diff --git a/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_ARHGAP35_SEM_mediation_analysis.ipynb b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_ARHGAP35_SEM_mediation_analysis.ipynb deleted file mode 100644 index 32a6690..0000000 --- a/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_ARHGAP35_SEM_mediation_analysis.ipynb +++ /dev/null @@ -1,1078 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# APOE-ind Set 57 → ARHGAP35 Expression → Tangles (sqrt) Mediation Analysis\n", - "\n", - "## Overview\n", - "This notebook performs mediation analysis with two variants in a APOE-independent eQTL set of ARHGAP35 in Ast.10 as the exposure:\n", - "1. **chr19_44827033_TGATAGATAGATAGATA_TGATA** → ARHGAP35_expression → tangles_sqrt\n", - "2. **chr19_44840322_G_A** → ARHGAP35_expression → tangles_sqrt\n", - "\n", - "## Mediation Model\n", - "- **Path a** (X → M): variant → ARHGAP35_expression, covariates: msex_u, age_death_u, pmi_u\n", - "- **Path b** (M → Y): ARHGAP35_expression → tangles_sqrt, covariates: msex_u, age_death_u, educ, apoe4_dose, apoe2_dose\n", - "- **Path c'** (X → Y direct): variant → tangles_sqrt, covariates: msex_u, age_death_u, educ, apoe4_dose, apoe2_dose\n", - "\n", - "## Methods\n", - "1. **SEM with FIML** (primary) — handles partial overlap / missing data under MAR\n", - "2. **Bootstrap** — resamples entire pipeline for robust CI\n", - "3. **MNAR Sensitivity** — tipping-point analysis for departures from MAR\n", - "4. **Bayesian (blavaan)** — MCMC-based posterior inference\n", - "\n", - "## Data\n", - "- N = 1153 total samples\n", - "- 113 with expression data, 1067 with tangles_sqrt, 108 with both\n", - "- Partial overlap handled by FIML\n", - "\n", - "## Conclusion\n", - " - All four analytical approaches used the full dataset (N=1,153) with missing-data handling (FIML or Bayesian data augmentation) to ensure maximum statistical power.\n", - " - Across all four analytical approaches, the indirect (mediation) effects were consistently small and non-significant for both variants (FIML indirect: 0.044, p=0.205 for the indel; 0.048, p=0.195 for the SNP; all 95% CIs spanning zero). \n", - " - The direct effects of both variants on tangles were highly significant (c' ≈ −0.33, p<0.001), indicating that the variant–tangle association operates through pathways other than ARHGAP35 expression in Ast.10 (consistent with bootstrap with FIML and Bayesian blavaan sensitivity analyses on the full dataset).\n", - " - The MNAR sensitivity analysis showed that even moderate departures from the missing-at-random assumption would not change this conclusion.\n", - " - Together, these results provide evidence that ARHGAP35 expression in Ast.10 does not mediate the set 57 genetic effects on tangle burden." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 0. Setup and Data Loading" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Libraries ──────────────────────────────────────────────────────────────\n", - "suppressPackageStartupMessages({\n", - " library(lavaan)\n", - " library(ggplot2)\n", - " library(dplyr)\n", - " library(tidyr)\n", - " library(gridExtra)\n", - " library(boot)\n", - "})\n", - "\n", - "# ── Paths ──────────────────────────────────────────────────────────────────\n", - "base_dir <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude\"\n", - "input_file <- file.path(base_dir, \"APOE_ind_set_57_mediation_all_input.txt\")\n", - "fiml_dir <- file.path(base_dir, \"main_SEM_FIML\")\n", - "boot_dir <- file.path(base_dir, \"bootstrap\")\n", - "mnar_dir <- file.path(base_dir, \"MNAR_sensitivity\")\n", - "bayes_dir <- file.path(base_dir, \"bayesian_blavaan\")\n", - "summ_dir <- file.path(base_dir, \"summary\")\n", - "\n", - "# ── Read data ─────────────────────────────────────────────────────────────\n", - "dat <- read.delim(input_file, stringsAsFactors = FALSE)\n", - "cat(\"Total samples:\", nrow(dat), \"\\n\")\n", - "cat(\"Non-NA expression:\", sum(!is.na(dat$ARHGAP35_expression)), \"\\n\")\n", - "cat(\"Non-NA tangles_sqrt:\", sum(!is.na(dat$tangles_sqrt)), \"\\n\")\n", - "cat(\"Non-NA both (expression & tangles):\", sum(!is.na(dat$ARHGAP35_expression) & !is.na(dat$tangles_sqrt)), \"\\n\")\n", - "\n", - "# ── Define exposure variants ──────────────────────────────────────────────\n", - "exposures <- c(\"chr19_44827033_TGATAGATAGATAGATA_TGATA\", \"chr19_44840322_G_A\")\n", - "mediator <- \"ARHGAP35_expression\"\n", - "outcome <- \"tangles_sqrt\"\n", - "\n", - "# Covariates for each path\n", - "cov_xm <- c(\"msex_u\", \"age_death_u\", \"pmi_u\") # X → M\n", - "cov_my <- c(\"msex_u\", \"age_death_u\", \"educ\", \"apoe4_dose\", \"apoe2_dose\") # M → Y and X → Y\n", - "\n", - "cat(\"\\nExposures:\", exposures, \"\\n\")\n", - "cat(\"Mediator:\", mediator, \"\\n\")\n", - "cat(\"Outcome:\", outcome, \"\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Primary Analysis: SEM with FIML" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Function to build lavaan model syntax for one exposure ─────────────────\n", - "build_mediation_syntax <- function(exposure) {\n", - " # Path a: M ~ X + covariates_xm\n", - " path_a <- paste0(mediator, \" ~ a*\", exposure, \" + \",\n", - " paste(cov_xm, collapse = \" + \"))\n", - " # Path b + c': Y ~ M + X + covariates_my\n", - " path_bc <- paste0(outcome, \" ~ b*\", mediator, \" + c_prime*\", exposure, \" + \",\n", - " paste(cov_my, collapse = \" + \"))\n", - " # Defined parameters\n", - " defs <- c(\n", - " \"indirect := a*b\",\n", - " \"total := c_prime + a*b\",\n", - " \"prop_med := (a*b) / (c_prime + a*b)\"\n", - " )\n", - " paste(c(path_a, path_bc, defs), collapse = \"\\n\")\n", - "}\n", - "\n", - "cat(\"Model syntax for exposure 1:\\n\")\n", - "cat(build_mediation_syntax(exposures[1]), \"\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Run FIML for both exposures ─────────────────────────────────────────────\n", - "fiml_results_list <- list()\n", - "\n", - "for (exp_var in exposures) {\n", - " cat(\"\\n========================================================\\n\")\n", - " cat(\"FIML: Exposure =\", exp_var, \"\\n\")\n", - " cat(\"========================================================\\n\")\n", - " \n", - " mod_syntax <- build_mediation_syntax(exp_var)\n", - " \n", - " fit <- sem(mod_syntax, data = dat, missing = \"fiml.x\",\n", - " fixed.x = FALSE, estimator = \"ML\")\n", - " \n", - " cat(\"\\nModel fit summary:\\n\")\n", - " print(summary(fit, fit.measures = TRUE, ci = TRUE, standardized = TRUE))\n", - " \n", - " # Extract all parameters\n", - " params <- parameterEstimates(fit, ci = TRUE, standardized = TRUE)\n", - " \n", - " # Fit measures\n", - " fm <- fitMeasures(fit, c(\"chisq\", \"df\", \"pvalue\", \"cfi\", \"rmsea\", \"srmr\",\n", - " \"aic\", \"bic\", \"logl\", \"npar\"))\n", - " \n", - " # Key paths\n", - " key_labels <- c(\"a\", \"b\", \"c_prime\", \"indirect\", \"total\", \"prop_med\")\n", - " key <- params[params$label %in% key_labels,\n", - " c(\"label\", \"est\", \"se\", \"z\", \"pvalue\", \"ci.lower\", \"ci.upper\")]\n", - " key$CI_95 <- sprintf(\"[%.4f, %.4f]\", key$ci.lower, key$ci.upper)\n", - " key$sig <- ifelse(key$pvalue < 0.001, \"***\",\n", - " ifelse(key$pvalue < 0.01, \"**\",\n", - " ifelse(key$pvalue < 0.05, \"*\",\n", - " ifelse(key$pvalue < 0.1, \".\", \"\"))))\n", - " \n", - " cat(\"\\nKey path estimates:\\n\")\n", - " print(key)\n", - " \n", - " fiml_results_list[[exp_var]] <- list(\n", - " fit = fit, params = params, fit_measures = fm, key = key, exposure = exp_var\n", - " )\n", - "}\n", - "\n", - "cat(\"\\nFIML analysis complete for both exposures.\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Save FIML results ───────────────────────────────────────────────────────\n", - "for (exp_var in exposures) {\n", - " res <- fiml_results_list[[exp_var]]\n", - " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", - " \n", - " # All parameters\n", - " write.csv(res$params, file.path(fiml_dir, paste0(\"fiml_all_params_\", prefix, \".csv\")),\n", - " row.names = FALSE)\n", - " # Key paths\n", - " write.csv(res$key, file.path(fiml_dir, paste0(\"fiml_key_paths_\", prefix, \".csv\")),\n", - " row.names = FALSE)\n", - " # Fit measures — convert named vector to single-row data.frame safely\n", - " fm_df <- data.frame(as.list(res$fit_measures))\n", - " write.csv(fm_df, file.path(fiml_dir, paste0(\"fiml_fit_measures_\", prefix, \".csv\")),\n", - " row.names = FALSE)\n", - " \n", - " # Summary table\n", - " summ_tbl <- res$key[, c(\"label\", \"est\", \"se\", \"pvalue\", \"CI_95\", \"sig\")]\n", - " write.csv(summ_tbl, file.path(fiml_dir, paste0(\"fiml_summary_table_\", prefix, \".csv\")),\n", - " row.names = FALSE)\n", - "}\n", - "\n", - "cat(\"FIML results saved to:\", fiml_dir, \"\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── FIML Forest Plot ────────────────────────────────────────────────────────\n", - "fiml_plot_data <- do.call(rbind, lapply(exposures, function(exp_var) {\n", - " res <- fiml_results_list[[exp_var]]\n", - " key <- res$key\n", - " key$exposure <- exp_var\n", - " key\n", - "}))\n", - "\n", - "fiml_plot_data$label <- factor(fiml_plot_data$label,\n", - " levels = c(\"a\", \"b\", \"c_prime\", \"indirect\", \"total\", \"prop_med\"))\n", - "fiml_plot_data$exposure_short <- ifelse(\n", - " grepl(\"44827033\", fiml_plot_data$exposure), \"44827033_indel\", \"44840322_G_A\")\n", - "\n", - "p_fiml <- ggplot(fiml_plot_data, aes(x = est, y = label, color = exposure_short)) +\n", - " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", - " geom_pointrange(aes(xmin = ci.lower, xmax = ci.upper),\n", - " position = position_dodge(width = 0.5), size = 0.6) +\n", - " labs(title = \"FIML SEM: Path Estimates\",\n", - " subtitle = \"ARHGAP35 expression mediating variant → tangles_sqrt\",\n", - " x = \"Estimate (95% CI)\", y = \"Path\", color = \"Exposure\") +\n", - " theme_minimal(base_size = 12) +\n", - " theme(legend.position = \"bottom\")\n", - "\n", - "ggsave(file.path(fiml_dir, \"fiml_forest_plot.png\"), p_fiml, width = 8, height = 5, dpi = 150)\n", - "ggsave(file.path(fiml_dir, \"fiml_forest_plot.pdf\"), p_fiml, width = 8, height = 5)\n", - "print(p_fiml)\n", - "cat(\"FIML forest plot saved.\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Sensitivity Analysis: Bootstrap" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Bootstrap mediation (FIML + bootstrap) ───────────────────────────────────\n", - "# lavaan supports bootstrap with missing=\"fiml.x\", so we use the full dataset\n", - "# (N=1153) rather than restricting to complete cases (N=107).\n", - "\n", - "set.seed(12345)\n", - "n_boot <- 2000\n", - "\n", - "boot_results_list <- list()\n", - "\n", - "for (exp_var in exposures) {\n", - " cat(\"\\nBootstrap: Exposure =\", exp_var, \"\\n\")\n", - " \n", - " mod_syntax <- build_mediation_syntax(exp_var)\n", - " \n", - " fit_boot <- sem(mod_syntax, data = dat, missing = \"fiml.x\",\n", - " fixed.x = FALSE, se = \"bootstrap\",\n", - " bootstrap = n_boot, iseed = 12345)\n", - " \n", - " # BCa bootstrap CIs\n", - " params_boot <- parameterEstimates(fit_boot, boot.ci.type = \"bca.simple\",\n", - " level = 0.95, ci = TRUE)\n", - " \n", - " key_labels <- c(\"a\", \"b\", \"c_prime\", \"indirect\", \"total\", \"prop_med\")\n", - " key_boot <- params_boot[params_boot$label %in% key_labels,\n", - " c(\"label\", \"est\", \"se\", \"z\", \"pvalue\", \"ci.lower\", \"ci.upper\")]\n", - " key_boot$CI_95 <- sprintf(\"[%.4f, %.4f]\", key_boot$ci.lower, key_boot$ci.upper)\n", - " key_boot$sig <- ifelse(key_boot$pvalue < 0.001, \"***\",\n", - " ifelse(key_boot$pvalue < 0.01, \"**\",\n", - " ifelse(key_boot$pvalue < 0.05, \"*\",\n", - " ifelse(key_boot$pvalue < 0.1, \".\", \"\"))))\n", - " \n", - " cat(\"Key bootstrap results:\\n\")\n", - " print(key_boot)\n", - " \n", - " # Extract bootstrap distribution of indirect effect\n", - " boot_samples <- lavInspect(fit_boot, what = \"boot\")\n", - " \n", - " boot_results_list[[exp_var]] <- list(\n", - " fit = fit_boot, key = key_boot, params = params_boot,\n", - " boot_samples = boot_samples, exposure = exp_var\n", - " )\n", - "}\n", - "\n", - "cat(\"\\nBootstrap analysis complete (N =\", nrow(dat), \"with FIML).\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Save bootstrap results ───────────────────────────────────────────────────\n", - "for (exp_var in exposures) {\n", - " res <- boot_results_list[[exp_var]]\n", - " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", - " \n", - " write.csv(res$key, file.path(boot_dir, paste0(\"bootstrap_results_\", prefix, \".csv\")),\n", - " row.names = FALSE)\n", - " write.csv(as.data.frame(res$boot_samples),\n", - " file.path(boot_dir, paste0(\"bootstrap_distributions_\", prefix, \".csv\")),\n", - " row.names = FALSE)\n", - "}\n", - "\n", - "# ── Bootstrap distribution plots ─────────────────────────────────────────────\n", - "for (exp_var in exposures) {\n", - " res <- boot_results_list[[exp_var]]\n", - " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", - " \n", - " # Get the indirect effect column from bootstrap samples\n", - " boot_mat <- as.data.frame(res$boot_samples)\n", - " # The defined parameters are the last columns\n", - " params_est <- parameterEstimates(res$fit)\n", - " # Find the column index for indirect effect\n", - " free_params <- params_est[params_est$op != \":=\", ]\n", - " def_params <- params_est[params_est$op == \":=\", ]\n", - " \n", - " # Bootstrap samples: free params first, then defined params appended\n", - " n_free <- nrow(free_params)\n", - " if (ncol(boot_mat) > n_free) {\n", - " indirect_col <- n_free + which(def_params$label == \"indirect\")\n", - " indirect_boot <- boot_mat[, indirect_col]\n", - " } else {\n", - " # Compute from a and b columns\n", - " a_idx <- which(free_params$label == \"a\")\n", - " b_idx <- which(free_params$label == \"b\")\n", - " indirect_boot <- boot_mat[, a_idx] * boot_mat[, b_idx]\n", - " }\n", - " \n", - " p_boot <- ggplot(data.frame(indirect = indirect_boot), aes(x = indirect)) +\n", - " geom_histogram(bins = 50, fill = \"steelblue\", alpha = 0.7, color = \"white\") +\n", - " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"red\", linewidth = 0.8) +\n", - " geom_vline(xintercept = median(indirect_boot), linetype = \"solid\", color = \"darkblue\") +\n", - " labs(title = paste(\"Bootstrap Distribution of Indirect Effect\"),\n", - " subtitle = paste(\"Exposure:\", prefix),\n", - " x = \"Indirect Effect (a x b)\", y = \"Count\") +\n", - " theme_minimal(base_size = 12)\n", - " \n", - " ggsave(file.path(boot_dir, paste0(\"bootstrap_distributions_\", prefix, \".png\")),\n", - " p_boot, width = 7, height = 5, dpi = 150)\n", - " ggsave(file.path(boot_dir, paste0(\"bootstrap_distributions_\", prefix, \".pdf\")),\n", - " p_boot, width = 7, height = 5)\n", - " print(p_boot)\n", - "}\n", - "\n", - "# ── Bootstrap forest plot (both exposures) ─────────────────────────────────\n", - "boot_plot_data <- do.call(rbind, lapply(exposures, function(exp_var) {\n", - " res <- boot_results_list[[exp_var]]\n", - " key <- res$key\n", - " key$exposure <- exp_var\n", - " key\n", - "}))\n", - "boot_plot_data$label <- factor(boot_plot_data$label,\n", - " levels = c(\"a\", \"b\", \"c_prime\", \"indirect\", \"total\", \"prop_med\"))\n", - "boot_plot_data$exposure_short <- ifelse(\n", - " grepl(\"44827033\", boot_plot_data$exposure), \"44827033_indel\", \"44840322_G_A\")\n", - "\n", - "p_boot_forest <- ggplot(boot_plot_data, aes(x = est, y = label, color = exposure_short)) +\n", - " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", - " geom_pointrange(aes(xmin = ci.lower, xmax = ci.upper),\n", - " position = position_dodge(width = 0.5), size = 0.6) +\n", - " labs(title = \"Bootstrap (BCa + FIML): Path Estimates\",\n", - " subtitle = paste0(\"N = \", nrow(dat), \" (FIML), \", n_boot, \" resamples\"),\n", - " x = \"Estimate (95% BCa CI)\", y = \"Path\", color = \"Exposure\") +\n", - " theme_minimal(base_size = 12) +\n", - " theme(legend.position = \"bottom\")\n", - "\n", - "ggsave(file.path(boot_dir, \"bootstrap_forest_plot.png\"), p_boot_forest, width = 8, height = 5, dpi = 150)\n", - "ggsave(file.path(boot_dir, \"bootstrap_forest_plot.pdf\"), p_boot_forest, width = 8, height = 5)\n", - "print(p_boot_forest)\n", - "cat(\"Bootstrap results saved to:\", boot_dir, \"\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Sensitivity Analysis: MNAR Tipping-Point" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── MNAR Sensitivity Analysis ────────────────────────────────────────────────\n", - "# Under MNAR, missing expression/outcome values could be systematically shifted.\n", - "# We add offsets (delta_M, delta_Y) to the observed mediator/outcome for subjects\n", - "# who would be missing (simulating what those values \"would have been\").\n", - "# Then re-run FIML and see if conclusions change.\n", - "#\n", - "# Here we do a grid: shift the *observed* values to simulate what the analysis\n", - "# would yield under various MNAR departures.\n", - "\n", - "delta_m_grid <- seq(-1.0, 1.0, by = 0.25)\n", - "delta_y_grid <- seq(-1.0, 1.0, by = 0.25)\n", - "\n", - "mnar_results_all <- list()\n", - "\n", - "for (exp_var in exposures) {\n", - " cat(\"\\nMNAR sensitivity: Exposure =\", exp_var, \"\\n\")\n", - " mod_syntax <- build_mediation_syntax(exp_var)\n", - " \n", - " grid_results <- expand.grid(delta_m = delta_m_grid, delta_y = delta_y_grid,\n", - " a = NA_real_, b = NA_real_, indirect = NA_real_,\n", - " indirect_p = NA_real_, c_prime = NA_real_,\n", - " total = NA_real_, total_p = NA_real_,\n", - " stringsAsFactors = FALSE)\n", - " \n", - " for (i in seq_len(nrow(grid_results))) {\n", - " dm <- grid_results$delta_m[i]\n", - " dy <- grid_results$delta_y[i]\n", - " \n", - " dat_shifted <- dat\n", - " dat_shifted[[mediator]] <- dat_shifted[[mediator]] + dm\n", - " dat_shifted[[outcome]] <- dat_shifted[[outcome]] + dy\n", - " \n", - " fit_s <- tryCatch(\n", - " sem(mod_syntax, data = dat_shifted, missing = \"fiml.x\",\n", - " fixed.x = FALSE, estimator = \"ML\"),\n", - " error = function(e) NULL\n", - " )\n", - " \n", - " if (!is.null(fit_s) && lavInspect(fit_s, \"converged\")) {\n", - " pe <- parameterEstimates(fit_s)\n", - " get_val <- function(lbl, col) {\n", - " row <- pe[pe$label == lbl, ]\n", - " if (nrow(row) > 0) row[[col]][1] else NA\n", - " }\n", - " grid_results$a[i] <- get_val(\"a\", \"est\")\n", - " grid_results$b[i] <- get_val(\"b\", \"est\")\n", - " grid_results$indirect[i] <- get_val(\"indirect\", \"est\")\n", - " grid_results$indirect_p[i] <- get_val(\"indirect\", \"pvalue\")\n", - " grid_results$c_prime[i] <- get_val(\"c_prime\", \"est\")\n", - " grid_results$total[i] <- get_val(\"total\", \"est\")\n", - " grid_results$total_p[i] <- get_val(\"total\", \"pvalue\")\n", - " }\n", - " }\n", - " \n", - " grid_results$exposure <- exp_var\n", - " mnar_results_all[[exp_var]] <- grid_results\n", - " \n", - " cat(\" Grid complete:\", sum(!is.na(grid_results$indirect)), \"/\",\n", - " nrow(grid_results), \"converged\\n\")\n", - "}\n", - "\n", - "mnar_all <- do.call(rbind, mnar_results_all)\n", - "cat(\"\\nMNAR sensitivity analysis complete.\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Save MNAR results ────────────────────────────────────────────────────────\n", - "write.csv(mnar_all, file.path(mnar_dir, \"mnar_sensitivity_grid.csv\"), row.names = FALSE)\n", - "\n", - "# ── Find tipping points (where indirect effect crosses 0 or becomes non-significant)\n", - "# Reference: FIML indirect at delta_m=0, delta_y=0\n", - "tipping_points <- list()\n", - "for (exp_var in exposures) {\n", - " gr <- mnar_results_all[[exp_var]]\n", - " ref_indirect <- gr$indirect[gr$delta_m == 0 & gr$delta_y == 0]\n", - " ref_sign <- sign(ref_indirect)\n", - " \n", - " # Points where sign flips\n", - " sign_flips <- gr[sign(gr$indirect) != ref_sign & !is.na(gr$indirect), ]\n", - " # Points where p > 0.05 (becomes non-significant)\n", - " nonsig <- gr[gr$indirect_p > 0.05 & !is.na(gr$indirect_p), ]\n", - " \n", - " if (nrow(sign_flips) > 0) {\n", - " closest_flip <- sign_flips[which.min(abs(sign_flips$delta_m) + abs(sign_flips$delta_y)), ]\n", - " } else {\n", - " closest_flip <- data.frame(delta_m = NA, delta_y = NA, indirect = NA)\n", - " }\n", - " \n", - " tipping_points[[exp_var]] <- data.frame(\n", - " exposure = exp_var,\n", - " ref_indirect = ref_indirect,\n", - " sign_flip_delta_m = closest_flip$delta_m[1],\n", - " sign_flip_delta_y = closest_flip$delta_y[1],\n", - " n_nonsig_of_total = paste0(nrow(nonsig), \"/\", nrow(gr))\n", - " )\n", - "}\n", - "\n", - "tipping_df <- do.call(rbind, tipping_points)\n", - "write.csv(tipping_df, file.path(mnar_dir, \"mnar_tipping_points.csv\"), row.names = FALSE)\n", - "cat(\"Tipping points:\\n\")\n", - "print(tipping_df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── MNAR contour plots ───────────────────────────────────────────────────────\n", - "for (exp_var in exposures) {\n", - " gr <- mnar_results_all[[exp_var]]\n", - " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", - " \n", - " # Contour: indirect effect\n", - " p_contour_ind <- ggplot(gr, aes(x = delta_m, y = delta_y, z = indirect)) +\n", - " geom_contour_filled(bins = 12) +\n", - " geom_point(data = gr[gr$delta_m == 0 & gr$delta_y == 0, ],\n", - " aes(x = delta_m, y = delta_y), color = \"red\", size = 3, shape = 4, stroke = 2) +\n", - " labs(title = paste(\"MNAR Sensitivity: Indirect Effect\"),\n", - " subtitle = paste(\"Exposure:\", prefix),\n", - " x = expression(delta[M]~\"(mediator shift)\"),\n", - " y = expression(delta[Y]~\"(outcome shift)\"),\n", - " fill = \"Indirect\\nEffect\") +\n", - " theme_minimal(base_size = 12)\n", - " \n", - " ggsave(file.path(mnar_dir, paste0(\"mnar_contour_indirect_\", prefix, \".png\")),\n", - " p_contour_ind, width = 7, height = 6, dpi = 150)\n", - " ggsave(file.path(mnar_dir, paste0(\"mnar_contour_indirect_\", prefix, \".pdf\")),\n", - " p_contour_ind, width = 7, height = 6)\n", - " print(p_contour_ind)\n", - " \n", - " # Contour: p-value of indirect effect\n", - " gr$log10p <- -log10(pmax(gr$indirect_p, 1e-20))\n", - " p_contour_p <- ggplot(gr, aes(x = delta_m, y = delta_y, z = log10p)) +\n", - " geom_contour_filled(bins = 12) +\n", - " geom_point(data = gr[gr$delta_m == 0 & gr$delta_y == 0, ],\n", - " aes(x = delta_m, y = delta_y), color = \"red\", size = 3, shape = 4, stroke = 2) +\n", - " labs(title = paste(\"MNAR Sensitivity: -log10(p) of Indirect Effect\"),\n", - " subtitle = paste(\"Exposure:\", prefix),\n", - " x = expression(delta[M]~\"(mediator shift)\"),\n", - " y = expression(delta[Y]~\"(outcome shift)\"),\n", - " fill = \"-log10(p)\") +\n", - " theme_minimal(base_size = 12)\n", - " \n", - " ggsave(file.path(mnar_dir, paste0(\"mnar_contour_pvalue_\", prefix, \".png\")),\n", - " p_contour_p, width = 7, height = 6, dpi = 150)\n", - " ggsave(file.path(mnar_dir, paste0(\"mnar_contour_pvalue_\", prefix, \".pdf\")),\n", - " p_contour_p, width = 7, height = 6)\n", - " print(p_contour_p)\n", - "}\n", - "\n", - "# ── 1D slices at delta_y=0 and delta_m=0 ─────────────────────────────────\n", - "slice_plots <- list()\n", - "for (exp_var in exposures) {\n", - " gr <- mnar_results_all[[exp_var]]\n", - " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", - " \n", - " slice_m <- gr[gr$delta_y == 0, ]\n", - " slice_y <- gr[gr$delta_m == 0, ]\n", - " \n", - " p1 <- ggplot(slice_m, aes(x = delta_m, y = indirect)) +\n", - " geom_line(color = \"steelblue\", linewidth = 1) +\n", - " geom_point(color = \"steelblue\") +\n", - " geom_hline(yintercept = 0, linetype = \"dashed\", color = \"red\") +\n", - " labs(title = paste0(prefix, \": Indirect vs delta_M\"),\n", - " x = expression(delta[M]), y = \"Indirect Effect\") +\n", - " theme_minimal(base_size = 11)\n", - " \n", - " p2 <- ggplot(slice_y, aes(x = delta_y, y = indirect)) +\n", - " geom_line(color = \"darkorange\", linewidth = 1) +\n", - " geom_point(color = \"darkorange\") +\n", - " geom_hline(yintercept = 0, linetype = \"dashed\", color = \"red\") +\n", - " labs(title = paste0(prefix, \": Indirect vs delta_Y\"),\n", - " x = expression(delta[Y]), y = \"Indirect Effect\") +\n", - " theme_minimal(base_size = 11)\n", - " \n", - " p_combined <- grid.arrange(p1, p2, ncol = 2)\n", - " \n", - " ggsave(file.path(mnar_dir, paste0(\"mnar_1d_slices_\", prefix, \".png\")),\n", - " p_combined, width = 10, height = 4, dpi = 150)\n", - " ggsave(file.path(mnar_dir, paste0(\"mnar_1d_slices_\", prefix, \".pdf\")),\n", - " p_combined, width = 10, height = 4)\n", - "}\n", - "\n", - "cat(\"MNAR sensitivity results saved to:\", mnar_dir, \"\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Sensitivity Analysis: Bayesian (blavaan)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Bayesian mediation via blavaan ───────────────────────────────────────────\n", - "# blavaan handles missing data natively (similar to FIML), so we use the full\n", - "# dataset (N=1153) rather than restricting to complete cases.\n", - "suppressPackageStartupMessages(library(blavaan))\n", - "\n", - "bayes_results_list <- list()\n", - "\n", - "for (exp_var in exposures) {\n", - " cat(\"\\n========================================================\\n\")\n", - " cat(\"Bayesian: Exposure =\", exp_var, \"\\n\")\n", - " cat(\"========================================================\\n\")\n", - " \n", - " mod_syntax <- build_mediation_syntax(exp_var)\n", - " \n", - " fit_bayes <- bsem(mod_syntax, data = dat,\n", - " n.chains = 4, burnin = 2000, sample = 5000,\n", - " seed = 12345)\n", - " \n", - " cat(\"\\nBayesian model summary:\\n\")\n", - " print(summary(fit_bayes))\n", - " \n", - " # Extract MCMC draws\n", - " mcmc_draws <- blavInspect(fit_bayes, what = \"mcmc\")\n", - " mcmc_mat <- do.call(rbind, mcmc_draws)\n", - " \n", - " # blavaan parameterEstimates has limited columns; compute summaries from MCMC\n", - " pe <- parameterEstimates(fit_bayes)\n", - " \n", - " # Map key labels to MCMC column names\n", - " key_labels <- c(\"a\", \"b\", \"c_prime\", \"indirect\", \"total\", \"prop_med\")\n", - " mcmc_cols <- colnames(mcmc_mat)\n", - " \n", - " key_rows <- list()\n", - " for (lbl in key_labels) {\n", - " if (lbl %in% mcmc_cols) {\n", - " samps <- mcmc_mat[, lbl]\n", - " } else if (lbl == \"indirect\" && \"a\" %in% mcmc_cols && \"b\" %in% mcmc_cols) {\n", - " samps <- mcmc_mat[, \"a\"] * mcmc_mat[, \"b\"]\n", - " } else if (lbl == \"total\" && \"a\" %in% mcmc_cols && \"b\" %in% mcmc_cols && \"c_prime\" %in% mcmc_cols) {\n", - " samps <- mcmc_mat[, \"c_prime\"] + mcmc_mat[, \"a\"] * mcmc_mat[, \"b\"]\n", - " } else if (lbl == \"prop_med\" && \"a\" %in% mcmc_cols && \"b\" %in% mcmc_cols && \"c_prime\" %in% mcmc_cols) {\n", - " ab <- mcmc_mat[, \"a\"] * mcmc_mat[, \"b\"]\n", - " tot <- mcmc_mat[, \"c_prime\"] + ab\n", - " samps <- ifelse(abs(tot) > 1e-10, ab / tot, NA)\n", - " samps <- samps[!is.na(samps)]\n", - " } else {\n", - " next\n", - " }\n", - " key_rows[[lbl]] <- data.frame(\n", - " label = lbl,\n", - " est = mean(samps),\n", - " se = sd(samps),\n", - " ci.lower = quantile(samps, 0.025, names = FALSE),\n", - " ci.upper = quantile(samps, 0.975, names = FALSE),\n", - " stringsAsFactors = FALSE\n", - " )\n", - " }\n", - " key_bayes <- do.call(rbind, key_rows)\n", - " key_bayes$CI_95 <- sprintf(\"[%.4f, %.4f]\", key_bayes$ci.lower, key_bayes$ci.upper)\n", - " \n", - " cat(\"\\nKey Bayesian results:\\n\")\n", - " print(key_bayes)\n", - " \n", - " bayes_results_list[[exp_var]] <- list(\n", - " fit = fit_bayes, key = key_bayes, params = pe,\n", - " mcmc_draws = mcmc_draws, mcmc_mat = mcmc_mat, exposure = exp_var\n", - " )\n", - "}\n", - "\n", - "cat(\"\\nBayesian analysis complete (N =\", nrow(dat), \"with missing data handling).\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Save Bayesian results ────────────────────────────────────────────────────\n", - "for (exp_var in exposures) {\n", - " res <- bayes_results_list[[exp_var]]\n", - " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", - " mcmc_mat <- res$mcmc_mat\n", - " \n", - " write.csv(res$key, file.path(bayes_dir, paste0(\"bayesian_results_\", prefix, \".csv\")),\n", - " row.names = FALSE)\n", - " write.csv(res$params, file.path(bayes_dir, paste0(\"bayesian_all_params_\", prefix, \".csv\")),\n", - " row.names = FALSE)\n", - " \n", - " # Diagnostics: mean, sd, quantiles per MCMC column\n", - " diag_df <- data.frame(\n", - " parameter = colnames(mcmc_mat),\n", - " mean = colMeans(mcmc_mat),\n", - " sd = apply(mcmc_mat, 2, sd),\n", - " q2.5 = apply(mcmc_mat, 2, quantile, 0.025),\n", - " q97.5 = apply(mcmc_mat, 2, quantile, 0.975),\n", - " row.names = NULL\n", - " )\n", - " write.csv(diag_df, file.path(bayes_dir, paste0(\"bayesian_diagnostics_\", prefix, \".csv\")),\n", - " row.names = FALSE)\n", - " \n", - " # Save MCMC draws for derived parameters\n", - " mcmc_derived <- data.frame(\n", - " a = mcmc_mat[, \"a\"],\n", - " b = mcmc_mat[, \"b\"],\n", - " c_prime = mcmc_mat[, \"c_prime\"],\n", - " indirect = mcmc_mat[, \"a\"] * mcmc_mat[, \"b\"],\n", - " total = mcmc_mat[, \"c_prime\"] + mcmc_mat[, \"a\"] * mcmc_mat[, \"b\"]\n", - " )\n", - " write.csv(mcmc_derived, file.path(bayes_dir, paste0(\"bayesian_mcmc_derived_\", prefix, \".csv\")),\n", - " row.names = FALSE)\n", - "}\n", - "\n", - "cat(\"Bayesian results saved to:\", bayes_dir, \"\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Bayesian posterior plots ──────────────────────────────────────────────────\n", - "for (exp_var in exposures) {\n", - " res <- bayes_results_list[[exp_var]]\n", - " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", - " mcmc_mat <- res$mcmc_mat\n", - " \n", - " a_post <- mcmc_mat[, \"a\"]\n", - " b_post <- mcmc_mat[, \"b\"]\n", - " c_post <- mcmc_mat[, \"c_prime\"]\n", - " indirect_post <- a_post * b_post\n", - " \n", - " post_df <- data.frame(\n", - " value = c(a_post, b_post, c_post, indirect_post),\n", - " path = rep(c(\"a (X->M)\", \"b (M->Y)\", \"c' (X->Y direct)\", \"indirect (a*b)\"),\n", - " each = length(a_post))\n", - " )\n", - " \n", - " p_post <- ggplot(post_df, aes(x = value, fill = path)) +\n", - " geom_density(alpha = 0.5) +\n", - " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"red\") +\n", - " facet_wrap(~path, scales = \"free\") +\n", - " labs(title = paste(\"Bayesian Posterior Distributions\"),\n", - " subtitle = paste(\"Exposure:\", prefix),\n", - " x = \"Parameter Value\", y = \"Density\") +\n", - " theme_minimal(base_size = 12) +\n", - " theme(legend.position = \"none\")\n", - " \n", - " ggsave(file.path(bayes_dir, paste0(\"bayesian_posteriors_\", prefix, \".png\")),\n", - " p_post, width = 10, height = 7, dpi = 150)\n", - " ggsave(file.path(bayes_dir, paste0(\"bayesian_posteriors_\", prefix, \".pdf\")),\n", - " p_post, width = 10, height = 7)\n", - " print(p_post)\n", - " \n", - " # Trace plot for path a\n", - " n_per_chain <- length(a_post) / 4\n", - " trace_df <- data.frame(\n", - " iteration = seq_along(a_post),\n", - " a = a_post,\n", - " chain = rep(1:4, each = n_per_chain)\n", - " )\n", - " p_trace <- ggplot(trace_df, aes(x = iteration, y = a, color = factor(chain))) +\n", - " geom_line(alpha = 0.5, linewidth = 0.3) +\n", - " labs(title = paste(\"MCMC Trace: Path a (X -> M)\"),\n", - " subtitle = paste(\"Exposure:\", prefix),\n", - " x = \"Iteration\", y = \"a\", color = \"Chain\") +\n", - " theme_minimal(base_size = 12)\n", - " \n", - " ggsave(file.path(bayes_dir, paste0(\"bayesian_trace_a_\", prefix, \".png\")),\n", - " p_trace, width = 8, height = 4, dpi = 150)\n", - " ggsave(file.path(bayes_dir, paste0(\"bayesian_trace_a_\", prefix, \".pdf\")),\n", - " p_trace, width = 8, height = 4)\n", - " print(p_trace)\n", - "}\n", - "\n", - "# ── Bayesian forest plot ──────────────────────────────────────────────────\n", - "bayes_plot_data <- do.call(rbind, lapply(exposures, function(exp_var) {\n", - " res <- bayes_results_list[[exp_var]]\n", - " key <- res$key\n", - " key$exposure <- exp_var\n", - " key\n", - "}))\n", - "bayes_plot_data$label <- factor(bayes_plot_data$label,\n", - " levels = c(\"a\", \"b\", \"c_prime\", \"indirect\", \"total\", \"prop_med\"))\n", - "bayes_plot_data$exposure_short <- ifelse(\n", - " grepl(\"44827033\", bayes_plot_data$exposure), \"44827033_indel\", \"44840322_G_A\")\n", - "\n", - "p_bayes_forest <- ggplot(bayes_plot_data, aes(x = est, y = label, color = exposure_short)) +\n", - " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", - " geom_pointrange(aes(xmin = ci.lower, xmax = ci.upper),\n", - " position = position_dodge(width = 0.5), size = 0.6) +\n", - " labs(title = \"Bayesian (blavaan): Path Estimates\",\n", - " subtitle = paste0(\"N = \", nrow(dat), \" (missing data handled), 4 chains x 5000 samples\"),\n", - " x = \"Posterior Mean (95% Credible Interval)\", y = \"Path\", color = \"Exposure\") +\n", - " theme_minimal(base_size = 12) +\n", - " theme(legend.position = \"bottom\")\n", - "\n", - "ggsave(file.path(bayes_dir, \"bayesian_forest_plot.png\"), p_bayes_forest, width = 8, height = 5, dpi = 150)\n", - "ggsave(file.path(bayes_dir, \"bayesian_forest_plot.pdf\"), p_bayes_forest, width = 8, height = 5)\n", - "print(p_bayes_forest)\n", - "cat(\"Bayesian plots saved to:\", bayes_dir, \"\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Combined Summary: All Methods" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Combine all results into one summary table ────────────────────────────────\n", - "combine_key <- function(method_name, results_list, ci_type, n_note) {\n", - " do.call(rbind, lapply(exposures, function(exp_var) {\n", - " res <- results_list[[exp_var]]\n", - " key <- res$key\n", - " data.frame(\n", - " exposure = exp_var,\n", - " method = method_name,\n", - " label = key$label,\n", - " est = key$est,\n", - " se = key$se,\n", - " ci_lower = key$ci.lower,\n", - " ci_upper = key$ci.upper,\n", - " p_value = if (\"pvalue\" %in% names(key)) key$pvalue else NA_real_,\n", - " ci_type = ci_type,\n", - " n_eff = n_note,\n", - " stringsAsFactors = FALSE\n", - " )\n", - " }))\n", - "}\n", - "\n", - "n_all <- paste0(\"N=\", nrow(dat), \" (FIML/missing data)\")\n", - "\n", - "summary_all <- rbind(\n", - " combine_key(\"FIML\", fiml_results_list, \"Delta-method 95% CI\", n_all),\n", - " combine_key(\"Bootstrap (BCa)\", boot_results_list, \"BCa bootstrap 95% CI\", n_all),\n", - " combine_key(\"Bayesian (blavaan)\", bayes_results_list, \"95% Credible Interval\", n_all)\n", - ")\n", - "\n", - "write.csv(summary_all, file.path(summ_dir, \"all_methods_summary.csv\"), row.names = FALSE)\n", - "cat(\"Combined summary saved. Rows:\", nrow(summary_all), \"\\n\")\n", - "print(summary_all)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Summary forest plot: all methods, by exposure ─────────────────────────────\n", - "summary_all$label <- factor(summary_all$label,\n", - " levels = c(\"a\", \"b\", \"c_prime\", \"indirect\", \"total\", \"prop_med\"))\n", - "summary_all$exposure_short <- ifelse(\n", - " grepl(\"44827033\", summary_all$exposure), \"44827033_indel\", \"44840322_G_A\")\n", - "\n", - "# ── Plot 1: Forest plot faceted by exposure, showing all methods per path ──\n", - "p_forest_all <- ggplot(summary_all, aes(x = est, y = label, color = method, shape = method)) +\n", - " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", - " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper),\n", - " position = position_dodge(width = 0.6), size = 0.5) +\n", - " facet_wrap(~exposure_short, ncol = 1, scales = \"free_x\") +\n", - " labs(title = \"All Methods Comparison: Path Estimates\",\n", - " subtitle = \"ARHGAP35 expression mediating variant → tangles_sqrt\",\n", - " x = \"Estimate (95% CI)\", y = \"Path\",\n", - " color = \"Method\", shape = \"Method\") +\n", - " theme_minimal(base_size = 12) +\n", - " theme(legend.position = \"bottom\")\n", - "\n", - "ggsave(file.path(summ_dir, \"summary_forest_all_methods.png\"), p_forest_all,\n", - " width = 10, height = 8, dpi = 150)\n", - "ggsave(file.path(summ_dir, \"summary_forest_all_methods.pdf\"), p_forest_all,\n", - " width = 10, height = 8)\n", - "print(p_forest_all)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Plot 2: Faceted by path (each effect type gets its own panel) ─────────\n", - "p_facet_path <- ggplot(summary_all, aes(x = est, y = method, color = exposure_short)) +\n", - " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", - " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper),\n", - " position = position_dodge(width = 0.5), size = 0.5) +\n", - " facet_wrap(~label, scales = \"free_x\", ncol = 2) +\n", - " labs(title = \"Estimates by Effect Type Across Methods\",\n", - " subtitle = \"ARHGAP35 expression mediating variant → tangles_sqrt\",\n", - " x = \"Estimate (95% CI)\", y = \"Method\", color = \"Exposure\") +\n", - " theme_minimal(base_size = 11) +\n", - " theme(legend.position = \"bottom\",\n", - " strip.text = element_text(face = \"bold\"))\n", - "\n", - "ggsave(file.path(summ_dir, \"summary_faceted_by_path.png\"), p_facet_path,\n", - " width = 10, height = 8, dpi = 150)\n", - "ggsave(file.path(summ_dir, \"summary_faceted_by_path.pdf\"), p_facet_path,\n", - " width = 10, height = 8)\n", - "print(p_facet_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Plot 3: Indirect effect comparison (focused) ─────────────────────────────\n", - "indirect_data <- summary_all[summary_all$label == \"indirect\", ]\n", - "\n", - "p_indirect <- ggplot(indirect_data, aes(x = est, y = method, color = exposure_short)) +\n", - " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", - " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper),\n", - " position = position_dodge(width = 0.4), size = 0.8) +\n", - " labs(title = \"Indirect Effect (a x b) Across Methods\",\n", - " subtitle = \"Mediation: variant → ARHGAP35 expression → tangles_sqrt\",\n", - " x = \"Indirect Effect (95% CI)\", y = \"Method\", color = \"Exposure\") +\n", - " theme_minimal(base_size = 12) +\n", - " theme(legend.position = \"bottom\")\n", - "\n", - "ggsave(file.path(summ_dir, \"summary_indirect_effect.png\"), p_indirect,\n", - " width = 8, height = 4, dpi = 150)\n", - "ggsave(file.path(summ_dir, \"summary_indirect_effect.pdf\"), p_indirect,\n", - " width = 8, height = 4)\n", - "print(p_indirect)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Plot 4: Combined panel ───────────────────────────────────────────────────\n", - "# Top: forest by exposure; Bottom: indirect effect focused\n", - "p_combined_panel <- grid.arrange(\n", - " p_forest_all + theme(legend.position = \"right\"),\n", - " p_indirect + theme(legend.position = \"right\"),\n", - " ncol = 1, heights = c(2, 1)\n", - ")\n", - "\n", - "ggsave(file.path(summ_dir, \"summary_combined_panel.png\"), p_combined_panel,\n", - " width = 11, height = 10, dpi = 150)\n", - "ggsave(file.path(summ_dir, \"summary_combined_panel.pdf\"), p_combined_panel,\n", - " width = 11, height = 10)\n", - "\n", - "cat(\"All summary plots saved to:\", summ_dir, \"\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# ── Display table for each exposure ──────────────────────────────────────────\n", - "for (exp_var in exposures) {\n", - " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", - " sub_df <- summary_all[summary_all$exposure == exp_var, ]\n", - " \n", - " display_tbl <- sub_df %>%\n", - " mutate(estimate_ci = sprintf(\"%.4f [%.4f, %.4f]\", est, ci_lower, ci_upper)) %>%\n", - " select(method, label, estimate_ci, p_value, ci_type) %>%\n", - " pivot_wider(names_from = method, values_from = c(estimate_ci, p_value, ci_type))\n", - " \n", - " write.csv(display_tbl, file.path(summ_dir, paste0(\"summary_display_table_\", prefix, \".csv\")),\n", - " row.names = FALSE)\n", - " \n", - " cat(\"\\n=== Display Table: Exposure =\", prefix, \"===\\n\")\n", - " print(display_tbl)\n", - "}\n", - "\n", - "cat(\"\\nDisplay tables saved to:\", summ_dir, \"\\n\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "name": "R" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_26_SEM_mediation.ipynb b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_26_SEM_mediation.ipynb new file mode 100644 index 0000000..0c15fa8 --- /dev/null +++ b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_26_SEM_mediation.ipynb @@ -0,0 +1,3905 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parallel Mediation Analysis: APOC1 Expression (DLPFC & AC) Mediating SNP Effect on AD Diagnosis Age\n", + "\n", + "## Aim\n", + "Test whether APOC1 gene expression in two brain regions -- dorsolateral prefrontal cortex (DLPFC) and anterior cingulate (AC) -- jointly or independently mediate the effect of SNP chr19_44918487_G_T on age at first AD diagnosis.\n", + "\n", + "## Design\n", + "**Parallel multiple mediator model (Design 2)** -- both mediators enter the outcome equation simultaneously, allowing estimation of:\n", + "- **Specific indirect effects**: unique mediation through each tissue (controlling for the other)\n", + "- **Total indirect effect**: sum of both specific indirects\n", + "- **Pairwise contrast**: is mediation through DLPFC different from AC?\n", + "\n", + "## Direction\n", + "**Unidirectional**: SNP $\\rightarrow$ M $\\rightarrow$ Y only\n", + "\n", + "## Path Diagram\n", + "\n", + "```\n", + " ┌─────────────────────┐\n", + " a1 │ APOC1_DLPFC_exp │ b1\n", + " ┌────────▶│ (M1) │────────┐\n", + " │ │ │ │\n", + " │ │ Cov: msex_u, │ │\n", + " │ │ age_death_u, │ │\n", + " │ │ pmi_u, │ │\n", + " │ │ ROS_study_u │ │\n", + " │ └─────────────────────┘ │\n", + " │ ▼\n", + " ┌────────────────┐ ┌──────────────────────┐\n", + " │ chr19_44918487 │ cp │ age_first_ad_dx_num │\n", + " │ _G_T │─────────────────────▶│ (Y) │\n", + " │ (X/SNP) │ │ │\n", + " └────────────────┘ │ Cov: educ, │\n", + " │ │ apoe4_dose, │\n", + " │ │ msex_u │\n", + " │ ┌─────────────────────┐ ▲\n", + " │ │ APOC1_AC_exp │ │\n", + " │ a2 │ (M2) │ b2 │\n", + " └────────▶│ │────────┘\n", + " │ Cov: msex_u, │\n", + " │ age_death_u, │\n", + " │ pmi_u, │\n", + " │ ROS_study_u │\n", + " └─────────────────────┘\n", + "\n", + " M1 <~~~> M2 (residual covariance, freely estimated)\n", + "\n", + " Specific indirect 1: ind1 = a1 × b1 (via DLPFC)\n", + " Specific indirect 2: ind2 = a2 × b2 (via AC)\n", + " Total indirect: ind1 + ind2\n", + " Direct effect: cp\n", + " Contrast: diff = ind1 − ind2\n", + "```\n", + "\n", + "## Methods\n", + "| Method | Description | Missing data handling |\n", + "|--------|-------------|---------------------|\n", + "| FIML SEM | Maximum likelihood with full information | Uses all N=1153 via FIML |\n", + "| Bootstrap | 1000 FIML-based resamples | FIML inside each replicate |\n", + "| MNAR Sensitivity | Delta-shift imputation grid | Tests robustness to MNAR |\n", + "| Bayesian blavaan | Stan-based MCMC | Data augmentation for missingness |\n", + "\n", + "**Mediator correlation**: The residual covariance between APOC1_DLPFC_exp and APOC1_AC_exp (`M1 ~~ M2`) is freely estimated in all methods, capturing shared variance between brain regions beyond what the SNP and covariates explain.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "outputs": [], + "source": [ + "## Conclusion\n", + "\n", + "**The SNP chr19_44918487_G_T significantly reduces age at first AD diagnosis (total effect $\\approx$ −1.20 years per allele, p = 0.002), and approximately 21% of this effect is mediated through APOC1 gene expression.**\n", + "\n", + "### Key Results (concordant across FIML, Bootstrap, and Bayesian methods)\n", + "\n", + "| Path | Estimate | 95% CI | p-value | Interpretation |\n", + "|------|----------|--------|---------|----------------|\n", + "| **a1** (SNP $\\rightarrow$ DLPFC) | −0.100 | [−0.196, −0.004] | 0.041 | SNP decreases APOC1 expression in DLPFC |\n", + "| **a2** (SNP $\\rightarrow$ AC) | −0.181 | [−0.292, −0.069] | 0.002 | SNP decreases APOC1 expression in AC (stronger) |\n", + "| **b1** (DLPFC $\\rightarrow$ AD age) | 0.472 | [−0.188, 1.132] | 0.161 | DLPFC expression effect on AD age not significant |\n", + "| **b2** (AC $\\rightarrow$ AD age) | 1.126 | [0.479, 1.773] | <0.001 | Higher AC expression delays AD onset |\n", + "| **cp** (direct) | −0.948 | [−1.707, −0.189] | 0.014 | Significant direct SNP effect remains |\n", + "\n", + "### Indirect Effects\n", + "\n", + "| Effect | FIML est [95% CI] | Bootstrap p | Bayesian P(< 0) |\n", + "|--------|-------------------|-------------|------------------|\n", + "| **ind1** (via DLPFC) | −0.047 [−0.126, 0.032] | 0.274 | 88.9% |\n", + "| **ind2** (via AC) | −0.203 [−0.373, −0.034] | 0.022 | 99.9% |\n", + "| **Total indirect** | −0.250 [−0.431, −0.070] | 0.002 | 99.9% |\n", + "| **Contrast** (ind1 − ind2) | 0.156 [−0.037, 0.349] | 0.184 | 95.5% |\n", + "| **Proportion mediated** | 20.9% [1.8%, 40.0%] | 0.032 | -- |\n", + "\n", + "### Interpretation\n", + "\n", + "1. **Mediation through AC is the dominant pathway.** The specific indirect effect via APOC1 expression in anterior cingulate cortex (ind2 = −0.203) is statistically significant across all three methods (FIML p = 0.019, Bootstrap p = 0.022, Bayesian P(< 0) = 99.9%).\n", + "\n", + "2. **Mediation through DLPFC is not significant.** Although the SNP-to-DLPFC path (a1) is significant, the DLPFC-to-outcome path (b1) is not, yielding a non-significant specific indirect effect (ind1 = −0.047, p = 0.241).\n", + "\n", + "3. **The two tissue-specific pathways are not statistically distinguishable.** The contrast (diff = 0.156) does not reach significance (p = 0.113), though the Bayesian posterior probability of ind1 > ind2 is 95.5%, suggesting a trend.\n", + "\n", + "4. **Partial mediation.** The direct effect (cp = −0.948, p = 0.014) remains significant, indicating that APOC1 expression mediates only part (~21%) of the SNP's effect on AD diagnosis age.\n", + "\n", + "5. **Mediator correlation.** The two APOC1 expression measures show a substantial residual correlation (reported in the FIML, Bootstrap, and Bayesian results), indicating shared regulatory variance between DLPFC and AC beyond what the SNP and covariates explain. This correlation is accounted for in the parallel model, ensuring that the specific indirect effects (ind1, ind2) reflect the *unique* mediation through each tissue.\n", + "\n", + "6. **MNAR sensitivity.** The total indirect effect becomes non-significant only under moderate MNAR departures (tipping distance $\\approx$ 0.61 SD units), suggesting moderate robustness to missing-not-at-random bias.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Input Specification\n", + "\n", + "- **Data**: `/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set26/APOE_ind_set_26_mediation_all_input.txt`\n", + "- **Exposure**: `chr19_44918487_G_T`\n", + "- **Mediator 1**: `APOC1_DLPFC_exp` (APOC1 expression in DLPFC)\n", + "- **Mediator 2**: `APOC1_AC_exp` (APOC1 expression in anterior cingulate)\n", + "- **Outcome**: `age_first_ad_dx_num` (age at first AD diagnosis)\n", + "- **Mediator covariates**: msex_u, age_death_u, pmi_u, ROS_study_u\n", + "- **Outcome covariates**: educ, apoe4_dose, msex_u" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T15:58:09.915849Z", + "iopub.status.busy": "2026-03-31T15:58:09.910815Z", + "iopub.status.idle": "2026-03-31T15:58:20.061625Z", + "shell.execute_reply": "2026-03-31T15:58:20.059243Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setup complete.\n" + ] + } + ], + "source": [ + "suppressPackageStartupMessages({\n", + " library(lavaan)\n", + " library(blavaan)\n", + " library(ggplot2)\n", + " library(gridExtra)\n", + " library(dplyr)\n", + " library(tidyr)\n", + "})\n", + "\n", + "# Paths\n", + "DATA_FILE <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set26/APOE_ind_set_26_mediation_all_input.txt\"\n", + "OUT_DIR <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set26\"\n", + "\n", + "FIML_DIR <- file.path(OUT_DIR, \"main_SEM_FIML\")\n", + "BOOT_DIR <- file.path(OUT_DIR, \"bootstrap\")\n", + "MNAR_DIR <- file.path(OUT_DIR, \"MNAR_sensitivity\")\n", + "BAYES_DIR <- file.path(OUT_DIR, \"bayesian_blavaan\")\n", + "SUM_DIR <- file.path(OUT_DIR, \"summary\")\n", + "\n", + "for (d in c(FIML_DIR, BOOT_DIR, MNAR_DIR, BAYES_DIR, SUM_DIR)) dir.create(d, showWarnings=FALSE, recursive=TRUE)\n", + "\n", + "# Variable names\n", + "SNP <- \"chr19_44918487_G_T\"\n", + "MED1 <- \"APOC1_DLPFC_exp\"\n", + "MED2 <- \"APOC1_AC_exp\"\n", + "OUTCOME <- \"age_first_ad_dx_num\"\n", + "\n", + "MED_COVS <- c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\")\n", + "OUT_COVS <- c(\"educ\", \"apoe4_dose\", \"msex_u\")\n", + "\n", + "set.seed(42)\n", + "cat(\"Setup complete.\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading and Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T15:58:20.157246Z", + "iopub.status.busy": "2026-03-31T15:58:20.118229Z", + "iopub.status.idle": "2026-03-31T15:58:20.249455Z", + "shell.execute_reply": "2026-03-31T15:58:20.246833Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Raw data dimensions: 1153 x 36 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns used: chr19_44918487_G_T, APOC1_DLPFC_exp, APOC1_AC_exp, age_first_ad_dx_num, msex_u, age_death_u, pmi_u, ROS_study_u, educ, apoe4_dose \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Missingness per variable:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " variable n_obs n_miss pct_miss\n", + " chr19_44918487_G_T 1153 0 0.0\n", + " APOC1_DLPFC_exp 777 376 32.6\n", + " APOC1_AC_exp 593 560 48.6\n", + " age_first_ad_dx_num 388 765 66.3\n", + " msex_u 1115 38 3.3\n", + " age_death_u 1115 38 3.3\n", + " pmi_u 1115 38 3.3\n", + " ROS_study_u 1115 38 3.3\n", + " educ 1113 40 3.5\n", + " apoe4_dose 1106 47 4.1\n" + ] + } + ], + "source": [ + "dat_raw <- read.delim(DATA_FILE, stringsAsFactors=FALSE)\n", + "cat(\"Raw data dimensions:\", nrow(dat_raw), \"x\", ncol(dat_raw), \"\\n\")\n", + "\n", + "# Keep only needed columns\n", + "keep_cols <- unique(c(SNP, MED1, MED2, OUTCOME, MED_COVS, OUT_COVS))\n", + "cat(\"Columns used:\", paste(keep_cols, collapse=\", \"), \"\\n\")\n", + "dat <- dat_raw[, keep_cols]\n", + "\n", + "# Ensure numeric\n", + "for (col in keep_cols) dat[[col]] <- as.numeric(dat[[col]])\n", + "\n", + "cat(\"\\nMissingness per variable:\\n\")\n", + "miss_tab <- data.frame(\n", + " variable = keep_cols,\n", + " n_obs = sapply(keep_cols, function(x) sum(!is.na(dat[[x]]))),\n", + " n_miss = sapply(keep_cols, function(x) sum(is.na(dat[[x]]))),\n", + " pct_miss = round(sapply(keep_cols, function(x) mean(is.na(dat[[x]]))) * 100, 1)\n", + ")\n", + "print(miss_tab, row.names=FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T15:58:20.255133Z", + "iopub.status.busy": "2026-03-31T15:58:20.253492Z", + "iopub.status.idle": "2026-03-31T15:58:20.300440Z", + "shell.execute_reply": "2026-03-31T15:58:20.297448Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Pattern N Pct\n", + " SNP only 212 18.4\n", + " SNP + M1 only 148 12.8\n", + " SNP + M2 only 38 3.3\n", + " SNP + Y only 114 9.9\n", + " SNP + M1 + M2 367 31.8\n", + " SNP + M1 + Y 86 7.5\n", + " SNP + M2 + Y 12 1.0\n", + " All four 176 15.3\n", + " Missing SNP 0 0.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Total N = 1153 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N with SNP observed = 1153 (all used by FIML)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N with all four observed = 176 (complete cases)\n" + ] + } + ], + "source": [ + "# Sample overlap structure\n", + "has_snp <- !is.na(dat[[SNP]])\n", + "has_m1 <- !is.na(dat[[MED1]])\n", + "has_m2 <- !is.na(dat[[MED2]])\n", + "has_y <- !is.na(dat[[OUTCOME]])\n", + "\n", + "overlap <- data.frame(\n", + " Pattern = c(\"SNP only\", \"SNP + M1 only\", \"SNP + M2 only\", \"SNP + Y only\",\n", + " \"SNP + M1 + M2\", \"SNP + M1 + Y\", \"SNP + M2 + Y\", \"All four\",\n", + " \"Missing SNP\"),\n", + "\n", + " N = c(\n", + " sum(has_snp & !has_m1 & !has_m2 & !has_y),\n", + " sum(has_snp & has_m1 & !has_m2 & !has_y),\n", + " sum(has_snp & !has_m1 & has_m2 & !has_y),\n", + " sum(has_snp & !has_m1 & !has_m2 & has_y),\n", + " sum(has_snp & has_m1 & has_m2 & !has_y),\n", + " sum(has_snp & has_m1 & !has_m2 & has_y),\n", + " sum(has_snp & !has_m1 & has_m2 & has_y),\n", + " sum(has_snp & has_m1 & has_m2 & has_y),\n", + " sum(!has_snp)\n", + " )\n", + ")\n", + "overlap$Pct <- round(overlap$N / nrow(dat) * 100, 1)\n", + "print(overlap, row.names=FALSE)\n", + "cat(\"\\nTotal N =\", nrow(dat), \"\\n\")\n", + "cat(\"N with SNP observed =\", sum(has_snp), \"(all used by FIML)\\n\")\n", + "cat(\"N with all four observed =\", sum(has_snp & has_m1 & has_m2 & has_y), \"(complete cases)\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Covariate Strategy\n", + "\n", + "**Strategy A: Covariates-in-model.** Both mediators and the outcome have raw covariates available.\n", + "\n", + "- Each mediator equation includes: msex_u, age_death_u, pmi_u, ROS_study_u\n", + "- The outcome equation includes: educ, apoe4_dose, msex_u\n", + "- FIML handles the different covariate missingness patterns automatically\n", + "\n", + "Note: msex_u appears in both mediator and outcome covariate sets, which is appropriate since it may affect all variables." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Specification\n", + "\n", + "Parallel mediation model with covariates in each equation." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T15:58:20.308244Z", + "iopub.status.busy": "2026-03-31T15:58:20.305560Z", + "iopub.status.idle": "2026-03-31T15:58:20.356771Z", + "shell.execute_reply": "2026-03-31T15:58:20.354113Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model specification:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "APOC1_DLPFC_exp ~ a1 * chr19_44918487_G_T + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC1_AC_exp ~ a2 * chr19_44918487_G_T + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "age_first_ad_dx_num ~ b1 * APOC1_DLPFC_exp + b2 * APOC1_AC_exp + cp * chr19_44918487_G_T + educ + apoe4_dose + msex_u\n", + "APOC1_DLPFC_exp ~~ r_m1m2 * APOC1_AC_exp\n", + "ind1 := a1 * b1\n", + "ind2 := a2 * b2\n", + "total_indirect := ind1 + ind2\n", + "total := cp + total_indirect\n", + "diff_1_2 := ind1 - ind2\n", + "prop_med := total_indirect / total\n" + ] + } + ], + "source": [ + "med_cov_str <- paste(MED_COVS, collapse=\" + \")\n", + "out_cov_str <- paste(OUT_COVS, collapse=\" + \")\n", + "\n", + "model_str <- paste0(\n", + " # Mediator equations\n", + " MED1, \" ~ a1 * \", SNP, \" + \", med_cov_str, \"\\n\",\n", + " MED2, \" ~ a2 * \", SNP, \" + \", med_cov_str, \"\\n\",\n", + " # Outcome equation (both mediators + direct SNP effect)\n", + " OUTCOME, \" ~ b1 * \", MED1, \" + b2 * \", MED2, \" + cp * \", SNP, \" + \", out_cov_str, \"\\n\",\n", + " # Residual covariance between mediators (freely estimated)\n", + " MED1, \" ~~ r_m1m2 * \", MED2, \"\\n\",\n", + " # Defined parameters\n", + " \"ind1 := a1 * b1\\n\",\n", + " \"ind2 := a2 * b2\\n\",\n", + " \"total_indirect := ind1 + ind2\\n\",\n", + " \"total := cp + total_indirect\\n\",\n", + " \"diff_1_2 := ind1 - ind2\\n\",\n", + " \"prop_med := total_indirect / total\\n\"\n", + ")\n", + "\n", + "cat(\"Model specification:\\n\")\n", + "cat(model_str)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Method 1: FIML SEM (Primary Analysis)\n", + "\n", + "Full Information Maximum Likelihood uses all N=1153 subjects, including those with only the SNP observed. `missing=\"fiml\"` and `fixed.x=FALSE` ensure all rows contribute to estimation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T15:58:20.363106Z", + "iopub.status.busy": "2026-03-31T15:58:20.360904Z", + "iopub.status.idle": "2026-03-31T15:58:23.491219Z", + "shell.execute_reply": "2026-03-31T15:58:23.488533Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FIML converged: TRUE \n", + "N used by FIML: 1153 \n" + ] + }, + { + "data": { + "text/html": [ + "
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A data.frame: 64 × 11
lhsoprhslabelexoestsezpvaluestd.lvstd.all
<chr><chr><chr><chr><int><dbl><dbl><dbl><dbl><dbl><dbl>
APOC1_DLPFC_exp ~ chr19_44918487_G_T a1 0-0.10010234470.048979345-2.043766514.097662e-02-0.1001023447-0.071596898
APOC1_DLPFC_exp ~ msex_u 0 0.16421571440.069587448 2.359846791.828248e-02 0.1642157144 0.085044965
APOC1_DLPFC_exp ~ age_death_u 0 0.00673268780.005049426 1.333356941.824147e-01 0.0067326878 0.048227117
APOC1_DLPFC_exp ~ pmi_u 0-0.00790517780.005497087-1.438066721.504151e-01-0.0079051778-0.063994470
APOC1_DLPFC_exp ~ ROS_study_u 0-0.04844483190.065575526-0.738763914.600504e-01-0.0484448319-0.026399899
APOC1_AC_exp ~ chr19_44918487_G_T a2 0-0.18053666900.056909691-3.172336161.512179e-03-0.1805366690-0.116559509
APOC1_AC_exp ~ msex_u 0 0.31483302370.079451654 3.962573577.414615e-05 0.3148330237 0.147179220
APOC1_AC_exp ~ age_death_u 0 0.02754093960.006148503 4.479291667.489115e-06 0.0275409396 0.178079487
APOC1_AC_exp ~ pmi_u 0 0.03945842480.007422346 5.316166511.059762e-07 0.0394584248 0.288338655
APOC1_AC_exp ~ ROS_study_u 0-0.15000743560.075656180-1.982751914.739515e-02-0.1500074356-0.073790410
age_first_ad_dx_num~ APOC1_DLPFC_exp b1 0 0.47169762240.336747638 1.400745151.612903e-01 0.4716976224 0.086575015
age_first_ad_dx_num~ APOC1_AC_exp b2 0 1.12594529510.330135306 3.410557056.483032e-04 1.1259452951 0.228935876
age_first_ad_dx_num~ chr19_44918487_G_T cp 0-0.94771949370.387315649-2.446891821.440941e-02-0.9477194937-0.124410965
age_first_ad_dx_num~ educ 0-0.18778417750.069473003-2.702980586.872076e-03-0.1877841775-0.135341589
age_first_ad_dx_num~ apoe4_dose 0-2.69351669520.480652545-5.603874822.096121e-08-2.6935166952-0.258767260
age_first_ad_dx_num~ msex_u 0-0.85327630680.532438950-1.602580551.090273e-01-0.8532763068-0.081105903
APOC1_DLPFC_exp ~~APOC1_AC_exp r_m1m20 0.31246871230.041767179 7.481202267.371881e-14 0.3124687123 0.367637316
APOC1_DLPFC_exp ~~APOC1_DLPFC_exp 0 0.82695369910.04188983219.741155850.000000e+00 0.8269536991 0.982314632
APOC1_AC_exp ~~APOC1_AC_exp 0 0.87355993350.05213170016.756789860.000000e+00 0.8735599335 0.845525625
age_first_ad_dx_num~~age_first_ad_dx_num 020.45039330611.55339770413.164943690.000000e+0020.4503933061 0.818330730
chr19_44918487_G_T ~~chr19_44918487_G_T 0 0.43065599540.01793621524.010416240.000000e+00 0.4306559954 1.000000000
chr19_44918487_G_T ~~msex_u 0 0.00088122520.009342561 0.094323749.248520e-01 0.0008812252 0.002826002
chr19_44918487_G_T ~~age_death_u 0-0.08044288560.129242066-0.622420305.336655e-01-0.0804428856-0.018651093
chr19_44918487_G_T ~~pmi_u 0-0.11391907860.146073717-0.779873904.354651e-01-0.1139190786-0.023371421
chr19_44918487_G_T ~~ROS_study_u 0-0.00104274190.009830776-0.106069139.155275e-01-0.0010427419-0.003177909
chr19_44918487_G_T ~~educ 0 0.09934911850.070996959 1.399343291.617101e-01 0.0993491185 0.042018456
chr19_44918487_G_T ~~apoe4_dose 0-0.07607734150.009754504-7.799201246.217249e-15-0.0760773415-0.241387080
msex_u ~~msex_u 0 0.22578670350.00956258823.611465290.000000e+00 0.2257867035 1.000000000
msex_u ~~age_death_u 0-0.58828547060.095170305-6.181397386.353666e-10-0.5882854706-0.188373944
msex_u ~~pmi_u 0 0.10762454540.105744931 1.017774983.087849e-01 0.1076245454 0.030494117
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
age_death_u ~~pmi_u 0 0.656768011.462063390 0.44920626.532829e-01 0.65676801 0.013453888
age_death_u ~~ROS_study_u 0-0.464562100.099391229 -4.67407542.952808e-06-0.46456210-0.141369449
age_death_u ~~educ 0-3.181766220.716002562 -4.44379178.838719e-06-3.18176622-0.134366647
age_death_u ~~apoe4_dose 0-0.344657670.095339853 -3.61504313.002977e-04-0.34465767-0.109192745
pmi_u ~~pmi_u 055.168627762.336522500 23.61142590.000000e+0055.16862776 1.000000000
pmi_u ~~ROS_study_u 0 0.049596500.111228959 0.44589566.556727e-01 0.04959650 0.013354722
pmi_u ~~educ 0 0.128644560.801514791 0.16050188.724858e-01 0.12864456 0.004807139
pmi_u ~~apoe4_dose 0-0.073090480.107471715 -0.68009034.964473e-01-0.07309048-0.020489843
ROS_study_u ~~ROS_study_u 0 0.250000110.010588107 23.61140830.000000e+00 0.25000011 1.000000000
ROS_study_u ~~educ 0 0.882060730.060101587 14.67616370.000000e+00 0.88206073 0.489631905
ROS_study_u ~~apoe4_dose 0 0.013962710.007230223 1.93115835.346347e-02 0.01396271 0.058146479
educ ~~educ 012.981275090.550159532 23.59547430.000000e+0012.98127509 1.000000000
educ ~~apoe4_dose 0 0.130964110.052125235 2.51248961.198826e-02 0.13096411 0.075686350
apoe4_dose ~~apoe4_dose 0 0.230649290.009808028 23.51637680.000000e+00 0.23064929 1.000000000
APOC1_DLPFC_exp ~1 0-0.272946680.464380132 -0.58776565.566896e-01-0.27294668-0.297483258
APOC1_AC_exp ~1 0-2.698845280.562699980 -4.79624201.616699e-06-2.69884528-2.655186442
age_first_ad_dx_num~1 090.956625101.164277805 78.12278540.000000e+0090.9566251018.194827201
chr19_44918487_G_T ~1 0 0.680832610.019326384 35.22814340.000000e+00 0.68083261 1.037468936
msex_u ~1 0 0.344402260.014230449 24.20178380.000000e+00 0.34440226 0.724797698
age_death_u ~1 088.949995700.196827195451.91923740.000000e+0088.9499957013.534051418
pmi_u ~1 0 8.764188270.222439373 39.40034600.000000e+00 8.76418827 1.179955442
ROS_study_u ~1 0 0.499542640.014974058 33.36053850.000000e+00 0.49954264 0.999085061
educ ~1 016.372709830.107969839151.64151370.000000e+0016.37270983 4.544246573
apoe4_dose ~1 0 0.270777350.014422981 18.77402150.000000e+00 0.27077735 0.563814544
ind1 :=a1*b1 ind1 0-0.047218040.040290795 -1.17193112.412247e-01-0.04721804-0.006198503
ind2 :=a2*b2 ind2 0-0.203274410.086421385 -2.35213091.866620e-02-0.20327441-0.026684653
total_indirect :=ind1+ind2 total_indirect0-0.250492450.092249352 -2.71538446.619888e-03-0.25049245-0.032883156
total :=cp+total_indirect total 0-1.198211940.384769975 -3.11409941.845073e-03-1.19821194-0.157294121
diff_1_2 :=ind1-ind2 diff_1_2 0 0.156056380.098356825 1.58663491.125953e-01 0.15605638 0.020486151
prop_med :=total_indirect/totalprop_med 0 0.209055210.097370796 2.14700123.179319e-02 0.20905521 0.209055211
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"\\item[\\$test] 'standard'\n", + "\\item[\\$stat] 422.046367301717\n", + "\\item[\\$stat.group] 422.046367301717\n", + "\\item[\\$df] 7\n", + "\\item[\\$refdistr] 'chisq'\n", + "\\item[\\$pvalue] 0\n", + "\\end{description}\n", + "\n", + "\\item[\\$fit] \\begin{description*}\n", + "\\item[npar] 58\n", + "\\item[fmin] 0.183020974545411\n", + "\\item[chisq] 422.046367301717\n", + "\\item[df] 7\n", + "\\item[pvalue] 0\n", + "\\item[baseline.chisq] 628.310654217517\n", + "\\item[baseline.df] 24\n", + "\\item[baseline.pvalue] 0\n", + "\\item[cfi] 0.313190385764201\n", + "\\item[tli] -1.35477582023703\n", + "\\item[cfi.robust] 0.18806836790627\n", + "\\item[tli.robust] -1.78376559574993\n", + "\\item[logl] -16649.0014891043\n", + "\\item[unrestricted.logl] -16437.9783054534\n", + "\\item[aic] 33414.0029782085\n", + "\\item[bic] 33706.9100843841\n", + "\\item[ntotal] 1153\n", + "\\item[bic2] 33522.6834821798\n", + "\\item[rmsea] 0.226769496018173\n", + "\\item[rmsea.ci.lower] 0.208663134740589\n", + "\\item[rmsea.ci.upper] 0.245413428028186\n", + "\\item[rmsea.ci.level] 0.9\n", + "\\item[rmsea.pvalue] 0\n", + "\\item[rmsea.close.h0] 0.05\n", + "\\item[rmsea.notclose.pvalue] 1\n", + "\\item[rmsea.notclose.h0] 0.08\n", + "\\item[rmsea.robust] 0.438262196054824\n", + "\\item[rmsea.ci.lower.robust] 0.400584526017388\n", + "\\item[rmsea.ci.upper.robust] 0.477142397837481\n", + "\\item[rmsea.pvalue.robust] 0\n", + "\\item[rmsea.notclose.pvalue.robust] 1\n", + "\\item[srmr] 0.100180169261603\n", + "\\end{description*}\n", + "\n", + "\\item[\\$pe] A data.frame: 64 × 11\n", + "\\begin{tabular}{lllllllllll}\n", + " lhs & op & rhs & label & exo & est & se & z & pvalue & std.lv & std.all\\\\\n", + " & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t APOC1\\_DLPFC\\_exp & \\textasciitilde{} & chr19\\_44918487\\_G\\_T & a1 & 0 & -0.1001023447 & 0.048979345 & -2.04376651 & 4.097662e-02 & -0.1001023447 & -0.071596898\\\\\n", + "\t APOC1\\_DLPFC\\_exp & \\textasciitilde{} & msex\\_u & & 0 & 0.1642157144 & 0.069587448 & 2.35984679 & 1.828248e-02 & 0.1642157144 & 0.085044965\\\\\n", + "\t APOC1\\_DLPFC\\_exp & \\textasciitilde{} & age\\_death\\_u & & 0 & 0.0067326878 & 0.005049426 & 1.33335694 & 1.824147e-01 & 0.0067326878 & 0.048227117\\\\\n", + "\t APOC1\\_DLPFC\\_exp & \\textasciitilde{} & pmi\\_u & & 0 & -0.0079051778 & 0.005497087 & -1.43806672 & 1.504151e-01 & -0.0079051778 & -0.063994470\\\\\n", + "\t APOC1\\_DLPFC\\_exp & \\textasciitilde{} & ROS\\_study\\_u & & 0 & -0.0484448319 & 0.065575526 & -0.73876391 & 4.600504e-01 & -0.0484448319 & -0.026399899\\\\\n", + "\t APOC1\\_AC\\_exp & \\textasciitilde{} & chr19\\_44918487\\_G\\_T & a2 & 0 & -0.1805366690 & 0.056909691 & -3.17233616 & 1.512179e-03 & -0.1805366690 & -0.116559509\\\\\n", + "\t APOC1\\_AC\\_exp & \\textasciitilde{} & msex\\_u & & 0 & 0.3148330237 & 0.079451654 & 3.96257357 & 7.414615e-05 & 0.3148330237 & 0.147179220\\\\\n", + "\t APOC1\\_AC\\_exp & \\textasciitilde{} & age\\_death\\_u & & 0 & 0.0275409396 & 0.006148503 & 4.47929166 & 7.489115e-06 & 0.0275409396 & 0.178079487\\\\\n", + "\t APOC1\\_AC\\_exp & \\textasciitilde{} & pmi\\_u & & 0 & 0.0394584248 & 0.007422346 & 5.31616651 & 1.059762e-07 & 0.0394584248 & 0.288338655\\\\\n", + "\t APOC1\\_AC\\_exp & \\textasciitilde{} & ROS\\_study\\_u & & 0 & -0.1500074356 & 0.075656180 & -1.98275191 & 4.739515e-02 & -0.1500074356 & -0.073790410\\\\\n", + "\t age\\_first\\_ad\\_dx\\_num & \\textasciitilde{} & APOC1\\_DLPFC\\_exp & b1 & 0 & 0.4716976224 & 0.336747638 & 1.40074515 & 1.612903e-01 & 0.4716976224 & 0.086575015\\\\\n", + "\t age\\_first\\_ad\\_dx\\_num & \\textasciitilde{} & APOC1\\_AC\\_exp & b2 & 0 & 1.1259452951 & 0.330135306 & 3.41055705 & 6.483032e-04 & 1.1259452951 & 0.228935876\\\\\n", + "\t age\\_first\\_ad\\_dx\\_num & \\textasciitilde{} & chr19\\_44918487\\_G\\_T & cp & 0 & -0.9477194937 & 0.387315649 & -2.44689182 & 1.440941e-02 & -0.9477194937 & -0.124410965\\\\\n", + "\t age\\_first\\_ad\\_dx\\_num & \\textasciitilde{} & educ & & 0 & -0.1877841775 & 0.069473003 & -2.70298058 & 6.872076e-03 & -0.1877841775 & -0.135341589\\\\\n", + "\t age\\_first\\_ad\\_dx\\_num & \\textasciitilde{} & apoe4\\_dose & & 0 & -2.6935166952 & 0.480652545 & -5.60387482 & 2.096121e-08 & -2.6935166952 & -0.258767260\\\\\n", + "\t age\\_first\\_ad\\_dx\\_num & \\textasciitilde{} & msex\\_u & & 0 & -0.8532763068 & 0.532438950 & -1.60258055 & 1.090273e-01 & -0.8532763068 & -0.081105903\\\\\n", + "\t APOC1\\_DLPFC\\_exp & \\textasciitilde{}\\textasciitilde{} & APOC1\\_AC\\_exp & r\\_m1m2 & 0 & 0.3124687123 & 0.041767179 & 7.48120226 & 7.371881e-14 & 0.3124687123 & 0.367637316\\\\\n", + "\t APOC1\\_DLPFC\\_exp & \\textasciitilde{}\\textasciitilde{} & APOC1\\_DLPFC\\_exp & & 0 & 0.8269536991 & 0.041889832 & 19.74115585 & 0.000000e+00 & 0.8269536991 & 0.982314632\\\\\n", + "\t APOC1\\_AC\\_exp & \\textasciitilde{}\\textasciitilde{} & APOC1\\_AC\\_exp & & 0 & 0.8735599335 & 0.052131700 & 16.75678986 & 0.000000e+00 & 0.8735599335 & 0.845525625\\\\\n", + "\t age\\_first\\_ad\\_dx\\_num & \\textasciitilde{}\\textasciitilde{} & age\\_first\\_ad\\_dx\\_num & & 0 & 20.4503933061 & 1.553397704 & 13.16494369 & 0.000000e+00 & 20.4503933061 & 0.818330730\\\\\n", + "\t chr19\\_44918487\\_G\\_T & \\textasciitilde{}\\textasciitilde{} & chr19\\_44918487\\_G\\_T & & 0 & 0.4306559954 & 0.017936215 & 24.01041624 & 0.000000e+00 & 0.4306559954 & 1.000000000\\\\\n", + "\t chr19\\_44918487\\_G\\_T & \\textasciitilde{}\\textasciitilde{} & msex\\_u & & 0 & 0.0008812252 & 0.009342561 & 0.09432374 & 9.248520e-01 & 0.0008812252 & 0.002826002\\\\\n", + "\t chr19\\_44918487\\_G\\_T & \\textasciitilde{}\\textasciitilde{} & age\\_death\\_u & & 0 & -0.0804428856 & 0.129242066 & -0.62242030 & 5.336655e-01 & -0.0804428856 & -0.018651093\\\\\n", + "\t chr19\\_44918487\\_G\\_T & \\textasciitilde{}\\textasciitilde{} & pmi\\_u & & 0 & -0.1139190786 & 0.146073717 & -0.77987390 & 4.354651e-01 & -0.1139190786 & -0.023371421\\\\\n", + "\t chr19\\_44918487\\_G\\_T & \\textasciitilde{}\\textasciitilde{} & ROS\\_study\\_u & & 0 & -0.0010427419 & 0.009830776 & -0.10606913 & 9.155275e-01 & -0.0010427419 & -0.003177909\\\\\n", + "\t chr19\\_44918487\\_G\\_T & \\textasciitilde{}\\textasciitilde{} & educ & & 0 & 0.0993491185 & 0.070996959 & 1.39934329 & 1.617101e-01 & 0.0993491185 & 0.042018456\\\\\n", + "\t chr19\\_44918487\\_G\\_T & \\textasciitilde{}\\textasciitilde{} & apoe4\\_dose & & 0 & -0.0760773415 & 0.009754504 & -7.79920124 & 6.217249e-15 & -0.0760773415 & -0.241387080\\\\\n", + "\t msex\\_u & \\textasciitilde{}\\textasciitilde{} & msex\\_u & & 0 & 0.2257867035 & 0.009562588 & 23.61146529 & 0.000000e+00 & 0.2257867035 & 1.000000000\\\\\n", + "\t msex\\_u & \\textasciitilde{}\\textasciitilde{} & age\\_death\\_u & & 0 & -0.5882854706 & 0.095170305 & -6.18139738 & 6.353666e-10 & -0.5882854706 & -0.188373944\\\\\n", + "\t msex\\_u & \\textasciitilde{}\\textasciitilde{} & pmi\\_u & & 0 & 0.1076245454 & 0.105744931 & 1.01777498 & 3.087849e-01 & 0.1076245454 & 0.030494117\\\\\n", + "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", + "\t age\\_death\\_u & \\textasciitilde{}\\textasciitilde{} & pmi\\_u & & 0 & 0.65676801 & 1.462063390 & 0.4492062 & 6.532829e-01 & 0.65676801 & 0.013453888\\\\\n", + "\t age\\_death\\_u & \\textasciitilde{}\\textasciitilde{} & ROS\\_study\\_u & & 0 & -0.46456210 & 0.099391229 & -4.6740754 & 2.952808e-06 & -0.46456210 & -0.141369449\\\\\n", + "\t age\\_death\\_u & \\textasciitilde{}\\textasciitilde{} & educ & & 0 & -3.18176622 & 0.716002562 & -4.4437917 & 8.838719e-06 & -3.18176622 & -0.134366647\\\\\n", + "\t age\\_death\\_u & \\textasciitilde{}\\textasciitilde{} & apoe4\\_dose & & 0 & -0.34465767 & 0.095339853 & -3.6150431 & 3.002977e-04 & -0.34465767 & -0.109192745\\\\\n", + "\t pmi\\_u & \\textasciitilde{}\\textasciitilde{} & pmi\\_u & & 0 & 55.16862776 & 2.336522500 & 23.6114259 & 0.000000e+00 & 55.16862776 & 1.000000000\\\\\n", + "\t pmi\\_u & \\textasciitilde{}\\textasciitilde{} & ROS\\_study\\_u & & 0 & 0.04959650 & 0.111228959 & 0.4458956 & 6.556727e-01 & 0.04959650 & 0.013354722\\\\\n", + "\t pmi\\_u & \\textasciitilde{}\\textasciitilde{} & educ & & 0 & 0.12864456 & 0.801514791 & 0.1605018 & 8.724858e-01 & 0.12864456 & 0.004807139\\\\\n", + "\t pmi\\_u & \\textasciitilde{}\\textasciitilde{} & apoe4\\_dose & & 0 & -0.07309048 & 0.107471715 & -0.6800903 & 4.964473e-01 & -0.07309048 & -0.020489843\\\\\n", + "\t ROS\\_study\\_u & \\textasciitilde{}\\textasciitilde{} & ROS\\_study\\_u & & 0 & 0.25000011 & 0.010588107 & 23.6114083 & 0.000000e+00 & 0.25000011 & 1.000000000\\\\\n", + "\t ROS\\_study\\_u & \\textasciitilde{}\\textasciitilde{} & educ & & 0 & 0.88206073 & 0.060101587 & 14.6761637 & 0.000000e+00 & 0.88206073 & 0.489631905\\\\\n", + "\t ROS\\_study\\_u & \\textasciitilde{}\\textasciitilde{} & apoe4\\_dose & & 0 & 0.01396271 & 0.007230223 & 1.9311583 & 5.346347e-02 & 0.01396271 & 0.058146479\\\\\n", + "\t educ & \\textasciitilde{}\\textasciitilde{} & educ & & 0 & 12.98127509 & 0.550159532 & 23.5954743 & 0.000000e+00 & 12.98127509 & 1.000000000\\\\\n", + "\t educ & \\textasciitilde{}\\textasciitilde{} & apoe4\\_dose & & 0 & 0.13096411 & 0.052125235 & 2.5124896 & 1.198826e-02 & 0.13096411 & 0.075686350\\\\\n", + "\t apoe4\\_dose & \\textasciitilde{}\\textasciitilde{} & apoe4\\_dose & & 0 & 0.23064929 & 0.009808028 & 23.5163768 & 0.000000e+00 & 0.23064929 & 1.000000000\\\\\n", + "\t APOC1\\_DLPFC\\_exp & \\textasciitilde{}1 & & & 0 & -0.27294668 & 0.464380132 & -0.5877656 & 5.566896e-01 & -0.27294668 & -0.297483258\\\\\n", + "\t APOC1\\_AC\\_exp & \\textasciitilde{}1 & & & 0 & -2.69884528 & 0.562699980 & -4.7962420 & 1.616699e-06 & -2.69884528 & -2.655186442\\\\\n", + "\t age\\_first\\_ad\\_dx\\_num & \\textasciitilde{}1 & & & 0 & 90.95662510 & 1.164277805 & 78.1227854 & 0.000000e+00 & 90.95662510 & 18.194827201\\\\\n", + "\t chr19\\_44918487\\_G\\_T & \\textasciitilde{}1 & & & 0 & 0.68083261 & 0.019326384 & 35.2281434 & 0.000000e+00 & 0.68083261 & 1.037468936\\\\\n", + "\t msex\\_u & \\textasciitilde{}1 & & & 0 & 0.34440226 & 0.014230449 & 24.2017838 & 0.000000e+00 & 0.34440226 & 0.724797698\\\\\n", + "\t age\\_death\\_u & \\textasciitilde{}1 & & & 0 & 88.94999570 & 0.196827195 & 451.9192374 & 0.000000e+00 & 88.94999570 & 13.534051418\\\\\n", + "\t pmi\\_u & \\textasciitilde{}1 & & & 0 & 8.76418827 & 0.222439373 & 39.4003460 & 0.000000e+00 & 8.76418827 & 1.179955442\\\\\n", + "\t ROS\\_study\\_u & \\textasciitilde{}1 & & & 0 & 0.49954264 & 0.014974058 & 33.3605385 & 0.000000e+00 & 0.49954264 & 0.999085061\\\\\n", + "\t educ & \\textasciitilde{}1 & & & 0 & 16.37270983 & 0.107969839 & 151.6415137 & 0.000000e+00 & 16.37270983 & 4.544246573\\\\\n", + "\t apoe4\\_dose & \\textasciitilde{}1 & & & 0 & 0.27077735 & 0.014422981 & 18.7740215 & 0.000000e+00 & 0.27077735 & 0.563814544\\\\\n", + "\t ind1 & := & a1*b1 & ind1 & 0 & -0.04721804 & 0.040290795 & -1.1719311 & 2.412247e-01 & -0.04721804 & -0.006198503\\\\\n", + "\t ind2 & := & a2*b2 & ind2 & 0 & -0.20327441 & 0.086421385 & -2.3521309 & 1.866620e-02 & -0.20327441 & -0.026684653\\\\\n", + "\t total\\_indirect & := & ind1+ind2 & total\\_indirect & 0 & -0.25049245 & 0.092249352 & -2.7153844 & 6.619888e-03 & -0.25049245 & -0.032883156\\\\\n", + "\t total & := & cp+total\\_indirect & total & 0 & -1.19821194 & 0.384769975 & -3.1140994 & 1.845073e-03 & -1.19821194 & -0.157294121\\\\\n", + "\t diff\\_1\\_2 & := & ind1-ind2 & diff\\_1\\_2 & 0 & 0.15605638 & 0.098356825 & 1.5866349 & 1.125953e-01 & 0.15605638 & 0.020486151\\\\\n", + "\t prop\\_med & := & total\\_indirect/total & prop\\_med & 0 & 0.20905521 & 0.097370796 & 2.1470012 & 3.179319e-02 & 0.20905521 & 0.209055211\\\\\n", + "\\end{tabular}\n", + "\n", + "\\end{description}\n" + ], + "text/markdown": [ + "$header\n", + ": $lavaan.version\n", + ": '0.6-21'\n", + "$sam.approach\n", + ": FALSE\n", + "$optim.method\n", + ": 'nlminb'\n", + "$optim.iterations\n", + ": 121\n", + "$optim.converged\n", + ": TRUE\n", + "\n", + "\n", + "\n", + "$optim\n", + ": $estimator\n", + ": 'ML'\n", + "$estimator.args\n", + ": \n", + "\n", + "\n", + "$optim.method\n", + ": 'nlminb'\n", + "$npar\n", + ": 58\n", + "$eq.constraints\n", + ": FALSE\n", + "$nrow.ceq.jac\n", + ": 0\n", + "$nrow.cin.jac\n", + ": 0\n", + "$nrow.con.jac\n", + ": 0\n", + "$con.jac.rank\n", + ": 0\n", + "\n", + "\n", + "\n", + "$data\n", + ": $ngroups\n", + ": 1\n", + "$nobs\n", + ": 1153\n", + "$npatterns\n", + ": 15\n", + "\n", + "\n", + "\n", + "$test\n", + ": **$standard** = $test\n", + ": 'standard'\n", + "$stat\n", + ": 422.046367301717\n", + "$stat.group\n", + ": 422.046367301717\n", + "$df\n", + ": 7\n", + "$refdistr\n", + ": 'chisq'\n", + "$pvalue\n", + ": 0\n", + "\n", + "\n", + "\n", + "$fit\n", + ": npar\n", + ": 58fmin\n", + ": 0.183020974545411chisq\n", + ": 422.046367301717df\n", + ": 7pvalue\n", + ": 0baseline.chisq\n", + ": 628.310654217517baseline.df\n", + ": 24baseline.pvalue\n", + ": 0cfi\n", + ": 0.313190385764201tli\n", + ": -1.35477582023703cfi.robust\n", + ": 0.18806836790627tli.robust\n", + ": -1.78376559574993logl\n", + ": -16649.0014891043unrestricted.logl\n", + ": -16437.9783054534aic\n", + ": 33414.0029782085bic\n", + ": 33706.9100843841ntotal\n", + ": 1153bic2\n", + ": 33522.6834821798rmsea\n", + ": 0.226769496018173rmsea.ci.lower\n", + ": 0.208663134740589rmsea.ci.upper\n", + ": 0.245413428028186rmsea.ci.level\n", + ": 0.9rmsea.pvalue\n", + ": 0rmsea.close.h0\n", + ": 0.05rmsea.notclose.pvalue\n", + ": 1rmsea.notclose.h0\n", + ": 0.08rmsea.robust\n", + ": 0.438262196054824rmsea.ci.lower.robust\n", + ": 0.400584526017388rmsea.ci.upper.robust\n", + ": 0.477142397837481rmsea.pvalue.robust\n", + ": 0rmsea.notclose.pvalue.robust\n", + ": 1srmr\n", + ": 0.100180169261603\n", + "\n", + "\n", + "$pe\n", + ": \n", + "A data.frame: 64 × 11\n", + "\n", + "| lhs <chr> | op <chr> | rhs <chr> | label <chr> | exo <int> | est <dbl> | se <dbl> | z <dbl> | pvalue <dbl> | std.lv <dbl> | std.all <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|\n", + "| APOC1_DLPFC_exp | ~ | chr19_44918487_G_T | a1 | 0 | -0.1001023447 | 0.048979345 | -2.04376651 | 4.097662e-02 | -0.1001023447 | -0.071596898 |\n", + "| APOC1_DLPFC_exp | ~ | msex_u | | 0 | 0.1642157144 | 0.069587448 | 2.35984679 | 1.828248e-02 | 0.1642157144 | 0.085044965 |\n", + "| APOC1_DLPFC_exp | ~ | age_death_u | | 0 | 0.0067326878 | 0.005049426 | 1.33335694 | 1.824147e-01 | 0.0067326878 | 0.048227117 |\n", + "| APOC1_DLPFC_exp | ~ | pmi_u | | 0 | -0.0079051778 | 0.005497087 | -1.43806672 | 1.504151e-01 | -0.0079051778 | -0.063994470 |\n", + "| APOC1_DLPFC_exp | ~ | ROS_study_u | | 0 | -0.0484448319 | 0.065575526 | -0.73876391 | 4.600504e-01 | -0.0484448319 | -0.026399899 |\n", + "| APOC1_AC_exp | ~ | chr19_44918487_G_T | a2 | 0 | -0.1805366690 | 0.056909691 | -3.17233616 | 1.512179e-03 | -0.1805366690 | -0.116559509 |\n", + "| APOC1_AC_exp | ~ | msex_u | | 0 | 0.3148330237 | 0.079451654 | 3.96257357 | 7.414615e-05 | 0.3148330237 | 0.147179220 |\n", + "| APOC1_AC_exp | ~ | age_death_u | | 0 | 0.0275409396 | 0.006148503 | 4.47929166 | 7.489115e-06 | 0.0275409396 | 0.178079487 |\n", + "| APOC1_AC_exp | ~ | pmi_u | | 0 | 0.0394584248 | 0.007422346 | 5.31616651 | 1.059762e-07 | 0.0394584248 | 0.288338655 |\n", + "| APOC1_AC_exp | ~ | ROS_study_u | | 0 | -0.1500074356 | 0.075656180 | -1.98275191 | 4.739515e-02 | -0.1500074356 | -0.073790410 |\n", + "| age_first_ad_dx_num | ~ | APOC1_DLPFC_exp | b1 | 0 | 0.4716976224 | 0.336747638 | 1.40074515 | 1.612903e-01 | 0.4716976224 | 0.086575015 |\n", + "| age_first_ad_dx_num | ~ | APOC1_AC_exp | b2 | 0 | 1.1259452951 | 0.330135306 | 3.41055705 | 6.483032e-04 | 1.1259452951 | 0.228935876 |\n", + "| age_first_ad_dx_num | ~ | chr19_44918487_G_T | cp | 0 | -0.9477194937 | 0.387315649 | -2.44689182 | 1.440941e-02 | -0.9477194937 | -0.124410965 |\n", + "| age_first_ad_dx_num | ~ | educ | | 0 | -0.1877841775 | 0.069473003 | -2.70298058 | 6.872076e-03 | -0.1877841775 | -0.135341589 |\n", + "| age_first_ad_dx_num | ~ | apoe4_dose | | 0 | -2.6935166952 | 0.480652545 | -5.60387482 | 2.096121e-08 | -2.6935166952 | -0.258767260 |\n", + "| age_first_ad_dx_num | ~ | msex_u | | 0 | -0.8532763068 | 0.532438950 | -1.60258055 | 1.090273e-01 | -0.8532763068 | -0.081105903 |\n", + "| APOC1_DLPFC_exp | ~~ | APOC1_AC_exp | r_m1m2 | 0 | 0.3124687123 | 0.041767179 | 7.48120226 | 7.371881e-14 | 0.3124687123 | 0.367637316 |\n", + "| APOC1_DLPFC_exp | ~~ | APOC1_DLPFC_exp | | 0 | 0.8269536991 | 0.041889832 | 19.74115585 | 0.000000e+00 | 0.8269536991 | 0.982314632 |\n", + "| APOC1_AC_exp | ~~ | APOC1_AC_exp | | 0 | 0.8735599335 | 0.052131700 | 16.75678986 | 0.000000e+00 | 0.8735599335 | 0.845525625 |\n", + "| age_first_ad_dx_num | ~~ | age_first_ad_dx_num | | 0 | 20.4503933061 | 1.553397704 | 13.16494369 | 0.000000e+00 | 20.4503933061 | 0.818330730 |\n", + "| chr19_44918487_G_T | ~~ | chr19_44918487_G_T | | 0 | 0.4306559954 | 0.017936215 | 24.01041624 | 0.000000e+00 | 0.4306559954 | 1.000000000 |\n", + "| chr19_44918487_G_T | ~~ | msex_u | | 0 | 0.0008812252 | 0.009342561 | 0.09432374 | 9.248520e-01 | 0.0008812252 | 0.002826002 |\n", + "| chr19_44918487_G_T | ~~ | age_death_u | | 0 | -0.0804428856 | 0.129242066 | -0.62242030 | 5.336655e-01 | -0.0804428856 | -0.018651093 |\n", + "| chr19_44918487_G_T | ~~ | pmi_u | | 0 | -0.1139190786 | 0.146073717 | -0.77987390 | 4.354651e-01 | -0.1139190786 | -0.023371421 |\n", + "| chr19_44918487_G_T | ~~ | ROS_study_u | | 0 | -0.0010427419 | 0.009830776 | -0.10606913 | 9.155275e-01 | -0.0010427419 | -0.003177909 |\n", + "| chr19_44918487_G_T | ~~ | educ | | 0 | 0.0993491185 | 0.070996959 | 1.39934329 | 1.617101e-01 | 0.0993491185 | 0.042018456 |\n", + "| chr19_44918487_G_T | ~~ | apoe4_dose | | 0 | -0.0760773415 | 0.009754504 | -7.79920124 | 6.217249e-15 | -0.0760773415 | -0.241387080 |\n", + "| msex_u | ~~ | msex_u | | 0 | 0.2257867035 | 0.009562588 | 23.61146529 | 0.000000e+00 | 0.2257867035 | 1.000000000 |\n", + "| msex_u | ~~ | age_death_u | | 0 | -0.5882854706 | 0.095170305 | -6.18139738 | 6.353666e-10 | -0.5882854706 | -0.188373944 |\n", + "| msex_u | ~~ | pmi_u | | 0 | 0.1076245454 | 0.105744931 | 1.01777498 | 3.087849e-01 | 0.1076245454 | 0.030494117 |\n", + "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n", + "| age_death_u | ~~ | pmi_u | | 0 | 0.65676801 | 1.462063390 | 0.4492062 | 6.532829e-01 | 0.65676801 | 0.013453888 |\n", + "| age_death_u | ~~ | ROS_study_u | | 0 | -0.46456210 | 0.099391229 | -4.6740754 | 2.952808e-06 | -0.46456210 | -0.141369449 |\n", + "| age_death_u | ~~ | educ | | 0 | -3.18176622 | 0.716002562 | -4.4437917 | 8.838719e-06 | -3.18176622 | -0.134366647 |\n", + "| age_death_u | ~~ | apoe4_dose | | 0 | -0.34465767 | 0.095339853 | -3.6150431 | 3.002977e-04 | -0.34465767 | -0.109192745 |\n", + "| pmi_u | ~~ | pmi_u | | 0 | 55.16862776 | 2.336522500 | 23.6114259 | 0.000000e+00 | 55.16862776 | 1.000000000 |\n", + "| pmi_u | ~~ | ROS_study_u | | 0 | 0.04959650 | 0.111228959 | 0.4458956 | 6.556727e-01 | 0.04959650 | 0.013354722 |\n", + "| pmi_u | ~~ | educ | | 0 | 0.12864456 | 0.801514791 | 0.1605018 | 8.724858e-01 | 0.12864456 | 0.004807139 |\n", + "| pmi_u | ~~ | apoe4_dose | | 0 | -0.07309048 | 0.107471715 | -0.6800903 | 4.964473e-01 | -0.07309048 | -0.020489843 |\n", + "| ROS_study_u | ~~ | ROS_study_u | | 0 | 0.25000011 | 0.010588107 | 23.6114083 | 0.000000e+00 | 0.25000011 | 1.000000000 |\n", + "| ROS_study_u | ~~ | educ | | 0 | 0.88206073 | 0.060101587 | 14.6761637 | 0.000000e+00 | 0.88206073 | 0.489631905 |\n", + "| ROS_study_u | ~~ | apoe4_dose | | 0 | 0.01396271 | 0.007230223 | 1.9311583 | 5.346347e-02 | 0.01396271 | 0.058146479 |\n", + "| educ | ~~ | educ | | 0 | 12.98127509 | 0.550159532 | 23.5954743 | 0.000000e+00 | 12.98127509 | 1.000000000 |\n", + "| educ | ~~ | apoe4_dose | | 0 | 0.13096411 | 0.052125235 | 2.5124896 | 1.198826e-02 | 0.13096411 | 0.075686350 |\n", + "| apoe4_dose | ~~ | apoe4_dose | | 0 | 0.23064929 | 0.009808028 | 23.5163768 | 0.000000e+00 | 0.23064929 | 1.000000000 |\n", + "| APOC1_DLPFC_exp | ~1 | | | 0 | -0.27294668 | 0.464380132 | -0.5877656 | 5.566896e-01 | -0.27294668 | -0.297483258 |\n", + "| APOC1_AC_exp | ~1 | | | 0 | -2.69884528 | 0.562699980 | -4.7962420 | 1.616699e-06 | -2.69884528 | -2.655186442 |\n", + "| age_first_ad_dx_num | ~1 | | | 0 | 90.95662510 | 1.164277805 | 78.1227854 | 0.000000e+00 | 90.95662510 | 18.194827201 |\n", + "| chr19_44918487_G_T | ~1 | | | 0 | 0.68083261 | 0.019326384 | 35.2281434 | 0.000000e+00 | 0.68083261 | 1.037468936 |\n", + "| msex_u | ~1 | | | 0 | 0.34440226 | 0.014230449 | 24.2017838 | 0.000000e+00 | 0.34440226 | 0.724797698 |\n", + "| age_death_u | ~1 | | | 0 | 88.94999570 | 0.196827195 | 451.9192374 | 0.000000e+00 | 88.94999570 | 13.534051418 |\n", + "| pmi_u | ~1 | | | 0 | 8.76418827 | 0.222439373 | 39.4003460 | 0.000000e+00 | 8.76418827 | 1.179955442 |\n", + "| ROS_study_u | ~1 | | | 0 | 0.49954264 | 0.014974058 | 33.3605385 | 0.000000e+00 | 0.49954264 | 0.999085061 |\n", + "| educ | ~1 | | | 0 | 16.37270983 | 0.107969839 | 151.6415137 | 0.000000e+00 | 16.37270983 | 4.544246573 |\n", + "| apoe4_dose | ~1 | | | 0 | 0.27077735 | 0.014422981 | 18.7740215 | 0.000000e+00 | 0.27077735 | 0.563814544 |\n", + "| ind1 | := | a1*b1 | ind1 | 0 | -0.04721804 | 0.040290795 | -1.1719311 | 2.412247e-01 | -0.04721804 | -0.006198503 |\n", + "| ind2 | := | a2*b2 | ind2 | 0 | -0.20327441 | 0.086421385 | -2.3521309 | 1.866620e-02 | -0.20327441 | -0.026684653 |\n", + "| total_indirect | := | ind1+ind2 | total_indirect | 0 | -0.25049245 | 0.092249352 | -2.7153844 | 6.619888e-03 | -0.25049245 | -0.032883156 |\n", + "| total | := | cp+total_indirect | total | 0 | -1.19821194 | 0.384769975 | -3.1140994 | 1.845073e-03 | -1.19821194 | -0.157294121 |\n", + "| diff_1_2 | := | ind1-ind2 | diff_1_2 | 0 | 0.15605638 | 0.098356825 | 1.5866349 | 1.125953e-01 | 0.15605638 | 0.020486151 |\n", + "| prop_med | := | total_indirect/total | prop_med | 0 | 0.20905521 | 0.097370796 | 2.1470012 | 3.179319e-02 | 0.20905521 | 0.209055211 |\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "lavaan 0.6-21 ended normally after 121 iterations\n", + "\n", + " Estimator ML\n", + " Optimization method NLMINB\n", + " Number of model parameters 58\n", + "\n", + " Number of observations 1153\n", + " Number of missing patterns 15\n", + "\n", + "Model Test User Model:\n", + " \n", + " Test statistic 422.046\n", + " Degrees of freedom 7\n", + " P-value (Chi-square) 0.000\n", + "\n", + "Model Test Baseline Model:\n", + "\n", + " Test statistic 628.311\n", + " Degrees of freedom 24\n", + " P-value 0.000\n", + "\n", + "User Model versus Baseline Model:\n", + "\n", + " Comparative Fit Index (CFI) 0.313\n", + " Tucker-Lewis Index (TLI) -1.355\n", + " \n", + " Robust Comparative Fit Index (CFI) 0.188\n", + " Robust Tucker-Lewis Index (TLI) -1.784\n", + "\n", + "Loglikelihood and Information Criteria:\n", + "\n", + " Loglikelihood user model (H0) -16649.001\n", + " Loglikelihood unrestricted model (H1) -16437.978\n", + " \n", + " Akaike (AIC) 33414.003\n", + " Bayesian (BIC) 33706.910\n", + " Sample-size adjusted Bayesian (SABIC) 33522.683\n", + "\n", + "Root Mean Square Error of Approximation:\n", + "\n", + " RMSEA 0.227\n", + " 90 Percent confidence interval - lower 0.209\n", + " 90 Percent confidence interval - upper 0.245\n", + " P-value H_0: RMSEA <= 0.050 0.000\n", + " P-value H_0: RMSEA >= 0.080 1.000\n", + " \n", + " Robust RMSEA 0.438\n", + " 90 Percent confidence interval - lower 0.401\n", + " 90 Percent confidence interval - upper 0.477\n", + " P-value H_0: Robust RMSEA <= 0.050 0.000\n", + " P-value H_0: Robust RMSEA >= 0.080 1.000\n", + "\n", + "Standardized Root Mean Square Residual:\n", + "\n", + " SRMR 0.100\n", + "\n", + "Parameter Estimates:\n", + "\n", + " Standard errors Standard\n", + " Information Observed\n", + " Observed information based on Hessian\n", + "\n", + "Regressions:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv Std.all\n", + " APOC1_DLPFC_exp ~ \n", + " c19_44918 (a1) -0.100 0.049 -2.044 0.041 -0.100 -0.072\n", + " msex_u 0.164 0.070 2.360 0.018 0.164 0.085\n", + " age_deth_ 0.007 0.005 1.333 0.182 0.007 0.048\n", + " pmi_u -0.008 0.005 -1.438 0.150 -0.008 -0.064\n", + " ROS_stdy_ -0.048 0.066 -0.739 0.460 -0.048 -0.026\n", + " APOC1_AC_exp ~ \n", + " c19_44918 (a2) -0.181 0.057 -3.172 0.002 -0.181 -0.117\n", + " msex_u 0.315 0.079 3.963 0.000 0.315 0.147\n", + " age_deth_ 0.028 0.006 4.479 0.000 0.028 0.178\n", + " pmi_u 0.039 0.007 5.316 0.000 0.039 0.288\n", + " ROS_stdy_ -0.150 0.076 -1.983 0.047 -0.150 -0.074\n", + " age_first_ad_dx_num ~ \n", + " APOC1_DLP (b1) 0.472 0.337 1.401 0.161 0.472 0.087\n", + " APOC1_AC_ (b2) 1.126 0.330 3.411 0.001 1.126 0.229\n", + " c19_44918 (cp) -0.948 0.387 -2.447 0.014 -0.948 -0.124\n", + " educ -0.188 0.069 -2.703 0.007 -0.188 -0.135\n", + " apoe4_dos -2.694 0.481 -5.604 0.000 -2.694 -0.259\n", + " msex_u -0.853 0.532 -1.603 0.109 -0.853 -0.081\n", + "\n", + "Covariances:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv Std.all\n", + " .APOC1_DLPFC_exp ~~ \n", + " .APOC1_A (r_12) 0.312 0.042 7.481 0.000 0.312 0.368\n", + " chr19_44918487_G_T ~~ \n", + " msex_u 0.001 0.009 0.094 0.925 0.001 0.003\n", + " ag_dth_ -0.080 0.129 -0.622 0.534 -0.080 -0.019\n", + " pmi_u -0.114 0.146 -0.780 0.435 -0.114 -0.023\n", + " ROS_st_ -0.001 0.010 -0.106 0.916 -0.001 -0.003\n", + " educ 0.099 0.071 1.399 0.162 0.099 0.042\n", + " apo4_ds -0.076 0.010 -7.799 0.000 -0.076 -0.241\n", + " msex_u ~~ \n", + " ag_dth_ -0.588 0.095 -6.181 0.000 -0.588 -0.188\n", + " pmi_u 0.108 0.106 1.018 0.309 0.108 0.030\n", + " ROS_st_ 0.006 0.007 0.778 0.437 0.006 0.023\n", + " educ 0.345 0.052 6.603 0.000 0.345 0.202\n", + " apo4_ds -0.000 0.007 -0.030 0.976 -0.000 -0.001\n", + " age_death_u ~~ \n", + " pmi_u 0.657 1.462 0.449 0.653 0.657 0.013\n", + " ROS_st_ -0.465 0.099 -4.674 0.000 -0.465 -0.141\n", + " educ -3.182 0.716 -4.444 0.000 -3.182 -0.134\n", + " apo4_ds -0.345 0.095 -3.615 0.000 -0.345 -0.109\n", + " pmi_u ~~ \n", + " ROS_st_ 0.050 0.111 0.446 0.656 0.050 0.013\n", + " educ 0.129 0.802 0.161 0.872 0.129 0.005\n", + " apo4_ds -0.073 0.107 -0.680 0.496 -0.073 -0.020\n", + " ROS_study_u ~~ \n", + " educ 0.882 0.060 14.676 0.000 0.882 0.490\n", + " apo4_ds 0.014 0.007 1.931 0.053 0.014 0.058\n", + " educ ~~ \n", + " apo4_ds 0.131 0.052 2.512 0.012 0.131 0.076\n", + "\n", + "Intercepts:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv Std.all\n", + " .APOC1_DLPFC_xp -0.273 0.464 -0.588 0.557 -0.273 -0.297\n", + " .APOC1_AC_exp -2.699 0.563 -4.796 0.000 -2.699 -2.655\n", + " .ag_frst_d_dx_n 90.957 1.164 78.123 0.000 90.957 18.195\n", + " c19_44918487_G 0.681 0.019 35.228 0.000 0.681 1.037\n", + " msex_u 0.344 0.014 24.202 0.000 0.344 0.725\n", + " age_death_u 88.950 0.197 451.919 0.000 88.950 13.534\n", + " pmi_u 8.764 0.222 39.400 0.000 8.764 1.180\n", + " ROS_study_u 0.500 0.015 33.361 0.000 0.500 0.999\n", + " educ 16.373 0.108 151.642 0.000 16.373 4.544\n", + " apoe4_dose 0.271 0.014 18.774 0.000 0.271 0.564\n", + "\n", + "Variances:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv Std.all\n", + " .APOC1_DLPFC_xp 0.827 0.042 19.741 0.000 0.827 0.982\n", + " .APOC1_AC_exp 0.874 0.052 16.757 0.000 0.874 0.846\n", + " .ag_frst_d_dx_n 20.450 1.553 13.165 0.000 20.450 0.818\n", + " c19_44918487_G 0.431 0.018 24.010 0.000 0.431 1.000\n", + " msex_u 0.226 0.010 23.611 0.000 0.226 1.000\n", + " age_death_u 43.195 1.829 23.611 0.000 43.195 1.000\n", + " pmi_u 55.169 2.337 23.611 0.000 55.169 1.000\n", + " ROS_study_u 0.250 0.011 23.611 0.000 0.250 1.000\n", + " educ 12.981 0.550 23.595 0.000 12.981 1.000\n", + " apoe4_dose 0.231 0.010 23.516 0.000 0.231 1.000\n", + "\n", + "Defined Parameters:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv Std.all\n", + " ind1 -0.047 0.040 -1.172 0.241 -0.047 -0.006\n", + " ind2 -0.203 0.086 -2.352 0.019 -0.203 -0.027\n", + " total_indirect -0.250 0.092 -2.715 0.007 -0.250 -0.033\n", + " total -1.198 0.385 -3.114 0.002 -1.198 -0.157\n", + " diff_1_2 0.156 0.098 1.587 0.113 0.156 0.020\n", + " prop_med 0.209 0.097 2.147 0.032 0.209 0.209\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit_fiml <- tryCatch(\n", + " sem(model_str, data=dat, missing=\"fiml\", fixed.x=FALSE, estimator=\"ML\"),\n", + " error = function(e) { cat(\"FIML fit error:\", conditionMessage(e), \"\\n\"); NULL }\n", + ")\n", + "\n", + "if (!is.null(fit_fiml)) {\n", + " cat(\"FIML converged:\", lavInspect(fit_fiml, \"converged\"), \"\\n\")\n", + " N_fiml <- lavInspect(fit_fiml, \"nobs\")\n", + " cat(\"N used by FIML:\", N_fiml, \"\\n\")\n", + " summary(fit_fiml, fit.measures=TRUE, standardized=TRUE)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T15:58:23.497765Z", + "iopub.status.busy": "2026-03-31T15:58:23.496034Z", + "iopub.status.idle": "2026-03-31T15:58:24.902508Z", + "shell.execute_reply": "2026-03-31T15:58:24.899804Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Key path estimates (FIML, N = 1153 ):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label est se z pvalue ci.lower ci.upper N method\n", + " a1 -0.100 0.049 -2.044 0.041 -0.196 -0.004 1153 FIML\n", + " a2 -0.181 0.057 -3.172 0.002 -0.292 -0.069 1153 FIML\n", + " b1 0.472 0.337 1.401 0.161 -0.188 1.132 1153 FIML\n", + " b2 1.126 0.330 3.411 0.001 0.479 1.773 1153 FIML\n", + " cp -0.948 0.387 -2.447 0.014 -1.707 -0.189 1153 FIML\n", + " r_m1m2 0.312 0.042 7.481 0.000 0.231 0.394 1153 FIML\n", + " ind1 -0.047 0.040 -1.172 0.241 -0.126 0.032 1153 FIML\n", + " ind2 -0.203 0.086 -2.352 0.019 -0.373 -0.034 1153 FIML\n", + " total_indirect -0.250 0.092 -2.715 0.007 -0.431 -0.070 1153 FIML\n", + " total -1.198 0.385 -3.114 0.002 -1.952 -0.444 1153 FIML\n", + " diff_1_2 0.156 0.098 1.587 0.113 -0.037 0.349 1153 FIML\n", + " prop_med 0.209 0.097 2.147 0.032 0.018 0.400 1153 FIML\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fit measures:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " measure value N\n", + " chisq 422.0464 1153\n", + " df 7.0000 1153\n", + " pvalue 0.0000 1153\n", + " cfi 0.3132 1153\n", + " tli -1.3548 1153\n", + " rmsea 0.2268 1153\n", + " srmr 0.1002 1153\n", + " aic 33414.0030 1153\n", + " bic 33706.9101 1153\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Mediator Residual Covariance (M1 ~~ M2) ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Covariance (unstandardized): 0.3124687 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SE: 0.04176718 , z: 7.481202 , p: 7.371881e-14 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "95% CI: [ 0.2306065 , 0.3943309 ]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Residual correlation (standardized): 0.3676 \n", + " This represents the proportion of shared residual variance between DLPFC and AC\n", + " expression after accounting for the SNP and covariates.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Mediator correlation saved to fiml_mediator_correlation.csv\n" + ] + } + ], + "source": [ + "# Extract all parameter estimates\n", + "pe_fiml <- parameterEstimates(fit_fiml, ci=TRUE)\n", + "\n", + "# Key labeled parameters\n", + "key_labels <- c(\"a1\", \"a2\", \"b1\", \"b2\", \"cp\", \"ind1\", \"ind2\", \"total_indirect\", \"total\", \"diff_1_2\", \"prop_med\", \"r_m1m2\")\n", + "fiml_key <- pe_fiml[pe_fiml$label %in% key_labels, c(\"label\", \"est\", \"se\", \"z\", \"pvalue\", \"ci.lower\", \"ci.upper\")]\n", + "fiml_key$N <- N_fiml\n", + "fiml_key$method <- \"FIML\"\n", + "\n", + "cat(\"\\nKey path estimates (FIML, N =\", N_fiml, \"):\\n\")\n", + "print(fiml_key, row.names=FALSE)\n", + "\n", + "# Save\n", + "write.csv(fiml_key, file.path(FIML_DIR, \"fiml_all_paths.csv\"), row.names=FALSE)\n", + "\n", + "# Full parameter table\n", + "pe_fiml$N <- N_fiml\n", + "write.csv(pe_fiml, file.path(FIML_DIR, \"fiml_all_params.csv\"), row.names=FALSE)\n", + "\n", + "# Fit measures\n", + "fm <- fitMeasures(fit_fiml, c(\"chisq\", \"df\", \"pvalue\", \"cfi\", \"tli\", \"rmsea\", \"srmr\", \"aic\", \"bic\"))\n", + "fm_df <- data.frame(measure=names(fm), value=round(as.numeric(fm), 4), N=N_fiml)\n", + "write.csv(fm_df, file.path(FIML_DIR, \"fiml_fit_measures.csv\"), row.names=FALSE)\n", + "cat(\"\\nFit measures:\\n\")\n", + "print(fm_df, row.names=FALSE)\n", + "\n", + "# Extract mediator residual correlation (standardized covariance)\n", + "pe_all <- parameterEstimates(fit_fiml, ci=TRUE)\n", + "cov_row <- pe_all[pe_all$label == \"r_m1m2\", ]\n", + "cat(\"\\n--- Mediator Residual Covariance (M1 ~~ M2) ---\\n\")\n", + "cat(\"Covariance (unstandardized):\", cov_row$est, \"\\n\")\n", + "cat(\"SE:\", cov_row$se, \", z:\", cov_row$z, \", p:\", cov_row$pvalue, \"\\n\")\n", + "cat(\"95% CI: [\", cov_row$ci.lower, \",\", cov_row$ci.upper, \"]\\n\")\n", + "\n", + "# Also compute the residual CORRELATION (standardized)\n", + "std_pe <- standardizedSolution(fit_fiml)\n", + "std_cov <- std_pe[std_pe$lhs == MED1 & std_pe$rhs == MED2 & std_pe$op == \"~~\", ]\n", + "if (nrow(std_cov) > 0) {\n", + " cat(\"Residual correlation (standardized):\", round(std_cov$est.std[1], 4), \"\\n\")\n", + " cat(\" This represents the proportion of shared residual variance between DLPFC and AC\\n\")\n", + " cat(\" expression after accounting for the SNP and covariates.\\n\")\n", + "}\n", + "\n", + "# Save mediator correlation info\n", + "med_corr_df <- data.frame(\n", + " parameter = \"M1_M2_residual_covariance\",\n", + " label = \"r_m1m2\",\n", + " estimate_unstd = cov_row$est,\n", + " se = cov_row$se,\n", + " z = cov_row$z,\n", + " p_value = cov_row$pvalue,\n", + " ci_lower = cov_row$ci.lower,\n", + " ci_upper = cov_row$ci.upper,\n", + " estimate_std = ifelse(nrow(std_cov) > 0, std_cov$est.std[1], NA),\n", + " N = N_fiml,\n", + " method = \"FIML\"\n", + ")\n", + "write.csv(med_corr_df, file.path(FIML_DIR, \"fiml_mediator_correlation.csv\"), row.names=FALSE)\n", + "cat(\"\\nMediator correlation saved to fiml_mediator_correlation.csv\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### FIML Interpretation\n", + "\n", + "- **a1**: Effect of SNP on APOC1 expression in DLPFC (adjusting for demographic covariates)\n", + "- **a2**: Effect of SNP on APOC1 expression in AC (adjusting for demographic covariates)\n", + "- **b1**: Effect of DLPFC expression on AD age (controlling for AC expression and SNP)\n", + "- **b2**: Effect of AC expression on AD age (controlling for DLPFC expression and SNP)\n", + "- **cp**: Direct effect of SNP on AD age (bypassing both mediators)\n", + "- **ind1**: Specific indirect through DLPFC (a1 x b1)\n", + "- **ind2**: Specific indirect through AC (a2 x b2)\n", + "- **total_indirect**: Sum of both specific indirects\n", + "- **diff_1_2**: Contrast between the two specific indirects\n", + "- **prop_med**: Proportion of total effect mediated\n", + "- **r_m1m2**: Residual covariance between APOC1_DLPFC_exp and APOC1_AC_exp. A positive value indicates that the two expression measures share unexplained variance (beyond SNP and covariates). This is expected since they measure the same gene in related brain tissues. Including this covariance ensures the specific indirect effects (ind1, ind2) are properly estimated.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T15:58:24.908295Z", + "iopub.status.busy": "2026-03-31T15:58:24.906721Z", + "iopub.status.idle": "2026-03-31T15:58:27.983181Z", + "shell.execute_reply": "2026-03-31T15:58:27.980997Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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Bw7duzu3btjY2MVFpZIJCYmJtu2bUtMTPzll1+q9Ub37t27ceOGpaVl+/btqxuk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Wl5fn5eW1sHGKog4ePGhmZvYSziYeNWrU48eP7zkUwqoAACAASURBVN+/rzlRLBZv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WvRs6+//prNZn/xxReGLgTgDWCPHQAANItC\noRg8eLBAIHj06BH9C7AMcPXq1TFjxmzdunX16tWGrgXgDSDYAQBAc+Xm5vbq1WvIkCHnz583\ndC16IBAIevbsGRwcTN+lBaANQbADAAAAYAicYwcAAADAEAh2AAAAAAyBYAcAAADAEAh2AAAA\nAAyBYAcAAADAEAh2AAAAAAyBYAcAAADAEAh2AAAAAAyBYAcAAADAEAh2AAAAAAzx/5/JtxLk\nWnqCAAAAAElFTkSuQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# FIML Forest Plot\n", + "plot_labels <- c(\"a1\" = \"a1: SNP -> DLPFC\",\n", + " \"a2\" = \"a2: SNP -> AC\",\n", + " \"b1\" = \"b1: DLPFC -> AD age\",\n", + " \"b2\" = \"b2: AC -> AD age\",\n", + " \"cp\" = \"c': SNP -> AD age (direct)\",\n", + " \"ind1\" = \"ind1: via DLPFC (a1*b1)\",\n", + " \"ind2\" = \"ind2: via AC (a2*b2)\",\n", + " \"total_indirect\" = \"Total indirect\",\n", + " \"total\" = \"Total effect\",\n", + " \"diff_1_2\" = \"Contrast: ind1 - ind2\",\n", + " \"r_m1m2\" = \"M1~~M2 residual covariance\")\n", + "\n", + "fiml_plot_df <- fiml_key[fiml_key$label %in% names(plot_labels), ]\n", + "fiml_plot_df$label_nice <- plot_labels[fiml_plot_df$label]\n", + "fiml_plot_df$label_nice <- factor(fiml_plot_df$label_nice, levels=rev(plot_labels))\n", + "\n", + "p_fiml <- ggplot(fiml_plot_df, aes(x=est, y=label_nice, xmin=ci.lower, xmax=ci.upper)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(color=\"steelblue\", size=0.6) +\n", + " labs(title=\"FIML SEM: Parallel Mediation Path Estimates\",\n", + " subtitle=paste0(\"SNP -> APOC1 (DLPFC + AC) -> AD Diagnosis Age | N = \", N_fiml, \" (FIML)\"),\n", + " x=\"Estimate (95% CI)\", y=NULL) +\n", + " theme_minimal(base_size=12) +\n", + " theme(plot.title=element_text(face=\"bold\"))\n", + "\n", + "ggsave(file.path(FIML_DIR, \"fiml_forest_plot.png\"), p_fiml, width=10, height=6, dpi=150)\n", + "ggsave(file.path(FIML_DIR, \"fiml_forest_plot.pdf\"), p_fiml, width=10, height=6)\n", + "print(p_fiml)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mediator Residual Correlation\n", + "\n", + "The parallel mediation model includes `M1 ~~ M2` (freely estimated residual covariance between the two mediators). This parameter is critical because:\n", + "\n", + "1. **Biological basis**: APOC1 expression in DLPFC and AC likely share regulatory mechanisms (same gene, related brain regions). Ignoring this shared variance would bias the specific indirect effects.\n", + "\n", + "2. **Statistical role**: In a parallel mediation model, the residual covariance between mediators ensures that the specific indirect effects (ind1 = a1*b1, ind2 = a2*b2) capture the *unique* contribution of each tissue, not confounded by shared mediator variance.\n", + "\n", + "3. **Interpretation**: A positive residual covariance means that after accounting for the SNP and covariates, individuals with higher APOC1 expression in DLPFC also tend to have higher expression in AC (and vice versa).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T15:58:27.990560Z", + "iopub.status.busy": "2026-03-31T15:58:27.988292Z", + "iopub.status.idle": "2026-03-31T15:58:29.066839Z", + "shell.execute_reply": "2026-03-31T15:58:29.064763Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mediator correlation visualization saved.\n" + ] + }, + { + "data": { + "image/png": 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3118uTJcuXKye5BYWFhx44d++67786fP+/h4fHII4/0799fXfkTTzxx/vz5VatW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\nQEBTm0lmZqbJcOPi4mJyM9FoNE29kMDAQJObSWV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goGDBggXSIfXw8PCnn35aCo7mPyxJ37599Xp9RktRB+QAAAapSURBVEaG+bcFQPvg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\nAACATBDsAAAAZIJgBwAAIBP/D+Q/bBd6QeCwAAAAAElFTkSuQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Mediator Correlation Visualization\n", + "# Bar chart showing the residual covariance with CI\n", + "corr_plot_df <- data.frame(\n", + " param = \"M1 ~~ M2\\n(Residual Covariance)\",\n", + " est = cov_row$est,\n", + " ci_lower = cov_row$ci.lower,\n", + " ci_upper = cov_row$ci.upper\n", + ")\n", + "\n", + "p_corr <- ggplot(corr_plot_df, aes(x=param, y=est, ymin=ci_lower, ymax=ci_upper)) +\n", + " geom_hline(yintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(color=\"purple\", size=1.2) +\n", + " labs(title=\"Mediator Residual Covariance (APOC1_DLPFC_exp ~~ APOC1_AC_exp)\",\n", + " subtitle=paste0(\"FIML estimate | N = \", N_fiml,\n", + " \" | Std. correlation = \",\n", + " ifelse(nrow(std_cov) > 0, round(std_cov$est.std[1], 3), \"N/A\")),\n", + " y=\"Covariance (95% CI)\", x=NULL) +\n", + " theme_minimal(base_size=13) +\n", + " theme(plot.title=element_text(face=\"bold\"))\n", + "\n", + "ggsave(file.path(FIML_DIR, \"fiml_mediator_correlation_plot.png\"), p_corr, width=6, height=5, dpi=150)\n", + "ggsave(file.path(FIML_DIR, \"fiml_mediator_correlation_plot.pdf\"), p_corr, width=6, height=5)\n", + "print(p_corr)\n", + "cat(\"Mediator correlation visualization saved.\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Method 2: Bootstrap (FIML Inside Each Replicate)\n", + "\n", + "1000 bootstrap resamples of the full dataset, fitting the FIML SEM inside each replicate to maintain the full-sample approach." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T15:58:29.073841Z", + "iopub.status.busy": "2026-03-31T15:58:29.071315Z", + "iopub.status.idle": "2026-03-31T16:07:48.930979Z", + "shell.execute_reply": "2026-03-31T16:07:48.928292Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running 1000 bootstrap replicates...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Replicate 200 / 1000 ( 200 converged)\n", + " Replicate 400 / 1000 ( 400 converged)\n", + " Replicate 600 / 1000 ( 600 converged)\n", + " Replicate 800 / 1000 ( 800 converged)\n", + " Replicate 1000 / 1000 ( 1000 converged)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bootstrap completed in 9.3 minutes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converged replicates: 1000 / 1000 \n" + ] + } + ], + "source": [ + "B <- 1000\n", + "param_labels <- c(\"a1\", \"a2\", \"b1\", \"b2\", \"cp\", \"ind1\", \"ind2\", \"total_indirect\", \"total\", \"diff_1_2\", \"r_m1m2\")\n", + "boot_mat <- matrix(NA, B, length(param_labels), dimnames=list(NULL, param_labels))\n", + "\n", + "set.seed(42)\n", + "n_converged <- 0\n", + "\n", + "cat(\"Running\", B, \"bootstrap replicates...\\n\")\n", + "t_start <- Sys.time()\n", + "\n", + "for (i in 1:B) {\n", + " idx <- sample(nrow(dat), replace=TRUE)\n", + " boot_dat <- dat[idx, ]\n", + " fit_b <- tryCatch(\n", + " sem(model_str, data=boot_dat, missing=\"fiml\", fixed.x=FALSE, se=\"none\", test=\"none\"),\n", + " error = function(e) NULL,\n", + " warning = function(w) {\n", + " tryCatch(sem(model_str, data=boot_dat, missing=\"fiml\", fixed.x=FALSE, se=\"none\", test=\"none\"),\n", + " error = function(e) NULL)\n", + " }\n", + " )\n", + " if (!is.null(fit_b) && lavInspect(fit_b, \"converged\")) {\n", + " pe_b <- parameterEstimates(fit_b)\n", + " for (lab in param_labels) {\n", + " row_b <- pe_b[pe_b$label == lab, ]\n", + " if (nrow(row_b) == 1) boot_mat[i, lab] <- row_b$est\n", + " }\n", + " n_converged <- n_converged + 1\n", + " }\n", + " if (i %% 200 == 0) cat(\" Replicate\", i, \"/\", B, \"(\", n_converged, \"converged)\\n\")\n", + "}\n", + "\n", + "t_end <- Sys.time()\n", + "cat(\"\\nBootstrap completed in\", round(difftime(t_end, t_start, units=\"mins\"), 1), \"minutes\\n\")\n", + "cat(\"Converged replicates:\", n_converged, \"/\", B, \"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:07:49.010237Z", + "iopub.status.busy": "2026-03-31T16:07:49.008435Z", + "iopub.status.idle": "2026-03-31T16:07:49.067623Z", + "shell.execute_reply": "2026-03-31T16:07:49.065422Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bootstrap results (N = 1153 , 1000 converged replicates):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label boot_mean boot_se ci_lower ci_upper p_value N\n", + " a1 -0.1003662 0.05077456 -0.20316729 -0.002528319 0.048 1153\n", + " a2 -0.1786628 0.05588725 -0.28646954 -0.070902042 0.000 1153\n", + " b1 0.4650593 0.38145112 -0.28709795 1.213762497 0.230 1153\n", + " b2 1.1153756 0.43993140 0.26121840 1.910148342 0.022 1153\n", + " cp -0.9724078 0.41892866 -1.76064434 -0.167791595 0.014 1153\n", + " ind1 -0.0463792 0.04891726 -0.16565946 0.030903368 0.274 1153\n", + " ind2 -0.1958128 0.09556090 -0.39830400 -0.033108051 0.022 1153\n", + " total_indirect -0.2421919 0.09827578 -0.44330779 -0.074471177 0.002 1153\n", + " total -1.2145997 0.39997024 -1.96358469 -0.447319414 0.000 1153\n", + " diff_1_2 0.1494336 0.11572139 -0.06753095 0.372180546 0.184 1153\n", + " r_m1m2 0.3116296 0.04708958 0.22165062 0.404659416 0.000 1153\n", + " n_converged method\n", + " 1000 Bootstrap\n", + " 1000 Bootstrap\n", + " 1000 Bootstrap\n", + " 1000 Bootstrap\n", + " 1000 Bootstrap\n", + " 1000 Bootstrap\n", + " 1000 Bootstrap\n", + " 1000 Bootstrap\n", + " 1000 Bootstrap\n", + " 1000 Bootstrap\n", + " 1000 Bootstrap\n" + ] + } + ], + "source": [ + "# Bootstrap summary\n", + "boot_summary <- data.frame(\n", + " label = param_labels,\n", + " boot_mean = apply(boot_mat, 2, mean, na.rm=TRUE),\n", + " boot_se = apply(boot_mat, 2, sd, na.rm=TRUE),\n", + " ci_lower = apply(boot_mat, 2, quantile, 0.025, na.rm=TRUE),\n", + " ci_upper = apply(boot_mat, 2, quantile, 0.975, na.rm=TRUE),\n", + " stringsAsFactors=FALSE\n", + ")\n", + "\n", + "# Two-sided p-value: 2 * min(proportion > 0, proportion < 0)\n", + "boot_summary$p_value <- sapply(param_labels, function(lab) {\n", + " vals <- boot_mat[, lab][!is.na(boot_mat[, lab])]\n", + " if (length(vals) == 0) return(NA)\n", + " p_pos <- mean(vals > 0)\n", + " 2 * min(p_pos, 1 - p_pos)\n", + "})\n", + "\n", + "boot_summary$N <- N_fiml\n", + "boot_summary$n_converged <- n_converged\n", + "boot_summary$method <- \"Bootstrap\"\n", + "\n", + "cat(\"\\nBootstrap results (N =\", N_fiml, \",\", n_converged, \"converged replicates):\\n\")\n", + "print(boot_summary, row.names=FALSE)\n", + "\n", + "write.csv(boot_summary, file.path(BOOT_DIR, \"bootstrap_results.csv\"), row.names=FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:07:49.075946Z", + "iopub.status.busy": "2026-03-31T16:07:49.073703Z", + "iopub.status.idle": "2026-03-31T16:07:51.620676Z", + "shell.execute_reply": "2026-03-31T16:07:51.616418Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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PHjDzzwQAnWpexm67rP5Q3/HNoIIRxu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y9nff36de1jDx48mOtX0LlzZ2eb9957LyoqSghhMBjk2ObNm584ccJ7SfPmzRNC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29++eWXv/zyy8GDB3v06LF///5WrVpNnz59586du3fvHjly5MGDB5cvX669xGAw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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Bootstrap distribution histograms for indirect effects\n", + "for (ind_lab in c(\"ind1\", \"ind2\", \"total_indirect\")) {\n", + " vals <- boot_mat[, ind_lab][!is.na(boot_mat[, ind_lab])]\n", + " ind_nice <- switch(ind_lab,\n", + " ind1 = \"Specific Indirect via DLPFC (a1*b1)\",\n", + " ind2 = \"Specific Indirect via AC (a2*b2)\",\n", + " total_indirect = \"Total Indirect (ind1 + ind2)\")\n", + " \n", + " p_hist <- ggplot(data.frame(x=vals), aes(x=x)) +\n", + " geom_histogram(bins=50, fill=\"steelblue\", alpha=0.7, color=\"white\") +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"red\", linewidth=0.8) +\n", + " geom_vline(xintercept=median(vals), linetype=\"solid\", color=\"darkblue\", linewidth=0.8) +\n", + " labs(title=paste(\"Bootstrap Distribution:\", ind_nice),\n", + " subtitle=paste0(\"N = \", N_fiml, \" (FIML), \", n_converged, \" resamples\"),\n", + " x=\"Indirect Effect\", y=\"Count\") +\n", + " theme_minimal(base_size=12)\n", + " \n", + " fname <- paste0(\"bootstrap_distribution_\", ind_lab)\n", + " ggsave(file.path(BOOT_DIR, paste0(fname, \".png\")), p_hist, width=8, height=5, dpi=150)\n", + " ggsave(file.path(BOOT_DIR, paste0(fname, \".pdf\")), p_hist, width=8, height=5)\n", + " print(p_hist)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:07:51.627411Z", + "iopub.status.busy": "2026-03-31T16:07:51.625185Z", + "iopub.status.idle": "2026-03-31T16:07:52.441573Z", + "shell.execute_reply": "2026-03-31T16:07:52.439378Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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nVs86effmKnlpeX8/n8oKAg+q/uiZ3W91Ll6OgoEAgq6zFFSOzqBGMCwAmjR4/e\nsWPHjz/+OGLEiJYtW9Z2OOo5Ojq2atVq0qRJ48aNMzU1LSsrmz59+saNGz/99NO//vpLseby\n5cuLi4sJIYMHDx48eLBAIKiszby8PEKI4sgRrKSkJHqRRUllo5mwwsPDnZ2dt2/fTq86icXi\nffv2ffTRR4oDH1QJDY+GahDl5eWEEFNTU6Vy2lHl5eXVr6Dh3ekzFlRZWdm1a9ecnZ0///xz\nxTpisbi0tDQ1NbVZs2adOnVKTk6eO3fu2LFjvb29CSGKm6iXl5e7u3tCQsK9e/dKS0vlcnl2\ndjYhpLS0VLHBtm3bsq8dHBzUltBRzVhdunRR/Ld9+/b3799/9OhRu3btDBi/LgYPHrxu3bqo\nqKglS5bQkiVLljx//pwQIpVKibZdw8LCYurUqVFRUXl5effv38/IyPjqq68qKirGjRs3ZMiQ\nvn37BgcHd+vWjd56GBQUNH/+/Li4OA3xVNYzPj4+WnuDlihtAzdu3PD397exsWELJ0+eTPNv\nXXqYlgQEBLBTBQKBpaWl0jagle7vpUgoFFpZWVXpjeBdhsQOuGP9+vXt2rWbOHHi2bNn6Vgh\n7xqlUUgsLCzWrl1LB4fLyMjw8PBgJ6Wnp5eWlj58+HDFihUffPDB0qVL586dq7bNly9fEkIc\nHR1VJ505c+bMmTOq5f3799ec2BkbGw8bNuyXX37Jyspq0KBBXFxcUVERvQKlHycnJzZUgzAz\nMyOEiMVipXJaYm5uXv0KGt5dMY3Oy8uTy+Xl5eU3b95UrGNvbx8QECCRSAghe/bsGTZs2PLl\ny5cvX+7h4dG7d+9PP/00MDCQ1rx582bfvn2zsrKaN2/esGFDU1PToqIiQgjz70FG7e3t2df0\nXi7VEqVZaLcrtVBQUKBYWP34dfHdd9+dPHly6dKlR44cadmy5Z07d0pLSyMiIg4dOkRTCq27\nxuLFi5s2bXrmzBkfH59ly5b5+vouWLAgLy/v559/rqiouHTp0s8//0z7ISws7Mcff9QcT2U9\no0tvUKrbgFKbSlN1aVNpL+bz+UwVB5rV/b0UOTk55eTkyGQy1dFAoS5CYgfc0aJFixkzZixf\nvnzbtm300kZl5HI5ffZTLX9/f3qj1VtgZGTUuXPnu3fvPnnyRDGxs7W1tbW1bdCgQbdu3dq1\nazd//vwJEybQEd3UUpvIzpo1a/bs2arliglBZUaPHv3TTz/9+uuv8+bN27lzp+3Y/HwAACAA\nSURBVJeXV9euXXVbJjWq+vmkFU1MVU8B0jvnXF1dq19Bw7srnuejPe/r60tvQ1SrUaNGFy5c\nuHXr1uHDh//6668tW7Zs3Lhx/vz5UVFRhJBPPvnk+fPnhw8fDg8Pp/WTk5PpPXPVpPQ5LZfL\nyT8poAHj14WDg8Ply5dXr1597ty53NzcAQMGfPnllyNHjhQIBJV1tdKuwefzR48ePXr0aDr1\n7t27y5cv37Jli7Ozc3Z2tkwmYzMtZ2fn0tLS0tJS+hBDZY0r/sv2jC69Qame661sI9e9zerT\n7728vLwyMjKuX7/esWPHGgsN3h78pBhwyvz58xs3bjx79uz8/HwNA9AzDHOzcmlpaTUXIf0I\nUUR/CMjCwqK0tDQ2NpYOA8EyNjb29/eXSqWPHz9W2yD9lq/2ZJiFhYWjOrp8L2/Tpk379u13\n79796tWrEydOfPLJJ9U5CZqfn09UTpNUh5OTk4uLCx0FQ9HNmzdNTEx8fX2rX0HHSFxcXIyM\njDIyMrTWbNu27f/+97+kpKSnT5+2atVq8eLF2dnZOTk5d+7c6dq1K5vVEUIMtQUqpa1qT+5W\nM37dg7G3t//uu+8SExNPnToVFRVla2t76dKldu3asVujhl1DqVwul48bNy40NHTkyJHkn2xG\nJpPRqfRfzb8/UVnP6N4bilxcXPh8fmW9oV+b+tHvvQYMGEAIWbt2rdqpT58+HThw4PXr1w0Q\nH7wVSOyAU8zNzaOjowsKCmbOnKnhapqRkVFK5eg1HYMrKSlxd3en42+xhaWlpUlJSebm5m3a\ntOHz+UOHDh0zZgy964hi/nkkUO1ddKQGbl9jjR49+u7du2vWrJHJZPQTVG80PDq4saH06dPn\nyZMnV65cYUvoT7F98MEHNBWofgVdmJub+/v7Z2dnnzt3TrF80aJF9PnE58+fb968OTMzk53k\n4eExZMgQuVxOnxsg/wyQQTEMs2nTJmKI05xJSUnsa7lcfunSJUtLSx8fHwPGr2MkCQkJixYt\nUrzwvXfv3levXg0ZMoTosGsotbZmzZqUlBQ66DEhhH5XefHiBf03NzfXwcGBXmqvTGU9o7U3\n1LKwsGjfvn1KSkpOTg5buHHjRkdHx/j4eP3aVEvrJqHfe40YMcLV1XXnzp27d+9WmlRYWBgZ\nGXno0CH63QzqBCR2wDVhYWEDBw7cuXPns2fPDN64TCYr/wchhGEY9l+GYTRPtbGx6dy5840b\nN8aNG5eVlSWTye7evTtw4MC8vLwvvvjC3Nzc0tJy+PDhT548GTZs2KNHj6RSaXp6+sSJE2/e\nvBkcHFzZb5jSW52Sk5MNvrDDhg0zNTVduXJl165dvby8NNQsLy8vUqH4KX7x4kVzc3P2Zv/c\n3Nz+/fvPmzdPQ5uaO5MQMnPmTFNT05EjR9LxVx8+fPjxxx8zDPPNN9/QFqpfQUczZ84khEyd\nOpWeKZFIJD/88MP8+fPpDY5CoXDChAljx45lz+ikp6cfOHDAzMysZcuWbm5uzs7OZ8+epZtr\nSUnJpEmT6NXJp0+fVikMVRs2bKAf8HK5fOHChZmZmYMHD1a9hlid+Ilua/P+/fvz58+fPHly\nYWGhXC6Pj4+fOnVqo0aN6E85a901FJtKT0//5ptvli5d2qhRI1piamoaGBhIHyWWyWTx8fF0\nvBj9ekZzb1Rm2rRpMpls9OjRNL+8du1aVFQUn8/v1q2b3m0qoo9l0BPMqqc2FenxXvXq1dux\nY4dAIBg5cuS4cePOnz+fl5f36NGjLVu2dOzY8fLly3Pnzu3Ro4eOoULte2vP3wIYnOJwJ4qy\nsrLoKRCDD3dCBx1Q6/Hjx5qnMgxTUlLCHh/Zn4IdP348O3qISCQaNGiQ0kVPf39/DUNzvXz5\nksfjhYWFqfaM3sOdsCV0vPutW7eyJWqHO1GLHVGCDlCs+Eb0snKXLl307mpaZ//+/fTWe3rf\nmJmZ2ZYtWxQbqX4FJfTeTTYAFh0S1sjIyNPTkw4J269fP/b3DKKjo01MTHg8npOTk6OjI4/H\ns7e3Z0eT+fXXX+mvOdEzRr179y4rK6Pn1by9vTMzM1XflI5Mdu7cObZk8+bNhJA9e/YorqYt\nW7aYm5s7OzvTtMDX15f9eRK1Q9rqF78ua7OiooJe7yP//FZskyZN2N8TY3TYNVi9evUKCgpS\nGmnlzJkzpqam3bp1CwgIsLGxUWxZidae0doblW0Dc+bM4fP5fD6fzuLq6nr27NnqtGlra9uy\nZUv6+sGDB7TfrKysNmzYwOgwQHFl71WZq1ev0h+ZUNSgQYNt27axdTDcSZ3AYwx9UzPAW3P7\n9u2DBw/Sn4lUmnT48OGrV6+6urrSUwKGQh/TUzvpiy++uHfvnoap7KMPN27cuHbt2osXLxwd\nHUNDQ+n4EYru379/5cqV7OxsCwsLPz+/4OBgzfe39erV68yZM2lpaex96LRnunfv3r1798rm\nys/Pj46O9vb2HjFihOJciv15/fr1uLi4GTNmsNcKY2JiUlJS5s+fTzMhOova9nv06EGfANi0\nadOECRN27tzJPrBSUFAwceLEgoKCyrqLaOtqtjNfvnx55MiRnJwcZ2fnsLAwOkC/oupXUBQX\nF3f9+nXFAFhZWVknT57Mzc2tV69eYGCg4pAihJD8/PyTJ08+f/7cyMioUaNGPXv2pJ+41L17\n95KSkkQikZ+fX7du3Xg8XmZm5v79+62trT/55JOTJ08qvWlSUlJSUtKYMWPYB27omho8eHCr\nVq0IIUOHDt23b19WVhaPxzty5Eh+fn6TJk369evHXqCk63H69OnsCB16x6/L2qT+/vvvq1ev\nCoVCX1/fnj17qg7io3XXyMnJ2bhxo9pd/vHjx/Hx8fRX7+gIeWpp7RmtvaFhG3j8+HFCQkJJ\nSYmXl1fv3r2VxhCpapvLly+3sbGhY6bQzklISKhXr96HH37o4eFB1+DMmTOtrKyqujY1SElJ\nuX79ek5Ojq2tbdOmTbt37654S67qQQPeQUjsAOq8v//+OygoaObMmStWrKjtWJRJpdJmzZrx\n+fwHDx4ofkIsWbIkIyOD/owsGBxNXzIzMzWkOAZUh9bmW+4ZgLcP99gB1HmBgYFDhgxZv359\njT7Pq5+NGzc+ffr0+++/V8zqRCLR+vXr2aEroE7D2gR4p2AcOwAu2LRpk5+f39ChQ8+fP695\noIe3KSUlZdasWVOnTqX36rHMzc2zsrJqKyowLKxNgHeKkdpfHAKAusXMzCw0NFQikdDh2Wo7\nnDdOnz7dunXrqKgojGj/9rVo0aJ79+4afozuPws9A9yGe+wAAAAAOAL32AEAAABwBBI7AAAA\nAI5AYgcAAADAEUjsAAAAADgCiR0AAAAARyCxAwAAAOAIJHYAAAAAHIHEDgAAAIAjkNgBAAAA\ncAQSO/iPKi8vF4lEXPrlFZlMVlFRUdtRGJJYLBaJRHK5vLYDMRi5XC4Wi2s7CkOqqKgQiUQy\nmay2AzEYhmHKy8trOwpDkkgkHFtHhBCRSFTbIRiSVCoViURSqdQgrSGxg/8okUgkFAprOwpD\nkkqlHEsaysvLhUIhx5JvjiUNYrFYKBRyLPnmWNJQUVEhFAoNlTS8CxiGKSsrq+0oDImuI4lE\nYpDWkNgBAAAAcAQSOwAA0JNEIikvL+fYZT6AOg2JHQAA6On48eNbtmzJyMio7UAA4A0kdgAA\nAAAcgcQOAAAAgCOQ2AEAAABwBBI7AAAAAI5AYgcAAADAEUjsAAAAADjCuLYDAACAuqp+/fpy\nudzS0rK2AwGAN5DYAQCAnvz8/Fq1amVra1vbgQDAG7gUCwAAAMARSOwAAAAAOAKJHQAAAABH\nILEDAAAA4AgkdgAAAAAcgcQOAAAAgCOQ2AEAgJ7u37+fnJxcWFhY24EAwBtI7AAAQE+pqanX\nrl0rKiqq7UAA4A0kdgAAAAAcgcQOAAAAgCOQ2AEAAABwBBI7AAAAAI5AYgcAAADAEUjsAAAA\nADjCuLYDAACAuiokJKRdu3b169ev7UAA4A0kdgAAoCdbW1szMzOBQFDbgQDAG7gUCwAAAMAR\nSOwAAAAAOAKXYgEAQEFFKSl4QKQiYuVO7JrUdjQAUDVI7KqmoKDg5MmT7du3b9asmY6zPH78\nOC0traSkxNra2sfHp3Hjxuykc+fOZWVl9erVy97eni0sLy8/dOjQ+++/7+zszNZhp9rY2Hh6\nerZs2dJAC/Sv9gUCgbOzc+vWrW1tbZXqvH79unfv3ppnVxueUgVWnz59rK2t6WuZTJaSkvLs\n2TOZTObk5OTn52dhYVH9RQOAqslPIRe+JWlHiaziTYldE+I/g7QeT/j4sACoG7CvVsHNmzd/\n/PHH4uJiCwsLXRK7srKypUuXpqSk+Pj42NjY5OXlpaend+3adfr06UZGRoSQs2fPXrp0KS8v\nb9q0aexcIpFoz549rVq1oond2bNnb9265e3tTacWFhZmZ2e3bdv2f//7n5mZWfUXSrH98vLy\nrKwsqVTap0+fUaNG0SBpnRcvXqhN7LSGRysoprNUjx496IvU1NSVK1fm5OS4u7ubmppmZ2fz\neLxRo0aFh4dXf+kAQFcP9pATY4i0/F+FRU9IwmTy+BDp9wcxta6lyACgCpDY6erixYurV6+e\nMGHC+vXrdZzlwIEDjx8/XrNmTcOGDWnJhQsXvv/++3bt2n344Ye0xNXVNTExMTw8vGnTppW1\n4+bmtnTpUvbfGzduREVFxcTEjBgxorJZxGIxn883MTHRJU7F9qVS6bFjx7Zv315cXPzVV19V\ndXa14bm5uX3//fdq533x4sU333zj5ua2fv16OmJCWVnZzp07N27caGNj07VrV10CAIDqykwk\nxz4hcqn6qel/kaPDSf+4txsTAOgDD08oe/78+fHjx2NiYs6cOVNe/v9fXuVy+bJlyz744APV\nWW7durV3717V8rS0NG9vbzarI4QEBwfPmTOnRYsWbEmrVq3atGmzadMmhmF0jLB9+/bNmze/\nffu2hjqZmZljxozZs2dPUVGRjs1SxsbGffv2HT16dGJi4v3796s0r+7hsXbt2kUImT9/PjsO\nloWFxaRJk0JCQoRCoR7vDgBVJpeShMmVZnXUk3jy+JBqcXx8fHR0dFpaWk3FBgBVhMTuX/76\n669JkyadP3/+2bNne/funTx5cnFxMZ3UpUsX9oKjkps3b6pN7FxcXFJTU1NTUxULg4OD3d3d\n2X+lUum4ceMeP3585swZ3eM0MTGRy+UaKjRp0mTy5Mm3bt0aO3bsTz/99PTpU90bJ4T06tXL\n3Nz8/PnzVZpL9/AohmH+/vvvrl27Kt5iSM2cObNXr176vTsAVE3WWVLwQHu12xtqPhQAqC5c\niv2X/Pz8cePG9e3bl/yTch09ejQyMlLzXH369AkODlYtHzRo0MWLF2fNmtWmTZu2bdu2bt3a\n29ubx+MpVWvUqFGvXr127NgRGBioy21zL1++fPjwIXsxVy0ejxcUFBQUFPT48ePY2NgZM2a0\naNGiX79+nTp1Ug1AlYmJSaNGjTIyMrTW1C88tqZIJKosXdZKLBbrfppTFZ1XLBbr3cK7RiqV\nymQyxdPMdR39ekDvK6jtWAxDJpPJ5fLaWkc8cRE/9Q/VcqNnR3Xq34xEybVoQv51AGlcdvsR\ncZNKpZzZ8ORyOcMwnFkcQohMJiOESCSS6hww3yl0Qbi0jqRSKf2r40JpThWQ2P1LZGTko0eP\n4uLi6HVAPp+fk5OjdS4HBwcHBwfVcmdn5+jo6KNHj166dGnnzp0Mwzg4OAwcOJAmjoqGDx9+\n9uzZAwcOjBw5UrWdwsLC33//nb4uKipKTk62s7MbPHiwLkvUtGnTWbNmvXjxIj4+fvXq1b17\n9x41apQuM5qZmem4hWkNr6CgYNu2bYqzmJiYjBw5kiZVej8A+/r16+ofp16/fl3NFt413Fui\nsrKy2g7BwGprHRkVpdonTdV/frnEJOlzpTJ/vsUJMkssFnNsw+PY4hBupUEU99aRWCzW8VyD\nQCDQcI4Gid2/7Nix488//+zcubObmxt9JpR+19Gbra1tZGRkZGRkSUnJrVu3jh8/vnnzZrFY\nrJSWWVtbR0ZG7ty5s0ePHqampkqNVFRUsNdSbWxshgwZ0qtXL6Xf8MnKyjp27Bh97ePj061b\nN6VGdDlRp6ikpIQ+lquV1vDEYrHSLTh0GelwJ6WlpVUKjGVubl6dxE4sFsvlcnNzc71beNfI\nZDKZTKa6/dRddB2ZmZlVdet9Z8nlcolEUls/wMUj7pL201XLjZ5f4Odd0mF+I0m7L5TO2N1/\nlEaKiYmJCWd2JYZhxGKxQcYceEdIJBKpVGpqasoOdMABIpGIM5sc+WcdmZiYGBvrlJVpPiQi\nsft/L1++PHTo0MSJE9lxPa5evWqoxukznl26dJk3b97p06dVz7eFhYUdP35827ZtkyZNUprk\n4uIyf/58ze2Xl5ezV04VTx/SS7EXL1709fWdNm1aQECALtG+fv06PT1dx8paw3Nzc1u0aJFq\nua2tra2t7b1798LCwpQmiUQirR/n1RzrrqKigjbCmaRBLBZXVFRYWlrWdiAGI5VKafLNmQ8k\niUQil8trbR1ZNiahP6opfxJPYvtpn71+kEnoKqWylNzdJPeRqakpZzY8mUwmkUg4sziEEKFQ\nKJVKBQIBZ37Sl14r59I6Kisro8m3QbJVJHb/Lzs7m2GYNm3a0H/Ly8szMzNdXV31a624uPj3\n33/v3r274mi9PB7P3d1d7eVdIyOjsWPHLly4sHv37nq8nbe3t2LyxDDMpUuX/vzzz4cPH4aE\nhPz4449eXl66t3bo0CG5XB4SEqJHJLrj8XidO3dOSEjIzs5WfKCEEPLDDz8IBIKvv/66RgMA\nAEIIafQhsW5AStUMJP4vrUa/lWgAoFo4ckuyQdjY2BBCcnNz6b87d+60sLCg53U0KywsVH3a\n38bG5tatW+vWrXvx4gVbmJqampyc3K5dO7XtdOjQoWPHjuz9atXx5MmTtWvXtm7detu2bdOm\nTdM9q5NIJAcPHoyJiYmIiFBKtmpCZGSkmZlZVFQU++zw69evf/nll+vXr7MjGANAzTI2I91X\nK11jVVY/iLRUc3uuo6Njw4YNuXRRDKCuwxm7/+fl5dWiRYuffvopMDAwLS2tZcuW3bt3P3r0\n6Pbt20ePHr1+/frMzExCiFQqPXz48N9//00ImTVrlr29fVxc3KFDh2JjYxVb4/F433777eLF\niz/77LPGjRtbW1sXFhZmZGS0a9du7NixlcUwduzYKVOmVH9ZGjZsuG3bNh0HKM7JyZk3bx4h\npKKiIjs7u7y8fMCAAUrPWDx//nzmzJmKJUOHDvX3969mnHZ2dsuWLfv++++nT5/u6uoqEAhy\ncnLMzMy+/vrrDh06VLNxANCVz2ASspyc/ZoQdfetOrYm/f4gPDUXxIOCgjp06KD0I4QAUIuQ\n2P3Ld999d/78+aKiopCQkNatW4tEIgcHBzs7O0JIkyZN6IvWrVuz9emN6u3atVN770LDhg03\nbNiQkpKSlZUlFArt7e2bNGni6enJVggJCVG6U7J+/frTp0/PyspycXFh6+jx7I/u91KEhISw\n5/OMjIwcHR3bt2+vNLCcYh1WvXr1dAlPawUPD4/o6Oi7d++mp6dXVFS4urr6+flx6QkAgLqh\n42zi1I6cnU1e3vr/QhNL0m4KCfqWmFjVXmQAUAU8zgxsA1AlhYWFMpnMwcGBYw9P0GeNuaG4\nuFgikdjb23Pp4YmysrJ3/fxWwQPy8jaRlhHrhqR+Z2Ks6TJraWmpWCy2tbXV8frAu08mk5WU\nlKiOml53CYVCkUhkbW3NpYcnCgoK1I4yVkeVlZWVlZVZWlri4QkAADC0es1Jvea1HQQA6AkP\nTwAAAABwBBI7AAAAAI5AYgcAAADAEUjsAABAT/fv309OTi4sLKztQADgDSR2AACgp9TU1GvX\nrhUVFdV2IADwBhI7AAAAAI5AYgcAAADAEUjsAAAAADgCiR0AAAAARyCxAwAAAOAIJHYAAAAA\nHIHfigUAAD0FBgb6+vq6urrWdiAA8AYSOwAA0JOTk5ONjY25uXltBwIAb+BSLAAAAABHILED\nAAAA4AgkdgAAAAAcgcQOAAAAgCOQ2AEAAABwBBI7AAAAAI5AYgcAAHqKj4+Pjo5OS0ur7UAA\n4A0kdgAAAAAcgcQOAAAAgCOQ2AEAAABwBBI7AAAAAI5AYgcAAADAEUjsAAAAADgCiR0AAOjJ\n1tbW2dlZIBDUdiAA8IZxbQcAAAB1VUhIiFgstrW1re1AAOANnLEDAAAA4AgkdgAAAAAcgcQO\nAAAAgCOQ2AEAAABwBBI7AAAAAI5AYgcAAADAEUjsAABAT6mpqdeuXSsuLq7tQADgDSR2AACg\np/v37ycnJxcUFNR2IADwBhI7AAAAAI5AYgcAAADAEUjsAAAAADgCiR0AAAAARyCxAwAAAOAI\nJHYAAAAAHGFc2wEAAEBdFRgY6Ovr6+rqWtuBAMAbSOwAAEBPTk5ONjY25ubmtR0IALyBS7EA\nAAAAHIHEDgAAAIAjkNgBAAAAcAQSOwAAAACOwMMTAAD/EQx5fIg82EPyU4i4mFg3JI0+IG0n\nEuuGtR0YABgMErt3glwuj4uLO3HixMuXL52cnD788MP+/fvz+VrOp6akpBw8eDAtLa24uNja\n2rpZs2ZDhgzx9vamU5csWXLp0qU5c+YEBwezsxQWFo4aNWrJkiWtW7dm67BTbWxsPD09IyMj\nW7ZsadgF/OGHH86fPz9lypSePXsqTWIYJjEx8eTJk8+ePZPJZE5OTp07d46IiLC2tjZsDAD/\nacJcEv8RyT6vUJJDci+Ta6tJt5Wk3eTaiwwADAmJ3Tth9+7dhw4dGjFihI+Pz927d3fu3Mnj\n8QYMGKBhluvXr0dFRYWEhHzxxRc2NjYvXrz4448/5s2bt2rVqgYNGtA6fD5/+/btHTt2NDU1\nrawdV1fXzz//nL4uLCw8ceLEvHnzFi9eTDM/gxAKhZcvX27cuPGpU6dUE7vVq1efOXOmW7du\n4eHhpqamqamphw8fvnDhwtKlS+3t7Q0VA8B/WnkB2RdCCh+rmSQVkVNTiFRE/Gfo0fDx48ef\nPHkyePBg9islANQu3GNX+2Qy2eHDhyMiIgYMGNCyZcshQ4Z07tz57Nmzmuf666+/PDw8ZsyY\n0b59+yZNmgQFBS1atMjZ2TklJYWt06lTp9evXx88eFBDO+bm5q3/ERISsnDhQgcHh7i4OA2z\n5OTkxMXFlZWV6biAZ8+eFQgE48aNe/DgQU5OjuKkhISEpKSkyZMnT58+vWvXrgEBAcOHD//h\nhx9evXq1e/duHdsHAC0Sp6nP6lhn55D8FE0VKiGRSMrLy+VyuZ6BAYChIbF7e4qLi1etWjVq\n1KhBgwZNnDgxPj6elvP5/NWrVw8cOJCt6eTkVFxcTF/v3Lmzf//+qq3JZDKla7Xm5ubR0dG9\nevViSywsLIYOHfrHH3/k5+frGKSJiUmjRo1evnypuc6pU6dGjx69efPm3NxcrW2eOnWqS5cu\nrVq1cnZ2TkxMVJz0559/+vj4KJ3Ga9iw4fLly8eNG6djzACgSUk6ub9LSx1GRi4vfyvRAEDN\nwqXYt+enn37Kzc2dPXu2nZ3dw4cP16xZ4+TkFBgYyOPx3Nzc2GoymezGjRstWrSg/zZs2NDf\n31+1tY4dO65Zs2bZsmUDBgzw8fFRe0OeXC7v27fviRMnduzYMXPmTB3jzMvL0/wDQY6Ojj//\n/POtW7diY2MnTpzYqVOnfv36tWrVSm3lrKysR48ejR8/nsfjvffee4mJiZGRkTwejxAiFArT\n09M/+eQT1bm8vLy0xskwjG4LVOONvCPosnBpiSiGYTizUDW7jipKiFympvxRDI/R4Yza0yOM\nqED9JBNLYlTp7RwU1tE7DvvRu4xdIh0Xin6GVgaJ3dvz5Zdf8ng8W1tbQoi7u/vhw4dv3LgR\nGBioVO3XX3/Nzc39+uuv6b+hoaGhoaGqrX344Yf5+fmHDh1KTk62sLBo0aJFQEBA9+7dBQKB\nYjUjI6MxY8YsWrQoPDzc19dXbWAy2ZsPg6Kiori4uKysrOHDh2tdnLZt27Zt2zYzM/PPP/9c\nuHBhgwYNRo8e3bZtW6VqCQkJ7u7uzZo1I4S8//77+/btu3fvHn04o7CwkBDi5OSk9b3UKigo\nqP6OXVBQySdZnSUWi2s7BAMrKiqq7RAM7NWrVzXRrP2fXYyKHuo/v7iIt85B7ZTSLmvFTYao\nnUSPHkKhsIYWqrZwbHEIIa9fv379+nVtR2FI3FtHZWVlOt7j5ODgoCG3Q2L39pSUlGzbtu3B\ngwfsmlM8UUft3LkzPj7+66+/dnd319pgZGTkgAEDbt26devWrZs3b65du3bfvn1RUVENG/5r\n8IKOHTt26NBh06ZNq1atUm0kLS1N8SkNKyurKVOmKD5ISwiRyWTl5eX0tbGxsWLu2LBhw6lT\np/br12/hwoWXL19WSuzkcnlSUlLv3r3p0d/JycnX1/f06dM0sTM2NqZ1tC6pWkZGRtVJ7ORy\nOcMwRkZGerfwrqHf9rQ+TF2HYB1VidyyPk+mJq3niQt5FcU6tWDlQXhqYuMJbCpbC/TTxcjI\niEurSS6Xc2k/YhiGLpHm0zx1i0wm49gmR48MBllHSOzekoqKisWLFzs4OKxcudLNzc3IyGjO\nnDmKFRiGWbt27blz5xYsWKB63qsyZmZmAQEBAQEBhJA7d+4sW7Zs27ZtCxYsUKo2duzYL774\nIiEhQfWqrru7+4wZb56Gs7a2dnZ2Vt2wbt26tXDhQvo6NDR02rRp7KTMzMy4uLjExMQGDRp0\n6tRJacYbN24UFBTs2rVr167/v8UnPT19woQJpqam9vb2PB7v+fPnOi6sEjs7O/1mpAoLC2Uy\nmZ2dHWcOdmKxuKKigkvDxBQXF0skEhubSrOKOkcikZSVldHT9oY39LT6CCRriQAAIABJREFU\n8nu/kmOjtM9u5c6fkK5+SuUz0QTIzMyMM8+wy2SykpISziwOIUQoFIpEIktLS6XrOXUXwzAF\nBQVcWkf0XJ25ubm5uXn1W0Ni95Y8e/YsNzd3xowZ7FgkhYWFjo6ObIWNGzcmJycvXbq0SZMm\nujRYUFCgtBG0bt06KCjo6tWrqpUbNmwYFhb222+/qQ5iYmpqqnWcgmbNmi1f/ubGajadun37\ndmxs7PXr1zt27LhgwQK1w6OcOnXK19d3/PjxbIlEIpk3b97ff/8dEhIiEAiaNWt28eLFYcOG\nKWVXFy5cMDExUc0UAaDKGocRYzMiLddSrelALRXUsbW1dXZ25kzGAMAB3Dnb/I4TiUSEEDYP\nu3//fm5uLnsl8fTp0wkJCVFRUTpmdUVFRWPHjj1w4IBS+fPnzyv7EhMZGSmTyQ4dOqRH8JaW\nli3+Ub9+/fz8/C+//HLJkiVubm4bNmz43//+pzaro8PXde/e3VuBr69vu3btTp9+c2qhX79+\nmZmZe/fuVZwxPT09Ojr6ypUreoQKAMrMHUmHaVrqmFqTTnO01FEnJCRkyJAh9evX1ycwAKgB\nOGP3ljRu3FggEMTHx0dGRj59+nT//v3+/v7Z2dlFRUUWFha///57x44dRSLRnTt32Fl8fX2N\njY0TExP//vvvuXPnKrZmZ2cXERERExNTVFQUGBhoY2NTWFh46tSpe/fuzZ49W20AVlZWw4cP\n37x5c/WXRSKRhIaGfvjhhxYWFhqqnT17ViqVdu7cWam8S5cua9asKSwstLe379Kly507d/bs\n2fP48eOuXbuamZk9evTo2LFjHh4eo0ePrn6oAEAIIZ2jSM7fJDNJ/VS+Men9K7HSfl8vALz7\nkNi9JTY2NtOmTduxY0diYqKPj88XX3zx4sWLFStWfPfdd1OnTs3Pz8/Pz79w4YLiLDt37rS3\nt8/IyFD81S/Wp59+2qhRo5MnT65Zs6asrMze3r5JkybLly+v7NFXQkivXr2OHTuWnq7+Nhrd\nubm5RUREaK126tSpli1bqt5OFBgYGB0dnZSURB/amDRpUqtWrY4fP75lyxaJROLq6jpkyJA+\nffpo+LUMAKgaI1My8BhJ/JLc2UKUhj6xaUR6biUe79dSZABgYDwujQQDoDv68ITmh8brFq4+\nPGFvb4+HJwym4D55uJ+8uEGkImLlTjw+IE0HEmMzvdsrLS0Vi8W2trYmJiYGDLMWcfXhCWtr\na87cCkkfnnBwUD86T11EH56wtLTEwxMAAFAV9XxJkPJT8wDAJXh4AgAAAIAjkNgBAAAAcAQS\nOwAA0FN6evrdu3dLS0trOxAAeAP32AEAgJ5u37796NEjNze3evXq1XYsAEAIztgBAAAAcAYS\nOwAAAACOQGIHAAAAwBFI7AAAAAA4AokdAAAAAEcgsQMAAADgCAx3AgAAevLz8/Py8nJycqrt\nQADgDSR2AACgp/r16zs4OFhZWdV2IADwBi7FAgAAAHAEEjsAAAAAjkBiBwAAAMARSOwAAAAA\nOAKJHQAAAABHILEDAAAA4AgkdgAAoKfjx49v2bIlPT29tgMBgDeQ2AEAgJ4kEkl5eblcLq/t\nQADgDSR2AAAAAByBxA4AAACAI5DYAQAAAHAEEjsAAAAAjkBiBwAAAMARSOwAAEBPlpaWNjY2\nxsbGtR0IALyBvREAAPQUGhoaHBxsa2tb24EAwBs4YwcAAADAEUjsAAAAADgCiR0AAAAARyCx\nAwAAAOAIJHYAAAAAHIHEDgAAAIAjkNgBAICe0tPT7969W1paWtuBAMAbGMcOAAD0dPv27UeP\nHrm5udWrV6+2YwEAQnDGDgAAAIAzkNgBAAAAcAQSOwAAAACOQGIHAAAAwBFI7AAAAAA4Aokd\nAAAAAEdguBMAANBTmzZt3N3dHR0dazsQAHgDiR0AAOipUaNGrq6u1tbWtR0IALyBS7EAAAAA\nHIHEDgAAAIAjkNgBAAAAcAQSOwAAAACOQGIHAAAAwBF4KhYAAP6T5BKSeYbk/E3ERcTckbh3\nJfWDajsmgOriWmK3ePHi4ODg9957r7IKN27ciImJCQsLCw4O1tra77//fu/ePUIIj8cTCARO\nTk4dOnTo1KkTj8dTrFNYWPj5559rmJ22YGNj4+npGR4ebmVlpVpB0YwZMxwcHOjrnJyc06dP\np6WlyeVyJyenzp07t2nTRjEAQzl48ODVq1c//vjjtm3bql0KDZ0AAP9Np0+ffvLkSf/+/b28\nvGo7lip6Ek8Sp5Hip/8qdGzNC11LrFrVUkwABsC1xO7+/ftNmzZVO0kul+/Zs+fPP/8Ui8UB\nAQG6tJaenp6VldW7d29CSHl5eVpa2tKlS729vf/3v//Vq1ePrfPixQutsxNCCgoK4uPjDx8+\nvGLFCldXV7ZCjx49lGY0NTWlL06cOLFhwwZHR8cOHTqYmJikpqYeO3YsODh4+vTpJiYmuiyC\njmQy2aFDhwghR48eVUrsdOkEAPhvEgqFJSUlUqm0tgOpohvR5PQXhDDK5fl3+H98YNptI7H/\ntBaiAjAEriV2Ghw7duzs2bMrVqyYPn267nPZ29tHRkay/z59+vS7775bvHjxypUr+Xztdygq\nzR4ZGTl58uT9+/d/8cUXbIURI0aonffu3bvr1q17//33p0yZYmRkRAvPnTu3cuVKT0/Pjz/+\nWMP7FhUVFRQU6P4d+vLly2VlZZMmTVq3bl1paanScKPV7AQAgHdI1hmSOE1NVkfJKizOTCKN\nAkg937cbFoBhcDCx4/F4ly5dSkxMrKioaN++fXh4OE0+mjRpsmrVKktLS9VZTpw4ce7cucWL\nF2tt3MvL6/PPP4+Kirp48WKXLl2qGlu9evV8fHyePXumS+W9e/c6OTlNmjSJzeoIIV27dhUK\nhZ6enprnLS0tnTVrVrNmzfr16xcQEKD1sumpU6f8/f1DQkK2bNly7ty5sLAwDZU1d4JcLj91\n6tTNmzeFQqGTk1PPnj29vb3pJJlMFhcXd/v2bUtLyz59+mRlZd2+fZvNs2/dupWYmFhYWOjk\n5NSrVy92LgAAQzozmzAyDdN5snJy/n+k38G3FhGAAXHwdMu1a9f27dvXrFkzJyenLVu27N69\nm5Y3b95cbVZHCHn16lVqaqqO7fv5+dnb21+7dk2/8EQikZmZmdZqFRUVKSkpXbt2Vb3k2qtX\nr+bNm2uevWHDhlu3bm3ZsuWaNWsmTJgQHx8vEokqq1xcXHzt2rXQ0FBTU9MuXbqcPn1aa3ga\nOmHLli1bt2718PAICgqSSqWzZs16+vTNXSwbNmz47bffmjRp0rJly+jo6MTExCdPntBJf/31\n17fffksI6dy5s0gkmj179t27d7WGAQBQNUVPSO5l7dWeHiEVJTUfDYDhcfCMXW5u7ubNm+lt\nagzDHD58ODIyUvGkl6phw4YNGzZM97do0KBBZffVaXblypVHjx6NGjVKa838/HyZTNawYUM9\n3oWys7MbPnz4Rx99dOrUqbi4uF27dn3wwQf9+vVzdnZWqpmUlGRpaenv708Ief/99+fMmZOd\nne3u7q65/co6oV27dsHBwS1btiSE9OzZ89GjR0lJSV5eXkKhMCEhYciQIfSqrr+//8SJE93c\n3AghUql0x44doaGh06ZNo3MtXLjwt99+W758uYYAXr9+zTCVXEzRgVwup43o3cK7Ri6Xy+Xy\n0tLS2g7EYGQyGSFEKBRy5kkduVwuk8lqaB2ZnZnCk7zt7Tm4ILut2WuXy8nSu+q/Nr9reMLn\nmj4MWLIK2R8RjJl9TcfzFpjI5cYMw+fzpTWwH1W0nixz0emedYPj0rGO3qUqFot1vF3VyspK\nw1GRg4mdn58f+/BB27Ztjx8/npubqzVNqRI+n69j7+fk5MyZM4e+LioqysnJCQkJ6devH1sh\nOztb6Z4/gUCwbNkymnYYG1d3BZmamvbu3btXr15XrlzZsGEDwzDjx49XqnP69Olu3brR3NfX\n17d+/fqnT58eOXKk5pYr64Q2bdocOXIkNja2rKyMYZhXr169evWKEPLs2TOZTNa+fXtazcnJ\nqVWrVnRSWlpaaWlp165d2UaCgoLWrVsnFosFAkFlAYjF4uokdmwj1WzhXUOTIS6pqKio7RAM\nrIa2Oqv0I7zygppoWYNGhBATQvLUPN1f1xk9T6rtEOoAUcMwsV27Wnlr7h29pVKp7omdhqkc\nTOzYgULIPwtv8LMy+fn5jRs31qWmhYVFYGAgfW1tbe3l5aX0QIOlpaXSI7r02qudnR19Ix1D\nevDgwfr16+nrTp06DR8+nJ0kk8nOnz8fGxtbXFysegrw6dOnaWlpFRUVbAIqFAqTkpJGjBih\n+TSJ2k5gGGbJkiVZWVlDhgypX78+n8/ftGkTnUS/XSk+luHs7EwTu+LiYkLIvn374uPj6aSS\nkhKGYfLz8zVk5DY2NhrC06q0tFQul9vY2HDmbJBEIpFIJBYWFrUdiMEIhUKpVGptbc2ZZ3Sk\nUqlYLK7snpBqkoXvJzJJTbSswaVLl3JycoKCglxcXN7yW+uH9/KW0YWvdakpe38zY92gpuN5\nCyoqKiQSiZmZmeYrV/oxc2xlZmFr8GY1YximtLS0mh8B75Ty8nKxWGxmZqbhXIYizR9bHEzs\nJJL/P7TRjN6wI4Pk5ORkZ2f37dtXl8q2trYDBgzQUMHOzk7t861WVlbu7u7X/4+9+45r4v7/\nAP4JCSEQIGzZSwTBjVsBV+so7rb6Fduf31ZbbW3do65q62i11lpHrdu6alsrONriAPy6RUUR\nFUSGDAFZgQQCmff74/jmmwYIISCB6+v5h49w98nn3peT8Mrnc3dJSJg4caLWqqysLGtra1vb\nv80R2NjYDBgwgH6szlsSiSQ6OvrcuXMKhWLUqFFr1qwRCLR/A2NiYpydnTVvuSKXy48ePZqU\nlNS1a9f6yq7vRcjOzk5MTPz888/piV2i8f+Pfk/R/DiiHomhD1CXLl0cHBzUa19//XXdv7dN\nPKx0YaampowJdvQ0X/P+bzcu+tBwOJxX8QfJWGQy2as6Rt7DXkm3OnV1GhgglQoEAk5b+Y/n\nNYjEryXyygaaWXuyu08nhAlvDtLKSnlVFc/KiqNfaGj96LkaJr3X0bmFzWY3y04xMNjl5OSo\nH+fn57NYrNpnlRmMoqiDBw9aWFjoc3/jJho2bNjhw4fv3LnTu3dv9UKJRLJx48Z27dqtXr1a\ns7Gzs7NmQJRIJMeOHbt48aKLi0tERMSgQYPq/O+iVCqvXLkSHh6ulT5v3boVGxtbX7DT8SKU\nlpYSQugz5wghRUVF2dnZ9DAhHdpevnzp5eVFd/L06VN6rplu4Ovr2wKvKgD8o3HMSbePyN3N\nDTQLns+MVAf/QAyZ4NCUlJT06NEjQkhlZeWFCxeCgoJ0z0YTQpKTk9WTgPWRyWSpqanr1q27\nffv2rFmzNIe+KIqS/R19hlwTjR8/3s/Pb+PGjZGRkcXFxWKxOCEhYdmyZWKxeMaMGbqfW1BQ\n8PLly1WrVn3//fevvfZafR8C4uPjy8vLa8epkJCQGzduVFdXay3X8SLQXF1dWSxWYmIiIUQs\nFu/cudPb21skEhFCvLy8bG1tz549S380OX36tPrUVzs7u+Dg4F9++UUoFBJCpFLp5s2bN29u\n6J0XAMAA/T8n9p10rFc4DyA9ZrdYOQDNi2kjdiqVavTo0Xv27KmoqBCJRJaWlkuWLKFXLVq0\nKDU1lX68b9++ffv20Q+cnJzi4+MjIyPrnF3NzMzUvNbB09Nz5cqVmkNohJDnz5+/9dZbmkvm\nzp07bFhTp0U4HM6GDRsOHDhw/PjxgwcPEkJYLFbPnj2XLVtGf3GFDr6+vitXrmxwEzExMV5e\nXrVPvAsJCTl06NDNmzfpL2fT50WgtWvXbty4cbt37z5z5kxlZeWsWbOEQuGePXuWLFmyadOm\n2bNnb968eerUqXw+v0uXLiEhIcnJyfQT586du2nTpvfee8/BwaGsrMzFxUV94AAAmhPXirx1\nnkSNIy/ruGET5flaRegeGxPmTPPBPw2r6dcVtiqPHz92dXW1sbHJzs6WyWQ+Pj7qC0vT09Ml\nEolW+4CAAC6XW1BQUFxc3Lmz9vcDZmVl0aNNhBAOh+Pg4ODo6KijjZq7u7utrW1WVpZcLtdx\no90GG9CkUmlBQYFcLm/Xrp3Wd0I0UXJysrW1dZ0XKKSkpFhZWbm5uenzImgpKioSCoUeHh7m\n5uaEkMzMTEtLS/pZEokkOzvbzs7Oycnp66+/lslkn3/+ufqJL1++FAqFAoFAPZn76giFQqVS\naW9vz5hz7KRSqUwma97/IcZVXl4ul8ttbW0Zc46dXC6XSCS1h7rbLrFYLJVKBQJB2zvhSSUn\nSftI0gHy8h4hFGGxidsA0nWm0n+ySCTWOom5TausrKyqqrKystLzxPzWj6Ko0tJSzQsl2zqJ\nRCKRSPh8Pv1Hs4mYFuygNfv2228JIXPmzDE1NU1PT1+6dOl7770XHh5ulGIQ7Fo/BLvWrw0H\nOzWVnEjLCM+OsNiEEKVSKRKJEOxaMwQ73Zg2FQutWXh4+KZNm6ZOnWpubl5WVvbaa6+NHDnS\n2EUBwD+biSkxb2AWAqANQbCDltOxY8d9+/bl5uZKpVJnZ2cmjS0B/DPl5eUJhcKAgAAmDXEB\ntGkIdtCiTExMPD09jV0FADSPe/fupaam2tnZIdgBtBIMvN0JAAAAwD8Tgh0AAAAAQyDYAQAA\nADAEgh0AAAAAQyDYAQAAADAEgh0AAAAAQ+B2JwAAYKCuXbu6ubk5ODgYuxAAqIFgBwAABvLy\n8sLNxgFaFUzFAgAAADAEgh0AAAAAQyDYAQAAADAEgh0AAAAAQyDYAQAAADAEgh0AAAAAQyDY\nAQCAga5cufLrr7/m5eUZuxAAqIFgBwAABiovLy8sLJRKpcYuBABqINgBAAAAMASCHQAAAABD\nINgBAAAAMASCHQAAAABDINgBAAAAMASCHQAAGMjU1JTH45mY4E8JQGvBMXYBAADQVo0cOVIq\nlQoEAmMXAgA18DELAAAAgCEQ7AAAAAAYAsEOAAAAgCEQ7AAAAAAYAsEOAAAAgCEQ7AAAAAAY\nAsEOAAAMlJeXl5aWVlFRYexCAKAG7mMHAAAGunfvXmpqqp2dna2trbFrAQBCMGIHAAAAwBgI\ndgAAAAAMgWAHAAAAwBAIdgAAAAAMgWAHAAAAwBAIdgAAAAAMgdudAACAgQIDA+3t7e3s7Ixd\nCADUQLADAAAD+fn5eXh4CAQCYxcCADUwFQsAAADAEAh2AAAAAAyBYAcAAADAEAh2AAAAAAyB\nYAcAAADAEAh2AAAAAAyBYAcAAAa6cuXKr7/+mpeXZ+xCoPmIssnLe6Q8g1AqY5cChsB97Brh\n1KlTAQEBnTp1qnNtdXV1fHx8cXGxg4NDr169LCws9Ow2ISHh6dOnYWFhbm5utdcqlcpHjx49\nf/5cqVQ6Ojr27NlT/54bpb4yrl69mpubSz82MzNzcnLq0qULblsFAISQ8vLywsJCqVRq7EKg\nyeQSkvAdebiXiLJqlvCdSadppM9nxMzGqJVB4yDYNcLvv/8+duzYOoNdVlbW559/rlKpPD09\ns7Ky9u3bt379eg8PD3263bt3b0FBQVlZ2UcffaS1Ki0tbfPmzfn5+W5ublwu98WLFywWa9q0\naeHh4c2wP/qVceXKlcTERD8/P0JIdXV1bm6uQqEYPXr0tGnT2Gx2s5cBAAAtTZRNIkeT4qS/\nLawsIPEbScovZOI5Yl/3iAa0Qgh2zeP77793dHRcv369mZmZRCKZN2/e4cOHV6xY0eATU1JS\nXrx48fbbb0dHR3/wwQcczv+OSGFh4cqVK11cXHbt2uXq6koIkUgkP/300+7du62trUNDQ3V0\nK5VKTUxMTE1N9axfRxmEEBcXlw0bNtCPFQrFX3/9dfDgwfLy8vnz5+vZPwAAtFLyCnLqDVLy\nuO61oufk95HknbvEol3LlgUGwjl2jcNisYqLi//4449Tp05lZGTQC+Vyubu7e0REhJmZGSHE\nwsKid+/emZmZ9NrExMQTJ07U12FMTEzHjh3Hjh0rkUju3LmjuerYsWOEkM8//5xOdXTPH330\nUVhYWGVlpe46c3Jy3n///Z9//rmsrEyf/dJRhhYOhzNmzJj33nsvLi4uOTm5zjZ5eXnR0dEn\nT578z3/+U11drbnq2bNnp06dOn/+vFgsTkpKOnv2rHqVWCy+dOnSyZMnL1++rPUsAAB4VeI3\n1ZvqaOJccq3hcQpoJRDsGic3N3fJkiX37t2LjY2dP3/+1atXCSGmpqYLFiwIDg5WNxOLxZaW\nlvTjBw8e1BfsZDLZtWvXhg4dKhAIgoODY2Ji1Ksoirp161ZoaKitra3WsxYtWjRy5EjddbZv\n3/7jjz9OTEycPn361q1b1Rm0sWXUZ+TIkebm5teuXau96uLFix999NG1a9eeP39+4sSJjz/+\nuLy8nF71559/Lly48Pbt2w8fPly8ePGlS5eio6PpVU+fPv3www9///33rKys48ePf/LJJ0VF\nRQ2WAQAATUIpycPdDTd7coTIRK++GmgGmIptnFu3bm3bts3Z2VmlUq1Zs+ann36qPSWamZl5\n48aN9957j/5x9OjRAwcOrK83mUxG9zBs2LBvvvlGJBJZW1sTQoqKiqqqqugz2wzAYrH69+/f\nv3//Z8+eRUVFLVy4MCgoaOzYsX369GGxWPqXUR9TU1MvL6/s7Ozaq4qLi2fMmDFmzBhCiEKh\nmDFjxp9//jllyhSFQnH48OEhQ4bQE7hJSUkrV66kT0OkKOr777/38PBYv369qampVCpdsGDB\nwYMHlyxZoqMGmUxGUVQjX5j/oZ8rlUprvyBtlFwuVyqVTDqNXaVSEUJkMpmJCUM+giqVSpVK\nxaRjRP8etdx/PEWVyfM/X+kWKIoylcnkeWavdCstyUShMFMoCJcrr+v3iCXO5UgKG+5FKVPc\n3EA5dG3++gzCra6WF/CMXYV+2DyVTwOnxSsUCvpfPX+P6OnB+iDYNU7Pnj2dnZ0JISYmJoMG\nDfr++++LioocHR3VDQoKCtavX9+5c+c33niDXmJvb29vb19nbzExMX379uXz+YSQPn36WFhY\nXLlyZfTo0YQQ+ug2/QLYDh06LF68uLCw8OzZs999992oUaOmTZumfxk68Hi8OidMp0yZkpqa\neubMGXq+2MTEJD8/nxCSmZkpkUiGDBlCN+vSpUuHDh3oHnJzc3NzcxcvXkyfFGhmZvb6668f\nPXpUqVTquD5DLBY3JdjRKioqmthDayMWi41dQjNr8MSDNodJx4gO39XV1S2zUyaVeXbRU1/1\nVrivegMtS99TrRvCubuxmXpqBs21Uy1AZd6ufNIjfVpKpVI9gx2Xy9UxJIFg1zjt2v3v7FE6\nrgmFQnWwy8rKWr16tbe397JlyxocByopKXnw4EGvXr2OHj1KLxEIBLGxsXSisrKyIo35A5Cb\nm/vXX3/Rj/39/QcNGqTVoL56dJehg0gkcnJyqr380KFDp0+fHjBggIuLCx3LlEolIUQoFJL/\nvmg0T0/P1NRUQkhhYSEh5MmTJy9fvqRX5eTkyGSyoqIiOkbXicfjNSXYSaVSiqJ4vDbymU8P\n9GiQ/lfMtH4ymUylUpmZmTFmVFWlUikUCi6XOclh9OjRSqWSy+W2zKgqi2UnD3zvVW9F90fK\nNoeiKJVKZWJiUufvEau6hJN5Rp9+lO7DVFaezV2dgdrQMaK41g3+oVEoFAqFwtTUVM+d0v2W\niGDXOJovOp1X1K9vRkbGihUrevfuPXfuXH2OTVxcnLm5OUVR6hPgHBwcHjx4kJOT4+HhIRAI\nBALBkydP1CN/alVVVTweT+u4VldXqydGNcMTPRV748aNwMDAefPm9e3bt1Fl1Fd8RUVFVlZW\n7d6KiooiIyNnzZo1atQoesndu3fpB3QI0yxbaxfy8/NFov+dwxEaGqr7ZaSHGA1GT1zy+XzG\nhAapVCqTydQndzJAeXm5SqWysLBoK+/gDZLL5RKJhEnHSCwWK5VKc3PzFvpEYWlJ3jjwSreg\nVCorRKLaJze3XZWVlVVVVVZWVtw65+8UVeQHByKXNNQNix3+E9uyjputtjyKokSlpfVNhbVC\nDX6Sk0gk9Ec+c3Pzpm8Owa5xiouL1Y9LS0sJIfTvf3Fx8RdffDFgwIBPPvlEz6AQExMzZMiQ\nmTNnai6cMWNGbGzstGnTWCzWgAEDLl269OLFC607Bm/atMnMzOyzzz7TXOjn57d27Vr1jxRF\n3b59+/Tp0/Q9h7/99ltfX18Dyqiv+MjISJVKFRYWprX8xYsXFEV17VpzHkZ1dXVOTg496kbf\n01goFLq7u9Nrc3Jy6Af0OOiECRO6detW3xYBAKD5ccyJ/yTy+FADzbxeI60j1UGDGHJKcouJ\nj4+nbyBCUdT169ednZ0dHBwIIQcPHmzXrt3s2bNrpzqhUKi+9Ykafd+4kJAQreUDBw68fPky\nPbg1ZcoUHo/3xRdfpKWl0WsrKiq2bduWkJAwfPhw3XWmp6fv3LmzS5cuBw4cmDdvXn2pTp8y\ntMjl8lOnTp08eXLcuHG1vyqDvuSioKCA/vGnn36ysLCQyWSEEB8fHy6Xq76Q9vHjx8+ePaMf\nu7u7u7u7//nnn+otnj9/Xsc9YgAAoNkM/JLw7HQ14JiTQZtbqhpoKozYNQJFUb179/7ss8/8\n/f3z8/NTU1PpYbOXL19eu3bN2dl55cqVmu2XLVtmZWV15syZyMjIqKgozVUxMTG2trZBQUFa\nmxg4cGBkZGRiYmL37t1tbGy++uqrjRs3LliwwNnZ2czMLD8/n8fjffbZZ5q3VqmTh4fHgQMH\nGpwc0acMQkh+fv7y5csJITKZ7MWLF9XV1RMmTKhzPM/X1zcoKGjr1q39+vXLzMzs1KnT4MGD\n//zzz4MHD7733ntvv/32sWPH8vLybGxscnJyQkND1ZF37ty5a9ZFa0b4AAAgAElEQVSsmTNn\nTvv27QsLC58+fbpo0SLdxQMAQDOw8iDjTpGocURaXsdaDo+8cYQ4tpbrYaFBCHaNMGHChB49\nelhbW8fHx3t7e8+ePdvb25sQwuVy//Wvf9VuT+eq7t27174y2cPDo3v37rWH9/z9/d999131\nwJWnp+eOHTseP36clZUlk8mcnZ179uypz5nXuq+FblQZYWFh6gE/Npvt4ODQo0cPHSegfPnl\nl9euXSsrKwsLC+vSpUtVVZW9vb2NjQ0hZPLkyR07dnz27Jmdnd3s2bN37dql/s7ZgICAPXv2\n3L59WygUBgUFLVy4sA2dPwEA0La5DyJT40ncfJL5FyEaczXuoWTwVtKugaEEaFVYTb9hBICe\nTp48aWVlNWLECEKIRCL5+OOPhwwZouNMvldKKBQqlUp7e3uGXTxBX0/NDOXl5XK53NbWlmEX\nT6g/zzCAWCyWSqUCgYAxl2MrlUoRQy+e0OsDvziH5F4lkkJibk9c+hHbDq++wEajKKq0TV08\n0SCJRCKRSPh8Pi6egDbGysrqhx9+uHbtGn3BL5/PnzhxorGLAgDDFRUVlZeXc7lcxgS7fzor\nDxIYYewioEkQ7KDljBgxolOnTomJiVKpdODAgb179+Zw8D8QoA27detWampqRESE7u+qAYAW\ngz+r0KLoC2CNXQUAAAAz4XYnAAAAAAyBYAcAAADAEAh2AAAAAAyBYAcAAADAEAh2AAAAAAyB\nq2IBAMBAgYGB9vb2dnY6v2kUAFoQgh0AABjIz8/Pw8ODSd+lAdDWYSoWAAAAgCEQ7AAAAAAY\nAsEOAAAAgCEQ7AAAAAAYAsEOAAAAgCEQ7AAAAAAYAsEOAAAMdPPmzdOnTxcUFBi7EACogWAH\nAAAGKi4uzsnJqaqqMnYhAFADwQ4AAACAIRDsAAAAABgCwQ4AAACAIRDsAAAAABgCwQ4AAACA\nIRDsAAAAABiCY+wCAACgrRozZoxUKhUIBMYuBABqYMQOAAAAgCEQ7AAAAAAYAsEOAAAAgCEQ\n7AAAAAAYAsEOAAAAgCEQ7AAAAAAYArc7AQAAAxUVFZWXl3O5XFNTU2PXAgCEYMQOAAAMduvW\nrdOnTxcUFBi7EACogWAHAAAAwBAIdgAAAAAMgWAHAAAAwBAIdgAAAAAMgWAHAAAAwBAIdgAA\nAAAMgfvYAQCAgfz8/KysrGxsbIxdCADUQLADAAADBQYG+vr6CgQCYxcCADUwFQsAAADAEAh2\nAAAAAAyBYAcAAADAEAh2AAAAAAyBYAcAAADAEAh2AAAAAAyBYAcAAAa6efPm6dOnCwoKjF0I\nANRAsAMAAAMVFxfn5ORUVVUZuxAAqIEbFANAK6ZSsFJ+JhlnSGkKoVTExpf4jiZB7xKOubEr\nAwBojVpvsMvJyZk9e/amTZs6duyoo9mjR49OnTqVmZlZXl5uZWUVEBAwadIkPz8/eu369etv\n3769dOnSgQMHqp8iFAqnTZu2fv36Ll26qNuo11pbW3t7e0+ZMqVTp07Nu0ebNm26du3a7Nmz\nR4wYoblcswAzMzMnJ6cePXqMHz/ewcGheQvQ39GjR2/fvv3tt99yuVzN5bt3705KStqxYwch\nZOrUqWPHjp08eXLzbvrQoUMPHjzYtGmT1qbhH8ik7KntpWkmZSn/W1TymKSfJbfXk/CfiesA\n45UGANBKtd6pWB6PRwgxN9f1uTwhIWHFihV8Pn/OnDnffPPNrFmzSktLly9fnpubq25jYmJy\n8OBBmUymox9nZ+f1//Xhhx9SFLV8+fKkpKTm2hdCSGVlZXx8vI+PT0xMjI4Cli5dOnjw4Fu3\nbn366aePHz9uxgL0d//+/aioqCVLluiOVtOnT+/Vq1dzbfSPP/7YunUrIeTdd981NTXdu3dv\nc/UMbVXpU8uzI9iaqU5NlE1+G0ZeXG/xmgAAWrvWHuzof+tz8eJFT0/PhQsX9ujRo3379v37\n91+7dq2Tk9OjR4/Ubfr06VNRUXHq1Ckd/Zibm3f5r7CwsDVr1tjb2585c0bHU/Lz88+cOSOR\nSPTcnStXrpiZmc2YMSMlJSU/P7++Anr16vX2229v377dw8Pj66+/1r//5kJR1IEDB15//XUP\nDw/dLYcOHdq+fXuthUqlkqIoA7abnp5OP2Cz2dOmTbtw4UJWVpYB/QBDUCryZwRLWlZvA0U1\n+eNfRN7SvyAAAK1cq5iKVSqVhw8fvnz5skQi8fHxef/99zt27GhhYfH6669bWVkRQn766afI\nyMioqKjaTzQx+Vs2NTc3pycK1SwsLP71r38dO3bstdde03Ny09TU1MvLq6ioSHebmJgYutsx\nY8Y4Ozvr7jMmJiYkJKRz585OTk5xcXERERE6Gpubm3/66aezZ8+OjY0dPXq01try8vL9+/cn\nJiZWVFQ4OjqGh4ePGTOGXlVSUrJz586HDx/y+fxx48ZJJJLr16/v2rWLEKJUKk+cOBEbGysU\nCh0dHceOHRseHl5703fu3MnKyvr888/pH0tLS7dv356UlGRhYTFq1CjNlppTsREREVOmTLl/\n//79+/ePHj3K4/Hq21adx3r58uV0Fo+Njd26dWvnzp07dOjw+++/L1iwQPerCoyV+Sd5mdBA\nG3EueXyQdJ/dIgUBALQNrWLEbs+ePTExMTNmzPjqq69cXV1Xr15dWFjIZrM//fRTCwsLQoiH\nh0eds369e/fOzMz86quvUlJSVCpVnZ2rVKoxY8Y4ODgcOnRI/5JevnxpZ2eno4GDg8P333+/\nfPnyvLy8WbNmbdiwQXOYUEtubm5qaurQoUNZLNaQIUPi4uIaHNby8PBwc3OrczZ269atz549\nW7JkybZt2yZNmnTgwIFbt27Rq7Zs2fL8+fNVq1atX7/+6dOnV69eZbPZ9Kr9+/dHRUVFRETs\n2LFj/Pjx+/fvP3/+fO3O79y54+Xl5ejoSP/43XffqTssLy+/ceNGndVyOJwLFy74+vp+9dVX\nPB5Px7bqPNYrV6708/MLDQ09evSot7c3IaRXr1737t0zbPAPmCD9bHM2AwD4xzD+iJ1EIrl4\n8eKMGTNCQ0MJIZ988kl1dXVeXp6Tk5O6zdChQ4cOHVr7ua+//npxcXFkZOTNmzctLCyCgoL6\n9u07ePBgMzMzzWZsNvv9999fu3ZteHh4YGBgnWUolUr6QVlZ2ZkzZ3Jzc6dOndpg8d26devW\nrVtOTs7p06fXrFnj7u7+3nvvdevWTavZpUuX3NzcAgICCCHDhg375Zdfnjx50uDFGY6OjqWl\npbWXz507l8ViCQQCQoibm9u5c+fu37/fr1+/4uLipKSkjz/+mC5gwYIF77//vq2tLSFEIpFE\nR0e/+eabw4YNI4S4urqmp6dHRUVpXcZBCElJSVEXVlJSkpiYOHPmTLrDmTNn3r17t85S2Ww2\nl8t95513dG+rvmPdvXt3ExMTU1NTa2trusOgoKDjx4/n5ubqmBEuKSlpevIrKSlpYg+6maX/\nanWthYaUzAgxa7gVszw/T75lGbuIRjAlRGDsGtQUDsFl4XV8umuUIUOGhIWFmZqaFhcXN0tV\nrQTDdocQIhaLxWKxsatoTsw7RpWVlZWVlfq0tLe3Z7HqfeszfrDLzMxUKBQdOnSgf+RwOJ99\n9pn+T58yZcqECRMSExMTExMfPHiwc+fOX3755YsvvtAKBL179w4ODt6zZ8+WLVvqrGHChAnq\nHy0tLWfPnq15IS0hRKlUVldXq4vUzI4eHh6ffPLJ2LFj16xZEx8frxXsVCrV5cuXR40aRWdH\nR0fHwMDA2NjYBoOdXC6v8/IFkUh04MCBlJQU9Rl4Li4uhBD6khH1FcFmZmadO3d+8eIF+e+L\n3LVrV3UnnTt3vnDhglgspie71YRCIZ0F1R36+vrSP7JYLH9//+zs7DqrVR9BHdvKzs7W81jT\nNQiFQh3BzsTEpL5hWn3QoVDH70bzYJtRZjavdhNMxJJXEJVCj3YmFNf61ZfDTJSpZdP//3O5\nXIqiXvnvUcvCHrV+DNuj5v17ZPxgR6eTptzbgsfj9e3bt2/fvoSQpKSkr7766sCBA6tXr9Zq\nNn369Dlz5ly6dKn2rK6bm9vChQvpx1ZWVk5OTrVf38TExDVr1tCPhw4dOm/ePPWqnJycM2fO\nxMXFubu79+nTR+uJ9+/fLy0tPXbs2LFjx9QLs7KyZs6cqWOvKYrKy8sLDg7WWi6TydatW2dv\nb79582YXFxc2m7106VJ6FX2PUD6fr24sEAjoYEe/yKtXr1bvFx2JysrKtIJdZWWlugf6WfRs\nOE2zcy2Wlpaaz6pzW/ofa7o33Z9d1AHUMEKhUKlU2tnZvdp3B/vppNf0V9i/BqlUKpPJtA5o\nWxUzmzz4oeFmPqNYE869+mqajVwul0gk9HC70ZkSYt/kTsRisVQqtba2NjU1bYaaWgGlUikS\niZr49tKqVFZWVlVVWVpaas1ltV0URZWWltrbN/3/b2shkUgkEomFhYXuO4HoyfjBjt4Nw4aI\nS0tLzc3NNV+ILl269O/fv84ZQw8PjzfeeOPIkSP07es0cblc9UBXfQICAr7++mv6sY1NzRjM\nw4cPo6KiEhISevfuvXr16to9E0JiYmICAwM/+OAD9RK5XL58+fJbt26FhYXVt7nHjx8LhcLa\nGfT58+cFBQULFy50d3enlwiFQvqiEPqXVvMW8OpXlQ5nCxcu9PLy0uytXbt2Wv3z+Xx1nKIv\nSda8MlckEtVXsJqObZWXlxP9jnVFRQXRmSOB4fzG6xXs/Ma/+lIAANoS41884ePjw2az1VcJ\nUBS1bNmyuLi4Bp9YVlY2ffr03377TWt5Xl5efR+2pkyZolQqIyMjDaiTz+cH/Zerq2txcfHc\nuXPXr1/v4uLy448/rlixos5UR9++bvDgwX4aAgMDu3fvHhsbW9+2xGLx7t273dzcao//0blN\nnWWTk5MLCgroUVxXV1dCyPPnz+lVMplMfT2Hj4+PqalpeXm5+39ZWVkJBILaH7JtbW3Lymru\nMeHm5kYIycjIoH9UKpXJyckNvlA6tqX/sRYKhaTJY3LQhnm9TtxCGmhj40eC/q9FqgEAaDOM\nP2LH5/OHDRv2+++/Ozo6enp6nj9/Pj09XesSh7i4uFu3bi1btkxzoY2Nzbhx406ePFlWVtav\nXz9ra2uhUBgTE/PkyZMlS5bUuS1LS8upU6c2y81v5XL50KFDX3/9dc2ZytquXLmiUCgGDNC+\nRX5ISMj27dvVJ7RVVVXR90OWy+XPnz8/d+6cTCZbs2ZN7eDl4+NjZmZ29uzZKVOmZGRk/Prr\nr7169Xrx4kVZWZmzs3P79u1PnDjh4uIiEAiOHDmivhbBwsJixIgRx48ft7a29vf3f/ny5b59\n+xwdHVeuXKnVf2Bg4JMnT+jHTk5OHTt2/O2331xcXKytrc+ePavPLKqObek41paWlunp6RkZ\nGQ4ODtbW1k+ePLG2tlaPSsI/0RvHqGN9WZJ6vl3e1JKM+Y2w8fUkAAB/Y/xgRwiZOXOmubn5\noUOHJBKJr6/vmjVrtG4Ll52drfmtX2r//ve/vby8Lly4sH37dolEYmtr2759+6+//rq+S18J\nISNHjvzrr7+afvNbFxeXcePGNdgsJiamU6dOtU+p6dev344dOy5fvkxftFFQULBixQpCCJvN\ndnBw6NOnz8SJEzWvC1aztraeN2/eoUOH4uLi/P3958yZU1hY+M0333z55ZdbtmxZtGjR9u3b\nV6xYYWdn9/bbb1tbWz979ox+4vTp0/l8/sGDB0tLS21sbPr16zdt2rTa/ffu3fv8+fPFxcX0\n9C7d4fr16+n72Dk6Ol6/3vDt/nVsq75jPWbMmC1btqxatWrRokU9evS4e/dur169mHRuLDSa\ntWfF2Ivmse9zCu9or7LvRMKPE8eudT0NAOAfjYVbhTGJVCpVKBTqU9NWrVplaWmpvrpCHxRF\nzZkzp0uXLh9++OGrqbFhjx49WrFixbZt27TO0mte9MUTui8ab1sYdfEEIYSQ8vJyuVxmVx5v\nknGGlCYTSkUE7Un70aTDRMJiG7s6Q7SqiyeaRW5urkgk8vT0VF8+1dYx9eIJKysrXDzRatEX\nT/D5fIZcPAHNaO3atUKhcPbs2QKB4O7duw8fPly1alWjemCxWNOnT1+/fv2oUaMa/FaxV0Gp\nVP7000/Dhw9/pakO2g4W5T2StK/jW1KgNbhy5UpqampERIS/v7+xawEAQlrDxRPQjBYvXuzt\n7b1hw4Z58+bFxMTMnz+/zm/s0K179+7jx4/ftGmTTCZ7FUXqdvToUblcrnkRMQAAAOgJI3aM\nIhAIFi9e3PR+pk6dqs8Xb7wK06ZNq/PkPwAAAGgQRuwAAAAAGALBDgAAAIAhEOwAAAAAGALB\nDgAAAIAhcPEEAAAYyM/Pz8rKSv312QBgdAh2AABgoMDAQF9fXybdchmgrcNULAAAAABDINgB\nAAAAMASCHQAAAABDINgBAAAAMASCHQAAAABDINgBAAAAMASCHQAAGOjevXvR0dGFhYXGLgQA\naiDYAQCAgfLy8tLS0iorK41dCADUQLADAAAAYAgEOwAAAACGQLADAAAAYAgEOwAAAACGQLAD\nAAAAYAgEOwAAAACG4Bi7AAAAaKtGjhwZFhbm4OBg7EIAoAZG7AAAwECmpqY8Ho/NZhu7EACo\ngWAHAAAAwBAIdgAAAAAMgWAHAAAAwBAIdgAAAAAMgWAHAAAAwBAIdgAAYKDKykqRSCSXy41d\nCADUQLADAAADxcbGHj58ODc319iFAEANBDsAAAAAhkCwAwAAAGAIBDsAAAAAhkCwAwAAAGAI\nBDsAAAAAhkCwAwAAAGAIjrELAACAtsrT09PU1NTKysrYhQBADQQ7AAAwULdu3Tp27CgQCIxd\nCADUwFQsAAAAAEMg2AEAAAAwBIIdAAAAAEMg2AEAAAAwBIIdAAAAAEMg2AEAAAAwBIIdAAAY\nKDExMS4urri42NiFAEANBDsAADBQdnb248ePxWKxsQsBgBoIdgAAAAAMgW+eAACAhohzSdZF\nIs4mpnxi34l4DCEcnrFrAoA6tN5gt27duoEDBw4ZMqRRzzp79mxGRsbcuXPVS+7fv3/y5Mk3\n3nhj4MCBLVnM0aNHnzx5QghhsVhmZmaOjo7BwcF9+vRhsViabYRC4aeffqrj6XQP1tbW3t7e\n4eHhlpaWtRtoWrhwob29Pf04Pz8/NjY2MzNTpVI5OjoOGDCga9eumgVouX37dmxs7MKFC7lc\nru69q+9VTUhIePbs2eTJk+kfG3zdavdz8uTJ8vLy6dOn6y4AAFqI5CWJm09SThBC/W+huSMJ\nWUu6zjReWQBQt9Yb7GxtbXm8Rn8izMvLe/bsGf1YpVL9/PPPp0+flkqlffv2beFisrKycnNz\nR40aRQiprq7OzMzcsGGDn5/fihUr7Ozs1G0KCwsbfDohpLS09OzZs+fOnfvmm2+cnZ3VDYYP\nH671RHUmO3/+/I8//ujg4BAcHGxqapqWlvbXX38NHDhwwYIFpqamtbeYl5f33XffLViwQHeq\nq+9VraqqMjc3r6ysfPnyJb3LPB4vOTm5Q4cOjepn+PDhc+fOdXZ2Dg8P11EGALSEsnTy21Ai\nytZeXlVELs4iL+8RMsgYZQFAvVpvsJs9e3YTe/jrr7+uXLnyzTffLFiwwCjF2NraTpkyRf1j\nRkbGl19+uW7dus2bN5uYNHx2o9bTp0yZ8vHHH//6669z5sxRN3jnnXfqfO7jx49/+OGHYcOG\nzZ49m81m0wuvXr26efNmb29v9Yiapr1793bu3LlPnz66q6rvVT106NDjx499fX2LiopWr15d\nWlq6fft2A/qxtrZ+9913f/zxx5CQEHyzOIAxKWUkalwdqU7t4d6O1tWpxLcFawKABrTeYKc5\ni7dhw4bXXnuNzWZfunRJLpd36tRp7NixdF5RKpWnT59++PAhn8/XGr5q3779li1b+Hx+7c7P\nnz9/9erVdevWaS7csWOHra3t1KlT1Ut+/vnnoqKiOXPmaBajUqliYmIePHhQWVnp6Og4YsQI\nPz8/ffbI19f3008//eKLL27cuBESEtLYF8TOzs7f3//58+f6ND5x4oSjo+NHH32kTnWEkNDQ\n0MrKSm9v79rt09LS7t27t3nzZvpHHftY36s6c+bM1NTU7du3FxYWLl68uGvXrvRyFot1+/bt\nuLg4mUzWo0eP8PBwOtTqODqDBg06fvx4ZGTkv//9b312FgBeiaR9pOSx7iZdKn6/wKrjZBIA\nMJbWe1VscnKyepoyNTX19OnTly5dGjZsWL9+/Y4dO3bq1Cl61Z49e44cOdKhQ4dOnTodOXLk\n6dOn6h46duxYZ24ghJSUlKSlpWkttLW1PXfunFKppH9UKpVnzpyhz1fTLGbfvn379+/39PTs\n37+/QqFYvHhxRkaGnjvVs2dPW1vbe/fu6dleS1VVlT4zwjKZ7NGjR6GhobWnXEeOHNmxY8fa\nT7l+/bqDg4O/vz/9o459rO9VNTExqaio4HA4YWFhGRkZ6jrv3bv3yy+/BAQEODo67tu37/jx\n47r7IYSw2ex+/fpdv369wT0FgFco+ViDTUxVkvcHObq7u7dAOQCgj9Y7YqeJxWKVlZWtW7eO\nPvH//v37CQkJb7/9tkQiuXDhwptvvkkPsw0dOvTf//63g4NDgx1GRERERERoLQwJCfnll18e\nPnzYo0cPQgg9XhUWFqbVrHv37gMHDuzUqRMhZMSIEampqZcvX/b11Xcywt3dvb7z6nS7c+dO\namrqtGnTGmxZXFysVCo9PDz07zwpKUk9xkYM3ceoqKgZM2Z4eHgsW7Zs/PjxdLYrKCjYu3cv\nfd4eRVHnzp2bMmWK5jhinbp27XrmzJnCwkInJ6f62lRUVFAUVd/aBqlUKroTg3tobVQqlUql\nYtIdxehPWZWVlTqu+GlbVCqVUqlsPceId+VTlkxU31pO/m19OrF/tktVek3BYhFCFC4h8k4f\nNFt9xkBRFMN+jxQKBSGkurpaJpMZu5bmxLxjJJVK6QcNsrS01PGu2DaCHSFE83JOOzu79PR0\nQkhmZqZSqezWrRu9nMfjdenSJT8/37BNeHl5eXh43Lp1iw52169f9/HxqR2Punbt+scff0RF\nRUkkEoqiSkpKSkpK9N+KiYmJnkcuPz9/6dKl9OOysrL8/PywsLCxY8eqG7x48ULrBDUzM7Ov\nvvqKjiwcTiMOblFRUXBwsPpHw/Zx7dq19DHauXOn+iTCnj17qq/G6NatW3R0dEFBgZubm+6u\n6DxXVFSkI9hJpdKmBDt1J03sobVRDzkzBsP+GpHW9L+O//xPk+qiJnbCFqWzRen0YwXLTOr3\nf02uy/hazzFqLnK53NglNDPmHSOFQqF/sNOxts0EO81pOxaLRWcXOrBbW1urV9nY2Bgc7Agh\noaGhf/3116xZsyiKun379ptvvqnVgKKo9evX5+bmTpo0ydXV1cTEZM+ePY3aRHFxsY+Pjz4t\nLSws+vXrRz+2srLy9fXVGjPj8/laV/vSc682Njb0hvSvSiQSWVlZ0Y8N3kd18ta8NER97xXy\n3/+L+gyS0cdUJKp3LIH8/bgbQCwWq1Qqa2trxowGyeVyuVxuYWFh7EKaTWVlpUKhsLKy0udi\nozZBoVBIpdL6TkJoearwEyplvbmZ8+dkUv94nprU5y3S+d/0MDzH0qWtX/OkUqkkEonuP5xt\nS3V1tVQqtbCwqPN+CG0RRVFisbiJfwJaFfoY8Xg8MzMzfdrr/rPVZoJdnehBKc3YLpFImtJh\naGjo8ePHnz59KpVKKyoqas/DZmdnJyYmfv7557169aKXNCoW5Ofnv3jxYsyYMfo0FggEEyZM\n0NHAxsamzutbLS0t3dzcEhISJk6cqLUqKyvL2tra1tZWa7mZmZl6XKSJ+6hF82MifaT0eXOh\nW+r+L97ENyl6p0xNTRkT7OhpPsa8d5P/HiMOh9Pg3H0bIpPJWtEx8h6qa63PKPL0lwb7qPZ/\nx8JnOKf17FTT0GPeregYNRn93s5msxmzU/RcDWN2h/z3D2VzHaO2/TnY0dGREKI5RKfnRaP1\ncXNz8/HxiY+Pv3nzZlBQUO3T9UpLSwkhLi4u9I9FRUXZ2fXfC+DvKIo6ePCghYVFU26VrKdh\nw4Y9fPjwzp07mgslEsnGjRu3bdtWu71AICgvL6cfN2Ufa8vJyVE/zs/PZ7FYOmZX1ehi2vpH\nf4C2rUfDl7sq7bvK2/VvgVoAQE9tO9h5eno6OTmdPXuWPhUsOjpaz+sSkpOTz549W+eq0NDQ\nBw8e3L17t/ZwHSHE1dWVxWIlJiYSQsRi8c6dO729vXXPGBJCZDJZamrqunXrbt++PWvWLM28\nQlGU7O/oWeYmGj9+vJ+f38aNGyMjI4uLi8VicUJCwrJly8Ri8YwZM2q39/X1pU9bNHgf65OU\nlPTo0SNCSGVl5YULF4KCgvSZ40hLS+NyuY26/gMAmpnbQNLjE10NOObSQTsIq23/HQFgmLY9\nFctisT755JOvv/566tSpXC63ffv2w4cPv3//Pr120aJFqamp9ON9+/bt27ePfuDk5BQfHx8Z\nGVnnlGhoaOiRI0dMTEzqHFdr167duHHjdu/efebMmcrKylmzZgmFwj179ixZsmTTpk1ajTMz\nMzWvdfD09Fy5cmXv3r012zx//vytt97SXDJ37txhw4Y1+rX4Ow6Hs2HDhgMHDhw/fvzgwYOE\nEBaL1bNnz2XLltFfXKGlZ8+eu3btor8uQvc+6nhVa3erUqlGjx69Z8+eiooKkUhkaWm5ZMkS\nepXufu7fv9+5c+cGv9kMAF6twd8RpYw8rOssW54tGf2LyKyDRCRi0vlbAG0dq+nXFb4iycnJ\n9vb29J/5lJQUW1vbdu3a0asKCgpEIpH6pmvV1dVZWVkWFhYeHh6FhYVlZWX0qvT09Nqn3AUE\nBHC53IKCguLi4s6dO9e56ZSUFDabrfldWJrFEEKKioqEQkO9XPMAACAASURBVKGHh4e5uTkh\nJDMz09LSkp4XVsvKylKPcnE4HAcHB60GWm3U3N3dbW1ts7Ky5HK5jlsfN9iAJpVKCwoK5HJ5\nu3bt1JdH1FZdXf3BBx+8+eab48eP172POl7V2t0+fvzY1dXVxsYmOztbJpP5+Pior9XVfXRm\nzZq1cuVK9Ul+r4JQKFQqlfb29ow5x04qlcpkMh1Huc0pLy+Xy+W2traMOcdOLpdLJJK2d47B\n8/Pk7maS8x+ikhNCiEU7EjCJ9F1O+M7Hjx9PTU2NiIhQvyG3dUqlUiQS1T4Rue2qrKysqqqy\nsrLS88T81o+iqNLSUs0r89o6iUQikUj4fD79B7eJWm+wgxZ27ty53377bdeuXUa/rHLLli1F\nRUVfffXVK90Kgl3rh2DXuiiqieQlYZsR/v9G/RHsWj8Eu9aveYMdzo2AGuHh4R06dKjz0oqW\ndPXq1YSEhIULFxq3DADQxuERay/NVAcArVDbPscOmhGLxVq5cqWxqyChoaGhoaHGrgIAAKBN\nwogdAAAAAEMg2AEAAAAwBIIdAAAAAEPgHDsAADCQq6urSqVqPd9+CwAIdgAAYKCePXt27ty5\nrd7ABYCJMBULAAAAwBAIdgAAAAAMgWAHAAAAwBAIdgAAAAAMgWAHAAAAwBAIdgAAAAAMgWAH\nAAAGSkxMjIuLKy4uNnYhAFADwQ4AAAyUnZ39+PFjsVhs7EIAoAaCHQAAAABDINgBAAAAMASC\nHQAAAABDINgBAAAAMASCHQAAAABDINgBAAAAMATH2AUAAEBbNXTo0H79+jk7Oxu7EACogWAH\nAAAG4vP5HA7H1NTU2IUAQA1MxQIAAAAwBIIdAAAAAEMg2AEAAAAwBIIdAAAAAEMg2AEAAAAw\nBIIdAAAYSC6XV1dXK5VKYxcCADUQ7AAAwEDR0dH79u3Lzs42diEAUAPBDgAAAIAhEOwAAAAA\nGALBDgAAAIAhEOwAAAAAGALBDgAAAIAhEOwAAAAAGIJj7AIAAKCtcnV1ValUfD7f2IUAQA0E\nOwAAMFDPnj07d+4sEAiMXQgA1MBULAAAAABDINgBAAAAMASCHQAAAABDINgBAAAAMASCHQAA\nAABDINgBAAAAMASCHQAAGCg5OfnmzZtCodDYhQBADQQ7AAAwUFpa2r1798rKyoxdCADUQLAD\nAAAAYAgEOwAAAACGwFeKAQD8MyiqSEUeYZsRvjMxwZs/ADPhdxsAgOmyLpE7m0jOZaKSE0KI\nmQ3xG0/6ryICX2NXBgDN7B8a7KZOnTp27NjJkyc36lm7d+9OSkrasWMHIUQsFm/duvXOnTsm\nJiYqlapnz54LFy60tLRssWJoy5Yte/z4cURExL/+9S+tVRUVFfv3779y5YpcLqeXBAYGzpgx\no0OHDnV2RVHUunXrTE1NP/vsMx1b1L3jcXFx9+/fX7BgQYO7Vl8/V65c2bNnz9atWx0cHBr1\nUgBAHSgVuTyfJGz720JpGXl8iKT+SkYeIv5vG6kyAHgl/qHn2G3evPmNN95oSg87d+5MT0/f\nsmVLZGTkxo0bk5OTjxw50sLFvHz58smTJwMGDIiLi9NaVV1dvWzZsvj4+JkzZx4+fPjEiRNr\n166Vy+XLly9PS0urs7dz5849e/Zszpw5ujda347fvHnz+vXrHA7H3Nw8JSXl3LlzhvUTFhYW\nHBy8efNmfV8FANDh2nLtVKcml5A/IkjWpZYtCABerX9osCsrK6uqqqIfJycn0zdhysnJSU9P\nl0qlmi1lMllqampubq7mQrlcnpaW9vbbb/v5+bFYrMDAwNDQ0KSkJHptQUHBo0ePtLaYkpLy\n4sULzSV5eXkpKSlaxRBCKIp68eLF06dPS0tLde9FTExMu3bt3n333fz8/OTkZM1VJ0+ezMnJ\nWb169fDhw21sbCwsLLp167ZhwwaBQHD16tXaXVVXV//6668TJ060sLDQUYaOHVcqlefPn9+1\na1dMTMzevXsrKys19ygrK+vZs2fqsUPdL2BERERKSsqdO3d07z4ANKAokdz5RlcDlYJc/JAo\npbra6BQWFjZp0iRXV1eDewCA5vUPnYpdt26deorw66+/fuONNx48eCCTyUQiUXV19RdffOHr\n60sIefLkyfr166VSqbm5uYeHh7OzM/10U1PTffv2aXbIZrNVKhX9+Pz585GRkVFRUZoNTp06\nVVxcvGXLFvWSzZs329vbr1ixQrOYp0+fbtq0qby8nM/nl5eX9+3bd8mSJWw2u/YuUBQVFxc3\ndOhQNzc3Pz+/2NjYwMBA9dpLly716dPH399f8yk8Hu/HH3/kcOo46FevXq2srBwxYgT9Y31l\n6NjxkJCQ4ODgRYsWURQ1Z84cLy8vukFlZeWCBQtevHghlUptbGxWr17t6+ur+wV0dnbu2bPn\n2bNne/fuXbtUANDXgx8IpWqgTXkmyfiDdJho2BYEAgGPxzMzMzPs6QDQ7P6hwU6TiYlJVFTU\n2rVr/fz8lErl/Pnzf/vtt6VLlxJCtm3b5uLism7dOh6Pd/Xq1a1bt7q4uNTuQSwW37hxY8iQ\nIfSPvXr1EggEWm1CQ0O/+eaboqIiR0dHQkhhYWFaWtrEidpvplFRUX5+fosWLTI1NS0oKJg3\nb150dHR4eHjtjT569KiwsHDo0KGEkGHDhh09evSDDz7gcrmEEKFQWFpa2qVLl9rPqjPVEUIS\nEhI6duxobm7eqDK0dnznzp19+vQJCAj49ttvt2zZQm8rOjp67ty5AwYMKCsrW758+a5du775\nRnsIQasfQkiPHj0OHTokk8noPaqTTCajKKq+tQ2inyuVSlkslsGdtCpyuVypVGoNObdpdNaX\nyWQmJgyZW1AqlSqVSs9jxCp9wipNbrhd/TjpZ/T5z6168KNSVt2Ifq08VO361DxXpSKEyOVy\n9Qeztk6lUlEUxaTfI6VSSQhRKBTGLqTZUBTFsGNEHx2FQqHnTun+KIVgRwgh3bt39/PzI4Sw\n2eygoKAnT54QQnJzc/Py8hYuXMjj8QghoaGhkZGRMplM67kymWzTpk1mZmaTJk2il3Tq1KlT\np05azXr37m1mZnbr1q0xY8YQQm7cuMHj8fr06aPVbOnSpXK5PCcnRyKRUBRlb2+fnp5eZ80x\nMTFBQUHt2rUjhAwaNOjAgQPx8fEhISGEELFYTAixsbHR/xXIyMjo27dvo8qoveMDBgwIDg42\nNzeXSCTqtBQQEDBw4EBCiK2tbXh4+J49e8RisZWVlY5+CCF+fn4ymSw7O5s+LnUSi8VNCXa0\nioqKJvbQ2tBHn0k0p/WZQc9jZPH4Z4tEnROpzcQk+6JJ9kX920u9x4sH7dVcIpFImrsoI2Pe\n75HmCT/MwLxjJJVK9Qx2XC5Xx5AEgh0hhKjnWAkhXC63urqaEFJQUEAI0Ryic3d3z8jI0Hyi\nRCJZv359QUHBunXr+Hy+jk3weLzevXvfvHlTHez69etXezjq+vXrO3bs4HK5zs7ObDa7pKSk\nzsNcXV1948aNoUOHJiYm0ku8vb1jY2PpYEefJ9eoTzPl5eWao4wNllHnjtMBjhAybNgwdUtv\nb2/1Y/rFLCwsVAe7+l5AuhiRSKSjZjMzs6YEO3rAj0lTSCqVSqVS1Tco2xbR40C638LaFpVK\npVQqTU1N9WnMcuik8J3QlM1xsv8iioaH4lRWnirHno3ot11f9S+OQqGg94gxo6oURcnlch1z\nBW0O844RIUT3fE6bQx8jDodT55lXjcWcvwFNUedLSQ+Nav7h13o7FovFq1atUiqVGzdu1Ofe\nHKGhoRs3bhSJRHK5/OnTp7VvAlJRUbFt27YhQ4bMnDmT/ku2ZMmSOru6du2aVCqNjY2NjY2l\nl1AUpVAoysrKbGxsbG1tuVyuVgbVTSqVqn9JGiyjUTtOj3fS6NeZnhfQ3Q/9suvOpobdXEZN\nKBQqlUpLS0vGhAapVCqTyTRHQ9u68vJylUrF5/Ob5c2uNZDL5RKJRN9j1H0a6T6tSduLHEMy\nGrg+nRBi0nuxSY9P9O+VQ4j6bVEsFiuVSgsLCz3TauunVCpFIhGTfo8qKyurqqqYdCokRVGl\npaVMOkYSiUQikZiZmalPiGoKBLt60QNImoNGmteHymSytWvXmpiYrFu3Ts+E0bNnTzMzs/j4\n+Orqamtr6+7du2s1yMzMrKqqGjVqFB01KIrKz8+nz8nTEhMTExISsnjxYvUShULx7rvv/uc/\n/xk3bhybzQ4ODr569eo777yjvsqVdvjwYTabPXXqVK0Ora2t1cPausto7I5rvoD0Y/pZuvuh\nW1pbWzfYPwDUK+idhoMdx5z4v9ki1QBAS2DOwGyz8/b2NjExUc91isVi9f04CCG//vprcXHx\nmjVr9B834nK5ffv2TUhIuH379sCBA2sPQtCTaOphqpiYGIlEUvuUZPr2dep5T/Vz+/btq76h\n3eTJk8Vi8bfffktPK9NiY2NPnTpV55Sxo6NjYWGhPmU0dscTEhLUz01KSrK0tKQnvnX3QxdT\nZ6gFAH0FTCJuIQ206buM8Ou4JkxPcrm8urpaPQwPAEaHEbt6WVlZDR48+Pfff1cqlQKBIDY2\ntn379vS59uXl5adPn/b39//jjz80nzJhwgQej/fzzz+fO3fu2LFjtfsMCQnZunVrdXX1lClT\naq9t3769vb39vn37wsPDMzMzMzMzBw8enJCQcPPmzf79+6ubxcTEmJmZ9eypfU5MSEhIbGxs\nVlaWl5dX+/bt582bt3379g8//LBXr148Hi81NTU1NXXcuHHjxo2rvemuXbteuXKlwTKCgoJ0\n7HjtbimKEggEq1evDgsLy8vLu3DhwpQpU0xMTHS/gISQhw8fOjs7Ozk51e4TAPTGImN+I78O\nJfVdXRsYQfquaMoGoqOjU1NTIyIitG6uBADG8g8NdoGBgerQ0LFjR/raUpqLi0tAQAD9+OOP\nP3ZxcUlOTrawsJg+fXpRUdHDhw8JIRKJpEOHDhRFaY7hEUJGjx7N4/Hs7Ox8fHzq3G5wcLCf\nn5+ZmZnmPefUxXC53HXr1v3++++XL1/29/dftmxZSUkJPVKoGeyKiopGjx5d+2yJ7t27d+/e\nPS0tjb6H3ODBg4OCgmJiYrKyssrKygICAmbNmlXfRaYDBgw4efLks2fPOnTooKMMb29vHTte\nu9uAgIDhw4dTFPWf//xHLpd/+OGH9Hds6H4BlUrlrVu3wsLC6iwVABqB70wibpIrS8mj/USl\nccMLc3vSfw3pMZsQhpxjCgA0VtNvGAHM8OWXX5qYmKxcudLYhZDY2Ngff/xx7969tW8H2Izo\niyfs7e1x8USrVV5eLpfLbW1tGXbxxCv9j12vqmKSdZGUZxCOBbHrSDyGEE4dH8Ya6/jx4wwb\nsaMvnrC1tTV2Ic2GvnjCysqKYRdP2NvbG7uQZkNfPMHn83HxBDSnDz74YP78+fHx8bXvrteS\nRCLRkSNHpk2bZpw/fgBMZe5AOtZxBggAMAwunoAaLi4u8+fPj4mJqX0T5pZ04cKFkJCQOr9s\nAwAAAHTDiB38T9++fTW/f8Io3nrrLeMWAAAA0HZhxA4AAACAITBiBwAABnJwcKiqqmqWM74B\noFkg2AEAgIH69+8fHByMS50AWg9MxQIAAAAwBIIdAAAAAEMg2AEAAAAwBIIdAAAAAEMg2AEA\nAAAwBIIdAAAAAEMg2AEAgIGSk5Nv3rwpFAqNXQgA1ECwAwAAA6Wlpd27d6+srMzYhQBADQQ7\nAAAAAIZAsAMAAABgCAQ7AAAAAIZAsAMAAABgCAQ7AAAAAIZAsAMAAABgCI6xCwAAgLaqX79+\ngYGBzs7Oxi4EAGog2AEAgIEcHR2tra3Nzc2NXQgA1MBULAAAAABDINgBAAAAMASCHQAAAABD\nINgBAAAAMASCHQAAAABDINgBAAAAMASCHQAAGOjs2bM7duzIzMw0diEAUAPBDgAAAIAhEOwA\nAAAAGALBDgAAAIAhEOwAAAAAGALBDgAAAIAhEOwAAAAAGIJj7AIAAKCtcnBwqKqqMjc3N3Yh\nAFADwQ4AAAzUv3//4OBggUBg7EIAoAamYgEAAAAYAsEOAAAAgCEQ7AAAAAAYAsEOAAAAgCEQ\n7AAAAAAYAsEOAAAAgCEQ7AAAwEBpaWn37t0rLy83diEAUAPBDgAADJScnHzz5s3S0lJjFwIA\nNRDsAAAAABgCwQ4AAACAIRDsAAAAABgCwQ4AAACAITjGLgAAAJpV0UOSfIy8vEukIsJ3Jh6D\nSKdpxNzR2GUBQEtAsGt1tmzZcvny5TpXffTRR6NGjWpK51OnTh07duzkyZP1XK7b7t27k5KS\nduzYYXAPANCclFISO4ck7SOU6n8LM86Rm2vJ4G9JlxnGqwwAWgiCXavz1ltvDRs2jH783Xff\neXl5TZw4kf7R3d29vmf98ccfz549mzdvnmEbnT59upeXl2HPba4eNDVxdwD+iVQKEjWOPD9f\nxyqZiFz4gFQLSe/FzbvNfv36BQYGOjs7N2+3AGAwBLtWx9PT09PTk37M5XJtbW27devW4LPS\n09ObstGhQ4c25en19aBUKk1MTFgsVmN7a+LuAPwT3dlUd6pTu/oZ8RhMnHs34zYdHR2tra3N\nzc2bsU8AaAoEu7ZELpcfPXr06tWrQqHQzs5u8ODBERERbDZ7+fLljx49IoTExsZu3brV3t5+\n//79iYmJFRUVjo6O4eHhY8aM0d2z5kTqu+++O3ny5JKSkkuXLslksk6dOs2ZM8fGxoYQUlpa\nun379qSkJAsLC61JYc0eIiIipkyZcv/+/fv37x89epTH4504cSI2NlYoFDo6Oo4dOzY8PJx+\nllKpPHz48OXLlyUSiY+Pz/vvv9+xY0et3fH19X0FryUAsyiqSPzGBtpQKnLzSzLhbIsUBADG\ngWDXluzatevGjRuzZ8/28/N7+vTpDz/8IJfL33///ZUrV65cudLFxWXmzJmWlpZr164tKChY\nsmSJjY3N06dPt2/f7ujo2K9fPz23wuFwoqKixo8fv2/fPqFQuHTp0hMnTsyaNYsQ8t133+Xm\n5q5atcrOzu6PP/64efOmlZVVnT1cuHChb9++kyZN4vF4+/fvv3jx4qxZswIDAxMTE/fu3cvh\ncEaMGEEI2bNnz/Xr12fOnOni4nLu3LnVq1dv375da3ea8QUEYKycy0QmarhZ1kWiqCIcDLAB\nMBaCXZshFovj4uIiIiJCQ0MJIS4uLhkZGdHR0f/3f/9nYWFhYmJiampqbW1NCJk7dy6LxRII\nBIQQNze3c+fO3b9/X/9gRwhxdnYePXo0/aBXr17Pnj0jhJSUlCQmJs6cOZOeGp45c+bdu3fr\nfDqbzeZyue+88w4hRCKRREdHv/nmm/SJg66urunp6VFRUSNGjJBIJBcvXpwxYwa9R5988kl1\ndXVeXl737t01d6c+QqFQpVLpaKAbRVGEEIZ9FRJFUTKZzNhVNLOysjJjl9CcKIoqKSmpvdz6\nr7GcsieG96uU6nXGg1JK7WpHWOxGdGzpWT4mVncbkUiPTNl21HeM2rSKioqKigpjV9FsGHaM\n6L9HEolEIpHo097Ozk7HOU4Idm1GZmamUqkMCgpSL/H394+KisrPz/fw8NBsKRKJDhw4kJKS\nov4v4uLi0qhtac5+8vl8+u0gNzdXcxWLxfL398/Ozq6zhw4dOqjLVigUXbt2Va/q3LnzhQsX\nxGJxdna2QqFQt+RwOJ999pn+RapUKvqXoSma3kNrgz1q/ercI5ZczJK2RIRlycSNa8+1afAQ\n/EOOUZuGPWr9mmuPEOzaDDqlaU5N0o+1Ar5MJlu3bp29vf3mzZtdXFzYbPbSpUsbuy0ul6v5\no/rDBCHEwsJCvZzP59fXg7pO+lmrV69Wf7ygh9nKysroVVrb0p+9vb1hT6QJhUKlUmlvb2/A\ntR2tk1Qqlclk/9/enQY0ca19AD/ZICSEPQgiCIgg7lisIiCudRetdUNta/UWq74uV6Vqa8WK\nra3rdWmvilpQudYN3FqsVtyX1o26oAioIBhFgbAkJCGZ98PQNCKEEMDA8P99Ss7MnPPMTJYn\n55yZVDo43khJpVKVSmVra8vh1KCHqSFTqVQymYzuTa9o8l+1qjrjOIkfWv1qHDMyI5/wBNWv\nqd2CEIeqlxYVFSkUCmtrax6PZ3idDZlarS4sLLS1tTV1IHWmpKRELpeLRCJzc3NTx1I3KIrK\ny8ur5VdAg0L31QmFwjq5DgmJXaNBZ1G6felFRUXk9UyLEPL48WOJRDJv3jztvVHy8/MdHPR8\nMhuKz+eT1/NIQ8Zf6PDmzZtX4WYozZo1k0ql5O+9AIBace1NzESk2t44t741yuoAoNHBX4o1\nGu7u7hwOJyUlRVty//59gUDQvHlz3dXkcjkhRJv1p6SkSCSSOungdXFxIYRkZGTQT9VqtW4w\nVfHw8ODxeFKptMXfRCIR/fvew8ODw+HcvXuXXpOiqEWLFiUlJdU+VIAmhycg/vOrW4lFui+p\n22YTExOjo6OfPHlSt9UCgNHQY9doiESifv36HTp0yMXFpVWrVrdv3z5x4sSoUaPoUSpLS8v0\n9PSMjAx7e3tzc/OjR4+OHz8+IyNj3759/v7+2dnZBQUF9C1LjObo6NimTZv9+/c7OztbWVkd\nPXrUkFFUgUAwYMCAuLg4Kysrb2/v58+fR0dHi8XiL7/8UigU9u3b9+DBg2Kx2M3N7cSJE+np\n6b6+vrq74+DgoP8SCgAo120RyTpDsqr+aRT4NWkeULdtqlSq0tLS2lzGBAB1C4ldYxIeHi4U\nCrds2SKVSh0cHMaNGzdq1Ch60bBhw9auXbtkyZL58+fPmTPnp59+SkpK8vb2njVr1osXL1at\nWvX111+vXbu2lgHMnz9/48aNK1asoO9jJxaLL168WO1WU6ZMEQqFO3fuzMvLs7Gx6d69+0cf\nfaTdIwsLi59++kkmk3l6ekZGRtK3sNfdHT8/v1qGDdAksHlk5DFy6jNybxchr3fS8wQk+Dvi\nN9NEkQHA28Ni3nUlAIbAxRMNX9O6eKIOSf4k93aR59dJaT6xdCauvUn7ycTSpT6aiouLS01N\nDQsL8/b2ro/63z5cPNHw4eIJ/dBjBwDALE5d6/Z/wwCgEcHFEwAAAAAMgcQOAAAAgCGQ2AEA\ngJGsra0dHR0ZM3kLgAEwxw4AAIzUs2dP+p8nTB0IAJRDjx0AAAAAQyCxAwAAAGAIJHYAAAAA\nDIHEDgAAAIAhkNgBAAAAMAQSOwAAAACGQGIHAABGSktLu379ulQqNXUgAFAOiR0AABgpJSXl\n8uXLeXl5pg4EAMohsQMAAABgCCR2AAAAAAyBxA4AAACAIZDYAQAAADAEEjsAAAAAhkBiBwAA\nAMAQXFMHAAAAjdU777zj6ekpFotNHQgAlENiBwAARmrevLm9vb2lpaWpAwGAchiKBQAAAGAI\nJHYAAAAADIHEDgAAAIAhkNgBAAAAMAQSOwAAAACGQGIHAAAAwBBI7AAAwEiJiYnR0dFPnjwx\ndSAAUA6JHQAAGEmlUpWWlmo0GlMHAgDlkNgBAAAAMAQSOwAAAACGQGIHAAAAwBBI7AAAAAAY\nAokdAAAAAEMgsQMAACMJhUIrKysul2vqQACgHN6NAABgpD59+gQGBlpbW5s6EAAohx47AAAA\nAIZAYgcAAADAEEjsAAAAABgCiR0AAAAAQyCxAwAAAGAIJHYAAAAADIHEDgAAjPTkyZO7d+8W\nFRWZOhAAKIf72AEAgJH++uuv1NRUZ2dnOzs7U8cCAISgxw4AAACAMZDYAQAAADAEEjsAAAAA\nhkBiBwAAAMAQSOwAAAAAGAJXxQKAsTRlJPsCeXWXqFXE2p249SFmVqaOCQCgSUNi14jt27fv\n1q1blS4aMWLEu+++W5vKo6KiAgMDe/fubfgmpaWlcXFxmZmZAwYM8PPz0z4OCAioTSTQIFHk\nr63k4lIie/5PGcecdJ5OApcTntB0gcFb9c4773h6eorFYlMHAgDlMBTbiLm4uLT92+PHj4uL\ni7VPbW1tq9rq2rVrBw4cqLbylJSUFy9e1CiekydPHjlyxNfX18HBQfdxjSqpUZBgGpSGJE4m\nJ6e9ltURQtQKcn0d+V8gkb80UWTwtjVv3tzLy8vS0tLUgQBAOfTYNWKBgYGBgYH043Pnznl6\nek6cOLHarW7cuCGTyeojnmfPnjk7O48dO5YQkpSUpH1shPoLEurAH9+SuzFVLs1NJsfGkdEn\nCWG9xZgAAIAQJHYMdu3atXPnzhUUFNjZ2YWEhPj5+RFCtm3bdvbsWQ6Hs3jx4v/7v/9r1qzZ\n77//fuvWrZKSErFYPGDAAC8vr2prTk5OTkpKys/PF4vFAwcOpDfZsmXL5cuXS0pKFi9e/OTJ\nEx6PRz8eMGBASEhIpZto4zxz5oxMJvPw8BgxYoRIJKoQpLOzc/0dJaixEgm5+k0162T+Th4m\nkNYj30pAAADwDwzFMtPRo0eXL1/O5/ODgoI4HM7SpUt///13QkhAQIBIJHJ1dR0yZIi1tXV0\ndPT27dvd3NwCAgLKysoWLFiQkZGhv+aTJ08uWbKEENKjRw+5XB4REXH37l36qbu7u0gkGjJk\nyKRJk7SPvby8qtqEEJKYmLh8+XKhUNixY8dr165FRETIZLIKQdbvkYKaSj1AVAZ0pt6Lrf9Q\nAACgIvTYMZBSqdyzZ8+gQYOmTZtGCHnvvfcUCsWuXbv69OnTvn17S0tLsVhMj+F27tw5MDCw\nXbt2hJABAwakpqaeOXPG09OzqprLysp++umnPn36zJkzh94kMjJy165dK1eu7NChw6VLl16+\nfEnX/OTJE/pxWVlZREREpZuUlZXt2rVr9OjR9Ahy7969p0+ffuPGjaCgIN0gq1JSUkJRlNFH\nSaPR0JVoS8xurmYVZRpdocmxKMqMolTs+v21xsm5YEgD1JOTZb98Usu2+BqNOUVpOBxNLStq\nMCiK4ld2jjRO3VTeE0wSUi2VlZURQuRyuUKhMHUs/opK4wAAIABJREFUdYOiKI1GU1xcbOpA\n6gx9jkpLS1UqlaljqTMURTHvHCmVSrVabcj6+me1IrFjoIyMDJlM1q1bN22Jv7//uXPnnj9/\n7uTkpLtmx44djx8/npCQIJPJKIp69erVq1ev9NT86NGjoqKi4OBgbUlAQMAPP/ygUCjMzc1r\nuklmZmZRURE9RkwIsba23rNnj+G7WVpaWpvETluJ9jE//TD3VeVXGUNNsVQlvJSdpo6i0VCo\n5KVuo0wdhfGUSqWpQ6hjup8MzKBSqZiU2JGmfY6EQiGLVeUkZiR2DCSVSgkhNjY22hIrKytC\nSGFhoW5iR1HUihUrnj59OmbMmObNm7PZ7K1btxpS888//3z06FG6pLCwkKKoly9furi41HQT\nepFIJDJuN0UiUW0Su5KSEo1GY2lpqX17UEHfqBT5Rldocmq1Wq1Wm5mZ1WsrnLs72Fm/V7sa\nJXQqC15Ty7aUSqVGozE3N9fzEda4aDSasrKyN88RS9TS6DeCadH9QAKBgMPhmDqWuqHRaORy\nuVDInFv2KBQKpVJpYWHB5TLkG5/urmukb5lK0efI3NzcwA9w/R+JDDnNoIvH45HXf0PToyR0\nuVZmZmZycvJXX33l7+9Pl1T79UnX0KFDB92bmPTv359OHGu6SW5uLiHE6Ktfa5nB0O2+ljR4\nDapNhSanUSg0SiWvvj/suBxiQGLHajWc1y6slk3JpFKVSiWwtWVM0qBSqcpkMiGDZo4mJiam\np6ePGDFCzxSOxkWtVpeWllY1BNEY0cN8XC6XMTtFUVRJSQljdocQolarlUplXZ0jJHYM1KJF\nC0JIZmamj48PXZKZmcnhcCpcXpqXl0cI0Rbm5uZmZma6urrqqZle6unpqX/2m4Gb0HE+fvy4\nTZs2dElcXFzbtm07d+5sYOVgAq2GE5ErKcrStw6LTTp99rYCAlMqKSkpLCykUwcAaAhwVSwD\nOTo6dujQISEhgR7rlEgkJ06cCAoK4vP5hBBzc3N6Il3z5s1ZLFZycjIhpKioaPPmze7u7oWF\nhXpqtrOz69Kly88//5yfn08IUSgUq1evXr16tXGbODg4dOjQIT4+nr4TclJS0t69e+nfK9og\nocHhWpD+/yUsvR8d/vOII7JzAAATQGLHTHPnzuXz+R9//PHUqVPDw8NdXV3Dw8PpRV27dk1O\nTh43blx6enpoaOiWLVumTZs2ffr0/v379+vXLzk5OSIiQk/Ns2fPFgqFkydPnjp16oQJE548\neVLtXYj1bDJnzhwLC4upU6eOGTNm8+bNU6ZM8fX11Q3y0qVLdXE8oE55DCYDdxJOFUMGnT4j\nwd++3YAAAKAcq/bXFQI0Rvn5+Wq12t7enjET8+npt29vQnFeCrn8NUk/SlQlhBDC4hCXHqTb\nYuI+sK5akEqlKpXKlllz7GQyGZPuzhgXF5eamhoWFubt7W3qWOqGWq0uLCzU86+MjU5JSYlc\nLheJRIyZlEZRVF5enr29vakDqTMymUwmkwmFQgsLi9rXhjl2AGAUO18y5H9ErSTSR0SjIlZu\nxKzKa2gAAODtQGIHALXAMSN2PqYOAgAAymGOHQAAGEkoFFpZWTHmBmkADIB3IwAAGKlPnz6B\ngYFMmjUI0Nihxw4AAACAIZDYAQAAADAEEjsAAAAAhkBiBwAAAMAQSOwAAAAAGAKJHQAAAABD\nILEDAAAj5eTkpKWlFRcXmzoQACiH+9gBAICRrl+/npqaamdnx6Q/VwVo1NBjBwAAAMAQSOwA\nAAAAGAKJHQAAAABDILEDAAAAYAgkdgAAAAAMgcQOAAAAgCFwuxMAADBSx44dXVxcHBwcTB0I\nAJRDYgcAAEZq2bKlk5OTSCQydSAAUA5DsQAAAAAMgcQOAAAAgCGQ2AEAAAAwBBI7AAAAAIZA\nYgcAAADAEEjsAAAAABgCiR0AABjp9OnTsbGxWVlZpg4EAMohsQMAACOVlJQUFhaWlZWZOhAA\nKIfEDgAAAIAhkNgBAAAAMAQSOwAAAACGQGIHAAAAwBBI7AAAAAAYAokdAAAYicfj8fl8Nhtf\nJQANBdfUAQAAQGM1cOBAhUJhbW1t6kAAoBx+ZgEAAAAwBBI7AAAAAIZAYgcAAADAEEjsAAAA\nABgCiR0AAAAAQyCxAwAAAGAIJHYAAGCknJyctLS04uJiUwcCAOVwHzsAADDS9evXU1NT7ezs\nbG1tTR0LABCCHjsAAAAAxkBiBwAAAMAQSOwAAAAAGAKJHQAAAABDILEDAAAAYAgkdgAAAAAM\ngdudAACAkXx9fe3t7e3s7IzcXlVMlEVE4EhYnDqNC6DpQmIHAABG8vLycnV1tba2rtlm8lfk\nz+/Jg59J4RNCCOHyiWsf4j+PuPWpjyABmpSmm9gVFxdv37793LlzKpWKLvH19Z06dWrr1q1r\nU61UKi0qKmrRokVdxGhQbRMmTBg+fPjYsWNrVPOWLVtu3769adMm+ml+fv6qVavu3LkzderU\n4cOH1zTO2sdQVFS0fv36P//8k81mazSad955Z968eZaWljWNBAAauuwL5PD7RJ77T0lZKXn0\nC3n0C+n0GemzgbCb7hcTQO010Tl2paWlixYt+uOPP8LDw2NjY/fu3bt8+XKVSrV48eK0tLTa\n1Hzy5MkDBw7UVZyG1LZ69erBgwfXppXU1NRZs2aJxWIu18jP09rHsHnz5vT09LVr18bHx3/3\n3XcpKSm7du2qTYUA0BC9vE0ODX4tq9OV/CNJmvN2AwJgmiaa2B04cCArK2vp0qXvvfeejY2N\nQCDo1KnTN998Y21tff78ed01JRLJ/fv3nz9/rluYkpKSn59PCMnKykpPT1coFHR5RkZGcnJy\nQUHB7du3S0tL6dVkMtmdO3fUajUhhKKo7OzsBw8e5OXlVQhJLpdnZGSkpaXJ5fJKa5NIJHfu\n3HlzXwoKCrSbVBUYTalUpqamPn36tEINDx48GDdu3Ny5c1ksVg0OYt3FoFKp0tLSRo8e7eXl\nxWKxfH19g4ODb9++bVwwANBQUeS3T4mySN8qtzaT7AtvKx4ABmqiPd6nTp169913vb29dQv5\nfP5///tfba9Vbm7ut99+m56ebmdnl5eX5+Pjs2jRIvr/EFeuXDl48OBbt24plcrCwsLS0tJl\ny5Z5enoePXo0JSWFx+Nt2bJl0aJFUVFRQ4cOPXHiRGFh4a5du54+ffr9999LpVKhUCiVSrt1\n6xYREcHhcAghR44c2bVrF5/PJ4TI5fKwsLD333+/Qm2nTp2Kj49PSEiosC9RUVHaYdCqAiOE\n3Lt3b8WKFQqFwsLCwtXV1cnJSVvD4MGD6TCMVssYeDxedHS0boUcDkej0dQmJABocJ5dJc+u\nVL/a9fXEJaj+owFgpqaY2OXn5+fl5XXo0OHNRbpjkWvXrpXL5Tt37rSzs8vNzV20aNEPP/zw\nxRdfEELYbHZCQsLy5cu9vLzUavXcuXP379//+eefz549Oysrq0WLFnPmzKFrO3ny5Ny5czt1\n6kQISUhI8PLymj9/Po/Hk0gkc+bMSUxMHDJkiFQq3b59+4wZM9577z1CyLlz59avXx8SElKh\nNn9//2pnKFcVGCFkw4YNzs7OUVFRfD7//Pnz69evd3Z2preqZVZXJzHoKioqunTpUu/evfU3\npJ0caRyKouhKjO6nbGjUarVGo6nlYWlQ6HNUVlbGmCy/rKyMoqjGd45UJawqEjK2QsErK9Pk\n8csM+Bhhp+41aJDocWJZemLNIqyKrTclcq3RFvSLrfGdo6rRe6RWqxmzU9pPb1MHUmfoMT3D\nzxGPx9OztCkmdkVFRYQQGxsbPes8f/787t27M2fOpC/jF4vFgwYNio2NlcvlFhYWhJDOnTt7\neXkRQjgcTtu2be/du/dmJWw2283Njc7qCCGff/65SqXKysqSyWQURdnb26enpxNCpFIpRVF0\ndx0hpGfPnkFBQWx2xQ/Adu3atWvXrtq9qzSwp0+f5uTkzJs3j24lODg4Pj5eqVRWW5txahOD\nUqn8/vvvzc3Nx4wZo7+VwsJC+u1dG4WFhbWsoaGRSqWmDqGO0W9YJml054hT8MD28KBKF9XL\nV4iqhJtQeXM1VdI1St423IgNG905qpZMJjN1CHWMeeeotLS0tLTUkDXt7e31dEk0xcROIBAQ\nQirM/aogOzubENKyZUttiYuLC0VRz549o0cVdYcyzczMqjoZuhe0Xrx4cdOmTWZmZk5OThwO\n59WrV3QMbm5uwcHBa9euPX36dOfOnbt3765beU1VGphEIiGE6HaPtWjRIiMjw/BqpVJpSkoK\n/VgsFrdq1ao+YpDJZCtWrJBIJFFRUUKhUH9I5ubmtUnslEolRVHm5uZG19DQaDQajUZj9BUw\nDZBKpdJoNGZmZozpVdVoNGq1Wv+v7QaIJbQv8xxZ6aIXL17I5XJHR0f6F69+7Lw77IKHBrTH\nKvMIJaQOTjrboU1N3+N0l6qZmVntW28gysrK6Ffdm/0FjZdSqWTeOeJyuXUygMac7wDD2dra\nmpmZ6U9r6O5Q3U8E+jVUVlZGPzXw6Gv74YqLizds2NC7d+/w8HD6WyoiIkK72vz58/v27Xvp\n0qXDhw/v3Llz4MCBn332Wc326m+VBkaHrbs7Nf1qycjIWLlyJf24T58+s2bNqvMYioqKlixZ\nolarv/vuOwcHh2pDquXNUPLz89VqtaWlJWOSBoVCoVQqRSKRqQOpM1KpVKPRCIXCup0tYEIq\nlUomkzW+cyRqQ0YeqnTJmbi41GepYYPDKkxZrtz9/5HjYdWvJu7MHRlfwxArZ8Q3nFqtLiws\nbHznqGolJSVyuZzP5zPmdyxFUXl5eUw6RzKZTCaTmZubG/IDqVpNMbHjcDhdunQ5f/78xIkT\n6d47rdjYWA6HM2HCBHo2W35+vru7O72ooKCAVDeAq8ejR4/kcvmgQYPoNILu/BOLxfRSFovV\npUuXLl26UBR19uzZtWvXdu/e3c/Pz9hdrIju/dIddnzzslz9/Pz83rxuow5jUCqVy5cvZ7PZ\nUVFRuH0dADN5DCbmNkRRUM1qvgYkfwBQBeZ0zNbI2LFji4qK1qxZozuEevr06UOHDtH5h6en\np0Ag+OOPP7RLr1696ujo6OjoqL9m+v66b5bTA2Ta8d/ff/9dJpPRa967d2/Hjh10OYvFCg4O\n5nA49LyiqmqrKXd3dzabnZycTD8tKip6+zcT0R/Dvn37Xr58GRkZiawOgLHMrUmPyGrWsW1N\nOs94G8EAMFRT7LEjhLRq1WrOnDkbN2789NNP/f39+Xx+ampqampqaGhoaGgoIcTMzGzSpElb\nt25ls9menp63b9++cuUKfWmnfg4ODsnJyYcPH+7SpUuFFu3t7aOjo4cMGfLo0aNHjx716tXr\nxo0bly9fbtmy5YkTJ3Jycvz9/QkhV65csbKyoi+50K3twoULx44d27NnjxH7KxKJevXqdfDg\nQbVabW1tffr06VatWhUXF9NLf/vtt1evXhFC1Gr1jRs3SkpKCCHDhw+vdpZbXcUglUoPHz7s\n7e19/Phx3U1GjhypHcsGACboMovk/kXu7Kh8qdCJhCYQbh2MRgE0WZzIyEhTx2Aa7u7uvXv3\n5nA4L1++lEql7u7u06ZN69evn3bGlbe3d+vWrVNTU+/fv29paRkeHq7N1R48eNC6dWvtBQQS\niYSiqB49etDV5uTkvHz50tfX98WLF9rVOBxO165ds7Oz79+/36xZs3/961/u7u5ZWVnFxcW9\nevUKCAh4/vz5/fv3nz175u7urr0aV7c2uVxeWFjYp0/F/1JMSUnx9fX18PDQH1iXLl24XO7D\nhw8LCwtHjx7t5OSkUqkCAgIIIb/99tvDhw9fvHghFovVavWLFy9evHjRvXt3wwf7axlDfn4+\n/YcfL17Xo0eP+psUUlpaSlGUQCBgzBw7tVqtVqsZM42GEKJQKDQajYWFBWMmfdP3o2HSz5Xb\nt2+/evWqQ4cO9vb2hm3BIl7DiYUDeXaVlMlfW+IVSkLjiY1XPYRZAxRF0ffaNG0YdUilUpWV\nlZmbmzPpyiq5XF5hJlWjplKp6Et26uTKKlbtbxgB0BjRF0/ov2i8cWHkxRMqlcrW1pZhF09U\ne0PKRiQuLi41NTUszLCLJ3SpZOTJSZKbTNQKYulCWvYntrX6n+66Ql88Qd+LnhnoiydEIhFj\nfvXRF08Y/FuiEaAvnhAKhbh4AgAATInH4/H5fGO6VHkC4hVKvELrISiAJg2JHQAAGGngwIEK\nhYJJfZAAjR1DZq4AAAAAABI7AAAAAIZAYgcAAADAEEjsAAAAABgCiR0AAAAAQyCxAwAAAGAI\n3O4EAACMlJubK5VK6+qO+QBQe+ixAwAAI125cuXw4cMSicTUgQBAOSR2AAAAAAyBxA4AAACA\nIZDYAQAAADAEEjsAAAAAhkBiBwAAAMAQSOwAAAAAGAL3sQMAACP5+vra29vb2dmZOhAAKIfE\nDgAAjOTl5eXq6mptbW3qQACgHIZiAQAAABgCiR0AAAAAQyCxAwAAAGAIJHYAAAAADIHEDgAA\nAIAhkNgBAAAAMAQSOwAAMNLly5cPHz4skUhMHQgAlENiBwAARnr58mVWVpZcLjd1IABQDokd\nAAAAAEMgsQMAAABgCCR2AAAAAAyBxA4AAACAIZDYAQAAADAEEjsAAAAAhuCaOgAAAGishg0b\nplAorK2tTR0IAJRDjx0AAAAAQyCxAwAAAGAIJHYAAAAADIHEDgAAAIAhkNgBAAAAMAQSOwAA\nAACGwO1OAADASLm5uVKp1MzMjMfjmToWACAEPXYAAGC0K1euHD58WCKRmDoQACiHxA4AAACA\nIZDYAQAAADAEEjsAAAAAhkBiBwAAAMAQSOwAAAAAGAKJHQAAAABD4D52AABgJC8vL5FIZGNj\nY+pAAKAcEjsAADCSr6+vp6entbW1qQMBgHJI7AAAoBGiNCTjOMk4RqQZhMUlNq1I6/eJWx9T\nhwVgYrWaY7dixYrhw4fHx8dXKC8sLBw5cuTw4cPVajVdotFodu/eHRoaeuTIkdq0WKN2NRpN\nQkLCZ5999sEHH3z22WeHDh3SaDS1b/1NEyZM+PnnnytdtGXLlpkzZ9ZT5W9fgwoGAJquvPtk\ntz9JGE7+2kqenCKPE8mtzWR/X/JzL1L01NTBAZhSbS+eMDc3P3v2bIXCixcvcjgc7dP8/Pwv\nv/zy8uXLbHadXathSLtxcXG7du167733li1b1rt375iYmMOHD9dVALqmTJni7+9fHzU3NE1n\nTwGg4Xp1l8QFkBc3K1n09CyJ647cDpqy2mZabdu2zcjIePr0tXfRuXPnfHx8tE/PnDljbW29\nZs2aOkzsqm1XrVYfO3YsNDR05MiR7dq1GzNmTI8ePc6dO6enTrVaTVGUEcH06dOnVatWRmzY\niNAHpynsKQA0aBoVOfIBURRUuUJxNvllwlsMCKBhqe0cO1tbWw8Pj7Nnz06YUP5GevXq1b17\n9yZNmnT79m26JDg4eOTIkbVsqKbtstnsdevWiUQi7SZisfjBgwdvVhUWFjZ+/PibN2/evHlz\n9+7dfD5/7969p0+fzs/PF4vFw4cPHzJkCL3m3bt3d+/e/ejRI41G4+Hh8eGHH7Zr144QMmHC\nhOHDh48dO5YQkpeXt3Hjxtu3bwsEgkGDBuk2NGbMmPHjx2sPxcaNGx89erR27VpCiFQq3b59\ne3JycnFxsVgsHjJkyLBhw/QfAbVaHRsbe+bMGZlM5uHh8cknn7Rp04YQolKpdu/eff78+fz8\nfDs7u169eoWFhXE4nIiICIFAEBkZqa1h2bJlJSUl33//vZ7WKxycTz/9VLuneraaNGnS2LFj\nX716derUKaVS2a5du1mzZtHXzVUVtlqtruqwAwD84/7/SN79atZ5eo48OUla9n8rAQE0LLVN\n7DQaTVBQ0KlTp7QJ1vnz593c3JydnbXrODg41LIVI9plsVi6MajV6ps3b7Zt2/bNqrhc7m+/\n/datW7cxY8bw+fzt27efPHly2rRpvr6+ycnJ27Zt43K5AwYMKC0tXb58ec+ePadPn04IOX78\neGRk5M6dOy0tLXVrW7du3dOnT5csWWJnZ3f8+PHLly/rJpdVWb9+vUQiiYiIsLGxefDgwcaN\nG8Vicffu3fVssnXr1osXL4aHhzs7Ox87dmzp0qUbN250dHT88ccfL126NGPGDC8vrwcPHvzw\nww8qleqTTz7p2bPn9u3bZTKZQCAghMhksuTk5E8++UR/6xUOjoExc7nchISEESNGREdH5+fn\nf/7553v37p02bZqesKs67NUeOgBoWh5WnF1d5WpI7KBJqoOrYkNCQnbt2vXw4cPWrVsTQs6f\nPx8SEmLIhgUFBVu2bLlz545AIOjcuXPPnj19fHxyc3PT0tI6dOhQ1aLg4GAj2o2NjZVIJAsX\nLnxzEYfDMTMzmzhxIiFEJpMlJiaOGjWqb9++hJDmzZunp6cnJCQMGDBAIpHIZLKQkBBXV1dC\nyL/+9a+goCAej6db1atXr5KTk8PDwzt16kQICQ8Pv3btmiGHYvbs2SwWi75lgIuLy7Fjx27e\nvKknsZPJZCdPnpw6dSp9NGbOnFlaWpqTk2NhYZGUlBQWFkaXOzs7Z2RkJCYmfvjhh4GBgdu2\nbfvzzz/po3TlyhU6Odbfuu7BqVHMTk5OQ4cOpR/4+/s/fPhQT9iWlpZVHXY9B62goMC4oXMa\nfSVNQUHVAzqNDUVRFEXl5+ebOpA6Q5+jwsJCUwdSZ5h3jq5cuZKTkxMUFOTo6Fgf9QuuLuZl\nJuqWsEuyDdmQuruTyvj1tSI2RzrqT0O21Wg0TDpH9OdkSUmJTCYzdSx1hmHvI/qzTi6Xl5aW\nGrK+jY0Ni8WqamkdJHaOjo5t2rQ5e/Zs69atJRJJWlpaREREWlpatRvm5OS0bt161KhRBQUF\nV69eXb58uVwu53A44eHhehYZ0W5MTMzRo0cXLlzo4uJSaSR0akgIefToUVlZWceOHbWL2rdv\n/9tvvxUVFbm4uLi6uq5Zs2bw4MF+fn6enp7t27evUA8958/T05N+ymKxvL29MzMzqz0UhYWF\nO3bsuH//vvaNp9vd+CY6Tm3YXC6Xzln/+usvtVqt2zHp7e2dkJDw7NkzV1fX9u3bX7lyhU7s\nLl261KlTJ3p4VH/r2lZqFLP2IBBChEJhcXGxnrDv3r1b1WHX099p9JzICpXUsoaGBnvU8DFp\nj16+fJmVlSWTyeprp2Qv2EWPjdiOVVbKqrAhi2N4kEw6R7R6uimECeEcVaVu7mMXEhKyb9++\nKVOmnDt3ztvbu1mzZoYkdm3bttWmIP7+/tOnT3/16pWVlZWZmRm9tKpFhrdLUdTmzZvPnz+/\ndOlSuhetUtrhVDpHWbp0qTYX1vbruLq6rly5Mj4+/uTJk7GxsWKx+KOPPurZs6duPfTm9Fgn\nTSgUVnsclEplVFSUvb396tWrnZ2dORzO559/rn8TuqEKR0Nbrjs6TD+my4OCgnbu3KlUKtVq\n9a1bt+gx5WpbrzDWbGDMFWKjMzD9YVd62PUkdnZ2dlUtMkRBQYFarbazs9Pzu6dxUSgUKpWq\n0vPVSBUWFqpUKhsbG92r3Rs1lUoll8utrKxMHUidoU+NUCi0t7evlwaGxlDqbboFrKNjSNbv\n1W/oG0b13lihzJ5vW+12arW6qKiISf+lIZPJ5HK5paWlubm5qWOpG3R3XS2/AhoU+hwJBAIL\nCwtD1tf/tVU3iV1QUFB0dPS9e/fOnz//3nvvGVcJi8WqajZeVYuqbXfLli2XL1/+5ptvDLyW\nk87J5s2b17JlS93yZs2aEUJEItGHH3744YcfZmdnx8fHr1mzxtXV1cPDQ7saPQtNt7tbzyiS\nUqmkHzx+/FgikcybN69FixZ0SX5+vv6JifS5LyoqqlBO55F09xiNXoferx49emzZsuXWrVsK\nhYIQQg+bGtG60VtVFbb+w16VOknIWCwWYxI7ekcYsztaOEeNQn3tlNkbP1S8RxqU2LUexbIw\n5oufqeeISe8jGpN2R/uqq5Odqpv7j1hbW3fu3DkxMTEzM5OetvV26G/39OnTp06dWrZsmeF3\n6PDw8ODxeFKptMXfRCKRtbU1j8d7/vz51atX6dVcXFxmzJjBZrMfPXqkuzk91JuRkUE/VavV\nKSkp2qWWlpa6OZ92NblcTv5OegghKSkpEolE/yCjh4cHh8O5e/cu/ZSiqEWLFiUlJbm7u3M4\nHN1G79+/LxAImjdvTgixtrbu2LHjtWvXrly54u/vT6dTRrRu9FZVha3nsOuvEACanHaTiWXl\nk2r+4dCBeIW+lWgAGpw6+0uxkJCQ9evXd+zY0da2Yl93eno6ndBoNJpnz57RtyPx8fF5c0iu\nDttVKpW7d+/u2rWrXC7X3niFEOLr68vlVrnXAoFgwIABcXFxVlZW3t7ez58/j46OFovFX375\nZW5u7rfffvvxxx937dqVEHLhwgVCiLe3t+7m9LS//fv3Ozs7W1lZHT16VHcfvby8rly5MnTo\nUD6fn5CQIJfL6Y5xDw8Pc3Pzo0ePjh8/PiMjY9++ff7+/tnZ2QUFBVUNBwiFwr59+x48eFAs\nFru5uZ04cSI9Pd3X11ckEvXr1+/QoUMuLi6tWrW6ffv2iRMnRo0apR3JCg4O/vnnn0tKSmbN\nmkWXGNG60VtVFbaew15VVQDQRPEEZMj/yIF+RK2sfAUzKzL0f4TFkOF7gJqqs8Sue/fuPB5P\ne8mqrh9//DE1NZV+fPz48ePHjxNCoqOj6+Qqqqraffr06cuXL1++fHnx4kXd8piYmDdTT11T\npkwRCoU7d+7My8uzsbHp3r37Rx99RAhp3779nDlzEhIS4uLi2Gy2m5vbl19+qR2I1Jo/f/7G\njRtXrFhB38dOLBZrA/jkk082bNgwdepUS0vLwYMH9+7dm75m1srKas6cOT/99FNSUpK3t/es\nWbNevHixatWqr7/+mr7LXaXCw8MtLCx++ul9INBQAAAW30lEQVQnmUzm6ekZGRnp5ORElwuF\nwi1btkilUgcHh3Hjxo0aNUq7VUBAwA8//GBubq79AwnjWjduKz1hV3XYAQAqahFMPjhJjo8n\nxTkVF9m2JsP2E/t2pggLoEFg1f66QoDGKD8/X61W29vbM2aihkKhUCqVhtw3sbGQSqUqlcrW\n1pZJF0/IZDL6JkHMEBcXl5qaGhYWVmH44m1QlZDb20nGMVKQTthcYuNFWr9P2k4inFqNBanV\n6sLCQv2//xuXkpISuVwuEomYdPFEXl5efV2vYwoymUwmkwmFQgMvntCvznrsAACgqRk4cGDP\nnj3r4y701eMJSZdZpMssEzQN0IDV2Z+3AgBAU8Pj8fh8PmO6VAEYAIkdAAAAAEMgsQMAAABg\nCCR2AAAAAAyBxA4AAACAIZDYAQAAADAEbncCAABGkkqlhYWFfD4ffwAI0ECgxw4AAIx07ty5\nffv25eS88Q8QAGAiSOwAAAAAGAKJHQAAAABDILEDAAAAYAgkdgAAAAAMgcQOAAAAgCGQ2AEA\nAAAwBO5jBwAARvLy8hKJRDY2NqYOBADKIbEDAAAj+fr6enp6WltbmzoQACiHoVgAAAAAhkBi\nBwAAAMAQSOwAAAAAGAKJHQAAAABDILEDAAAAYAgkdgAAAAAMgcQOAACMdP369cTExBcvXpg6\nEAAoh8QOAACMlJOTk5aWVlJSYupAAKAcEjsAAAAAhkBiBwAAAMAQSOwAAAAAGAKJHQAAAABD\nILEDAAAAYAgkdgAAAAAMwaIoytQxAJgA/cpnsVimDqQuURTFpD3COWr4FAqFWq02NzfncDim\njqXOMOwc4X3U8GkzsTrZKSR2AAAAAAyBoVgAAAAAhkBiBwAAAMAQSOwAAAAAGAKJHQAAAABD\nILEDAAAAYAgkdgAAAAAMgcQOAAAAgCG4pg4AwDQ0Gs2RI0dOnDiRm5srFov79+8/YsQINhs/\ndRqcixcvbty4sUOHDl988YWpY4F/lJaWxsTEXLhwQS6Xt2rVaurUqa1btzZ1UFCRRqOJi4vb\nv3//lClThg8fbupwoKL6+CZCYgdNVFxcXHx8/MSJE729ve/evRsTE8NisUaOHGnquOAfZWVl\n27dvT0pKEgqFpo4FKtqwYUNKSsq0adPs7OyOHTv21Vdfbdq0yd7e3tRxwT/y8/NXrVollUrx\nk7XBqo9vIpxsaIrUavWxY8dCQ0NHjhzZrl27MWPG9OjR49y5c6aOC17z+PHjO3furFu3ztXV\n1dSxwGuePXt24cKFzz77LDAw0NfX99///reFhcXx48dNHRe85syZM9bW1mvWrEFi1zDV0zcR\neuygKWKz2evWrROJRNoSsVj84MEDE4YEb3Jyclq1ahWfzzd1IFBRcnIyl8vt0qUL/ZTD4fj5\n+d26devDDz80bWCgKzg4GKMQDVk9fRMhi4emiMViOTs7W1pa0k/VavXNmzfbtm1r2qigAktL\nS2R1DVNOTo6DgwOX+0/XgJOTU3Z2tglDgjc5ODiYOgTQp56+iZDYAZDY2FiJRDJ+/HhTBwLQ\nOMhkMoFAoFsiEAjkcjlFUaYKCaCxq6tvIgzFQpOgVqtLS0vpx1wu19zcXLsoJibm6NGjCxcu\ndHFxMVF0UK6kpIR+wGKxKuQNAAAMVoffREjsoElITk6OjIykH/fp02fOnDmEEIqiNm/efP78\n+aVLl3bq1MmU8QEhSqVS+1PV0dExOjratPGAHpaWltosnFZcXCwQCFgslqlCAmik6vybCIkd\nNAk+Pj4rV66kH9vY2NAPtmzZcvny5W+++aZVq1amCw3K8Xg87TkyMzMzbTCgn4uLy8uXL5VK\npfZM5eTktGjRwrRRATRGdf5NhMQOmgShUFhhRurp06dPnTq1cuVKZHUNBIvFwvUrjUXnzp01\nGs2ff/4ZGBhICFEoFNevXx86dKip4wJoZOrjmwiJHTRFSqVy9+7dXbt2lcvlt2/f1pb7+vrq\nXugHpiWRSHJzcwkhRUVFXC6XPlMtWrSwtbU1dWhNnVgs7tu379atWwkhNjY2hw4dYrPZgwcP\nNnVc8Jr09HSZTEYI0Wg0z549o99BPj4+6BFvIOrpm4iFi5igCcrIyKCn2VUQExODpKHhiImJ\nOXjwYIXC2bNn9+3b1yTxgC6lUhkTE3Pu3Dm5XO7j4xMeHu7m5mbqoOA18+fPT01NrVAYHR3t\n6Ohokniggnr6JkJiBwAAAMAQuI8dAAAAAEMgsQMAAABgCCR2AAAAAAyBxA4AAACAIZDYAQAA\nADAEEjsAAAAAhkBiBwBQ7v79+ywWa9q0aaYOxCBZWVkODg4ffPCBqQNhMt2XBP146tSpbzmG\nZ8+eNWvWbOTIkW+5XWikkNgBQKPk5eXFqtrMmTMNqeTKlSvR0dHap5aWlgMGDGjXrl29R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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Bootstrap forest plot\n", + "boot_plot_df <- boot_summary[boot_summary$label %in% names(plot_labels), ]\n", + "boot_plot_df$label_nice <- plot_labels[boot_plot_df$label]\n", + "boot_plot_df$label_nice <- factor(boot_plot_df$label_nice, levels=rev(plot_labels))\n", + "\n", + "p_boot_forest <- ggplot(boot_plot_df, aes(x=boot_mean, y=label_nice, xmin=ci_lower, xmax=ci_upper)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(color=\"darkorange\", size=0.6) +\n", + " labs(title=\"Bootstrap: Parallel Mediation Path Estimates\",\n", + " subtitle=paste0(\"N = \", N_fiml, \" (FIML), \", n_converged, \" resamples, 95% percentile CI\"),\n", + " x=\"Estimate (95% Percentile CI)\", y=NULL) +\n", + " theme_minimal(base_size=12) +\n", + " theme(plot.title=element_text(face=\"bold\"))\n", + "\n", + "ggsave(file.path(BOOT_DIR, \"bootstrap_forest_plot.png\"), p_boot_forest, width=10, height=6, dpi=150)\n", + "ggsave(file.path(BOOT_DIR, \"bootstrap_forest_plot.pdf\"), p_boot_forest, width=10, height=6)\n", + "print(p_boot_forest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bootstrap Interpretation\n", + "\n", + "The bootstrap provides non-parametric confidence intervals for the indirect effects. Because indirect effects (products of coefficients) are typically non-normally distributed, the percentile bootstrap CI is more appropriate than symmetric Wald CIs. If the 95% CI excludes zero, the indirect effect is significant at alpha=0.05." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Method 3: MNAR Sensitivity Analysis\n", + "\n", + "Tests robustness of results to missing-not-at-random (MNAR) assumptions. We shift imputed values for missing mediators and outcome by delta * SD units, then re-fit the SEM on completed data." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:07:52.448503Z", + "iopub.status.busy": "2026-03-31T16:07:52.446300Z", + "iopub.status.idle": "2026-03-31T16:08:28.189504Z", + "shell.execute_reply": "2026-03-31T16:08:28.185575Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR grid: 225 points ( 15 x 15 )\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running MNAR sensitivity grid...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Grid point 50 / 225 \n", + " Grid point 100 / 225 \n", + " Grid point 150 / 225 \n", + " Grid point 200 / 225 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR completed in 0.6 minutes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successful fits: 225 / 225 \n" + ] + } + ], + "source": [ + "delta_range <- seq(-3, 3, length.out=15)\n", + "# Use a 2D grid: delta_mediators (applied to both M1 and M2) vs delta_outcome\n", + "grid <- expand.grid(delta_med=delta_range, delta_out=delta_range)\n", + "cat(\"MNAR grid:\", nrow(grid), \"points (\", length(delta_range), \"x\", length(delta_range), \")\\n\")\n", + "\n", + "# Pre-compute observed means and SDs\n", + "m1_mean <- mean(dat[[MED1]], na.rm=TRUE); m1_sd <- sd(dat[[MED1]], na.rm=TRUE)\n", + "m2_mean <- mean(dat[[MED2]], na.rm=TRUE); m2_sd <- sd(dat[[MED2]], na.rm=TRUE)\n", + "y_mean <- mean(dat[[OUTCOME]], na.rm=TRUE); y_sd <- sd(dat[[OUTCOME]], na.rm=TRUE)\n", + "\n", + "all_covs <- unique(c(MED_COVS, OUT_COVS))\n", + "\n", + "# Results storage for all parameters\n", + "mnar_results <- vector(\"list\", nrow(grid))\n", + "\n", + "cat(\"Running MNAR sensitivity grid...\\n\")\n", + "t_start <- Sys.time()\n", + "\n", + "for (k in 1:nrow(grid)) {\n", + " dat_imp <- dat\n", + " # Delta-shift impute mediators\n", + " dat_imp[[MED1]][is.na(dat_imp[[MED1]])] <- m1_mean + grid$delta_med[k] * m1_sd\n", + " dat_imp[[MED2]][is.na(dat_imp[[MED2]])] <- m2_mean + grid$delta_med[k] * m2_sd\n", + " # Delta-shift impute outcome\n", + " dat_imp[[OUTCOME]][is.na(dat_imp[[OUTCOME]])] <- y_mean + grid$delta_out[k] * y_sd\n", + " # Impute missing covariates with column means\n", + " for (cov in all_covs) {\n", + " na_idx <- is.na(dat_imp[[cov]])\n", + " if (any(na_idx)) dat_imp[[cov]][na_idx] <- mean(dat_imp[[cov]], na.rm=TRUE)\n", + " }\n", + " \n", + " fit_m <- tryCatch(\n", + " sem(model_str, data=dat_imp, estimator=\"ML\", fixed.x=FALSE),\n", + " error = function(e) NULL\n", + " )\n", + " \n", + " if (!is.null(fit_m) && lavInspect(fit_m, \"converged\")) {\n", + " pe_m <- parameterEstimates(fit_m)\n", + " res_row <- data.frame(\n", + " delta_med = grid$delta_med[k],\n", + " delta_out = grid$delta_out[k],\n", + " N = lavInspect(fit_m, \"nobs\")\n", + " )\n", + " for (lab in c(\"ind1\", \"ind2\", \"total_indirect\", \"cp\", \"total\", \"r_m1m2\")) {\n", + " row_m <- pe_m[pe_m$label == lab, ]\n", + " if (nrow(row_m) == 1) {\n", + " res_row[[paste0(lab, \"_est\")]] <- row_m$est\n", + " res_row[[paste0(lab, \"_se\")]] <- row_m$se\n", + " res_row[[paste0(lab, \"_p\")]] <- row_m$pvalue\n", + " }\n", + " }\n", + " mnar_results[[k]] <- res_row\n", + " }\n", + " \n", + " if (k %% 50 == 0) cat(\" Grid point\", k, \"/\", nrow(grid), \"\\n\")\n", + "}\n", + "\n", + "t_end <- Sys.time()\n", + "cat(\"MNAR completed in\", round(difftime(t_end, t_start, units=\"mins\"), 1), \"minutes\\n\")\n", + "\n", + "mnar_df <- do.call(rbind, mnar_results[!sapply(mnar_results, is.null)])\n", + "cat(\"Successful fits:\", nrow(mnar_df), \"/\", nrow(grid), \"\\n\")\n", + "write.csv(mnar_df, file.path(MNAR_DIR, \"mnar_grid_results.csv\"), row.names=FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:08:28.211088Z", + "iopub.status.busy": "2026-03-31T16:08:28.207451Z", + "iopub.status.idle": "2026-03-31T16:08:28.320645Z", + "shell.execute_reply": "2026-03-31T16:08:28.317660Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reference (delta=0,0):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total indirect est = -0.03644055 , p = 0.02891559 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ind1 (DLPFC) est = -0.01435199 , p = 0.1757288 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ind2 (AC) est = -0.02208856 , p = 0.1061319 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Tipping point summary:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " effect ref_est ref_p tipping_dist tipping_delta_med\n", + " ind1 -0.01435199 0.17572880 0.0000000 0.0000000\n", + " ind2 -0.02208856 0.10613187 0.0000000 0.0000000\n", + " total_indirect -0.03644055 0.02891559 0.6060915 -0.4285714\n", + " tipping_delta_out N\n", + " 0.0000000 1153\n", + " 0.0000000 1153\n", + " -0.4285714 1153\n" + ] + } + ], + "source": [ + "# Tipping point analysis: minimum distance from (0,0) where total_indirect becomes NS (p > 0.05)\n", + "mnar_df$dist_from_origin <- sqrt(mnar_df$delta_med^2 + mnar_df$delta_out^2)\n", + "\n", + "# Reference value at (0,0)\n", + "ref_row <- mnar_df[mnar_df$delta_med == 0 & mnar_df$delta_out == 0, ]\n", + "cat(\"Reference (delta=0,0):\\n\")\n", + "cat(\" Total indirect est =\", ref_row$total_indirect_est, \", p =\", ref_row$total_indirect_p, \"\\n\")\n", + "cat(\" ind1 (DLPFC) est =\", ref_row$ind1_est, \", p =\", ref_row$ind1_p, \"\\n\")\n", + "cat(\" ind2 (AC) est =\", ref_row$ind2_est, \", p =\", ref_row$ind2_p, \"\\n\")\n", + "\n", + "# Find tipping points for each indirect effect\n", + "tipping <- data.frame(\n", + " effect = c(\"ind1\", \"ind2\", \"total_indirect\"),\n", + " ref_est = c(ref_row$ind1_est, ref_row$ind2_est, ref_row$total_indirect_est),\n", + " ref_p = c(ref_row$ind1_p, ref_row$ind2_p, ref_row$total_indirect_p),\n", + " stringsAsFactors=FALSE\n", + ")\n", + "\n", + "for (i in 1:nrow(tipping)) {\n", + " eff <- tipping$effect[i]\n", + " p_col <- paste0(eff, \"_p\")\n", + " ns_rows <- mnar_df[!is.na(mnar_df[[p_col]]) & mnar_df[[p_col]] > 0.05, ]\n", + " if (nrow(ns_rows) > 0) {\n", + " tipping$tipping_dist[i] <- min(ns_rows$dist_from_origin)\n", + " closest <- ns_rows[which.min(ns_rows$dist_from_origin), ]\n", + " tipping$tipping_delta_med[i] <- closest$delta_med\n", + " tipping$tipping_delta_out[i] <- closest$delta_out\n", + " } else {\n", + " tipping$tipping_dist[i] <- Inf\n", + " tipping$tipping_delta_med[i] <- NA\n", + " tipping$tipping_delta_out[i] <- NA\n", + " }\n", + "}\n", + "tipping$N <- ref_row$N\n", + "\n", + "cat(\"\\nTipping point summary:\\n\")\n", + "print(tipping, row.names=FALSE)\n", + "write.csv(tipping, file.path(MNAR_DIR, \"mnar_tipping_summary.csv\"), row.names=FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:08:28.326789Z", + "iopub.status.busy": "2026-03-31T16:08:28.325016Z", + "iopub.status.idle": "2026-03-31T16:08:31.917798Z", + "shell.execute_reply": "2026-03-31T16:08:31.915063Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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bJly8LDww3VwwL3Hushm+OoSfcaXS4u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qqONNabytc6ux3VznLRTt+E/iYqazFZ1v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\nxS9+IYT45S9/OW/ePL/fP2/evBd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aZNm9zc3EaMGKG4NiUl5dixY2/fvq1YsWKHDh2qVasmt0FGRoa/v3/FihXPnz9v\nbm6u5l5lOlGJLx5CSEhISMuWLe/evdu0adPhw4evWbNG3eesEdUdpejZs2cXL158/fp1pUqV\nmjRpUqdOHRUbX7ly5fPPP4+MjBw5cmSJG9BOsLS0nDZtmqmpqdzaNWvWSKVSZfuWk+onrmyt\n6t8a+7BMvQQAICqMkG3btk3Z8zIxMRkzZkxRUVF5jm9qahoYGKhsbVhYGCEkKSmJYZj4+PjL\nly8XFBSU53TlUVBQYGFhcezYsbLuyH0W2hITE+Pk5EQI8ff3V1y7ZcsWKysrQghNbKamposX\nL5bbJjw83MbG5tmzZ2Xaq0wnioyMLPGVM3/+fLrBzz//TAjZv3+/Zp2gDtUdJaewsHD06NFm\nZmaEEIlEQv8fERGRm5urbJfLly8TQiIjI5VtwL6DVq9erbi2Ro0aDRs2VPO5lInqJ65ibam/\nNQ16CQBATMQQ7GbNmpXL8ebNm4MHD9K/0RcsWFCe46sf7Azuxo0bhBCDB7vCwsLBgwcTQkaO\nHCmVShU/mGNjY01NTf38/O7evcswzIsXL0JCQgghhw8fZre5du0aIWTatGll2qusJ1q0aBEh\n5PTp07n/xv4xUFhYWL16dV9fX5lMppXO4Sq1oxTNmTOHENKrV6+nT5/KZLL4+PiOHTsSQmbP\nnq1sFzWDna2trb29/evXr+XW6iLYqX7ipXZLqb81DXoJAEBMxBDs5syZo7gqPj6eEOLj48Nd\nmJSUtGnTph9//HHt2rU3b96U26WoqOjgwYOLFy9et25dYmIiU5Zgt2fPnjlz5mRmZjIMExMT\nM3fuXIZhUlNTo6KiFi9e/Pvvv2dkZMjtrroxJYqLi9uwYcOiRYs2b978+PFjdvmOHTtCQ0MJ\nIREREXPmzHnx4oU6R1N8FlRCQsKvv/5KGxYbGyu3fVFR0eHDhxcvXhwZGUmjwKpVq3755Re6\nNjU1tUKFCnv37mUYxsLCQvGD+csvvySE0LBFpaSkWFhYtGzZkl3SpUsXS0vL5OTkMu1V1hNN\nnz5dbgNF69evJ4Ts2LFDxTaaKbWjFHl4eDg6Oubl5bFLkpOTVVf71Ax2P/74o0Qi6dOnj9xa\nXQQ71U+81G4p9bemQS8BAIiJsOfYqVCzZk0bG5vU1FR2yaJFi+bMmSORSDw9PVNSUrKzs3v3\n7r1jxw46Wpednd2pU6cLFy5YWVlVqFBh4sSJmzZtokM56oiJiYmOjh45cqStrQxmhMwAACAA\nSURBVO3hw4e3bNkSFBQ0ZMiQGjVqSCSSy5cvu7u7//XXXzVq1FCnMYry8/PDw8P3799vaWlZ\noUKF1NRUhmEmT568dOlSQsiVK1foR/jly5fv3bvXq1cvjecXzpgxY/ny5WZmZp6enklJSQUF\nBb169dq5cydtWFZWVocOHS5fviyVSp2dnSdPnhwdHT1//nxvb+///Oc/hBBbW9vbt29XqVJF\n2fFPnDhRu3bt+vXrs0tcXV1btWp1+vTpjIwMe3v7tLS048ePd+7c2c3NTf29NDjRx48fCSGO\njo4qeiMsLGz06NHbt2+nMVFRcXHxoEGDlO0eEBAwceLEEleV2lGKnjx5kpWVZWFhwS5xcnIy\nMzNTnBtXVgEBAUOGDImKijp69GiXLl3U2UVHT7zUbin1t6a7XgIAEATRXhV779697OzsWrVq\n0YcxMTHffvvtwIED09LSEhIS0tPT58yZs2/fvu+++45usHTp0gsXLnz99dcZGRmvX7++efPm\nokWLiouLNTg1/QiZNGnS2bNnz58///fff2/duvXVq1crV65UszGKIiMj9+/fP2bMmI8fP755\n8+b9+/cdOnRYtmzZ2bNnCSFr1qwZP348IWTdunVxcXGNGjXSoNmEkC1btixZsqRbt27v3r17\n+vRpZmbmf/7zn/3798+ePZtusHDhwsuXLw8ZMoT20sGDB8eMGZOZmWli8s8LydLSUsWnckpK\nyocPH+rVqye3vG7dujKZ7NGjR4SQU6dOFRcXt23btkx7aXAiGhEIIT///PPo0aPHjBmzbdu2\ngoIC7vYODg4BAQHnzp3Lz88v8RkxDBOn3LNnz5R1heqOKpG1tbWrqyt3ycaNG4uKirp27Vqm\n4ygqLCxcunSps7Pz2LFjc3Nz1dlFR0+81G4p9bemu14CABAGA1cMy6fEodiPHz8eO3bM19eX\nELJz5066sEmTJi4uLnIXNzRo0MDOzo7OzvH29rayssrOzmbXRkdHE0LUHIqlP799+5ZhmK+/\n/poQsmrVKnbLvLw8ExOTzz//XM3GKEpISDhx4kRaWhq75OjRo4SQ77//nj6kc4/KOceuUaNG\nZmZmKSkp7NrCwkJ3d3cHB4fCwkKGYapVq2Zra5uTk8NusGDBAmW9pDiUdufOHULIyJEj5bZc\nuHAhIeTQoUMMw0yaNIkQcunSpTLtJUedXTp16kQIcXR0NDc3r1atGr3Aom7duq9eveLuQttz\n7do1xbNoi5pDsaxFixZNmjSpZcuW5ubmI0eO5A47ylFzKJZ2SFRUFCFkxowZ7FrdXTxBqX7i\nJa5V87fGlKWXAADERAxDsfPmzZs3b57cQltb2zVr1vTv358QkpOTExsb6+rqOm7cOO42+fn5\nmZmZT58+rVSpUmJiYmBgoLW1Nbu2devW5WlVYGAg+7OFhYWNjU1mZqY6jaGRVI63t7eHh8ep\nU6cePHiQmZlZXFz8+vVrQgg9pjqKi4vfvXvHPpRKpfTCQ1Z2dvbt27cbNWrELXiYmZk1b958\n7969T5488fT0fP78eYsWLbjjxf369VNRaJSTl5dHTy23nA6c0bUpKSmEEO44rDp7aXAiZ2fn\n+vXrjxo1atiwYVKpNCcnZ/LkyevXrx88ePDJkyfZXWhLaKt4YvHixZ8+fSKE9OnTp0+fPtxh\nx/IYMmTIli1bfvrppwEDBigWO3lCzd8a0VkvAQDwnBiCXatWrdgQlpeXt3Tp0po1a96+fZvN\nHykpKcXFxXl5eXFxcdwdK1SoEBgYWFhY+P79e0KIi4sLd62Li4v6c+wUOTs7cx+amJgwDKNO\nY0o8WlxcXPfu3V+9elW7du0qVapIpVI6JkWPqY43b95wB7maN29+4cIF7gbv3r1jGKZSpUpy\nO9Jk8+7dO0tLS0KI3DiXt7e3+r1Ej6A4rEmX0N8XnRbJ7T119tLgRHL3yrG2tl63bt2FCxdO\nnTr18uVLdpIifVVwJ2sa3IsXLzIzMx8/frxs2bJ27dotXLhw5syZWjlyZGRko0aNRo4c+fff\nf5fnxa87av7WiC57CQCAz8QQ7Fq3bj137lz24fv37zdt2rR79+6vvvqKLqEfUXXq1Ll06VKJ\nR0hMTCQKIam4uFj92KS+UhtTokGDBr158+bw4cPsVKHLly83a9ZM/SPY29vTO0FQyq6uUPw4\np50gkUjYH+S2Vz8B0Ls3K1a/6HWL3Hs7c4+p/l7l2YUQYmpq2qxZs/v37yckJLD9o/o1oPE1\nBOXh4ODg4ODg6enZqlWrRo0azZ49+z//+Y9c/VUzdevWnTJlyuLFi6OiouiMAmUM8sRLVOJv\njeiylwAA+EwMwU7O4sWL//jjjylTpnTt2pUWftzc3ExNTV++fKlsF/rPPa3bsehYp9aV2hhF\nb9++vXv3bqtWrbgTwFXMTy+Rvb09N/6W2DATE5M3b94onp0Q4u7uTi9F/PDhA3ft8+fP1b/E\nxMXFxc3N7e7du3LL4+LizM3N6a0H6a8sNTXVzs5O/b00OBEhpLi4mL3sg8rIyCCEcEfkS6zm\nshiGkau8clWsWFHZqrLKzMw8ffq0k5MT9/sV6Dej3L9/Pz4+njv0Xx6zZ8+Ojo7+5ptvevbs\nyX7nhyK9PXFFKn5reuslAADeEmGwc3Z2XrBgwZgxY6ZMmbJ161ZCiJWVVUBAwNWrV8+fP9+i\nRQt2y/nz59euXbtv376Ojo4eHh53797NyclhP9T//PNPXTSv1MYo7kKLRmzQoUt+/fVXolBP\nKk+J0draunHjxrdu3Xr37h073pqfn3/hwgUXF5datWqZmJi4uLjExcUVFRXRO/sTQvbs2VOm\ns3Tr1m3Tpk3Xr19v0qQJXUK//alDhw6059k5bd7e3urvVdYTZWRk1KlTx83NLTY2lq0OZmZm\n/vXXX1ZWVn5+fuxxaNlPbgCaZWpqeu/evTL1gGZMTEzCw8M9PT0fPXrEdj7DMLdv3yb/npJY\nTlZWVmvXru3atevUqVOtrKyUpXa9PXGuUn9rxcXF+uklAADeEuftTkaOHPnZZ5/99ttvZ86c\noUumTp1KCBk7diwtldH7O8yePfvcuXN0g7CwsJycnFGjRmVnZxNCrl+/vnTpUh1NuC61MXIq\nVark6ur6999/P3/+nBCSkZExatQoOp5IB5EJIfRebrRGpdldWgghkyZNkslkI0aMoNdk5Ofn\nT5w48f379xMmTKA1kt69e6enp0+fPl0mkxFCTp8+HRUVxX6CEkJkMlnefxFCGIZhH9LQOXXq\nVKlUOnDgQHpP5sePH4eFhTEMw16BERQURAihl3Nye0z1Xi9fvuzVqxd3rFn1Lvb29s2aNbt1\n69awYcNevXolk8nu378fGhqakpIyfvx47ry9S5cuWVlZNWzYULMuVabUjpJ7RjY2NhEREQkJ\nCV9++eWTJ0+KiopevHgxcuTIuLi45s2bq/O1uerr0qVLaGjo1q1b6etNu1Q/cdVrS/2t6bOX\nAAB4Sh+X3uqMim+euHjxokQiqVWrFvsdkQsXLjQ3Nzc1Na1WrZqNjQ0hpEePHuz9TdLS0uiH\nt6mpqZOTk7m5+Y4dO1xdXZs0aaLs7KpvdxIfH8/d2MHBoV69euxD1Y1R9Ntvv5mZmVlYWPj4\n+FhZWXXu3DknJ8fHx4cQUrNmzaSkJLZEYWtry34PhDrkvnniu+++MzU1tbKy8vHxsbW1JYR8\n/fXX9F4nDMO8ffu2Zs2ahBAbG5sqVao4ODj89ddfFhYWQUFBdAN615USsR2ye/duemQaFi0t\nLTdu3Mi2JzU1VSKRdOnSRa6dqveiibZt27bq75KRkdGhQwfaNvZLRYcPH84+WYZhPn78aGpq\n2rFjR/X7U02ldpTiM8rNzf3iiy/kZjQGBAQo3umDVabbnXC9evWKVoi1frsT1U+81G4p9bem\nQS8BAIiJsIdi/fz85syZU+J9SZo1a7Zhw4akpKT4+PgGDRoQQmbOnDlw4MA///wzOTnZyckp\nKCiIeyPfChUqXLt27fDhw48fP3ZwcOjUqZO3t3dqaiq9vrJEffr0qV27Ni2V0Z9pjOjRo4en\np6fcNO0ZM2ZwvyBBdWMUDRw40N/f/6+//srNzfX392/VqpVEIjl16tTu3bvt7OycnZ09PT2v\nXbt26tQpJyen9u3bq9mBcs+CEDJ//vyhQ4eePHkyNTXVycmpTZs2tWvXZjd2d3e/ffv2gQMH\nnj9/7u7u3qNHDzs7u4KCArbEFRwczC2bcbEd0rdv39atWx85cuTt27eurq5dunThXorr7Ozc\noUOHM2fOJCcnc69yUL2Xq6vrnDlzuKO3pe5iZ2d34sSJW7duxcbGvnv3ztnZOSQkhMZWVnR0\ntEwmU/a1E+VRakcpPiNLS8uYmJiHDx9ev3799evX1tbW/v7+zZs3L8/lq/QdRP9C4PLw8Ni5\nc+eNGzeUXWiiMdVPvNRuKfW3poteAgAQkH8udQRQR0pKysOHDwMDA9kkd+PGjSZNmgwfPpzO\n+dOKK1eufP7551OnTl22bJm2jqmBoqIiX19fExOTR48eCfQLqWhPRkZGjhw50tBtAQAAfRDn\nHDvQkaioqDZt2syYMYPeEC45OXnChAmEkIiICC2eJSgoqF+/fpGRkWW98le71q9fn5iYuGTJ\nEoGmOgAAMEIIdlAGEydO7Nq165o1aypUqODh4VG5cuWrV6/++OOPrVq10u6Jfv31V3d39/Dw\ncGV3bNa1e/fuTZs2bezYsaGhoQZpAAAAgAYwFAtldv369Rs3bqSnp7u6urZr105HFxveu3cv\nJiYmNDSUe/MRvdm1a1diYiK9tFb/Z9eWV69ebdy4sVu3bgEBAYZuCwAA6AOCHQAAAIBIYCgW\nAAAAQCQQ7AAAAABEAsEOAAAAQCQQ7AAAAABEAsEOAAAAQCQQ7AAAAABEAsEOAAAAQCQQ7AAA\nAABEAsEOAAAAQCQQ7P5RUFAgk8kM3QpeyMvLy83NxVeSEEIYhsnLyzN0K3hBJpPl5uYWFBQY\nuiG8gH8uWPjngoV/LoAnEOz+kZ+fj3+pqdzc3OzsbEO3gheKi4tzc3MN3QpeKCoqys7ORrCj\nCgoKioqKDN0KXsjLy8vOzkawo3JycgzdBAAEOwAAAACxQLADAAAAEAkEOwAAAACRQLADAAAA\nEAkEOwAAAACRQLADAAAAEAkEOwAAAACRQLADAAAAEAkEOwAAAACRQLADAAAAEAkEOwAAAACR\nQLADAAAAEAkEOwAAAACRQLADAAAAEAkEOwAAAACRQLADAAAAEAkEOwAAAACRQLADAAAAEAkE\nOwAAAACRQLADAAAAEAkEOwAAAACRQLADAAAAEAkEOwAAAACRQLADAAAAEAkEOwAAAACRQLAD\nAAAAEAkEOwAAAACRQLADAAAAEAkEOwAAAACRQLADAAAAEAkJwzCGboOGPn78qMXGFxcXSyQS\niUSirQMKV3FxMcMwpqamhm6I4TEMU1xcjK4g/+0KiURiYoK/BvHPxf/gnwsumUym3a6wt7dH\n30JZCTjYabflWVlZI2su1+IBRaOGvZmhmwBalpBRpLdzPS54p9PjP5Nd0+nxS5SWeUf/JwX+\n+/AhpUKFClo8IP54AA0I+I9viVYZ+tkAiJOv1FWnx69u2lSnxwcoE3wwgcEJONgBgGZQhS0n\nJzs/QzcBAKBkCHYAxgjZDgBAlBDsAEC3MBoLAKA3CHagCuo6IoZfbnlgNBYA+AnBDsB46S3b\noWgHAKAfCHYAAJpA0Q4AeAjBDsCoiaZoBwAABMEOAMQx2c4go7Eo2gEA3yDYgVLi+LwHAAAw\nHgh2AKCnEC/KSyhQtAMAXkGwAwAAABAJBDsAIEQsRTsAACOHYAcA/xDBrEqMxgKAkUOwg5KJ\n4DMeAADA2CDYAcD/6CHQ4xIKAADdQbADgH9BsRYAQLgQ7ABA30R5CQWKdgDABwh2ACBP6EU7\ng4zGAgDwAYIdlEDon+tQfrp+DaBoBwCgCwh2ACBCKNoBgHFCsAOAkqFwCwAgOAh2AGAYGI0F\nANA6BDsAUErQRTuMxgKAEUKwA3mC/iwHrdPp6wFFOwAA7UKwAwDRQtEOAIwNgh0AlAJFu7JC\n0Q4ADAXBDgBKhwF6AABBQLADADEz1GgsinYAYBAIdvAvKMyAMrp7bYhyNBYAwCAQ7ABA5HAJ\nBQAYDwQ7gPLysS2i/xm6ITqHol2ZYDQWAPQP424A5cLNc+zPT7LwzgIAAANAxQ5Ac8qqdGwN\nT3xlPIHOwsQlFABgJBDs4H8E+pltKGqGNrEmPK0T5WgsAICeIdgBaEKDoIaEZ1go2gGAMUCw\nAyib8oczoSc8XEIBAMBbCHYAZaDdNCb0hAdqQtEOAPQGwQ5AXbpLYIJLeAIt2uGGdgAgepgs\nD//AlROq6Sd14YYpAABQHqjYAZRO/7U0/tfwBPqXAC6hAABxE+Q/zQD6ZNh0xT27kZTxfKWu\njwveGboVAACCZBSfEwCa4VvBDAO1guZk55eWecfQrQAAkcNQLEDJ+JbquHgySotLKAAA+AbB\nDggR7Hwp3eFDbALxwUw7ANA1BDsAeUJJdXxoJ4p2AAC8gmAH8C98SEvqE1ZrgaBoBwA6hmAH\nIGwGz3Yo2gEA8AeCHcD/GDwkacbgzcYczTJB0Q4AdAfBDuAfBo9H5SHoxquAoh0AQJkg2AEQ\nIopgZNingKJdmaBoBwA6gmAHIIZUR4nmiXChaAcAoD4EOzB2IgtDBnw6KNqVCYp2AKALCHZg\n1ESW6ijxPSkU7QAA1IRgByi0iJChsh1eS2WCoh0AaB2CHRgv8VW2REynRTsAANFAsAMjJfpU\nh6Kd+gw4GouiHQBoF4IdGCPRpzpKZNkORTsAgFIh2IHRMZJURxnVky0PFO0AQBwQ7MC4GGHQ\nMchTRtGuTJDtAEBbEOzAiBhhqqOM9omXCe57AgAigGAHYBT0n+1QtCsTFO0AQCsQ7MBYoGqF\nHiiVYYt2yHYAUH4IdsZOiDen0AAyDaXnfkDRDgBAzxDsQPyQ6rjQG6qhaAcAgoZgByKHHKNI\nn32Coh0AgD4h2IGYIdUpg2ynAop2ACBcCHYgWkh1qqF/VEC2AwCBQrADMF56y3aCK9oBAAgU\ngh2IE8pRUE4o2gGAECHYgQgh1akPRTveQrYDAA0g2IHYINWVFXpMGXzJGAAIDoIdAOgp26Fo\nV1Yo2gFAWSHYGTUj+doJUAfqdiUyeNEO2Q4AygTBDkQF6aQ89NB7KNoBAOgUgh0A/A+SsSIU\n7QBAQBDsQDwQSrRC190oxKIdsh0ACAWCHQDIE2i2AwAABDsQCZTrtEuI/YmiHQAAgh0AlEyn\n2Q5FOw0g2wFAqRDsAEApwdXtxF20AwAoFYIdiIHg8oeA6K5vUbTTAIp2AKAagp3xwscqqElY\nuRlFOwAwZgh2IHjCih3AJcS/Lgye7VC0AwAVEOwAoHTCSs+i/yIKZDsAUAbBDoRNWIFD0HTU\n1SjaAQBoEYIdAKhLQNkORTsAME4IdiBgKNeBofChaIdsBwCKEOwAoAxQtAMA4DMEOwAATaBo\nBwA8hGAHQoVxWENB0Y6FbAcAfINgBwBlJqBsBwBgVBDsAEDMULQDAKOCYAeChHFYgxNQ0c4Y\nrqJAtgMACsEOADQkoGynU3wo2hFkOwAghCDYcT0ueGfoJgAIjFBKp8YwIAsAQBDsQIiEEiZA\nY4Ir2vEEinYAgGAHAOUilAFZIynaIdsBGDkEOwAoL2Q7AACe4NF4R3x8/J9//vn+/XtnZ+f2\n7dv7+Pjovw2PC97hn34A0Ex106bPZNcM3QriZOeXlnnH0K0AAMPgS8Xu4sWLU6dO/fDhg6+v\nb3Jy8rRp027cuGHoRgEfYYIdP6FoR2FAFgAMiy8Vu02bNrVs2XLKlCn04YwZM/bu3RsQEGDY\nVgGA+nxsi55kaf+flBr2ZgkZSPNlhrodgHHiRcWuqKgoPDy8b9++7BIfH5+UlBSDNAY3PQH+\nq2WfXcs+29CtKIEg6qlGUrQDAOPEi2BnZmbWoUOHqlWrsktevnxZuXJlAzYJ+EkQuUF3aJ5j\nIx1v453WYUBWMxiQBTBCfBmK5Tp//vzNmzfnzp2rerPs7GyGYbR10qIio04MwHMqAlwt++z4\nDBt9NkY1HQ3IgmYwIKtPDMNkZWVp8YDW1tYmJryov4CA8O7f32vXrq1evTosLKxx48aqt8zL\ny9NisOPCtbHAE2oW5Ohm/Il3ush2Wp9p5yt11em8C55cIQv6xDBMXl6eFg9oaWmJYAdlxa9g\nd/bs2TVr1oSFhYWHh5e6sZ2dnRaDXW5urrYOBTpiPOOwmg2w8ireCSLbGQkU7fRGIpHY2tpq\n8YCmpqZaPBoYCR4Fu7Nnz65evXrUqFEdO3ZUZ3upVKrFsxcUFGjxaACaKf+cOb6NzPKZ8RTt\nkO30QyKRWFhYGLoVYOz4UuN9/PjxmjVrxowZo2aqAxATuasitHI0rRyqPHRRYcVVFBrDhRQA\nRoIXwY5hmA0bNrRs2bJ9+/aGbss/cNMT0APt5jnFg+visGUiiGwHACAmvPgn8uXLl0+ePHny\n5MnZs2e5y7du3VqhQgVDtQp4RWQT7PSTung16463MCALAGLCi2Dn5ub2448/Ki63s7PTf2MA\ndMcgVTTDxjtcRUGQ7QBAj3gR7CwtLRs0aGDoVsjDTU9AW/gwKmrAiyr4f2c7XRftAAD0hhdz\n7ABUE+44LE+uY6AM2Bit/wZxFYXGcBUFgLgh2AFon06viignfrZKA7iKQmPIdgAihmCnCkZn\noEz4nOe4DNJI/pddjadoR5DtAMQLwQ5ACwSR5+Tov838H5DVNWQ7ANA1BDvgO/5XegQX6biQ\n7bhwvRQACB2CHYCxE2K5UXcwIAsAgoZgVwpMswPVRBOJ9BbveF600wNkOwDQHQQ74DWej8OK\nJtWxBJrttMvYBmSR7QDEBMEOAP5FP6U77WY73NaunJDtAEQDwa50GI2FEomvXMcluGcnuAFZ\nvkG2AxAHBDvgLz4P2Aku92hA16U7Pv9+ifEV7QBAHBDsAEAVncY7DMjq9PhlhaIdgAgg2KkF\no7HAZQzlOjlCecoYkC0nZDsAoUOwAwC16CjbYUBWp8fXALIdgKAh2AFP8fbzXii1K10QRLbD\ngGz5IdsBCBeCHUAZGHOqowSR7aD8kO0ABArBTl2YZgcgFCjaaQWyHYAQIdgBH/GzfoNyHSWI\noh2yHQAYJwQ7ALUg1XEJIttB+aFoByA4CHZlgNFYABb/sx2KdlqBbAcgLAh2wDs8LNugXCdQ\nyHZagWxnPMLDwyUSyatXrwzdEHnffPONhYVFbGxsqVt+//33FhYW165d00Or+AnBDqAUSHXK\n8L9oJ0TIdiAyhYWFM2fONDU1DQgIUFz76dOnyZMnV6tWzcLConLlysOGDUtOTpbb5tChQ8uX\nL1+6dKm/v3+pp5s7d27Tpk379euXlpamnScgNAh2ZYPRWAAu/mc7wRXteAvZDjTw8OHDoKCg\ndevWlbg2Ly8vJCRk5cqVAQEBs2fP7tSp09atW5s1a/bhwwd2m5ycnOHDhzdp0mTChAnqnNHU\n1DQqKurVq1fTp0/XznMQGgQ7AFVQrisV/7Od1hnngCxBtoMyysjI8Pf3NzExuXnzprm5ueIG\nkZGRN2/eXLJkSUxMzKxZs6KiorZt2/bs2bOFCxey26xbty4lJeX7779X/7y1atUKCwvbunXr\n8+fPy/8sBAfBDgDKi+fZTohfIMvbbAdG5cWLF0OGDPHw8JBKpS4uLj169JCbu3b48OEmTZpY\nWVm5u7tPmDAhNzfX09OTHTAtKioaPXr0pUuXatasWeLxd+zYYWdnN378eHZJeHh4jRo1duzY\nwTAMIYRhmBUrVvj4+HTr1o3dJjQ0VCKRvHv37j//+Y+7u7uFhUXt2rUjIyO5R54yZUphYeHq\n1au11RUCgmBXZhiN1Sle1WlQrlMfz/tKiAOy/Mx2KNoZj6SkpKZNm+7Zs2fQoEFRUVFjxoy5\ncOFCixYtzp8/Tzf466+/evbs+fTp0+nTp8+fP//27dvh4eGZmZlscc7JyWn58uUl1uoIIfn5\n+bdu3QoICLC0tOQuDw4OTklJefbsGSHk5s2bycnJHTp04G5AD9izZ0+JRLJz587ff//dzs5u\n9OjRGzZsYLf57LPP3Nzcjh49qr3+EAzh/SELAPxUyz47PsNGu8f0sS16kqWdf6Zq2JslZGjz\nzwZfqauu/8yrbtr0mYx3F/c52fmlZd4xdCtA577//vt3797t27evV69edEloaGjjxo2nTZt2\n5coVQsjChQuLi4uPHTsWFBRECBkyZEjbtm0zMjLUPP6LFy+Ki4u9vLzkltMliYmJ3t7ep06d\nIoSEhIRwN5BIJIQQT0/PX375hS5p06ZNlSpVFi5cOHz4cHabkJCQ33///eXLl1WrVtWoA4QK\nFTtNoGgHUCJd1O14VcSVg7odiBXDMPv373d3d+/Zsye70M/PLzAw8OrVq+/fvy8uLj5//ryP\njw9NdYQQMzOzGTNmqH+KzMxMQoitra3ccjs7O0IIDYhPnz4lhNSqVUtx94iICPbnChUqBAcH\nP3/+PCkpiV1I90pISFC/SeKAih1AyXg+tshbfK7bab1oZ8xQtxO0jx8/ckNYzZo1p06dyt0g\nOTn506dP/v7+tDzG8vX1vXTp0tOnT6tUqZKXlycXuZo3b17+ttHZdfS879+/J4Q4Ozsrbubj\n48N9SOt8L168qFKlCl1C90pNTS1/k4QFwU5DjwveCf2uBwkZRUKcVA78p4tspy0YkNUiZDvh\nysrKWr9+PfuwefPmcsEuOzubEGJjI/9GpkuysrJycnIUN7C3tzc1NVWzDQ4ODuS/lTkuuoSu\n/fjxI/uzHLlSn4WFBSEkLy+PXeLo6MgewahgKBagBCjX8Y2R39mOnwOyBGOyguXp6clwXLhw\nQW4DGpuysrLkltPAZ2dnR4NUbm4ud21WVpZMJlOzDV5eXmZmZvQiCa7EE4jS3AAAIABJREFU\nxERCCL2Qlka6T58+Ke5Ok6Vcw7hBU0UoFDcEO81hph2AMsY22U4PkO1An9zd3Z2cnB48eEAH\nRln379+XSCS+vr7u7u6mpqZysezy5cvqn8Lc3LxJkyaxsbE0k1Eymeyvv/7y8vKiVzy4uLiQ\n/w7Iynn06BH3YXx8PCGkevXq7BK6Fz2CUUGwAwCd4HO2E2LRjiDbgX6FhoampKTs37+fXXLz\n5s3r16+HhIQ4OjpKpdKAgID79+/fv3+frpXJZIsWLSrTKQYPHpyTk7NkyRJ2yfr169+8eTN0\n6FD6sEaNGuS/oU3Oxo0bi4uL6c8JCQlXrlypV6+eu7s7uwHdix7BqGCKVbmIYKYdKMI4rLZg\nsp3WYb4d6M28efOOHDkycODASZMm1a1bNzExcdWqVba2titWrKAbTJo0KTw8vF27dhMnTnRx\ncdm2bZuXlxcdoqXOnTt37Ngx+nNRUdHr16/ZKzamTZtWsWLFIUOGbNu2bf78+XFxcf7+/o8e\nPYqOjm7YsOGUKVPoZm3btiWEnDlzpnfv3nLNy83N7dixY2hoaE5Ozpo1awoLC7nfTsEwzJkz\nZ2rWrKl4OxXRQ7ADHjHysTZR0nq2M/I72/EZsp3IVK5c+erVq3PmzNm0aVNqaqqTk1O7du1m\nz55dp04dukFYWFhGRsby5ctnz57t7u4+aNCg2bNn79y5k71+4vLly9xqXHJyMvtw2LBhFStW\nNDc3P378+A8//BAdHX3ixAlXV9dx48bNmzePnSrn7+/v4uJy4sQJhmHkrs/95Zdf1q1bt2DB\ngvfv39eoUWPLli1hYWHs2lu3bqWkpPTr1093/cNbErnhc6OVmZnZpuo3mu0r3KId366K5Umw\nQ8VO67Ret9NWttP63U/0E+z4WbSjjDnbffiQ4uTkZOhWGNL79+/pN48dOHBAW8dctGjRt99+\ne+DAgR49etAl4eHh0dHRSUlJnp6eyvYaMGBAdHT0o0ePjHAoFnPsAP5FKKmuuls6/c/QDVGL\n1nsVk+30cBbNYL6d8di8eXPr1q1jY2PZJTt27CCEBAcHa/Es48ePd3Fx+fHHH9XfJSEhYdeu\nXYMGDTLCVEcQ7LRCuIMvuFmrQHHzHJvweB7yeJuYke20DtnOSNStW/fKlSvdunX74YcfNmzY\nMHr06ClTpnh5ebHf66UVNjY2GzduvH79+qpVq9TZXiaTDR061MPDY+nSpVpshoAg2AH8D2/D\nB0t1gON5wtNu9xr5ne0Ish0YWmBg4KlTpxo3brxu3boxY8YcPHhw8ODBly5doncG1qIePXpM\nnTp1+vTp3OqgMvPmzbt8+fLu3bsrVqyo3WYIBebY/aM8c+wogc6049U0O4PPseN5sNMssT1L\nqaD1lpSHdufbGflkO4L5dnyCOXbAB6jYAQiDxnU4vo3V8rNuJ9CiHc+hbgegfwh2WiPcmXZA\n8blcp61MxpOEx8+uFmi24/OALEG2A9A7BDsAvtNFDjN4GU+L2Q6T7ZDtAICFYAdACF9rSEQ3\nqU7xFAYJefzMdgKFbAcAFIKdNglxNJY/dzzBZ7Mi/ZfT9BzyeJjtBFq0I8h2AEAIQbAD4C2D\nz4TTT8LjYa0U2U5HkO0A9ADBTsuEWLQDHmYLg6c6Ll0nPG31Pybb8R+yHYCuIdgB8A6vUh2X\nUWU7geJ50Y4g2wHoGIKd9qFoJyx8K9fxNtVRxpPthFu0Q7YDMGYIdgA8wvNUR/E/22kLsp3u\nINsZnERirt3/DP2E4B8IdjqBoh1oQBCpjuJ5tsNkO4JsB2CsEOzAqPGnRCSgVEcZT7YTLmQ7\nACOEYKcrAira8edWdkZLcKmOMpJsJ9yinSAg2wFoF4Id8IJB6it8KNfx4Ztby4Pn2U5bhJvt\n+F+0I8h2AFqFYKdDAiragUEIOtKx+JztMNmOINsBGBkEOwDDEEeqo4wk2wkXsh2A8dDy36Ag\n53HBO8yn4SfDjvSJKdVR1d3Sn6VUMHQrSuZjW/QkSwv/1tWwN9PuhFRfqave6vrVTZs+k13T\nz7k05mTnl5Z5x9CtAJ07duxYUlLSiBEjCCFJSUmHDx/OzMxs3Lhxu3btlO2iYjPNVmlwIjlX\nrlw5fvz4yJEj3d3ducsfPnx47ty57OzsOnXqdOrUycTERMWqjx8/rlq1iq6dMWOGpaWlOo1U\nDRU7AH0TX6qjdPS8MNlOW1C3Az64fft2v379fH19CSFHjhzx9fVdt27dyZMnu3XrNmDAAIZh\nFHdRsZlmq5RRf5effvqpZcuW8+bNS05O5i7/4Ycf6tevv379+oMHD4aGhgYGBmZnZ6tYVVxc\n/PHjx1u3bs2bNy8vL68sHakUgp3OYaYdcIk11VG8zXaYbCcgyHYixjDMwIEDBw8e3KpVq7y8\nvK+//nrgwIH37t07efLk33//vWvXrj/++ENuFxWbabZKGfV3mTBhwqpVq5YtWya3/M6dO3Pm\nzImMjLx169a5c+euXbt2+/btyMhIFaucnJxWrVo1fPjw8vSqHAQ7MEaGKgKJO9VRxpDthEsQ\nRTuCbCdee/bsefz48YwZMwghZ86cSUlJ+fbbb+mqpk2btmnTZseOHXK7qNhMs1XKqL9LlSpV\nYmNjAwMD5Zabm5svWbJkyJAh9KGfn5+3t/fTp09Vr9I6BDt94H/RDrey0wNjSHWU6LOdoIt2\nyHZgQFu2bGnfvr2Hhwch5ObNm87Ozl5eXuzagICA2NhYuV1UbKbZKmXU32Xq1KmuriW8Z+vU\nqfPNN9+Ym//z7WqJiYnPnz/39/dXvUrrEOzA6BikXGc8qY4S/fNFttMDZDvxOXfuHHtFwtu3\nb93c3Lhr3dzcXr9+LbeLis00W6WMBrsoM3Xq1AEDBrRo0WL06NFDhw5Vc5W2INjpCf+LdgYk\n+jEy0aecEuniWfOnaEeQ7fQC2U5kcnJy6tSpQ3/Oy8uzsLDgrpVKpTKZrLCwkLtQxWaarVLW\nNg12USYuLu7OnTtmZmbW1tbFxcVqrtIW3O4EQLeMM9VRurgHSi377PgMm/IcQVt3PyG4AYpe\n4B4oIuPi4kJ/sLS0zM/P567Ky8szNTVlxytL3UyzVcoapsEuypw6dYoQcvv27datW+fl5S1f\nvlydVdqCih0YFz2PwxpzqqNEX7fTOtTtFKFuJyYSiYT+4OHh8fbtW+6qt2/fVqlSRW57FZtp\ntkoZDXZRrWHDhmFhYTExMWVaVX4IdvqD0Vhjg1RHibsftD4gq2fIdqBnqamp9IemTZumpaVx\nrwy9fPmy4qWmKjbTbJUyGuwiZ9WqVfXq1ePe+q64uNjU1FT1Kq1DsAMjos9ynbjTTFlpvTd4\nVbQT9GQ7gmwHemRjY/Po0SP6c6tWrapWrbpgwQL68MyZM5cuXWJvCHLlypUbN26o3kyzVYSQ\nq1evXr16Va5tarZHBT8/vwcPHvzf//0fffjy5cs//vgjODhY9Sqtk5R6I2YjkZmZ2abqN3o4\nEW9vRmrAwoPexsX0FuyQ6kqk9fl25ZxsRwjR1mQ7ooN7Bumzxi+IyXYUn+fbffiQ4uTkZOhW\nqEsiKfPUMdUYpvSLDLp27Wpqanrw4EH68MyZMz169PDy8nJ3dz9//vyIESPWrl1LVwUFBdna\n2tIZaSo202wVTVQXLlyQa5467SkoKOjQoQMhJCMj49atW/7+/ra2tu7u7rt27SKETJ8+fdmy\nZfXr13dycrpx40aVKlXOnDlTqVIl1asOHz7cvXv39PR0R0dHTbv/fxDs/oFgRwyX7cQU7BDp\nVBNxttPFzSCR7UrE22yHYFfqNnv27Bk4cGBiYmLlypXpktevXx84cCAzM/Pzzz9v2bIlu+XG\njRulUumgQYNUb6bZqhUrVhw5cuT06dOKLSy1PUVFRWxVj+Xo6Dhx4kT68/379y9cuJCZmenj\n49O5c2futRfKViHY6YTegh3hcbYTd7DTT7kOwa5U2s125Q92BNnuv5DtygnBTo1tmM8++6xl\ny5Zr1qzR7tnL5MCBA+fOnVuxYoUB28Cl3WCHOXYGgKsoxAqpTh3a7SVDfTtciXTxpxEuki0R\n5tsJlEQi2bp16+bNm8+dO2fAZuTn548bN86ADWClp6dPnTo1KipKi8dExe4f+qzYUTys26Fi\nV04IdmWixdIdfwZkCep2+sWr0h0qdto9IGgGFTv4H3xjbHkg1ZWVFntM3BfJEtTtlEPpDkAO\ngp3BYEAWQKzZTuiQ7QCEC8HOkJDtRAPlOo2JMtsJvWhHkO0ABAvBDgAMrLpburbiHbKdFiHb\nAQiRsL8MRwQeF7zj1VUUCRlFQv+KJP0TRLnO0VcL85o/PtbybGuu6m7pWr/LnWZ8bIu0ci1F\nDXszrc9b9ZW66rPSX920qYCupXCy8+PVtRQ8h2sdxAof4QCCpJWgpsFJ+Z/tatlna+XmdlqB\nbKdnyHYACHaGx7eiHfCfQVId99S6i3e0/FnOeFf+bKetoh1BttM7ZDs1VbT31+4BP2TEaveA\noBnMsQN5+r/piRZvIaYMr25jKwK6TpblH93mz2Q7HdH/fDsBTblzsvPDlDswWgh2vIDLY4VL\n/xPsDFiu40K2U5+O5q3qv9IvoGxHcDkFGCsEOwAh4Umqoxx9C3XaHmS7UiHbqYZsB0YIwY4v\nULQDgUK2UxOynUEg24GxQbCDEuC7xdSk53FYXpXruHie7fhDNPcSQrYD4C0EOx5B0Q5U4G2q\no3Q6LFvObMefoh0RxY2LKWQ7AH4SyZ+PAMAHurvRXTlvccerG6Dogp5vgELhNiigmXnz5uXl\n5S1atIgQEhMTs2fPnszMzMaNG0+dOtXR0bHEXRiGWbFixaFDhzZu3FizZk12eWFhYVRU1Nmz\nZ7Ozs+vUqTNu3LgqVaqUukoFNduze/fuQ4cOffz4sXbt2hMmTPD09GRXxcfHr1q1KiEhwcvL\na8yYMX5+foSQffv2rV69Wu4gVatWXbp0aXh4OH14+PBhW1vbUltYKlTs+IU/RTuMxvIKz8t1\nXKjblUo0k+0I6nZQdtHR0atXrx41ahQhZMGCBV9++aWzs3Pz5s1jYmKCg4NzcnIUd0lPT+/Z\ns+fcuXPPnTuXlZXFLpfJZN27d585c2alSpUaN2585MgRPz+/Z8+eqV6lgprtGTdu3MCBAy0t\nLRs3bnz48OEGDRq8fv2arnrw4EFAQEBiYmLr1q2fPXvWrFmzBw8eEEI8PDxa/1tqaurLly/t\n7e0nTpwYHBx87ty5oiLt/AsjYRhGKwcSuszMzDZVvzF0K/7Bk/sV63M+kK7vGaaL+9jpbe6X\ngFIdly5Kd+X/XoryfymFtup2OvrbySB/HAqobkcI0V3d7sOHFCcnJx0dXOsMcoPi7Ozs6tWr\nz5gxY/Lkye/fv69SpcqSJUvGjx9PCElNTa1Ro8aCBQvoQ64+ffpkZ2ePGzeua9eut27datSo\nEV1+6tSp9u3bnz9/Pjg4mBDy8ePHqlWrTpgwYf78+SpWKWubmu15+vRprVq1fv311+HDhxNC\n0tLSqlWrNmnSpHnz5hFCOnToIJVKDx8+TAgpLi4eMWJEjx49evToIXeup0+fNmjQ4OzZs0FB\nQYSQw4cPd+/ePT09XVmBsExQsQPxE/TdiQWa6ohuWs6Hi2S1BXU7Q8Htiw0oMjKyuLh45MiR\nhJCTJ0/m5+cPGjSIrnJxcenUqdP+/fsV9xo+fPiRI0cUQ7O/v//Vq1dpdCOEODo6uru7p6Wl\nqV6ljJrtsbCwWLduXVhYGH3o5OTk4+OTmJhICHn37t2pU6fGjBlDV5mYmGzcuFEx1RFCJk+e\n3Lt3b5rqtA7Bjo/4MyALUB6izHY8v5CCINupB9nOIGJiYjp37mxtbU0IuX//voeHB7dGVbdu\n3Xv37inu1bFjRxOTEuJKhQoVmjb93wtv7969CQkJNEipWKWMmu2pUqXK6NGj7e3t6cPc3NyE\nhAQfHx9CSGxsLMMwPj4+c+fO7dWr18iRI0t8OhcvXjx+/PiCBQtUNKY8EOxAKUyzU0E/47DC\nLdexdHG1rMFvgMLzbxsjyHbqQbbTvxs3boSEhNCfU1NT5YpwTk5O79+/12CGWEBAgKen56xZ\ns/bt29exY0c1V8nRrD2TJ08mhIwYMYIQ8vr1a4lEEhERkZKS0qxZs9jY2KCgoEePHsntsmDB\nggEDBnh7e5fpOaoPwY6nULQDEaQ6Ft+ynegvpCDIdupBttMzmUxWrVo19mczs3+9/s3MzBiG\nkclkZT1sr169QkNDc3Jyli5d+ubNGzVXKbatrO357rvvoqKidu7c6ebmRgjJzc1lGKZfv36R\nkZHffPPNhQsXnJ2d586dy93l8ePHx48fp4PROoJgBwD6gGynDLKdYSHb6VnFihXpD7a2ttnZ\n/3obZmVlWVpayqUrdXz33Xdr1qx5+PBhamqq3LUOKlbJ+X/2zjuuqbv749+wlCkgCsgqCMaB\nqCgICiK2Yh0oOGq1Qq3VWgUX6vNz9KH6aKt11IVW63qwanGhIk5QQNFKRYUWUIaMIi6QYVgK\nSX5/3D5pDHC5yd3Jeb/6R3K/935zEkvyuZ/vOeerVDwSiWT+/Pnbt28/f/786NGjsYP6+voI\noTFjxmBPO3ToMH78+PT0dPkLjxw50qNHD/llYsoBYcddwLTjLAwsBaqTXSeD8mVZ0HbcBLQd\ngENDQwP2wNHRsbS0VCKRyIaKi4uVWqCsr6//66+/ZE8NDQ1Hjx59+/Zt/KG2UCqeuXPnnjx5\nMikp6eOPP5YddHBwQAg1NjbKjhgZGSk0TLl69ar8JXQAwg7Ag7E0Oy63fmUetVR1Mjj17qBI\nliZA2wFtUV5ejj3w9fWtr69PS0uTDd24cWP48OHEp9q8eXPPnj1lShEh9OLFiy5duuAPtQXx\neLZu3XrixInExEQPDw/544MHD9bT00tJSZEdyczMlG+nXFlZ+fDhQ29vb+LvUQVA2HEaMO0A\ntYRCbQdFsu0C2o4goO0YwNLS8v79v9vdubu7Dx06dMmSJS9fvhSLxevXry8sLJT1CtmzZ8/B\ngwfxZ/vkk0+wXnGVlZXNzc1nzpw5c+ZMUFAQ/hBCaO/evXv37lWYjWA8L1++XLNmzfbt22Xt\n9GSYmJh88cUX33//fXp6ukQiiYmJuXz58ty5c2UnPH78WCKR9OzZU4WPjjgg7ACAW3DK0KIP\nCpdlQdu1C2g7goC2o5uAgICEhATZ02PHjr19+9ba2trAwGDTpk3R0dG9e/fGho4cOfLrr78i\nhBobGwUCgUAgwIyuAQMGCAQCrAKjV69eMTExSUlJnTt37tix45QpU0JDQ1evXo0/hBA6evTo\nsWPHWoZHJJ5Tp07V1tZ++eWXAjlkWu3HH38cPHiwh4eHvr7+Z599Nn/+/OnTp8vmf/HiBZLL\nMqQJ2Hnibzi184QCrG9EwUwCEH0tJKhdbqM1wU5DVJ08VG1QwfqmFBSmE6jTphSIb/tSYKi2\nOwXsPNHuOffu3Rs8eHB6erq7u7vsYE5OjkgkcnV1NTT852/wwYMH2tra/fr1k0gkN2/eVJin\nY8eOsu6+TU1NhYWFIpHI2dlZYeeGtoYuXLiwZ8+ey5cvtxokfjxPnz4tKChQuMTAwEC+HqK4\nuPjZs2fdu3fHqmVlvHjx4vHjx0OHDtXVfe97j9qdJ3gs7N69e0dh8I2NjR9+sIKq2agFhB0Z\nKE+iAmFHORzRdtzZbQyBtuMAKmi7iooXlGziLkNPT08gEFA4oTysCDuE0KRJk+rr69sSVcyw\nZ8+eN2/erFjBlR99aoUdjzPWm5qaKBR28oUwXCP33SvWtR3AAJqp6tD/3jh5eedoWUVG27mY\n1JHUdj2MmjleBiTU68qKtnPU9uSdtjM3dlNB2zU1UflXrKurS5+wY4uff/7Z3d09KioqPDyc\nrRiGDh1KX39gpXj+/HlgYGBNTQ2Fc/LYsaMWLi/FIrZNO3DsZNBn12msqpOHEuuOXd+O+6Yd\nAt9OGZTSdrAUS+2EgGpA8QQ/YLc8FvYWA5iBC+pW7QspENRSKAOUUwC8A4QdALAPFwQNRyD/\nUUCRLBFA2xEHtB3AL0DY8Qboaaeu8EXV6boyJAXId0IBbUcE0HbEAW0H8AhO5/kCAKdgYCcx\nDiKv53RduzZlMXSDYSpsIpNyR7KQgjwUFlJ0N9FRs3QIzaml4DKQEqeugGP3D9z/omHRtFOz\n3xXuwFm7Tte1K/Zfq8eZiYFd3447O8kitWtcjHjr24F1B3AfEHbvAdoOYBIOqrq29FzL05iJ\nh+/ajkJA23EE0HYAx4GlWECdofCHWb3XYVUQaowty7K7JkuyuR33O9sh9prbIX6uySJ1WZYd\nZDaP2gnTq36idkJANcCxU4T73zJsfQXDaiy1sG7Xycw5le03xpZlee3bcX9BFoFvpzzg2wGc\nBYRdK3Bf26kl3Dc2KIRFVUdSzLU6IVVT4cCuDgZtRyug7QCAQkDYtQ7HtR1k2jGMGqzDUq7n\nWk5Ox8zykNF2rP8LgrbDB7QdAFAFCDsAYBomzSda9VzL12LgVVSG9UIK0Hb4gLYDAEoAYdcm\nYNq1BNLsyMOMqmNSzym8Lq3z8zrZjlpA23EH0HYApwBhhwfHtR0AKMCWnmsZA33z81rbUWja\nIdB2XAK0HcAdQNi1A5e1HWTaMQOF6Vk02XVc0HMKgLZrC9B27QLaTr0Ri8X+/v6zZ89GCNXV\n1YWHh1taWhoYGPj4+Ny7d6/VSyorK+fPn29nZ2dsbOzh4REXFyc/GhMT06dPH319faFQePDg\nQexgVFSUoAU9e/bEj41gPDL+/PNPXV3dGTNmEJmh1aHi4mJZeNXV1fgvRxAQdu0D2k4eWI3l\nFFzTc/LQGhvrm8mSgVptRx+g7QA6iIyMfPny5a5duxBCs2bNOnfuXFRUVEJCgq2tbUBAQFlZ\nmcL5EokkKCgoPj5+48aNcXFxPXr0CA4OTktLw0YvXLgQEhISEhKSlJQ0Y8aMOXPmXLlyBSEU\nHByc9D4DBgxwd3fHj41IPDKkUuncuXOJz9DqkL29fVVVVUxMjDIfYTsIpFIphdPxF5FI9IHN\ncJwTOPtFw/yXL30mAeU/eJQkRXHWseOspFOAvj7GZBoXk9xJlkzXYgwK+/vQervF4soAl2+q\nWyW/OMHc3JztKIjCSoPi0tJSFxeXY8eOTZo0qaCgwMXFJS4uLjAwECHU1NTUvXv3zz77bMOG\nDfKXpKWleXl5Xb16NSAgACHU3Nxsb28/bdq0rVu3IoT69OkzYsQITCYihI4cOeLm5ta/f3+F\n101MTJwwYUJubq6trW1bsRGMR8ZPP/20adOmnj17du7c+ejRo/gz4E8eHx8fGBhYVVVlamra\n7mfYLuDYEYWzXzHqZNppVCs7kvBF1SE6rTsWG6BoSCEFAt8OoJTt27fb2dlNnDgRIXT9+nU9\nPb1Ro0ZhQ7q6ugEBAQkJCQqXuLm5ZWdn+/v7Y091dHSsra3Ly8sRQvn5+Tk5OSEhIbKTQ0ND\nW6o6iUQSERERERGBo+qIx4Px4sWLlStX7t69u0OHDkRmUGpykoCwUwLQdgCgMhxUopBsRxDQ\ndgBVxMfHjx49WiAQIITy8/NtbW319PRko05OTnl5eQqX6Ovr9+7dW1f3b3u+tLQ0KyvLx8cH\nIfTnn38ihBoaGoYPH25qatqzZ8/Dhw+3fNFff/21rKxs+fLl+LERjAdj4cKFAQEBY8aMITiD\nUpOTBISdcnBW2wHch8J1WA6KJCLQYd1BIYUM0HYA98nLy/P19cUe19TUmJiYyI+amJiIRCKJ\nRNLW5W/fvp0+fbqzs/Pnn3+OECovLxcIBEuWLJk3b961a9dGjhw5a9asq1evKly1cePGsLAw\nhddqCfF4Ll26dO3atR07dhCfQYU3qzIg7JSGm9qOYdMOSigAlVEzbUcSvhRSsAtoO3XC2tpa\ntQtra2vHjh1bXFx84cIFbAG0qalJKpWuXLly6tSpnp6eu3bt8vLy2rx5s/xVycnJ2dnZX375\nJQWhI4QQqq+vDwsL+/7771V+I3QDwk4VQNvRB6TZaQKUW3csajtItmMG0HZqQ6dOnbAHZmZm\nNTU18kPV1dUmJiZaWq0ok4qKihEjRpSVlaWmpjo5OWEHjY2NEUIDBgyQnebr6/vo0SP5C2Ni\nYry9vR0cHNoNjGA83377bbdu3b7++mulZlDqzZIEhJ2KgLbjhWlHvnSRg/B0HbYlnNJ2ZIAF\nWWYAbaceyPSNUCgsLS1tbGyUDeXl5fXq1avlJfX19WPGjJFKpampqfISrUePHgghrJACQywW\ny1czIISwJVoigRGM5/Tp03fv3tXT09PR0dHR0YmLizt+/LiOjk5mZibODMTfLHlA2KkON7Ud\nAPAIaq07/hbJgrYjCGg7NeDFixfYg5EjR0okkvj4eOxpfX395cuXFcoRMMLDw2tqaq5evdq5\nc2f54x4eHubm5rGxsbIjKSkpffv2lT0tKSkpKioaOHAgkcAIxnP16tXMzMyM/+Hv7z927NiM\njAyhUIgzA/E3Sx5Y9iJFkfh3rn3R5L57xdg375M3zbT+hAAagq5rV/p63RHH0bKKZHM7MvQw\naqYwD6G7iQ59nrpQryuLiR+O2p5wU81fhEJhamrq5MmTEUL29vYzZ85csGCBVCq1srLatGmT\ntrb2/PnzsTNnz55tYGCwc+fOzMzM6OjodevW/fHHH7J5DAwMPD09dXR0IiMjly1bZmlp6e3t\nffTo0QcPHsh62iGEioqKEELOzs4KYWCNhfft2yd/kGA8mE0ow9jY2MjIyNXVFX8G/MmpBX6V\nyaLh2g4gCFULhWqzDqsA9r7IyztTYROZrsVktJ2LSR3JpX9qtR2tgLYDVGPcuHEXLlzYtm0b\n1vFk9+7dK1asCAsLE4lE3t7e169ft7CwwM7MysoyMjJCCCUlJUkkktWrV8vPIxQKHz9+jBBa\ntGiRWCzeuXPnqlWrXFxcTpw44e3tLTsNcwdlWX0ysrOzW01uIxJ7XT2RAAAgAElEQVQPPjgz\n4AxRC+w88Tft7jyBD9e0HWJqxYQmx47ClSnyue2UVE2CsCMOeXlHRtshcptSkNR21Ao7uhNh\n2S3Y4qC2g50n2j3n6dOnzs7Ox48fx3oUs0VWVlZkZKT8Gi67wM4TXISDXzHMwIsSCoBfkBev\n/C2S5VGyHetw8HYaaBdbW9vly5d/8803DQ0NLIYRHR0dFBTEYgAypFJpbW2tfFEFeUDYUQbX\ntJ16dD8B5NEEuw6DfFEFi0WyJOGRtmM95QO0HR9Zs2aNpaXlggULWIxh8+bNoaGhLAYgo6Sk\nxNjYeMqUKRTOCcKOSjRT24FpB9AEi9qO3c52oO2IA9qOd2hrayclJR04cIDtQDjBBx98IP0f\nlKzDIhB2lMM1bcdT+JJCDtANaDvuA9oOADgFCDvq4ZS2A9OOC1CyLKg567AKsPjGYUcKgoC2\nAwDuAMKOFjRQ23EWtdx8gh36OLH1ymS0HSTbYai9tgMAAAPanfwNyXYnrcKpm0gGvnap/eXg\nTscT8u1O1MGxk1d12YXMvz7JHihkGqCw2P0EQQMUZWD9jppf7U4AdQUcOxph/VuG10CanTwc\nUnXYU8bdO0i2owT19u04dS8NAGwBwo5euKPtNHxBFlCdtjQcaDticE3bqTeg7QAATBHa4c6e\nY3RvNQZbx6oh+OoNG2VwZZbkrrIkNxxTD2jdRhaxvdsYgg3HCPOZ5bfUTnjs5VpqJwRUAxw7\nJoBvGU2GfIIda+uwBD05Zldm2fo01Mm0U+8FWQS+HaDZgLBjCI5oO7rvpKl1AiDNjk2U1WoM\nyju2imRB2xEHtB0AsAUIO+bgiLYDgPZRWaKx1xKFOKDtMEDbAYBaAsKOUbig7fhl2gFMrzyS\nN94Yse6gkIISQNsBgPoBwo5puKDtAKB1KBRk9Ms7FrWdOgHaDgDUDBB2LFAk/p1deadpph1s\nPkEIOnQYzfKOLW2nTqYdA4C2AwAmAWHHGmDdEYHv9RMkbSHm1mFpddfolHdQJEseTehSBNqO\nO4jFYn9//9mzZyOE6urqwsPDLS0tDQwMfHx87t27h3/tn3/+qaurO2PGDNkRnBmUnZz4JcbG\nxoL3iYmJwYaKi4unTZtmZWVlbGzs6el5/vx52VWtDhUXF8smqa6ubjdCIoCw01CgXzHwD8xU\nPHBS20GyHYbaL8gi0HacITIy8uXLl7t27UIIzZo169y5c1FRUQkJCba2tgEBAWVlZW1dKJVK\n586dq3AQZwalJid+iVQqrauri4yMTJJjxIgRCCGRSPThhx/m5+fv27cvLi5OKBROnDjx1q1b\nOEP29vZVVVUyXUgJ6n+jxmW407uYcqBZMW9gso6VtobGZBoX87drcQ+jZgotbbVvXIygdzEH\nKC0t3bp167Fjx/T19QsKCk6ePBkXFxcYGIgQ8vT07N69e1RU1IYNG1q9du/evc+fP//oo49k\nR3BmUHZy/NnkT6utrZVKpYMGDRo+fLjCDDdv3nz+/PmNGzccHBwQQsOGDUtMTDx79qyvry/O\nkKmpqaEhlflC4NixDIvfMqx/yQL4MLHIyEp3Eja2msWHp8l2lAO+HUA327dvt7OzmzhxIkLo\n+vXrenp6o0aNwoZ0dXUDAgISEhJavfDFixcrV67cvXt3hw4dZAdxZlBq8nZnk+fNmzcIISMj\no5YzjB07tr6+HpNuCCFtbW1dXV0tLS38IcoBYcc+6noHSdXdP9/T7LgLu+qKankHhRSUANoO\noJX4+PjRo0cLBAKEUH5+vq2trZ6enmzUyckpLy+v1QsXLlwYEBAwZswY+YM4Myg1ebuzySMS\niRBC+B5bbW1tQUHBwoUL37x5M2fOHIJDVAHCTqMB005D4Y5nRmkkoO0oAbQdQB95eXm+vr7Y\n45qaGhMTE/lRExMTkUgkkUgUrrp06dK1a9d27NihcBxnBuKTE5lN/iAm7KKjo11cXAwNDd3c\n3A4fPqwwlbGxsYuLy7Vr165fvy4UCgkOUQUIO04App1aQiYxn8Z1WI5IOnn4r+3YBRqgqABo\nO7awtrZW6vz6+vqwsLDvv/9e2QtporGxsVOnTk+fPt2xY8elS5eGDRs2a9as/fv3y59z69at\nM2fOuLq6fvTRR7/99hvBIaqARS6uwFYhRe67V1z4kgUYgoOqDoO6ugoyhRQq42hZVfTSTLVr\nXUzqyLda5FchBYJaCg2mU6dO2AMzM7Oamhr5oerqahMTE4XMs2+//bZbt25ff/11y6lwZiA4\nOcHZ5A/6+vrK9yXx8/MrKirasWOH/Lqqj48PQmjixIl+fn7Lly9PTU0lMkQV4NhxCLX8iqHk\n5wHS7KiBs6pOBkUrs6w0QGG9kIJfDVAQ+Haaikw8CYXC0tLSxsZG2VBeXl6vXr0Uzj99+vTd\nu3f19PR0dHR0dHTi4uKOHz+uo6OTmZmJMwPByeVR4RIMNze34uJihFBGRoZC45KBAwfm5+fj\nD1EOCDtAIzLt+LX5BC3rsNxXdTLYTgHkr7ajFtB2AB28ePECezBy5EiJRBIfH489ra+vv3z5\nskJ5BELo6tWrmZmZGf/D399/7NixGRkZQqEQZwaCk8tD8JLz589/+umn7969kx1JS0tzcnJC\nCN28efOzzz57+vSpbCgjI8PR0RF/iHLACOEWatzZDmATHqk6GeQWZ0kuyKrc3I7dNVlqF2QR\nrMkCVCMUClNTUydPnowQsre3nzlz5oIFC6RSqZWV1aZNm7S1tefPn4+dOXv2bAMDg507d/bo\n0UN+BmNjYyMjI1dXV/wZ8CfHGh3v27dPfmaC8XTv3v38+fPBwcFLlizR0dE5fvx4UlLSsWPH\nEELTp0/ftGnTuHHjIiMjzc3Nz5w5k5SU9Msvv+APUQ44dpyDle8X1r9YARrho6qTQSJ4tnYb\nYxfeFcki8O00iXHjxl2+fFkqlWJPd+/ePXXq1LCwsICAgLq6uuvXr1tYWGBDWVlZOTk57U6I\nMwPOUHZ29qNHj5SaTRaPq6vrtWvXGhsbp0yZEhwcnJ2dffHixenTpyOELCwskpOThUJhWFjY\nuHHjbt++feTIEWwDNJwhyhHIPl8NRyQSfWAznO0o/oaVrxhav1sp+W0g+Yul8lKXautrKi/n\nUSlHeC3pZJCrqCDj26m8I4XKph2iKG2A8rRUun07jtxbkryvzi9OMDc3pyoYuvnM8ltqJzz2\ncm275zx9+tTZ2fn48eNYj2K2yMrKioyMjI2NZTEGeeLj4wMDA6uqqkxNTcnPBo4dFwHTDqAA\n9VB1iOwbgUIKStCE5nYIfDv6sbW1Xb58+TfffNPQ0MBiGNHR0UFBQSwGIEMqldbW1spXbJAH\nhB1HgYQPgBRqo+owQNtpAKDtNIQ1a9ZYWlouWLCAxRg2b94cGhrKYgAySkpKjI2Np0yZQuGc\nIOy4C/Pajj7TTsM7FROHmnVYNVN1GDzUdmTQwB0pEGg7zUBbWzspKenAgQNsB8IJPvjgA+n/\noGQdFoGwAwB1Qy1VHQbf3hoZ044SQNupDGg7gL+AsOM06mTakQfaFLcP36SP0rBUJMvfBVnQ\ndioD2g7gKSDsuI7aJNuxvhrLrx7FAB3wrgGKZibbIc5oOwDgI2CB8ACGuxZzeffYvFod3u13\nDlBMHydKtpRVFlZaFlMCH7sWcwT1blxMpDsJwEfAseMHDH+5cHlBFgDIAKYdX+Ds7SUAcBwQ\ndv9QKfqD7RDwUIMbRw25ywdoh6VUQlYy7SiBj5l2HAEy7QDeoSl/nASpFP1hbuzGdhRtwuSa\nLGcXZMmsxua/MVTB/yh6acb6DzNAISS3kWUe8nvI8hQubCOrxkS6rKN2wv/k/5vaCQHVAMdO\nEY77dkxCx1eq5ph2Ku9GBRACTDslAdNOZcC0A/gFCLtW4LK2U4MFWfJA3xNWkPTuLendm+0o\nqAEy7fgCR9YNQNsBPAKEXeuAtsNQP9NOM5e0KIRD8o6lvSjAtJOhOaYdAPAIEHZtAtoOANqC\nK9pOk+CmaQf9igGAa4CwwwO0HeJq6xOGV2PZ7UPGBVoqOU5Yd2DaKQm0gQQAtQeEXTtUiv7g\nrLzjr7bTnBIKtYd9badJgGnHLmDaAbwAhB0hQNtxEJVNOy6n2ZFqw8FSoSjL1h2YdkrCU9MO\ntJ3a8Pr1awcHh6ioKIRQfn5+3759tbS0OnToYGBgsG/fvlYvMTAwELxPTEwMNnTlyhV7e3uB\nQKCjo6Onp7d06VLZVThDbUEwHolEMm/ePG1t7Y4dO2ppaYWGhjY3N+MPRUVFCVrQq1evZ8+e\n9e/f38nJSSAQVFdXK/NBtgkFwi45Ofn//u//vvvuO4RQWVlZYSELu/0wAGe1HTOAaafJENFt\nbMo7lhQtK2isaQeoDbNnz+7du3d4eLhUKp06daqpqemLFy8aGho2btw4f/789PR0hfPFYnFD\nQ8PJkyelcnz66acIobKyskmTJo0cObK6urqhoSEqKurHH388evQo/lBbEIwHIbRly5ZTp07d\nvn27sbExJSXl1KlT+/fvxx/C3q8MsVg8YMCAKVOmdOvWLSMjY+fOnZR8thhkhd3KlSuDg4Nv\n376dkJCAEEpLS3N3d8/OzqYiNs7BTW3H3wVZtUcDW9nxcWVWA007ngKmnRqQlpZ27ty5devW\nYY8fPny4ffv2rl27amlpLVy4sF+/fnv37lW45M2bNwghQ8NWVlqKior8/Py2bdvWqVMnXV3d\nr776ytnZ+c6dO/hDOLERiUcsFm/ZsmXZsmVeXl4IIV9f3z/++CM0NBR/SIF9+/aVl5evWLGC\n8CenBKSEXVVV1d69e7OystavX48dmThx4uLFi3ft2kVFbFxEw7UdtZA37ZgsoYD6CYKwY92B\naacMdKzGgmkHECEqKsrLy2vQoEEIoTt37hgZGbm7u8tGfX19U1NTFS4RiUQIISMjo5az+fj4\nXLp0ycTEBHtaX1//+vXrbt264Q+1BcF4MjIyysvLg4ODa2trMzIynj9/7uLigulOnCGFd7Rm\nzZp169YZGBjgxKMypIQdthptY2Mjf9DX17eoqIhcVJxGk7Wd2ph2XE6zUw/4Zd1poGkHmXZk\nANNOZa5duzZq1CjscUlJiY2NjUAgkI3a2dkVFxcrXIIJO0NDw7Kysnv37r1+/brltPHx8QcO\nHBgxYoSTk1NYWBjBIQUIxpObm4sQunHjhrW19dChQ7t16/b5559jiXQ4Q/Ls3LnTzMwsJCQE\nJxgykBJ2Xbp0yc/Pb2hokD949epVfFGsBmiytuMasAsF3ags0Zi27liqomAFyLRjHdB2qvHq\n1auBAwdij2traxUsKwMDg7dv3yooIUzYzZ8/387ObujQoRYWFp9//rmC8AgKCpozZ051dfV3\n331nZmZGcEgBgvFUV1cLBIITJ07k5+fX1taePn366NGjWJkFzpCMt2/f7tixY8mSJdra2rgf\nleqQEnaOjo5eXl7Dhg07fvz48+fPf/jhh7Fjx+7atSs8PJyq+DiLxmo7ak07KKFQe/hl3amA\nyqYd64BpB7BC165//wvq6uqKxWL5oebmZoFAoKB4DAwMJkyY8Omnn4pEovr6+lOnTsXExHzz\nzTcKF75+/TosLGzs2LGHDx8mOKQAwXgQQlKpdM2aNVZWVgKBYNKkSePHj4+Ojm53COPs2bO1\ntbVY8QdNkC2eOHHiRGhoaG5ublNTU0xMjIWFxf3792V6XL0BbadpQJqdajBn3fHKtCO5Ggum\nHeuAaaca+vr62IMuXbpUVFTID1VUVHTp0kV+MRQh5Obmdu7cuSVLlhgaGuro6EyePHnGjBmx\nsbEK05qbmy9YsGDy5MlbtmwhPiQPwXjMzc0RQra2trIjvXv3LikpwR+SERsb++GHH3bq1Akn\nEpKQFXZ6enoLFixISUkpLCx8+PBhdHR0b3W/QZeHy+2L+QJbJRSQZkcECgWZGlt3YNoxDJh2\nvEaWJOfm5vb8+XP5nLmMjIz+/fu3O4OZmRmmwGJjYxXS5rp27Yp1g8MZaguC8fTt2xchJF9L\nUF1dbWFhgT+EIZFIbty4MWLEiHbfIxko6GP3119/3b17N1WOP//8k/y0PIJr2g5MO+6ggR1P\ncGDCugPTjm2YMe04ou3AtFMWbW1tmYMVEBBgaGh46NAh7Olff/2VkJAwZcoU7Om7d++ampoQ\nQvv377eysnr27JnseHx8vKenJ0KooaFhz549t2/fxoaampoSExNdXV3xh7BJ3r17pxAbwXj6\n9OnTs2fPPXv2YMcbGhri4uKGDBmCP4Tx+PHj169fu7m5kfoQ24PUX6BEIgkKCrpw4YJAINDS\n+kcj+vn5Xb9+nXRsfKJS9Ie5Mb3/VEpRJP6d7m+c3HevqPpuffKmmeSPQV6tDk+9Bw1E0ru3\nVk4O21FQjKmwiacivodRM1QgkcFR21MzC9dUw8PDIykpaebMmQihTp06rVu3bvny5cXFxVZW\nVocOHXJzc5N1fRs2bJiRkVFiYuKECRO+//57Ly+v0NBQHR2dM2fOPH/+/JdffkEITZ06dd++\nfaNHj545c6a5uXlcXFxhYSGW04YzhBDCPDOFViYE40EI7dy5c8yYMYGBgV5eXmfOnKmtrY2M\njMROwxlCCD19+hQh5ODgQN8njEg6dllZWY8fPy4oKGh+H01TdRga6NsB6g197hq91h2Ydmyj\nUaYdoBRTpky5dOmSrKZ18eLFp06dKi8vv3PnzhdffHHjxg09PT1syN3dvV+/fgihrl27/v77\n73PmzMnOzs7MzBw/fnxubq6HhwdCSEdHJzEx8YcffigvL09PTx8+fHhWVla7QwihPn366Oi0\n8n8pkXgQQiNHjrx9+3bnzp1TUlK8vb3v3btnZ2fX7hBCSEtLy8/PD0vFow+BVCpV+eKcnJxv\nv/321KlTFAbEFiKRyMSEgs+aU74don+lgKrvVvK/BKo5dir8Lir7S6xsApbqwoLqJr3MZMXR\n5d5lq763oco79qpm2pEsyqEkW5Ry046Zgnfu5IRgN9L5xQl0/2ZTSKTLOmon/E/+v9s9p76+\n3tHRccWKFUuWLKH21ZXi9u3bP//8s0K9KovEx8cHBgZWVVWZmpqSn42UY9e7d28DA4PTp083\nNjaSD0U9AN9ONfi1CwVAIRwsqtBA047yTAYw7YBWMTAw2LVr17p16xRqRRnmzp07c+bMYTEA\nGQ0NDfHx8ffu3aNwTrLFE3Z2dp988om+vn5HOQICAigJjqdolLbjzh0zwF9oWZllY5Mx/pbH\nAiSBKgrifPLJJ4sWLWq5ByuTLF++3MfHh8UAZNTU1GzZsiUlJcXPz6/V1WEVIDVLbm5uVFTU\nTz/91L17d/mAKPESeQ3XailohaoqCiih4BTMG2mcKqrQde2q8oKsCjhaVpFZkHUxqSO/IEt5\nFUV3Ex0GFmSFel05cnsJ2o443377LdshcAUrK6vk5GRq5yT1Z/zmzZuPP/547ty5VEWjTnBK\n2zFQJMtT8t8Y0p1+Xp2ry4SRw4ZBRTmYmqRM3vVxIpNppxr8LY8FAEA9ILUUO2DAgDdv3jx/\n/pyqaNQMTrUv5sWCLC92GKN7/wkmjSJuwpGsO8i0Iw9k2gEA85D6qyssLOzYsaOzs7OHh4f8\n8qurq+v69etJx6YmcMe6o9W3o7CtHRlgNZYSWJdWlFl3YNppBtxZkAUA1iEl7LS1tZ2dnXv0\n6KFw3N7ensy06oeGaDuewsBqLKAarGfdQaYdeZjJtANUgEh3EoCPkPoD7t69+6ZNm6gKRb3h\njrajD0pMOyihAOShQNvxyrQjqe00GTDtAACDgjuzs2fPXr169eXLl/r6+v369Zs1a1aXLl3I\nT6t+cETbgWkH4MP6OiynYNi0IwmYdgBxjg6guDR1xsO11E4IqAbZPnYRERHTp08vKSkxMzMT\ni8U///xzv379Xr3izfcgw3CkloK+QgpK7pi5/xsAngqTUCA0edXTjmQVBSXw1PPmQpovALAO\nKWFXVVV18ODBnJycy5cvHzp06MSJEwUFBaNHj966dStV8akfHNF29MGF1RBl/QZKNmVSH15V\noyoR0ZMb3qK/2P8Xbx8S2o7h8liScDNhlJnyWAAAEElhV1BQ0LdvX0dHR9kRgUAwderUrKws\n0oGpM1zQdhzfaoz7pp1S8KZGUiJFWy5oWwdrWwZpfXMAtbeRtCD2prbNJG3HqVqec1FFDY1x\nadjqMJh2KgOmHQCQEnZ2dna5ubk1Ne99oaenp/NoF2S24IK2ow8umHaAKtzOEURfQQghsUSw\n4ZjWkigcbSc4maQ17T+opg4hJLifp7UlhrEwVYRx046tHcbAtAMATYbUX5qVldXw4cMHDRo0\nbdo0GxubhoaG33///dy5c4mJiVTFp8awXkuhCW3t6KPopRkXbBWKqXgj/0ywK1YLIcm2cCQQ\nKJwoOJmkFfIdahb/c+hFJa2hsd76hGG4UB7L0yoKKI8FNByyxRNHjhyZO3ducnLyDz/8cPjw\nYS0trd9++23IkCGUBKf2sO7bcXlBluQPAKTZqcLwvqjrexs9C3bFtvTtWlF12lrSmaOZiZEU\nYNoBAKDukL0b09fXX7p06dKlSwUCAULo3bt3enp6qk114cKFCxcuvH792tLScsqUKf7+/iRj\n4wWs+3b0ofamnfohGeopuLJFK2CpfMKcYFes1tsmyZ4lmG8nOJOiqOq0BJID/5IO7097eGDa\nMQ6YdgAdXL58ubS09KuvvkIIlZaWxsfHi0Qid3f3jz76qK1L3r59GxcX9+TJEwcHh6CgIH19\nffyh33///dKlSwqTWFhYhIeH48dGMJ7Hjx+npKTU1dX16tVr1KhRWlpaRGZoOVRdXb19+3Zs\ndMWKFR07dsQPjwhkHbtr167Z2dnJtosdP378jBkzGhsblZ0Hq6sdO3bs999/7+fnt3379vT0\ndJKx8QV2fTs1Nu0AFZD26y65thVZdJI/KPj5gtb8bUgqFZxJ0Zq+TlHVHfw/aegopgNVGTDt\nNAO4q+QsmZmZn3zyiVAoRAhdvHhRKBTu3r07ISFh3LhxM2bMkLaW11tdXT148ODw8PDk5ORF\nixb179//9evX+ENlZWXJ77N///4DBw7gx0Ywnk2bNvXr1y8uLu7+/fszZ8708vKqra1td4ZW\nhyQSSXV19cOHD9euXauCdmoVUsKurq5u2rRpERERnTt3xo5s27YtNzdXJj+Jc+rUqcDAwAkT\nJgiFwqlTp/r4+Jw4cYJMbPxCXbUduzfN1DoNmkOb2m70v1hXdVAeyzyUl8dCFYUmI5VKQ0JC\nZs6c6efn19jY+OWXX4aEhGRlZSUkJNy8eTMmJiY2NrblVcuXLxeLxfn5+VeuXHn8+LFYLJad\n1tZQcHCwvKo7f/68WCxetmwZTmwE4ykrK1u5cuW2bdsuXrx47NixjIyM7Ozsbdu24c/Q1pC5\nufn27dvnzJlDyceLQUrY5eTk2NraRkREdOjQATvSq1evf/3rX6mpqUrNU1ZWVlFR4eHhITvi\n4eGRl5dXX19PJjx+wXq+HU2Q1HZMmnbKptkptUzG/Y4n8pqpdW2XkM5vr04GmHZKwlNtB6Yd\nBzl16lRubu6KFSsQQjdu3Hj58uWqVauwIU9PT39//2PHjilcUl9ff/To0aVLl5qYmCCEzM3N\nCwoKMCWEM6TA2rVrHRwcPvvsM5zYCMaTl5cnkUh8fX2xp9bW1s7Ozo8fP8afgeDklEBK2JmY\nmDx79kxBfmVmZnbq1KmtS1rl2bNnCCFra2vZESsrK6lUKlvhbRUppSgVME2wqO24vCALtA8N\nOyu0qu3+gT1VB6YdwGXU74eJWv773/+OHDnSxsYGIfTgwQMLCwsHBwfZ6KBBg+7fv69wSXp6\nemNj40cffZScnLx9+/aYmJiGhoZ2h+QpLi7evXv3pk2bBC0K/OUhGE+vXr10dXUfPHiAPa2t\nrS0pKenXrx/+DAQnpwRSt01CobB///5eXl5TpkyxsrKqra29e/fu+fPnExISlJqnrq4OIWRg\nYCA7giU/YsfborKyUi3/vwcAjoBpOy2/hUj0vncuQJID/PTqZPRxQtmFql2q2u6xpsImVlxb\nSnaPRVBFQQyJRCLL/aIEU1NTHR21WrlOSUn57rvvsMfPnz+3tLSUH7W0tCwrK1O4pLCwUCAQ\nREZGpqWlOTg43L59e+3atXfu3DEzM8MZkp9hw4YNPj4+fn5++LERjMfKymrXrl3Lli27d+9e\nly5dzp8/7+3tHRYWhj8DwckpgWzxxPnz5z///POEhIQNGzYcPnxYR0fnzp07MouSVrQpBV/I\nM4ZamnYsrsbytOmJ0rpBVY3SPvmlqL5FPq8UCX7LandfCvoA0w7gJgKBQC1/mCikvr6+V69e\n2OPGxkZZHheGnp6eWCxuanovdaG2thYzcbKysi5duvTw4cOysrL169fjD8moqKiIjo5etGhR\nu7ERjAchZGVlZWZmlpmZ+eDBg/LyckdHR21tbfwZiE9OHlK3AnV1da9fv8bancgOPnv2LDs7\nu0+fPsTnMTIywmaTmXaYV4cdbwtTU1OcUWURiQhvjkkzLDZAobVlMRmevGnmZsK1erYp/h+C\n08lan61HYkkrQ/vjtRCS/BTRsncxAHAKJk07gUCg4BUBLenSpQv2oGPHjm/fvpUfamxs1NbW\n1tV9z9vGPMsFCxZgMtfJyWny5MnXrl3DH5Jx7NgxExOTMWPGtBsYwXgePXo0adKkgwcPfv75\n5wihqqoqd3d3HR2dnTt34sxAcHJKIOXY3bt3b/78+QoHb9++/c033yg1j62tLfpfph3Gs2fP\ntLS0unXrRiY8/qJ+hRQslsdypzaW+/UT8vyt6uSrJRRO2B+vNe9HFn07gO9w824NoBuZDWlj\nY6OQSf/8+XM7OzuF87H8e/nGdd26dXv58iX+kIz4+PiPP/6YyIo2wXjOnTunp6cXGhqKPTUz\nM5swYUJcXBz+DAQnpwTVhd3cuXPnzp2bmpo6SI6BAwfOmYSyqVsAACAASURBVDNH1v2EIFZW\nVtbW1nfv3pUduXPnTp8+fSjp1MdT2NJ2nK2iYKw8VmNXY+V7/7ai6rQEkv/MUqyTZUnbaVSb\nYgBQM8rLy7EHnp6elZWVBQUFsqHffvtt8ODBCucPGjQIIZSZmSk78tdff9nb2+MPYTQ2Nqam\nprabXadUPAKBQCwWy6f4Nzc3Y2oVZwaCk1OC6sJu/fr1M2fOtLGxmSFHSEjIrl27duzYoexs\nn3766ZUrV86dO5eXl3fkyJH79+9PmzZN5djUAzXTduraCJ71vQEop3VVd/D/pKtDWumBAr4d\nYaDpCQAYGhpinUEQQn5+fvb29rKUuBs3bty5c+eLL77Ant69exfbp8DGxuajjz7asGHDmzdv\nEEL5+fmxsbETJkzAH8LIzs5ubGxsmRuWlpaWlpamcJBgPEOGDHn37t3Jkyex43V1dZcuXcJ2\nUsWZAX9yahGQKSytrKwsLi52d3enJJTLly+fPXu2oqLCxsZm+vTp3t7elExLEJFIZGJizuQr\nEoStfDuaku1IdpZSee1Gqd8kZX8IlUqzI/7rrnTLNNJNTyS9ewtOJWvNaKHqDq2QhgRgzwQZ\nBVoBS9HrN/IXShdMlGxfQPLViUONY0fC41ShKhZD5eV4kvcPVPnQlCc2MObEM3Njee3Jd+bm\nXPwdaZWjA76ldsIZD9e2e87YsWO1tbWxhUuE0I0bN8aPH+/g4GBlZXXr1q2vvvoqKioKG/Ly\n8jIyMkpMTEQIFRYW+vn5icXiPn36pKWlOTs737p1y9DQEH8IIRQfHx8YGFhYWOjo6Cgfho+P\nD0KoZc9dgvFERETs2rVr5MiR5ubmN2/e1NfXv3HjBtbDBWcGnCEszqqqKkqKB0gJu4qKilZ7\nEZuZmRF0PrkDCDsFNFnYITq1HY3CDpHVdhJtY+3+s9A7uc/qfVWH0aq2kxxewVgDFBB2ygLC\nDoRdS1gRdqdOnQoJCSksLJTl0JeVlZ0/f14kEnl7ew8bNkx25oEDB+RT2d68eRMbG/vs2TMX\nF5egoCD5mgOcoczMzLNnzy5fvlwm9TB+/PHHixcvXr9+vWWEBOP5448/fvvtN5FI5OLiMmbM\nGPkXbWsGnCEOCbvbt2/Le55NTU1v3rzp3LnzuHHj/vvf/5IPjkk4K+wQaLv3UU3b8VHYIeZN\nu9OpgrW//vO0NVWH0VLbSad9KDmqXNWUyoCwUxbOCjvElLYDYdcSVoSdVCodMGDAsGHDdu7c\nSe2rK8X58+dTUlJ+/PFHFmOQh1phR6oqdujQoRVy1NTUlJSUjBgxYvHixeQjA2SoWbIdK3Cn\noR29tbEkSyh62P7zuG1VhxCS9neWXNuKOpv8c6SfM6mXZh7GNxYjgxp31QE0CoFAEB0dffjw\n4ZSUFBbDePv27YIFzGWP4FBVVbVs2bJDhw5ROCfZBsUK2Nvbr169euvWrdROC6hTAxQe7R5L\nHDUpoXD7QBK1GNl3RUI7ycm1bak6DGl/Z8n1bVKfvqiLqfTrCdLFkxkLEwAA/tKvXz+RSMRu\nvtYnn3yikHXHFmZmZlu2bImNjZVKpVR156XeV6+pqamurqZ8WoCVxsWcbVmsAnm1Orwr62vK\nesWwOSTwcxHPO0HwZGlfJ2kKm+spAMNQvrEYYzC8txgAsAipP9FHjx7JNn3DqK2tTU5OXru2\n/YV2QAXURtvlvntFJtOOmY0o8t8YUtUnQoHqXF0aO19kF5IvjwUAAAB4CqlfR21tbYVdv7p1\n6xYaGhocHEwuKqBNWNxwDMBHvbcXA3iNi0kdRzpvt6S7iQ438ysAgKeQEnY9evTYu3cvVaEA\nnEVtTDtlV2PpM+2Iw/xqrFZOjqR3byZfEQAAAKAKCtazzp49e/Xq1ZcvX+rr6/fr12/WrFmy\nLX4BOlCbBVlNBlZjNRNTYRO/tgxWJyDNTgEi3UkAPkK2KjYiImL69OklJSVmZmZisfjnn3/u\n16/fq1fwx0Mv6lEky0p5LK2p32pSGwsAAADwFlI/clVVVQcPHszJyZGVDUul0tmzZ2/duvWH\nH36gIjygTZj37TTTtOPraiyYdmqHo2UVF+4c+FsYCyiQ5reM2gkHp2yhdkJANUg5dgUFBX37\n9pVvBiMQCKZOnZqVlUU6MKB9mPftKG9ZrJY97QjC5SU5arZ2AAAAABiHlLCzs7PLzc2tqamR\nP5iens6jPVX4jnqsyTIMrbtQcMFTAQB+wUD3IgyS+xkCAC8g9edkZWU1fPjwQYMGTZs2zcbG\npqGh4ffffz937lxiYiJV8QHtwvCaLOULsrzoacdL1HQ1FtxEAAAAHMgWTxw5cmTu3LnJyck/\n/PDD4cOHtbS0fvvttyFDhlASHMBNuLaHrAoLshwpoSC+GqvyrvMqA/pJzWA9VRQAAGYg+/Om\nr6+/bNmyZcsozsEElILvXYtJmnYMwIUSCgAAAABoFxUdu/z8/EOHDm3cuPH48eOvX7+mNiZA\nBRhOtlMD004jyC5kOwK1heGu0QBVcPweEgDI045jV1RU9MUXXxw4cMDZ2Vl28F//+tePP/4o\nFouxp0ZGRnv37v3ss89oDBMgAK+T7Zg37WjdhYL49mLEOxUzvwUFALQKHR1PYGMxjWLt2rWN\njY0bNmxACJ0+ffrUqVMikcjd3X3ZsmWmpqYtz29ubj58+PCNGzfq6up69eq1YMECW1tb2SjO\nDEQmV4DIJRKJ5PDhw5cuXWpoaBg6dGhERIS+vj421NTUdOjQoaSkJFmodnZ2OEMvXrz49NNP\nsRPi4+MVtmlVjXYcu71796ampurq/pMJtGrVqq1bt06ZMmX//v2HDx9evHixRCL5/PPP09LS\nyEcDkESTfTv4VaAcSLOjAzI7jsBmxIAacOLEiR07dsybNw8htH79+unTp1tYWAwdOvT06dM+\nPj719fUtL5kyZcq3337r7u4eGBj44MEDV1fXgoICbAhnBoKTy0PwkvDw8Hnz5llaWvbt23fr\n1q2jR4+WSCQIIbFYHBgYuHLlSmtra3d394sXL7q5uRUVFeEMmZiYLF682MfHJyUlpbmZml8x\ngVQqxRl2dXV1cHC4ePEi9vT3338fMmTIL7/8Mm3aNNk5WVlZHh4eY8aMOXPmDCUxsYJIJDIx\nUZMuLUz6dtRWyJI07VQoj1XKtFMqzY74bzDxX3oVHTsStbEc3DSWSrlJbqla5YoWMi0MyfTT\nUapxDz50lB8xdm9G38Zi1558x6NuX6w0KK6rq3N0dFyxYkVERERFRYWdnd0PP/ywcOFChFB5\neXn37t3Xr1+PPZWRk5PTp0+f8+fPjx8/HiH09u1bW1vb0NDQrVu34sxAcHJ5iMfTt2/fvXv3\nzpkzByF0584dTAVOmjQpMTFx5MiRt27d8vHxQQhVV1fb29svWrRo3bp1OEMIofj4+MDAwKqq\nKiKeYru049gVFRUNGjQIeyyVSsPCwr744gt5VYcQcnV1nTRpUmpqKvloAEpg0rfTKNOOpoZ2\nXK6NBQAAoJCffvpJIpF8/fXXCKGEhIS3b9+GhoZiQ126dPn444/PnTuncEl5eTlCyMHBAXva\noUMHa2tr7CDODAQnl4fgJXfv3pVIJLL0syFDhvTv3//8+fMIoYEDB6alpWHSDSFkampqZWVV\nWVmJP0Q57Qg7gUCgra2NPf7555/T09O1tFq5xNHRsaoK1gg0FAq1HWzRTQskfClYjVUnoLIb\nA+onWOT06dOjR482MDBACGVnZ9vY2Mh7VL179265c9WgQYMsLCxOnDiBPc3Ly8vLywsICMCf\ngeDk8hC8pKKiokOHDthbwHBycnr06BFCyMzMzNPzn1WsM2fOPHnyBDMacYYopx07vWfPnjEx\nMVOnTs3JyVm6dOnQoUMvXbr09OlT+bxFhFBhYaGFhQUd8QGqwfcGKCqjbL9iWksoAAAgDtRP\naALp6elz587FHpeXlyusXJubm1dUVEilUoFAIDtoaGh48eLFL774Ii4urkuXLn/++eeaNWtm\nzJiBPwPByeUheIm9vf3bt2/lhVB5ebmC9zZo0KAXL14YGRmdPXt21KhRBIeooh3HLjw8/NGj\nRz179pw4caKhoeGRI0eCgoKmT59eUVEhO+f+/funT58eNmwYHfEBKsPTBVl1Mu3o2F4MVmMB\nLqDU7RAAyBCLxR988IHssY7Oe/fhOjo6UqlU1nNDxsmTJysrK4ODg8ePH9+/f/9ffvmluLgY\nfwbik8vHRuSSkSNHGhsbr1mzBitRiI+Pv3v3rkK5QlBQ0MSJE+vr6zdt2vTs2TOCQ1TRjrcx\nc+bM5ubmixcv2traLlu2zMHB4d///veAAQPc3NymT59uZWWVm5v7yy+/NDc3R0RE0BEfQAYm\nfTvKtxpTGbpNOzog3vRERdR0ezEAAHhH586dsQdGRkZ1de8tgNTW1nbs2FFBXSUmJm7duvXe\nvXtYxv/ixYuHDBkSERERGxuLMwPByeUheEnnzp23bNkyb96869evm5ub19fXBwYGYkJTxjff\nfIMQ2rBhg7u7+8KFC0+fPk1kiCra//2bPXv27NmzZU+7du2akJAwefLkrVu3YkcMDQ0PHDgg\nv3gMcAc+rslyfCMKTVuN1crJ4WBtLDX0cYIezpqJUK+rOi0O8IuGhgbsgaOjY2lpqUQikeXu\nFxcXOzkp3oKmpqYaGxvL6jgFAoGvr+8vv/yCPwPByeUhfslXX33l5+d369atrl27fvzxxxMn\nThQKhQih+vr6iooKe3t77DRDQ8PRo0dj2YE4Q5Sjys4TWDrhjRs3Dhw4EBsbW1ZWhq12A9yE\nsTVZ7lTIcidTh47VWECTgVZ2AN/BCloRQr6+vvX19fJNcG/cuDF8+HCF883Nzevq6mRyECFU\nUVGBJcPhzEBwcnkIXiIWi/Pz8z/44IPZs2ePHz++vr7++vXrWDHH5s2be/bsKR/qixcvunTp\ngj9EOSpuKaalpeXv7//ll18GBwd36tSJ2pgAyuGdtmP4ZlrZplwU9gOTQXvTE7CmAM6jQitK\ngF9YWlrev38fe+zu7j506NAlS5a8fPlSLBavX7++sLAwLCwMG92zZ8/BgwcRQqNGjdLR0Vm9\nejWW65aZmXn69OkJEybgz4A/+d69e/fu3asQG8F4qqur3dzcVq1a1dzcXFtb+/XXX1taWmK7\nR3zyyScSieSrr76qrKxsbm4+c+bMmTNngoKC8IcoR0VhBwCA5gBNT6iF3nxKAOAwAQEBCQkJ\nsqfHjh17+/attbW1gYHBpk2boqOje/8v8ePIkSO//vorQkgoFB49evT48eNmZma2trYDBw6c\nNGlSZGRkuzPgDB09evTYsWMtwyMST+fOnffv3//zzz8bGRmZm5s/ePDgwoULHTt2RAj16tUr\nJiYmKSmpc+fOHTt2nDJlSmho6OrVq/GHKKednSc0B3XaeaItGEu2o6qKguGNKFjfhYL2LSiQ\n6rtQcCTNjnqJqWGbTyDYf0IOylcGYOeJds+5d+/e4MGD09PT3d3dZQdzcnJEIpGrq6uh4T//\ncz548EBbW7tfv37Y06ampsLCQpFI1L17dzMzxT+BVmfAGbpw4cKePXsuX77capBE4qmtrX38\n+LGenp6rq6tCf19ZqM7Ozgo7SbQ1RO3OE+B7axCMFVJwp0KWPugooSBeG9uU9Up1bQcAAMAS\nHh4ewcHBq1evlhdVvVu7b5RXfgghXV1drEChVVqdAWeotLTUz89PqUsU4jEyMpLVcyiAEyr+\nu6AKWIrVLJhsbkcekvfT3CmhAAAAADB+/vnnnJycqKgoFmMYOnSoLHmOXZ4/fz5o0KAlS5ZQ\nOCc4dgAt8NG0o28XiqKXZlwpZlS1oZ06Nz0BuATsP6H2dO7cuaSkhN0YZCuqrGNtbZ2enk7t\nnODYaRz8qpDVtF5TtNfGAgDwP7jcLxMAVAaEnSbCrwVZMih7609HMjgAUAtX3F8AADgJCDuA\nRtTetCNeYMihTsWq1oFC0xMAAADuA/6EhqI5FbLKbh3LOlyujYVMOzXAxaSOqo4nPYyaweTm\nL0S6kwB8BBw7zYVHC7JMmnZc2IWCy2jl5IB1Rx7oUYwDv+7EAIBrgLADaIc7e8iyCx2rsWxt\nLwbyDlAPoH4CUD/gxkijYWxBljy5716p/BWs7Gqssn1PKIf4aiy7yLQdrM8CAO949cmX1E7Y\n9eRBaicEVAMcO02HmQVZ9TbteLkaS9q0kwcz8Oj28MAjBAAAaBcQdgBvIJNpx5G+J8RXY3na\n0E6m8PghwihVt0pBZq9YAAAAHEDYAWDaUQC7ph1bmXb4UCvyqFeK7Kk6AAAA+gBhByDEnwpZ\nJk07muBQQzsGIanwuOn/ccorBQAAwABhBzAHv/oVs973RKnVOm6adi3hylotn+06kvcGvEwJ\nBQCAMFAVC/wNjypk+U7RSzPYFQq978Ph1NVy064DAADgJuDYAf/AwIIsu6YdR0ooiKOWpl2r\nMOrkUfGWyazDQuUEoMmIxWJ/f//Zs2cjhOrq6sLDwy0tLQ0MDHx8fO7du9fqJRUVFSEhIZ07\ndzYwMBg5cmRubq5sqLi4eNq0aVZWVsbGxp6enufPnycyhENlZWVgYKBAIMjIyGjrHPywY2Ji\n+vTpo6+vLxQKDx78uwVMVFSUoAU9e/YsLi6WPa2uriYSYbuAsAMAyuDl1rGIK9pOhrzIg5oJ\nAFAzIiMjX758uWvXLoTQrFmzzp07FxUVlZCQYGtrGxAQUFZW1vKSSZMmJScn//TTT2fOnKms\nrPzoo49qa2sRQiKR6MMPP8zPz9+3b19cXJxQKJw4ceKtW7fwh3BIS0sbMGBASUkJ/mk4YV+4\ncCEkJCQkJCQpKWnGjBlz5sy5cuUKQig4ODjpfQYMGODu7m5vb19VVRUTE6P8B9kmsBQLvAcD\nC7KU7B6rcr9i9W5WzPzusYAmw7qlDfCO0tLSrVu3Hjt2TF9fv6Cg4OTJk3FxcYGBgQghT0/P\n7t27R0VFbdiwQf6SlJSUmzdvJiYmfvjhhwghNzc3R0fH6OjosLCwmzdvPn/+/MaNGw4ODgih\nYcOGJSYmnj171tfXF2cIJ7zvv/9+7ty5/v7+Q4YMaesc/LBXrFjx9ddfr1ixAiHk5eXl4OBg\nZWWFELKxsbGxsZFNkpiYmJubGxcXp6WlZWpqamhIZeYrOHaAInypkOUmrGem83pBlnY05G3y\nHI4UsAN0sH37djs7u4kTJyKErl+/rqenN2rUKGxIV1c3ICAgISFB4ZKsrCxtbW0/Pz/sqY2N\njZeXV2JiIkJo7Nix9fX1mHRDCGlra+vq6mppaeEP4bB79+5Vq1YJBAKcc3DCzs/Pz8nJCQkJ\nkZ0cGhrav39/hRkkEklERERERIStrS1+PKoBwg5gAci0Q/Q0KwaYARqdAIBqxMfHjx49GlNO\n+fn5tra2enp6slEnJ6e8vDyFS969e6elpSWvySwtLZ88eSJ/Tm1tbUFBwcKFC9+8eTNnzhyC\nQy0horRwwv7zzz8RQg0NDcOHDzc1Ne3Zs+fhw4dbzvDrr7+WlZUtX7683ddSDRB2QCuAaUcG\nMO04CjfeIEmZzq3sTABQkry8PNliaE1NjYmJifyoiYmJSCSSSCTyB11cXJqamv74459fpUeP\nHolEIvlzjI2NXVxcrl27dv36daFQSHBINXDCLi8vFwgES5YsmTdv3rVr10aOHDlr1qyrV68q\nzLBx48awsDCFSSgEhB3ADmDaIfiRZhJuqDoAAKytrZU6f9SoUQ4ODvPnzy8qKhKJRMuXL3/6\n9KmOznvfybdu3Tpz5oyrq+tHH33022+/ERyinKamJqlUunLlyqlTp3p6eu7atcvLy2vz5s3y\n5yQnJ2dnZ3/55Zf0hQHCDmgdvph2jPUrZhdlbR4w7eiD1+uwrNvJANCpUyfsgZmZWU1NjfxQ\ndXW1iYmJQiacrq4utnbp5ORkZmZWUVExYcKErl3fqxLz8fGZOHHi6dOn3dzcFJY4cYZUAyds\nY2NjhNCAAQNkQ76+vo8ePZI/OSYmxtvbW5b8Rwcg7IA2oVvb8Wv3WKVMO772PVFXQLACAGeQ\nqSKhUFhaWtrY2CgbysvL69WrV8tLvL29i4qK8vPzy8vLDx8+/OTJk759+yKEMjIyFBqFDBw4\nMD8/H3+IJDhh9+jRAyFUXl4uGxKLxR06dJC/HFuiJR8GDiDsAN6jmmnHu8o7MO3UAKiDAYAX\nL15gD0aOHCmRSOLj47Gn9fX1ly9fHjNmjML51dXV+/fvLy8vd3Z2NjMzy8/Pv3PnTlBQEELo\n5s2bn3322dOnT2UnZ2RkODo64g+RBCdsDw8Pc3Pz2NhY2ckpKSmYBsUoKSkpKioaOHAg+TBw\ngC5EAB50t7WjpKcdYyjV0y7/jaGLSR2RMzm3w1h2IerjxHYQ1AFSFQA4g1AoTE1NnTx5MkLI\n3t5+5syZCxYskEqlVlZWmzZt0tbWnj9/Pnbm7NmzDQwMdu7caWBgsHbt2piYmDVr1jQ0NCxd\nunT48OEff/wxQmj69OmbNm0aN25cZGSkubn5mTNnkpKSfvnlF/whhNDcuXMRQvv27ZOPTSKR\n3Lx5EyGUk5ODEEpPT6+uru7YsaOXl5d8PDhh6+joREZGLlu2zNLS0tvb++jRow8ePMBaMWMU\nFRUhhJydnWn9kEHYAe3Aiz1kVetXrGyzYtZRqlkxgn7FNMBugh0s3AN8Z9y4cRcuXNi2bRvW\n8WT37t0rVqwICwsTiUTe3t7Xr1+3sLDAzszKyjIyMkII6enpXbp0adGiRaNHj9bT05s4ceLW\nrVuxcywsLJKTk1evXo3N0KNHjyNHjsyYMQN/CCGUnZ3dsqfdu3fv/P39ZU+x3igODg7FxcXy\n8eCHvWjRIrFYvHPnzlWrVrm4uJw4ccLb21s2J+ZWyrIMaUIglUppfQG+IBKJTEzM2Y6Co9At\n7Cgx7VTbiEIFYafURhQETTvijp1Swg5DRW2nHqYd1XYdSWHHeq8TaosnaKoWZzhNgsICrGtP\nvjM3583vyKtPKC7M7HryYLvnPH361NnZ+fjx41iPYrbIysqKjIyUXzNll/j4+MDAwKqqKlNT\nU/KzQY4d0D68qJDldaYdNCumBViEBQCOYWtru3z58m+++aahoYHFMKKjo7EsPdaRSqW1tbXy\npRjkAWEHEIJWbcev8ljeAVUUHEHNRDlsFAuoxpo1aywtLRcsWMBiDJs3bw4NDWUxABklJSXG\nxsZTpkyhcE74ywTUB2Yy7TSlhILv0CBMed3BDgA4gra2dlJSEttRcIUPPviA8ow4cOwAooBp\nxxFUMH7AtAMAANAQQNgBSsD9ZDtmNqKAZsVcByRpa8C2EwCgCYCwA7gCVaadCtqOIyUUxAHT\nDg96YmZ9HRZ0PwAARIAcO0A5eNHWjgGUyrTjCCq2tVOzfsUsoWaVE4AaQKQ7CcBHwLEDOASL\nmXa0mnZ0rMaCUGgdPlqMAAAA1AHCDlAayLTD4GO7Bw1akAUAANBI+PfLBHAB+hZkWdw9ltYd\nxujoe6LsDmPqD20ClPUEOwCgnHf/priRm966I9ROCKgGOHaAegKmXVuorWnH/QgBAvCukgkA\nuAYIO0BF6FuQhUw7yLRTM8j/M0FJLAAABAFhB6gOx5PtNMS0g9YnCNEbG6zDAgDAI0DYAVwE\nNqLgqEOTXchpeQcAAKDxgLADSKF+pp0Kq7HETTuaWv8zZ9phcE3ecSoYrkLt/3usG9UAALQF\nCDuALDRpOzDt6IbsCiMm71gXVawHAAAAwCVA2AGAIhwx7ZRajWWzioIL8o42yCfYQYELAMgj\nFov9/f1nz56NPa2srAwMDBQIBBkZGW1dUldXFx4ebmlpaWBg4OPjc+/ePfnRmJiYPn366Ovr\nC4XCgwf/3k4jKipK0IKePXu2Gx6ReCorK+fPn29nZ2dsbOzh4REXF0ck1FaHiouLZeFVV1e3\nGx4RQNgBFMBl046ZEgqeQmVZACvyTn0FJQCoK5GRkS9fvty1axdCKC0tbcCAASUlJfiXzJo1\n69y5c1FRUQkJCba2tgEBAWVlZdjQhQsXQkJCQkJCkpKSZsyYMWfOnCtXriCEgoODk95nwIAB\n7u7u+C9EJB6JRBIUFBQfH79x48a4uLgePXoEBwenpaW1G2qrQ/b29lVVVTExMQQ+OaJAngRA\nDWq2hyzvmhUjjvQrxpQW7C1LKRytpAEA5SktLd26deuxY8f09fURQt9///3cuXP9/f2HDBnS\n1iUFBQUnT56Mi4sLDAxECHl6enbv3j0qKmrDhg0IoRUrVnz99dcrVqxACHl5eTk4OFhZWSGE\nbGxsbGxsZJMkJibm5ubKW2utQiSee/fu3bp16+rVqwEBAQghX1/fpKSkkydPDh48GCdUnCFT\nU1NDQypTYMGxAzgNj0w7LqSTM11F0RbMuHdg1wEA39i+fbudnd3EiROxp7t37161apVAIMC5\n5Pr163p6eqNGjcKe6urqBgQEJCQkIITy8/NzcnJCQkJkJ4eGhvbv319hBolEEhERERERYWtr\nix8ekXjc3Nyys7P9/f2xpzo6OtbW1uXl5fih4gxRDgg7gDK4vCCrAhzpgM9jt4ZWeceIqoMO\ndgBALfHx8aNHj5Ypp3aVFkIoPz/f1tZWT09PdsTJySkvLw8h9OeffyKEGhoahg8fbmpq2rNn\nz8OHD7ec4ddffy0rK1u+fHm7r0UkHn19/d69e+vq/n0XXVpampWV5ePjgx8qzhDlgLADNAKu\nmXY09T1B3DHtZKh1aUW7QOUEAMh4/fp1Xl6er6+vUlfV1NSYmJjIHzExMRGJRBKJpLy8XCAQ\nLFmyZN68edeuXRs5cuSsWbOuXr2qMMPGjRvDwsIUJqGEt2/fTp8+3dnZ+fPPP8cPFWeI8qhA\n2AFUomamneZAuzVFrbzTYKUIAPzlxYsXCCFra2uqJmxqapJKpStXrpw6daqnp+euXbu8vLw2\nb94sf05ycnJ2dvaXX35J1YvKqK2tHTt2bHFx8YULRWJ3zgAAIABJREFUFzp06ED5/CoDwg6g\nGI63LFYK3m0di8Fdl4hX7p06rcPS5xADAHGwdh6dOnVS6iozM7OamhqFeUxMTLS0tIyNjRFC\nAwYMkA35+vo+evRI/uSYmBhvb28HBwfV426NioqKESNGlJWVpaamOjk5tRsqzhC1gSEQdgBf\nIG/acW01lmswp2NIyjv+SENK4HGSJQC8j6mpKUJIQd+0i1AoLC0tbWxslB3Jy8vr1asXQqhH\njx4IIaxwAUMsFiuYZ9gSLZmwW1JfXz9mzBipVJqamiovGXFCxRmiHBB2APWAaUcQTTTtZKgm\n7zRM1QGAOoE1IsEWZIkzcuRIiUQSHx+PPa2vr798+fKYMWMQQh4eHubm5rGxsbKTU1JS+vbt\nK3taUlJSVFQ0cOBACqKXIzw8vKam5urVq507dyYYKs4Q5fDVXQA4Dh1t7YrEvztqe5KZIffd\nK6FeV6riaYu8Wp0eRpyoqFWWpqxXuq60fz7vodZ973ggr1WCv7Y0wC6dO3cWCoWpqamTJ09G\nCEkkkps3byKEcnJyEELp6enV1dUdO3b08vJCCM2ePdvAwGDnzp329vYzZ85csGCBVCq1srLa\ntGmTtrb2/PnzEUI6OjqRkZHLli2ztLT09vY+evTogwcPsNbHGEVFRQghZ2dnhUjmzp2LENq3\nb5/8QYLxZGZmRkdHr1u37o8//rEwDAwMPD09cULFGaIc+PsEgHagtVkxcZRqVow40q+YIATl\nHYN2nTol2AEAdxg3btyFCxe2bdsmEAjevXsn6waHEJozZw5CyMHBobi4GCGUlZVlZGSEDe3e\nvXvFihVhYWEikcjb2/v69esWFhbY0KJFi8Ri8c6dO1etWuXi4nLixAlvb2/ZnJg72DKrLzs7\nu2VyG8F4kpKSJBLJ6tWr5a8VCoWPHz/GDxVniFoEUqmUjnl5h0gkMjExZzsKdYOOvShImnYI\nIRVMOxWEHXHTjuAuFAghpYQdhgrajmnTToG25B2zi7Dc2SKWkhw7aosn6HPsmG8eSW3q7bUn\n35mb8+Z35N2/Q6mdUG/dkXbPefr0qbOz8/Hjx2U9ilkhKysrMjJSfg2XXeLj4wMDA6uqqrA0\nRJJAjh0AtI9GNStm2axqNfeOb6qOKjio6gCADLa2tsuXL//mm28aGhpYDCM6OjooKIjFAGRI\npdLa2lr5ogrygLADaISOKgr162lH6+8uX9O85OUdFEwAgBqxZs0aS0vLBQsWsBjD5s2bQ0Mp\nNixVo6SkxNjYeMqUKRTOyX7mEKDe0FFFQRLVSiiUzbSjqYRC2Uw71WChiqJV2JB0lNh1fNXT\nBIDKCYAk2traSUlJbEfBFT744APKM+LAsQP4B5h2SqHGIoNyuLMIi6CDHQAAKgHCDqAdDra1\nUy1jWtlMO+54G5zbQJaTUPWWOaWkIcEOADQNEHYAL9Fk0w6MHDrQQCHLNThSogQAfIcrjgIA\nMIymNStWoa0dVzLt6IdCVcepLicAgAOR7iQAHwHHDmACtSmP5ekOYyqjCT6WJrxHquBOdgEA\nAG0Bwg4ANAVO5X5xBGpVHdc+YUiww4fa7sQAwBHg9gtgCA7uHsvBvif5bwwJbkTBTN8TpNYL\nspz16mAdFmAA6Yll1E4omLqF2gkB1QDHDgA0CK5ZSizCWVUHAABABhB2AHNwMNOO131PVPN1\noPUJoucdgWgmA5TEAgBV8HgptqamhsJ+zWKxmKqpAIAMxFdjESzIKo/6idS2oDzBDion2kUi\nkVRXV1M4obGxsba2NoUTApoAj/9QjYyMKJytro7oTynANdQv045uVGh9oh7Qp+ootOsgwY6/\naGlpGRsbUzshhbMBGgKPhR219zECgYDC2YC24ODWsRwETDs60ByvDmARMNgA1oG7AUAdUL+e\ndnSjmsPEX21Ea+SQXQcASvHXX3/99ttvsqdVVVXJycm1tbU4l0gkkidPnty/f7+mpqbl6KNH\nj37//XeRSKTUUFsQiae5ufnx48f3799vufiu1FBjY2Py/2hupuY3BYQdwDRqs3WssiiVoqRU\nghSTi3dNWa94J+94FDBV/5TQwQ7gLK9fv/b19b1//z729O7du/379/f39y8oKGjrkocPH7q5\nuTk7Ow8ePNjCwmL58uWyofz8/L59+/bp08fX19fS0nLfvn1EhnAgEs+VK1ecnJx69eo1ePDg\nrl27Ll26VOWhysrKxYsXz5o1y9/fH19KEgeEHaAmgGmnLGR8Jh7JO7rj1BC7jtbKCVb+jqA7\nMVvMnj27d+/e4eHhCKEDBw6MGDFi+PDhOOc3NDRMmDChS5cuT58+bWho2Llz55YtW06ePIkQ\nkkqlU6dONTU1ffHiRUNDw8aNG+fPn5+eno4/hAOReMrKyiZNmjRy5Mjq6uqGhoaoqKgff/zx\n6NGjqg1169YtIyNj586dSnyC7QHCDmABMO2IwIBpR1KUcF/bcT9CANAo0tLSzp07t27dOuzp\n3bt3r1y5Mm/ePJxLMjMzGxsbd+zYYWNjo6urO2/evB49eiQlJWGzPXz4cPv27V27dtXS0lq4\ncGG/fv327t2LP4QDkXiKior8/Py2bdvWqVMnXV3dr776ytnZ+c6dOyoPUQ6PiycAQAGS5bF8\nh7EqCnkw5cTNogreqTqohwXUnqioKC8vr0GDBmFP9+7dq6Ojc/fuXZxLvLy8Xr16729ZR0cH\n61B2584dIyMjd3d32ZCvr+/Vq1fxh3AgEo+Pj8+lS5dkT+vr61+/ft2tWzeVhygHHDuAHdTG\ntKO1WTEDmVKUrCRycGWWmXg0ZB0WAChBKpVeu3Zt1KhRsiM6Okq7S6mpqTk5ORMmTEAIlZSU\n2NjYyDe1sLOzKy4uxh/CQal44uPjsaVbJyensLAw8kNUAY4doFaAace8aSeDO/1QuKYyGQYq\nJwBu8uzZs1evXg0cOFDlGYqLi6dNmxYcHDx27FiEUG1trYGBgfwJBgYGb9++bW5uxhlSQU22\nSlBQkFgsFgqFO3bsMDMzIz9EFeDYAQBZaE395otph8G6dcdkANTadVxeh4U9JwBKwFZUu3ZV\n8fbv0aNHvr6+PXv2xMoREEK6uroKu0Y1NzcLBAJtbW2cIdVevSXNzc2vX78OCwsbO3bs4cOH\nyQ9RBQg7gDVoWo0lUx7LwRIKZWGlikIBtuSdhht1PIXXpeWAUjQ0NCCE9PX1Vbg2PT3dx8dn\n6NCh8fHxMiuuS5cuFRUV8qdVVFR06dJFIBDgDKkafiuYm5svWLBg8uTJW7ZsoWSIEkDYAQAF\nwI9TSxiWWQy/HGTXAYCyWFhYIIRev36t7IVPnjwZM2bMxIkTjx8/3qFDB9lxNze358+fy0+Y\nkZHRv39//CGSxMbGKuTGde3aFWs4rNoQ5YCw+wfY6op5wLQjgrKrsVww7TAYs+747tVRuA4L\nCXYAZ+nWrZu2tnZJSQmRk9+9e9fU9PeW1jNnzhw0aNC+ffsUNs8NCAgwNDQ8dOgQ9vSvv/5K\nSEiYMmUK/hA2+bt375QKXhZPQ0PDnj17bt++jR1vampKTEx0dXVVeYhyIHPiPcyN3ThYrQnw\ngidvmrubcOUPit0qCgXobonCvKoDu47vQHdiVjAyMvLw8EhKSpo5cyZCqLm5ef369Qihp0+f\nIoT27t1rZWVlamq6ePFihNCwYcOMjIwSExMvXryYmpr6xRdf/Oc//5FNZWFhER4e3qlTp3Xr\n1i1fvry4uNjKyurQoUNubm6hoaEIIZwhhNCIESMQQqmpqfLhEYxn6tSp+/btGz169MyZM83N\nzePi4goLC6OjoxFCqg1RDld+h7gDaDuGqRT9wTWvNPfdK6Ee7dWdebU6PYyILuDmvzF0Mfn/\n9u49Pory3uP4s7lflhRigEA0hItBDAKNIB5uSTlyEUXRVqBYevQoGLmIclF8lVNIm5Si6FFb\ng3LRYoHWFqMiFgscQwpYQZCLEINAgHALEAkhgYTc5vwxdrsG2Gx2Z2eemfm8/9qd2Z15spDs\nd3+/Z569FNDxCCEuHAht2bU2QAcP0DWzZq/VyY8rJ6Chhx56aP78+VVVVZGRkQ0NDZs2bVK3\np6WlFRYWFhYWxsfHq1tSU1PV2XhVVVVpaWlFRUVFRUWu4yQmJqo3nn766aSkpFWrVhUVFT36\n6KMzZswICwtrcldKSsqBAwcajc3L8YSEhGzcuHHZsmX/+Mc/ioqK0tPT//KXv3Tp0sXnXZpz\nKIoSiOOaTkVFRVJCunqbYKezAAU7f9Y98S3YNbdi532wE0L4EOx8KNoFLti5aBjvjEp1mlfs\nZG7FBjrYWen7xNYfzo6NjQ3EkQNBeXemtgd0jGn6UoDLly937Nhx9uzZzzzzjLZnb5atW7cu\nXrw4QAUzH6xdu3bkyJFlZWUtW7b0/2jMsbsG2QpIlidhktZnseJm0WfulA5NRq0m3pHqrma6\nCXZcdWQ3UVFRv/vd73796197OdMuQD777LMJEyYYOACXqqqqtWvXfvHFFxoek2B3bWQ7C/Dn\nEgp9BLoWIs9VFFfzM5bRgQVMavTo0dOmTWvya1sDatasWQMGDDBwAC7l5eULFy7Mz89PS0vT\nauVkJk9cF5Pt4IOAXkKhz0w73fh2UYWxkU7mch1gFnPnzjV6CLKIj493TezTChU7T6jb6UbC\nDC3huic+kLlop2pWZ5ZCnc64cgIwHYIdrEz+bmxz+TCJyhQ1IW8Sm+GpTvJVTkw3wQ5AIBDs\nmkDRTjeWKdo1dz64nEUR/UOM59Kd4akuEEyRuQGYi4zvKLJhsp2pHanf7s+6J9Yg1XrFnl1z\n4p0MqU7ycp0ZGXVJLKsTq7xZnQRmRMXOK9Tt9CFhgJawaKdbx83AKOOe5GRIdQBgFlTsvEXd\nDqbmW9EuoN9F4Znl85y2fdhAxH05JwlAK8q+HG0P6Og+SdsDwjdU7GB9hlxCYYHFiuFCHxaA\nWRDsmoGGrA4kLItaY90TYYalT+yDyyYABAjBrnnIdibFuicq8oQPiLYATIRg12xku0CzTNHO\nGuueCJKN9MzYmudbYoEAIdj5gmwHGehZtLNttgvED26Kuqm0HzMAeEawg4wCUbTzsxsrZ9HO\njKUaAEDg8JnMR6x+AvMy3dInRrFtuc7CWJ1YBuvWrTt+/PjEiRPVu59//vknn3ySkZERHx9/\nvad8/fXX+fn5ly5d6tat2/Dhw4OCgvzc5YE341HV1ta+8MIL3bp1e/DBB10br1y5smbNmsOH\nD3fo0GHUqFGRkZFCiO3bt//tb39r9PS4uLif/exnr7zyinp39uzZERER3ozQMyp2vqMhG1AS\nFu3kpGfRzrYNWZlRtYW57NmzZ/To0V27dlXvvvTSS4MGDcrMzCwpKbneU371q1917979zTff\nXLNmzYMPPti3b99Lly75s8sDb8bjsnDhwjlz5uTm5rq2XLhwoW/fvlOmTNm0adO0adN69er1\n7bffCiFOnjy56fuWLFmydOnShoaGCxcu7Nq1KzMzs7q6uskzeoNgB3hLzm6sb3yuG9kn29m5\nXBfo/4RcOWFbiqKMHz/+kUceSUtLE0JMmzbtlVdeefHFFz08Ze/evXPnzl20aNGuXbvy8/O3\nb9++Z8+eRYsW+bzLA2/G41JUVJSdnd2lSxf3jbNmzaqvrz948OAnn3xSWFhYX1+vxr4HHnjA\nPdV9+OGH9fX1M2fOjI2NfeWVVyZMmODNGb1EK9YvNGQD6nzFXsqi3jh4MfrmmKY/iWrFhj1Z\nAJr461//euDAgXXr1ql3b7rppp07dxYVFXl4Smho6IIFCx599FH1bo8ePTp16nTo0CGfd3ng\nzXhcJk2aNG7cuLNn//2B//LlyytWrFi0aFFMTIwQIjY29npnzMzM7NChw8MPP+zNiZqLYOcv\nsp25HKnf3jH4Dp+ffqDmbNewNk0/7vsOX6zrHNOM37VvKkOSnQEvafg2006lVrMsHO/MUq6j\nDwtz+cMf/jBkyJCEhAT17syZM4UQnoNUt27dunXr5rpbVFR09OjR22+/3eddHngzHtWqVat2\n7dr1pz/9yZUdhRA7duyorq6+6667Nm3atHv37vj4+Pvvv1+dY+fu6NGjr7/++vr16x0OR5Mn\n8gHBDrACnYt2Kkp3ALxXU1OTn5+fnZ3t29NnzpxZUlKSl5c3adKk//7v//Z/l8/KysqmT5/+\nv//7v61afe/TWlFRkcPh+OUvf7lt27YOHTps3bo1MzPzs88+a/Sw+fPnDxgwQG1GBwJz7DRA\nuzBwJKyGWumqOv9rSJaccmeWcl2AWHgFOyv98prRyZMnL1++7F5Ia5bdu3fv3bs3JCQkKiqq\noaHB/10+e/bZZ3v06DFu3LhG2ysrKxVFEULs27fvb3/7265du06ePJmVleX+mNLS0uXLl0+b\nNk2TkVyTZX+BdUZD1kT87Mb6RodurCFFO2G5up2JoqpJ+7BcOWFb586dE0K0bt3at6dv3LhR\nCLFnz5709PTq6uqFCxf6ucs3W7duXbVq1d6913jHDwkJEUJMnTpV7bF26tTpJz/5yfr1690f\ns3LlypiYmBEjRvg5DA+o2EF2JOaA0qSSZKIwZAgTleuAQPNzYlnPnj3HjBmzevVqrXY11/Tp\n07t16/anP/0pKysrKyvrm2++2bdvX1ZW1pkzZ9q1ayeEcJ9U1759+zNnzrg/fe3atcOHD1cj\nYIAQ7DRDQ9ZELPktFELfLxlr5MKBULPHOwv8CP6zcB8WhlNrdWrdznuvvPJKSkqK2uJUNTQ0\nBAcH+7zLT7fffntSUtLufykvLy8rK9u9e3dVVVXv3r2FEHv27HE9uLi4ODEx0XW3urp6y5Yt\ngZtdpyLYaYlsFyAU7czCpMEooJEuQOU6k/ZhYWc33nhjdHR0YWGhNw/+/PPPd+zYIYTo0aNH\nQUFBTk6Our24uDg3N3fAgAE+7xJCbNu2bdu2bc0avGs8OTk5q9306dNn4MCBq1evTkpKSkhI\nuOuuu+bPn3/x4kUhxMGDB3Nzc++//37XQfbv319dXZ2SktKsUzeXwz3P2llFRUVSQromhyKF\nBILmodn/aXY+rHvSrGl2Kh/WPfFtpp3PS59czVxT7gIdRs0V7HSo2Bk4xy7QF0+sP5wdGxsb\n0FNoSNmXo+0BHd0nNfmYe+65Jzg4eM2aNUKImpqaoUOHCiEuXry4a9eu22+/3el0xsfH//nP\nfxZC3HnnnU6nU50h99xzz7344ovdu3ePjY3dsWPHTTfd9Omnn6p9T992qQlvy5Yt7mPzfjzu\nRo0a5XQ6V6xYod4tKipKS0urr69PSUnZtm1bly5dNm/eHB393W/r2rVrR44cWVRU1LFjR/eD\nqNvLyspatmzZ3Jf9alTdYQ6aL1ZsiksoTMosl1PoUF9kdl0jFk518MYjjzwyfvz4U6dOtW/f\nPigoKD09Xd1+3333qTdcyebxxx8PCwtTby9YsODnP//5li1bKioqpk+ffvfdd4eGhvqz68EH\nH/z4448bjc378bgbO3as+/ZOnTrt378/Nzf31KlTEydOHDVqlOukQoibbrpp7ty5bdo0uyjQ\nLFTsvqNhxU5QtAsM2Yp2PlTsRPOLdr6tVGx40U4lbbzTrWVMua4Rawc7KnZNn1RRfvjDHw4a\nNOi1117T9uzN8uGHH+bn57/88ssGjsGdthU75tgFBJPtAkG2uCztJRTykHPKndlTHWBeDodj\n+fLlb7/9dn5+voHDuHLlytSpUw0cgEtZWdnMmTPfeustDY9JsIN9+XltrMwMvDy2EamyHde9\nAobr2bOn+o2xBo5h9OjRjWa5GcXhcDidzh49esydOzciIkKTY5q4GCA5liy2A9++Ora59Pnq\n2MCRYcqd/nkucOU6roeF2Q0fPtzoIciiZcuW8+bN0/aYVOwCiIas5jTPyoYU7fSZZiRP0U4Y\nXSqjSucNa0+wA+yDYAf4RZ9L7Uw9085F/4BlVKCkXAfAKAS7wKJopzka3N6Tqmin0jNmUaiD\nC2udwD6sUAaQHJPtJMeCdjrTYcqdsZGOi2FhCt6sTgIzomIH85EtKMvcjZWwaCcC2SG19nWv\ngevDWqPXD0BQsdMHRTtczc5FO5XmpTsZIh3luuvhygnZ1Dds0vaAwUHp2h4QvqFipxMm22lL\n26Ds/7WxFO18o1UUs3ahDgC8R7ADYCQ/A5lUkS6gaZg+LABvEOz0Q9FOW7IV7XygW3NK5qKd\n8CPbyRPpIDMuiYWtEOx0RbazMJm7sfJrbuFNqkKditl1AGRAsAOMRNHOnTdZTcJIpwOzr0vM\nlROAbgh2eqNopyELdGN9YNWincpzaJM20pm3XGft/06ADRHsDEC2syrJp/KYomgnrpPe7Fmo\nAywpMzPz+eefV28rivLSSy+lp6cfOnSoySeWlpYOGTIkOzvbfePBgwcnT548fPjwJ554Yu/e\n7z7tv//+++lX+fnPf97kKbwczzVPqlq9evWYMWNGjBgxZ86cCxcueN5VUlLiGl5lZWWTw/MG\nwc4YZDutWGCBQB+6VJavsjSKcZJHukBnX7P3YQF377777quvvvrkk08KIcrKyu6///558+bl\n5+d7E2tmzJixcePGr7/+2rWloKCgd+/eRUVF6enpR44c6devX0FBgRAiISGhUao7d+5ccXGx\n5+N7OZ7rnVQIkZWVNW7cuLi4uP79+69evXrAgAGXL1/2sCsmJubpp58eMGBAfn5+XZ02MxYs\n/vYANIshXy+mp4MXo2+OudTcZx0506pj27JAjMczyfMcgOa6dOnS1KlT58yZk5iYKISYMGFC\nbW3tu+++e8899zT53Ly8vPfff//OO+903/j0008PHDhw7dq1Qohnn3124sSJhw4duvXWW++4\n44477vj3H/NDhw4tWLBg2bJlnk/h5Xiud9LS0tLs7OyFCxc+9dRTQoiJEyd27tx56dKlTz31\nlIddo0aNCgnRMoxRsTMMRTutSFW0860by9Ry8zLv7DqhV+nX2P/ekk+QsJtFixY1NDRkZGSo\ndydMmPDxxx/HxsY2+cQrV65kZGTMmzevbdu2ro1nz57duHHj5MmT1btBQUFLly697777rn76\n9OnTH3jggUah8GrejMfDSTds2HDlyhVXw7d169bDhw//4IMPPO/SHMEO+B7LX0Jhlpl2UNGH\nhZWsXr367rvvjoqKUu8OGzYsKMirHJKdnR0VFTVt2jT3jTt37lQUJTk5ed68eaNGjcrIyNi3\nb9/Vz926desnn3ySlZXV5Fm8GY+Hk+7fvz8hIaFly5auB996663qXg+7NEewMxJFO61YoGgH\nMyLvAt6rrKzcsWPH4MGDm/vEwsLChQsXLl68ODg42H37yZMnHQ7Hww8/fObMmX79+u3cufPO\nO+8sLCxs9PSsrKyf/exnnTp18mv0Xpz03Llzjap9sbGxpaWliqJ42KXJqNwR7AxGtoNKz0so\nKNoB0N+pU6fq6+uTkpKa9SxFUZ544onHH3+8T58+jXZVVVUpijJ69OhFixY9++yzW7ZsiYuL\nmzdvnvtjDhw48Mknn7iav/7zcNL6+vpGs+VCQkIURamvr/ewS6uBuRDsYBEaFu3M0o3V35Ez\nrYh3Ljq8FAHtw1r+2mrIprS0VAhxww03NOtZb731VlFRUaMlTlSRkZFCiBEjRqh3w8PD77vv\nvh07drg/5p133klOTna/kMJPHk7qdDovXfre1WmVlZUREREhISEedmk1MBd+sY0X26KHVJ1E\n+O9AzdmuYW10ONE3lSHJTl9mpvt2eazKqItkYUZcGAQXNRJVVVU161kvvPBCSEjIAw88oN7d\nu3dvcHDwXXfd9eabb3bo0EEIUV1d7Xqw0+l0LS+i+vvf/z58+HB/h+7Gw0k7dux4/PjxhoYG\n10S9o0ePqi1gD7s0R7ADZHH4Yl3nGHP8SpLtqFyaAhNepdKmTRshxLlz55r1rF/84hcXL150\n3T137lx4ePioUaN+8IMf9O3bNywsLD8/v1evXurePXv2dOnSxfXg8+fP79q1a+bMmVoM/zse\nTjpw4MDLly9v27btP/7jP9Rdn3766X/+53963qU5c7yLWB5FO02cr9ir1ZxFEy1oZ0jRTpDt\nAo8+LCymffv2bdu23blz57333tvkg3NycsLDwx977LFGXxexceNGp9M5ZcoU9e6jjz76m9/8\npn///qmpqX/5y1/WrVv3xz/+0fXgwsLChoaGW265pdHB33jjDSFEsybeucYTExNzvZOmpqb2\n79//mWee+fDDD+Pi4ubPn19UVJSbm+t5l+b43QYCwrdurImKdsLG2Y5yHeADh8MxdOjQDRs2\nzJ07VwhRXV2tNmdVP/zhD4UQHTp0OHr0qBDinXfecTqdjz32mOdjvvzyy6dOnerTp09YWFhd\nXd2kSZPGjRvn2ltSUiKuNatvxYoVDoejUbDzfjweTrpy5cpRo0a1a9cuNDQ0PDx8+fLlt956\na5O7tGWatxDLo2inCYp2zeJn0U7YONsB8MHUqVP79u375ZdfpqamhoWF5eXlNXpARESEeiMn\nJ6fR4iaqrKws9+1RUVFr1qw5evToqVOnOnfu7L58sRCiX79+eXl58fHxjQ7y3HPP5eTkNNro\n/Xg8nLRDhw67du0qKCioqKjo3r17dHS0N7u05QjEGipmVFFRkZSQbuwYCHaa0HAFGf+DnQ9F\nO98qdr4FOyGEn8HOxT7xTp9yXaDXJdatFWurr51Yfzjbmy9RkER9wyZtDxgclO7Nw3784x9f\nvnx53bp12p69WXJyci5evDh79mwDx+Bu7dq1I0eOLCsrc1/B2GcsdyIR1rTThNnzsW9vhDqv\naXc1upMmYpNUBzktXry4oKDg97//vYFj6N+/v+s7wYx1+vTp3r17P/PMMxoek1asXGjISsVE\n3VgZ2KEtS341ES6JldMNN9ywefPmkydPGjiGnj17Gnh2d61atVq4cKF62+l0anJMgh0QQHpe\nQmHgTDsXO2Q7HfD9sLC2xMTExMREo0chhYgCHd22AAAYKUlEQVSIiPT0dG2PSStWOjRk/UfV\n00AWrmlZ+EcDYBkEO8ATc329mCSLkxGAZCbJfxIAAcJvuIyYaWclpljQTsNurMp6PVnd0qpl\n+rBcOSE5Ly9ihelQsYM1aZiMKdqBGiQAsyDYARAiAIUiwpCEyP2A5RHsJMUlFFbi27ILFuhk\nWSPb6flTWKYPazjWOoFtEeyAptmkGxuIVGGNbAcAZkGwkxdFOz9Z4AIUCxTthMmznakHbyBr\n/NcFzIhgB+hB58aQVEU7Ydp4pPOwLfP9sAAMRLCTGkU7eZirGyshk2Y7ADAXgh2sjG6sVMyV\n7SxWrgNgEwQ72VG0swybd2NhLPqwgE0Q7ABv0Y31n1mKdmYZp5wMLzOz1gnsjGBnAhTtbM7n\nt0k5i3ZkpqtRJQWgFYIdLE7baXZ+Fu0oJKgkz3aSDw8APCDYmQNFO/hGzqKdkDg8STswfzDB\nDrAPgh1gAoZPWgoE2SLUkTOtDBkSfVgAGiLYmQZFO59J1Y2FO0mynVGRzpIs+SEEMBGCHaAr\nn6fZWewSChmoec7YSKfDi0wfFrAVgp2ZULSD9eifq2TIcwAQIAQ7oNlM142VvGinT8aSMM9Z\nviZqCK49h80R7EyGop1vpPpuMf27sfILXN6SMM8BQOAQ7ABfmK5o5zPdqkraZi/585w+L6zO\nE+ws/NkDMAuJJtVWV1dv3769tLQ0Li6ud+/eUVFRRo9IUrEtekhVf4IpfFMZkuyU/U33yJlW\nHduW+X8QTQYDAGYkS8Xu2LFjTzzxxJIlS3bu3Ll48eKMjIzjx48bPShYilRp2FzdWD2ngvkc\ny+Qv0bljdh2AAJEl2L366qutW7deunRpdnb24sWLIyIi3nnnHaMHBXhium6sJZe9MFeeA4BA\nk+IPfW1t7Y033pienh4eHi6EiIqK6tOnz7Zt24wel7zoxsLCvGnImjrJ6Vaus2SUB+CZFBW7\n0NDQ6dOnp6amurZUVFQ4nU4DhwQEmiHdWMnXPXG5Xm6zQH3Owk1YGa6cYK0TQMbPc0eOHPns\ns88effRRzw+7fPmyoihanbSuzvg/Sc1C0c4H5yv2artezJH67R2D79DwgHBpVLczdZiDTSiK\ncunSJQ0PGBkZGRQkRf0FJiJdsCspKcnOzu7evfuIESM8P7KqqkrDYAeYyOGLdZ1j9P7lPXgx\n+uYYLd+0mmS9MKdnuY4+rP4URamqqtLwgOHh4QQ7NJcxv/knTpxYt26dejs5OTktLU29fezY\nsblz5yYlJT3//PMOh8PzQaKjtfwTWV1dreHR9EHRzuwO1JztGtZG55P6s+6J/tkOMBGHw6Ht\nGxOpDj4wJthVV1cXFxert2+44Qb1RlFR0S9+8Ys+ffpMmzYtODi4yYNERERoOKTa2loNjwZp\n0Y2FUSw8u07IMcHOcA6HQ9s3JsAHxgS7Ll26/PrXv3bfUlpampmZ2a9fvylTpjRZq4MLRTvb\n8qcbS9EOAKxKljLv22+/3bZt28mTJ5PqYDr+LGhnxov4rF15ChCdXzQm2AG2JcUv/5kzZ7Zs\n2RIfHz9nzhz37c8//3yLFi2MGpVZULSzLUMuoVBRt4OEzPgxCdCcFMEuLCxs7NixV28PDQ3V\nfzCwPM2n2ZmR/18dS7bzHjVOALqRIti1atXqpz/9qdGjMDGKdobz5xIKQ66N1QTZTk7692G5\ncgKQhyxz7AD4wJBvoXBHLapJvEQA9ESwswh6i81CgVNDBBcAkAfBDjCeP5O+DS/awQNSLwCd\nEeysg6KdsfxZ9MTsiC/yIKwDNkewA6ABst3VbPKaSHLlBGudACqCnaVQtPOebNPsLNCNtUmO\nAQCZEewAzdi5G6si27kY8lLQhwVAsLMainb2JEnRTpDtAMBQBDvYl5W6sZAK6RaAUQh2gJbo\nxgpijZ1IcuUEABeCnQXRjbUnebqxwt7ZzqifnQl2AATBDpAK3Vizs3OiNRC/OIALwc6aKNp5\nSbZpdlZCxAEA/RHsAI0ZOM1Oqm6ssF+2M/DnpQ8LQEWwsyyKdiZFU8mk7JZiBVdOAFIi2MHu\n6Ma6ULQDALMj2AGWQhHFEMbmV/qwAFwIdlZGN9YorGbnjqIdAOiGYAdIx8BpdgGq/Vg721n7\np7seeWrDTEsF3BHsLI6inTcsNs1OnndcOzA81dGHBeCOYAdAD4YHIACwA4Kd9VG0M4Sf0+ys\n140VVsx21vuJAJgdwQ4Qgm4szIk+LIBGCHYA9GOlEpeVfpbm4pMDIC2CnS3QjTUjS3ZjLcPO\nqQ6AzAh2QKAYu5qdtDUVIpFWyN+CtU6AqxDs7IKiXZMsNs3OTwENDWbPdmYfPwALI9gB8qIa\nAQBoFoKdjVC0sxtpu7HCzEUvSUZuYB9W5v9XAAh2wL9p3o019ZfGBjo6SJKQmsWMYwZgK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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# MNAR Contour plots for total indirect effect\n", + "if (nrow(mnar_df) > 10) {\n", + " # Contour: indirect effect magnitude\n", + " p_contour_ind <- ggplot(mnar_df, aes(x=delta_med, y=delta_out, z=total_indirect_est)) +\n", + " geom_contour_filled(bins=12) +\n", + " scale_fill_viridis_d(option=\"viridis\") +\n", + " annotate(\"point\", x=0, y=0, shape=4, size=3, color=\"red\", stroke=2) +\n", + " labs(title=\"MNAR Sensitivity: Total Indirect Effect\",\n", + " subtitle=paste0(\"SNP -> APOC1 (DLPFC + AC) -> AD age | N = \", ref_row$N),\n", + " x=expression(delta[mediators]), y=expression(delta[outcome]),\n", + " fill=\"Indirect\\nEffect\") +\n", + " theme_minimal(base_size=12)\n", + " \n", + " ggsave(file.path(MNAR_DIR, \"mnar_contour_indirect.png\"), p_contour_ind, width=8, height=6, dpi=150)\n", + " ggsave(file.path(MNAR_DIR, \"mnar_contour_indirect.pdf\"), p_contour_ind, width=8, height=6)\n", + " print(p_contour_ind)\n", + " \n", + " # Contour: -log10(p-value)\n", + " mnar_df$neg_log10_p <- -log10(pmax(mnar_df$total_indirect_p, 1e-300))\n", + " \n", + " p_contour_p <- ggplot(mnar_df, aes(x=delta_med, y=delta_out, z=neg_log10_p)) +\n", + " geom_contour_filled(bins=12) +\n", + " scale_fill_viridis_d(option=\"magma\") +\n", + " annotate(\"point\", x=0, y=0, shape=4, size=3, color=\"red\", stroke=2) +\n", + " labs(title=\"MNAR Sensitivity: -log10(p) of Total Indirect\",\n", + " subtitle=paste0(\"Red line at -log10(0.05) = 1.3 | N = \", ref_row$N),\n", + " x=expression(delta[mediators]), y=expression(delta[outcome]),\n", + " fill=\"-log10(p)\") +\n", + " theme_minimal(base_size=12)\n", + " \n", + " ggsave(file.path(MNAR_DIR, \"mnar_contour_pvalue.png\"), p_contour_p, width=8, height=6, dpi=150)\n", + " ggsave(file.path(MNAR_DIR, \"mnar_contour_pvalue.pdf\"), p_contour_p, width=8, height=6)\n", + " print(p_contour_p)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:08:31.934308Z", + "iopub.status.busy": "2026-03-31T16:08:31.931637Z", + "iopub.status.idle": "2026-03-31T16:08:33.302846Z", + "shell.execute_reply": "2026-03-31T16:08:33.300610Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR slice plots saved.\n" + ] + }, + { + "data": { + "image/png": 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eXlCYLw8MMPE1FAQEBiYqJcLv+///u/S5cuKRQKlUo1Y8YMMWFrNJobb7yRiIKC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\nG7py5UpJScmLL76oVCoZhklPT4+JicnLy+vGtOzs7NDQ0JSUFDuWDwAuBsE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TN27c+MUXX3z66add/VUAgFvppO911HYUCoVer+cHGxsb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\nCASCHQAAAIBAINgBAAAACMT/AwGt6ug4tz4OAAAAAElFTkSuQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# 1D sensitivity slices\n", + "# Slice 1: varying delta_med, holding delta_out = 0\n", + "slice_med <- mnar_df[abs(mnar_df$delta_out) < 0.01, ]\n", + "# Slice 2: varying delta_out, holding delta_med = 0\n", + "slice_out <- mnar_df[abs(mnar_df$delta_med) < 0.01, ]\n", + "\n", + "p_slice1 <- ggplot(slice_med, aes(x=delta_med, y=total_indirect_est)) +\n", + " geom_line(color=\"steelblue\", linewidth=1) +\n", + " geom_point(color=\"steelblue\", size=2) +\n", + " geom_hline(yintercept=0, linetype=\"dashed\", color=\"red\") +\n", + " labs(title=\"Total Indirect vs delta_mediators\",\n", + " subtitle=\"(delta_outcome = 0)\",\n", + " x=expression(delta[mediators]), y=\"Total Indirect Effect\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + "p_slice2 <- ggplot(slice_out, aes(x=delta_out, y=total_indirect_est)) +\n", + " geom_line(color=\"darkorange\", linewidth=1) +\n", + " geom_point(color=\"darkorange\", size=2) +\n", + " geom_hline(yintercept=0, linetype=\"dashed\", color=\"red\") +\n", + " labs(title=\"Total Indirect vs delta_outcome\",\n", + " subtitle=\"(delta_mediators = 0)\",\n", + " x=expression(delta[outcome]), y=\"Total Indirect Effect\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + "p_slices <- grid.arrange(p_slice1, p_slice2, ncol=2)\n", + "ggsave(file.path(MNAR_DIR, \"mnar_1d_slices.png\"), p_slices, width=12, height=5, dpi=150)\n", + "ggsave(file.path(MNAR_DIR, \"mnar_1d_slices.pdf\"), p_slices, width=12, height=5)\n", + "cat(\"MNAR slice plots saved.\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MNAR Interpretation\n", + "\n", + "The MNAR sensitivity analysis tests how robust the mediation findings are if missingness is informative (not at random). The tipping point distance tells us how far the missing data would need to deviate from the observed distribution (in SD units) before the indirect effect becomes non-significant. Larger tipping distances indicate more robust findings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Method 4: Bayesian SEM (blavaan)\n", + "\n", + "Bayesian estimation via Stan, with full posterior distributions for all parameters. Missing data handled automatically via data augmentation." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:08:33.309576Z", + "iopub.status.busy": "2026-03-31T16:08:33.307705Z", + "iopub.status.idle": "2026-03-31T16:32:46.204791Z", + "shell.execute_reply": "2026-03-31T16:32:46.202660Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting Bayesian SEM via blavaan/Stan...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using n.chains=2, burnin=1000, sample=2000 for tractability\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 1).\n", + "Chain 1: \n", + "Chain 1: Gradient evaluation took 0.004647 seconds\n", + "Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 46.47 seconds.\n", + "Chain 1: Adjust your expectations accordingly!\n", + "Chain 1: \n", + "Chain 1: \n", + "Chain 1: Iteration: 1 / 3000 [ 0%] (Warmup)\n", + "Chain 1: Iteration: 300 / 3000 [ 10%] (Warmup)\n", + "Chain 1: Iteration: 600 / 3000 [ 20%] (Warmup)\n", + "Chain 1: Iteration: 900 / 3000 [ 30%] (Warmup)\n", + "Chain 1: Iteration: 1001 / 3000 [ 33%] (Sampling)\n", + "Chain 1: Iteration: 1300 / 3000 [ 43%] (Sampling)\n", + "Chain 1: Iteration: 1600 / 3000 [ 53%] (Sampling)\n", + "Chain 1: Iteration: 1900 / 3000 [ 63%] (Sampling)\n", + "Chain 1: Iteration: 2200 / 3000 [ 73%] (Sampling)\n", + "Chain 1: Iteration: 2500 / 3000 [ 83%] (Sampling)\n", + "Chain 1: Iteration: 2800 / 3000 [ 93%] (Sampling)\n", + "Chain 1: Iteration: 3000 / 3000 [100%] (Sampling)\n", + "Chain 1: \n", + "Chain 1: Elapsed Time: 275.978 seconds (Warm-up)\n", + "Chain 1: 419.143 seconds (Sampling)\n", + "Chain 1: 695.121 seconds (Total)\n", + "Chain 1: \n", + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 2).\n", + "Chain 2: \n", + "Chain 2: Gradient evaluation took 0.001511 seconds\n", + "Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 15.11 seconds.\n", + "Chain 2: Adjust your expectations accordingly!\n", + "Chain 2: \n", + "Chain 2: \n", + "Chain 2: Iteration: 1 / 3000 [ 0%] (Warmup)\n", + "Chain 2: Iteration: 300 / 3000 [ 10%] (Warmup)\n", + "Chain 2: Iteration: 600 / 3000 [ 20%] (Warmup)\n", + "Chain 2: Iteration: 900 / 3000 [ 30%] (Warmup)\n", + "Chain 2: Iteration: 1001 / 3000 [ 33%] (Sampling)\n", + "Chain 2: Iteration: 1300 / 3000 [ 43%] (Sampling)\n", + "Chain 2: Iteration: 1600 / 3000 [ 53%] (Sampling)\n", + "Chain 2: Iteration: 1900 / 3000 [ 63%] (Sampling)\n", + "Chain 2: Iteration: 2200 / 3000 [ 73%] (Sampling)\n", + "Chain 2: Iteration: 2500 / 3000 [ 83%] (Sampling)\n", + "Chain 2: Iteration: 2800 / 3000 [ 93%] (Sampling)\n", + "Chain 2: Iteration: 3000 / 3000 [100%] (Sampling)\n", + "Chain 2: \n", + "Chain 2: Elapsed Time: 296.057 seconds (Warm-up)\n", + "Chain 2: 424.606 seconds (Sampling)\n", + "Chain 2: 720.663 seconds (Total)\n", + "Chain 2: \n", + "Computing post-estimation metrics (including lvs if requested)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_model_vcov_se(): \n", + " The following boostrapped defined parameters have a high (>5) ratio of \n", + " standard deviation to median absolute deviation: prop_med. P-values and \n", + " confidence intervals may not match.”\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian SEM completed in 24.2 minutes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "blavaan fit obtained.\n", + "N used: 1153 \n" + ] + } + ], + "source": [ + "cat(\"Fitting Bayesian SEM via blavaan/Stan...\\n\")\n", + "cat(\"Using n.chains=2, burnin=1000, sample=2000 for tractability\\n\")\n", + "t_start <- Sys.time()\n", + "\n", + "fit_bayes <- tryCatch(\n", + " bsem(model_str, data=dat, fixed.x=FALSE, target=\"stan\",\n", + " n.chains=2, burnin=1000, sample=2000, seed=42),\n", + " error = function(e) { cat(\"blavaan error:\", conditionMessage(e), \"\\n\"); NULL }\n", + ")\n", + "\n", + "t_end <- Sys.time()\n", + "cat(\"Bayesian SEM completed in\", round(difftime(t_end, t_start, units=\"mins\"), 1), \"minutes\\n\")\n", + "\n", + "if (!is.null(fit_bayes)) {\n", + " cat(\"blavaan fit obtained.\\n\")\n", + " N_bayes <- lavInspect(fit_bayes, \"nobs\")\n", + " cat(\"N used:\", N_bayes, \"\\n\")\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:32:46.381239Z", + "iopub.status.busy": "2026-03-31T16:32:46.376766Z", + "iopub.status.idle": "2026-03-31T16:32:47.205441Z", + "shell.execute_reply": "2026-03-31T16:32:47.202692Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Labeled parameters from parTable:\n", + " label est se\n", + " a1 -0.101 0.051\n", + " a2 -0.181 0.058\n", + " b1 0.449 0.333\n", + " b2 1.131 0.330\n", + " cp -0.854 0.392\n", + " r_m1m2 0.314 0.042\n", + " ind1 -0.045 0.044\n", + " ind2 -0.205 0.089\n", + " total_indirect -0.250 0.097\n", + " total -1.104 0.388\n", + " diff_1_2 0.159 0.102\n", + " prop_med 0.226 0.686\n", + "\n", + "Posterior draws matrix: 4000 x 64 \n", + "Column names: a1, APOC1_DLPFC_exp~msex_u, APOC1_DLPFC_exp~age_death_u, APOC1_DLPFC_exp~pmi_u, APOC1_DLPFC_exp~ROS_study_u, a2, APOC1_AC_exp~msex_u, APOC1_AC_exp~age_death_u, APOC1_AC_exp~pmi_u, APOC1_AC_exp~ROS_study_u, b1, b2, cp, age_first_ad_dx_num~educ, age_first_ad_dx_num~apoe4_dose, age_first_ad_dx_num~msex_u, r_m1m2, APOC1_DLPFC_exp~~APOC1_DLPFC_exp, APOC1_AC_exp~~APOC1_AC_exp, age_first_ad_dx_num~~age_first_ad_dx_num ...\n" + ] + } + ], + "source": [ + "if (!is.null(fit_bayes)) {\n", + " # Extract results using parTable\n", + " pt <- parTable(fit_bayes)\n", + " labeled <- pt[pt$label != \"\", c(\"label\", \"est\", \"se\")]\n", + " cat(\"Labeled parameters from parTable:\\n\")\n", + " print(labeled, row.names=FALSE)\n", + " \n", + " # Get posterior draws\n", + " draws_list <- blavInspect(fit_bayes, \"draws\")\n", + " draws <- do.call(rbind, draws_list)\n", + " cat(\"\\nPosterior draws matrix:\", nrow(draws), \"x\", ncol(draws), \"\\n\")\n", + " cat(\"Column names:\", paste(head(colnames(draws), 20), collapse=\", \"), \"...\\n\")\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:32:47.215801Z", + "iopub.status.busy": "2026-03-31T16:32:47.213298Z", + "iopub.status.idle": "2026-03-31T16:32:47.304527Z", + "shell.execute_reply": "2026-03-31T16:32:47.302342Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Regression parameters with labels:\n", + " lhs op rhs label est se\n", + " APOC1_DLPFC_exp ~ chr19_44918487_G_T a1 -0.101 0.051\n", + " APOC1_AC_exp ~ chr19_44918487_G_T a2 -0.181 0.058\n", + " age_first_ad_dx_num ~ APOC1_DLPFC_exp b1 0.449 0.333\n", + " age_first_ad_dx_num ~ APOC1_AC_exp b2 1.131 0.330\n", + " age_first_ad_dx_num ~ chr19_44918487_G_T cp -0.854 0.392\n", + "\n", + "Defined parameters:\n", + " lhs op rhs label est se\n", + " ind1 := a1*b1 ind1 -0.045 0.044\n", + " ind2 := a2*b2 ind2 -0.205 0.089\n", + " total_indirect := ind1+ind2 total_indirect -0.250 0.097\n", + " total := cp+total_indirect total -1.104 0.388\n", + " diff_1_2 := ind1-ind2 diff_1_2 0.159 0.102\n", + " prop_med := total_indirect/total prop_med 0.226 0.686\n", + "Extracted M1~~M2 draws directly by label.\n", + "\n", + "Bayesian results (N = 1153 ):\n", + " label post_mean post_sd ci_lower ci_upper pp_positive\n", + " a1 -0.10110738 0.05057047 -0.20002086 -0.001546526 0.02300\n", + " a2 -0.18079054 0.05798105 -0.29462748 -0.064209890 0.00150\n", + " b1 0.44872863 0.33259779 -0.20848154 1.089715875 0.91100\n", + " b2 1.13140637 0.33006988 0.46288206 1.784125484 1.00000\n", + " cp -0.85414119 0.39192742 -1.63232013 -0.079134304 0.01525\n", + " ind1 -0.04500375 0.04388993 -0.14750136 0.023367715 0.11150\n", + " ind2 -0.20379149 0.08899261 -0.39823542 -0.052234669 0.00150\n", + " total_indirect -0.24879524 0.09679774 -0.45850588 -0.082965349 0.00125\n", + " total -1.10293643 0.38797084 -1.86588920 -0.344174542 0.00275\n", + " diff_1_2 0.15878773 0.10159833 -0.02843153 0.378349829 0.95525\n", + " r_m1m2 0.31421474 0.04199963 0.23337575 0.398405809 1.00000\n", + " N method\n", + " 1153 Bayesian\n", + " 1153 Bayesian\n", + " 1153 Bayesian\n", + " 1153 Bayesian\n", + " 1153 Bayesian\n", + " 1153 Bayesian\n", + " 1153 Bayesian\n", + " 1153 Bayesian\n", + " 1153 Bayesian\n", + " 1153 Bayesian\n", + " 1153 Bayesian\n" + ] + } + ], + "source": [ + "if (!is.null(fit_bayes)) {\n", + " # Map parameter labels to draw columns\n", + " # blavaan may use different column naming -- map via parTable\n", + " pt_full <- parTable(fit_bayes)\n", + " \n", + " # For defined parameters (:=), compute from draws if not directly available\n", + " # First, find column indices for labeled regression parameters\n", + " bayes_results <- data.frame(label=character(), post_mean=numeric(), post_sd=numeric(),\n", + " ci_lower=numeric(), ci_upper=numeric(), pp_direction=numeric(),\n", + " stringsAsFactors=FALSE)\n", + " \n", + " # Try to extract draws by label\n", + " # blavaan draws columns may be named like \"a1\", \"b1\", etc. or by pxnames\n", + " draw_cols <- colnames(draws)\n", + " \n", + " # Get the mapping from parTable\n", + " reg_params <- pt_full[pt_full$op == \"~\" & pt_full$label != \"\", ]\n", + " def_params <- pt_full[pt_full$op == \":=\", ]\n", + " \n", + " cat(\"\\nRegression parameters with labels:\\n\")\n", + " print(reg_params[, c(\"lhs\", \"op\", \"rhs\", \"label\", \"est\", \"se\")], row.names=FALSE)\n", + " \n", + " cat(\"\\nDefined parameters:\\n\")\n", + " print(def_params[, c(\"lhs\", \"op\", \"rhs\", \"label\", \"est\", \"se\")], row.names=FALSE)\n", + " \n", + " # Try direct column matching first, then fall back to pxnames\n", + " get_draws_for_label <- function(label, pt_df, draws_mat) {\n", + " # Check if label is directly a column name\n", + " if (label %in% colnames(draws_mat)) {\n", + " return(draws_mat[, label])\n", + " }\n", + " # Check pxnames\n", + " if (\"pxnames\" %in% names(pt_df)) {\n", + " row_idx <- which(pt_df$label == label & pt_df$op == \"~\")\n", + " if (length(row_idx) == 1) {\n", + " pxname <- pt_df$pxnames[row_idx]\n", + " if (pxname %in% colnames(draws_mat)) {\n", + " return(draws_mat[, pxname])\n", + " }\n", + " }\n", + " }\n", + " return(NULL)\n", + " }\n", + " \n", + " # Extract regression parameter draws\n", + " a1_draws <- get_draws_for_label(\"a1\", pt_full, draws)\n", + " a2_draws <- get_draws_for_label(\"a2\", pt_full, draws)\n", + " b1_draws <- get_draws_for_label(\"b1\", pt_full, draws)\n", + " b2_draws <- get_draws_for_label(\"b2\", pt_full, draws)\n", + " cp_draws <- get_draws_for_label(\"cp\", pt_full, draws)\n", + " \n", + " # Compute derived parameters from draws\n", + " if (!is.null(a1_draws) && !is.null(b1_draws)) {\n", + " ind1_draws <- a1_draws * b1_draws\n", + " } else {\n", + " # Fall back to parTable estimates\n", + " ind1_draws <- NULL\n", + " cat(\"WARNING: Could not compute ind1 draws from components.\\n\")\n", + " }\n", + " \n", + " if (!is.null(a2_draws) && !is.null(b2_draws)) {\n", + " ind2_draws <- a2_draws * b2_draws\n", + " } else {\n", + " ind2_draws <- NULL\n", + " }\n", + " \n", + " if (!is.null(ind1_draws) && !is.null(ind2_draws)) {\n", + " total_ind_draws <- ind1_draws + ind2_draws\n", + " diff_draws <- ind1_draws - ind2_draws\n", + " } else {\n", + " total_ind_draws <- NULL\n", + " diff_draws <- NULL\n", + " }\n", + " \n", + " if (!is.null(total_ind_draws) && !is.null(cp_draws)) {\n", + " total_draws <- cp_draws + total_ind_draws\n", + " } else {\n", + " total_draws <- NULL\n", + " }\n", + " \n", + " # Extract M1~~M2 residual covariance draws\n", + " r_m1m2_draws <- NULL\n", + " # Check for covariance parameter in draws\n", + " cov_rows <- pt_full[pt_full$op == \"~~\" & pt_full$label == \"r_m1m2\", ]\n", + " if (nrow(cov_rows) > 0) {\n", + " if (\"pxnames\" %in% names(pt_full)) {\n", + " pxname <- cov_rows$pxnames[1]\n", + " if (!is.na(pxname) && pxname %in% colnames(draws)) {\n", + " r_m1m2_draws <- draws[, pxname]\n", + " cat(\"Extracted M1~~M2 draws via pxnames column:\", pxname, \"\\n\")\n", + " }\n", + " }\n", + " if (is.null(r_m1m2_draws) && \"r_m1m2\" %in% colnames(draws)) {\n", + " r_m1m2_draws <- draws[, \"r_m1m2\"]\n", + " cat(\"Extracted M1~~M2 draws directly by label.\\n\")\n", + " }\n", + " if (is.null(r_m1m2_draws)) {\n", + " cat(\"WARNING: Could not extract M1~~M2 draws from posterior; using parTable estimate.\\n\")\n", + " }\n", + " } else {\n", + " cat(\"NOTE: r_m1m2 label not found in parTable; trying unlabeled M1~~M2.\\n\")\n", + " cov_rows2 <- pt_full[pt_full$op == \"~~\" & pt_full$lhs != pt_full$rhs, ]\n", + " if (nrow(cov_rows2) > 0 && \"pxnames\" %in% names(pt_full)) {\n", + " pxname2 <- cov_rows2$pxnames[1]\n", + " if (!is.na(pxname2) && pxname2 %in% colnames(draws)) {\n", + " r_m1m2_draws <- draws[, pxname2]\n", + " cat(\"Extracted M1~~M2 draws via unlabeled covariance row:\", pxname2, \"\\n\")\n", + " }\n", + " }\n", + " }\n", + "\n", + " # Build results table\n", + " all_draws_list <- list(\n", + " a1 = a1_draws, a2 = a2_draws, b1 = b1_draws, b2 = b2_draws, cp = cp_draws,\n", + " ind1 = ind1_draws, ind2 = ind2_draws, total_indirect = total_ind_draws,\n", + " total = total_draws, diff_1_2 = diff_draws,\n", + " r_m1m2 = r_m1m2_draws\n", + " )\n", + " \n", + " bayes_results <- do.call(rbind, lapply(names(all_draws_list), function(lab) {\n", + " d <- all_draws_list[[lab]]\n", + " if (is.null(d)) {\n", + " # Fall back to parTable point estimate\n", + " pt_row <- pt_full[pt_full$label == lab, ]\n", + " if (nrow(pt_row) > 0) {\n", + " return(data.frame(label=lab, post_mean=pt_row$est[1], post_sd=pt_row$se[1],\n", + " ci_lower=NA, ci_upper=NA, pp_positive=NA,\n", + " stringsAsFactors=FALSE))\n", + " }\n", + " return(NULL)\n", + " }\n", + " data.frame(label=lab, post_mean=mean(d), post_sd=sd(d),\n", + " ci_lower=quantile(d, 0.025), ci_upper=quantile(d, 0.975),\n", + " pp_positive=mean(d > 0),\n", + " stringsAsFactors=FALSE, row.names=NULL)\n", + " }))\n", + " \n", + " bayes_results$N <- N_bayes\n", + " bayes_results$method <- \"Bayesian\"\n", + " \n", + " cat(\"\\nBayesian results (N =\", N_bayes, \"):\\n\")\n", + " print(bayes_results, row.names=FALSE)\n", + " \n", + " write.csv(bayes_results, file.path(BAYES_DIR, \"bayesian_results.csv\"), row.names=FALSE)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:32:47.311236Z", + "iopub.status.busy": "2026-03-31T16:32:47.309055Z", + "iopub.status.idle": "2026-03-31T16:32:52.054417Z", + "shell.execute_reply": "2026-03-31T16:32:52.052048Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in RColorBrewer::brewer.pal(n, pal):\n", + "“n too large, allowed maximum for palette Set2 is 8\n", + "Returning the palette you asked for with that many colors\n", + "”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in RColorBrewer::brewer.pal(n, pal):\n", + "“n too large, allowed maximum for palette Set2 is 8\n", + "Returning the palette you asked for with that many colors\n", + "”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in RColorBrewer::brewer.pal(n, pal):\n", + "“n too large, allowed maximum for palette Set2 is 8\n", + "Returning the palette you asked for with that many colors\n", + "”\n" + ] + }, + { + "data": { + "image/png": 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XI+kS4ul2tlZaWoqBgeHu7u7k6j0T5+/DhhwoTTp09HRESEhISQKb99+7Z27VqE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ZngQrJyenrq6uwVFWVlb4wpAcUFBQwGKxzMzMBGeOio0AACAASURBVAtr5uTk4I+H\n8GbyRE1NTWFhoYaGhqB3WnV1dV5enqWlpYqKSnFxcWlpaYOjTExM+P5DkuW38gs2ZrPZX79+\nlaHH+LcqC9/CZrOLiorIZLKhoaGsDA1H+GtYVlZWVFTU4BAajcZf4pRy5FXNyofyhIYdBAKB\nQCAQiJwAfewgEAgEAoFA5ARo2EEgEAgEAoHICdCwg0AgEAgEApEToGEHgUAgEAgEIidAww4C\ngUAgEAhEToCGHQQCgUAgEIicAA07CAQCgUAgEDkBGnYQCAQCgUAgcgI07CAQCAQCgUDkBGjY\nQSAQCAQCgcgJ0LCDQCAQCAQCkROgYQeBQCAQCAQiJ0DDDgKBQCAQCEROgIYdBAKBQCAQiJwA\nDTsIBAKBQCAQOQEadhAIBAKBQCByAjTsIBAIBAKBQOQEaNhBIBAIBAKByAnQsINAIBAIBAKR\nE6BhB4FAIBAIBCInUCUtAEQqqKurS01N7dGjh5qaWnNtqqqqMjIyevbsqaqq+tsTJiQkIAiC\nf1ZXV7ewsGh8VEJCgr6+voWFhfDDSSSSoaGhiYkJhUJpsgEfRUXFvn37Cm4pLy8vKCig0Wgm\nJiZChtZGPn78WF1dPWDAABKJJLhdlIsAgUAI48uXLwiCdO/evbkG1dXV375909TUNDU1JZNF\nnfjAdaOJiYmpqWmTDRAEycnJqamp0dPTMzY2Fv3MLaI5MYqKivLy8vDPioqKenp6ZmZm7SEA\nRFrAIBAM+/z5s5GR0bt375prkJSU1L9/fyMjo48fP4pyQisrKyMBzMzMpk+fnpmZ2aDNpk2b\nRDncyMioX79+Dx8+FNLAyMioT58+giPy8fExNjbmC7Bw4cLS0lJRhG8RNTU1nTt3NjIyio+P\nFz6KJi8CBAIhjHnz5k2aNKnJXSiKBgYGmpmZmZqaGhkZDRkyRERdh2HYvn37jIyMfHx8Gu9C\nEOTo0aPdu3fn6wFHR8fw8PDWj6HlYpw5c6aBquzdu/fJkycRBGkPMSASBy7FQn7PtWvXJk6c\n6Ojo2KKj5s6dW1hY+OPHj0+fPp05c6a4uHjMmDEfPnxo0eGFhYX5+fkvX77s1q3bvHnzvnz5\nwm8wZ86c7/9LQkICviszM9Pb27uiouLy5cuZmZkpKSkHDx58/fq1l5dXbW1ti0bxW6KiohQV\nFR0dHW/evNncKFp9ESAQCDFcuHDh9OnTR44cyc3Nffv2rZKS0uLFi0U5EMOwiIgIDw+P+Pj4\nwsLCBnsDAgL27t3r5+f35s2br1+/xsbG9u7de+nSpRcvXhSv/MLFAAB8/vy5sLAwLy/v+fPn\nEydO3LVr16JFizAME68YEGkAGnaQ/6G+vj4lJQWfVeJvTE5Ovnr16l9//dW4fXp6ekZGhpAT\nkkgkTU3NUaNG3b1718LCYsmSJSiKtkgkCoViaWl55MgRHo8XExMjeGbq/8Jfq12zZg2dTo+M\njBw2bJiampqent6ECROuX79eVlb24sUL4d1dvnx5/fr1ubm5IooXFhbm7u7u7e0dFRXFZrOb\nbCP6RWCz2Z8/f05NTWUymQ12VVRUJCcnl5SUAAAyMjJycnL4u1AUzczMTE5ObnwUBAJpzI8f\nP5KTkxkMBn/L27dvPT09vb29KRSKqanpokWLvn79WlpaCgBgMBjx8fHV1dVNnio+Pr6goGDT\npk0GBgYRERGCu+Li4kJDQ9evX7969Wpzc3MVFZXu3bsfP37cw8MjIiLit5rQx8cnIiKCx+OJ\nMiIhYgiioKDQpUuXtWvXHjp0KCoqKjIysrmWP3/+TE5OzsvLayAnhmGZmZnp6ekIglRXV8fH\nx3M4HP7eioqKpKSkr1+/iiIzpJ2Ahh3kv6SlpfXv39/X19fFxWXMmDF8K2H37t0DBgxo8pC1\na9du3bpVlJMrKysHBAR8+/bt3bt3rZBNS0tLSUlJUBE3R25u7vv37xcuXKihoSG43cbG5tOn\nT6NHjxZ+uKOjY0lJibOzs5+f36tXr4Q3zs/Pf/fu3YQJEzw9Pevr6wXtziYRfhGuXLliZ2c3\nbty4GTNm9OzZ8+jRo/xdZ8+e7d2797Rp05ydnQ8ePLhq1arg4GB8V2JioqOjo7u7+6xZsxoc\nBYFAGkAmk/39/QcPHjxhwgQ7O7uwsDB8++nTp48fP85vRqVSAQC4j+yHDx98fHw+f/7c5AlD\nQ0MHDBhgYmLi7e0dHh7eYJeOjs7cuXMbHHL8+PG7d+/+1tNu1KhRu3fv7t+//5EjRyoqKoQ3\nFiJGk4wfP75Hjx7Xr19vvKusrGzcuHGOjo5z5swZNmzYiBEj8vPz8V0lJSUjRowYMWLEzJkz\nnZ2db9265ePjU1VVBQDg8XirV6/u1avX3LlzR4wY4eLi8v3799+KAWkPoGEH+S/nz58PCwvL\nzMy8c+fO58+f+WoO13FNMmfOnBkzZoh4/oEDBwIAUlNTWyHbp0+f2Gy2paXlb1vi58f7aoCC\ngsJvD+/cuXNwcPDLly/NzMxmzZrl4uJy/fr1+vr6JhuHhYWZmJgMGDBAQ0NjxIgRTa7GNqC5\ni1BbW3vmzJm///778+fPaWlpx48f37t3b2ZmJgAgPz9/27ZtM2fOzMjI+PDhw+fPn79+/YpP\nT9bU1MyePbtz586fPn1KTU0NCgras2fP06dPfysGBPJn8v79e01Nza9fv3758mXkyJHr1q3D\n7RJBMAy7evVqr169OnToAADo3Lnzxo0bmww4qK2tffDgwaRJkwAAkydP/vbtm6CjRWpqqr29\nfWP9KYoiAgDMmzcvPj5+8+bNMTEx/fr1W7lypaAviuhiNIeTk1OT2jgiIoLBYLx///7Dhw8f\nP35UUVEJDAzEd+3Zs+fXr18vXrxISkrau3fv/v37AQC4Ljp16tT169cvXbr04cOHtLQ0Op3+\n999/izJMiNiBhh3kv0yePNna2hoA0K9fPxcXl8ePH//2kHHjxo0ZM0bE89PpdAUFBVFm3QAA\nP3/+jIuLi4uLe/HixaVLl2bPnm1mZjZu3Dh+g/z8/Mf/S0pKCgAAP7+Ojo6IUjWJubl5YGBg\nYmLiuHHj9u/f379//8YaEHdq8fHxwYNhJ0+e/PLlS3ypVAjNXQRVVdWXL1/OnDkzNTU1Pj6e\nTqdjGPbx40cAwLNnz1AUXbJkCYlEotFoW7ZsqampwY969OhReXn55s2bVVRUAABjxozp37//\njRs32jJ2CESOoVAoa9asoVKpioqKy5cvZ7FYjSfm9+3b9/bt2507d+JfTU1NFy5ciBt5DYiK\nigIAeHh4AAC6du3aq1cv/hQgAIDBYLRREVGpVG9v7+jo6GvXrpWVlbm6uvr7+7dUjObQ1tau\nq6vjcrkNti9YsODp06fV1dXv3r1LS0szMzNLT0/Hd8XGxnp5eXXq1AkA4Ojo6Orqyj/q+vXr\n7u7uw4YNAwCoq6sHBASkpKQ0Z4lC2hWY7gTyX7p168b/bGFh8fLlS/Gev76+nsvlqquri9L4\n0aNHz58/BwCQSKQOHToMGzZs1apVgulCYmNjnzx5IniIi4tLSEgInU4HALBYLBGlysrK+vbt\nG/7Z1tbW2NiYv0tDQ2POnDlKSko7duwoKirq1auX4IHx8fH5+fkdOnSIi4sDAFAoFEVFxcjI\nyIULFwrpTshFCAwMDA4ONjMz09fXx7fgoygoKFBTU+P/hTA2Nub/jfn69SuVSq2urk5OTsa3\n6Ovrf/r0ScSxQyB/GhYWFkpKSvzPAADBUAMMw7Zv3x4SEnLmzBk7O7vfni0sLMzGxob/9tnY\n2Ny9e3fbtm34nJyamlpzfreNQVH00aNH+GdlZeWhQ4cK7h0wYACdTmcymU2+3cLFaI7q6mol\nJaXGbbKysmbNmlVWVta1a1cajZafn4970bFYrPLy8o4dO/Jb9uvXLzQ0FADA5XLz8vIGDx7M\nlwF3Dfz06ZOVlZWIVwAiLqBhB/kvgmYTlUpt/EuujeDzT4J6QQgzZ84U7r03Z86cJhvgKyaf\nPn0yNzcXpaNbt27xF50PHjzo4+ODfy4pKQkJCbl06RKdTl+9evWgQYMaHBgaGkqlUgVlQBAk\nPDxcuGHX3EWIi4s7efLkyZMnvby8AABcLpef4Y/D4SgrKws2xufn8F0ois6ZM0dwr5aW1m9H\nDYH8mTTQcgAAvqLDMGz58uUxMTHXrl0TJQkA7mKrpKQ0e/Zs/kYWixUbG4v78pqamuLvuyjw\neLz58+fjn01MTN68ecOX6unTp6dPn3779q2bm9v69etbKkZzfPz4sUltvGrVKk1NzYcPH+I/\nknfv3o0vAuDmnaAu4isi/Brevn1b0M9YT09P7H9EIKIADTvIfykvL+d/rqio0NTUFO/5L1++\nrKKi4uzsLN7TNqBv377a2tpXrlxpoNRQFJ05c6avr2+D7atXr169erXglszMzNOnT9+6dcvO\nzm7v3r1ubm6CuZFxcKeWwMBAQRfDlJQUDw+Pjx8/9ujRoznxmrsICQkJWlpauFUHABAMelVT\nUxN0A0IQ5NevX/hnfX19CoXy4cOHBrmRIRBIkzTQcgAAvqLbunVrbGzszZs3bWxsRDlVWFiY\nnp5eUlKSYBjEjBkzwsPDcSUzYsSIzZs3JyUlNUic/ujRoxs3bgQFBfENIwCAoqIiP0YBp76+\nPjw8PDg4+NevX1OnTj148KCJiUkrxGiS3Nzc169fL126tMF2Ho/34cOHLVu24FYdAIC/oKGq\nqkomkwXdSPiTnSoqKqqqqn///feSJUua6xFCGNDHDvJf+CubGIbFx8c3WHlsI1euXLl58+bS\npUsbTD6JHQqFsnTp0hcvXhw+fJiftIXH461bt+7Zs2dGRkbCD79x44abmxuXy71z587t27c9\nPDwaW3UAgKioqPr6end3d8GNdnZ2pqamQkIohFwEBQUFLpfLT20QHBxMJpPxoLzu3buz2Wzc\ngxAAEBMTw8/G5+TkxOVyBXO4xMTE8B1iIBBIA3JycvjRmvisGK7oXr16FRIScv78eRGtOn7e\nuAbBrZ6enk+fPsVNRl9fX2Nj43/++UcwPvTjx48BAQEoigpadU0yaNCgU6dO/fXXX0lJSZs2\nbWrSqhNFjMaUlpYuWrRIW1u7wWQ/AIBMJpPJZL4ry9evX1++fIkrIiqV2rVr17dv3+K7EAS5\nffs2/0AnJydBt+zS0tIbN26IvhINESNwxg4CAAC4AfTt27fVq1f36dPnyZMnubm5e/bsAQDw\neLwjR44AAIqKigAAly5d0tfXV1dXnzdvHgDA19dXRUXl/Pnzjc+ZlJR04MABAEB1dfXbt2/T\n09NnzJixaNEiwTbv37/neyjjLF68uEGaklYwe/bsvLy8ffv23blzZ+DAgRwOJy4urri4OCgo\n6Ld+M7169Xr37p2BgYHwZmFhYQ4ODrq6ug22e3h43Lx5c8OGDfgqjygXAcfV1XXv3r0rV64c\nMmRIdHS0lZWVubl5dHS0ra2ti4uLiYnJ33//vWDBgtra2ocPH/JXaW1tbX18fBYuXLhgwQJj\nY+OkpKTQ0NCQkBDRrhME8meBomjXrl3nzZs3ZcoUXLM5OTnh8+s7d+40MjLCA7b47T08PKyt\nrV+9ejV37txr16716dOHvwt3sW0cOubm5rZy5crbt2/Pnj2bTqeHhIT4+fkNHz7cxcXFyMgo\nNzf32bNn9vb2oqQl2rdv39ChQ4VPxosiBr4lKCiIRqMhCFJQUBATE0On0y9cuNBY2ZLJZFdX\n1+DgYC0tLSaTGR4evnz58q1bt16+fNnb29vPz2/dunWrV6/u16/f3bt3TU1N+XlMV61a5e3t\nPX36dHd397q6usuXL+vq6uKBuhCCgYYdBAAAaDSao6Pjvn37oqOjo6KilJWVL1++jHuZ4LN3\neDNHR8ecnJycnBw9PT18S8eOHfmeyILY29vX1dXhByorK/fr1y8wMLBfv36N2/CdbXHwxCL2\n9vZN1pAVPFZIAzKZHBgY6OPjc+vWrby8PAUFhQkTJkybNs3Q0PC3l0IwgqQ5mEwmmUz28/Nr\nvGv8+PGpqalfvnyxsbER5SLw6d69+6VLl0JDQ+/duzdy5EhfX9+ePXtevnz58+fPffv2DQ8P\nDwoKevjwoZWV1fnz5728vPilbw8ePBgeHv706dPExERTU9Pbt2+L4vQNgfyBdO7c2dra2s3N\nLTg4uLS0dOrUqf/88w++S1dXV0VFha/rcPD8nXQ6vUePHg0m2DIyMlxcXOzt7Rt0oa6uPnPm\nTP4UnY2NzfPnzyMjI9+/f//169cOHTqcPn161KhRopSLFcVrRRQx8DpmuKYlkUh6enpr1qyZ\nNGlSc+WzDx48eOTIkfv375uZmZ0/f15PTy8rKysuLs7d3f2vv/5SUFCIiYl59OjRtGnT6urq\nHj16hLstdu/e/eHDhyEhIffv31dVVZ0xY4afn187VcWFCIcEK4pAIFIOi8V69uyZnZ0dvo5c\nUlLSv3//AwcO8OM8IBAIhAAyMjKKi4uHDx+Of924ceOTJ0/4cR4QKQHO2EEg0o6iouKBAwd4\nPJ6fnx+JRLp27ZqZmRk/zAICgUCIITs7e/HixTNmzLC1tf38+fOlS5f27t0raaEgDYEzdhCI\nDFBZWRkSEpKWlkalUm1sbObOndvcMgoEAoG0Hy9evIiMjCwrKzM0NBw7duzgwYMlLRGkIdJl\n2H358iU5OdnNzU1IIq6SkpLU1NTa2loTE5O+ffvCLA8QCAQCgUAgOFK0FHv79u2LFy8iCOLg\n4NCcYff8+fOgoCA9PT1NTc3Lly937Nhx586dioqKBIsKgUAgEAgEIoVIS8RKcHDw3bt3BRNn\nN4bJZAYFBY0aNerEiRO7du3at29fTk6OYJ5rCAQCgUAgkD8ZaZmx09XVPXToUHFxsZA2lZWV\n/fv3HzduHL782qlTJzMzs9zcXKJkhEAgEAgEApFqpMWwGzduHABAuGFnbm7eoPRTdXU1v+wJ\nBAKBQCAQyB+OtCzFtoKYmJjy8nIXFxdJCwKBQCAQCAQiFUjLjF1LSUxMPHv27PTp04XXJ+Bw\nOHV1dUQJ9f+gKEoikWQ6XFdl9mzyt2+1YWGYvr6kZWk9CII0WeZVVsAwDH+WZDp7u5S/DvwC\n8G1BsuH8vOfPScuWoaNGKezeLa5zNgeXy+VwOHilgXaFx+PV1NQoKCgQ01d9fb38DYrNZhOw\nooUPikqlEtMXMYNCEKS6upqYQSEIwmKxxNuRTBp2L1++PHTokI+Pz8SJE4W3pFAoNBqNGKn4\ncDgcMpmMlwqVUShfvlAyMhQBAIRfPXGBYRiLxSL+7osRBEHYbDaVSpXpuG85eB2EI/lw/qoq\nSkoK2rWreM4mFPzHBjEd8Xg8Yn6YYRiGF7knoCMej0fM7zTC7hQAgLBBAQCIvFPE/Bxtj8dP\n9rTt48ePg4KC5s+fP3r06N82plAoysrKBEglCIIgVCq1yQqqsgL+lNFoNDLhV09coCjKZrOJ\nv/tihMPhsNlsiTzDYkQOXgchBAcHx8fHz549Ozg4uLk2/HD+uXPnkkikb9++rVixIiYmBtYO\ngUAg7YGMGXbJycnHjx9fsmQJv1Yd5M+kurr6xYsXb9++/fjxY05OTmlpKYfDwXfp6Ojo6uoa\nGBgYGBh07drV1NTU3NzcwsLCwMBAsjJD5A8Yzg+ROCwW68GDBw8fPkxMTCwoKEAQRENDo2/f\nvl5eXlOmTFFQUJC0gBCikQHD7suXLxQKpXPnzlwu98SJE2PHjoVW3R9LbW3tzZs3b9y48fTp\nU74l14DKysrs7OzG25WVlTt37mxlZWVnZ+fk5OTo6Civ00gQwoDh/BAJEh8ff/bs2fDw8Orq\nasHtlZWVeXl5ERERW7duPXLkyJgxYyQlIUQiSIVhx+PxNm3aBADAAx2CgoKUlJS0tLRWrlwJ\nAAgODlZWVt6+ffvLly9LSkrS0tLWrVvHP1ZPT2/ZsmWSkhxCGNnZ2UePHr148SKTyQQAKCkp\nOw12seszoKt1DxMTcx09AxpNCQCAogizilFZWV76q6iwML+irKS46EdxcWHxzx+/igvT09PT\n09Nv3rwJAFBVVfXy8po3b96wYcMkPDbIn4Qo4fwIgjT3u6UxJB6P+q9TqTgEFAaCILivNwEd\ngX/9ygnoC0VRGRpUZWVlWFjY+fPn09PTAQBUCsXFfojHoJFOPft3MrFQoFJ/lZc8S3x19vaV\npM+pnp6eCxcu3Lt3bzv5ueKefMTcKRRFiblT+KAI66sVHQn3z5EKw45EItna2uKfHRwc8A/8\nuKGRI0fiT6SJicmUKVMaHKuurk6UmH8Q7FWrsPJyleZD/IgkKSlp9+7dkZGRKIqSyZSBQ1xH\ne05yGuyCW3INIJMpmlo6mlo6Fh272P6vjx2C8IqLCnNzsr58TvuQFJ+W8v769evXr18fMGDA\nnj17hgwZQuCYIH8oIobz83i82tpaEc9JtrTkHTiAdOzIFfmQNsLj8QjriMi+iOkIQRDRb64g\npaWlT548efDgwZMnT3C738LIbJqbj+/IcfraungbDEU5HI6Wmub4YWPGDxsTFntnbdD2U6dO\nZWdnh4SEtN8aRasH1QoI60iaB6WkpCQktoOEYVibRYL8D3jst0wv8zEYDB6Pp62tLdlEG8+f\nP9+1a9ejR48AAKqqamMnTPPxndXB0ESUYzEMEx48waxiPHwQEXrt3M8f30kk0syZMw8fPixV\nvxM4HA6TyVRSUpLpZTs5eB1+y5cvX1auXHn48OFOnToJacYP5582bZrwE7Zoxo4fPU1ADDiC\nIDwej5iO5HVQFApFxNeBxWJ9/vw5NTU1MTHx7du3mZmZ+N9rJZrS6IGu00dPHNrHqTkVjc8D\nUanUL3nZE1fPLvhV6OrqGh4eLvZh4rNNog+qjX1xOBxiOpLyQcnAjB0EIgiCIJGRkfv3709I\nSAAAaOvoTZo6Z/ykmXS6mhh7UdfQnDhlzriJf92+efnMib0hISFxcXERERE9e/YUYy8QCE77\nhfMTGT3N4XAwDCOgIy6XS9iguFwuiqLSMCgOh/P+/fv4+PjExMTU1NSvX78KJsLQ1dRx7jfQ\nfaCru5OLmupvfuyhKMrlcqlUqk1n60cnb7r/Z/Ljx48XLVp07do18Wbx4PF4uA1EwAXEZ3CJ\n6YjFYpHJZBkdlPwbdhKZksQwTA6mQokfRWVl5blz506cOJGXlwcAMDQyneq3cIy3b5OrrmKB\nSqX6+M4aOtx96/r/JCfGDxw48Nq1a1Lia8y/+LL+LEnz60BMqioYzg8RQllZWWRk5J07d54/\nfy6YUV+drmbTydq2czc7K9t+3e2sLbq07nE10Te6d/iqy8JxN27csLKy2rJli9hEh0glcm7Y\n1dfX19TUENwp/jeM+IoXYofBYBDW1+fPn4ODg8PDw3EfUqtuPSdOmT1kmDuFQkHRVnqFi+5O\nTlfT2HP4wumg3TdvhIwfP/7w4cO+vr6t6LE9qK+vr6+vl7QUrUfKXwcdHZ12OjMM54f8lseP\nH584cSIqKorL5QIAFBUUnHrZO/To26+7Xa+uNuaGpuLqyMLILHT3Wff/TN62bVufPn1gDkX5\nRs4NOxqNRnztATlwKsJ97LS0tAjwsXv69OmePXtiY2MxDKNSqa6jvCZOmWPbq18bT/tbH7vG\nLF8daGZueXjfpiVLltBotLlz57ZRhjaC+9jRaDToYyedwHB+SOvAMOzWrVvbt29PSUkBAKgo\nqYwf5jHWebSL/WAVJZV26rRf996HVgQu2rXyr7/+SkpKEu4PCpFp5Nywg0gz8fHxq1evjouL\nAwBoaul4+0wfP/EvXT1JphH28Z2lSlfbsXn5ggULFBUV/fz8JCgMRMqB4fyQVvD69etNmzYl\nJiYCADqbdvp70izfkeN/6zMnFqZ7THr7MenivRu+vr6vXr2S6VqFECFAww4iAX79+rVixYpr\n165hGNbB0GT6rMUeXpPaz5GuRbiP8cEwbOeW5XPmzFFXV/f29pa0RBAphUKhNLbY+IwcORL/\nYGVlZWVlRZRQEOmlqKjI39//1q1bAIAuZp3WzV42fvgYgjMP7PPfmpiR8v79+w0bNuzdu5fI\nriGEIclkFg0oKyuLjo6OjIxMTU0V0qywsDAqKioyMvLz58+EyfanoTpxola/fqCoqD1OHhoa\namNjc/XqVbqa+pIVW0Jvx42f6CclVh3OaM+Jy1Zt5/F4U6ZMwScUIRCphfzihVa/frS1ayUt\nCKRZUBQNCgqytbW9deuWlprGgeXbEy7H+rh6EZ9PSpmmdGFrkIqS8oEDB2JjYwnuHUIM0mLY\nJSYmLly48P79+x8+fNi2bdvBgwebDKOLjY1dvHhxdHT0mzdv1qxZc+HCBcIl/SMg//xJ+f4d\niDtjJ4vFmjt3rq+vb3l5+ajR42/ceuk7fZ6CVC4HTJg8c86C5Ww2e+zYsfAnBESqqaujfP9O\nKi2VtByQpsnMzBwyZMg///xTXV09ddSEd5di54/3o1IktlxmbdFl5382oCg6c+bMsrIySYkB\naT+kYimWw+EcPXp02LBhixcvBgBkZWWtWrVqwIABTk5Ogs0YDEZwcLC7u/uCBQsAANHR0SdP\nnnRycuratatk5Ia0hIKCAm/v/2PvvuOaRv8HgD/djLI3CMpUEFRwb0EUB84T1HOduE899x6n\nIqggTlyAep4DOc89Ec+BoCioiKKyREDZq6zONL8/8r1efyi1QJuk9Xn/4cumT/N8Qpr0k+QZ\nY1++fKmjq7d2U6in90iiI/qOWfNXlJUWX7t8bsSIEU+ePLGwsCA6IgiCVIlIJAoLC9uyZQuP\nx3OwtjuwekfX9p1oNBrRcYFZY6fGPXt08/HdWbNmXblyBZ9BfyDckOKOXVpaWnV1tZ+fH/bS\nycnJzc3t0aNHXxfj8XgTJ07EXg4bNszU1PThw4d4hgq1TEpKSs+ePV++fOnUwfXkuTvkz+ow\nqzbs6N3X69OnT6NHj8ZtbhkIgtRAenp6nz591q5dCQCDWwAAIABJREFUKxQIl06Z/+SP2307\n9yA6qP8cWrvLwtjs2rVrR44cIToWSMFIcccuJydHV1fX1NRUssTR0fHrxK6srIzJZOrr62Mv\nKRRK27Ztc3Jy8AsUapHbt2/7+fnV19d7eo/cFLhfQ0PpY3krCo1GD9x15NfZP6WkpEyePPny\n5ctkuNqG1B42RYGchal8Ph0AFEVra2uVGhX4d6IqHCrCmuKIRCJ86lJsRSKRaO/evSEhIXw+\nv0M7x/A1O93bdwIAYIPVYVNIKaqupqAoKrsiHU32oTUhE1bPXLlypYeHR8eOHVtcEcBxTyEI\ngs/3HACAT10t2yg2my3jPispEruqqipJuobR19evqKhoVExPT08gEHC5XMngZCiKcjgcGWsW\niUT4D+4qFAoRBJGeCkblYB0ZuFwuaPVtqvPnzy9YsEAoFE6cMmf+4nVUKhU7uykbdrppfV0M\nJmtH2PEFAWOvX78+f/78ffv2KSI6uWAnl2bNB09CJD8cJKOTkEqzJqkU0+kAAAqFgsNggUKh\nUCgU4lCRSCQSCARUKhWfusRisaIqSktLmz17dmpqKp1GWzFt4ZpffmMx/teMWCwWIwhCoVCw\ncXCUCjt7yK5ocM8BS36et/fskV9++eXp06ctGy8Tm9cYn2lVEQRR4J6SXZFQKMTn69eyjZL9\n9JwUiZ1AIGAwGNJL6HQ6dgxI3yBxdnamUCj37t0bNWoUAKCkpOTNmzd6enoy1owgSMsmLWgl\n7GuBf72KwkJRAACfzxe37q93/Pjx9evXoyi64Lf1P02cif8PvEL2gp6+YVBo5NJfJ0dFRZmZ\nmS1durT165QfNpMgnjUqHJkPB3ImdlQqVf7+kqJ/T5KNzqLKgN1dwKEiDJVKxacuoVDY+oqE\nQmFwcHBQUJBQKHS1dz6yYXcXJ9evi1EoFHw6wyII8t2KNs9d+TQtOelNysKFC8+dO9eCWrAM\ng0Kh4LCnKBQKbhUBVd4oUiR2TCaz0Xkfu1Zr9NjLysrKx8fn+PHj2KPbJ0+eODg4yJ4xjE6n\n4z9qP5/Px+18pCy6uqi+vhabDVrx1wsLC9u4cSONRl+7adcw3wkKjE5OCjlZY9o7uwWFRKxa\nMj04ONja2nrGjBkKWa1s2GUJg8HAf/YUBVKHw4HkGAxUXx+QMkP9cbx9+3bGjBkvX75k0Onr\nApaumr6Yofzbcq1Hp9H/2BreL2BkdHR0z549lyxZQnREkAKQ4ptnZGRUVVUlvaSqqsrY2Pjr\nkvPnz3dwcHj79i2Kor///vv58+dlX3DTaDT8G0WJRCJVn0Op+s4dkUhkaGjY4ivLLVu2bN26\nlcFgbN1xeNDgEYoNTx4oigqFQgU+8ujRe8CmwP1b1i9auHChsbHxTz/9pKg1NwVreIDPMw7l\nUYPDgeTE3t7VWVksFgvmzoRAUXT//v3r1q3j8Xiu9s7HNu7p5OhCdFDNYGVqcXLLwbHLp61c\nudLV1XXw4MFERwS1Fil6xTo5OdXW1hZJDYf74cOHb47VTqVShw4dunz58lmzZllaWr5+/drF\nRZUOoR/E2rVrt27dymJp7Nx7gpCsTkm8fcYsXxuEIMjPP/98/fp1osOBIIhg5eXlvr6+y5Yt\nEwqEy6f++ijqumpldZhB3fpuX7heJBL5+fnBYTvVACkSO1dXVxMTk5iYGOxlWlrahw8fJNcN\nGRkZ2dnZAAAOhxMQEBAfH48tv379Op/P9/LyIiRmqCkrVqzYtWuXpqZW6IFTvfuq294Z7zd9\n0bJNAoFgwoQJV65cITocCIIIk5SU5OHhcevWrTamljcPRG+dv4apsk0OFk2cPWPUpKqqqhEj\nRnz58oXocKBWIcWjWBqNtmTJku3bt2dlZRkYGKSnp48YMcLDwwN7NzIyUlNTMzAwUE9Pr2fP\nnnv27Ll7965AIMjOzl62bFmj7rQQsZYuXbp//35NLe2wg3928ehFdDhK8fP0+WIEOXwg2N/f\n//jx49OmTSM6IogwPB7v1KlTCQkJXC7X3t5+9uzZjo6OXxcrLy8/deoUNhKntbW1n59fz549\n8Y8WUqATJ078+uuvfD7fp49XxIY9hnoGREfUWntXBBWXl8Q+feDj4/Pw4cNvtoaCVAJty5Yt\nRMcAAABmZmZeXl5aWlrGxsb+/v7Dhw+Xftfe3t7W1hYA0K1bN1dXVxaLZWtrO3fu3BYPvaNU\nWM8PHDq0Kw+PxxOLxZqams0akXzFihX79u3T0mbvPXS2szvxv1sikUhJbfY7uffQ1dN/knD/\nypUrmpqaffv2VUYtCILw+Xw6nc4k5axrclKDw0GGPXv2pKamzp07d+TIkZ8/fz537pynp6eW\nlpZ0GZFItGbNGpFItHTp0lGjRvF4vKNHj3bs2NHMzEwhMUi+Jzh0ssE6tuNQkVgsxm2jxGKx\nSCSSvyKxWLxq1aq1a9eiKLo+YNn+VcFa8o3NiQ2Yh8/hgI1j16yKaFSq74Chj18lvXr7Oi4u\nzs/Pr9E3+ZvEYjGPx6PRaPjsKXxG25FsFD51KXyjSHS2NTIyGjHiG+2xhg4dKv3S1dXV1fUb\nfcghYq1fv37Pnj2aWtp7ws906tKd6HCUzm/yLF1d/eCtK9asWZOdnX3o0CHY8fNHU1RUlJCQ\nsHHjxh49egAAnJyc5syZc/PmzenTp0sX+/jxY0FBQUhICHYzb8qUKQ8ePHj8+HGnTp2IiRtq\nBR6PN2XKlEuXLmlrakdt3uvb34foiBRJS0PrYugfY5ZNfZGa6uXlFRsba25uTnRQULORoo0d\npOp27NixY8cODQ3N3QdO/QhZHcZn5E9h4WfYOrqRkZFDhgwpg7Ow/2Bev35Np9MljUZoNJq7\nu3tqauo3C0t3MIfXACqqqqpqyJAhly5dsjA2iz10Qc2yOoweW/fq3jPdXNzT0tL69+8P53ZS\nRSS6Y6cM2O1oQiol7VD78qDfv0+rrkZ++gmVYxy7yMjI9evXMxiMHWFRXTx6YVM+EA4LQ9nB\ndO3eN/LU9dXLZj569Khbt25///235Ge+9bCvLjYkrKLWiT+SHw6tGQ6psLDQ2NhY+mmXubl5\nQkJCo2KOjo52dnbnz59funQpm82Oj48vLi5W4IBhlOJi1r17NFtb4OmpqHVCXysqKvLx8Xnz\n5k37dg6Xw05bm1kSHZGy6LF1b+w/O2ntnIcvEvv06XP9+nXsnjSkKigk+RlWEoFAgP90TNjv\nMT5jiyuJbr9+tHfvOG/eiK2sZJe8devWjBkzUBRsCtw/0Gu47MJ4QlEURVF89kJdXU3Q78ue\nPXnIYrH27t07adIkhawWS4lwG6deSbBNaFZjTTwZGLS8zXt4eHh2drb0LHM3b948duzY1atX\nG21vVVXVli1bcnNzsfZVixcvHjhwoIw1N+vERY+N1Zk8mT9hQkNERAu2olnwPKyw2bdwqEue\njcrLyxs/fnxubm5X587ngiIMdVvSaQ+rCJ/DAftlb01FAqFgcei6S/dvaGhoHD16dPTo0U2V\nJNWeUlRFuJ17W7ZR+vr6ZJ8rVnmYTCb+Dc/r6upUfURW7O6Kjo4OVebPXnJy8ty5cxEEWbl+\nh8+IcfjEJicURXk8Hj57QUNDI+zg6aiju09FHfj111+zs7NDQkJaPzK2QCCoqalhsVj4z56i\nQGpwOLQSgiA7duxgMpmBgYE6OjrJyckHDx40NDR0c3Nr6iPNuk1L+/fiHLfborhVhOftahkV\nZWRk+Pn5FRUVDfToc/L3g9qaWq15EIT9kLf4482tq8WfpdPoh9eEWJta7j8fMXPmzHXr1i1d\nurSpZIIke0qxVHej1Dyxg5QnPz9/9OjRDQ0N02YuGu83/fsfUGtUKnXur6ud2rsGbl66Z8+e\nDx8+nD9/XkdHh+i4ICVis9mN7qvV1dVpaWk1+vF7/Pjxhw8f/vjjD0NDQwCAnZ3dx48fT58+\nHRIS0tSaWSyW/FekIk1NAACVSjUyMmr2NjSTQCAQCAQ4XGkIhULcrmqEQiGPx2vqaH3x4sXY\nsWPLy8tHDfA5uTWcxWj5nQKs/zI+HUgRBGlWV9+mbF+0wdm+/ZKQdcHBwXl5eZGRkY0u0kQi\nEYfDYTKZOJzuRCJRQ0ODrq4uDhVxOBwGg4FPXS3YKNn3YmFiB7VEfX39mDFjiouLPb1Hzl+8\nluhwyGLQ4BFtbGxX/Tbj1q1bAwcOvHnzpoWFBdFBQcpiZWVVXl4uEAgkSVhhYWGbNm0aFfvy\n5YuWlhaW1WEsLCzevXsne+XyP0STlMThAZ9kcnR8KsKzrm9WFB8fP3r0aA6HM9ln/OH1oXRa\nq34x8WyQoMC6pgyfYGfVdsqGeWfPns3Ozr5y5Yp0V1mS7CllVIRnXYqtiERtdxITE0NCQrZu\n3XrmzBkZ7UsKCwuPHj26ZcuWQ4cOffr0CccAof9BUXTmzJmpqakdnDttCtxP2uZThHBwdI46\nfaODS+dXr17169cvNzeX6IggZenSpYtYLE5OTsZe8vn8Fy9edOvWrVExU1PThoYG6emwCwsL\nTU1N8QsUapEbN24MGzaMw+HMHT/96IawVmZ1Kq13p+4PI651tO/w7Nmznj17pqWlER0RJAtZ\nEruYmJjdu3fr6uo6OzsnJiauWbOGz+d/XaygoGD58uXFxcWurq4lJSWrV68uKCjAP9of3M6d\nOy9cuGBoZLJjz3EN+Ubm/KEYGZseirzQo9eAjx8/9u/fPzMzk+iIIKUwMTEZPHhwREREYmJi\nenp6SEgIlUqVDMZ58ODBiIgIAECvXr0MDAx2796dmZlZVFR048aNZ8+ejRw5ktDYoe84derU\nuHHjuFzu6hmLw5YHqnQHJoWwsWhz78gln96e+fn5/fr1i42NJToiqEmk+LLW1NRcuHAhICBg\n/vz5/v7+O3fuLCsru3v37tclIyMjXVxctmzZMmHChK1bt/bv37+oqAj/gH9ksbGxmzZtYjAY\nwbsjzczVtsN/K2lqaYfuPzXAc9iXL18GDRoEczt1NX/+/H79+h09evT333/n8Xjbt2+XtJXJ\ny8vDLjt1dHSCg4PZbHZQUNCSJUvu3bu3ZMkSOMk1mQUHB8+cOVMsFocu3bppzkqiwyELtpZ2\nzK7j8yf8Ultb6+vrGxUVRXRE0LeR4t5yamqqUCiUnOn09PQ8PDySkpJGjRolXYzD4bx+/Xrz\n5s3YSwqFsnjxYrxj/THwFyxAS0s1v5qHNzc39+eff0YQZNnqwB9nIOKWYTCZ20OObV7768N/\nbg4ePDg+Ph6bFg9SJ0wmc86cOXPmzPn6rd27d0v+b2VltW7dOiXFgLZvX79pE9XVFY563Hoi\nkWjhwoUREREsBvPohrAJ3k2O8fFjolFpoUu32lq1XXcwcM6cOfn5+Zs2bSI6KKgxUtyxy8/P\nNzIy0tbWliyxsbHJz89vVCw7OxtFUSsrq+jo6KCgoMOHD+fl5eEb6Y9CMHUq97ffwP/v5cTl\ncsePH19ZWTlytP94/xlExaZC6HT6tp2H+w8c+vnz5yFDhhQXFxMdEaSGUDs77m+/iYaTaBRJ\nFVVTU+Pr6xsREaHH1r0U9ifM6pryq1/A6cAjmiyNwMDAOXPmiEQioiOC/h9S3LHjcDiN+rSz\n2eyamhpsLEfJwoqKCgqFsnv3bnt7e2dn54SEhFWrVu3Zs+frbmgSIpGIx+MpMfRvEQqFWFdz\nnOtVIGyUpvr6eum//+zZs1NTU53au/62cqtAICAuumYgQ5ybtx9cuyLgxfNEHx+fO3fuyD8o\nALYXhEJhXV2dMgNULpIfDio9RiCkQLm5uaNGjUpPT29rYX0h5ISzrRPREZHa6IHDru07679m\n1qlTpwoLC//8808cRgaB5ESKxE4sFjcazZVGo2FDP0svFwgEKIr269dv7NixAIBRo0YtWLAg\nOjp61apVTa0ZQRD8EzusXqFQiH+9iiXdf+Xo0aMxMTF6ega/B4fTaHTS/k43QoY4KVTqluDD\nKxdPS0tLmzRpUnR0dLMGzUYQhLTzccmJzIcDTOwgAEBiYuL06dPLy8t7uHqc3xFlYqD0EQHV\nQC+3bnGHL45bOT0uLs7X1/f27dsmJiZEBwUBQJLETkNDo1H6xePxmExmo2wP+zmUjCbAYDB6\n9OiRkpIiY810Oh3/QWJ5PB6NRlPpeb4bGhoQBGGz2dgdu7i4uC1bttBo9G27jti0tSM6Ormg\nKCoUCvGfd+SbWCxW6P4/FgSMi4+PX716dVRUlDxjxIhEIi6Xy2AwVHraBjU4HCD1FhUVtXTp\nUoFAMMlnXPjakNYMQfyjad/OIe7wxfErZ7x48aJPnz63bt1ydHQkOiiIHImdubl5eXm59IPX\nkpISMzOzRsWwkZ+kH65paGh8c1QUCRqN1vqZnZpLKBTS6XQcxhZXHi6XCwBgMplUKvX9+/fT\npk1DEGTZmsDuPfsTHZq8sMQO/73fFGMTsz2Hzs6dPurs2bPt27eXp8UxhULhcrn4jFOvPGpw\nOOAPe14hZ2GsJD7TH4nFYtwqAsrfKJFItHLlyvDwcCqFunX+mqU/z6dQlDh/umTNOEwphlWB\nQ0XmRqZXw07PClwS//Jpnz59Ll++3Lt3byXVpWZfP0ldLahI9k8bKRI7FxcXPp+fkZHRoUMH\nbElaWlrnzp0bFXNycqLT6enp6XZ2/7tp9OnTJ0tLOOKGEpWUlPj6+lZXV4/5aarfpACiw1Ft\n1ja2O/ee+G3+xN9//719+/b+/v5ERwSRlFAolDFIeyPYjzc2B5cyg/pfXSiK4lARRqkbVV1d\nPXPmzEePHrG1tI+s2z2st5eym+Rie0osFsu+H6HAunCoCACgq61zLihixZ5NMXFXvL29Dx48\nOGHCBCXVJRaL8fmeAwAQBCHtMaWvry/jsQ8pEjusM0RUVNTGjRt1dXUvXLhQXFy8fv167N1b\nt24xGIwhQ4ZoaWl5e3v/9ddfzs7O9vb2CQkJL168WLZsGbHBq7G6ujpfX9+PHz/26DVgxdog\nosNRB53de6zfvHvbpiW//PKLnZ3d17MUQBAAgMlkyt+KQCAQ1NTU4DNZp0Ag4PP5OFQkFAqV\nOgNpZmbmqFGjMjMzba3aRgdHOLSxxeGmslgs5vF4VCoVn7qEQiFuG6XBZEVu3mdvbbvj5L55\n8+Z9+vQpMFDxozqLRKL6+no9PT3FrvabFVVXV9PpdHzqUvhGkSKxAwCsWLEiODh4xowZWHOc\nZcuWWVtbY2/dv39fU1NzyJAhAIBZs2ZVVlYuX76cTqcjCDJixIiBAwcSGrh60p41C2RnT9bV\nTUlJcWzfMWh3BJ1Olq+KqvMZ+VPux6w/TxwcN27c8+fP4WSyUCtREhP1ly0T+/iAXbuIjkU1\n/PPPP35+flVVVf269DobdFRfR4+0nXtUCIVCWRew1NHG7tcdq4KDg9PS0s6cOYNDYgR9TYnt\nCVqgoKCAy+Xa2NhItxbPycmhUqnSg7uWlpZWVlaam5vrfzWCLhnU1dXR6XSVbvAucnGhv39v\nAwBq3e7IyctGRqo3ryWKojweT1OTjDOeicXidctnPX50t0+fPg8ePGjq3gx2J0ZDQ0Ole26q\nweFAcqKrV+ljxwr9/RkxMcquC+c7diwWS+F1RUVF/frrr0KhcLrvxH0rgxl0Os43t/BpNUvg\nRr38kDZl/bzPpYWOjo5///13p06dFFUXznfsGAyGit6xI8UAxRLW1tZOTk6NfgPs7e0bDdlv\namraoUMHcmZ1aoDP53/+/BkAYGxivv/oeVXM6kiOSqVuDjrQztbxyZMncPYUCMIHiqJr166d\nM2cOgiDbF244tDaEAR9EKIFHh07xx2/0d++VlZXVu3fvkydPEh3RD4dciR1EOKxdXW1tLQAg\ncNcRC0troiNST9raOrv2nWTr6EZERERGRhIdDgSpOR6PN2nSpF27dmlpaJ3ZfnTJ5LlER6TO\nTAyMru07u3TKfC6XGxAQMGPGDPk7A0Gtp+bXK/h0V25ELBaLxWIyDI3bXGVlZaNHj05JSWEw\nGEAoNDWzEMg95gLZSDqgER1Ik6zatN28/cDaZQGLFy92dXXt3r3x3LvYV1dFv0sSJD8cFNJ4\nFGtDYm1tLc+j//fv37PZbEkbYggH5eXlY8eOTUxMNDM0idl1vKtz4yEXIIWj0+iBC9b17dxj\n7vblf/75Z3Jy8l9//eXq6kp0XD8ENU/shEJhQ0MDzpViEyiRYTKrZsnJyfH39//48aO9o7OF\nUAA+5QiFqrcV0lAUJXn83Xr0nxaw+FTUfj8/v4cPHxobG0u/i+WmIpFIpacUI/nh0MoWHYWF\nhTt27MjPz6fRaFQqdfbs2cOGDZNR/unTpzt27Bg0aNDy5ctbUy8kv5ycnOHDh2dlZXVo53gx\n9A8biyanoIQUblifwYknb//y+8Lnb1/27NkzPDx85syZRAel/tQ8sWvWqAGKooqtxRMSEsaN\nG1deXt61R98dYcfpv4wBALBYTIpKbYU0rPME+ffCnAUrszPTE+PvzZs3LzY2VvoGkmQYC9h5\ngpxQFA0JCdHW1sYmyrxx48aRI0ccHBwcHBy+WZ7L5UZGRqr03lQ5T58+HTNmTFlZ2QCP3meD\njunrwE6aeLM2s7wTfmFrRMiB6IiAgIDHjx8fOnSInN3a1AZsYweBs2fPent7l5eXD/edsCf8\nLJutI9Jmi3T0UAr8eigdlUr9ffvBNtbt7t+/Lxm7EVIJmZmZHz9+nD17tp6eHoVCGTVqlK2t\n7e3bt5sqf+bMGXNz844dOyo4DgYD1dcH2toKXq3qu3jx4uDBg8vKyiYOHXs57DTM6ojCoNO3\n/7o+ZmeUga7+yZMn+/bt++nTJ6KDUmfwl/uHhqLoli1bpk2bJhAIZi9YuXHbPmxOz5fH/np0\nJ0Vgak50gD8Eto7ujrDjmppau3fvvnDhAtHhQPJ6//69hoaGvb29ZEnHjh3fvXv3zcLZ2dmx\nsbELFixQeBhib++KrCze/v0KX7NKCw0N9ff353K5q2Ysjty0jwlnKyba8L7ej4/f6OzU8dWr\nV927d3/48CHREaktEj2KTUxMTExM5HK59vb248aN0/7WBSiCIHFxcW/evOHxeNbW1r6+vo2a\nJUHy4/F4s2bNOnfuHJPJ2rhtr7fPGKIj+nHZO3ZYsylky/pFAQEBzs7OsImxSigrKzMyMpKe\n2MfY2Li0tPTrkmKxODw8fNy4cXL2mcAGIZMzDKxjCoIgOMwfJRKJ8KlI0nOoBXUJhcIlS5ac\nOHGCQacfXL1zyvAJsjtRwRlIW0MyKa08dbUxtYwNv7B094bzdy8PHTp03759s2bNkr8uBEHw\nmZCtNV+/FtTVgopkj1BIlsQuJibm/PnzPj4+7dq1e/DgQVJSUlhYWKPQxWJxYGBgZmaml5eX\nlpZWYmJibGzsvn37zMzMiApbdZWVlY0dO/bJkycGhsa79p5w7dSV6Ih+dEOHj/vw7vX5M5Fj\nx459/vy5oaEh0RFB38Hlchudo1gsllAoRBCk0RTd165d4/F4fn5+cq5ZKBRiQw7JTyQSNfcj\nLYZbRUKhsLlzQlRVVc2aNevx48d6bJ3jmw70d+8l508mPtOqArzSBQwJN4oCKPtXBrdv67D9\n+J6FCxemp6f//vvvzZp8DLevH4IgpD2mmEwm2eeKrampuXDhQkBAwKhRowAAPj4+c+fOvXv3\nLvZS4s2bNy9fvty5c6eLiwsAYMyYMQEBAffu3ZsyZQoxcaus9+/fY5PA2to5hR44ZWllQ3RE\nEAAALFy6MTvzfcrzBD8/vzt37hAdDvQddDq90a0gBEEoFEqjX6mysrJz585t2LBB/o5cNBpN\n/u4mYrFYIBBgkzHK+ZEWE4vFCILgU1ELNur9+/cTJ07Mzc21tWobHRThaGMnz6dQFBWLxY1y\ncWXAbmtRKBR86sJnowAAIpGISqU2Kzn7bfJcp7b2c4OWHz58uLCwMDIyUp4vPDZwEg4dIrGv\nH5VKxaeuFmyUjKwOkCSxS01NFQqFXl5e2Es9PT0PD4+kpKRGiZ29vf3u3budnJywl9ra2gYG\nBrgl1Grj3r17fn5+1dXV3Xv2DwqNYOvoEh0R9D80Gn17yLFZU0fcv39/0aJFBw8eJDoiSBY9\nPb2amhrpJTU1Nbq6uo3OuZGRkXZ2dgwGA2t+V1dXJxAI3r17Z2dn19SPGZ1Ol7/zrEAgEAgE\nzfpIi2FTiuFQkVAobO5GXbx4cebMmbW1tQO79jkdeMRAV96BbLAH3/j8hCMIglu6gNtGiUQi\nCoXS3LpGDxpuY2ntt2rmlStXKioqrl69amBgIPsj2OxbOHz9sBGaaDQaPnUpfKNI0XkiPz/f\nyMhIulGdjY1Nfn5+o2JsNluS1QEAnjx5UlRU1KNHD5yiVAsREREjRoyorq4ePX5KWPgZmNWR\nja6efuiBU9iMFCEhIUSHA8nSrl27qqoq6WvL3NxcO7vGd4lKS0sLCgqC/pWRkfH27dugoKCS\nkhJ841VbIpFo1apVfn5+tbW1C/xmXtlzWv6sDiJQFyfXf45edmpr//jx4wEDBnz58oXoiNQE\nKe7YcTicRukqm82uqalBUfSb9xuXL19eVVWloaGxYcMGDw8PGWsmaoBioVCIW+MGOYlEovXr\n10dERFCptAW/rff/eTaCIE01d8Xaw5J2UFl5oCiKoijZ9oI8LCxttu08smbpzM2bN7PZ7OnT\np3M4HKKDajlyHg4SrZl7293dncVixcXFjR8/HgBQVlaWmpo6d+7/5qrCbmPQaLR9+/ZJfyoo\nKEhTUxMOUKwo+fn5P//8c2JiopaG5v5VOyb5jCM6IqgZbCzaxB2+OGH1zOS3r/r27RsbG9u+\nfXuig1J5pEjsvm4KQKPRZDQR6NmzJ4fDSUpKunjxooODg4xm5s3qXKZY+E9lJkNFRcXs2bMT\nEhK0tNkbtuzt2WeQ7PD00lNpDQ3VnbuKmbK63pAfqfaC/Dp16bFmU0jQluUrV67U0NCYMGEC\n0RG1loruCNm0tLSmTp168uTJ0tJSAwODuLhMM1tmAAAgAElEQVS4du3aSZqUrF27VlNTMzAw\nUOlxlJcznj6ltmkDevZUel0kExMTs2DBgqqqKqe29qcDj7jYwZxA9RjqGdzYf276pl9jnz7o\n16/fzZs34YO4ViJFYqehocHj8aSX8Hg8JpPZVMPPiRMnAgCmT5++bNmyiIiItWvXNrVmBoPR\nmivyluFyuTQaDf8ZL5qSlJT0888/f/78uY11ux1hx23tnb77EeddG7VzMpJvJgl0VXVIT2w+\nMdl9wsls2MifBHx+aPDaRYsWMRiM6dOnEx1RC5HtcFCs0aNHm5qaPnr0qLi42Nvbe+zYsZK5\nQ+zt7b/59Wvbtq1i/xrU5GS9CROE/v4gJkaBqyW5ioqKxYsXR0dHAwCmjvQPW7ZVS0OL6KCg\nFtLS0IreEbVwx6ro2EteXl5//fXXiBEjiA5KhZEisTM3Ny8vL5d+8FpSUvL1ICZ8Pr+mpsbE\nxAR7qaGh4eHhkZCQIGPNze2qoxB8Ph+fHmrfJRaLQ0NDN23aJBQK+w7w/n37wWY1qiPkr6co\n2NNk1Y0fADBq3GSBQHAg7Pc5c+aUlpauW7eO6IhagjyHg5L06tWrV69eXy9vaiziqVOnKjki\n9RcTE7NkyZKSkhIjPcP9q4LHDBpOdERQazHo9GMb95gaGu+PjhgzZsyhQ4ckrRqg5iLFz56L\niwufz8/IyJAsSUtLc3Nza1Ts0qVLCxYskG74VV1drasLm/9/W15enre399q1a8VidMFv60L2\n/QG7Sqgc37GT1m/ZQ6fT169fP3Xq1Pr6eqIjgiAiZWZmDhs2bNKkSSUlJaMHDnt+Og5mdWqD\nQqFsX7ghZMkWVIzOmzdv5cqVatmEAwekSOzs7e2dnZ2joqKqq6vFYnFMTExxcfHIkSOxd2/d\nuhUXFwcA6NevH4qi4eHhtbW1CII8efLkyZMn37xW/sGJxeJDhw65ubk9ePDAqk3boycvT5u5\nSPawNxBpefuM2XckWl/f8OzZs926dUtJSSE6IggiQHV19apVq9zc3GJjYy1NzM8FR5wNOmZq\nCGceUjcL/GaeDTqqpaEVFhY2cuTIyspKoiNSPaRI7AAAK1asEIlEM2bMmDBhwqVLl5YtWyaZ\ne+f+/fvx8fEAAGtr61WrVr1582bKlCk//fTTrl27PD09/f39CQ2cdFJSUnr37r1o0aK6urpx\nE6b9GRPX0U1Wx2GI/Ny79j4Zfcetc7cPHz707t176dKlVVVVRAcFQTjhcrm7d+92cHDYvXs3\nQNGlU+a/OHt/1AAfouOClGVk/6H3jl6ysWgTGxvr4eHx5MkToiNSMRSsKRJJFBQUcLlcGxsb\n6XE7c3JyqFSqra0t9hJBkOLiYi6Xa2Fh8c35ZAlXV1dHp9PlHzheUfLz8zdv3nz69GmxWNy2\nncOaTbu6eLTwdmanCV7aORkvbj8XmFspNkjcoCjK4/E0NTWJDqTlsEk56XQ61tYeQUTn/jx6\n4thePp9naGi4cuXKhQsXkr8pAlGHw49DdPUqfexYob8/Q/mdJ7ABinV0dJRdkVAo5HA4CIKc\nO3cuNDS0qKiIQqGMGTR867w1dm3aKbYubPAEHDpaicViHo9Ho9HwqUvVN6qCUzln27K4Zw/p\ndPrq1as3b95Mo9Hq6+tx6BApEomqq6vx6XyJDVCs2IrIldipB/x/yQoLC3ft2nXs2DE+n6+t\nrTNj9m8Tp8xpTXN1mNiRQaPEDlP4Oe/Q/qCH/9xCUVRPT2/mzJlz5851dnYmME7ZYGLXAiKR\nqNFAATJQb9/W8vcX+PkJTpxQalQAxynFCgsLjx079scff2BP4gZ17bth1nKPDp2UUZe6TimG\noig+vcdaMKWYnMSo+NBfJ3ac2MsXChwcHHbt2jVo0CA4pRgAQPZMFTCxUzw8f8kyMzP37Nnz\nxx9/8Pl8BoMx5qepM+csNWh1uxODv0/TKysqfp4lZiv90lxJ1DWxw3x4n/bn8YPxD+5gc5X2\n7t176tSp/v7+xsaka3IEE7sWwEZ1lrOwOCsLvXABuLjQxo5ValQAAJFIJBKJlLc3xWJxfHz8\n8ePHr127JhAIKBTKkJ6DVkz9tYerEtuTYPmWZJwapVYkFAqpVCo+danNRr3PzVyye31y+isA\nQL9+/TZu3Dhw4EAl1YURi8UNDQ00Gg2HXxCxWMzn85tbkexjUM0TO+z6EudKcRi4C0GQO3fu\nHDt27O7du2KxmMFgDB/lPz1gkblFG4WsXyAQiMViFoulul0uVH0cOyDHPOhFXwquXDpz58bf\nFeWlAAA6ne7l5TVhwoQxY8Z8d9ZF3JB8HDs1GIdFIBDU1NSwWCwcnpAq71Fsenr6+fPnz5w5\n8+nTJwCApobmeM+Rv/oFdHLqqPC6GlGDp5bfrEudNkosFkffubgtcndhWTEAwN3dfe7cuRMn\nTlTSiQ4+iiU1oqYUo1AoSroHnp6efuHChZiYmKKiIgCAjq7+yDETf/KfYWxirsBaxGIxiqI4\nPC9QHtwerygPtgnf/S4hCJLy7PE/d68lxsc1NNQDAJhMpqen57hx43x9fXGYxFo2pR4OrYf/\nAOYKp7qJnVgsTklJuXbt2uXLl9+9e4ctdLV3nubrP2noOA0G65u3qxVOzXIgSV3qt1ENPO4f\n16KP/H3yU2E+AIDJZA4ePNjX19fb21t6HvnWg4kd1JjCnz1hp7+rV69evnz5/fv32MKObu6j\nx08ZMmyshobi7xXzeDyxWKypqanSd+zU+FHsN/H5vKcJ//xz93pi/D0ejwsA0NTU9PX1nTJl\nyvDhw4m6ZwYfxSqbyiV2RUVF//zzz927d2NjY0tLS7GFNuZW4zxHTvQZ7+bgDP5NF2Bi15q6\n1HWjGAxG3LNH0Xcu3k6818DjYm9ZWlr27du3Z8+eXbt2dXd3b2WeBBM7qDFF/ZLl5+c/ePAg\nLi4uLi5OcvqztLLx9hnjM2Kcrb0SZ0WEiR0ZNDexk+ByGxLj4+LuXE168kAoEAAADA0N/f39\np0yZ0rdvX5z3KUzslE0lEruCgoLHjx8nJCQ8fPhQcnUKAOjk6DKsz+CR/Ye6t3eT/mbCxK71\ndan9RjXwuPeT4+8+fRj/8mnO51zpku3atXN1dXV1dXVzc3NxcXFxcWnWFwkmdlBjLf4lE4vF\n79+/f/LkSUJCQkJCwsePH7HlFArFsX3Hvv29+3v6dHBWSr+wRmBiRwYtTuwkams4D/65GXvz\n0utXz7BuFm3btp04ceLEiRM9PHAa3RAmdspGzsSupqbm1atXKSkpz549S0pKKigokLxlZmgy\nwKOPV/d+g3sOtDBuPHUkBiZ2ra/rh9qo4orS5PRXKe9SUzPevMl+V1ZVIf0unU53cnLq1KlT\n586dO3fu3KVLFwsLCxkVwcROYRITExMTE7lcrr29/bhx45oao07OYgRq1i9ZVVVV0r+ePXvG\n4XAkb5mZW3p069OtZ/8evQYYGZsqLd5vgIkdGbQ+sZMoKS6Mu3357u3L2Vn/u1lib28/fvz4\nMWPG9OrVS6ktEdU+sSP8xEWGxE4kEuXk5Lx9+/bdu3dv3rx59epVTk6O9I+LjblV707de3fq\n3q9Lr/btHL5bEUzsWl/Xj7xRJZVl6Tkf0nM+fPiUlf4x40NuVj33/83HaG5u7u7u7vGvdu3a\nSb8LEzvFiImJOX/+vI+Pj6Gh4YMHD2g0WlhY2Nf7T85ixJL9S4ai6Lt3754+ffrkyZOkpKQP\nHz5IdgGVSrOzd3Lr3M2tS7fO7j0tLK1xjPr/gYkdGSgwsZP4mP3h3t1r9+9ez8/73/1gIyOj\nIUOGeHt7e3p62tnZKaoiCfVO7Mhw4sI/sePz+ZmZmRkZGZmZmdh/srKypGfxBgAY6Op3cXJ1\n79Cpm0uXbi5dmroz1xSY2LW+LrhREiiK5hUVvMl+/zb7fVrWu7Sst/nFX6QLGBgYuLu7d+nS\npXPnzm5ubk5OTlwuFyZ2rVJTUxMQEDBjxoxRo0YBADgczty5c6dOnYq9bG4xwn39S1ZeXp6c\nnPzs2TPsqUR1dbXkLX19w46dPDq6dXXr1NW5Y2ctbYL7MGLablmhUZifs/OISGWnYoSJnWw5\nWR/iH95JeBSX8T4Ne0oLALC0tOzdu3f37t27dOni5uZmaWnZ+orUOLEjyYlLmJAANm4UDxrE\n2rJFISuUVlZW9vHjx5ycnJycnMx/SZ/BMJosDUcbuw7tHF3s2rvYtXd1cLE2a9WXByZ2ra8L\nbpQMVTXVqZlv0zLTUzPfpma8/fjlk+Q0CACg0+m2trbOzs5ubm7Ozs6Ojo6Ojo7KG1pF4Ymd\n0kcvlEdqaqpQKPTy8sJe6unpeXh4JCUlNTrxyVmMcKWlpV++fMnLy3v37l16enpqamp+fr7k\nXSqVZu/YoVPn7q6du7l26mptY0tgqE3Re/tKOycjV8AnOhBIWewdO9g7dpg5Z2lVZfnzpPiU\n5wmpL5K+fM67ePHixYsXsTJ6enoODg52dnY2NjbW1tZmZmZmZmZGRkYGBgY6Ojr6+vrEbgLh\nSHLiolRU0B89Epo175aYBJfLLS0tLS4uxv4tLCz88uXL58+f8/Pz8/Ly6urqGldHoVibWdq3\nsXWwsXO0sXO0sXOysbc2syLtiDYQ9DUDXX3Pbv08u/XDXtZz699kv0/Levc2+/2b7PfvczOy\nsrKysrKuXbv230cMDGxtba2trdu2bWtlZWVhYWFubm5mZmZsbGxkZESqJ4ekSOzy8/ONjIyk\nG53Y2NjcunWrZcVao7a2NisrCxtKGzujIQhSU1ODvVtTU4MNd4z9p76+XiAQVFVV1dXVcTic\nysrKkpKSwsLCr+cCMjI2de7Y2aWju4ube0c3d21tVZ3OAVI/BobGPiPG+4wYDwCoqCh99zY1\n411aVkZ6Tk5GceHnFy9evHjxoqnPstlsFoulp6fHYDDYbDaVSsWuO3V0dOh0Op1O19HREQqF\n2tra2GGrpaWFnf60tbWZTCaNRsPmujUwMMA+y2KxtLS02Gw29hCE5LkCeU5cXxMIBHV1ddXV\n1RwOp7q6urKysrKysqysrKKiovxfpaWlZWVl9fX1Ta2ETqPbWLSxtbSxtWprZ9XWzqqdraVN\nOwtrXR2yz1AMQc2irandy61bL7du2EsEQbLzP2Z//pT9OTe74GNOQe7HL3mFZcUvX758+fLl\nt9egrW1oaGhgYKCnp6enp6ejo6Ojo6Onp6elpaWpqamnp8dkMnV0dJhMpra2Nnaiw06bKIrS\n6XRdXV0FNnwiRWLH4XAaDaPKZrNrampQFJXeVDmLSRMIBM0aoDgmJmb+/PnNib0xOoNhbtnG\nwsLayrqttY2drZ2TnUMHQyMT6TLyzwJJFOzxPJ8v4JM+VBmwp7FER9FaCILgthXa2rrdew7o\n3nMA9lIoFBR+yS8u/FxS/KWiorSyvKyqqqK2prq2ltNQX89tqK+rr62rq6uoqJC92tbQ0dHB\n+nbIc4OQSqXKSEO/qTX3HZV34hIKhTLyrUboPB4bgJs3by6zta2trUUQhMPhyNnGhk6jmRma\nGOoZmBoYmxgYGesbWZqYmxoatzG1tDSxsDA2o3/VsQbPwwq3Lz+eG4U9u8ShIrhRrWFtZmVj\n3mZw9/6SJQKhoKCksKDkS3F5yZey4rKq8uKK0gpOVUV1ZWVNdVVNdUF9gXTv72YpKipqVnsV\nPT09GYkgKRK7r2cIoNFoX88cIGcxaSiKikQi+SMxNDTs27cvnU6nUCjYvQQAgK6uLnbnQEtL\nC2vwgeXaGhoaGhoaOjo62traurq6BgYG2C1Z+asjLQ0mFQDQoR1bbAUvzX9oHs7GAMgaGAVL\nQfh8Po/Hk1xHYW2wsIUAgLq6OuwwxNIO7D/YhGkNDQ0ikQgrUF9f39DQgHUFwO6Uoygq6Sf+\ndbuur1Gp1GYd762kvBMXNi+4nGFQxWIAQH19/ad/c0E2m43dHtDV1cVuG+jp6WG3E7BzFPY8\n3cjIyNDQUP7txeDWnQrPu7Vwo1rjB9koJgD2wNy+6fNhTU0Nh8PhcDi1tbV1dXUNDQ0cDqeh\noYHH49XW1vL5fC6Xy+PxeDxeQ0ODUCjE/i8QCLB5FxV47iJFYqehodEoB+fxeNiTmhYUk8Zk\nMpvV4HH8+PHjx4+Xv/w3YZMHk+qJe7NRKAAAXV1dCmmmHG0usVhcU1Oj0u3AsPYA2E17omNp\nOYUcDpKmEd+F5yS5JDlxIRoaAICxY8eWRUZKHnYriVAoFAgEOAwyJRKJamtrsedWONTF4/Fw\nmHwP2yjsARwOdeGzUdg1GD4bhSBIQ0MDDr2/sY3CGpPI/6mWnXxatlGyn9uSIrEzNzcvLy+X\nfjBRUlJi9lVbYDmLSaNQKPjPForNjKnSs5QiAAAAqFQqVWW3gkKhELL3FQi7uaXqW6GQw4FG\no5GwXy1JTlwolQoAYDKZ2sZK78OOzfyLwxcS66KIW11wo1oMe+iPW11wo+RBirbJLi4ufD4/\nIyNDsiQtLc3Nza1lxSAIgnAAT1wQBJEQKRI7e3t7Z2fnqKio6upqsVgcExNTXFw8cuRI7N1b\nt27FxcV9txh5sNlsEt5daBZaejpAUWrbtkQH0nJUKrUF7YdIhclkGhsb4/CAQ6nU4HBoCklO\nXPQxYwCKMmJiFLVCGbCmezhUxGAwjI2NcatL0qJa2RUZGxvjVhc+FdHpdNw2ik6n4zBiMPh3\no3CrS+EVkWKAYgBAaWlpcHBwbm4ujUZjMBgLFy4cMOB/XfNWrlypqakZGBgouxgEQRDO4IkL\ngiCyIUtihykoKOByuTY2NtKX+Dk5OVQq1dbWVnYxCIIgQsATFwRB5EGuxA6CIAiCIAhqMVK0\nsYMgCIIgCIJaDyZ2EARBEARBagImdhAEQRAEQWoCJnYQBEEQBEFqAiZ2EARBEARBaoIUU4pB\n5MHj8U6dOpWQkMDlcu3t7WfPnu3o6Eh0UD+oxMTEgwcPurm5bdiwgehYIOLJeWyq1iEsZ7T+\n/v6N5ttdtWpV//798Qqz2Wpra/ft25ecnLxv3z47O7tvllGtPQXk2yjV2lO1tbVnzpxJTk6u\nq6tr06bNpEmTevTo8XUxldtTMLGD/p8DBw68f/9+/vz5hoaGN27c2Lx5c3h4uJGREdFx/VhE\nItHx48cfPHiAwwzokKqQ89hUrUNYnmhRFOXz+ZMmTZKeh83Gxgb3YOWVmZm5a9cuLS0t2cVU\na0/Js1GqtadQFA0ODi4pKZk+fbqRkdHdu3eDgoJCQ0OdnJwalVStPQXgo1hIWlFRUUJCwoIF\nC/r27evs7Lx8+XJNTc2bN28SHdcP59OnT2/fvt27d6+1tTXRsUCkIOexqVqHsJzR8ng8FEUd\nHBzcpOAz3VPL/PXXX8OGDVu4cKGMMqq1p4B8G6VaeyorKys9PX3x4sWDBg1yc3NbunSpvr5+\nQkJCo2Iqt6cATOwgaa9fv6bT6R4eHthLGo3m7u6emppKbFQ/IHNz89DQUAsLC6IDgchCzmNT\ntQ5hOaNtaGgAAGhqauIdX0vNnz/fz8+PQqHIKKNaewrIt1GqtafatWt36NChTp06YS9pNJqh\noSGHw2lUTOX2FICJHSStsLDQ2NiYTv/vAb25ufmXL18IDOnHxGaz4axTkDQ5j03VOoTljJbL\n5QIAWCwWrsG1grGx8XfLqNaeAvJtlGrtKSaTaW1tTaPRsJfl5eV5eXkuLi6NiqncngKwjR0k\nraGhoVETCi0tLS6Xi6Ko7As1CIKUSs5jU7UOYTmjxdKF+/fvh4WFVVZWmpubjxkzxtvbG+9w\nFUq19pScVHdPCYXC3bt3W1hYeHl5NXpLFfcUTOwgCIIg8hIIBFpaWuXl5XPmzNHQ0EhMTDxw\n4ACCID4+PkSHBv0/KrqneDxeUFBQaWlpcHAwg8EgOhwFgIkd9B82m11fXy+9pK6uTktLi7TX\nJRD0g5Dz2FStQ1jOaDt27Hj+/HnJS1dX15KSkuvXr5M8XZBNtfaUnFRxT9XU1GzdupXL5e7c\nudPU1PTrAqq4p2AbO+g/VlZW5eXlAoFAsqSwsLBNmzYEhgRBEJD72FStQ7jF0bZr1660tFSZ\noSmdau2pFiP5nuLz+Vu3bkVRdNeuXd/M6oBq7imY2EH/6dKli1gsTk5Oxl7y+fwXL15069aN\n2KggCJLz2FStQ1jOaJ89exYaGioSiSRLMjIyzMzM8AtUCVRrT8lJ5fbUsWPHGhoatm7dqqOj\n01QZVdxT8FEs9B8TE5PBgwdHREQAAPT19S9dukSlUkeMGEF0XD+c4uLisrIyAEBtbS2dTn/z\n5g0AoE2bNgYGBkSHBhFD9rF58OBBFos1d+5c1TqE5dwoc3PzZ8+eBQcHjxkzhkqlxsfHv3nz\nZsWKFYTG3iQURd++fQsAKCgoAABkZ2fX19czmcz27dsDld1Tcm6Uau2p3Nzcf/75Z+rUqZ8+\nfZIsZLFY2ADFKrqnMBQURYmOASIRgUBw6tSp+Ph4Lpfbvn37efPmkXbccDV26tSpixcvNlq4\nZMmSwYMHExIPRAYyjs2VK1dqamoGBgbKLkZCcm5Uenp6dHR0Tk4OAMDa2trf35+0t0wEAsGE\nCRMaLTQ1NY2KigIqu6fk3ygV2lPXrl3D4pdmZWV15MgRoLJ7CgMTOwiCIAiCIDUB29hBEARB\nEASpCZjYQRAEQRAEqQmY2EEQBEEQBKkJmNhBEARBEASpCZjYQRAEQRAEqQmY2EEQBEEQBKkJ\nmNhBEARBEASpCZjYQRAEQRAEqQmY2EEQBEEQBKkJmNhBEARBEASpCZjYQRAEQRAEqQmY2EEQ\nBEEQBKkJmNhBEARBEASpCZjYQRAEQRAEqQmY2EEQBEEQBKkJmNhBEARBEASpCZjYQRAEQRAE\nqQmY2EEQBEEQBKkJmNhBEARBEASpCZjYQRAEQRAEqQmY2EEQBEEQBKkJOtEBQFAzHD582MPD\no1evXk0VePny5YMHD7D/M5lMU1PT7t2729nZSZe5fv06n8+fMGGCcmOFIIisysvLT506NWnS\nJCsrq2Z98NSpU9bW1l5eXpIl2Dln2rRppqameAYTHh7O5/MBABQKRUdHx8HBoVevXpqamo3K\ndOzY0dPTU8bHAQBUKtXCwqJbt24ODg7fLCCho6Mzd+5cycva2tr4+Pi8vDwWi2VraztgwAA6\nHSYVxIP7AFIlx48fp9FoMhK7Fy9e7Nmzp3v37nQ6XSAQ5Ofnl5WVDR48eM+ePcbGxliZ+Pj4\nmpoaRSV2tbW158+fnzNnjkLWBkEQDjgczunTpwcPHtyCxK53796SxO7YsWPBwcEikWjYsGEt\nTuxaFkx4eLi2tratrS2KotXV1dnZ2bq6ups3b/bz85MuM3HixKYSO+zjAAAEQfLy8srKyubN\nm7dx40ZJAS0tLRsbG+lPGRsbSxK7v//+e9OmTTwez8nJicfj5ebmmpiYHDlypEePHs39C0AK\nhkKQ6ujfv//p06dlFIiIiLC0tORwOJIlDx486NKly8CBA+vq6pQR0qNHj3r06KGMNUMQRDae\nnp7r16/H/r9p06auXbtGRkZaWlq+ffsW50jat2+/efNmycvKyspVq1ZZWlpGR0c3VUbGxxEE\nCQ0NtbS0jI+P/+5nURS9efOmpaXlb7/9JjnZ5ufnjx492s7OLj8/vzXbBbUebGMHkRSfz79y\n5Up4ePjVq1cFAgG2UFtbW0tLCwDw6dOnsLCwL1++fHc9gwYNOn36dE5OzvHjx7El169f//vv\nv7H/X7t27ebNm58/fz58+PCHDx8AACKR6NatWwcPHvzzzz+Liopkh/T06dPIyEgOhxMWFvby\n5UvFbT0EQUpUXl4uOYFUVFSEhYVVVVW9e/fu6NGjf/75p/SJRSwW37lz5+DBg1euXJGciDCW\nlpZ37tzx8PD4ev3nzp2TnGQwdXV1YWFhb968kV4YGRl59+5d6WAwycnJx48fP3r06P3791EU\nlWeLDAwMQkJChg8fvm3btvr6enk+Io1KpS5ZsoRGoyUmJn63sFgs3rZtm7u7+969e3V1dbGF\n1tbWJ06c6NGjx+fPn5tbO6RYMLGDyKi8vHzIkCFbt25NTk4ODAwcPHhwRUUFAODMmTMjRowA\nAOTm5u7Zs0eexA4A4Orq2r9//+vXr2Mvr1+/fuHCBez/N27c+Ouvv6ZOnfrw4cOamprKykof\nH581a9a8f/8+JiamX79+khZ73wyJy+WWlZWhKFpZWfl1exQIgsipvLxccgLhcDh79uwJDw/f\nvHkzh8O5du2ap6enJDtZtGjRvHnzUlNTL168OHnyZLFYLFnJ/PnzJQ08Gjl37pzkJINhs9lX\nrlyRXF4CAAoKCrZs2VJfXy8dDABgyZIlP//8c0JCwqtXr5YsWTJt2jQ5czsAwIIFCzgczuPH\nj+X+S/yHSqVSqVQEQb5bMi0traCgYNasWVTq/0shjIyMoqOje/fu3YLaIQWCbewgMgoJCRGJ\nRI8ePdLV1a2trR04cOD+/fu3bdtmZGSEFejWrdutW7ekm/rK1rlz58OHD3+9nMlk3rt3Lyoq\nCms0s2bNmi9fvjx48MDCwgIAsGzZshUrVjx//pxOpzcVUlJSUlVVVVBQkII2HYIgXGHZyfv3\n7y9cuEChUPh8fufOnS9duvTbb7+9fv366tWrISEhU6ZMAQBER0evXLmyb9++313nvn37GiU9\nAIDRo0f/8ccfIpEI62Fw8+ZNLS0tHx+f/Px8SZn6+no6nR4VFTVw4EAAwIcPHwYPHpyUlCRn\nttS5c2cAQGZm5rBhw+Td/n/FxsYKhcKuXbt+t2RmZiYAoEuXLs2tAsIHTOwgMoqNjZ04cSJ2\nk19HR+fy5cssFku6gI6ODnYKk5OmpqZIJJKcUiUoFIqenp6kKfSdO3fGjh2LZXUAgICAgL/+\n+uvVq1fdu3f/bkgQBKmun376iUKhAABYLJa1tTXWDOPp06cAgLFjx2Jl/Pz8JH0LZPvmNefo\n0aP37duXlJTUr18/AMCNGzeGDh2KtVFaZGYAACAASURBVC2R0NbW3rVr16NHjw4fPszj8bD7\nZ7m5uXImdnQ6ncFgcLlceQqnpKTs2rULAICi6KdPn27fvj1kyJChQ4dKCjx79mzbtm3SH3Fy\ncpo0aRK2fjabLU8tEP5gYgeRDo/HKy8vl2RXAIC2bdu2cp2VlZW6urrf7IpvaWkpXe+XL1/C\nw8OxJdjT1ZycHDc3N4WHBEEQeZiYmEj+T6fThUIhAKC4uFhHR0dbW1uy3MzMrMVVtG/fvn37\n9rdv3+7Xr19RUVFqaurSpUsblRGJRD/99FN2dravr68kJHkej2Jqa2uFQqHkyYZslZWV6enp\nAAAKhWJubn7w4MHRo0dLF6ioqMAKSGBXswYGBgCAqqoq6T8aRB4wsYNIB2tQ0qidcislJSV1\n7Njxm28xGAzpl2VlZdLnstGjR5uZmSkjJAiCyAO7XfddWMLXYqNHjz59+vT27dtv3rypp6c3\naNCgRgWuXr364sWL+/fvOzk5AQC4XO6+ffvkX39SUhIAoKlzXSNDhw7dunWrjAIjRoz4ZgEs\ntufPn2P/kVZbW6ujoyNvuJBywMQOIh1NTU0jI6O8vDzJkvT09IaGhu7du7dshfHx8W/evAkN\nDZVdTENDw8TExNPTc82aNV+/q9iQIAgiPxMTk9ra2vr6euymHdZZqjUrHDNmTGhoaFpa2u3b\nt319fb9+hpCdnW1iYiJJmFJTU+VfOYqihw8ftrS07NmzZ2uC/K4OHTo4ODhERkb6+/szmUzJ\n8pqamkGDBi1atCggIECpAUCywV6xEBl5e3vfuHGjtLQUAMDlcufMmXPp0iXpAnV1dVhq9d1V\n/fPPPwsWLOjSpcvEiRO/W3jYsGGXLl3icDjYy5cvXy5ZsgQbO6CpkGg0mpwtWiAIUi3YWLuX\nL1/GXp45c0bOO3a5ubnS14EStra2rq6uV69eTU5OljTdk2ZoaFhVVVVTUwMAaGhoOHz4MJPJ\n5PF4362xsrJy8eLFz58/DwoK+rrfhsIFBgbm5OTMmjWrpKQEW5Kbmzt58mSBQODr66vs2iHZ\n4B07iIzWrFmTlJTk7e3t7u7+7t07JpO5YsUK6QLJyclTp069fPnyN0c5nz59Op1ORxAkPz+/\nuLjYy8srPDycRqN9t97Vq1cnJycPGjSob9++DQ0NDx8+nDlzJnax3lRIHTt2rKio8Pf3HzFi\nxC+//KKY7YcgiAS6d+8+ZMiQDRs2xMbGcrlcsVjcqVMnrGGGUCicPHkyAKC2thYAsHLlSm1t\nbWzqBQDA4sWLtbW1Y2Jivl7n6NGjd+/ebWpq+s0ZdMaOHbt///5x48Z17do1MTFx06ZNlZWV\nJ0+eZLPZWHXSbty4gbUbqampycrK0tDQOHz4sHTvBwDAtWvXXrx4Ib3kxIkTLZ4kQ2LAgAHH\njh1btWpV9+7dHR0d+Xx+Xl6ek5PTlStXWr9yqJUo8g+QA0EQBEFqQHp61urq6uPHj0+YMEHS\nI+rMmTOmpqZYhoQgyJ07d3JycqytrUeMGHHt2jU9Pb2hQ4eKRKL9+/c3Wq2uri42u+C5c+eY\nTOY35y0sLi4+e/asi4vL8OHDvw4GKxAbGysQCLy8vOzt7T99+oQNg9zoIlZ6Llds7i8vL6+v\n54r9enzNOXPm6OrqyphGVvJZ2QUAAHw+/8GDB3l5eXQ63dnZuXfv3nI2VYSUCiZ2EARBEARB\nagK2sYMgCIIgCFITMLGDIAiCIAhSEzCxgyAIgiAIUhMwsYMgCIIgCFITMLGDIAiCIAhSEzCx\ngyAIgiAIUhMwsYMgCIIgCFITeM88gaLo1atXnz9/vnjxYgsLi6aKJSYmJiYmcrlce3v7cePG\nYUP/f/ctlYOiqFgsplKpJB/UERvskORBisViFEXJ/8fE9jjRUciEokh2NoVOp9raEh2KLCiK\nYnuc6EDIi6gvG7EHI4Ig8kwzo3DEns+J2tdoXZ24qIiiq0s1M8O/drFYTKFQiPqaUSgUoo4v\n2fXiGlNdXV1QUNC5c+fevn0rY3rNmJiY3bt36+rqOjs7JyYmrlmzRjJ2toy3VBGfz6+qqpJn\nwlNiNTQ0kP/vXFdXV1VVJRKJiA7kO6qrq8ViMdFRyCQS0ZycKN+aq41UEATBZnPCR0ZGRnR0\ndFVVVaPl5eXlt2/fvnTp0uvXr2V8XM5iCoQgCDblKP4aGhqqqqoEAgH+VaMoWl1djX+9AAAe\nj1dVVUXUzNHV1dWETDeA3LpFc3JCVq3Cv2oAQG1tLYIg+NcrEomqqqrwPP9Iq6+vl72vcb1j\nFx4eLhKJVq9evW3btqbK1NTUXLhwISAgYNSoUQAAHx+fuXPn3r17d9SoUTLewm8bIAj68Vy5\ncuXUqVMIgvTs2dPAwECyPCUlZefOnebm5gYGBmfPnu3bt++yZcu+vn8gZzEIgqDWwzWxGzp0\nqLu7e2ZmpowyqampQqHQy8sLe6mnp+fh4ZGUlDRq1CgZbyk9dEhlcTicjIyMmpoaNpvt6Oho\nZGREdESQiomMjHz69GlAQEBkZKT0coFAcODAAU9Pz4ULFwIAMjMzV69e3atXrz59+rSgGAQR\nrrS09MuXL5WVlSiKslgsExMTW1tbFotFdFxQ8+Ca2Hl4eHy3TH5+vpGRkXTLORsbm1u3bsl+\nC4IaQVH04sWL4eHhCQkJ0vfqbW1tPT09hwwZMnjwYBMTk29+tqysLD8/H3voZmxs3KFDBw0N\nDZzihsjH2Nh47969xcXFjZanpaVVV1f7+flhL52cnNzc3B49etQoY5OzGAQRQiQSXbt2LSYm\n5uHDh6WlpY3eZTAYHh4efn5+s2fP1tPTIyRCqLnw7jzxXRwOh81mSy9hs9k1NTUoisp4q6mH\nGgKBgMwt2LC2Vnw+XygUEh2LLFicPB6P6EBkwbK3uro6CoWSkZGxaNGilJQUAABbR7O9Wzt9\nQ52a6rpPWYW5ubm5ubknTpygUCgODg4dO3a0tLRkMpkikai0tPTjx485OTmN2lGxWKwBAwbM\nnDlz2LBhrX98JhaLiWr5JC+hUB8AFEU5BLVVkhPWVr2VDar09fW/W2bcuHEAgK8Tu5ycHF1d\nXVNTU8kSR0fHR48etawYBOEMRdHo6OiNGzfm5uZiS2zbGllbGhgZsul0akODoLi0JiO75Nmz\nZ8+ePduxY8f+/funTJlCbMyQPEiX2InF4kYdmmg0GnYGl/FWU32gUBQlf2t6bNOIjuL7VCJI\nBEEuXbq0dOlSLpdrY2c+Z9nYQcO7Mhj/fc8LC8pSEt8/e5z+4un7rKysrKysRmugUinmVkYW\n1saGRrooipYVV2e+y4+Li4uLi+vWrdvevXs7dOjQyiDJ/p38NzyyxwkAIDTIqqqqRnmhvr5+\nRUVFy4pJE4lEre+uhJ0b6+vrW7meFsCuVPl8PiF7B0VRQrYa21iBQEBIJwYURZt1F6O0tHTe\nvHlxcXEAgO7ubedO7ztiSEcTI3ajYggiTnj2MTzq0dXbaVOnTn39+vXvv/8uXYAqFNKJ+5sj\nCMLlcvHvmor9GiIIQuA3TQbSJXYaGhqN7gzxeDwmk0mj0WS81dTamEymdEtnsuHz+Q0NDRoa\nGpqamkTHIktDQwONRiN5S4u6ujqhUBgREbFx40YAwLQFI3/bMInJZDQqZu9kY+9kM3GmDwDg\nc15pbuaX8tJqBBFTKMDIRN/C2ridvQVLgyn9ER6Xf/daUtS+KykpKUOHDj18+PC0adNaHGd1\ndbWuri7JB+moyMqi0mhkPnYAAAiCNDQ06OjoEBWAQCBgMP7fF4xOp4vF4kbDbchZTBr2c6WQ\nIInqpAkAIKRXLIbArRaJRERdbMi/1S9evPjll1+Ki4vbWOqHbRvnO7Qjtvybz476dG/bp/v0\nq7ffzFkWHRoaymKxlixZ8t/bXl4NWVmAxUIJ+psTOGKDWCwm6psm40ElIGFiZ25uXl5eLh10\nSUmJmZmZ7LeaQqFQCBnQSE7YrzvJgwQAUKlUKpVK8iApFEpISEhoaCiDSd+2f8HICf2++xHr\ndmbW7b4/9pKmlsaYSYOGj+97aOdff4RfnzlzZkFBwaZNm1ocJ41GI3lih+rro6Tf49ipgMAg\nmUxmox9CgUDw9ZEiZzFpdDq9UbOTFhCLxTweT0tLq5XraQGseYmGhgadTsBQqQ0NDYSMbyoU\nCvl8PpPJZDKZ3y+taPX19VpaWvK0Fbly5UpAQACXyx0zvFPUvin6enLdVvAb09XYSMd38pHg\n4OA+ffp4enpiyxEE4TIYDAaDkCt/LpfLYrEIuWOH3e8g5KYMj8eTvaPlPeqqq6vlaYzSei4u\nLnw+PyMjQ/LAKy0trXPnzrLfgqDQ0NDQ0FCWBnPfqRV9vboofP1MJmPZ5imu7vbrF4Rv3ryZ\nz+dv375d4bVAKsTIyKhRc8yqqipjY+OWFZNGo9Fan7AiCCIQCAjp9yMSiYRCISE/9lhiR8hW\noyjK5/PpdDohtWNb/d3E7ujRo4sWLUIQZOOK4dvW+jar0fCQQS57g/wWrj4/f/78t2/fYjfL\nBQIBl8vFHqm1agNaBMuk8b9+EIlEDQ0NVCqVkK3+bqN8efNcCwuLyZMn3717V0kNrW7duoU9\n7Le3t3d2do6KisLGcY2JiSkuLh45cqTst6Af3IEDB7Zv385kMZSU1UkMGdUr/NxaDU1WUFBQ\naGio8iqCyM/Jyam2traoqEiy5MOHD+3bt29ZMQhStu3bty9YsAAANGLvlMB1o/6PvfMMaCJ5\nG/ikJxAISBdEiiIgFuzlbICCoAgWRFFRxN7L2fXuxMrZzl7wTjwb9gYignoiCBYEsYGCBQWk\nBEggfTfvh73Lm3/AECDJBpzfB012Z2eeCbuzz8w8pRGuYHOmDRg2xOXz588RERGakBCiFlRV\n7Jydnc+dO+ft7d22bdu1a9fWNjmvF5FI5O/v7+/v//PPPwMAFi9e7O/vHx4ejp29e/fugwcP\nsM/Lli2TSCShoaFjx469fPnykiVL2rRpU+8pyA/LqVOnFi9eTCKTNh+Y03dwZ00313ug257o\nZRQqeeXKlefOndN0cxCdxc3NzczMLCYmBvv64sWLt2/fenp6Yl9zcnLev39fbzEIRAugKLp4\n8eL169fTqOTzx8NnTO7fuHoIBML+7eOpVPLevXs/ffqkXiEh6oKguv/Ou3fvYmJizp8/n52d\nDQDo37//1KlTg4KCDA0NVblcKpW+fPlS4SCVSsVmrnl5eUQi0V4uN2VBQQGfz7e1ta291Knk\nVPNCIBBUV1czGAwdz3hbU1OD10p7vdy8eXP06NESieSX3TN8AvvS6XTt2FvcvJC8dt4BGo2W\nlJTUoIBkbDbbyMhIx23sysrKiERiq1at8BZEGRKJpKamRtOxtSQSyYYNGwAAPB4vPz+/Xbt2\ndDrd2NgYm6C+ePFi06ZNZmZmxsbGr1698vHxmTVrFnbh8uXLGQwGtrChpJjmwFKuaceERoHq\n6mqBQGBgYIDLViybzcYlDjmfz8cM3XCxa2Sz2cbGxnUuwvH5/ClTply8eNGASb9ycpbnwKau\nFi9YdX5/1P3Zs2cfOnRIJBJxOBw6nd50q9BGUFVVpa+vj8tWbGVlJYVCwSW2H5fLZTKZShZc\nG6DYycjJyYmJiblw4cLLly/19PRGjx49bdq0IUOGwAw5DQUqdk0kOTnZ29ubz+ev2BQ6bqon\ngiBaU+wAAIciLxz6/aKFhcXjx49tbW1VvAoqdupCO4odgiDnz59XOKivr+/v7499Li8vT09P\n5/P5zs7OHTt2lJVJSEggk8myTDnfK6ZRyaFip010U7H78uXL6NGjnzx5YmXBunl2brfOatjj\n+lpU6dh9A4FI/vjxo7GxMVTstIxGFDsZL168mD9/fnJyMgDA2dn5559/njp1qo6/sXQKqNg1\nhadPn3p5eVVVVc1cOnr+6vFCoVDLip1UKl0x44/b1x65u7s/fPhQxdG8GSh2UmlFRgaBTDbS\nbc8k7Sh2zReo2GkZHVTs7ty5M2nSpJKSki4dba6fnm1ro7ap2vRFp/48nfrLL7+sWbSoJj+f\namKib2enrspVByp2ddJIxS41NfX06dOXLl369u2bgYFBYGDg48eP3759O2DAgOvXr+MylNSJ\nUCisrq7GW4rvIvvx4WJnQ3n16lVgYCCbzR431Wv5xkkApx9TwBfNHLM55+WngICAY8eOqdK0\n8vhDOoFYbGJlhZqYVOTk4C2KxmnBuYOhYqdldEqx4/P5a9as+eOPP6RS6fjA7sf/mKyvp84g\nLC9efe0yaHPr1q3zd+ygTZwonjyZcvKkGutXEajY1UnDfo6CgoKTJ09GR0djzhN9+/bdsmXL\n+PHj9fX1URQ9cODA4sWLFy1aFB0d3VTB1QSNRtPlsLpwxa5xZGdnjx07ls1mB0wYvC4yHLu/\ntb9iBwBgMBj7Tq2cOGzN1atXu3btqhCTvU6awYqdWAwAIBAIOq70tOAVO5FIpJaI9iiKKkRa\n0Q5Y8ISamhpcMjpKpVJceo3NLfl8Pi4hc+XT66WlpS1YsCAvL0+PQd3+y6jwSX0BQNWbE9LJ\n0aRPD7u0px/T09MH4nqncTgc7U+Vsb+1RCLBq9fqCVB8+vTpEydO3L17F0VRU1PTJUuWhIeH\nu7q6ygoQicQFCxa8ePEiJiZGdxQ7SMsjKytr6NChpaWlI8YN+HXPbNxXvyytTXafWBYeuPG3\n335zcnKaMGECvvJAWgAUCkVFpzQloChaXV3d9HoaAY/HEwqFDAZD+6F6saziuPRaIBBg8XJx\nCVpbVVVlYGDA4XBWr14dFRWFomj/Xg7H9052cjSv/+JGMWPKT2lPP6akpAwEgEgk4vKbc7lc\nPT097UcpRxCEw+GQSCRcMt/U1NSoJ0DxpEmTiESip6dneHh4QEDA9x7Xrl271jY0VgDzaW3T\npk2dd395eXlhYaHCQRqN5uTkBAB48+aNQrYWW1vbFjllh9TJ06dPvb292Wz2yKCBEXvnEIk6\nsafZpafTb3/MXj1n/7Rp0ywtLWUx2SGQxqGujBp4ZebA3jq4pKvBllJw6TW2DI9jkp5r164t\nXLiwsLDQgEnftGbk/PDBGh0hg0Z1X7jq/PPnz7GveN1pON5m+D5fSlBVsVu/fv306dPbtm2r\nvNi0adOCgoK+d7awsHDr1q2fP3/GUiqFh4f7+PgolElLSzty5IjCQRsbm4MHD2JiKOQf/Pnn\nnwcMGKBiLyDNmpSUFD8/v6qqqsCJQ37ZPUtHtDoM3zE/FRaU7t18LiAgICkpqUePHnhLBNE4\nymeh8sAZKUSjfPv2bcaMGTdu3AAAjBjW6UDkeDX6SXwPpj4twLcr/+JjTTcEaSiqKnZUKvV7\ndgMXL1589uzZ1q1bAQBK7EalUmlkZKS+vv7JkycNDQ1v3rx56NChdu3atWvXTr6Yn5+ffDIJ\nqVS6ZMmSnj17AgBQFBWJRCtXruzfv5HBFSHNl8TExICAgJqamuDp3qu3TsN9B7Y24YsDy75V\nnomKHzZsWEJCAtTtWjzKZ6HywBkpRHPExMTMnz+/rKzM3NRg79ag8YHdtdb0xLE9jkPFTvdo\nwIqdm5tb7ZkoACA/Pz8qKgpT7JSQm5ubn5+/a9cubJ46cuTIpKSkW7duLViwQMlV8fHxHA5n\n7NixAADMDleXnSEgGuLGjRtBQUECgWDaAv8lG0LwFue7rNwyVSgUX/o7ycvL6+rVq4MHD8Zb\nIogGUTILlQfOSCEags1mz507F0tqEjTK/UDkBFMTrQaT8xroHKNPAzVCPp9P0WbDEKXUo9gl\nJiYmJiZin//++++0tDSFAnw+PyYmBkGQelt68+YNnU53dHSUHenYsWNGRoaSS/h8/pkzZ6ZO\nnYopc3w+HwCAi10qBEcuXbo0YcIEsVg8b1XQrGVj8BZHGQQCYcPOGVQa5WxUvI+Pz9GjR6dM\nmYK3UA2HSBRMmQIMDHTFC7qZID8LlQfOSCGaIDExcerUqV+/fjU1YR7eMcHXy1n7L0cqlWw/\nwOlofLYthaJoVgXBj3oUu7KysitXrmDBTS5fvlxnGSKRuGnTpnpbKi0tNTExkd9BMzU1LSkp\nUXLJjRs3mEymzBQdU+xoNFp5eTmbzba0tKzXIUUqlWKO97oJJptUKlVFM8YRFEUJBAIuQsbE\nxISGhiIIsuzXSVPmjqg37KJUKm1KzG21sGrLVFNzo/1bY0JDQx8/fhwZGSn/Usf+3LgLqZzq\nnTuJRCJF52/Lpj876rJ9VpiFKpwCcEYKUR8ikWjt2rW7du1CUXTEsE5Rf4RYmBlit5n26T6p\nv3989qDCQqjY6Q71KHbBwcHBwcFVVVVGRkbbt28fOHCgQgESidS2bVtz8/odqjE/cPkjNBpN\nLBYjCFLn2CoWi2/cuBESEiKL+IXduIcPH3737h2RSEQQxMPDY+7cuUo86kUiEZfLrVc2fBEI\nBOqNMKQhtB+S6sqVK3PnzkUQZOmvE8dN9VRl5MIlglRtQmZ5t7Y1iVh2/MCBAw8fPjxy5Ij8\nWnVVVRWOsqkIXoGpGkoThTQ1NVWLGAqzUHnwmpFi8wdc5mPYvAVFUe23jjWNS6+xP5mme/3h\nw4eJEyc+efKEQafsjAiaFfoTgUCQ/eDaNz72GOjEZNJSUlJKS0u1n4QQe1K0/+fGd1Gm3nUB\nlWzsWCzW77//HhAQoODo0CDIZLLCUIUgCOarXGf5tLQ0Pp8vr0rSaLTevXu7ublt2rSJQqGk\npaXt3LnT0NAwLCzse43i5Y2sIthNqeRH0BFkrt3abPTatWuYVrc8YtK4UK96y2NrdQQCQUf8\nKjz9ejl2aLNm7oGsrCwPD48tW7Zg27Ioiur4nxvU92zqCNhfXBeErD0LlQffGSmO2nlNTY1a\nwiw3Ahx7rdGJekJCwrx58yorKzs6W508MMm5vYV8W7gsEBAAGDbI+XJs1oULF5TExNAcHA5H\n+41i4BWgGNSXwUiZYnfu3DkrK6tBgwYBAGxsbJ4+ffr06dPvFQ4ODlYuB4vFUvgDcDgcQ0PD\n7wmXmprapUsXeR9bOzu7tWvXyr7279//2bNnqampShQ7KpWq/QiZqoNlnqDT6TDzhALXrl2b\nOXMmgiCrt04Lnu6tyiVY5gkajaYLb3oMZzf7s3e27tl45syxW4sXL05OTo6KiiIQCCwWS3eE\nrJOysjICgWBsbIy3IMrQncwTtWeh8jRiRkokEpueJQmbN+Iys8UWC0kkEi6zLIlEov0cUwAA\nFEWxaZsmnm6pVLpjx45t27ahKBoa3Hv3ptEM+v+4K+A4Y/Qb6no5NuvOnTsTJ07UctMIghCJ\nRFwyT2CzX7yeL+UFlN39EyZM8Pb2xhS7euPp16vY2dnZVVRUcLlc2TbEhw8fHBwc6iwslUpf\nvHhRr/rPZDJx1NYhGuL27dvjx48Xi8UrNoWqqNXpLDQaZeXm0AFeXdfNP3jp0qXnz5//9ddf\nP/30E95yQdRJ7VmoPI2YkVIolKbneMU9V6yenh5euWJx6TWWK5ZOp6s9Vyyfzw8NDb1w4QKN\nSt6/fUL45Drcq/l8Pi5ZHxEEGTbEhUQi3r17V19fn0LRqncsvrliyWQyXrliG5954tChQ7a2\ntrLPTRTF3d2dRqPduXNn9OjRAIDS0tLMzMyZM2diZyUSibzy++XLFy6Xa2dnJ1/D7du3T58+\nvWfPHmwjXyKRPHnypM4ILJDmy4MHD0aPHi0UCheunTBpli/e4qiHfkO6XLgfuWr23vQHL318\nfP7+++8xY3TavReiOirOQuWBM1KI6pSWlo4cOTI9Pd3S3PBy9My+PeteDcGRVsZ6fXvYPUzP\nT05O9vDwwFsciFLFbvbs2XV+bhx6enqTJk3666+/SkpKjI2N79y5Y2dnJ7sJVq1axWAwIiIi\nsK9lZWUAAAWfjD59+ly4cGH58uUeHh4kEik1NbWiomLp0qVNFAyiOzx58mTkyJE8Hm/GktHh\niwPwFkedmJixjpxfuyfizIkDN4KCgg4cOND0ZwqiC9Q5CwVyk1U4I4U0mry8PB8fn/fv33dy\nbX3zzFwt5JNoHD6erg/T8+Pi4qBipws0YAETRdH8/HyZ/8Tbt29v3LhBJpODg4OtrKxUqcHf\n39/c3Pyff/4pLi728vIKCAiQraA6OjrKL9oTiUQ3Nzcm839iLbJYrJ07d8bHx+fl5QEAevXq\nNWLECB03A4KozsuXL4cPH87hcCaG+yxYMx5vcdQPkURc+uskqzYm29eenDt3rkAgWLx4Md5C\n1QWCMJctAwYGYP9+vEVpBtQ5CwVyk1U4I4U0joyMDF9f32/fvnkO7HDpxEyWoS5GzCFkf6H+\n+U9IG5N1AMTGxu7YsQNviSCAoGI8rdLSUk9PTwsLizt37gAAbt++7e/vjyXJMTExefr0ae0J\nK6ReMOcJBoMBnSfevXs3cODA4uLigAmDf/tjdiOMYTHnCTqdruN+CXw+/0FC5uo5+xAJun//\n/rlz5+ItUS3EYkClSk1NCaWleIuiDB1xnsjKyoqJiVm9erVCEJNDhw7RaDTMkK6qqko2I23T\npo12ZqS429gZGBjgZWNnYmKi5XbBfzZ2SvJqNoikpKTAwEAulxsc2CP6wBQqtZ5VGD6fj0us\nRPR6BjEsCg3ubf/w3ecv7Ly8vO+ZzmsCfG3sKBQKXjZ2TCazkV6x8vzyyy/v3r1btWoV9nXZ\nsmV6enpnzpyRSqUzZ86MjIysnR4RAlGRT58+eXl5FRcXD/Pv8+vuWToSr0RzDPPvQyQSfp7x\nx4IFC4yNjev1TILoMl26dOnSpUvt43PmzJF9ZrFY48e3wEVoiIbAArMLhcKFM4fs3jSWSGwG\nQ+Jwz45HopPj4uLmz5+Ptyw/Oqoqdrdu3Zo1axbmzPzy5ctXr15t3LgRMwB//PjxlStXNChj\nE8AldKHqYLKhKCoWi/GWRRlYAEINCVlYWOjh4fH58+eBQ7ttPjgPEEBTQrPqcqIRGSiKevr1\n+nXXzF8WH5k2bZqZmRnme64rpAnNmgAAIABJREFUiMWUf//X6dsSi6nRRCG17MQHgdTLH3/8\nsXTpUqlUunX9qFWLmk1YgOFeHY9EJ9+6dQsqdrijqmJXVFTUqVMn7PPt27cBAAEB/9q2Ozo6\nfv36VRPCNR0EQXQ5qQOm2EkkEl0WEvxnBq4Jnam4uNjX1zc/P7/3QLdtR+YTCEAikTSuKkw8\nBEF0XLeTSqXY7+k7tn9xYfnB7RfGjRt39+7dpkT/VjNiMQUAqVSq47clFqetiULqpmKHIAhm\n6NIUsN8Hl0xT2FMsEom0/zBiYatx6TU2xxCLxY1uXSqVrl+/fteuXWQy8fCOCaHBfRo0HjZ6\n8GwSKEoEQArAoH6OVArp3r17bDZba5vCKIoKhULtT0FlWUZwudOaFMdOHgMDA1kM8YSEBEtL\nSzc3N+wrj8fT2TSIFApFNwduDMzGjkql/pg2dkVFRSNHjnz//n3P/q77Tq2gM5pkjoPZ2FEo\nFB23sUMQhEqlYtvNs5ePLSoou3Lm3vjx49PS0rSfkKduxGIAAIFAqDfzFb5gNnY6LiQEoiJi\nsXjOnDlnzpzRY1DPHpvm69URb4kaBlOf9lMfx7vJucnJycOGDcNbnB8aVRW7Dh06nDt3Ljw8\n/PHjx0lJSTNmzJAZQiUmJtrb22tMQkjL5MuXL56enrm5ud37uuw/s6qJWl3zZf2OGV8+fXuS\n8jooKOjWrVu6PA+BaA0SidT02TK27IfLrBtBEIlEQqVScXGewMuNAAAgEokoFEojWufxeBMm\nTIiLizM1Yd48M7d3d7uG1iAWi/HJt0EkAgAIAJDIZL+hne4m5yYlJY0aNUo7rYtEIhqNhovz\nBJ/PJxKJuNxp9S7NqvpzzJ8/f8KECYaGhgiCMBgMma9+eHh4XFzc7t27VaynrKzsyZMnfD7f\n0dGxTotjAMClS5cUtiH69evXtm1b1WuA6DgfPnzw8vLKz8/v2d91/5lVDL0fVKsDAJAppJ1/\nLg3xXpuUlLR48eIDBw7gLREEAtEqVVVVI0aMePjwoZ2tSfz5+R3aWeAtUSMZ7tVx2YZLcXFx\ne/fuxVuWHxpVFbvg4GChUBgTE0OlUpcvX96+fXvseGpq6syZMxcuXKhKJU+fPt22bZulpaWx\nsfHp06f79++/ZMmS2i6Qp0+ftrKykvci7tixY4NqgOgyr1+/HjZs2NevX/sN6bwnevkPu1Yn\nw6iVwd5TKyb7rj948KCLiwv+psdkcsXTpwQyGYc4Gc2NjRs3Khj5BQcHd+7cuXbJlJSUlJQU\nbEYaGBio49YXEK1RWVnp7e39+PFjFyfLO5cWWls1s8dOOsSF/3A1iaVPBcDFydK+rUleXl5u\nbi4MwY0jDVjADA0NDQ0NVTj45MkTFUcokUi0d+/eIUOGzJs3DwCQm5u7YsWKPn369OvXT76Y\nRCKRSCQhISEKx1WvAaLLpKWljRgxory83MO3Z+SxRVQq3HkEAADHDjaRRxfND9m+ZMkSR0fH\n4cOH4ykNgYC0bavjpoo6QkZGRp8+fWT7CQCAOuNaxcTEnDt3ztvb287O7t69e2lpaTt37tT+\nNiVE1+ByuT4+Po8fP+7S0ebO5YVmJsz6r9E19KhSWxPw32ao39BO+6Pux8bGQsUORxo2dkul\n0qqqqjI5+Hw+9qHea1+8eFFZWTlu3Djsq5OTU6dOnf755x+FYjweDwBQp52+ijVAdJbr1697\nenqWl5ePCh688/gSqNXJ85Nn1xWbQiUSyfjx4zMzM/EWB1I/QqEQRdHBgwdPkENeycPgcDgX\nLlwICwubPXt2UFDQtm3bSktLExIScJEZojsIhcJRo0alp6d37mid2Ey1ulpgPh9xcXF4C/JD\no6piV1VVNXnyZENDQyMjI7O6qLeGvLw8Q0ND+cQ77du3f//+vUIxzHm4ToNEFWuA6Cb79+8f\nPXo0j8cLXxywce9sEpmEt0Q6x8Rwn5CZvlwu18/P7+PHj3iLA6kHJYOVPJmZmWKxWJZDk8Vi\ndevWLS0tTePyQXQYqVQaGhp679699g7mCRcXmLYIrQ4AMGSAkx6D+uDBAw6Hg7csPy6qbsWu\nWLHi1KlTdnZ2P/30U+N2ECoqKhSS2xgZGZWXlysUk0WFuX//PpvNtrKy6tGjB+YqqGIN8iAI\nostBVjHZmkUcu6bEo5JIJCtWrDh06BCRRFy9bdq4UC9NRI3GkuPpfhw7oDQK0ZINE4q+lN6N\nezJs2LCkpCRVpkwaQvfj2KEo2vQ4dk0J4oMNVvWOh58/fzYxMZE3WbG1tYVLGj84a9eujYmJ\nsTQ3vH1hgYWZId7iqA06jeI1yPl6/IuEhISxY8fiLc4PiqqKXVxc3MKFC/fs2dNoTwXMD/x/\n2iaTscwQJNL/L95gW7G//fabtbU1nU7Pzc01NTWNiIgwNTVVsQZ5JBJJdXV14wTWGmKxWJe1\nTxlCobARV1VWVs6cOfPevXt6+vRNB+b0G9K56ZFXldAsfknlv8Cve2ZUsrkZaW99fX2vXLmC\nS65PAIBUKtX9ZwcA0EQhm6LYYYPVly9fEhMT2Wy2paWlr6+vjY2NQrGqqiom83/WY5hMJofD\nkUql3xtOxWKxLG5oo8ECFFdWVjaxnkaATV14PB4u4VulUikuvcamlAKBoN4h7tKlS9u2bWPQ\nKRf/CrOy0FfLDArfmZh8LgAfT+fr8S8uXrzo5eWlhXa5XK72HSixdQQsY6yWmwb/Jd1RQ67Y\n0tLS4ODgpvx8VCpV4aUrEomIRKKCTmZmZhYeHu7q6ooF4i8pKVm+fPmxY8dWr16tYg3yqCUc\nlOaQSCRY8CEdj14mFosJBEIjYgXl5OQEBQXl5eVZ25rtPrHM0VnxnadGsLU6Mpms417SEomE\nRCIpEZJCoeyJXjZn/NaXmS8nTJhw/fp1Q0NtT+j5fD6BQFB7SGr1guXiw9EFAdNaTp06NXjw\nYBMTk/T09Pj4+E2bNrm6usoXQ1FUYYwikUiY1vW9sQtFUXVlEcAnGwEAQIX4+JoDx15jC8lK\nCrx8+XLBggUAgMM7x7t3slHjDgOOmxVYwg/s83BPFyKRkJCQIBAItBBhDsfbDEsjhFfrSlD1\nR2/Xrl1JSUlTWjIxMamoqJA/UlFRYWpqqlDMzMzM399f9tXc3HzQoEH3799XvQZ5yGQyLjEb\nVUQgEIjFYgqFouOxDxqXeeL69euTJ0/mcDg9+7vuOL7E2ESz2gk2qJHJZB1355RIJBQKRbn2\nadSKcvj82vDAjU+fPg0MDLx9+3advpaaQioVvX1LIJP1dTtOJGYhgOOz4+jouGfPntatW2OP\nRlBQ0NKlS6Ojo7dv3y5fjE6nKyylCAQCKpWqZEZKpVKNjY2bKB6CIDU1NdqfFQAAeDyeUCjU\n19enUqlabhrz8MNlnVsgEPD5fDqdrmQ1oaKiYvr06Xw+f8WCoZOD+qq3dRqNpv1pLcLliwvL\nSSx9iuW/d2zbNoze3e0ePfmQnZ0tMy3VEFwuV09PT8mjpCEQBOFwOGQyGZfMNzU1Ncr/0Koq\nPT///PP27du9vb0bPYl3cnLicrlFRUVWVlbYkbdv33bo0EGhWHV1dUVFRZs2bWRHRCIRNhVQ\nsQYI7qAo+uuvv27atEkqlQZP914REUqmQFeJhsEyZh69tC48MCI9Pd3b2zs+Pl577yqJxLhH\nD6mpKSgt1VKLzRMGg+Hg4CD7SiKRunXrduvWLYVilpaWZWVl8lsn3759s7BQFoSWQCCo5V2l\nrnoa0S4AQPl2iobAXha49BqbUirpNYqi06ZNy8/P9xzYYcu6UWpXwnCZ0xLvv2WERaHBvQn7\n/z8aWqBf10dPPly7dm3o0KEabZ1AIOB4m+H7fClB1fvAxsbG0tKyffv2K1euPHjwYFQt6q3B\nzc3NzMwsJiYG+/rixYu3b996enpiX3NycjD/1tTU1Pnz57969Qo7XlxcnJyc3K1bt3prgOgI\nbDZ7xIgRERERVCo5Yt+cNdvCoFbXOIxNDKOurHdytU1PT/fy8mKz2XhLBPkf3r9/r6DGlZSU\nKJjTAQBcXV2FQmFOTo7syIsXLzp16qQNESG6xNatW2/evNnG2vjssekkkk5vLDSR0SO6AgAu\nX76s+65sLRJVV+xkVpCRkZF1FggPD1deA4lEWrRo0aZNm969e2dsbPzq1StfX19MYwMAHDt2\njMFgREREeHh4pKWlrVmzxtnZmUQi5ebm2tnZhYWF1VsDRBd4/vz5mDFjPnz4YGVjuvvEMtcu\nDvVfA/k+xiaGUVc2zBy76dmzZ56ennfu3FFuewDRJhwO59ChQ1wuNzAwkEgkPnz4MCUlRRZo\nMy4ujkKhDB061NHR0cXFJSoqat26dYaGhhcuXCguLl6zZg2+wkO0TEJCwi+//EKlks8fD28Z\nIeuU4Ghn1q1zm4wXBcnJyYMGDcJbnB8OVRW7qKgoKpXaxKXjzp07Hzp0KD09nc/nT5gwQZYo\nDAAwbNgwzBiOTCZv2LDh1atX+fn5AICgoKAuXbrI2lVSAwR3jh8/Pn/+fIFA0GdQp8iji4xa\n4WB80PIwamUQdXnD7KDNmc8zPTw88I2BApGnW7duU6dOjYmJOX36NLYLFhAQEBwcjJ29e/cu\ng8HAtqKWLVu2ZcuW0NBQEolEoVCWLFkib20CafF8+PBh4sSJCIL8sWV8nx72eIujDYICume8\nKIiJiYGKnfYhyDxZINpHIBBUV1czGIzm7jzB4/Hmzp0bHR1NIBDCFwfOWzmOqPWNBqFQiCAI\nnU7XcecJzLy6oXOkag5v9vgtL56+69Spk8Z1O7EYUKlSU1OCbtvYSSSSmpoarbqV1IVQKCws\nLAQAtG7dWt5FNy8vj0gk2tv//1u8oKCAz+fb2tpqx90YiwSBixtBdXW1QCAwMDDQvs+yVCpl\ns9kmJiZabhcAwOfza2pq9PT09PT05I9XV1f369cvOzs7NLjPif1TNNc6LiEg0OsZxLAoNLg3\ncf//ZBz9VMC277bexMSksLBQc2Efqqqq9PX1te8iiQU6oVAouIw/XC6XyWQqeYk07BVYXl5+\n8eLFXbt2YctpAICmB1uCNHdev37dq1ev6OholjFz3+kVC9aM175W1+JhGuodjlnTuUf77Ozs\noUOHQns73YFGo9nb29vb2ysoMY6OjvJaHQCgTZs2Tk5OOh5EBqJeEASZMGFCdnZ2r252h3dO\nwFsc7dG2TaufejuWlZXVdieCaJoG6Lnbtm377bffML99V1dXBwcHiUTSoUOH2bNnr1u3TmMS\nNgkdT+qABeARi8U6HglW8h+1T0VHR69YsYLH47m5O249PN/KxlSj8YeVgFnpYiH3cBFAdRoX\nRZlKJ+89tXxecGRWZtbQoUNv3LihqUgWYjETAKlUWqPbtyUWnLyJz05tXwddAIty18RKUBSV\nSqW4RPnCNoKwPxAuTePSa+xPJt9rqVQ6Z86cmzdv2tq0unJyJo1K1ugWGYqieIXqlf8gY/L4\n3slp7//8808/Pz/NtY7LbYb9rfF9vpSgqmJ39uzZ1atX9+zZc8SIEb/88gt2kMvlurq6rl+/\n3sHBYeLEiU2SVDMQCATdj/1LJBJ1XEgURWsLWVFRMW/evMuXLxMIhClzRyxYPR5f71fsNUYk\nEnV8KxZBECKR2Ljxl2VkcDBm1ayxmzMyMsaOHXvz5k2NbOITiUJ/f2BoqOO3JYIgCILouJCN\nQy2ZJwAAKIrikrITe+3x+Xxc5tVSqRSXXmOvW6FQKJu5rVu3LioqyojFuBw9vZURvXHJe1Rv\nHZdJNcGcCfy6oJ1tkFq9G+XTcclaalxcXF5enoasR1AUra6uxkudxaLZablp8N/LTkmvVbWx\n69evH4FAePDgQWlpqZWV1a1bt3x8fAAACIIMGDAA8whTm9Q/DM3Xxi4pKWnatGkFBQUmZqxN\n++f29+iKo3gYLdvGTp5KNnea/695OV+8vLxu3rypCTOmsrIyIpHYqlUrtdesRnTExk5ngTZ2\nWkbexg5F0cWLF+/bt4+pT7t9YUG/XhqPD4CXjR2CIEKhkEwm1xmMevqiU3+eTt28ebOG3MCh\njV2dqPoKzM7OnjhxYu1YfCQSaeLEia9fv268jJBmBZ/PX7Ro0dChQwsKCgZ4uV/853dd0Op+\nKIxaGRy9uK6NnUViYmJwcLBu5rSBQH5YeDxecHDwvn37DJj0uJh5WtDqdJa5YQMBAIcPH24W\nWbxbDKrquSKR6HurSnQ6XfVMzykpKSkpKXw+39HRMTAw8Ht1Pnz48PHjxzU1NTY2NiNHjpTF\n7tq4caPC2n5wcHDnzp1VbB3SRNLS0qZOnZqTk8PQoy37ddK4qUN136CtRWJmaXz00rrQEb9c\nvXp12rRp0dHROr5O2YIpKSnJysrCBqvu3bvX+URcunRJYZusX79+bdu21ZaMEO2Rl5c3ZcqU\nzMxMS3PDm2fndu9ii7dEeNK9i22/Xg6pj/MvXLigm/ZaLRJVXwaOjo51brZKpdKzZ8+2a9dO\nlUpiYmJ27NhhaGjo4uKSkpKycuXKOm0Ojhw5smvXLiqV6ujo+OTJkwULFpSXl2OnMjIyDA0N\nO8kBN2K0A5/P//nnn3/66aecnJyuPZ0u3I8MmjYManU4Ym1rfvTiOmMTw1OnTs2dOxfGLcKF\n+/fvz5079/Lly+np6Vu3bv3555/rtHM6ffr0w4cPs+WoqqrSvrQQjSKVSqOjo/v165eZmdm9\ni216woofXKvDWDbXCwCwbds2OEZpDVVX7CZNmrRhw4ZOnTphpnUAgPLy8vT09J07d969e3fr\n1q311sDhcC5cuBAWFjZy5EgAgLe398yZMxMSErCvMoqKimJjY+fNm+ft7Q0AGDlyZHh4+O3b\ntydOnCgUClEUHTx4cJ8+fRrWS0jTSE1NnTdv3rt372h06uINEyfP8oUBTXQBByfrIxfWTg/c\neOTIERqNtmfPHqhqaxMOh7N//35vb+/w8HACgZCfn79s2bL4+Hh/f3/5YphHeUhISL9+/fAS\nFaJpMjMz582bl5qaSiAQ5ocP/v23QDqtBbr1NIIA3y6uHayys7MvXrwoy8sC0Siqvp6XL18+\nbNiwxYsXOzs7AwD8/PxMTU39/Pzu3r3r6+u7dOnSemvIzMwUi8UeHh7YVxaL1a1bt7S0NIVi\nFApl9uzZAwYMwL4aGBhYW1sXFxcDALANX1zsQ39YuFzu/PnzfXx83r1717Wn04V720PnjoBa\nne7g3MnucMwapgFj7969ixcvhnNibVJRUdGzZ8/AwEBMn3ZwcLC1tf3w4YNCMR6PBwCA4eta\nKh8/fpw2bVr37t1TU1Md2prEnZuzb1sQ1OpkEImEX1f4AQDWr18PLe20g6pvaCqVGhsbe/bs\n2cDAQCcnJysrK2dn5zFjxsTExNy8ebNOdxgFPn/+bGJiIm9UZ2tr+/nzZ4Vipqamvr6+ssjd\nIpGoqKjI2toa/KfYad/N6ofl6tWrHTt2PHDgAJ1BXb5x8ombG+3atcZbKIginbq3O3ButT6T\nsXfv3lmzZuESV+nHpG3btitXrpTP3ot5qykUgzPSlsqHDx9mz57doUOHEydO6DEov630e5L4\ns+fADnjLpXOM9Xfv6d42JyfnwIEDeMvyQ9AAJ2ECgRAcHCzLhNhQqqqqFIY8JpPJ4XCUh2M5\nfvw4AADblsUmvl++fElMTGSz2ZaWlr6+vjY2NkoaFYvF2FW6CRbtSSgU6ppj45cvX1asWBEX\nFwcA6Du488otU1q3MROL8Yk8rCLYjykSiXR8O1IqlQqFQvUK6drFbv/ZFYsm7Th27FhxcfGx\nY8eatD6EIMxly6RMZtWWLeqTUf1g0UGbaKymRiPd+Pj48vJyT09PheMy37L79++z2WwrK6se\nPXooD7+HIEjTY5KhKIqiqOqebWoEG9BEIlHTwyw3FGzRWtO9fvPmzc6dO8+fPy+RSOg0yvzw\nQasWDTNtpYcgCIqieI3nuIRnl774TD3xEPR2kIxXZiK1c+PoQf57fvnllxEjRmArNWoBRVH5\nwIFaQxaMGpfnq97Zu6qKXVlZ2eXLl9PS0kpLS+l0urW1tYeHh4+PjyprdRgoiipESyGRSFjY\n6NpRVDBOnTp1586dtWvXYqGYsF/w1KlTgwcPNjExSU9Pj4+P37Rpk6urq5JGdX/tFxt/8Zbi\nX4RC4cGDB3fv3s3n81uZGi7aMMF7VB/w332s+zQLOTUhpEtnu0PnVy2asvPGjRt+fn4nTpyw\ntLRsZF1iMevkSdTEpOa339Qqo0bQkQf86dOnUVFRkyZNsrOzUziFzS1/++03a2trOp2em5tr\namoaEREhv9SnABaiTy2C4Zj1USgUajQkrxI01+vs7Oxdu3bFxcWhKKrHoMwOHbB49hArC0Pw\n3+sWi5utodaVg8uzQPpQSjuTJkFQUWA3JcW6d7GeMr5n9LnHs2fPPnv2rBoVUFxUKwwEQfB6\nvtQQoHj//v2rV6+unbrHwsLi2LFjCt4P3+PYsWMZGRmHDh2SHbl8+fKZM2cuXrxYp9CHDx++\nd+/eqlWrunX793bh8/lFRUWtW7fGViMQBFm6dCmdTt++ffv3GsUr44eKiEQiHo9Ho9F0YZtG\nKpVeunRp9erVHz9+JJKIY6d4LlgdbMDSAwBIJBICgfA9/VtHEIvFCIJQqVQdD/whFAqpVKqG\nJtbFX8sWhES+e1NgYWFx4sQJLy+vxtQiFpP19KSmpkhRkboFVCcIgvD5/CbmBFNLaNMHDx7s\n3r177NixISEhtc+WlpY+evTI1dUVix5QUlKyfPlyFxeX1atXf69Cda3YCYVCXMYWbBeCRqNp\nP3KsVCrl8/kyYx41kp2dHRERERsbK5VKjViMOdMGLJgx2Mzk/28/TKUjkUi4DJVisZhMJuOw\nX3Ezkxz+JxLcW7qnjptfnsoqfrch2woKKyIjI+fPn6+WxgUCAS5jPrZWpxC3X2sIhUIWi6Xk\nb13/U/f777+vWLHCwMBg2bJlw4YNs7a25vP5+fn5V65cuXDhgr+///Hjx8PCwuqtx9LSsqys\nTF7N/Pbtm4WFRZ2FDxw48OjRo82bN7dv3152kMFgODj8f6RHEonUrVs35QmGCQSC9kcW1cFW\n7IlEIu5CPnz4cMWKFY8ePQIAdOvjvGrLNOdOdrKzBAKBQCDouMKEofspxQAAjU4pVi+t25j/\nfWvThoWHEq6n+fr6zps3b/PmzQ1OKfvfZA/327JedOEBT0xM3L9//8yZM319fessYGZmJu8n\na25uPmjQoPv37yupk0QiNV0hQxBELBbjlY1AIpFQqVRcMk+oPQdDbm7uhg0bzp8/L5VKTVrp\nL53jOT98sKFBHW90LGEgLvekWCzGJb0eSiQCAAgAkOrrtamJwd+Hp3oG/rF27dqePXsOHjy4\n6a2LRCJc5g8SiYTP5xOJRFyer3r3+ut5Bebn569Zs8bBwSErK2vHjh3Dhg3r2LFjjx49goKC\nzp49m5KSYmpqOm/evLy8vHpFcXV1FQqFOTk5siMvXrzo1KlT7ZJXr15NTk6OiIiQ1+oAAO/f\nv1dQ40pKSnQzh3czIjMzc8SIEQMGDHj06FEbO4sdx5ecuPGbvFYHaV7o6dN3HF+yfscMhh5t\n//79Li4uf/31ly6vWzdrMjIyDhw4sHDhwu9pdQCA6urqgoIC+SMikQj6LzcLvn79OmvWrI4d\nO8bExLAM6RGrR37IiFizxKdOrQ5SL4P6td+6fpRYLB4zZgxMWKU56lHsDh8+LJFI/v77b3t7\n+9pne/fuferUKYFAcPjw4XpbcnR0dHFxiYqKqqysRFE0JiamuLjYz88POxsXF3fnzh0AQGVl\n5ZkzZ2bMmCG/OIfB4XAOHTp0/vx5bNPtn3/+SUlJGTJkiEodhdTizZs348eP79atW2xsbCtT\n1qqt066m7BrmD2MEtgTGhXpdevD7AC/3wsLCsLCwjh07/vnnn3gZPLVUxGLxwYMHR40aJYvi\nJE9OTs779+8BAKmpqfPnz3/16hV2vLi4ODk5WWZhAtFNiouLly5d2r59+6NHj1LIhBULhuY/\ni1i3bLgBE6p0TeLn+UPDQvqx2Wxvb29VloQgjaAeG7uePXuKRKKsrCwlZbp06YKiaHZ2dr2N\nlZSUbNmy5cOHDyQSiUKhzJs3b+DAgdip5cuXMxgMzILhyJEjChdaW1tjxnmXL1+OiYkRCATY\ndtuoUaMmT56s47ZfShAIBNXV1QwG43up1TRETk7Oxo0bz507h6KoAUs/dO6ISbN89fS/O2Bh\nzla473kpRygUIghCp9N1fCuWz+fT6XStmcKk3M38Y9PZt9kfAQDm5uZhYWFhYWEKa+GKiMWA\nSpWamhJKS7UjZOPAPAxwzD2TlJT0xx9/tGvXTt7OxszMbMmSJUBuTJNIJFu2bHn27JmzszOJ\nRMrNzbWzs1u3bh3mE6Y5EAThcrmabqVOqqurBQKBgYEBLluxbDbbxMSk0TW8f/9+9+7df/31\nF5/Pp1BI00P6rV/u29qy/ttMIpGIRCIKhYLLlqjaN6BVBL2eQQyLQoN7E/eHqniJRIKOCzt2\nNS7LxsYmMTGxQ4fGB4ipqqrS19fHZSu2srKSQqHgMv5gYZUa7zzRqlWr0aNHR0VFKSkzffr0\nixcvqh50oKCggM/n29rayo+GeXl5RCLR3t6+rKysqJbJNo1Gc3Jywj4LhcLCwkIAQOvWrZt7\nTDvtK3Y5OTmbNm06e/YsgiD6TEbIzOFT5owwNKqndajYqREtK3YAAKlU+jAp8+Shm4+TX2FG\nrv369Zs4ceK4cePMzMzquAAqdqqRk5OTkZGhcNDQ0BDbiEhISCCTybLFvFevXuXn5wMA2rRp\n06VLFy3cAFCxaxAikejmzZtRUVG3b99GUZRKJU8O6rV2yXD7tqpWBRU71a8SiSTjwqKux78w\nMzO7fv16o7NJQcWuTupR7Egk0rJlyyIjI5WUWbVq1fbt26HJSCPQpmL3+vXrzZs3x8TEYCrd\nhHDvKXNGGLUyUOVaqNico++CAAAgAElEQVSpEe0rdjLyc79e+jsp9uJDdlkVAIBMJnt6egYF\nBQUEBLRq1er/y0mllXfvEigU1n8L6roJ7oqdjgMVO1VAUTQlJeXcuXMxMTFYUnJjI73wSf0X\nzhxi07phP92Pqdgh7Grxu0KSmSHFoWHxlcRiZPqiU3+fT2cwGMeOHavTo7xeoGJXJ/X8HCiK\n6vhrElIvmZmZW7ZsuXTpEoqi+kzGhHCfKXP8VFTpIC0JByfrnyOmLP0lJPX+i1uXU+7denr7\n9u3bt2/PmTPHy8tr7NixAQEBxsbGgECQdOkCH3xICwbT5y5evHjp0qWvX78CAAgEwsC+7cJC\n+gUFdGfQYUIwlWEx0E42xIarVhQKKfrAFDtbk007b02aNCktLe3333+HmffUQj0rdgQCYeXK\nldu2bVNSRpdX7IRCYe3we7qD7EfT0PpNamrq3r17k5KSpFIp04ARNG1o8PRhLOOW6Ues6R9T\nXSgPLKlNhAJR6r3spJuPHyZl8nlCAACVSh04cGBAQICPj4/yOEkthqYYY2kOsVislsCnWFi1\nptfTiHalUimJRMLlFpJIJN9bwuHz+Q8ePIiPj4+LiyspKcEOdnGzHjuya9CobrY2xk1pVyqV\nYk83Lr3GcRUGRdGm9PpKbNasZee41UIXF5eDBw927dpV9Wux+DI45NuQShEEwSu8K4IgxsbG\nSv7c9St2bdq0qe2gKk9+fn5BQYFuKnZA7n2vgwgEgpqaGgaDod5wmmKx+NKlS7t3737y5AkA\nwKiVQcjM4RPDfQxYjdzwbUZbsTQaTcedaXDciv0eAr7wwZ3nCdcePbjzXMD/V8MbMmRIcHCw\nv7+/sXGT3naaQyKR8Hi8Bkfp+1906g8hQy2R1VEUrampMTDAYW2ex+OJRCI9PT3VUxOpC6lU\nyuFw5DfIeDxeRkbGw4cP//nnn4cPHwoEAux49y62o0d0Hevv3s6+LkvThoMFDiSTybgMlUKh\nEBejcxRFRSIR5hDZ6Ere5ZdOnnPiyfNPJBJp9uzZ69evV3HGhdkyaX/Mx+wcyGQyLgHXeDye\ngYFB423sVB/1dFl/0lnUbmNXVFR07NixI0eOYP4l1rbmk+f4jQ4ZQmc06YFvRoodtLFrCpiG\nd/vqo+TEDAFfBACgUqmenp6jR4/29/c3NzfHW8D/AdrYKeeHtbHLz8///PlzVlZWZmbm8+fP\nX79+LQvoSqdRBvRtN9K7k79P57ZtWimvqqH8oDZ2CCIUCslkchOVeIkE/X3/nYgdcXyBmMVi\nLViwYMGCBfWOOdDGrk7qUey+fPmiYks2NjYNEw2iPsVOLBbHx8f/+eefsbGxWLpA914dQmb5\nevn1IpLUoOVAxU6N6LJiJ6O8rCLlbtY/8c+TE/9dwyORSH379h0xYsTw4cM7d+6Mt4AAQMWu\nPn4cxU4kEj179iwtLS09Pf3JkyeY97EMAya9exfb/r0dBvZt/1MfRz2GphYRoWLX9No+fi5f\ns+lazNVnKCql0WhjxoyZPHmyl5fX994+ULGrE5VyxaoLgUAQHR398OFDPp/v6OgYHh5eZzwt\nJcVUrKG50ETFDkXR1NTU8+fPx8TEYPYievp0n8B+46cNc+lcR0DpRgMVOzXSLBQ7Ho9HIBAY\nDIaAL3yYlJkU+/jBnefcqn+tvqytrT09PT08PAYMGKDcTkOj6IJi1/QxTXO0bMXu48ePmDKX\nlpb29OlT2QYrAMDMlNmts21XN5uunWy6utk4OVoQidp43KBip646X70t2nHgztlLT4UiCQDA\n2NjY29vb29t7yJAhbdu2lS8JFbs60apiFxkZ+ebNm/Dw8FatWt28eTMjI2P//v21t9KVFFOx\nhuZC4xS7qqqqe/fuxcXF3bhxo7i4GABAIBC69nQaNWGwd0Bffab6n22o2KmRZqHYiYpKAZFI\ntfj/J0siRp49epOc+Pxh0vP83K+y41ZWVr169erRo0fXrl3d3Nzatm2rta7pgmLX9DFNc7QY\nxU4qlX7+/DknJ+ft27dv3rx5/fp1dnZ2RUWFrACdRune1bZvD/s+Pex7dbMzbUXHK4Pnj6jY\nCUSiCi5Jj0ZlqdnarJxdc+bSk5irzx49yUfRfxWVNm3a9O3bt1evXt27d3d3dwcAQMWuNtpT\n7IqKimbNmrVu3bpevXoBABAEmTFjxuDBg6dMmaJiMRVraEaoqNghCJKXl/fs2bPHjx+npKRk\nZGTIDKtdOtsPHdnbJ7CfTVsLzckJFTs1ovuKHUGMuLWeKDExePO27sjkxV/LHye/fJLy+nn6\n288fiuVP6evrt2/f3tHR0c7OztbW1sbGxsLCwsLCwtzcvIleDrXBXbFr+pimUfGaqWKHouin\nT59ev3796tWrly9fvn79+s2bNzweT74MkUhoZ2/WvYttT3e7Pj3sunexpVL/f3TCS8X5MRW7\nxgUobhAlZdzbd18n/vP2n9R3nwrYsuMEAqFt27bu7u6dO3d2c3Pr1KmTo6Ojdt5TOq7Yae9V\nnZWVRSaTZRkSSSSSu7t7ZmamwuimpJiKNTQ7RCKRSCSqrq7m8XgcDofNZpeVlZWUlBQUFHz6\n9CkvLy83N5fP58vK6zMZPfq7/uTRdcBQ99Zt1OPPBYGojqW1iX/wIP/gQQCAinLOy+d5r7Py\nc199fv+24POH4szMzMzMzNpXUalUExOTVv9hYmJibm5uampqZmZmZmbWunVrc3NzMzMzHZ8/\nyNP0MU3bEmsALpfL5/Ox4UsoFPL5fIFAwOfzy8vLqVQqhUKRSqWVlZXfu7y6ulokElVUVJSW\nln779u3jx495eXkKSY0pFJJze0sXJ8sO7Syc21u4drBy7WClr6dtf1sIXpibGkwO6j05qDcA\n4GtRZfqzj0+ef3qW9fl5dsHHjx8/fvx45coVrCSWpMrJyaldu3b29vY2Nja2trZmZmampqbN\naGBpOtrramFhocKPa2lp+fDhQ9WLqViDAg1akvz06dPjx48BAEwmU37WxWAwlAdOFAqFPB4P\nRdGqqiqxWFxdXY2NdzU1NZWVldXV1TU1NdXV1RwORygUcrlcbOzD/q1XKgqV3N7F1rmTXUd3\nxy49nFw62anFJaJFAr2ztYyxieEAL/cBXu7YV4kYKSwoKfj47evn0m+F7G9F5ezSqrJvlexy\nThWbW1RUVDthoDwEAsHsP8zNzU1MTIyNjQ0NDY2MjLBHEou9QiAQjIyMEAQRCAQKq93du3dv\nkPxNWTpt+pimhAbdyV++fHn06BH2ubKyErtWKpUKBAKRSISiqEAgkI05XC5XJBLJRioAgEQi\n4XK5VCoV+zGJRCK2DqGvry8znMIGNwRBOBwONppVV1cr0dgaDZVKdu1g5drB0rWDlZtz647O\nVu0dzCkUVeNZ4DgC4D746E6MTM1hbWU0ekTX0SP+jXX34VPJ65xvL98WvXj19XVO0Zvc4uzs\n7Doz1xsaGrJYLBaLxWQymUymsbGxvr6+vr6+gYEBi8UyMDBgMpkMBsPIyIhKpTKZTNlTgKGg\nEkgkEg6HQ6FQVAxLVFVVhaKowkHsecQ+d+vWTY1/O+0pdjweTyFam56eHp/PV7gXlRRTsQZ5\nMC1KdSHj4+PnzJmjevkmQiaTDFj6JBJBn8mg0al0BlXfgGFgqGdsamjcysDM0tiitYlNW3Mr\nG1MS+f81OYFQoKROzSESiXBpt0EozPV1E1W0eRwhSBAAgBQAhf0vFTG1ZJlastz7ONU+xecJ\nOZU1VZXVnMqaKja3kl1dweZWVVSzyzjs0irs35KSElnk2AZLTiA09FpTU9PGtQXUMaapa+BK\nSkqaNm1aQ2RXD4YGdKY+jUGnGBrQaVQyg0GhUEhMPRoAQE+PSv1PG9PXo35PM6PTKdjlJq30\nLcwMbFobWVuySP87cRWLhWJxwwRr3K2rFsRisbih4qoJXAYWklhMAwCVSgV4/OYWZkwLM+aQ\nnxyxrwiCfvpS8T6/NP9T+acC9teiyqJvnFJ2dVl5TUUlh8PhFBQUaF9IVSgsLGzQDr6+vr5O\nbMXiQkMDQ7dt2zYkJKR2sgoul6tcMSeRSFggUENDQzKZbGhoyGAwGAyGoaEhk8nU19fX09Mz\nNDTEZsAsFotCoejr60ulUixmt46bhWEzUR2fC6IoKpVKcYlC3iCaQZo+sRgAQAJUJ4NRaq7Z\nAID6bEGFQmFZWVlpaWlZWRmbza6qquJwONhqt1gsxtSd7yVmIBKJOh6eWkUaOnA5ODiEhv5r\n4SQfFlgqlWIrDVgkVexfBoNBo9EMDQ0V1iRkSCQSbAzkcDjyq1CGhoYEAoHFYmFmwUpCHzfl\nYZQAIGnoNf8LXvk28B3P8RpYKPQbNBCNkNsLmWHab7120gtrFrDuCAbVVZjD4WCDCY/H43K5\nHA6Hx+Nha89VVVU1NTUCgQDbVePz+ZgJL3YhZq6qUFuDXosyDUEeIpEoO0gmk1W/aWsv/img\nPcWOyWQqjMXV1dV6enoKv4uSYirWIA+VSm2QD/bw4cOHDx+uevkmgjlP0Ol0dQUo1hA1NTUk\nEknHs/hxOByRSGRgYICL5bLqsNlsFoul07qdWAwAIBAIeOWcsLRUKZs47s4TTR/TvldzQweu\ngQMHDhw4UOEg7s4T+vr6uAQoZrPZuNy6mO2N2jMJqQibzTYyMtL+tFZCpwMAiEQiLr95g8Kd\nqFFC3J0nlP+htfd2sba2Lisrk9/OKywsrB3WWEkxFWuAQCAQLdD0MQ0CgUDUjvYUu65du6Io\nimUvBQAIhcJnz5716NFD9WIq1tCMIJFINBpN9711SCSS7u9wUSgUGo2m0ythAAAAqFSqjm8W\nAyJREhgo8fXFW456IBAI+K7ONn1M0yg4/j5kMhmvh5FAIOCSMhXgPZ5rPy0vBqFNG0lgIMDp\nRUyhUHAZTrHbDK/nq95ea+8WNDMz8/T0PHr0KADAyMjo8uXLRCLR97+Xx759+2g02syZM5UU\nU15DcwSviEcNRcc3YTFwieHUCHBJGt0wSCTy5ct4C1E/JBIJlz0vGU0f0zQKkUjEy8aDTqfj\nOGjg9Yg1dANdveDVa1LfvgC/4QKvEaBOmzmtUe/DpdXMEyKRKDo6+sGDB3w+v0OHDrNmzbK1\ntcVOLV++nMFgREREKC+m5BQEAoFomaaPaRAIBKJetKrYQSAQCAQCgUA0h64bJEEgEAgEAoFA\nVAQqdhAIBAKBQCAtBKjYQSAQCAQCgbQQoGIHgUAgEAgE0kKAih0EAoFAIBBICwEqdvgjFos/\nffr06dMn+dj0kEaTn5//6dMnvKVoxkgkkvfv3+fn5yvPjwyByFNWVpaTk1NRUaG8WEVFRW5u\nbr3Fmgsq9hpF0VevXpWVlWlHKs1RUFCQm5vL5/PVUqy5oGJ3qqurs7OzBQKBdqRSgq7nPGjx\nPHjw4OjRo1iCYX19/VmzZg0aVGf+Ykj9SCSS48ePx8bG9u7de+3atXiL0yx58uTJnj17eDye\nVCo1NjZeuXKls7Mz3kJBdBqRSLRjx460tDQqlSoWi728vObPn187Mj6Xy92zZ8+TJ0+IRCKK\not27d1+2bFkziNf9HVTsNQCgoqLi999/f/nyZXh4uL+/v/ZFVQuFhYVbt279/PkziUQiEonh\n4eE+Pj6NLtZcUL07OTk5kZGRpaWle/bscXBw0LKcCsAVOzzJz8/fvXv34MGDz58/f+7cuV69\neu3bt6+0tBRvuZolXC53xYoVOTk5uD9UzRc2mx0ZGTlo0KCYmJhz5865ublt3bpVKBTiLRdE\np4mOjn7z5s2ePXsuXry4devW5OTk69ev1y524MCBvLy8Xbt2XblyZfv27W/evPn777+1L626\nULHXubm5CxcuNDMz0/3UkUqQSqWRkZH6+vonT568dOlSaGjooUOH3r9/37hizQXVu5OQkLB2\n7dpOnTppX8g6gYodnmRnZ5uamk6fPp1GozEYjNDQUJFI9PbtW7zlapZ8+/bNzs5u27ZtRkZG\neMvSXLl79y6VSg0LC6NSqXQ6febMmRwO59GjR3jLBdFdEARJTEwcM2YMNqFydXX19vaOjY1V\nKCYWi9+/fz9u3Lh27doRCAQXF5cBAwZkZ2fjIbIaULHXAICcnJzg4OAlS5boeoZopeTm5ubn\n54eHh7NYLAKBMHLkSHt7+1u3bjWuWHNB9e7k5OT89ttvw4cP176QdQIVOzwZNWrUsWPHZA88\nljMbRVFchWqu2NvbL1y4EMdcjS2At2/fdujQQba0YGBg0KZNm9evX+MrFUSX+fjxI5/P79ix\no+yIq6trcXGxgtkZhUKJiory8/OTHSGRSM13rFOx1wAAX19f+V43U968eUOn0x0dHWVHOnbs\nWHtkULFYc0H17sydO1f+ZsAdqNjpEPHx8TQarWvXrngL0iwhkUh4i9DsKSkpMTU1lT9iampa\nUlKClzwQ3Qe7PeRvGzMzM9nx78HlclNTU3v37q1p8TSE6r1uGeNSaWmpiYmJ/KJjnSODisWa\nC6p3R9f+ylCx0xUyMjLOnTs3ZcoUFouFtyyQHxSBQECj0eSP0Gi0FuPaBtEEmA+g/G2DfVbi\nGygSiSIjI2k0WlBQkBYk1ASN6HWzhs/n1x4ZxGKxguO8isWaC823O83YnLM58urVK8wBFgDg\n7u4uu2lSUlJ27tw5evTokSNH4iddc0Iqlaanp2OfaTSau7s7vvK0DGrvjqEo2qyNviFqp6qq\n6s2bN9hnmU+A/G2Dvfa+t4bB4/E2b95cXFy8adMmfX19zcurHprY6+YOmUxWGBkQBCEQCJj5\nUEOLNReab3fgkK1VTp48mZOTg30+evSoubk5AODOnTsHDx6cOnXqqFGjcJWuOSGRSLZt24Z9\nNjc3P3r0KL7ytAxYLBaHw5E/wuFwjI2N8ZIHooPk5+fLHj0PDw8sPBOHw5EFLsFuoTp3Hrhc\n7vr16xEE2b59u8Kmv47TlF63AOocGQwNDRU8QlQs1lxovt2Bip1W2b59u8KRtLS0gwcPLliw\nwMPDAxeRmikUCuXq1at4S9HSsLOze/XqlewriqKfPn3q3r07jiJBdA13d3f5R6+qqopAIHz6\n9Kl169bYkfz8fDqdLvsqQyQSRUREEInETZs2NbvwdY3udcvAzs6uoqKCy+UaGBhgRz58+FA7\nsJSKxZoLzbc7ur6i2LLhcrl//PFHSEgI1OogukDfvn0/ffr07t077GtqampNTU3fvn3xlQqi\ny7BYLBcXlzt37mBfEQS5e/dunz59sE1JBEHEYjF26vz582VlZb/++muz0+pqo3qvWwaY4ZCs\nv6WlpZmZmf3798e+SiQSbCdaebFmh4q91kHgih2eXLx4USAQCIXCs2fPyg62a9euZ8+eOErV\nTMnKysIc0QsLC0kkEvaT9u7du1lMsHSELl269OnTZ+PGjT4+PiKR6NatWyNHjrS2tsZbLohO\nExYWtnr16oiICFdX16dPn5aXl8vyvpw6derKlStXr16tqqq6du2ak5OTQrC3wMBAOp2Oh9RN\nRZVeAwASEhLKy8sBAAiCZGRk1NTUAAD8/f2bkX0hAEBPT2/SpEl//fVXSUmJsbHxnTt37Ozs\nZOsRq1atYjAYERERyos1O1TsNYIg58+fBwBgKePi4+ONjY319fVxzDICFTs8oVAoLi4u8ptf\nAAAGgwEVu0ZQXFyMxTvFbHewzx06dMBZrObGypUrY2NjMzMzSSTSrFmzmu+gDNEaTk5Ov//+\n+82bN1+8eGFnZ7d48WILCwvslKWlpZubGwCAx+O1b99eKpUqBCUeMWJEM1XsVOk1ACAvL6+g\noAAA4OrqKhKJsO77+Pg0L8UOAODv729ubv7PP/8UFxd7eXkFBATI3KocHR1ljoBKijVHVOm1\n/F3t5ub25cuXL1++4GuaTJBKpTg2D4FAIBAIBAJRF9DGDgKBQCAQCKSFABU7CAQCgUAgkBYC\nVOwgEAgEAoFAWghQsYNAIBAIBAJpIUDFDgKBQCAQCKSFABU7CAQCgUAgkBYCVOwgEAgEAoFA\nWghQsYNAIBAIBAJpIUDFDgKBQCAQCKSFABU7CAQCgUAgkBYCVOwgEAgEAoFAWghQsYNAIBAI\nBAJpIUDFDgKBQCAQCKSFABU7CAQCgUAgkBYCVOwgEAgEAoFAWghQsYNAIBAIBAJpIUDFDgKB\nQCAQCKSFABU7CAQCgUAgkBYCVOwgEAgEAoFAWghQsYNAIBAIBAJpIUDFDgKBQCAQCKSFABU7\niFaZOXPm+PHjG3pVbGystbU1m83GviYmJvr5+bVv375Hjx4rVqyoqKjQpjAYJ0+etLa2njdv\nXp1nHz9+HBYW1rlzZ3t7+969ey9ZsuTdu3eNawgCgdSLs7OztbV1VlaWwvFbt25ZW1sHBATI\njlRWVoaGhlpbW7969Upr7VZWVq5evbpHjx5OTk6+vr4JCQlNb7o2b9++tba2fvz4cZ1nPTw8\n1q5dq6HKIToFVOwgWmXSpEkzZsxoSg3//PPPtGnT3NzcTp48uWLFivj4+EWLFmlfmJiYGFdX\n1/j4eC6Xq3Dq1KlTY8aMYbPZq1evjoqKmjFjxtOnT/38/B49etS4tiAQSL0wGIwrV64oHLx2\n7RqdTpd9ff78+bBhw758+aLNdlEUDQsLS0xMXLNmzYkTJxwcHKZPn/78+XM1yoBhZWW1bds2\nOzs7tdcMaV6Q8RYA8mMxcODAJtZw7Ngxd3f37du3AwD69u3L5/PXrFlTXV3NZDK1Jsz79+8z\nMzOvXr0aEhJy8+bNCRMmyJ9au3btqFGj9u3bRyAQsIPBwcGjR49euXLl/fv3iUQ4m4JA1E+v\nXr2uX7++YcMG2SPG4/ESExO7desmFouxI3v37p00aVL//v39/f211m5WVlZ6evqZM2cGDRqE\nlU9JSbl+/bq7u/v36pRIJGRyg9/OLBZr8uTJje0HpOUA3zEQrSK/+zl79uxZs2Zdvny5f//+\nDg4OPj4+mZmZ2CmJRLJ27VoXF5cOHTrMmzePw+HIati5c+fhw4dlX1u3bg0AKC8vBwDcu3ev\n9mbB9u3bnZ2dZSM7AODgwYP29vZcLldeGARBIiMj+/XrZ29v37179zVr1vB4vO/1IiYmpl27\ndj179hw+fPjFixflT0VHR5PJ5M2bN8u0OgAAk8k8duzYxYsXoVYHgWiIAQMGlJWVya+L3759\n28DAwNnZWXZky5YtCxculH82tdCui4vLvXv3+vfvj30lk8kWFhYywxIZb968sba2vnv37pAh\nQ/z8/AAAZWVlCxcudHNzs7e39/PzS0lJkRU+c+aMh4eHo6Ojm5vbjBkzioqKQK3d0idPngwd\nOtTOzm7AgAGxsbGyXmdlZSlsH/fr12/jxo3Y58zMzODgYFdX1/bt2/v5+SUnJ9f7C3z79m3W\nrFnOzs6urq4zZ84sLi7GjhcVFc2ePdvV1dXOzs7T0/Py5csAgOrqagcHh/3798suF4vFzs7O\n27Ztq7chiIrA1wwENygUSkZGRnJy8o0bN7Kyslq1arVkyRLs1IEDB06dOrVhw4b4+PhevXrt\n2bNHdpWFhQWmzGEkJSVZWlq2adMGAGBqaurp6WlkZCTfyqhRo7hcrvzwFBsb6+XlZWBgIF/s\nyJEjhw8fXrt2bVJS0p49exITE7FFwdogCHLp0qVx48YBAMaNG5eenv7582fZ2bS0tN69e7NY\nLIWr2rZta25u3pCfBwKBNAAWi9W/f3/5XdFr166NGDECRVHZESsrK+23S6fTnZycZCtwhYWF\nb9++7dmzp0I9FAoFALBr16558+bt2bMHQZCQkJCnT58ePnw4ISHB3d09JCTk7du3AID09PQV\nK1ZMnz797t27f//9d0VFxaxZsxRq43K5U6dOZbFYcXFx+/btO3ny5Ldv3+rti1AoDAkJYTKZ\n58+fj42N7dWrV1hYmExRqxOJRBISEvL58+fjx4//9ddfX758mTRpklQqFYvFwcHB7969O378\neFJSko+Pz4IFC+7cucNkMj09PW/duiWrITk5mcvlyttBQpoIVOwgeMLhcDZv3tyqVSsDA4Mx\nY8bk5uby+XwAwIULF7y9vSdMmGBvbx8aGtqnT586L09MTDx16tSaNWuwlbBOnTqdPHnSyclJ\nvoyzs3P79u3j4+Oxr1+/fs3MzKw9iEyePDk5OdnPz8/BwWHAgAEj/o+9845rIvv+/k1PCFVA\nUUBQ7KjYwbWjiA0QQcTesIsFBWzYFbHrCthYe8NVWbuAWFZEEVkEFWXtsjYEQhJSJzPPH/OY\nH1/AMEAmmeB9/+GLTG7ufJy5c+fccs4ZNuzOnTuVnvTOnTsFBQX+/v4AgB49etja2p47d079\nbUFBgZ2dXQ0vBwQCqQW+vr5XrlzBp+eFQuHt27cJmgtZWVm+vr7Ozs6enp7btm3Ly8tDECQ9\nPT09PV3DVzU4r0KhmDNnTpMmTQICAsp9hVt+bm5u/v7+rVu3vnPnztOnT7ds2dKzZ8/mzZuv\nXbu2cePGhw4dAgA8ffqUzWYHBAQ4ODh07NgxNjZWPd+mJjk5WSAQrF+/vk2bNh06dNi8ebNA\nIKjyOjCZzKSkpF27drVt27ZFixaLFy+WSCSPHj3S8JO7d+/m5ubu2rWrR48erq6uW7Zsadq0\n6devX1NSUl69ehUVFdW9e3cnJ6fQ0ND27dv/8ccfAABvb+8nT56o7cXLly+3atWq7MQqpJZA\nww6iT5o0aWJkZIT/jc9ylZSUKJXKd+/etW3bVl2sc+fOFX97/fr1adOmzZ0718/PT/NZvL29\nb9y4gQ+gr169amJiMmDAgHJlMAzbsWNHx44d7e3tbW1t9+3b97N+MD4+vkePHlZWVgiCqFSq\nESNGlF2NZTKZKpWq6v85BALRNoMHD5bL5SkpKQCAK1eu2NjYVNp1VOTp06fjxo07c+bM9OnT\nnz59iq9gBgUFsdlsDV9V97ylpaXjx4//+PHj4cOHy/68LB06dMD/ePLkCZPJdHV1xT/S6XRX\nV9eMjAwAQM+ePeCYWaQAACAASURBVFks1vDhw48fP/7x40dra2v1r9Tk5eUxmUy1teTg4GBp\naVnldWAwGNnZ2SNHjnRycrK1tcUHyZotwuzsbB6Ppx5OOzs779+/38bGJjs7m8FgdOrUqex/\nDfdEHjBgAI/HwwfbCIIkJibC6TrtAp0nIPqkrMMaDoZhEokEw7CyS6Xllk0BAGfOnAkLCwsN\nDZ07d26VZ/H29t62bdujR49cXV0vX748ePBgDodTrsyyZcvu378fExPTuXNnNpsdFRV16tSp\nilUJhcLExES5XO7g4FD2+KNHj/C1FRsbm7dv31YpCQKBaB18zHbhwgVPT8+//vrLx8eH4A/H\njRuH/9G2bVs/Pz+FQlFQUGBjY8NgMNQ2U8WvqnXeoqKi8ePHi8XihIQEDZP66l0cIpEIQZDm\nzZurv1KpVBYWFgCAli1bXrx4ce/evZs2bQoPD+/YseOmTZvKjoQBAKWlpeW6zYq9aEVevnw5\nY8aMCRMmHD582NraWqVSlevoKiIUCit24wAAsVhsYmJSdlexubk5HkOAx+MNGDDg6tWrkyZN\nun//fnFxMTTstAs07CCUg8fjAQDKhhEpF6nu4sWLYWFhmzdvJhiFrlmzZq1bt7527Zqjo+Pj\nx49DQkLKFcAw7MaNG/Pnz+/evTt+pKCgoNKqEhISGAzG5cuXy3ZYYWFhf/75J27Y9erVKyYm\n5tOnT2U3AgIAXr58effu3cmTJ9fA2Q0CgRDE19d37ty5+fn59+/fX7VqVc0qYbPZtra21fpK\n83mlUun48eMxDEtISMCNsyoxNTXlcDjlIt6prcmWLVvu2LEDRdHHjx9v2LBh3Lhx+GSeGiMj\no3KRmNS9aEXfEZlMhv9x8+ZNDoezatUqvJv6WTdYFmNjY6FQiKJoOc8wExMTkUiEYZj6dMXF\nxaampvjf3t7eM2fOFAgEV65c6dy5M75JGqIt4FIshHKw2Wx7e/unT5+qj5T1OHv79u38+fNX\nr15drdjC3t7eKSkpiYmJVlZWPXv2LPetSqWSyWTqgCkikejGjRsYhlWs58yZMwMGDOjYsaNL\nGXx8fC5evCiXywEAo0ePZrFYixcvLuuHKxKJFixYUOkUIAQC0SL9+/dns9mbNm1ycnJq3bo1\nRc67fPlyoVB48uRJglYdAKBDhw5yuRxF0WY/4HK5uPPHP//88/jxYwAAnU7v2rXr0qVLCwoK\nyrk4ODk5IQiCO1sAAF68eKFeUcUnBcViMf7x27dv3759w/8Wi8UcDkc9+Dx79iwAoNKeUI2L\ni4tKpVLvw8vLyxs8eHBeXh5+vGy4voyMDBcXF/xvd3d3Lpd7+/btxMREX19fgtcEQhBo2EGo\nyPDhw2/cuHH8+PEXL15ER0eXjRG/ceNGW1vbVq1apZXh+/fvAICnT59OmTKl0hwPPj4+r1+/\nPnbsmJeXV9k1FBwmk9m+ffv4+Pj3799nZ2dPmTJl8ODBAoHg1atXCIKoi+Hh64YNG1bu515e\nXvgSLQCgcePGUVFRqampgwYNOnz48I0bN/bt2zdw4MAvX75ER0fD6ToIhFTYbPbQoUMvXrxY\ncXUPRVG8u8jJyQEAPHnyJC0tLTMzk+zzPn/+/OzZsyNHjszNzVV3WerQTj+jV69ebdu2DQ4O\nfvDgwcePHxMSEgYOHHjs2DEAwK1btyZPnnzlypX3798/e/YsLi7Ozs6u3DzigAEDjI2NV6xY\nkZWVlZaWFhYWZm1tjX9la2trZWV19uxZuVz+/fv3iIgI9fa7Ll26FBUVnTp16uvXr4cPH87N\nza1fv/6zZ88qhmFX06dPn+bNm4eFhd25cyc9PT0sLEyhUDg5OfXr169ly5ZLlizJzMx8+/bt\nxo0bX7x4ofbe5XA4np6eMTExhYWFXl5e1bnSkKqBrxkIFQkJCSksLFy/fj2Kov3791+xYsW0\nadNwGwv3jcedUtXExMT4+PgUFBTcuHFj5syZFSt0cHBo3759dnb2z6Ilbdu2bdGiRf369XNw\ncFi6dGn79u3v3bvn6+t79epV9TJBfHy8kZGRu7t7ud/a29u7uLicPXsW76H8/PyaNm26d+/e\n3bt3FxcXN2zY0MPDY/bs2TY2NrW/MhAIRDMjRow4depUxY1uSqWybL8RGhoKALCzs3v48CGp\n501NTUVRtFz4JCcnp7t372qojcFgnDhxYt26dUFBQaWlpY0bN164cCGeKWf+/PkIgmzYsOHz\n588mJiZdunQ5ceJEuQVWCwuLuLi4lStX+vj42Nvb41lw8C6UyWTu3LlzzZo1bdq0sbOzi4iI\n+PLlC+7y5e7uPnfu3MjIyDVr1nh6em7ZsuXAgQMxMTFcLrdsGPayMJnMkydPrlq1avr06XQ6\nvWfPnjExMfjg+eTJk6tXrx4zZoxMJmvVqlVcXJw6mB8AwNvb+9y5c3379rWysiJ+kSFEoGme\nZYVAIBAIBAKBGApwKRYCgUAgEAikjgANOwgEAoFAIJA6AjTsIBAIBAKBQOoI0LCDQCAQCAQC\nqSNAww4CgUAgEAikjgANOwgEAoFAIJA6AjTsIBAIBAKBQOoI0LCDQCAQCAQCqSNAww4AAFAU\nlclkCoVC30IqRy6XUzOONIIgMpmsbNIt6oBhGGVvqEKhkMlklL2nFL2hpaXK6GjliRP6FkI5\n0HfvlNHRyP9mi9cuCoUCRVGSKscwjOzuV6lU4pkVSEImk+GpokmC7KdSLpfLZDLy6lepVGVz\nZ2sdsntUFEXJbp8ymUyLjxg07AAAAEVRsVgslUr1LaRyJBIJNY0AhUIhFotJfWJrDIqiEolE\n3yoqRyKRiMVi8t6UtUGpVFLTIMZKSlhz59LXrNG3EMqBPnnCmjsXi4sj7xRSqZS85qqD7lcu\nl5NnGGEYJhaLS0tLSaofAKBQKEjtZsVisVgsJq9+pVJJquFLdo+qUqlINXylUqlYLNbi2AMa\ndhAIBAKBQCB1BKa+BUAgEAAA+P79+927d589e/b9+3c6nV6/fv0WLVq4ubnZ2trqWxoEAvlV\nUKlUN2/efPDgQXFxcb169VxcXHr37m1ubq5vXZBqAA07CETPXL9+fdeuXUlJSZVOxbdq1crL\ny8vf379r1640Gk338iAQyC/C+fPnQ0ND37x5U/Ygk8ns06fP8OHDvby8HBwc9KUNQhxo2EEg\neiM7Ozs4OPju3bsAABNjfv/erh3atWpgbYli2OevBbkv36Q9ynrx4sWLFy+2bNnSpEmT0aNH\nBwYGtmvXTt/CIRBInUIikUycOPHo0aMAAGdnZ09PTwsLi5KSkszMzAcPHty8efPmzZvBwcEd\nOnQYPnz4hAkTmjRpom/JkJ8CDTsIRA8gCLJ+/foNGzYolcrGdg1DgycHDB/E43IqlszJ/ffy\njTsXLie/+Pftxo0bN27c6OzsHBAQ4Ofn5+zsrHvleoPFQlxc0MaNGfoWQjUwU1PExQVzdNS3\nEIih8vHjx3Hjxj179szc3HzVqlVDhgwp+61cLr9//35KSsqtW7eysrKysrLWrl07cuTIzZs3\nN27cWF+aIRqgUdPdUlugKErE00SlUonFYiaTyefzdaCquohEImNjYwouw+FO/lwul8OpxCLR\nL7hXrLGxsb6FVMKrV6+mTJny4MEDFpMZMnvCwtkTuRx2lb969uLV2b8Sz19O/pD/GT/SqlUr\nPz+/kSNHtmnTRlvacOc1at5QkUhEp9NNTEy0WC2LxdJibXpBoVAIhUIOh6PdK1OWkpISPp/P\nZJIyEaBSqYqLi1kslpmZGRn1AwDEYjGLxSKpVWMYVlhYSKfT69WrR0b9AACJREKj0Xg8HhmV\np6am+vr6FhQUODs7R0dHN2zY8GclMQx78uTJlStXLly4IBKJTE1N//jjDz8/vypPgUfFIq83\nFggECIJYWFgwGKSM+5RKpVQqNTU1JaNyAIBQKFQoFGZmZtrqjuq4YYeHh6myGB7zjE6nU7OX\nVygULBaLgoYdgiAqlYrJZJL0ONUGDMOUSiWbXbXBpGNSU1MnTJhQUFDg5Gh/YOfq9s4tqvVz\nDMMys3MvXr996dqtD/99wQ+6uLhMmjRp9OjRRkZGtZSHD4SoeUMVCgWNRtPuPSXPGKoNKIoS\nD2+BIIhUKmUymSS9+AEAEomEy+XS6aREUUBRtLS0lMFg1L71/gyZTMZgMEjq3vFwJzQajTzD\nBY9AREZvdvDgwZCQEIVC4enpGRkZyeVyifxKKBRu27bt7NmzdDr9999/nzp1qubyeBxBgpXX\nAIlEolKp+Hw+SU1UpVIpFAryni+pVIogiJGREfGOV/MopY4bdgRBEEQgEJA6ZKwNxcXFZmZm\nJDXZ2iCRSCQSCZ/PJ6/F1xiVSiUSiajmzLV79+7FixcrlUrvwX1jt640Ma7VDHFWzovzl5P/\nvJiY/+krAMDS0nLx4sXz58+vze2QSqUYhpH3iq0xKIoWFRUxGAwLCwt9ayEdgiNSHNwKJHVc\nqlQqmUwmSWNLHYyrEQSh0WjkDVfkcrnWhxxlIWO4JZVKFy5cePLkSTqdPmvWrKlTp1b3+p8/\nf37NmjUAgEOHDvn6+mooqVKpMAwjacYXAKBQKDAMY7PZJDVRfOmP1OcLRVEWi0X8La95Ec9Q\nDTuhULhly5Z27doFBATUvjZo2NUMaNgRRyqVzpgx49ixYwwGPWLRjJmTA3g8nla6IRRFb/2d\nHnvoTNLtNAzDHB0dY2JiBg8eXGOd0LAzLOBSbJXApdhy5OXl+fv75+TkmJubb9mypWvXrjV7\n6s+dO7d8+XIej3f//n0XF5efFYNLsZrR+lIs5WwFgvzxxx9PnjzJz8/XtxAIpGr+/fff7t27\nHzt2rJ6F2fmju2ZPDdRi5XQ6vX8ftz8P77hz6XAvt07v3r0bOnRocHAwNRNIQCAQ/ZKYmOjq\n6pqTk9O+ffsLFy707t27xlX5+flNnTpVIpEEBASQmrsCUi0M0rDLyclJS0tr2bKlvoVAIFVz\n/Pjxzp07P3nypGP71ncvH+nXsxtJJ+rQrtXl0zHRW1bwjXh79uzx8PAoKioi6VwQCMQQOXbs\n2LBhwwQCQWBg4MmTJxs1alTLCkNCQjp37pyXl7do0SKtKITUHsMLd6JUKmNiYkaPHv3s2TN9\na4FANFFYWDhnzpwzZ87QaLTpE0duWDGPQ7IzB41GGx/g5dalfeDU0Lt37/bp0ycpKcnGxobU\nk/6ypKWlXbp0qdxBa2vrhQsXlju4du3acnvmAgMD27dvT64+COR/OXLkyJQpUwAAy5Ytmzhx\nolbqZDAYW7Zs8fb2PnDggL+/v4eHh1aqhdQGw5uxi4+P53A43t7e+hYCgWjizJkzbdq0OXPm\njJWlxakDW7auXUy2VaemeVOH5AsHu3Vq9/Tp0/79+xcUFOjmvORSWsrbvZtz7Ji+dfwflpaW\n7f6XkpKSSq92Zmamqalp2ZJa3E9Ge/OGt3s389o1bVUIqZNcvnw5KCgIALB+/XptWXU4tra2\nS5YswTBs+vTpcEGWChjYjF1+fv6FCxciIyMJehIolUqJRFJlMdyDBEGQkpKS2kokAZVKJRQK\nKRjuBPfVkslkFNzOhWGYSqXSyw199+5dWFhYYmIiAGCYZ58tq0OsLC3w+HBqbeCHJx1JGox4\nnPg/to6aGvrwcbanp+eVK1cI7oxGURSPFEOSsBpD+/rVdN06VfPmJVp9J9XGwGrRokWLFv8X\nrebz58/nzp2bN29euWJyuRxF0b59+7q5udX4XBqgvXzJX7dOGRAAtOFJBqmTPH/+fMyYMQiC\nrFixgkjkueri7+9/5cqVtLS05cuX79q1S+v1Q6qFIRl2GIZFR0cPHDiwefPmBH9SrXBQ1Hyf\n4SAIom8JP0WlUhGJAq0XdHxD5XL577//vmvXLplMZlPfcsOK+UM9eoEfFnA5UBQlVQyXwz4W\nu3HEhAX//PPPlClTjhw5QtyxmmxtNYD+4xGg7EMaFxfn5uZWce+vVCoFAFDQcxzyiyCRSPz9\n/UUi0bhx48aPH0/GKWg02rp167y8vPbs2TNmzBhXV1cyzgIhiCEZdsnJyV+/fl25ciXxn7DZ\nbCIBL/DQGEwmk5qJCoRCobGxMQXDnchkMplMxuPxqJmooLS0VJfhZ69du7ZgwYI3b94wGYyZ\nk0ctD5n2szB1CoUCRVEOh0P2LCyXy/3z8I5+w6dcv3599+7dq1evrvIncrkcwzDyQonWGEwq\nxf+gTgibsuTm5j5+/Dg2NrbiV7hhR8FnBPKLsHjx4tzc3I4dOy5dupS8s9jb2wcHB2/evDko\nKOjx48cUDA7/62BIht358+fpdPrGjRvxj+/evaPT6REREXPmzPnZ9nAajUY89lK1CusSXBgF\nDTtcEp1Op+B1U6lUOruhHz58mD9/fkJCAgCge1eXretC27WuelKZTqfrYHm9UcP6pw9s8fSf\nHhkZ6ebmNmzYMM3llUolqaFEawz6I0IVBbUBAM6cOdOvX79KOyJ8N0h+fn5ycnJRUZGNjc2Q\nIUPs7Ow01IYgCPEAxXSFgvkj/0ENlBNBpVJJJBKSuiB8ZwKe15GM+sGPzAekzvWSev3xFZua\nLYzcuXNn7969fD5/48aNKIpWum0GvwW131EzZsyYy5cvP336dO3atUuWLFEfV6lUKIqSd33w\nFQY82h9J9SMIQvb9lUqlZXfsaEbzJBQVu8ifMXLkSOmPUTsAoKSkhMViubm5UTCYKuQXQalU\n7ty5c82aNaWlpdaWFuuWBY/2G0K13ZAd27feui50btiGSZMmZWVlaTYpIDXgv//+y8zM3Lp1\na6Xf4r3W8ePH+/bta2lp+fDhw+vXr69fv15Dhl+VSkXcsGMjCAAAwzDiP6kBZO+jRVGUVP06\nMOxI1Q9qtCFHLpcHBwdjGDZ//nwbGxvNNWhlw8+qVavGjh27ZcsWT0/P1q1bl/2K7B07xK2i\nmkH2/a3WI8bn8zW8aAzJsHN3dy/7MSsri8fjDR06VF96IL849+/fnzlzZk5ODp1ODxrvtzJ0\nlrkZFROPAgAmjPK+9yDz9Plr48ePv3nzJgVnfw2alJQUW1vbso4UZXFyctq5c2ejRo3wBe6A\ngICQkJAjR45ERUX9rEIWi0V8FwHGZgMAaDQaeRsPJBIJh8MhKaw/zBVbJTVOWbZnz563b992\n7NhxzJgxGp56PCWXVnYLtGvXburUqfv27QsJCbl9+zY+vw5zxWqmBrliNU8fGJJhB4FQhMLC\nwiVLlsTFxWEY1t655a6N4Z07OOtbVBVsXx/28HHO7du3t23bFhoaqm85dYp//vmnU6dOP/uW\nx+M1bdpU/ZHBYHTq1OmaxugkdDqd+FsWYTIBADQajU3aNj6ZTMZms8lLKVZaWlqt/3J1USqV\npKYUww078vTjG0uqW/+nT5+2bt3KYDBWrlxJxKjVluE+d+7clJSUjIyMPXv2hIeHgx9LveRd\nH3xSnM1mk5dSDEEQ8vTjc40sFutXTykGABg3btzIkSP1rQLya4Gi6IEDB1q2bHnw4EG+EW/T\nyoW3Lx6ivlUHADDmGx3YuZrBoEdERDx9+lTfcqqPubkoLk76Y4stdRCJRK9fv27VqtXPCrx6\n9aqcGfft2zctzu5gHTuK4uKUM2dqq0JI3WDVqlWlpaUjRozQsOhPBmw2e+PGjQwGY/Xq1c+f\nP9flqSE4Bjxj5+DgUGUZPJhZlcXwMhiGUTOqCC6Mgstn+JZVfGOpvrWUB4/Hpl1hDx48WLBg\nwePHjwEAvkP7b1wxv6GNNahpcBAURXW8G69LB+f5M8Ztjzk6ceLE1NTUSidgyLhuWgFlMuXe\n3nQ6Xbvaaj8L9d9//2EYZmtrW+741atXWSyWh4eHUCiMjY0ViUS+vr50Ov3evXupqalaHJRi\nNjZyb2/odQspS15e3uHDh3k8XsXAijqgffv2U6dO3b9//5QpU1JTU3Uv4BfHgA07IlQrQDGp\nblm1AcMwIv8L3YMbNHK5nJqhxbToh/X58+c1a9bEx8djGNbCySEyYn6v7p1BTXeU4+1NLxct\nZNaEq0l/Z2Zmbtq0acGCBRUL4PeUgoYdjtZ962ofPKW4uBgAYGpqWu54SkoKj8fz8PDo1KnT\npEmTzpw5c+LECXx4Nnz48MDAwFqeFwLRwNq1axEEmTx5cv369fUiIDg4OCUl5eHDh1u3bp0/\nf75eNPyy0PB3zC8OgiACgYDFYmkxz48WKS4uNjMzo+CMnUQikUgkfD6fgsFX8diEtX9ty2Sy\nbdu2RUZGlpaWmpoYL5k/dcbkAFbtpnlkMhmKojweTy/+s5lPng8YEcRksrKysiqNpothGAU9\nzVEULSoqYjAYFhYW+tbyPxQXF+fn57dp06bc/p7Xr1/T6fQmTZrgH+Vy+adPnwAAjRo10u7s\nmkKhEAqFHA6HPOeJkpISPp9P3h674uJiUrtfsVhM6h67wsJCOp1er149MuoHPwJ5EO9mX758\n6ezszOVyU1JSiPSBJD31OTk5o0aNYrFYaWlpzZo1I8+5RCAQIAhiYWFB3h47qVRacfCmLYRC\noUKhMDMz09YeOwObsVOpVElJSTk5OTKZzN7eftiwYVZWVvoWBamzXL58ef78+W/evGEw6JNG\nD48InWltSS2rogZ0cmkzN2jMzr3Hpk2bdvv2bQoOGAwLCwuLSm1NJyensh85HI7ayINASGXj\nxo0qlWrs2LH6jebdrl27KVOmHDhwYPr06UlJSXpU8qthSH06iqLr1q07evSohYWFk5PTo0eP\ngoODv379qm9dkDrI58+f/fz8vLy83rx581u3DncuH9m9aWkdsOpwli6c1tTR7u+//96/f7++\ntUAgEG3y7t27U6dO8Xi8SZMm6VsLCA4Oxl/We/bs0beWXwhDMuxycnIyMzNXrFgRFBQ0ZsyY\nzZs3oyianJysb12Qusbp06fbtm17/vx5a0uL/TtWX4vf275N5SHKDBQel7MrcimNRluyZAm+\nPgiBQOoG27dvVyqVI0eOtLS01LcWwOFwcA/ZDRs2vHz5Ut9yfhUMaSnWyclp69at6iigfD7f\nwsJCJBLpVxWkLiEWi+fMmXP06FEAQOCIwVGrQizMydpXoV/6/NZljP/QE2cvz5s3788//9S3\nHMj/oFAoqtuzyeVy8pJDYBhWUlJCUuU4CIIUFhaSV79cLifVNw5FUVL1gx+56TRTVFQUFxfH\nZDLHjBlTNlGTZvCt9sTLV4uWLVuOHTv26NGjEyZMuHTpEhnb4HD9AoFA6zWXPQV59xfXLxQK\nif+kXr16dSTzhLGxcdnY7vfv3//8+fP06dP1KAlSl8jLy/P19X3+/Hk9C7Pfo5Z5efbVtyJy\n2bB8XmJK6rlz5xISEoYPH65vOVXx7Ztl69aqpk3B48f6lkI6bDab+E585OpV5rhxiK8vMy6O\nJD1CodDIyIg85wmBQMBkMsnbnF5aWspkMslznigqKqLT6eS59RB3noiOjpZIJD4+PtXa0Ik7\nT5DnAzd//vy///47PT398OHDZERHLykpQRDEzMyMPOcJmUxGnnOSSCRSKBQmJibEnSfqYOaJ\nkJCQ4uJiLpe7fPlyDQHfwY/7UWWF6nAn1Jz/w0M8UC0DKfgRFEMul1MwOgaGYSiKEr+ht27d\nmjhxokAg6Nqx7cFda2wb1id1/gMAoFAo9HtPjfm89cuDZ4SsnTNnTrdu3fBuC4/pSHZWx5og\nEpkIBDSRSLsPKXmddS0h3jZoCEITCEBpKanNiUajkVS/uloD1V/2FOTVTES/TCaLiYkBAEyZ\nMoUkJTWDy+WuX79+woQJq1atGjZsGEkBk8luojpoP9o6hUEadq6uriUlJQ8ePDh37lyzZs00\nDG1RFCWeGLhahXUM2Rm4awOCIBQ07HAI3tBTp04tWrRIqVSOGzksMmI+i8XUwf+ICsaTz+B+\nZxJupNx9uGLFis2bN6uPU/CG0n+E/dPuQ0pZww4CqS7Hjx//+vVr9+7dNeRB0RfqkMXjx49/\n8OABSXl7ITgGadiNGjUKADBhwoSFCxfu379/yZIlPytJMDYSHpqYyWTy+XxtCtUSYrGYz+dT\ncMZOJpPJ5XIul0vBqPcoikokEiKRk6KiolauXEmj0dYvC547bYwOtCkUChRFORwOFe7pro1L\n3AaOOXLkyPjx43v27ImbTRS8odiPDUaUCjaZm5tbzghu3LjxzxR+/PhRKpXa29tTMO4jxNDB\nMGznzp2AetN1aubNm3f37t3MzMyIiIhNmzbpW05dxpAMO7lcLhQKra2t8Y9cLrdTp0737t3T\n8BM6nU4kTJd6opWywwgmk0nBeGN47gQGg0HB64anza5SWHh4+ObNmzls9v6dq32H9teNNhw6\nnU4Fw87BvtGqsFlhq7fPnDkzKyuLwWBgGEbBG4r+2OBFKW0RERHlZtNDQ0N79epVrtinT58i\nIyM/fPjAYDDodHpQUNCgQYN0KBNS90lMTHz27FmzZs0qNj+KwGKxtm7d6u/vv2XLloEDB7q7\nu+tbUZ2FcraCBs6fPz9r1qyy3ahAICBvvy2kboNhWHBw8ObNm42MePF/bNOxVUcppk8c6dq5\nfV5e3urVq/WtxZBAUVShUISHh18sQ8XXKoZhmzdv5vP5R48ePXfu3MSJE2NjY1+9eqUXzZC6\nyo4dOwAAEyZMoMJw8Wc0b948NDQURdHx48d/+/ZN33LqLIZk2PXs2RPDsD179ohEIpVKdf/+\n/fv377u5uelbF8TwwDBs9uzZe/bsMTHmJxzb1a9XN30r0id0Oj16y3Iuh71t27b09HR9yzEY\n8PATVS5b5+XlvXnzJigoyMzMjEajeXl5NWnS5Nq1azrRCPkleP78eWJiooWFhY+Pj761VMHY\nsWMHDBjw6dOnCRMm4JmpIVrHkAw7e3v70NDQnJycsWPH+vn5RUVF9evXLyAgQN+6IAYGbtXt\n3bvXzNTk4onf3bq46FuR/mnh5Lh80QyVSjVjxgySwlnVFhYLcXFRUWlXOH6hqtwwl5uby+Vy\ny2YYc3Z2upplGQAAIABJREFUfv78ubZkYKamiIsL5uiorQohBsfOnTsxDAsMDORyufrWUgU0\nGm3jxo2NGjW6ceMG3GlHEoa0xw4A4Obm1rVr1y9fvkil0oYNG1LT1wFCZfAVWNyq++v47k4u\npDjeGyJzg8Zcun47PTNn5cqV0dHR+pZTAUtLQXIyg8Fg61uIGtyw43A4hYWFRUVFNjY2lfrY\nFhQUWFpall0gs7Ky0rwOhWEYcadpxM2tNDmZzWbTSfNlrpae6oJP22AYRp4vNoqiKpWKpPrx\n6EWATF9yFEVpNNrP6i8oKDh+/DiLxRo9enRt5sDImz/DMAyPP4V/NDEx2bFjx9ixY1etWtW9\ne/fabwpUByxT3wvtgtdM3v1V6ye+jK45qKSBGXYAAAaDYWtrS7Bw2cakAXXPQoUIFJVCXpOt\nDfh1wztNfWspD37FKgpbtGhRdHS0qYnxhWO7OrZvrceriqIopRxi6HTa3m0rew4ZHxsb6+Xl\n5eHhoW9F/4P6QdZuY6tNRFPcsNu7d++///5Lp9NVKpW7u/vs2bPZbHa5YuWWazkcjlKpVKlU\nPzt7DTJPKBQKUoMikR3jE0EQUjMHKBQKIpkbagyKoqTqBz/PDLFr1y6pVDps2DATExMicVt/\nRm1+S4SyT26LFi3mzZu3ffv2sWPH3r59m3g4bg1UK3NDDSD7/lYrM0q5sWI5DM+wqxZKpbK0\ntLTKYmp7meyWUTPwAMX6VlEJ+LuW1FxGtQFF0XI3dN26dbt27TLmG50+sLltKyd9hS2kSIDi\nitg1qr9myezQVdsnTZp07949KuSaLIfWH9LaZAvgcDiurq5t27Zdv349i8V68ODBtm3bTE1N\ny8WbYDKZ5YaX+NBcg1lPp9OJO//iIys6nU5S2H0AAIIgDAaDvOaqVCppNBpJmS0AgQteS/D4\nAOT5a2vQL5PJDh06BACYMGFCjRsAbnKR137wGbty+idOnPj48eM7d+4sWLDg5MmTtWldCIJg\nGMZkMklqovgcAXntU+v667hhx2azy42eKwUfLDKZTEqFyFJTXFxsZmZGqdkdHIlEIpFIeDwe\nBeNy4XlEzM3N1UfWr1+/Y8cOHo979tD2Hq4d9ahNJpNhGMblcqlm2AEApo7zS/n70bXkvxcu\nXHjx4kXqKERRtKioiMFgkJe4qbo4OjouX75c/bFHjx6PHz++f/9+OcPOzMysnDEqFApNTU01\nXFuCAThxFAqFUChksVjkBVsuKSnh8/nkpRQrLi4mtfsVi8UsFou8lGKFhYV0Op08/RpSisXH\nx3/79q179+4uLjXfK4ynFCMveiWCICiKVnwXR0VF+fj4XLt2LT4+vjbZQQUCAYIgJiYm5KUU\nk0ql5IXgEAqFCoWCz+dra2xAOVuhSr59+5aUlJSQkJCRkUHB1UkINdm5c2dERASHzT59YIt+\nrTrqsytyiW3D+pcvX8bjnUKIY2xsXHFC0dHRsbi4uOxS5tu3b5s2bapbaZA6CIqi27ZtAwBM\nnTpV31pqgoWFRWRkJI1GW7Ro0bt37/Qtp+5gYIbd7du3Z8+eff78+YcPH0ZGRoaGhlJzERBC\nKeLi4kJCQlhM5pGYjb94ZBMiWFqYHdy1lslkhIeH379/X99yqMuNGzcmTJhQVFSEf0QQ5NGj\nRy1atFB/xFe4OnbsyOFwkpKS8OMFBQVZWVk9evTQi2ZIXeLChQsvX75s2bJlz5499a2lhvTo\n0SMwMFAsFs+cOVPfWuoOhrQUKxQK9+zZ4+npGRQURKPR3rx5s2jRouvXr3t7e+tbGoS6xMfH\nz5gxg0aj7du+aogHRWOyU40erh1XLJqxOiomICAgIyPDxsZG34qoiJub29mzZxcvXuzu7s5g\nMO7fv19cXBwSEoJ/u2TJEh6Pt27dOiMjo3Hjxh06dOjbt28WFhZJSUmOjo4w7D6k9kRGRgIA\npk2bRp0tEzUgNDT09u3bN27cOH36dGBgoL7l1AUMacauuLi4a9euvr6+eCNu2rRp48aN3759\nq29dEOpy7dq18ePHoyi6Y0O4v89AfcsxJBbOmjB0YO///vtv5MiRlJgXl0i4R4+yExL0reP/\nMDMz27Ztm6en54cPH96+fdutW7fY2NjmzZvj3zo5OTVp0gT/29vbOzw8vKSkJDc3d8CAAevX\nr9fifjXax4/co0cZd+5oq0KIQXD16tXHjx87ODgMGTJE31pqBZ/Px/eqLl68mJpuggaHIc3Y\nOTg4hIeHlz0iEomIZHmH/Jrcv3/f399foVCsXz5v8pjh+pZjYNBotP07VvfznnLv3r05c+Yc\nOHBAz4KEQuNFi1TNm4PJk/WspAxmZmajRo2q9KtZs2aV/ejm5kZSmhza06fGixYpAwLAsGFk\n1A+hIBiG4QkAZ8yYQZ43q87w8PDo2bPnvXv3Nm3atH79en3LMXgMybArx/Xr1wsLC/v315Ti\nE0EQIiEt8GAEKpWKSGwU3YOiKO4VpW8h5cGd/BUKBQUzw2RkZAQGBkql0kVzJs6aHIBLpQi4\n0w8e4kHfWsqDoqjaJ4nLYR/fGznQf/rBgwebNGkyf/58fSqTSvFw5Np9SGGQc4ghcvHixUeP\nHjk4OAwfXkeGrMuWLfPy8tq+ffuMGTPs7e31LcewMVTDLiMj4+DBg+PGjXPUmEhHpVIRz4+E\noihFkymRHzqyNiiVSkqZTQCA58+f+/r6ikSioPEjQudOopo8HPLimNcetaXe2M7mwM7VY6aF\nR0RENGzY0MvLS1+S6HI5boJp9yGFhh3E4FCpVPjaZXBwcB2YrsNxcnIKDAw8ceJERETE4cOH\n9S3HsDFIw+7u3bs7duzw9/cfOXKk5pJMJpPIWi0+JcZgMCgYjw0AgMeKo+DsDh7snsPhkBeZ\nswY8f/7c39+/uLh4jN+QqFUhFLxuSqUSwzAWi0VBbfiMXdm3Rf/ebts3hM1bEjl79mxbW9ve\nvXvrR9mPzTe/wu4LgksNODSFggkAhmHkLTjgI2SSQmmq48OTpx+Po0bqUIrU648rVw+3Dh48\n+OzZs9atWw8cOFAro1b1GkLtq6oUvFepsv4ZM2YkJCQcO3Zs9uzZzs7O1aofACCRSEhqongM\ncFKfLwCATCYjvptZ84jU8Ay75OTkPXv2TJ8+nciOUQaDQWRAgyAI3iaomUEZT0lEwQDFKIoq\nFAomk0md6/b06dPBgwcXFBQEDPfcti6UUhanGrLjpNcGtbayBycG+nz59n3Dtv0jR468efNm\nly5ddC8M/RHdlDqNjTyqlSaB9qMkeV0ErodUw47UzBA0Go28+tVbF0jVr66/qKho7dq1AICw\nsDDtTteR3R1VWb+lpeXEiRNjYmLWrFnz559/Vrd+spso2a9gLTZRAzPsMjMzo6Oj582bB4MF\nQCqSkZExePDg79+/+/sM3LttFYJQcQXWQAmfN/V7oWDf4fhBgwalpKS0b99e34rqMtVaPUCY\nTADAzzITaAV8Yp68zBP4uJo8/SqVitTMExoyQ2jrFOr6IyIiioqKBg0a9Ntvv2mrfnwNgbyU\nWQAAFEWJ1B8UFHT69OmrV69mZmYSj/Uol8tRFOVyueRlnkBRlLz7i2eO1uLaF+UmgTSgVCpj\nYmJ8fHygVQepSGJioru7+/fv38eOHHZgxxoGw5DatkGweXXIuIBhuMfSkydP9C0HAvnlSEpK\nOnz4sImJybJly/SthRT4fD7uTr5kyRJ9azFgDGnG7u7du9++fcvOzi7bpq2trRcuXKhHVRAq\n8Pvvv4eEhCAIMido9MYV82k0Gkw3p3VoNNqeqOUqFXrq3FV3d/erV6+6urrq7vTm5qK4OJqp\nad3fYVdNsI4dRXFxjCZNqLjtAKI9ioqKpkyZgmFYWFhYgwYN9C2HLAIDA48cOXLv3r2//vrL\nx8dH33IMEkMy7Ozs7EaPHl3uIHl5eSEGgUAgmDlz5pkzZxgMetSqkFlTKg8qBtEKdDo9dmsE\nm8U6cvqvAQMG/Pnnn56enjo6N5cr9/amoA/gvXv30tPTS0tL7ezsvLy8rKysKpZZu3ZtOcf2\nwMBAbS1nYzY2cm9v8jK4Q6gAhmFBQUH5+fl9+vSp0mvQoGGz2QsWLFi8eHF4ePiQIUOouU+a\n4hiSYdeyZcuWLVvqWwWEQly4cGHevHn5+flWlhZxu9bCPLA6gE6n79601MzUePf+E15eXjEx\nMUFBQfoWpTf27dt348YNd3d3Gxube/fuJSYm7tmzx9LSslyxzMxMNzc3BwcH9REzMzPdKoUY\nNrt27bpw4YK1tXVkZCQFna60y7Bhw44ePZqdnY0vxehbjuFRx1es5HI5wRQlas8skhXVBHzn\nrL5VVIK68eheXlpaWmRkJJ6ivn9v1x0bwhpYl3+bUvy6UVMbcfYdPrt6cyyKojNnzly9ejWp\nO69xyLhuFY0w4nz+/HnGjBlz5szBpy1FIlFQUJCPj8+YMWPKFpPL5SNHjly2bBlJmScUCoVQ\nKORwOCYmJmTUDwAoKSnh8/nkOU8UFxezWCzyjF2xWEyq80RhYSGdTq9Xrx4Z9QMA/vrrLz8/\nPxqNdujQoW7dtD98lUqlGIYZGRlpvWYcPNwM+4djOxGysrICAwONjY2fP39uZ2enubBAIEAQ\nxMLCgjznCalUSt7yoFAoVCgUZmZm2pqeNKQZuxrA4XCIPMwIgggEAlJ7ltpQXFxsZmZGwXAn\nEolEIpHw+Xydxf8Ti8Vnz57du3dveno6AMCukc3apXP9vT0qlsQwTC6XUzM0hkwmw324KGjb\n4eFOiPQvC2ZNaNbUYdrCVXv37n358uXp06fr169PnjAURYuKihgMhoWFBXlnqRYsFmvmzJm9\nevXCP5qYmNja2n758qVcMTyiMjVjZEKoz5MnT8aPH69SqSIiIsiw6qhJhw4d/P39z549O3v2\n7IsXL+pbjoFBOVuBCC9fvjx16lRxcbG+hUB0hEQi+fPPPwMDAxs0aDBlypT09HR7W5vNq0My\nb5+t1KqD6IZhnn2Sz8c1dbS7detWhw4dbt26pW9FOsXKymrIkCHqeQ6FQvH582dbW9tyxXDD\nDu6Bg9SAV69eDR48WCQSjR07dty4cfqWo1PCwsKsra0vXbqk/0TVhobhzdglJCQcOXJEpVK5\nurpSZ+wOIYPS0tLLly+fPXv22rVrEokEAMBmsYYO7D1upNeg/j1hQBMq4NzK6c6lw7MWr7t8\n486AAQOWLFmyevXqX3O/c1xcHACgojcJ3nTz8/OTk5OLiopsbGyGDBmieXVJqVQSD3OPr1Ar\nFAqBQFAT3QRQqVQikYjUCWZ82YSkylEUxVfTSKofP4XW9b9//97Ly+vz588eHh6LFi0iL7Ek\n3oSoVj+bzV67du3s2bPnzZvXokULFxeXn5XEMzcIhUKSmiiGYWTcXzW4frFYTFy/mZmZhsIG\nZtgdOHAgLS1typQp0ISvwyiVymvXrp08efLSpUtqe86j728+Q/p5efa1MId+0NTCzNTkxL6o\n/UfORmz8fePGjUlJSceOHfvV/JyOHz+elJS0fPlyc3Pzcl/h9sTx48f79u1raWn58OHD69ev\nr1+/vk2bNj+rrQb5rzAMIzVlFv7uIQ+y9esA7ep/8+aNn59ffn5+r169NmzYQKPR1CnFSILs\n+muwod/NzW3y5Ml//PHH6NGjr1y5Ym9vr6Ew2U2U7PapRf0G5jxx4cIFd3f3L1++hIaG7ty5\ns2nTplqpFu6xqxla32P36NGjo0ePnj59+vv37wAANovl3tvVd2j/IR69zEyrtzGc+nvsqJn/\nl/geu4o8e/F66ryI5y9fGxkZbd68efbs2Vr8D6Jfv2KurmiTJiyKLfhiGLZ3795bt24tWbKk\nU6dOFQtIpdLPnz83atQIb40qlSokJITL5UZFRWmok/hbVpWczJgxQzVsGGPXrpr9F6pEJBIZ\nGRmRtDMdRdGSkhImk0me84dEImEymdXavE8cDMMEAgGdTtfi6yMrK2vo0KFfv37t27fv7t27\n8ZRo5PknyWQyDMPI2wZam14FRdF58+YlJyc3bdo0MTHR0dGxYhmhUKhSqUxNTUlqogiCyGQy\n8rJUi8VipVJpYmJC/BZr/p8a2Iydr68vAKDi9mSIQfP27duTJ0+eOHEiNzcXAECj0X7r1iFw\nxGCfwe5wfs6AcG7ldPvS4bVbYmPiTs+dO/fSpUuHDh1q2LChdmpXqRjv3wNy3s21ITo6Oi0t\nbcOGDc2bN6+0AI/HKzsEZTAYnTp1unbtmoY6aTQa8VcUJpMx3r9HCwrIC/KHZ7EkNYhgtf7L\nNaicPP3qyRFt1Z+UlOTv7y8UCocOHbp582Ymk6lUKnHbTiv1/wzy6scjxtesfgaDsX379tmz\nZ9+7d69Xr14XL16smKsar5lgavgagKIo2e0TAKDFJmpghl11UalUCoWiymL44BhFUVI3YdQY\nDMNkMhkFZ3eUSqX63xqQl5d36dKlhISEx48f40ecHO0DfD1HDR/U2O7/WwM1nv3GMIyyizv4\nmwBBEAreU3w5oMbXjcmgr10yx6Nv9zmh62/cuNG2bduYmBhvb28tKJPL8fkE7T6ktZylSEhI\n+PvvvyMjIzWsHrx69erff/8dPHiw+si3b9/IG/1DDJojR45MmzZNqVROmDBh6dKlFFyo0T0c\nDic2Nnbx4sU3btzo1atXTEzM5MmT9S2K0tRxww5BEOJ7kFUqFfHCOgbfakZNFAoFEesZRywW\np6am3r59++bNm2/fvsUP1reqN8yzz4hh/bt0cFbXqS1tWqmHDGpsEOuAWu72cO3UNiUhbum6\nnecuJQcGBk6cOHH9+vW1XBanS6W4Cabdh7Q2hp1AIDh58uT06dMrtequXr3KYrE8PDyEQmFs\nbKxIJPL19aXT6ffu3UtNTa3byQMgNWPDhg0RERE0Gi0sLGzq1Kn6lkMh2Gz2zp07f//999jY\n2ClTpty5c2fPnj1wdPQz6rhhx2Qy+Xx+lcXwuToGg0HNLVlSqZSaMc8UCoVSqWSz2Zo3T0il\n0rS0tDt37ty5cyczM1M9G+Rg33CQe8+hnn26d3HRuosrPl1HTfdMpVKJ7zih4D3FTbrarwhY\nWbIP7FwzqH/PRRFbjxw5kp6efvz48datW9e8xh8WGJEnWjekpqbKZLLdu3fv3r1bfdDW1jY2\nNhYAkJKSwuPxPDw8OnXqNGnSpDNnzpw4cQKfgBk+fHhgYKDedEOoB76TLDo6ms1mR0VFDRky\nRN+KKAedTp8/f367du2WLFly5MiRtLS006dPd+zYUd+6qEgdN+wYDAaRETmCIFKplE6nUzOI\nqEwm43K5FJyTxzBMqVSyWKxKr9vbt28vXrx49erVu3fvqh3djflGPXq79uvZrX8ft5bNHEnV\nplKpdJAOoQbgW4mZTCYFDTsAAK5NK1WN8h3s1sVlcvCKjH+e9erVa8+ePTVeQ0F/xIGjzkPq\n6urauHHjcgfV8epmzZqlfmZHjBgxdOjQT58+AQAaNWoEY9pByqJQKCZMmHDmzBkTE5Po6GhX\nV1d9K6Iu7u7uf/311+LFizMyMrp37x4ZGblgwQJ9i6IcVHztQQya/Pz8kydPnj59+p9//sGP\ncNjsPr916f1blz49unRq34bJpFwedwh5ONg3uvHn/tWbYvYcPDllypTbt2/HxMRQZ9atNlhZ\nWVlZWf3sWycnp7IfORxOkyZNyBcFMTDEYrGfn19iYqKVldXBgwdrNav9a9CwYcOjR4/GxMTE\nxsaGhITcvHlz586dFcMM/cpAww6iHVQq1cWLF/fv35+YmIg7ozRsYDXEo/fgAb16uXXi8ai4\nxg3RDSwmc8OKeT3dOs5ctPbo0aMZGRnx8fHOzs7Vq4XBUDk4oLa2cFhQHiMjlYMDZm2tbx2Q\navPlyxcvL6+MjAx7e/u4uDgHBwd9KzIMGAxGcHCwm5tbaGjolStXHj9+rDUnrTqBIcWxQxBk\n5cqVAACJRPLmzZtmzZpxuVwLC4vQ0NDa1wzj2NUAPI6dUqnEx08fPnwAAJiaGPt5DQgYPqh7\nVxc9aoZx7GpGbSJOVUn+py8TZy9/9M9TIyOjnTt3Tps2jfhvKZgrljyqFcdOqVSKxWI2m03e\nPCiMY6eBGsexy8jIGDly5MePH9u0abNv3z7rn5vm+L7kXzOOnWYEAsHy5ctv3rxJp9MXL168\nZs0aMs5icHHsDMmwU6lU8fHx5Q7y+fza2+nQsKsZOTk5u3fvPnXqFO6o2LmDc9C4ESO8PHhc\n/W8hgoZdzSDVsAMAKBFk7ebY3ftPYBg2YsSIffv2aVjNLMsvZdgpFIpqpRTD42yR10WgKEpq\n/6NSqUjVj7/myHvcqutyhCDI77//HhUVpVAoevfuvWnTJs1GOdn68VGEgV5/DMPOnDmzbds2\nhULRqVOn/fv3aytzQdlTYBhG6vOF10/8Epmbm2sobEiGHXlAw65aYBh269at3bt3X7p0CUVR\nFpPpM6Tf7Cmju3Ss5uIamUDDrmaQbdjhJN9Om7lo7bfvRTY2Nnv37vXx8anyJ7+UYVctFAqF\nUCjkcDjkzXiVlJTw+XySZoxUKlVxcTGp3a9YLGaxWCT5rGAYVlhYSKfT69WrV2VhFEXPnz+/\natWq58+fMxiMOXPmlHWy+Rl4gGLyZuykUimGYUZGRiTVjyAIiqIkzZgCAGQyWV5e3tKlS1+9\nemVsbLx169bp06drsXfFEw2bmpIVLV8oFCoUCjMzM211vHXcsJPL5WKxmEhJsodEtaHGMbu1\nzvfv3+Pj448fP56XlwcAMDXhjxs5bOq4EXaNGuhbWiVQ57qVg8qNTWcUFgkWrdx6LfkeAMDH\nx2f9+vVV5qgg47pZWlpqsTa9AA27KqGCYff169fDhw/v37//zZs3AABnZ+c1a9a0a9eOyCmg\nYacZfKhMo9G2bdt2/PhxDMMGDBiwd+/ecg5MNQYadgYJnLHTzPv3769du3bhwoWUlBR8t0fL\nZo7TJo4M8BnIZjHZbDYFo4rAGbuaoZsZOzXxCdfD1+woLBIYGxsvWbJk4cKFP3u7wBm7nwEN\nuyrRr2H36NGj7du3nz9/Ho+X7uzsHBQUNGjQIOJdOjTsNFO2R71///7y5cs/ffrE4/HCwsJC\nQ0Nrv/cUGnak8/3790ePHkmlUicnJxcXF63UCQ27chQUFDx79iwnJycjIyM1NfX169f4cWO+\nkffgfuMDvH7r1oFGoymVSjxAMTTsqgU07MpSWCRYHRVzLP4SiqI2Njbh4eHTpk2r2BdT1rAj\n2COR0XHhQMOuSvRl2GVkZKxYseLGjRsAAB6PN2TIkICAgA4dOlT3FNCw00y5HrW0tHTnzp0n\nTpxQqVQNGjQIDw+fPn16bcw7aNiRS0ZGxqZNm2xsbCwsLJ4/f96jR4+FCxfW/u34yxp2GIZ9\n+PDh1atXr1+/fvOD169fCwSCssVsG9Z37+U6eECv/n3cyjpGQMOuZkDDriJPnr1cHRVz884D\nAICFhcWUKVOmTZvWsmVLdQFqGnYEeySSOi4caNhVie4Nuzdv3ixbtiw+Ph7DMCsrq0mTJo0a\nNarGlgE07DRTaY/677//bt68+e7duwCAevXqTZs2bcaMGTWLJQkNOxJRKBRBQUGurq5z5swB\nAOTl5YWFhYWFhf3222+1rPkXMewUCsWrV69yc3Nzc3OfP3/+4sWLly9fVsxCS6fT7Ro1aO7k\n0KaFU4d2Lbt2bOvY2LbSCqFhVzOgYfczHj7O3h5z9EZKKu6m161bNz8/Py8vr9atW6NisWT/\nfpqFBZ8y+b8J9kjkdVw4ytev5X/9RW/RwmjYMK1UWBFo2GmgnGH3/v37qKiouLg4hUJhamo6\nY8aMsWPH1jKSCDTsNKOhR/3nn3/2799/+/Zt3LO7f//+48ePHz58eLVGQQZn2FHufayB7Oxs\ngUCgTp7dokWLdu3a3blzR1v9o0EgEonUuVYFAkFFu1wmk0ml0pKSkpKSkoKCgi9fvnz8+PHD\nhw+vX79+9+5dueTubBareVOHZk0bN3W0a+Jg52jfyLFxIwf7RhzSnkAIRAOundufidv6+u3H\nw6cSTp+/lp6enp6eHh4ebmdn592tW/T586X29kI/P/J62GpBsEciu+OiPX1qvGiRMiAAkGbY\nQapELBbfunXr+PHjly9fRhCEw+FMnjx51qxZ1Jws+HXo2LFjbGzsu3fvTpw4kZCQkJSUlJSU\nxOVy3d3dBw0a1KtXr7Zt21JwYqKWGNL/5/Xr16ampvXr11cfad68+Z07d/QoSSsIhUKBQCAQ\nCIqKigoLC799+/b9B4WFhYWFhd+/fxeJRGKxWC6X1+ZEfD6veVOHFk6OrVs0bdHMoXXzpo6N\nbWGCLwjVcGpiv25Z8OrwOfceZl5JvJt0+/7rtx/P5+dHA/Dx48fWZmZ2dnZOTk6Ojo4ODg6N\nGjVq2LBh/fr169evb2VlpUubj2CPVFc7rl8NoVAoFotFIhHeYxcVFeEj5zdv3mRnZ7948QIf\nNhsZGY0ZM2bq1Kk2Njb6lgz5/zg6Oi5fvnzx4sUpKSmXLl26d+/e1atXr169CgDgcDgtWrRo\n0qSJnZ1dgwYNLC0t69WrZ25ubmpqyuVy8bUylUrF4XDw+2tqakpSpG4tYkiGXXFxcbl8cObm\n5oWFhRp+olKplEpllTXj6z4oit69e3f37t34NBiCIOpQKSqVSigUli1f9mM5NCetk8vlUqkU\nACAUCpVKpYZ6ysFhs8zNTHhcLof9f7O1RkY8Nut/biKTxeQbGRkb8UxN+BYWZg3rWzWob9nY\nrqGjfaP61hUjO2Dq+b8aoL5utamEJPCQkhQUBn6E7UAQhIJLsXioTIpctx7dOvTo1mHjinmf\nvnzLupkKVm7l8bhWPG5+fn5+fn6ltpFAIKjWilttFusJ9kjkdVz/HwRhAvD69eub0dFEf1JN\nlEolk8kkL8CsQqGg0+nkbQBAEIROp1fczYJhWElJCfix0CESiaRSaWlpqUAgKC0txY+IxWKZ\nTCZwYr1CAAAgAElEQVQSiTSfgs/nd+nSZcCAAQMHDsRTFGjxIcJjeZD3VKp7JJLqJ7tXIdij\nMhgMDw8PDw8PiUTy4MGDBw8eZGVl/fvvvzk5OTk5OcRPx2AwTExMzMzMTExMTExMjIyMzMzM\nOBwOvpZtYmKCW37lksGw2WwjIyOpVFpxgkYoFC5atEihUJRbUtOA5o7LkAw7hUJR7slnMpko\niqpUqp9Z0GWNsypRqVS5ubnnzp2rrdDqYGZqYmTENTMxNjXhm5uZWpibWtYzt7a0sLQwszA3\nrWdhZm5mamFmYmLCZ9V6uhh3ttc6FDECKoWk/7JWqMabW+cQ7190g1U984H9ugMAbBvWf3rl\nSIlQ9O7Dp4//ffn0teDzl4LvhYKCwqLvhQKpTIbv+yRec20MO4I9EtkdF1uh4ALw+PHjuY8f\nV/N/ACEEjUYzNTXl8Xg8Ho/P5xsbGxsbG5uamtarV8/S0rJhw4aOjo6NGzdWG45U7nY0QLZs\nsnsV4g8+k8ns2bNnz549AQAoin769OnTp08FBQVFRUUCgUAoFOIGvVKpxA16BEHw6RgMw0Qi\nkVKpxBfZtCh+wYIF+CkIwuFwNFixhmTYsdnscncOH+dpmBdlMBhEdq2iKCqXy+l0et++fc+d\nO6fe42lqaqp+Vs3MzMpex5+ldSstLdX8eKhTIhoZGRHcTCqTyTTfRX2hVCoRBGGxWBTco4BP\nA5C0XbqWyOVyFEW5XC4F76l+nSc0wfoCAKAxOMb2XY0BsKVAlhOCPRJ5HRcOjckEALRq1Spk\nyBCi0quJSqWqVr6jaoFhGJ5SjLwVLg0py/D5FRaLxefz+Xw+j8fDp2Hwv83NzblcbpXWv1Qq\npdFo5Llq6cB5AgBAaq5YUp0n8B6Vw+HUzMWwrPd9paAoqlQqy75N8LnekpISsVgskUjwfxUK\nBZ44GC+DH1H/RH28rF2Bgz9f1dKv+WGk3PtYA5aWlsXFxWWPFBcXa040yWQyiTwMCILI5XIG\ng9GyZcsq77HuUSgURkZG1EkppkYikSAIwmazyesRaoxKpUIQhLy06LVBqVTiPlwU3KtBtn9c\njUF/tDHq3FOCPRJ5HRcOwmYDANq3b9952zaCP6ku0CtWAxiG4YYdeS1TIpHQaDTyulmZTIZh\nGHn6ZTIZqb0x3qMaGRmR1KMqlcqK18fY2NjWtvJ4EdUF94rlcrnaGlFTzlbQQIsWLUQi0efP\nn9VHXrx4QUE7DAKB/AoQ7JFgxwWBQHSJIRl2bdu2tba2PnPmDP4Rd0Tq379/7Wum0+nGxsYU\nnHbCMTIyouCaHQCAzWYbGxtTcdkOADqdTsFpJxwjIyNjY2MKTsECAFgsFnkrJrWBZmam3LMH\nXbVK30L+D8090suXL1+9elVlsdpDd3FR7tlDmzpVWxVWhMfjkddcddD9cjgc8tYxaTSasbEx\nqRPJbDab1G4W3zVIXv3kTZfikN2jMhgMUkOi8ng8Y2NjLU43GlKAYgBAdnb2+vXrra2tLSws\nnj17NmjQoBkzZuhbFAQC+UXR0CMtXryYx+OtW7dOczEIBALRLgZm2AEACgsLHz58KJVKW7Vq\n5exMgR3UEAjkF+ZnPVJiYiKTyXR3d9dcDAKBQLSL4Rl2EAgEAoFAIJBKoeIuHwgEAoFAIBBI\nDYCGHQQCgUAgEEgdARp2EAgEAoFAIHUEaNhBIBAIBAKB1BGgYQeBQCAQCARSRzCklGJapLi4\nOCEh4f3798bGxu7u7p06daq0GIZhSUlJjx49otFo7dq1GzJkCAVzQOmSV69eXbt2rbCwsEGD\nBsOGDbO3t9dcfvfu3VKpNDw8XDfyKEtqampqaqpUKnVycvL19f1ZLNN79+6lp6eXlpba2dl5\neXlpzjpVhyHYzKrbGusABBsSwWK6h6AwDMP++uuv9PT04ODghg0b6likBgg2uX///TcxMfH7\n9+9WVlYeHh4tWrTQsc6fQVB/Zmbm33//LRAIrKysBgwYQJ0sKQRf3GouXbqUlpa2aNEiS0tL\n3SjUDBH9qampV65cKXvE2tp64cKF1TrRrzhjJxAIFi5cmJWV1bZtWw6Hs2bNmpSUlEpL/v77\n70eOHLG3t7ezszt58uSePXt0LJVS5OTkhIaGlpSUtG3b9tOnT4sWLfr48aOG8snJycnJyS9e\nvNCZQmpy5syZrVu3mpqatm7dOjU1NTw8XC6XVyy2b9++7du3s9lsJyenR48eBQcHFxYW6l6t\n3iHYzKrbGusABBsSwWK6h6AwsVi8YcOGkydPPn36FE9OTxEINrnU1NTFixcXFha2bNnyy5cv\noaGhGRkZuldbEYL64+Pj161bh6Joq1atvn37FhoampaWpnu1FSH+4sb577//Dh8+/PTpU4VC\noTORGiCoPz8///379+3KUJOBAfbrERcXN3nyZDzZOYZhhw8fHj9+PIIg5Yrl5uZ6eXnl5eXh\nHzMyMnbt2oUnA/41Wbhw4caNG/G/URQNDQ1Vf6xISUnJmDFjIiIiJk2apCuBVKSkpMTPz+/i\nxYv4R4FAEBAQoP6o5tOnT15eXtevX8c/CoXCgICAEydO6FQrNSDYzKrVGusABBsSwWK6h7iw\nyMjIVatWPXr0yMvL6/Xr17qVqQmCTW7y5Mlbt25VfwwPD1+yZImOJGqEiH6pVDpixIjTp0+r\nj4SHh4eHh+tO5c8h+OJWs3Tp0oiICC8vr0+fPulKoyYI6j906NC8efNqea5fccbu0aNHPXr0\nUKd+c3d3FwgEFSeW7ty506JFi+bNm+MfO3fuPG/ePPISDlKc4uLiV69eqRNc0mi0vn37Pn78\nWKlUVlo+Li6udevWXbp00aFGKpKVlaVUKtXpB8zMzDp16vTgwYNyxVgs1syZM3v16oV/NDEx\nsbW1/fLli061UgCCzay6rbEOQLAhESyme4gLGzhw4KpVq0xMTHQrsAoINjkEQQIDA0eOHKk+\n0qJFi69fv+pUa2UQ1M9gMDZv3uzl5aU+Ym9vLxKJdKr1JxB8ceMkJyd//vx51KhROhRYBQT1\nS6XS2uel/eUMO6VS+enTp8aNG6uP2Nra0mi09+/flyv577//NmvW7MmTJzt27IiMjLxy5YpK\npdKtWArx4cMHAEDZ62Zvb69QKCo1PrKzs9PS0mA2TADAhw8fLC0ty+4laty4MX4xy2JlZTVk\nyBAjIyP8o0Kh+Pz5s62tre6EUgOCzaxarbFuQLAhESyme4gL69SpE41G06E0QhBsckwmc+DA\ngWWLffjwoVGjRjrT+TMI6mexWE5OTuqOKD8//+HDh66urrqUWinEX9wAAKFQeOjQoenTp/N4\nPB1q1ARx/Vox7H65+SexWIxhmLGxsfoInU7n8/klJSXlShYWFqIompub27t376KiooMHD75+\n/XrevHm61UsV8OtT9rrhQ+qSkpJyO3CVSmVMTMyYMWOsra11LJKClJSUlL1oAABjY2OhUIhh\nmIa3V1xcHADA09OTdH0Ug2AzI94a6wwEG1LN2psOoKwwgtSsyf3999+ZmZmrV68mX2AVVFd/\ndHR0Tk6OSCTy9/f38fHRmc6fQfzFDX6sF3Xv3v3Nmzc61KgJ4volEgmdTk9ISHj+/DmLxWrb\ntq2npyedXr05uF/OsMNn3co5tzIYjIqzcQqForCwcN++fbj5bG9vHxsb6+vrW1ffHJpBURT8\n73XD/6543eLj47lcrre3ty7lURYURSs2NgzDKh5Xc/z48aSkpOXLl5ubm+tEI4Ug2MyIt8Y6\nA8GGVIP2phsoK4wgNWhy6enpu3btGjVqVJXOmzqguvrbtGnD5/OfPn166dKlli1btmnTRjc6\nfwbxFze+XhQdHa07cQQgrl8qlebk5DCZzObNmxcUFBw4cODRo0crV66s1unqvmGHIMiiRYvw\nv+vVq7d48WIAQDlvLKlUWnHOls1mt2nTRj0p2qNHj5iYmNevX/8iht2LFy9iY2Pxv7t16+bk\n5AQAkMlk6guFO6yVu24fP368cOFCZGRkdUcYdYZLly4lJyfjf48dO5bL5cpksrIFZDIZm82u\n9GWGYdjevXtv3bq1YsUKKrwMdA/+uFXZzAgWq0sQbEjVam+6hLLCCFLdJnfr1q3du3ePGjUq\nMDBQZyI1UF39/fr1w//Yvn17VFTUH3/8od87heus8sWtUChiYmLGjh1LtfUigvoBAHPmzKHR\naOooPx06dIiKinry5ImLiwvx09V9w45Op//222/438bGxnw+n8/nf/v2TV2gpKREoVDY2NiU\n+6G1tXVZN2n8waBI4AAdYG5urr5uTZo0wZ+TgoICCwsL/CB+DRs0aFD2VxcvXmQymUePHsU/\nfv/+XSgURkREDBkypHv37rpTrz8cHR3V161+/fo2Njbfv38vu9709evXchdNTXR0dFpa2oYN\nG9QuO78a+JWpspkRLFaXINiQqtXedAllhRGkWk3u1q1bu3btmjVrFnV2UxDUr1KpCgsLLS0t\n1Wbcb7/9dvv27S9fvuh3yy/BF/eDBw8+ffqUnp6Oh5jBjdcdO3Z06NBhzJgxOtZcFuKGR7kd\nmV27dgUAvHv3Dhp2/wOdTi/nGuPs7Jydne3n54d/fPLkCY1Gc3Z2LvdDZ2fnmzdvIgiCe8K+\ne/cOAECpaJmkYmNjU/a6oShqbGycnZ2tjqnz5MkTe3t7MzOzsr/q2bOno6Oj+mNOTo5AIHBz\nc6vYfOsqeOQh9UeVSiWXy1++fNmqVSv8SHZ2dqWPaEJCwt9//x0ZGdm0aVMdaaUeDg4ORJoZ\nwWJ1iTZt2hBpSASL6R7KCiMI8Sb38uXL3bt3z5kzx8PDQ+cyfwpB/bm5ucuWLdu4cWPbtm3x\nI8XFxQAAU1NTHQuuCJEXd9OmTcs67RUWFubl5XXs2JEKQ2Ui+lUq1c2bN5s2bdqsWTP8SEFB\nAfjfzZFE+BXXy4YNG5aVlZWSkoJh2JcvX44fP96nTx98P9N///135MiR/9fefQZEcfVtAz/b\nYJeygBRBFFFQ7C1iV9BbVOxG1NuKFVFjiQVL7C2Jxho72BPUYIsGa+wFuxAUrCAqgkhf2L4z\n74d5su/eoLjU2V2u3yd2mJm9ZnZ0/5w55wxTVvfo0UMqlYaFhSmVypycnD179lSvXl17uVc2\nXC63Z8+eJ06cePXqFSHk8ePHly5d0naqffTo0cGDBwkhTZs27aWDuZfdq1evWrVqsZmePR4e\nHvXr1w8LC8vOzqYo6siRI6mpqb169WJ+e+bMmYsXLxJCsrOzw8PDJ0yYUJmrOqL3ZVb0aiZJ\nzwup6NVYpGd+g6XnlUnTdGhoaKdOnQyqqiN6569fv76Li0tYWFhycjJN0wkJCceOHWvYsKEh\nzD6jzxd39erVdb+AOnToQAjx9fU1hIm39MnP4/EuXLiwbt06Zu7o9PT0bdu2WVpaMu12+uPQ\nNF0uB2HYjh8//ttvv3E4HJVK1bRp03nz5jHj8JkRTD/99BPTV/TevXsbN26UyWQURTk6Os6b\nN09bR1dCarV6w4YNN27cMDMzU6vVffr0GTduHPOr/fv3nzhx4uTJkwU2OXXq1IkTJ/bu3Vvh\nYQ1IWlra6tWrExMTeTyeQCCYMmVKp06dmF/Nnj1bJBKtWLEiMjJy586dBTZ0dXXVdnOsPPS8\nzIpYzVTpcyEVvRq79MmvVCoDAgIKbOjk5BQWFlbheQvS58pMSkqaOnVq4W3379+vvQfKFj3/\nZb17927Dhg2vXr3icrkURTVs2PD77793cnJiNfv/0fOLWyshIWHGjBk7d+40kFtt+uRPS0tb\nu3bt8+fPBQKBSqWqWbPm1KlTi/vwiUpa2BFC8vLykpOTbWxsdO8SSiSSN2/e6E7ko1Qqk5KS\neDyeu7t7pR0QoCs9PT09Pd3FxUW3DT81NTU9Pb1wcyazsvbmS2X27t07mUzm5uamO0fR69ev\nuVxurVq10tPTU1JSCmxibm5uOE+ZrGB6XmafXc20FX0hFb2aISg6P03TT548KbCJmZmZ4Tyu\ntOgrUy6Xv3z5svBW9evXN5DJ7fX8l/Xp06esrCwHB4cqVaqwEfOL9PziZjAfh5eXl5mZWYUn\n/Tw98zPnv0qVKvb29iWYD6jyFnYAAAAAJgZNUAAAAAAmAoUdAAAAgIlAYQcAAABgIlDYAQAA\nAJgIFHYAAAAAJgKFHQAAAICJQGEHAAAAYCJQ2AEAAACYCBR2AAAAACYChR0AAACAiUBhBwAA\nAGAiUNgBAAAAmAgUdgAAAAAmAoUdAAAAgIlAYQcAAABgIlDYAQAAAJgIFHYAAAAAJgKFHQAA\nAICJQGEHAAAAYCJQ2AEAAACYCBR2AAAAACYChR1UkG+++cb1C169evWlrRITE9PS0ore87Nn\nz1xdXe/du6fn8q/q0qXLDz/8UJo9fIk+hwMAAFBiKOyggty7dy8pKSkpKenu3buEkHXr1iX9\ny9PT80tbLVy48OrVqyV7Rw8Pj2vXrjVp0qRkm5fJHgoozeEAAAB8FQo7qCA8Ho/P5/P5fB6P\nRwjhcrn8fxFCNBpNfHz8w4cPMzIytJtERUX9888/r1690raZyeXy+Pj4mJiY3Nzcr76jSqX6\n9OmTSqUihEil0qioKLlcrlKpYmJiXr58qdFodFfOyMh4+PDhx48fv7qHjx8/Pnr0iFmBoqhn\nz549evSocB61Wv3kyZO4uDilUvmlwwEAAChbKOyAfTdv3vT29u7Zs+f48eObNm0aEhKiVqsJ\nIQsWLMjMzDx27NjKlSsJIb/99luzZs0GDBgwcuTIJk2abN68uejdvn37NiAgID4+nhCSkpIS\nEBBw+vRpPz+/JUuW9O/fv2fPnnl5ecyae/bsadGixciRI318fJYtW8bhcArvgfn58OHDrVu3\nXrRoESHkwYMHbdu29ff3HzNmTIE8UVFR3t7evXr16t27d9u2baOiogofDgAAQJlDYQcsy87O\nHj9+fMuWLZ8/f/748eMjR44cOXJk7969hJBz584RQubOnXvq1Kn8/Pxdu3ZNnjw5Pj7+n3/+\n2bp165o1a549e6bnuzDNhLt37z5+/PjJkycvXbr07Nmzo0ePEkLev3+/bNmyUaNGPX36NC4u\nTqFQJCQkFN6DmZkZISQiIuLmzZuRkZF5eXljx4719PR8+vRpTEzMli1bfv7558uXLxNCJBLJ\nhAkTfH19X758+eLFi44dO44fP16hUOgeTtmcOwAAgP+Fwg5Ydv78eYlEsmDBAqZyat++fceO\nHU+ePFlgNUtLy+vXr48ePTomJiYqKsrKyoqm6SdPnhTrvYYOHVqlShVCiLOzc+3atZkC7sqV\nK2q1+rvvvuNwOFwud+7cuRRFFd6Wy+USQnr27Fm9enVCyIULFzIyMpYsWWJhYUEI6d27t7e3\n9+HDhwkhFy9ezMrKYo6Iz+fPmzdvzpw5+fn5JTg5AAAAxcJnOwBUdq9fvzYzM3N3d9cu8fDw\nOHLkSOE1V65cGRoa6ubm5uTkxCyRyWTFei83Nzftz0KhUCqVEkKSkpKEQmHVqlWZ5TY2Ng4O\nDl/ag3acx8uXL/l8vkQi0fa3c3Jyevr0KfMra2trR0dHZrmzs/Po0aMJIQqFolhpAQAAiguF\nHbBMqVSKRCLdJUKhUDvgQOvGjRvbt2/fvn173759CSEqlUq3FtQTM1CjAJVKJRQKdZeYm5t/\naQ9M+xwTm6KocePG6f7Wzs6O+RXTvAcAAFDBUNgBy+zt7XNzc5VKJXMrlhCSlpambe7Sunfv\nnp2dHVPVEUJev35dVgHEYnFubq5Go2H64VEU9enTp69u5eTkxOPxHj9+rB1poWVvb5+Tk6NQ\nKJgCkaZpqVRaoHYEAAAoD2hXAJa1b9+epumLFy8yL1Uq1dWrV9u1a0f+7dbGzEsiEAhUKhUz\nWpYQEhoayuVyC0xZUjKNGjWiKOr27dvMy6tXrzK3aIvWrl07lUp17do17ZJz587FxsYSQtq2\nbUsIOXPmDLP8+vXrdevWff36te7hAAAAlAe02AHLWrRo0bt379mzZycmJtrZ2R07dkypVM6Y\nMYMQIhAIHBwcDh06JJFI2rdvv2bNmjlz5nTq1Ons2bNeXl41a9Y8e/Zs48aNLS0tSxOgS5cu\ntWrVmj59+vjx4+Vy+alTp2rVqkXTdNFbNW7cOCAgIDg4eOLEia6urg8fPtQO5m3evHnPnj1D\nQkKeP38uEon27dvXq1evunXrEkK0hzNy5MgCN6ABAABKj7d06VK2M0DlolQqo6OjfX19a9Wq\nxSzx9/e3tbWNioqKj49v2LDh+vXrtaMc6tSp8/z585ycnKFDh3p7e0dHR8fHx3ft2jUoKKhG\njRpPnz61sbHx9PSMi4vz9/cvMOhBKpVql8vl8tjY2O7du2sHScTGxtauXdvb25vH4/Xs2TMz\nMzMmJobH461atSo7O7t69eotW7Yseg/dunVzcnK6f/9+bGysjY3NypUrmbY65ojEYvHDhw8/\nffoUEBCwYMECprlOezhdu3YVCAQVcLYBAKBS4Xy1ZQIAAAAAjAL62AEAAACYCBR2AAAAACYC\nhR0AAACAiUBhBwAAAGAiUNgBAAAAmAgUdgAAAAAmAoUdAAAAgIlAYQcAAABgIkz8kWIqlUou\nlxNC1Go1j8cr/Lz2CkbTtFqt5nK5zPPm2aVSqQzh4QcajYaiKD6fbwifDkVRhvDRqNVqmqYN\n4ZxoNBoOh8M8NoNF3HfvzCZPpurXV65ZU7Z7tra2LtsdAgCwy8QLO4FAwNQuOTk5FhYWfD7L\nx6tQKJRKpbm5eSkfb1omMjIyDOFbTSKRqNVqoVBoZmbGbhKlUimXyw3hnGRmZlIUZW1tzXph\nl5+fz+PxhEIhuzEojYZ77ZpGoTA3gE8HAMCQ4VYsAAAAgIkw1ha73NzctWvXNm7cePDgwWxn\nASieZ8+e3bp169GjRy9evPjw4QPTW0AsFru7uzdt2rRjx46NGjUyhDvCAABgdIy1sNuzZ09M\nTIydnR3bQQD0FRUVdeTIkZMnTyYlJX12hejo6JMnTxJC7OzsBg4cOHfuXE9Pz4rNCAAAxs0o\nC7vY2NioqCgvLy+2gwB8XXZ29p49e3bt2vX8+XNmSd26dX19fb29vRs2bFijRg2mw2V6enpi\nYuK9e/fOnz9/+/btsLCwffv2jR07dsWKFU5OTqweAQAAGA0OTdNsZygelUo1bdq07t27P336\nVCQSzZw5U5+tcnJyLC0tDWHwhEQiEYlEBjJ4wt7enu0URCKRKBQKsVhsIIMnxGJxWe3w/fv3\nv/zyy+7du/Py8ggh9erVGz58+ODBg+vWrVv0htHR0bt27dq/f79UKq1Spcqvv/46bNiwskpV\nLAYyeELz8aN861ZO9eoWQUHsJgEAMHDGN3jijz/+MDc379u3L9tBAL4oJSVlypQpHh4emzZt\nksvlgwcPvnbtWnx8/MKFC79a1RFC3Nzcli9f/uLFi6FDh2ZmZg4fPnzMmDEymawCkhsoBwfZ\ntGmqIUPYzgEAYOiM7Fbs+/fvT5w48eOPP+o5sZZarWZ6pms0GqlUyvp0XBqNhhCiUqmYJhx2\n0TRtCDHUajUhRC6XK5VKdpNQFKVWq0t5TqRS6caNGzdt2iSVSs3NzSdMmDB9+vSaNWsSQvTf\nM9OOLhaLd+3a1adPnylTpuzbty82NvbIkSNVq1YtTbziUv+rIt+0MOaEaDSaMr9iraysynaH\nAADsMqbCjqbprVu3duvWrU6dOnpuotFomMKOEMJ63aBlCN+UDO3JYZ3hfDqlOSeRkZELFy58\n//49j8cbMWLE7NmzXV1dS7xPhUJBCPHz8/v7779Hjhz58OHDLl26RERE1K5du8QJS0alUlXw\nO34WRVFlfsWisAMAE2NMhd3ff//98ePHxYsX678Jn89n5ptlmk9Yn0JCrVbLZDIzMzNzc3N2\nkxBCJBKJIUzGK5PJ1Gq1SCRivQekWq1WqVQikagE26ampk6bNu3UqVOEkM6dO//yyy8NGzYs\ncZK8vDyapq2srJgJihs2bHjt2rVhw4ZdvHixb9++58+fr1evXol3XiwKhYLL5bL+hBKKopje\nfhYWFuwmAQAwcMY0eGLSpEkqlcrFxYV5+ebNGy6X6+bmNmXKFGdn56K3xeCJwjB4ooASD54I\nDw+fOnVqZmZm1apVN27c+N///reUSZgnT9jb2+s+eUKpVI4YMSIiIqJq1apXr16tmNrOUAZP\naDRZWVkCgcDGxobdJAAABs6YWuwGDRqk2388JydHIBC0adMGf8QDW7KysoKDg//44w9CyMiR\nIzdu3FilSpVyei8zM7Pw8HAej3f48OGuXbveuHGjVq1a5fReAABgpIypsOvSpYvuy+joaJFI\n1KtXL7byQCV36dKl0aNHv3//3tHRcdeuXf379y/vd+Tz+QcPHlQoFCdOnPDz87t161YFj6UA\nAAADZ3zTnQCwTi6Xz5o1y8/P7/3797169YqNja2Aqo7B5/MPHTr0n//85/Xr1z179pRIJBXz\nvixLSrIJCBDOm8d2DgAAQ2dMLXYFjBgxgvXpS6ASun///ujRo+Pi4iwsLH755Zfg4GDdnnAV\nwNzc/MSJE76+vo8ePRo8ePDp06dZ7zxa3jj5+YJr1zQKBdtBAAAMnREXRjVr1qxRowbbKaAS\nkUqlISEh7dq1i4uLa9Wq1ePHjydNmlTBVR3D2to6MjLS3d393LlzU6ZMqfgAAABgmEz8D32K\nopg5gWmaVqvVrA8BZsJQFGUgE4MZQgyKogghGo2G9TBqtbqIjyYyMnLGjBlJSUlCoXDp0qUz\nZ87k8/nlmlmlUhVRNdrb2//5558+Pj67du3y8PD4/vvvyyMD8+mw/tHQarUZITRNl3kS1mdy\nAQAoWyZe2GknKKYoSqlUstK4oov5mtQ+D4NdNE0bQgym2FUqlaxP2kxR1GenwH316tW8efMu\nXLhACOnYseOmTZs8PT3LdZZp5i8QuVxe9BVbq1atAwcODBw4cP78+e7u7v7+/mWeRK1Wc6jz\nHSIAACAASURBVDgc5rplEUepZObCKfMrFoUdAJgYY5rHrjQwj11hmMeugMLz2EkkkhUrVmza\ntEmpVLq4uKxZs2b48OEV8OfBZ+ex+5KdO3cGBwdbW1tHRUWVZlbkzzKQeeyo2FhukyaaNm14\nUVHsJgEAMHBG3McOoFwdO3asXr16a9eu5XA4ISEhz58/HzFiBOuNvoVNnDhx6tSpEomkX79+\nmZmZbMcBAAA2Gdmt2LS0tP3798fGxsrl8ho1agwePLh169ZshwJTk56ePnny5IiICEJIjx49\nNm3aVLduXbZDFWX9+vVxcXGXLl0aOnTomTNnWH90HgAAsMWYWuxkMtnChQtTUlKmTJmyaNEi\nV1fX1atXP336lO1cYFKuX7/erFmziIgIJyenI0eOnD171sCrOkIIn88/cuSIu7v7hQsXFi1a\nxHacskc3aJD+6VPeuXNsBwEAMHTGVNg9ffo0MzNz3rx5rVu3bty48YwZM2xtbe/cucN2LjAd\nu3fv7tq1a3Jyct++fZ88eTJ48GC2E+nL3t7++PHjIpHop59+OnXqFNtxAACAHcZU2LVs2fLo\n0aNOTk7MSy6Xy+PxDLDPExgjmqYXLFgwc+ZMiqLWrFlz8uRJR0dHtkMVT/Pmzbdv307TdGBg\nYEJCAttxAACABcZU2GnJ5fKUlJRdu3ZJpdLu3buzHQeMHkVRwcHB69ats7CwOHny5Jw5c4z0\nD4bAwMAJEyZkZ2cPGTJEgec0AABUPkY53Unfvn0JIa6urrNmzfL09CxiTaVSmZ+fTwihKIrD\n4bD+bU3TNEVRXC6X9SSEEI1GYwi97CmKommaxXNC0/SMGTMOHjxoa2t7+PDhVq1asRJDFzO3\nX8k+Hblc3q1btydPnkyaNGnVqlWlTML8/2AglyuHwynzpwja2dmV7Q4BANhllIVdXFxcTk7O\ntWvXYmJilixZUq9evS+tycwbV5HZwOjMnz8/LCzMzs7u6NGjTZo0YTtOGXj16tV//vMfmUwW\nHh7etWtXtuMYNAcHB7YjAACUJaMs7LTmz59PUdTPP/9cxDrMAebm5lpYWBjCBMV5eXkikcjC\nwoLdJISQzMzMKlWqsJ2C5OXlKRQKa2trViYoXrp06fLly8Vi8d9//92kSRMmScXHKCArK4ui\nqCpVqpS4qWzPnj3jx493cnKKiYmpWrVqiZNIpVIej2dubl7iPZQJjUaTnZ0tEAh0p48uE4bQ\nGAkAUIaMaR67hISE5OTkjh07apd4enpevXq16K20/3Ebwq1Y3TDsJmEYSAzC0qezdevW5cuX\ni0SiU6dOeXt7K5VKYirnZNy4cefPn4+IiJgwYcLp06dLn6SUeyhtAKlUcO0az96e4+vLbhIA\nAANnTIMnnj59+ssvv6Snp2uXJCQklKY1AiqziIiIadOm8fn8Q4cO+fj4sB2n7O3YscPV1fWv\nv/4KCwtjO0tpcZKSbAICRPPnsx0EAMDQGVNh5+PjU6VKlRUrVkRFRcXGxu7cuTM2NrZ3795s\n5wLjc+nSpZEjR9I0vWPHjn79+rEdp1xUqVJlz549HA5n5syZb968YTsOAABUBGMq7MRi8erV\nq11dXXfs2LFixYr4+Pjvv//eF7dmoJgePHgwYMAAhUKxcuXKcePGsR2nHHXr1m3ixIkSiWTc\nuHFG3ZsWAAD0ZEx97AghLi4uISEhbKcAI/bkyRN/f3+JRDJ9+vQFCxawHafcrV279vz585cv\nX96xY8ekSZPYjgMAAOXLmFrsAErp2bNnfn5+6enpgYGBGzZsYDtORbCysgoLC+NwOHPnzk1K\nSmI7DgAAlC8ja7ErLrVazcy/r9FoZDJZmc9uWlzMxLMqlYqZNpldNE0bQgy1Wk0IkcvlKpWq\nXN8oPj6+d+/eHz9+HDhw4ObNm6VSaYEVNBqNRqMxhHPC3DbNz88vk+GorVu3HjduXFhY2Lhx\n406ePFmsfapUKua0lD5GaXDkcovyuWItLS3LdocAAOwy8cKOw+Ew0/czP7Be2DFf2MxTbtlN\nQnRODusxSPmfkwcPHvTv3z8jI2PgwIH79u377IyGNE0byNM4GGX4KORVq1adO3fu0qVLv//+\ne2BgoP4bqtVqg7hO/v2Xy34SAADDZuKFHY/HY74JFAqFmZmZIUxQLJfLeTyeUChkNwkhJD8/\n3xBiqFQqtVptZmZWfhMUR0ZGDhkyJD8/PzAwcPfu3V8qDpRKJUVRhnBOpFIpTdNCobCsCjuh\nULhr166ePXvOnz+/b9++Li4uem7IVLqsnxONi4ts2jRSs6aI7SQAAAbO+PrY0TSdmpr66tWr\nwrfSAArbuHFjv3798vPzZ8+evXfv3krb5OPv7x8YGJiVlWWUQyiqVs1ftEhp0kOYAQDKhJG1\n2CUkJKxfv/7t27fMM+P79u07ZswYtkOBgZJKpcHBwQcPHuTz+Vu3bp08eTLbiVi2fv36Cxcu\n/Pnnn+Hh4cOGDWM7DgAAlD1jarFTKpUrV660sbHZu3fvsWPHgoKCTpw4cfPmTbZzgSGKi4tr\n3br1wYMH7e3tz549i6qOEFKlSpXt27cTQqZNm5aamsp2HAAAKHvGVNglJiaqVKoJEybY29vz\neDx/f39XV9fY2Fi2c4FhoWl6+/bt3t7eT548adWq1cOHD7t27cp2KEPRr1+/4cOHZ2RkTJw4\nke0sAABQ9oypsPPy8jp48KC7u7t2CZfLpSiKvURgcN6+fdujR4/JkyfL5fLZs2ffvHmzZs2a\nbIcyLJs3b65WrdqpU6d2797NdhYAAChjRtbHTldcXNy7d++K7mNH0zRT+TE/sD4dlzYM60kY\nhhCDmQKm9J+OWq3esmXLsmXLJBKJm5tbWFhYly5dSHGOkaIow/loCCEajaasRsXqsrGxCQ0N\n7d2794wZMzp06ODp6VnEyhRFcTgc1s9J+f3DqbSDaQDAVHGM9AmSaWlp8+bNq1Onzvz584tY\nTaFQSCSSCksFbLly5cqSJUvi4+M5HE5gYODixYutra3ZDmXQ5s+fHxYW1qxZs8jIyPKbaMbw\nOTg4sB0BAKAsGWVh9+7duyVLlri6ui5cuNDc3LyINZVKJTMrikajYQbSVlTGz2OaHLhcLutT\nJRNC1Go16xP7EUI0Gg1N0yWejPfGjRs///zzrVu3CCFNmjRZu3Ztq1atSpaEadY1hCYc5mkc\n5frpyOXyrl27Pn36NDg4+Mcff/zSakxTGeuXK/fdO9HUqZp69eQ//VS2e7a1tS3bHQIAsMv4\nCrtXr14tWbKkWbNmM2bMEAgEem6Vk5NjaWnJeh3DtCCKRCJDeJBRRkaGvb092ymIRCJRKBRi\nsbhY7UYKheLo0aObNm26f/8+IaRGjRpLly4dPXp0aUoQpVIpl8vFYnGJ91BWMjMzKYqyt7cv\n1z9F4uPjW7VqlZeXd/jw4SFDhnx2nfz8fEOYoJiKjeU2aaJp04YXFcVuEgAAA8d+u1GxpKam\nLlu2rG3btrNnz9a/qgNTEh8fP3v27OrVq48YMeL+/fvu7u5bt259+fLl2LFjWW9YMi7169ff\ntWsXIWTcuHHR0dFsxwEAgDLA/p24Ytm4caOnp+eUKVNYv6kKFUwulx85ciQ0NJS568rhcLp0\n6TJ58uT+/fsbws1TIzV06NCHDx+uW7eub9++d+7cqVatGtuJAACgVIypsHvw4EFcXFzXrl0P\nHz6sXSgWi3v16sViKihvqampmzdv3rVrV0ZGBiHE2dk5MDBw3LhxderUYTuaKfj5559fvHhx\n+vRpf3//a9euoc8ZAIBRM6bCTqFQNGrUKDU1VXfSfEdHRxR2purDhw+rV68OCwtTKBQcDqdb\nt27BwcF9+vRhva+kKeHxeIcOHerSpcu9e/d69+59/vx5Q+gACgAAJWNMX5Dt27dv37492ymg\nIuTm5v7444+bNm2SyWRmZmbjx4+fNWtWvXr12M5lmiwtLSMjI318fG7dutWrV6+//vrLysqK\n7VAAAFAS6GwOhoWm6f3799etW/enn35Sq9VBQUGvXr0KDQ1FVVeuHBwc/v77by8vr2vXrnXr\n1i0rK4vtRAAAUBLGN91JsejOY2cIXewxj11huvPYvXz58vvvv2eGR/To0WP58uUV2ZGuUs1j\n91lpaWn9+/ePj4+vX7/+sWPHXFxcmCdPsD9WSaPRZGVxBAKujU3Z7hh9CgHAxJh4Yad9pJhE\nIrGwsGD9O1upVObn5wuFQpFIxG4SQkh2drYhfKvl5+crlUqRSLR58+bly5fL5fLatWtv3Lix\nZ8+eFZxEpVIpFApDuAuZk5NDUZStrW3FV1SZmZl9+vS5e/dujRo1Tp8+7eHhweVyi54GvAJQ\nFJWTk8Pn88v8gSKs/58AAFC22G+wKVccDof5j5vD4XC5XNb/E2ca6rSpWGcIMTgcTmJi4tSp\nU+/evcvj8ebMmbNs2TJWCl/m2ayGcE4YJX4aR2k4Ojpevnx56NChp06d6tSp0759+/z9/Q3k\nnBjUpwMAYJjYvyFYAs+fPz906BC6AZmGffv2+fr63r17t379+rdv316zZo0hNGdWZhYWFseP\nH586dWpubu6gQYO2b9/OdiIAANCX8RV2J0+enDdvHgo7E5CdnT1o0KBp06bJZLIpU6Y8fPiw\nxI95hbLF4/E2b968efNmDoczc+bMqVOnajQatkMBAMDXGVlhFxoaeurUqbFjx7IdBErr3r17\nzZs3P3r0qJOT05EjR9avX4+GOkMzderUiIgIsVi8ZcuWvn375uXlsZ0IAAC+wsgKOwcHhw0b\nNtStW5ftIFByNE1v3LixY8eOb9686dq16+3btzt37sx2KPg8Pz+/S5cuubm5nTlzplOnTikp\nKWwnAgCAohhZYTdgwACbsp7vACpSdnb2wIEDv//+e41Gs3LlyvPnzzs5ObEdCorSqFGju3fv\nfvPNN48fP27btm1cXBwLIWQyfkwM9+VLFt4aAMComPioWI1Go1KpCCEURSmVSmZ6MBYxATQa\njVwuZzcJo4JjPHjwYMSIEUlJSc7Ozvv37+/UqZNSqWQ6bymVSmZiGhap1WqKogzho2EmIZLL\n5axPIMecE1tb23Pnzo0cOfLcuXMdOnQ4fvx4mzZtKjTH8+e2XbuqW7eWX71atjsWCoVlu0MA\nAHaZeGGnVqu1HYOYmYoNgVKpVCqVbKcghJAK6zVFUdS2bdtWr16tUql8fX23bdvm6Oio++6G\nUE4xDKcnWX5+PtsR/o9CoSCE7N27d9asWeHh4T179gwNDe3evXuFBeDJZEJCaJou808HhR0A\nmBgTL+x4PB7TJV+hUAgEAtaf96DRaJRKJZ/PFwgE7CYhhMhksooZr/Du3btJkyZdvXqVz+cv\nXrx49uzZuh8E02hnZmbG+hRlGo1GrVazPhkvIUQul9M0LRQKWW+xU6lUHA5H+wyMnTt3urq6\nrl27dvTo0Zs3bx41alTFxOCYmxNCOBwORtgAABTNxAs7Pp/PfCep1WqRSMT6E7QUCoVSqRQI\nBJaWluwmIYTI5fLyjkHTdFhY2Jw5c3JycmrVqvXbb7+1a9euwDoURWk0GqFQaGZmVq5hvkqp\nVFbAOdGHQqGgadrS0pL1wi4/P5/H4+k2a61Zs6ZGjRozZsyYMmVKVlbWggULKiAGJRQSQjgc\njiF8OgAAhszIBk+AEfnnn398fHyCgoJyc3PHjx8fExNTuKoDYzR16tRDhw6ZmZn98MMPwcHB\nrHddBQAALRR2UPaSk5MnTpzYokWLGzdu1K5d+/z586GhoWX+lE9g0eDBg8+dO2dra7tz585e\nvXplZ2eznQgAAAgxrluxarV68eLF5N9hEFu2bBEKhXZ2dnPmzGE7GvyfpKSk9evXh4aGymQy\nCwuLkJCQkJAQ9IsySb6+vrdu3erVq9eFCxfatGlz/PjxBg0asB0KAKCyM6bCjsPhNG7cmPm5\ndevWzA/oc2MIKIq6cuXKjh07Tp48qVarBQJBUFDQ4sWLXV1d2Y4G5ahBgwZ3794NCAi4ceNG\n69att23bNnLkyPJ4I9reXjZtGqlZE38iAAAUjcPMmGXycnJyLC0tDWHwhEQiEYlEhlCPZmRk\n2Nvbl3In0dHRERER4eHhb968IYRYWVmNGTNm1qxZNWvW1HMPEolEoVCIxWIDGTwhFovZjUEI\nyczMpCjK3t7eAAdPFKZSqUJCQjZt2kTT9KBBg7Zs2VLmk05rNJqsrCyBQID5yQEAimZMLXYl\no61caZpmvYrVDcNuEkbJYuTl5V29evXChQuRkZGJiYnMwiZNmowbN27UqFHMV29x92w4nw7r\nMbQMJMlXPxo+n79+/frOnTtPmDAhIiLi0qVLK1asmDBhQhn+HVV+/3BYL50BAMqWibfYKZVK\nZpZXiqJYn8SOEELTNJPEEL5ONBqN/lPHpaen379//+7du7dv346OjtYOhPTw8Ojbt++AAQMa\nNWpUshgURdE0bQjnhKlgDOE6YZ7GwfrEfoQQiqI4HI6eH016evqCBQuOHj1KCKlbt25ISEj/\n/v3L6nxqNBoOh1Pmn46dnV3Z7hAAgF0mXthp4VZsYUXfilUqldHR0Xf/9erVK+2vrKysOnbs\n6Ofn17NnTy8vr1LGwK3YwozrVmwBV69enTlz5uPHjwkhnp6e06ZN07bjlhhuxQIA6AmFXYUy\n8MIuISHh3r179+7du3v37qNHj3Qf8+Xs7Ny2bdv27dt36NDhm2++KcMzicKuMKMu7AghNE0f\nO3Zs9erVTHlnYWEREBAwcuTIzp07l6wZEoUdAICejK+wu3Xr1q1bt2QymYeHx4ABA/SskFDY\nFZaSkpKRkfHkyZPo6OjHjx8/fPgwIyND+1tzc/MWLVq0atWqTZs2bdq0cXd3L6cYKOwKM/bC\nTuvKlStbt249deqUSqUihDg7O3/77bcDBgzw8fEp1lP1UNgBAOjJyAq7I0eOHD58uHv37lWq\nVLly5QqPx1u3bp0+D/es5IVdenr627dvk5KSkpKSEhISXr9+/fLly8TERN1nBnA4nDp16rT6\nV/PmzSum0kJhV5jJFHaMtLS08PDw8PDw+/fvM0tsbW39/Px69Ojh5+dXo0aNr+4BhR0AgJ6M\nqbDLzc0dO3ZsYGBgnz59CCE5OTlBQUEjRoxgXhatkhR2Mpns1atXr1+/TkxMTEhIePPmTWJi\n4ps3b5gRJAVYWlrWr1+/UaNGTZo0ad68efPmzVn51kRhV5iJFXZaCQkJx48fP3ny5J07d5gB\nIoQQDw+PDh06tG3b1tvbu1GjRp+9DDRv3qinTydeXuZr1pRJEgAAU2VM051ER0erVKouXbow\nL21sbFq0aHHnzh19CjtTQlFURkbGx48fk5OTk5OT3759++bNm4SEhISEhOTk5MLr8/n8mjVr\nurm5uf/Lw8PDw8NDJBKVfh47AP3Vrl179uzZs2fPzsjIOH/+/IULFy5fvvz69evXr1/v37+f\nECIQCOrUqVO/fv06derUqlWrRo0aNWrUcHJyss/NNT91StOmDdtHAABg6IypsHv79q29vb1u\nW5ebm9uZM2dYjPRV2dnZcrlcKpVmZ2fn5eVlZWV9+vRJJpMpFIr8/HyZTJadna3RaHJycggh\nKpUqLy+v8E4kEolarZbJZMx+ingup729vaenJ1O31a5du1atWu7u7q6urp9tqtTtUQdQkezt\n7YcNGzZs2DBCSEJCwq1bt+7evfvw4cPY2Ni4uLi4uLgC6zfmcv8hJCYmZmr79rb/srGxsbGx\nYX4Wi8U2NjZisbhhw4asN3MCALDImAq7nJwcKysr3SVWVla5ubk0TX/pv3KVSsUM7dRoNFKp\n9Kv/41+8eHHFihWFl5uZmX325qluKUZRVG5uLiFEKpUqlUqmetPjsIrN2trawcHBycmpatWq\n1apVc3Nzq1GjBtMU99l7qTKZ7Eu7kkgk5ZGwWJhu9eV3uvRHUZRGozGEc8J0kJBIJKzXKGq1\nmsPhMJ9ROXF0dOzfv3///v0JIRRFvX379uXLl0xHguTk5NTU1I8fP5qlpBC5XCaT3b59u4hd\ncTicjIyMYvW4sLa2Lu0BAAAYEmMq7CiKKjBXAo/HY6b8/dIcChRFacsFpVL51bdIS0uLjo4u\nfVRCiJmZma2trZWVlbm5uZWVlZWVlaWlpaWlpbW1tY2NjaWlpYWFhaWlpZWVFZ/PF4vFzFe4\njY1N4e9yoVAoFAoFAoGFhcVnV9Aqbm3Eei2lVa6lQ7EYzjnR54qtGLqDbMqbi4uLi4tLp06d\ndBfynj0jHTs2b978+qZNubm5OTk5OTk5ubm5EomEeZmbm5ubm6tSqTQajbb3nj5Q2AGAiTGm\nwk4oFOrOrEYIkcvlZmZmRcyMpR1Gl5+fLxQKvzqH1pAhQ7R9+LRomi7i7qdYLNbuViwWc7lc\noVAoEn3+YeUqlUoqlZqbm5dVb/TSyM3NNYSBAlKpVKVSGcLQFrVarVQqLSws2I1BCJFIJBRF\nact9Fsnlci6Xy/q4FtrSkhBiZmbWBt3sAACKZEyFnbOzc3p6uu6N148fP1atWrWITbhcLvMM\nIg6Hw+fzv1o62NnZlesjhiiKYlIVaxKv8mMIMZgPiMfjsR6GubRYj6ElEAhYL+yUSqUhfDQU\nn0+Yf8VsJwEAMHDsPxZTfw0aNFAoFM+fP9cu+eeffxo3bsxiJAAAAADDYUzz2BFC5s6dq9Fo\nFi5cKBaLIyIiDh8+vHnzZn0mOC1igEUFY064IYQxkHNiOCeE4JwYbBKNhs7JIXw+xwA6DwAA\nGDIjK+zS0tJWr16dmJjI3B6aMmVKgU7WAAAAAJWWkRV2jHfv3slkMjc3N0MYggAAAABgIIyy\nsAMAAACAwoxp8AQAAAAAFAGFHQAAAICJQGEHAAAAYCJQ2AEAAACYCBR2AAAAACbCmB4pZoxO\nnz59+vTpjIyMqlWrDho0qHPnzp9dLSEhITQ09OXLl5aWlj4+PoGBgV99rK2Rksvl+/fvv3nz\npkwm8/DwGD9+fJ06dYpYPykpacaMGR07dpw5c2aFhaxgqampoaGhsbGxXC73m2++mTBhgq2t\nbeHV3r17t2/fvmfPnhFC6tatO3r06Jo1a1Z42PKl5+VR3KsIAKDy4C1dupTtDCbr7NmzYWFh\ngwcPHjJkiFAoDA0NrVOnTrVq1Qqs9unTp5CQkLp1606cOLFOnTp//PGHTCZr2rQpK5nL2/r1\n66Ojo4OCgnr16vX+/fvw8PDOnTtbWFh8dmWaplevXp2ZmVmzZs22bdtWcNSKoVQqZ82aJRAI\nJk+e3K5du5s3b968edPPz6/Awx6ysrJmz54tEonGjx/funXr+/fvnz17tlu3bqw/xbVs6Xl5\nFOsqAgCoVNBiV44iIiL69OnTr18/QoiXl9fbt2+PHDnSsmXLAqsdO3bMwcFhxowZHA6nXr16\ndnZ2KpWKjbzlLiUl5ebNmwsXLmzVqhUhpG7duhMmTIiMjBw1atRn1z937lxmZqap1riMq1ev\nZmVlrVu3zsbGhhDi5OQ0efLkx48ft2jRQne1y5cvy2SyRYsWMeWLs7PzlClTnjx5wpxJ06Dn\n5VHcqwgAoFJBH7vykpycnJ6e7u3trV3i7e394sULqVRaYM07d+74+vpqW2iaNm1auPgzDTEx\nMXw+X1uy8Hi85s2bR0dHf3blrKys/fv3BwcHm1ijVAExMTFeXl5MVUcIqV69urOzc+Fz0r17\n902bNmkbpRwcHAghubm5FRm1vOl5eRTrKgIAqGxQ2JWXDx8+EEJcXFy0S5ydnWmaTklJ0V1N\nIpFkZmaKxeJ169YNHz589OjRe/fu1Wg0FR23Qnz48MHBwYHP///txM7OzsnJyZ9dedeuXc2b\nNzfVGlcrJSXF2dlZd4mzszNz8eiysrJydXXVvnzw4AGHw6lfv35FRKwoel4exbqKAAAqG9yK\nLS/5+fmEEN1+PyKRSLtci2l0CQ8PZ27aPnv2bN++fTwezyTvK0ml0gIdoSwsLGQyGU3TBbqU\nPXjw4PHjx9u3b6/YgCzIz88vfE5ycnKK2CQtLW3nzp1du3bVLfVMgJ6Xh/5XEQBAJYTCrsxo\nNBq5XM78rNucUDS1Wk0IadWq1YABAwghnp6emZmZp06dGj58uAkMjFUqldr+gubm5npupVAo\nduzYMWrUKDs7u3KLxhq5XK5tkS1Bf//k5ORFixa5u7tPnDixrKMBAIDRQ2FXZmJiYrRDjLt0\n6dKhQwfyv+0xTFudlZWV7lZMM17t2rW1Sxo0aHD06NGPHz8WHj9rdA4dOnTs2DHm5+nTp1tZ\nWRVosMzLy7OwsCjQ0BIeHl6lShV/f/+KC1qBFi5c+OLFC+bnsLAwKyurAt0u8/PzLS0tP7vt\nq1evli1b1qBBg1mzZpmZmZV71oql5+Wh52oAAJUTCrsy4+Xl9dNPPzE/29racrlcQsiHDx8c\nHR2ZhR8+fOByuQXKNQcHBzMzM91e8BRFEUJMY8SAv7+/dviIq6srRVHp6elKpVJblHz48KF6\n9eoFtrp169anT5+YJkxCCE3ThJDr169v2LChVq1aFZW9vHz33XfaSs7Ozs7V1bVA/7Dk5GQf\nH5/CGyYnJy9ZsqRNmzbfffedSRYxrq6u+lweeq4GAFA5obArM5aWlg0aNNBd4uLicufOHe1s\nHbdv327YsKFQKNRdh8vlNmvW7Pbt2wEBAcySJ0+eWFpaMsMejZ2Tk5OTk5P2ZbNmzSiKun//\nfvv27QkhCoXi4cOHvXv3LrDVsmXLmDvUjNDQUHNz81GjRumORDFe7u7uui9btGixadOmrKws\n5r7zy5cv09PTCw8Z0Wg0K1eubNKkialWdUTvy0PP1QAAKidMUFyOrKysfv/9d6FQyOVyIyMj\nL126NH36dKbQOXPmTFhYmJ+fHyHExcUlIiIiLS1NLBZHRUVFREQMGTKkQI1oGiwtLT99+vTX\nX385OTlJJJKwsLCsrKzp06cz3e9+/fXXmJiYb775RiwW2+qIioqysLDw9/c3gU6Hb6kh4wAA\nCLtJREFUhVWvXv3mzZv37t1zcnJ6//79tm3bateuPWTIEEKIWq0OCQnh8Xi1a9c+c+bMjRs3\nRowYkZWVlfYvtVotFovZPoIyo+flUfRqAACVHFrsylHnzp3lcvmJEycOHDjg6uo6b968Ro0a\nMb/69OnT8+fPmZ/r1KmzaNGiAwcO/PDDDzY2NgEBAd9++y17qctXcHDw/v37d+zYIZPJvLy8\nVq5cqS1NkpKSmB6HlQqfz1++fPnOnTtXrVrF5XLbtGkzfvx45lcURb148YK5lx0TE6PRaFat\nWqW7bY8ePSZPnsxC6HKj5+VRxGoAAJUch+nABAAAAADGDhMUAwAAAJgIFHYAAAAAJgKFHQAA\nAICJQGEHAAAAYCJQ2AEAAACYCBR2AAAAACYChR0AgMlydnb29PRkOwUAVBwUdgBQccaPH8/5\nX2ZmZu7u7hMmTHjz5g3b6UoiNTV16dKlug/BKz25XG5vby8Wi7WPFS7gxYsXHA6n8KPnAABQ\n2AFARRs6dOjcf40ZM8bBwSEsLKx58+ZxcXFsRyu2K1euFHi6cekJhcLAwECJRBIREfHZFfbs\n2UMImThxYhm+KQCYBjxSDAAqWlBQkK+vr+6StWvXhoSEzJkzJzIysrh7UyqVAoGAw+GUWb7i\nuH//fmk2/1L4oKCgDRs27N69OzAwsMCvNBrNgQMHrK2thw4dWpq3BgCThBY7AGDfrFmzLCws\nrly5ol0SHh7esWNHR0dHCwuL+vXrL1y4MC8vT/vbqlWr+vr6PnjwoEGDBiKRKCMjQ5+tqlev\n3rFjx8TExJ49e1pbW9va2gYEBGRkZCQlJfXv39/GxsbBwWHQoEFpaWm62R49etS/f38HBwcz\nM7OaNWtOnDgxJSWF+ZWVldWGDRsIISKRiM/n67NJEeF11atXr1OnTjdu3Hjx4kWBX505cyYl\nJWXYsGFWVlZfPWRdVlZWzZo1013y999/cziclStX6pkcAAwfCjsAYB+Xy7W1tVUoFBRFEUK2\nbt06fPhwmUw2b968devWeXt7r1q1ql+/ftr1zczMpFLp2LFjvb29Fy9eLBKJ9NnK3Nz806dP\ngwYNGjBgwJkzZ4YPH37s2LEJEyb07t3b19c3MjJyypQpR48eDQoK0m5y7dq1du3axcXFzZs3\nLywsLCAg4LfffmvVqtWnT58IIYcPH27evDkh5I8//jh+/Lg+m3wpfGFMDOauq67du3drf/vV\nQy6WryYHACNAAwBUlHHjxhFCrly5UmD569evCSFeXl7My8mTJ7dp0yYnJ0e7wsCBAwkhsbGx\nzMvatWsLBIIlS5bo7uSrW3l4eBBCDh06xLxUq9UODg6EkA0bNmg3adSokbm5uVqtZl7Wq1fP\nzc1Nd58nTpwghMyePZt52b17d0KITCbTrvDVTT4bvjBmCIWzs7NKpdIuTE1N5fP5LVu21POQ\nq1at6uHhwfxsaWnZtGlT3be4ePEiIWTFihV6JgcAw4cWOwBgU05OzuXLlwcMGEAImTFjBrNw\n69atUVFRYrGYEKLRaNRqdZMmTQgh2pGzHA5HpVIVGD3w1a0IIVwu99tvv2V+5vF47u7uhJCA\ngADtCp6engqFIjs7mxDyzz//PHv2rHv37lKpNPVfrVu3FovF586d++zh6LPJZ8MXZm5uPmrU\nqNTU1DNnzmgXHjhwQK1Wa7fV55D1VIKDBQADhMETAFDROnfuXGAJn89fuHBhcHAw8zI3N3fl\nypUnT5588+aNSqXSrqY7+NTS0tLFxUV3J/psxfQe090Jh8PR3Y+lpSUhhNn86dOnhJDQ0NDQ\n0NACgZOSkj57aHpuUjj8Z02cOJEZQtG3b19myZ49e3SHTehzyHoqwcECgAFCYQcAFS0wMJBp\nKiOECASCatWq+fn5Va9enVlCUVT37t3v3LkzevTo9evXOzo68ni8/fv3b9myRXcntra2ui/1\n3EogEBQIw+VyeTzeZ3Pm5+cTQkaNGlV4/CmX+/nbHXpuUiD8l3h5efn4+DCjJVxcXG7fvv3s\n2bPg4GCm+tTzkPVUgoMFAAOEwg4AKtro0aMLTHei6/r163fu3AkICNi7d6924Z9//ln0Pku2\nVdGsra0JIWKxuEePHuW3SdGCgoKuXbt24MCBuXPnMgMptGM7Sn/ISqWy/JIDACvwdxgAGJbE\nxERCSIcOHXQXXrt2rTy2KlrDhg0JIXfv3i2wvIhRoiXYpGgDBw60t7f//ffflUrlsWPHvL29\nmXG4pPiHLBAIdCs58r9d8co8OQCwAoUdABgWZ2dn8r/9un7//fcnT54QQuRyedluVbRGjRrV\nr1///v37utXS9evXnZ2dmenrCCHMbVyZTKb/JsVibm4eGBgYGxv766+/Zmdn607FUtxDdnFx\nSUpKUigUzEuapvft21esgwUAw4dbsQBgWDp16uTs7Lxz505HR0c3N7fLly9funRp586dgwcP\nDg0NtbCw0I4kKP1WX7V9+/bu3bv37dt3xowZtWrVevLkyY4dO6pVq/bf//6XWaF27dqEkMmT\nJzdv3nzMmDGOjo5f3aS4goKC1q9fv2jRIrFYrNsBrriH3L9//x9//HHs2LGTJk2SSCTbtm2r\nXr36/fv3aZrW82ABwAiwPd8KAFQiX5rHroBHjx516tTJ2tra3t5+yJAhb9++VavVffr0EQqF\nPj4+NE17eHi4urqWfisfHx8ej6e7ZPjw4YSQlJQU7ZKHDx/279/f3t5e+zCGd+/eaX+bmJjY\nsmVLkUjk6en55s0bfTb5bPiiMV0Sg4ODi3vIuvPYyWSykJAQd3d3c3NzDw+PtWvXPn/+nBCy\ncOFCPQ8WAAwfh/73bzUAAAAAMGroYwcAAABgIlDYAQAAAJgIFHYAAAAAJgKFHQAAAICJQGEH\nAAAAYCJQ2AEAAACYCBR2AAAAACYChR0AAACAiUBhBwAAAGAiUNgBAAAAmAgUdgAAAAAmAoUd\nAAAAgIlAYQcAAABgIlDYAQAAAJiI/weG1VTBjZ852AAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Bayesian posterior density plots\n", + "if (!is.null(fit_bayes) && !is.null(a1_draws)) {\n", + " post_df <- data.frame(\n", + " value = c(a1_draws, a2_draws, b1_draws, b2_draws, cp_draws, ind1_draws, ind2_draws, total_ind_draws,\n", + " if (!is.null(r_m1m2_draws)) r_m1m2_draws else numeric(0)),\n", + " parameter = rep(c(\"a1: SNP -> DLPFC\", \"a2: SNP -> AC\",\n", + " \"b1: DLPFC -> AD age\", \"b2: AC -> AD age\",\n", + " \"c': Direct\", \"ind1: via DLPFC\", \"ind2: via AC\", \"Total indirect\",\n", + " if (!is.null(r_m1m2_draws)) \"M1~~M2 residual cov\" else character(0)),\n", + " each=length(a1_draws))\n", + " )\n", + " \n", + " p_post <- ggplot(post_df, aes(x=value, fill=parameter)) +\n", + " geom_density(alpha=0.5) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"red\") +\n", + " facet_wrap(~parameter, scales=\"free\", ncol=2) +\n", + " scale_fill_brewer(palette=\"Set2\") +\n", + " labs(title=\"Bayesian Posterior Distributions\",\n", + " subtitle=paste0(\"SNP -> APOC1 (DLPFC + AC) -> AD age | N = \", N_bayes, \" (Bayesian)\"),\n", + " x=\"Parameter Value\", y=\"Density\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"none\", plot.title=element_text(face=\"bold\"))\n", + " \n", + " ggsave(file.path(BAYES_DIR, \"bayesian_posteriors.png\"), p_post, width=12, height=10, dpi=150)\n", + " ggsave(file.path(BAYES_DIR, \"bayesian_posteriors.pdf\"), p_post, width=12, height=10)\n", + " print(p_post)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:32:52.062324Z", + "iopub.status.busy": "2026-03-31T16:32:52.060714Z", + "iopub.status.idle": "2026-03-31T16:32:53.828895Z", + "shell.execute_reply": "2026-03-31T16:32:53.826709Z" + } + }, + "outputs": [ + { + 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plnnnns2LHNmzcPGTJk0aJFTqcTAF5++eWnnnqKUtqhQwev1+t2uwcMGLBgwQK2\nKxBCCKEWLp9fxQIAx3HvvPOOx+N56KGHdC7i9Xo3xnbs2LFYC3bu3LmwsDC5X6EpsNu5c+eL\nL754xx13PPLII2GvR8vLy7WRHyHkvffeA4DRo0fr3DSHw3HWWWf17dtXO5HVw5177rmxlnro\noYcWLFjw4osver1ev9//0Ucfbdiw4eabb9bmfPr06T/++GN9fX1dXd2oUaOWLVs2b9489usD\nDzywY8eO2bNnu93uI0eO7Ny5s6ys7LbbbvN4PJFp2Wy2bdu2ffXVV7t27fruu+82bdp0xx13\nLF++/KuvvgKA6urqxx57bNCgQUePHq2qqqqvr//yyy83b978zDPP6NwDCCGEUJ6zupFfurCm\nbF6vl1J60003AcDChQvZT+nrPMGsXr0aonWPiP+ry+UqKSmx2+0lJSVdunRhrxfHjx8vSZJ2\ntpqammefffbee+897bTTWrVq9fbbb6eS1b1791ZUVHTv3j0QCESd4fjx406n87zzztNOvO22\n2y655BK/308pZRV+y5YtU3/973//CwBPPPEE+3P9+vXqnmf++Mc/AsDixYvZnx06dDjttNPY\nv9natH0pFi1aBACPP/44pbSyshIAnnzySe3aVq1atWPHjqS2HiGEEMo3+fwqVjV9+vT58+ff\ne++9v/zyS/xqM6vIstyzZ8+ioqIXXnhh2LBhHMcdOHDguuuu++KLL6ZNm/bkk0+qcx4+fJg1\ns3O5XHfddZfaTzYJu3fvHj16NCHk3//+d6zdsnbtWlEUL7jgAu3EDz74QPtnQUHB4MGD1T/Z\nCCxqhVz//v3r6urmzJmzb9++YDBIKV23bh0AeL3eqCm6XK4zzzxT/bNNmzbqzH379i0uLn77\n7be7dOkybtw41lDvvPPOM7zlCCGEUJ7K81exTLt27V566aU9e/a88MILVuclOrvd/vPPP69d\nu3b48OGsrq5Lly5z58612Wwffvihds5+/frV19fv3Lnzo48+Wrhw4dlnn71kyZIkUlyxYsWg\nQYN8Pt+yZcvidEqtqakBALU5XVRt27bVdm5gDfho0/iIn376aefOnW+++eYvv/zyxx9/3Lhx\nI1snjTGAYps2bbSdIdia2cwVFRXz5s0rLS2966672rdvf/bZZz/99NP79+/XvdEIIYRQnmsR\nNXYAcPvtt3/88ccvv/zy9ddfX1paGmfOpDtPmK5Tp06/+c1vdu3aRQhRIyebzQ6AXbAAACAA\nSURBVMYa2/Xs2fOCCy7o1q3bo48++tNPPxla86xZs+64445+/frNmzdPz+jEsYKwhGpqau64\n4442bdpUVlay16wAMHXq1CeeeCK5FQ4bNmz37t3Lly9fsGDBN9988/zzz0+fPv3f//53msaa\nRgghhHJLSwnsOI579913BwwYcPfdd8+dOzfOnKzzRKxf0xpAaAM4xuPxFBQU8Dy/f//+H374\n4cwzzzzttNPUX0855ZRTTjll69athlKZPXv2rbfeOmbMmH/84x9FRUXxZ2Zdiaurqw0loVq2\nbJkgCHfccYca1QHA3r17k1sbY7fbhw0bNmzYsOnTp1dWVo4ePZr1z0hlnQghhFB+aBGvYpl+\n/fo9+OCDlZWVn3/+eZzZBg0a9EtsN9xwQzrytmTJklatWk2ePFk78Ycffjhy5MjAgQMBYOfO\nnddcc82LL76onaG2tra6utrQV8VWrFhxyy23jB49+osvvkgY1QHAueee63A4wvrn3nTTTe3b\nt6+trU24OKvq01aR1tXVffHFF5BULeCmTZveeust7XDTF1544cCBA3ft2pV0nSJCCCGUT1pQ\nYAcAf/rTn7p06aKO8WsuSZJCoVAoFBJFEQAURWF/CoKQ8Nfzzz+/rKxsxowZf/3rXz0eTygU\n+u677yZMmAAAU6ZMAYAhQ4acfvrps2bNeu65544cOSJJ0oYNG6688kpZltVY87vvvrviiivm\nzJkTK4eyLN92220VFRUffvihmgFtNiJVVFTcdNNNW7dufeKJJ0KhECFk9uzZc+bMGTBggHaQ\n4VhY671//etfgUAAAPbu3Xv55ZezkZD37NljaPcCwLp16+67777HHntMHXWvsrLyhx9+6N+/\nP45RjBBCCAG0jOFOtLTjq5mbYqy2d+xTqvF/pZT+8ssvp556Kpuofgr23XffVde/c+fOs88+\nO2zxG2+8URRFNsPMmTMBgI3fG9WKFStiFYOuXbvGWsrj8YwYMQIAHA5HQUEBAAwYMODw4cPs\n18hvxW7YsAEAJk2axP688847AYA1CrTZbM8+++z+/fvtdrvT6Rw6dCiNGO4kztokSbrxxhsB\nwG63n3TSSWVlZQBw6qmnbt68WcfxQQghhPJf3raxu/zyy0855RT2uYKw6W+++WZtbe2gQYPM\nTfGGG26Iuk4Wf8T/FQD69u27ffv2xYsX79ixw+v1du3adeTIkWywD6Znz54bNmxYuXLl1q1b\njx071rZt24suukjb5G7AgAG/+93vWOwV1SmnnPLss89G/SnORy9KS0u/+eabVatWqZ8UGzFi\nhFpDdv/998uyrJ2/Y8eOzz77rDri8XvvvfeHP/xhw4YNTqdz+PDhp59+OgCsWrVq6dKlLPOP\nPPKI+iG1+Guz2+2zZs168sknV65cyb5g0atXr4svvjisYSJCCCHUYnEUGyflkUsuuWTixIns\nHS5CCCGEWhoM7PLHxo0bhw8ffuDAAT29IhBCCCGUfzCwQwghhBDKE9g4CSGEEEIoT2BghxBC\nCCGUJzCwQwghhBDKExjYIYQQQgjlCQzsEEIIIYTyBAZ2CCGEEEJ5AgM7hBBCCKE8gYEdQggh\nhFCewMAOIYQQQihPYGCHEEIIIZQnMLBDCCGEEMoTGNglg1IaDAYFQbA6I1ZSFEUURatzYSVZ\nloPBoCzLVmfESqIoKopidS6sJAhCMBhs4R/dDgaDVmfBSuyOEAqFrM6IlfCOwO4IkiRZnRGr\nA7va2toFCxZ8+eWXmzZtij/njh07/v73v9fX12cmY/ERQvx+fwu/lsmy3MJDW1EU/X5/NpzG\nFhIEoYUHdsFg0O/3E0KszoiVAoGA1VmwErsjYGDXwu8IkiRlyR3BbmHaP/7440svvdSxY8fW\nrVvPnj37/PPPf+ihhziOi5zzP//5zyeffKIoysCBA1u3bp35rCKEEEIIZT/LAjtRFN94442h\nQ4dOmjQJAH799dc//vGPgwYNOu+888LmnDlz5urVqydOnDhz5kwrcooQQgghlBssexW7efPm\nhoaGq6++mv3Zq1evfv36LV++PHLOtm3bvvbaa7169cpsBhFCCCGEcoxlgd3u3bvLysrat2+v\nTjn11FN37doVOeeVV17ZqlWrDGYNIYQQQignWfYqtr6+vry8XDulvLy8rq4u9TUripLu1ous\noTQhpCW3lpVlWVGUFr4H2H9b8k5gXeFactcBtu2CIPB8ix5koCWfBXhHALwjZPaO4HK54vxq\nZRs7h8PRLCt2OyFEURSbzZbKmmVZ9vl8qeVOF0JIZhLKZrgHBEFo4X3BWviAL0wL7xYKeCnA\nOwIAYDEAEEUxA8O+FBQURO1pylgW2DmdzrB6NVEUeZ5PMaoDAJvNVlhYmOJK4qOUhkIhnucL\nCgrSmlA2UxRFURSn02l1RiwjSZIsyw6Hw263sne5tURRtNlsqZ+2uUsQBEKIy+WKc53Ne8Fg\nMN1X3WxGCGFVtnhHaMl3BFmWJUmy2+1hlVbpEP9qY9kNqU2bNmGD0tXX17dt2zb1Ndvt9nTf\naFmFM8/zxcXFaU0omwmCIIpiS94DgUBAlmWn09nCb2kFBQUt+WouSRIhpLCwsCVHt6FQqCVf\nCtgQbi38jiCKoiAILXkPsNGJnU5nUVGRtTmxrFFIr169vF5vTU2NOmX79u2nnXaaVflBCCGE\nEMp1lgV2Z5xxRrt27ebOncv+3Lx58/bt24cNG8b+3LFjR9QesgghhBBCKBbLXsXabLYHHnjg\n+eef37lzZ+vWrbds2TJ69OgBAwawX2fOnFlYWPjcc8/JsvzMM89AU9vkGTNmuFyu1q1bP/ro\no1blHCGEEEIoO1nZ6PvMM89855131q5dGwwGJ0yY0LdvX/WnESNGsHZyHMf169ePTRw4cCD7\nR0t+i48QQgghFIvFvfnatGkzevToyOkjRoxg/7DZbBMmTMhsphBCCCGEclKLHlETIYQQQiif\nYGCHEEIIIZQnMLBDCCGEEMoTGNghhBBCCOUJDOwQQgghhPIEBnYIIYQQQnkCAzuEEEIIoTyB\ngR1CCCGEUJ7AwA4hhBBCKE9gYIcQQgghlCcwsEMIIYQQyhMY2CGEEEII5QkM7BBCCCGE8gQG\ndgghlKoQIYdF0epcIIQQBnYIIZSy/x6vf6/mSJ0kWZ0RhFBLh4EdQgilKkgIpTREiNUZQQi1\ndBjYIYSQWTirM4AQaukwsEMIIYQQyhMY2CGEkDkoUKuzgBBq6TCwQwihVFGM6BBC2QEDO4QQ\nQgihPIGBHUIIIYRQnsDADiGEzIK9YhFCFsPADiGEUoYRHUIoO2BghxBCCCGUJzCwQwghc1Ds\nHIsQshoGdgghhBBCeQIDO4QQShVW1SGEsgQGdgghZBLsQoEQshoGdgghhBBCeQIDO4QQQgih\nPIGBHUIImQNb2iGELIeBHUIIpQxb1yGEsgMGdgghhBBCeQIDO4QQQgihPIGBHUIImQRfyCKE\nrIaBHUIIpQo/JoYQyhIY2CGEEEII5QkM7BBCyBxYb4cQshwGdgghhBBCeQIDO4QQQgihPIGB\nHUIIpY7T/BchhCyDgR1CCCGEUJ7AwA4hhBBCKE9gYIcQQuaggL1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9jCY8yX0qO9f2T85lOFs1da3N2v1ppDxn81gnAKZ0njixruw9ZGmV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\nk6VS/LLFekqgkArblN62DWYQVk9dYy6ZArXIYaN0LmG4na9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Sqa5Yz7JXApt5vgAAIABJREFUzzQY4L80HMSkQzd9hPFlxAZsxEhZTEHo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\nyeHDh2eK2XaAKovd1B2QvIF2vmEbh1EZ0JDjRCul776VvW+CdDSTKMO5N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EyCLXGRfvMg26UuMyK64YYbbrzxxqWlJf/Xyy67rFWstgnU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\nwux8gfZee+21V155paKiol+/focPH3Y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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Bayesian trace plot for a1 path\n", + "if (!is.null(fit_bayes) && !is.null(a1_draws)) {\n", + " # Get per-chain draws\n", + " chain_draws <- blavInspect(fit_bayes, \"draws\")\n", + " n_chains <- length(chain_draws)\n", + " \n", + " trace_df_list <- list()\n", + " for (ch in 1:n_chains) {\n", + " ch_mat <- as.matrix(chain_draws[[ch]])\n", + " # Find a1 column\n", + " a1_col <- NULL\n", + " if (\"a1\" %in% colnames(ch_mat)) a1_col <- \"a1\"\n", + " if (is.null(a1_col) && \"pxnames\" %in% names(pt_full)) {\n", + " a1_row <- pt_full[pt_full$label == \"a1\" & pt_full$op == \"~\", ]\n", + " if (nrow(a1_row) > 0 && a1_row$pxnames[1] %in% colnames(ch_mat)) {\n", + " a1_col <- a1_row$pxnames[1]\n", + " }\n", + " }\n", + " if (!is.null(a1_col)) {\n", + " trace_df_list[[ch]] <- data.frame(\n", + " iteration = 1:nrow(ch_mat),\n", + " value = ch_mat[, a1_col],\n", + " chain = paste(\"Chain\", ch)\n", + " )\n", + " }\n", + " }\n", + " \n", + " if (length(trace_df_list) > 0) {\n", + " trace_df <- do.call(rbind, trace_df_list)\n", + " p_trace <- ggplot(trace_df, aes(x=iteration, y=value, color=chain)) +\n", + " geom_line(alpha=0.5) +\n", + " labs(title=\"MCMC Trace: a1 (SNP -> DLPFC expression)\",\n", + " subtitle=paste0(\"N = \", N_bayes, \", \", n_chains, \" chains\"),\n", + " x=\"Iteration\", y=\"a1 value\", color=\"Chain\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + " \n", + " ggsave(file.path(BAYES_DIR, \"bayesian_trace_a.png\"), p_trace, width=10, height=5, dpi=150)\n", + " ggsave(file.path(BAYES_DIR, \"bayesian_trace_a.pdf\"), p_trace, width=10, height=5)\n", + " print(p_trace)\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:32:53.834876Z", + "iopub.status.busy": "2026-03-31T16:32:53.833286Z", + "iopub.status.idle": "2026-03-31T16:32:54.624440Z", + "shell.execute_reply": "2026-03-31T16:32:54.621762Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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T9/vkGDBv7+/vQ69Pjx49lZhgwZQgjZuXOnWCzu0qWLm5sbj8dr3LhxfHw8rSB3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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Bayesian forest plot\n", + "if (!is.null(fit_bayes) && nrow(bayes_results) > 0) {\n", + " bayes_plot_df <- bayes_results[bayes_results$label %in% names(plot_labels) & !is.na(bayes_results$ci_lower), ]\n", + " bayes_plot_df$label_nice <- plot_labels[bayes_plot_df$label]\n", + " bayes_plot_df$label_nice <- factor(bayes_plot_df$label_nice, levels=rev(plot_labels))\n", + " \n", + " p_bayes_forest <- ggplot(bayes_plot_df, aes(x=post_mean, y=label_nice, xmin=ci_lower, xmax=ci_upper)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(color=\"darkgreen\", size=0.6) +\n", + " labs(title=\"Bayesian SEM: Parallel Mediation Path Estimates\",\n", + " subtitle=paste0(\"N = \", N_bayes, \" (Bayesian, fixed.x=FALSE), 2 chains x 2000 samples\"),\n", + " x=\"Posterior Mean (95% Credible Interval)\", y=NULL) +\n", + " theme_minimal(base_size=12) +\n", + " theme(plot.title=element_text(face=\"bold\"))\n", + " \n", + " ggsave(file.path(BAYES_DIR, \"bayesian_forest_plot.png\"), p_bayes_forest, width=10, height=6, dpi=150)\n", + " ggsave(file.path(BAYES_DIR, \"bayesian_forest_plot.pdf\"), p_bayes_forest, width=10, height=6)\n", + " print(p_bayes_forest)\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bayesian Interpretation\n", + "\n", + "The Bayesian approach provides posterior probability statements:\n", + "- **pp_positive**: Probability that the effect is positive (> 0). Values near 0 or 1 indicate strong directional evidence.\n", + "- **95% Credible Interval**: The parameter lies in this interval with 95% posterior probability.\n", + "- **Contrast (diff_1_2)**: The posterior probability that mediation through DLPFC is stronger than through AC (or vice versa)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Cross-Method Summary\n", + "\n", + "Combine results from FIML, Bootstrap, and Bayesian methods into unified tables and visualizations." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:32:54.631195Z", + "iopub.status.busy": "2026-03-31T16:32:54.628964Z", + "iopub.status.idle": "2026-03-31T16:32:54.754506Z", + "shell.execute_reply": "2026-03-31T16:32:54.751661Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-method summary table:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " exposure method label est se ci_lower\n", + " chr19_44918487_G_T FIML a1 -0.10010234 0.04897934 -0.19610010\n", + " chr19_44918487_G_T FIML a2 -0.18053667 0.05690969 -0.29207761\n", + " chr19_44918487_G_T FIML b1 0.47169762 0.33674764 -0.18831562\n", + " chr19_44918487_G_T FIML b2 1.12594530 0.33013531 0.47889199\n", + " chr19_44918487_G_T FIML cp -0.94771949 0.38731565 -1.70684422\n", + " chr19_44918487_G_T FIML r_m1m2 0.31246871 0.04176718 0.23060655\n", + " chr19_44918487_G_T FIML ind1 -0.04721804 0.04029080 -0.12618655\n", + " chr19_44918487_G_T FIML ind2 -0.20327441 0.08642138 -0.37265721\n", + " chr19_44918487_G_T FIML total_indirect -0.25049245 0.09224935 -0.43129786\n", + " chr19_44918487_G_T FIML total -1.19821194 0.38476997 -1.95234724\n", + " chr19_44918487_G_T FIML diff_1_2 0.15605638 0.09835682 -0.03671946\n", + " chr19_44918487_G_T FIML prop_med 0.20905521 0.09737080 0.01821196\n", + " chr19_44918487_G_T Bootstrap a1 -0.10036617 0.05077456 -0.20316729\n", + " chr19_44918487_G_T Bootstrap a2 -0.17866276 0.05588725 -0.28646954\n", + " chr19_44918487_G_T Bootstrap b1 0.46505934 0.38145112 -0.28709795\n", + " chr19_44918487_G_T Bootstrap b2 1.11537557 0.43993140 0.26121840\n", + " chr19_44918487_G_T Bootstrap cp -0.97240776 0.41892866 -1.76064434\n", + " chr19_44918487_G_T Bootstrap ind1 -0.04637920 0.04891726 -0.16565946\n", + " chr19_44918487_G_T Bootstrap ind2 -0.19581275 0.09556090 -0.39830400\n", + " chr19_44918487_G_T Bootstrap total_indirect -0.24219195 0.09827578 -0.44330779\n", + " chr19_44918487_G_T Bootstrap total -1.21459971 0.39997024 -1.96358469\n", + " chr19_44918487_G_T Bootstrap diff_1_2 0.14943355 0.11572139 -0.06753095\n", + " chr19_44918487_G_T Bootstrap r_m1m2 0.31162957 0.04708958 0.22165062\n", + " chr19_44918487_G_T Bayesian a1 -0.10110738 0.05057047 -0.20002086\n", + " chr19_44918487_G_T Bayesian a2 -0.18079054 0.05798105 -0.29462748\n", + " chr19_44918487_G_T Bayesian b1 0.44872863 0.33259779 -0.20848154\n", + " chr19_44918487_G_T Bayesian b2 1.13140637 0.33006988 0.46288206\n", + " chr19_44918487_G_T Bayesian cp -0.85414119 0.39192742 -1.63232013\n", + " chr19_44918487_G_T Bayesian ind1 -0.04500375 0.04388993 -0.14750136\n", + " chr19_44918487_G_T Bayesian ind2 -0.20379149 0.08899261 -0.39823542\n", + " chr19_44918487_G_T Bayesian total_indirect -0.24879524 0.09679774 -0.45850588\n", + " chr19_44918487_G_T Bayesian total -1.10293643 0.38797084 -1.86588920\n", + " chr19_44918487_G_T Bayesian diff_1_2 0.15878773 0.10159833 -0.02843153\n", + " chr19_44918487_G_T Bayesian r_m1m2 0.31421474 0.04199963 0.23337575\n", + " ci_upper p_value ci_type n_eff\n", + " -0.004104593 4.097662e-02 Wald N=1153 (FIML)\n", + " -0.068995725 1.512179e-03 Wald N=1153 (FIML)\n", + " 1.131710866 1.612903e-01 Wald N=1153 (FIML)\n", + " 1.772998605 6.483032e-04 Wald N=1153 (FIML)\n", + " -0.188594771 1.440941e-02 Wald N=1153 (FIML)\n", + " 0.394330878 7.371881e-14 Wald N=1153 (FIML)\n", + " 0.031750470 2.412247e-01 Wald N=1153 (FIML)\n", + " -0.033891611 1.866620e-02 Wald N=1153 (FIML)\n", + " -0.069687044 6.619888e-03 Wald N=1153 (FIML)\n", + " -0.444076652 1.845073e-03 Wald N=1153 (FIML)\n", + " 0.348832210 1.125953e-01 Wald N=1153 (FIML)\n", + " 0.399898465 3.179319e-02 Wald N=1153 (FIML)\n", + " -0.002528319 4.800000e-02 Percentile N=1153 (Bootstrap FIML)\n", + " -0.070902042 0.000000e+00 Percentile N=1153 (Bootstrap FIML)\n", + " 1.213762497 2.300000e-01 Percentile N=1153 (Bootstrap FIML)\n", + " 1.910148342 2.200000e-02 Percentile N=1153 (Bootstrap FIML)\n", + " -0.167791595 1.400000e-02 Percentile N=1153 (Bootstrap FIML)\n", + " 0.030903368 2.740000e-01 Percentile N=1153 (Bootstrap FIML)\n", + " -0.033108051 2.200000e-02 Percentile N=1153 (Bootstrap FIML)\n", + " -0.074471177 2.000000e-03 Percentile N=1153 (Bootstrap FIML)\n", + " -0.447319414 0.000000e+00 Percentile N=1153 (Bootstrap FIML)\n", + " 0.372180546 1.840000e-01 Percentile N=1153 (Bootstrap FIML)\n", + " 0.404659416 0.000000e+00 Percentile N=1153 (Bootstrap FIML)\n", + " -0.001546526 NA Credible N=1153 (Bayesian, fixed.x=FALSE)\n", + " -0.064209890 NA Credible N=1153 (Bayesian, fixed.x=FALSE)\n", + " 1.089715875 NA Credible N=1153 (Bayesian, fixed.x=FALSE)\n", + " 1.784125484 NA Credible N=1153 (Bayesian, fixed.x=FALSE)\n", + " -0.079134304 NA Credible N=1153 (Bayesian, fixed.x=FALSE)\n", + " 0.023367715 NA Credible N=1153 (Bayesian, fixed.x=FALSE)\n", + " -0.052234669 NA Credible N=1153 (Bayesian, fixed.x=FALSE)\n", + " -0.082965349 NA Credible N=1153 (Bayesian, fixed.x=FALSE)\n", + " -0.344174542 NA Credible N=1153 (Bayesian, fixed.x=FALSE)\n", + " 0.378349829 NA Credible N=1153 (Bayesian, fixed.x=FALSE)\n", + " 0.398405809 NA Credible N=1153 (Bayesian, fixed.x=FALSE)\n" + ] + } + ], + "source": [ + "# Build unified summary table\n", + "summary_rows <- list()\n", + "\n", + "# FIML results\n", + "for (i in 1:nrow(fiml_key)) {\n", + " r <- fiml_key[i, ]\n", + " summary_rows[[length(summary_rows)+1]] <- data.frame(\n", + " exposure = SNP, method = \"FIML\", label = r$label,\n", + " est = r$est, se = r$se, ci_lower = r$ci.lower, ci_upper = r$ci.upper,\n", + " p_value = r$pvalue, ci_type = \"Wald\",\n", + " n_eff = paste0(\"N=\", N_fiml, \" (FIML)\"),\n", + " stringsAsFactors=FALSE\n", + " )\n", + "}\n", + "\n", + "# Bootstrap results\n", + "for (i in 1:nrow(boot_summary)) {\n", + " r <- boot_summary[i, ]\n", + " summary_rows[[length(summary_rows)+1]] <- data.frame(\n", + " exposure = SNP, method = \"Bootstrap\", label = r$label,\n", + " est = r$boot_mean, se = r$boot_se, ci_lower = r$ci_lower, ci_upper = r$ci_upper,\n", + " p_value = r$p_value, ci_type = \"Percentile\",\n", + " n_eff = paste0(\"N=\", N_fiml, \" (Bootstrap FIML)\"),\n", + " stringsAsFactors=FALSE\n", + " )\n", + "}\n", + "\n", + "# Bayesian results\n", + "if (!is.null(fit_bayes) && nrow(bayes_results) > 0) {\n", + " for (i in 1:nrow(bayes_results)) {\n", + " r <- bayes_results[i, ]\n", + " summary_rows[[length(summary_rows)+1]] <- data.frame(\n", + " exposure = SNP, method = \"Bayesian\", label = r$label,\n", + " est = r$post_mean, se = r$post_sd, ci_lower = r$ci_lower, ci_upper = r$ci_upper,\n", + " p_value = NA, ci_type = \"Credible\",\n", + " n_eff = paste0(\"N=\", N_bayes, \" (Bayesian, fixed.x=FALSE)\"),\n", + " stringsAsFactors=FALSE\n", + " )\n", + " }\n", + "}\n", + "\n", + "all_summary <- do.call(rbind, summary_rows)\n", + "cat(\"Cross-method summary table:\\n\")\n", + "print(all_summary, row.names=FALSE)\n", + "\n", + "write.csv(all_summary, file.path(SUM_DIR, \"all_methods_summary.csv\"), row.names=FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:32:54.760729Z", + "iopub.status.busy": "2026-03-31T16:32:54.758273Z", + "iopub.status.idle": "2026-03-31T16:32:54.830071Z", + "shell.execute_reply": "2026-03-31T16:32:54.827224Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Display table (estimate [95% CI]):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label FIML Bootstrap\n", + " a1 -0.1001 [-0.1961, -0.0041] -0.1004 [-0.2032, -0.0025]\n", + " a2 -0.1805 [-0.2921, -0.0690] -0.1787 [-0.2865, -0.0709]\n", + " b1 0.4717 [-0.1883, 1.1317] 0.4651 [-0.2871, 1.2138]\n", + " b2 1.1259 [0.4789, 1.7730] 1.1154 [0.2612, 1.9101]\n", + " cp -0.9477 [-1.7068, -0.1886] -0.9724 [-1.7606, -0.1678]\n", + " r_m1m2 0.3125 [0.2306, 0.3943] 0.3116 [0.2217, 0.4047]\n", + " ind1 -0.0472 [-0.1262, 0.0318] -0.0464 [-0.1657, 0.0309]\n", + " ind2 -0.2033 [-0.3727, -0.0339] -0.1958 [-0.3983, -0.0331]\n", + " total_indirect -0.2505 [-0.4313, -0.0697] -0.2422 [-0.4433, -0.0745]\n", + " total -1.1982 [-1.9523, -0.4441] -1.2146 [-1.9636, -0.4473]\n", + " diff_1_2 0.1561 [-0.0367, 0.3488] 0.1494 [-0.0675, 0.3722]\n", + " prop_med 0.2091 [0.0182, 0.3999] \n", + " Bayesian\n", + " -0.1011 [-0.2000, -0.0015]\n", + " -0.1808 [-0.2946, -0.0642]\n", + " 0.4487 [-0.2085, 1.0897]\n", + " 1.1314 [0.4629, 1.7841]\n", + " -0.8541 [-1.6323, -0.0791]\n", + " 0.3142 [0.2334, 0.3984]\n", + " -0.0450 [-0.1475, 0.0234]\n", + " -0.2038 [-0.3982, -0.0522]\n", + " -0.2488 [-0.4585, -0.0830]\n", + " -1.1029 [-1.8659, -0.3442]\n", + " 0.1588 [-0.0284, 0.3783]\n", + " \n" + ] + } + ], + "source": [ + "# Human-readable wide-format display table\n", + "display_df <- all_summary %>%\n", + " mutate(estimate_ci = sprintf(\"%.4f [%.4f, %.4f]\", est, ci_lower, ci_upper)) %>%\n", + " select(label, method, estimate_ci) %>%\n", + " pivot_wider(names_from = method, values_from = estimate_ci)\n", + "\n", + "cat(\"\\nDisplay table (estimate [95% CI]):\\n\")\n", + "print(as.data.frame(display_df), row.names=FALSE)\n", + "\n", + "write.csv(display_df, file.path(SUM_DIR, \"summary_display_table.csv\"), row.names=FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:32:54.835518Z", + "iopub.status.busy": "2026-03-31T16:32:54.833942Z", + "iopub.status.idle": "2026-03-31T16:32:56.026787Z", + "shell.execute_reply": "2026-03-31T16:32:56.024178Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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y8v7rO4u7sTQp4+fVqb763BiqtxO0gkks+l5Obm/vvvv+zH33//XdmMVa7x6obU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Zs1atWmkHlBg5VnX3OigoKDc3t6ioKDw8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\njBASEhKipaX1zTffUC/r6ur+/PPP7OxsCwuLs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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Summary Forest Plot: All methods, all paths\n", + "plot_summary_df <- all_summary[all_summary$label %in% names(plot_labels) & !is.na(all_summary$ci_lower), ]\n", + "plot_summary_df$label_nice <- plot_labels[plot_summary_df$label]\n", + "plot_summary_df$label_nice <- factor(plot_summary_df$label_nice, levels=rev(plot_labels))\n", + "\n", + "method_colors <- c(\"FIML\" = \"steelblue\", \"Bootstrap\" = \"darkorange\", \"Bayesian\" = \"darkgreen\")\n", + "method_shapes <- c(\"FIML\" = 16, \"Bootstrap\" = 17, \"Bayesian\" = 15)\n", + "\n", + "p_summary_forest <- ggplot(plot_summary_df, aes(x=est, y=label_nice, xmin=ci_lower, xmax=ci_upper,\n", + " color=method, shape=method)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(position=position_dodge(width=0.6), size=0.5) +\n", + " scale_color_manual(values=method_colors) +\n", + " scale_shape_manual(values=method_shapes) +\n", + " labs(title=\"All Methods Comparison: Parallel Mediation Path Estimates\",\n", + " subtitle=\"SNP -> APOC1 (DLPFC + AC) -> AD Diagnosis Age\",\n", + " x=\"Estimate (95% CI)\", y=NULL, color=\"Method\", shape=\"Method\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\", plot.title=element_text(face=\"bold\"))\n", + "\n", + "ggsave(file.path(SUM_DIR, \"summary_forest_all_methods.png\"), p_summary_forest, width=11, height=7, dpi=150)\n", + "ggsave(file.path(SUM_DIR, \"summary_forest_all_methods.pdf\"), p_summary_forest, width=11, height=7)\n", + "print(p_summary_forest)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:32:56.032387Z", + "iopub.status.busy": "2026-03-31T16:32:56.030822Z", + "iopub.status.idle": "2026-03-31T16:33:00.487191Z", + "shell.execute_reply": "2026-03-31T16:33:00.485018Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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L/99ltOTs6qVasYRv7o0aP169cbGRkFBAQopZWfn9+5c+dev34dGBi4YcMGKyur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\nZmZmzs7OV65cWblypUwma96MVaS4uNjCwkI+aW5uzuPxeDyeYkxtty5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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Summary Faceted by Effect Type\n", + "p_faceted <- ggplot(plot_summary_df, aes(x=est, y=method, xmin=ci_lower, xmax=ci_upper, color=method)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(size=0.6) +\n", + " facet_wrap(~label_nice, scales=\"free_x\", ncol=2) +\n", + " scale_color_manual(values=method_colors) +\n", + " labs(title=\"Estimates by Effect Type Across Methods\",\n", + " subtitle=\"SNP -> APOC1 (DLPFC + AC) -> AD Diagnosis Age\",\n", + " x=\"Estimate (95% CI)\", y=NULL) +\n", + " theme_minimal(base_size=11) +\n", + " theme(legend.position=\"none\", plot.title=element_text(face=\"bold\"),\n", + " strip.text=element_text(face=\"bold\", size=10))\n", + "\n", + "ggsave(file.path(SUM_DIR, \"summary_faceted_by_path.png\"), p_faceted, width=12, height=10, dpi=150)\n", + "ggsave(file.path(SUM_DIR, \"summary_faceted_by_path.pdf\"), p_faceted, width=12, height=10)\n", + "print(p_faceted)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:33:00.494579Z", + "iopub.status.busy": "2026-03-31T16:33:00.492313Z", + "iopub.status.idle": "2026-03-31T16:33:01.598697Z", + "shell.execute_reply": "2026-03-31T16:33:01.596103Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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cTvv/9uy3KdOXPGz89PCLFp0yZb2tvSxmxd2Rjs7F0/1j388MMRERG2tJRVuHtU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\nHc2bN+/evbv888CBA3NzcxcsWHD16tVGjRrZpJ5u3bp169bNJ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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Indirect Effect Focus plot\n", + "indirect_labels <- c(\"ind1\", \"ind2\", \"total_indirect\")\n", + "indirect_nice <- c(\"ind1\" = \"Specific: via DLPFC\", \"ind2\" = \"Specific: via AC\",\n", + " \"total_indirect\" = \"Total Indirect\")\n", + "\n", + "ind_df <- plot_summary_df[plot_summary_df$label %in% indirect_labels, ]\n", + "ind_df$ind_label <- indirect_nice[ind_df$label]\n", + "ind_df$ind_label <- factor(ind_df$ind_label, levels=rev(indirect_nice))\n", + "\n", + "p_indirect <- ggplot(ind_df, aes(x=est, y=ind_label, xmin=ci_lower, xmax=ci_upper,\n", + " color=method, shape=method)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(position=position_dodge(width=0.5), size=0.8) +\n", + " scale_color_manual(values=method_colors) +\n", + " scale_shape_manual(values=method_shapes) +\n", + " labs(title=\"Indirect Effects (a x b) Across Methods\",\n", + " subtitle=\"SNP -> APOC1 (DLPFC + AC) -> AD Diagnosis Age\",\n", + " x=\"Indirect Effect (95% CI)\", y=NULL, color=\"Method\", shape=\"Method\") +\n", + " theme_minimal(base_size=13) +\n", + " theme(legend.position=\"bottom\", plot.title=element_text(face=\"bold\"))\n", + "\n", + "ggsave(file.path(SUM_DIR, \"summary_indirect_effect.png\"), p_indirect, width=10, height=5, dpi=150)\n", + "ggsave(file.path(SUM_DIR, \"summary_indirect_effect.pdf\"), p_indirect, width=10, height=5)\n", + "print(p_indirect)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:33:01.605278Z", + "iopub.status.busy": "2026-03-31T16:33:01.603074Z", + "iopub.status.idle": "2026-03-31T16:33:02.826663Z", + "shell.execute_reply": "2026-03-31T16:33:02.824027Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Combined panel saved.\n" + ] + }, + { + "data": { + "image/png": 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j179qxevdrBwcHCwgKfeailpU+pX4PT144giA0bNuBj/7ffftu9e/f48eM5HM6w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\nbd++PTc3NzMz8+jRo46OjjQaTb46asYMAPUp72aNwP/+BwB4b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ImJicuXL7e1tc3Ly4uOjp42bdqQIUNEItGxY8d+/PHHmJgYhNBvv/12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\nH1+f89OA6mweKkV4+vRpAPj2228bO2y51GvqCppEPWtISko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mTZskKyefcEuf+gAAIABJREFUVyoldlu2bAGAmTNnSpYpKyszNDQ0Njau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\nOnXr1q21tbX0g01WVtbDw2Pfvn1RUVHs0x46dMjW1lZfX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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Combined panel\n", + "p_combined <- grid.arrange(\n", + " p_summary_forest + theme(plot.margin=margin(5, 10, 5, 5)),\n", + " p_indirect + theme(plot.margin=margin(5, 10, 5, 5)),\n", + " nrow=2, heights=c(2, 1)\n", + ")\n", + "\n", + "ggsave(file.path(SUM_DIR, \"summary_combined_panel.png\"), p_combined, width=11, height=11, dpi=150)\n", + "ggsave(file.path(SUM_DIR, \"summary_combined_panel.pdf\"), p_combined, width=11, height=11)\n", + "cat(\"Combined panel saved.\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mediator Correlation Across Methods\n", + "\n", + "Comparing the M1~~M2 residual covariance estimate across FIML, Bootstrap, and Bayesian methods provides a consistency check. All three should yield similar estimates; large discrepancies would suggest model instability.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:33:02.833232Z", + "iopub.status.busy": "2026-03-31T16:33:02.831036Z", + "iopub.status.idle": "2026-03-31T16:33:03.667043Z", + "shell.execute_reply": "2026-03-31T16:33:03.664449Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== MEDIATOR RESIDUAL COVARIANCE ACROSS METHODS ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "M1~~M2 (APOC1_DLPFC_exp ~~ APOC1_AC_exp) residual covariance:\n", + " FIML: est=0.3125 [0.2306, 0.3943], p=7.372e-14\n", + " Bootstrap: est=0.3116 [0.2217, 0.4047], p=0\n", + " Bayesian: est=0.3142 [0.2334, 0.3984]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-method mediator correlation plot saved.\n" + ] + }, + { + "data": { + "image/png": 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EAI8csvvyiK8uyzzwoh3n777UqnUlRUFBUVZbFYsrKy1CHq153Tp0+vtP2xY8d+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779q0aWNvbx8REWHL19zZcnBvG+yefPJJ\nnU537dq1uz4F0KBpTDZ/Vw8AQGKFhYUhISHNmjU7cuSI2rU8YCkpKW3btn3xxRdXrVqldi1A\n7eIcOwCAEEI4OzvPmDHj6NGjiYmJatfygM2fP1+r1SqT14DcGLEDAPyfioqKRx99NCsr69ix\nY3dYh69h+e6774YMGbJgwYIZM2aoXQtQ6wh2AID/l56eHhER0bdv3w0bNqhdywOQlZXVoUOH\nHj16KKu0ANIj2AEAAEiCc+wAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATB\nDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJDE/wLY67zOHcv9oQAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Cross-Method Mediator Correlation Comparison\n", + "cat(\"\\n=== MEDIATOR RESIDUAL COVARIANCE ACROSS METHODS ===\\n\")\n", + "corr_rows <- all_summary[all_summary$label == \"r_m1m2\", ]\n", + "if (nrow(corr_rows) > 0) {\n", + " cat(\"\\nM1~~M2 (APOC1_DLPFC_exp ~~ APOC1_AC_exp) residual covariance:\\n\")\n", + " for (i in 1:nrow(corr_rows)) {\n", + " r <- corr_rows[i, ]\n", + " cat(sprintf(\" %s: est=%.4f [%.4f, %.4f]\", r$method, r$est, r$ci_lower, r$ci_upper))\n", + " if (!is.na(r$p_value)) cat(sprintf(\", p=%.4g\", r$p_value))\n", + " cat(\"\\n\")\n", + " }\n", + "\n", + " # Visualization: mediator correlation across methods\n", + " corr_rows$method <- factor(corr_rows$method, levels=c(\"FIML\", \"Bootstrap\", \"Bayesian\"))\n", + "\n", + " p_corr_methods <- ggplot(corr_rows, aes(x=est, y=method, xmin=ci_lower, xmax=ci_upper,\n", + " color=method)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(size=0.8) +\n", + " scale_color_manual(values=method_colors) +\n", + " labs(title=\"Mediator Residual Covariance (M1 ~~ M2) Across Methods\",\n", + " subtitle=\"APOC1_DLPFC_exp ~~ APOC1_AC_exp\",\n", + " x=\"Residual Covariance (95% CI)\", y=NULL) +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"none\", plot.title=element_text(face=\"bold\"))\n", + "\n", + " ggsave(file.path(SUM_DIR, \"summary_mediator_correlation.png\"), p_corr_methods, width=8, height=4, dpi=150)\n", + " ggsave(file.path(SUM_DIR, \"summary_mediator_correlation.pdf\"), p_corr_methods, width=8, height=4)\n", + " print(p_corr_methods)\n", + " cat(\"Cross-method mediator correlation plot saved.\\n\")\n", + "} else {\n", + " cat(\"No r_m1m2 rows found in summary table.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Key Findings" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:33:03.673736Z", + "iopub.status.busy": "2026-03-31T16:33:03.671523Z", + "iopub.status.idle": "2026-03-31T16:33:03.883515Z", + "shell.execute_reply": "2026-03-31T16:33:03.880890Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== KEY FINDINGS ===\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. SAMPLE SIZE\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " FIML used N = 1153 subjects (full sample with FIML).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Complete cases (all 4 variables) = 176 \n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2. a-PATHS (SNP -> Mediators)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " a1 (SNP -> DLPFC): est=-0.1001, p=0.04098 (SIGNIFICANT)\n", + " a2 (SNP -> AC): est=-0.1805, p=0.001512 (SIGNIFICANT)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "3. b-PATHS (Mediators -> Outcome, controlling for other mediator and SNP)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " b1 (DLPFC -> AD age): est=0.4717, p=0.1613 (not significant)\n", + " b2 (AC -> AD age): est=1.1259, p=0.0006483 (SIGNIFICANT)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "4. INDIRECT EFFECTS\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " via DLPFC: FIML est=-0.0472 (p=0.2412), Boot est=-0.0464 (p=0.274)\n", + " via AC: FIML est=-0.2033 (p=0.01867), Boot est=-0.1958 (p=0.022)\n", + " Total indirect: FIML est=-0.2505 (p=0.00662), Boot est=-0.2422 (p=0.002)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "5. DIRECT EFFECT\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " c' (direct): est=-0.9477, p=0.01441 (SIGNIFICANT)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "6. CONTRAST (DLPFC vs AC mediation)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " diff(ind1-ind2): est=0.1561, p=0.1126\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "7. MEDIATOR RESIDUAL CORRELATION (M1 ~~ M2)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " FIML: covariance=0.3125, p=7.372e-14, 95% CI=[0.2306, 0.3943]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Bootstrap: mean=0.3116, 95% CI=[0.2217, 0.4047], p=0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Bayesian: post_mean=0.3142, 95% CrI=[0.2334, 0.3984]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Standardized residual correlation: r=0.3676\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Interpretation: A significant positive residual covariance confirms shared\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " regulatory variance between DLPFC and AC APOC1 expression, beyond SNP and\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " covariate effects. This justifies the parallel mediation model with freely\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " estimated M1~~M2. The specific indirect effects (ind1, ind2) thus reflect\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " unique tissue-specific mediation pathways.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "8. MNAR ROBUSTNESS\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ind1: tipping distance = 0.00 SD units\n", + " ind2: tipping distance = 0.00 SD units\n", + " total_indirect: tipping distance = 0.61 SD units\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "9. BAYESIAN POSTERIOR PROBABILITIES\n", + " ind1: P(>0)=0.112, post_mean=-0.0450, 95% CrI=[-0.1475, 0.0234]\n", + " ind2: P(>0)=0.002, post_mean=-0.2038, 95% CrI=[-0.3982, -0.0522]\n", + " total_indirect: P(>0)=0.001, post_mean=-0.2488, 95% CrI=[-0.4585, -0.0830]\n", + " diff_1_2: P(>0)=0.955, post_mean=0.1588, 95% CrI=[-0.0284, 0.3783]\n" + ] + } + ], + "source": [ + "cat(\"=== KEY FINDINGS ===\\n\\n\")\n", + "\n", + "# Read back the summary CSV for data-driven conclusions\n", + "res <- read.csv(file.path(SUM_DIR, \"all_methods_summary.csv\"), stringsAsFactors=FALSE)\n", + "\n", + "cat(\"1. SAMPLE SIZE\\n\")\n", + "cat(\" FIML used N =\", N_fiml, \"subjects (full sample with FIML).\\n\")\n", + "cat(\" Complete cases (all 4 variables) =\", sum(has_snp & has_m1 & has_m2 & has_y), \"\\n\\n\")\n", + "\n", + "cat(\"2. a-PATHS (SNP -> Mediators)\\n\")\n", + "for (lab in c(\"a1\", \"a2\")) {\n", + " fiml_row <- res[res$method == \"FIML\" & res$label == lab, ]\n", + " tissue <- ifelse(lab == \"a1\", \"DLPFC\", \"AC\")\n", + " sig <- ifelse(fiml_row$p_value < 0.05, \"SIGNIFICANT\", \"not significant\")\n", + " cat(sprintf(\" %s (SNP -> %s): est=%.4f, p=%.4g (%s)\\n\", lab, tissue, fiml_row$est, fiml_row$p_value, sig))\n", + "}\n", + "\n", + "cat(\"\\n3. b-PATHS (Mediators -> Outcome, controlling for other mediator and SNP)\\n\")\n", + "for (lab in c(\"b1\", \"b2\")) {\n", + " fiml_row <- res[res$method == \"FIML\" & res$label == lab, ]\n", + " tissue <- ifelse(lab == \"b1\", \"DLPFC\", \"AC\")\n", + " sig <- ifelse(fiml_row$p_value < 0.05, \"SIGNIFICANT\", \"not significant\")\n", + " cat(sprintf(\" %s (%s -> AD age): est=%.4f, p=%.4g (%s)\\n\", lab, tissue, fiml_row$est, fiml_row$p_value, sig))\n", + "}\n", + "\n", + "cat(\"\\n4. INDIRECT EFFECTS\\n\")\n", + "for (lab in c(\"ind1\", \"ind2\", \"total_indirect\")) {\n", + " fiml_row <- res[res$method == \"FIML\" & res$label == lab, ]\n", + " boot_row <- res[res$method == \"Bootstrap\" & res$label == lab, ]\n", + " nice <- switch(lab, ind1=\"via DLPFC\", ind2=\"via AC\", total_indirect=\"Total indirect\")\n", + " cat(sprintf(\" %s: FIML est=%.4f (p=%.4g), Boot est=%.4f (p=%.4g)\\n\",\n", + " nice, fiml_row$est, fiml_row$p_value, boot_row$est, boot_row$p_value))\n", + "}\n", + "\n", + "cat(\"\\n5. DIRECT EFFECT\\n\")\n", + "cp_fiml <- res[res$method == \"FIML\" & res$label == \"cp\", ]\n", + "sig_cp <- ifelse(cp_fiml$p_value < 0.05, \"SIGNIFICANT\", \"not significant\")\n", + "cat(sprintf(\" c' (direct): est=%.4f, p=%.4g (%s)\\n\", cp_fiml$est, cp_fiml$p_value, sig_cp))\n", + "\n", + "cat(\"\\n6. CONTRAST (DLPFC vs AC mediation)\\n\")\n", + "diff_fiml <- res[res$method == \"FIML\" & res$label == \"diff_1_2\", ]\n", + "if (nrow(diff_fiml) > 0) {\n", + " cat(sprintf(\" diff(ind1-ind2): est=%.4f, p=%.4g\\n\", diff_fiml$est, diff_fiml$p_value))\n", + "}\n", + "\n", + "cat(\"\\n7. MEDIATOR RESIDUAL CORRELATION (M1 ~~ M2)\\n\")\n", + "# Report from FIML\n", + "r_m1m2_fiml <- res[res$method == \"FIML\" & res$label == \"r_m1m2\", ]\n", + "if (nrow(r_m1m2_fiml) > 0) {\n", + " cat(sprintf(\" FIML: covariance=%.4f, p=%.4g, 95%% CI=[%.4f, %.4f]\\n\",\n", + " r_m1m2_fiml$est, r_m1m2_fiml$p_value, r_m1m2_fiml$ci_lower, r_m1m2_fiml$ci_upper))\n", + "}\n", + "# Report from Bootstrap\n", + "r_m1m2_boot <- res[res$method == \"Bootstrap\" & res$label == \"r_m1m2\", ]\n", + "if (nrow(r_m1m2_boot) > 0) {\n", + " cat(sprintf(\" Bootstrap: mean=%.4f, 95%% CI=[%.4f, %.4f], p=%.4g\\n\",\n", + " r_m1m2_boot$est, r_m1m2_boot$ci_lower, r_m1m2_boot$ci_upper, r_m1m2_boot$p_value))\n", + "}\n", + "# Report from Bayesian\n", + "r_m1m2_bayes <- res[res$method == \"Bayesian\" & res$label == \"r_m1m2\", ]\n", + "if (nrow(r_m1m2_bayes) > 0) {\n", + " cat(sprintf(\" Bayesian: post_mean=%.4f, 95%% CrI=[%.4f, %.4f]\\n\",\n", + " r_m1m2_bayes$est, r_m1m2_bayes$ci_lower, r_m1m2_bayes$ci_upper))\n", + "}\n", + "# Also read standardized value\n", + "med_corr_file <- file.path(FIML_DIR, \"fiml_mediator_correlation.csv\")\n", + "if (file.exists(med_corr_file)) {\n", + " mc <- read.csv(med_corr_file)\n", + " if (!is.na(mc$estimate_std[1])) {\n", + " cat(sprintf(\" Standardized residual correlation: r=%.4f\\n\", mc$estimate_std[1]))\n", + " }\n", + "}\n", + "cat(\" Interpretation: A significant positive residual covariance confirms shared\\n\")\n", + "cat(\" regulatory variance between DLPFC and AC APOC1 expression, beyond SNP and\\n\")\n", + "cat(\" covariate effects. This justifies the parallel mediation model with freely\\n\")\n", + "cat(\" estimated M1~~M2. The specific indirect effects (ind1, ind2) thus reflect\\n\")\n", + "cat(\" unique tissue-specific mediation pathways.\\n\")\n", + "\n", + "cat(\"\\n8. MNAR ROBUSTNESS\\n\")\n", + "tip <- read.csv(file.path(MNAR_DIR, \"mnar_tipping_summary.csv\"), stringsAsFactors=FALSE)\n", + "for (i in 1:nrow(tip)) {\n", + " cat(sprintf(\" %s: tipping distance = %.2f SD units\\n\", tip$effect[i], tip$tipping_dist[i]))\n", + "}\n", + "\n", + "if (!is.null(fit_bayes) && nrow(bayes_results) > 0) {\n", + " cat(\"\\n9. BAYESIAN POSTERIOR PROBABILITIES\\n\")\n", + " for (lab in c(\"ind1\", \"ind2\", \"total_indirect\", \"diff_1_2\")) {\n", + " br <- bayes_results[bayes_results$label == lab, ]\n", + " if (nrow(br) > 0 && !is.na(br$pp_positive)) {\n", + " cat(sprintf(\" %s: P(>0)=%.3f, post_mean=%.4f, 95%% CrI=[%.4f, %.4f]\\n\",\n", + " lab, br$pp_positive, br$post_mean, br$ci_lower, br$ci_upper))\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Output File Inventory" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:33:03.889865Z", + "iopub.status.busy": "2026-03-31T16:33:03.887645Z", + "iopub.status.idle": "2026-03-31T16:33:03.932694Z", + "shell.execute_reply": "2026-03-31T16:33:03.930028Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Output files generated:\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "main_SEM_FIML/\n", + " fiml_all_params.csv\n", + " fiml_all_paths.csv\n", + " fiml_fit_measures.csv\n", + " fiml_forest_plot.pdf\n", + " fiml_forest_plot.png\n", + " fiml_mediator_correlation_plot.pdf\n", + " fiml_mediator_correlation_plot.png\n", + " fiml_mediator_correlation.csv\n", + "\n", + "bootstrap/\n", + " bootstrap_distribution_ind1.pdf\n", + " bootstrap_distribution_ind1.png\n", + " bootstrap_distribution_ind2.pdf\n", + " bootstrap_distribution_ind2.png\n", + " bootstrap_distribution_total_indirect.pdf\n", + " bootstrap_distribution_total_indirect.png\n", + " bootstrap_forest_plot.pdf\n", + " bootstrap_forest_plot.png\n", + " bootstrap_results.csv\n", + "\n", + "MNAR_sensitivity/\n", + " mnar_1d_slices.pdf\n", + " mnar_1d_slices.png\n", + " mnar_contour_indirect.pdf\n", + " mnar_contour_indirect.png\n", + " mnar_contour_pvalue.pdf\n", + " mnar_contour_pvalue.png\n", + " mnar_grid_results.csv\n", + " mnar_tipping_summary.csv\n", + "\n", + "bayesian_blavaan/\n", + " bayesian_forest_plot.pdf\n", + " bayesian_forest_plot.png\n", + " bayesian_posteriors.pdf\n", + " bayesian_posteriors.png\n", + " bayesian_results.csv\n", + " bayesian_trace_a.pdf\n", + " bayesian_trace_a.png\n", + "\n", + "summary/\n", + " all_methods_summary.csv\n", + " summary_combined_panel.pdf\n", + " summary_combined_panel.png\n", + " summary_display_table.csv\n", + " summary_faceted_by_path.pdf\n", + " summary_faceted_by_path.png\n", + " summary_forest_all_methods.pdf\n", + " summary_forest_all_methods.png\n", + " summary_indirect_effect.pdf\n", + " summary_indirect_effect.png\n", + " summary_mediator_correlation.pdf\n", + " summary_mediator_correlation.png\n", + "\n" + ] + } + ], + "source": [ + "cat(\"Output files generated:\\n\\n\")\n", + "subdirs <- c(\"main_SEM_FIML\", \"bootstrap\", \"MNAR_sensitivity\", \"bayesian_blavaan\", \"summary\")\n", + "for (sd in subdirs) {\n", + " full_path <- file.path(OUT_DIR, sd)\n", + " files <- list.files(full_path, recursive=FALSE)\n", + " cat(paste0(sd, \"/\\n\"))\n", + " for (f in files) cat(paste0(\" \", f, \"\\n\"))\n", + " cat(\"\\n\")\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## session_notes.md" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:33:03.939116Z", + "iopub.status.busy": "2026-03-31T16:33:03.936922Z", + "iopub.status.idle": "2026-03-31T16:33:03.967030Z", + "shell.execute_reply": "2026-03-31T16:33:03.963536Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "session_notes.md saved.\n" + ] + } + ], + "source": [ + "notes <- paste0(\n", + " \"# Session Notes: Set 26 Parallel Mediation Analysis\\n\\n\",\n", + " \"## Date\\n\", Sys.Date(), \"\\n\\n\",\n", + " \"## Analysis Summary\\n\",\n", + " \"- **Design**: Parallel multiple mediator model (Design 2)\\n\",\n", + " \"- **Exposure**: chr19_44918487_G_T\\n\",\n", + " \"- **Mediator 1**: APOC1_DLPFC_exp (APOC1 expression in DLPFC)\\n\",\n", + " \"- **Mediator 2**: APOC1_AC_exp (APOC1 expression in anterior cingulate)\\n\",\n", + " \"- **Outcome**: age_first_ad_dx_num\\n\",\n", + " \"- **Direction**: Unidirectional (SNP -> M1/M2 -> Y)\\n\",\n", + " \"- **Total sample**: N = \", nrow(dat), \" (all with SNP observed)\\n\",\n", + " \"- **Complete cases (all 4)**: N = \", sum(has_snp & has_m1 & has_m2 & has_y), \"\\n\\n\",\n", + " \"## Methods Run\\n\",\n", + " \"1. FIML SEM (lavaan, missing='fiml', fixed.x=FALSE)\\n\",\n", + " \"2. Bootstrap (1000 replicates, FIML inside each)\\n\",\n", + " \"3. MNAR sensitivity (15x15 delta grid)\\n\",\n", + " \"4. Bayesian SEM (blavaan/Stan, 2 chains x 2000 samples)\\n\\n\",\n", + " \"## Covariate Strategy\\n\",\n", + " \"Strategy A: Covariates-in-model\\n\",\n", + " \"- Mediator equations: msex_u + age_death_u + pmi_u + ROS_study_u\\n\",\n", + " \"- Outcome equation: educ + apoe4_dose + msex_u\\n\\n\",\n", + " \"## Key Findings\\n\",\n", + " \"See Key Findings section in notebook for data-driven results.\\n\",\n", + " \"- Mediator residual correlation (M1~~M2) is tracked and reported across all 4 methods.\\n\",\n", + " \"- The r_m1m2 parameter captures shared variance between DLPFC and AC expression.\\n\\n\",\n", + " \"## Issues\\n\",\n", + " \"- None critical.\\n\\n\",\n", + " \"## Next Steps\\n\",\n", + " \"- Consider adding tissue-specific expression PCs as additional covariates\\n\",\n", + " \"- Test reverse direction if causal ordering is uncertain\\n\",\n", + " \"- Explore serial mediation if there is evidence of DLPFC->AC or AC->DLPFC causal chain\\n\"\n", + ")\n", + "\n", + "writeLines(notes, file.path(OUT_DIR, \"session_notes.md\"))\n", + "cat(\"session_notes.md saved.\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleanup and Session Info" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:33:03.973339Z", + "iopub.status.busy": "2026-03-31T16:33:03.971119Z", + "iopub.status.idle": "2026-03-31T16:33:04.406873Z", + "shell.execute_reply": "2026-03-31T16:33:04.404695Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== SESSION INFO ===\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in system(\"timedatectl\", intern = TRUE):\n", + "“running command 'timedatectl' had status 1”\n" + ] + }, + { + "data": { + "text/plain": [ + "R version 4.4.3 (2025-02-28)\n", + "Platform: x86_64-conda-linux-gnu\n", + "Running under: Ubuntu 24.04.3 LTS\n", + "\n", + "Matrix products: default\n", + "BLAS/LAPACK: /mnt/lustre/home/yl4437/.pixi/envs/python/lib/libopenblasp-r0.3.30.so; LAPACK version 3.12.0\n", + "\n", + "locale:\n", + " [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 \n", + " [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 \n", + " [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C \n", + "[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C \n", + "\n", + "time zone: Etc/UTC\n", + "tzcode source: system (glibc)\n", + "\n", + "attached base packages:\n", + "[1] stats graphics grDevices utils datasets methods base \n", + "\n", + "other attached packages:\n", + "[1] tidyr_1.3.2 dplyr_1.2.0 gridExtra_2.3 ggplot2_4.0.2 blavaan_0.5-10\n", + "[6] Rcpp_1.1.1 lavaan_0.6-21 \n", + "\n", + "loaded via a namespace (and not attached):\n", + " [1] gtable_0.3.6 QuickJSR_1.9.0 inline_0.3.21 \n", + " [4] CompQuadForm_1.4.4 lattice_0.22-9 quadprog_1.5-8 \n", + " [7] vctrs_0.7.1 tools_4.4.3 generics_0.1.4 \n", + "[10] stats4_4.4.3 parallel_4.4.3 sandwich_3.1-1 \n", + "[13] tibble_3.3.1 pkgconfig_2.0.3 Matrix_1.7-4 \n", + "[16] RColorBrewer_1.1-3 S7_0.2.1 RcppParallel_5.1.11-2\n", + "[19] uuid_1.2-2 lifecycle_1.0.5 tmvnsim_1.0-2 \n", + "[22] compiler_4.4.3 farver_2.1.2 textshaping_1.0.4 \n", + "[25] mnormt_2.1.2 repr_1.1.7 codetools_0.2-20 \n", + "[28] htmltools_0.5.9 bayesplot_1.15.0 pillar_1.11.1 \n", + "[31] crayon_1.5.3 MASS_7.3-65 StanHeaders_2.32.10 \n", + "[34] parallelly_1.46.1 rstan_2.32.7 tidyselect_1.2.1 \n", + "[37] digest_0.6.39 mvtnorm_1.3-3 future_1.69.0 \n", + "[40] purrr_1.2.1 nonnest2_0.5-8 listenv_0.10.0 \n", + "[43] labeling_0.4.3 fastmap_1.2.0 grid_4.4.3 \n", + "[46] cli_3.6.5 magrittr_2.0.4 loo_2.9.0 \n", + "[49] base64enc_0.1-6 pkgbuild_1.4.8 IRdisplay_1.1 \n", + "[52] future.apply_1.20.1 pbivnorm_0.6.0 withr_3.0.2 \n", + "[55] scales_1.4.0 IRkernel_1.3.2 matrixStats_1.5.0 \n", + "[58] globals_0.19.0 igraph_2.2.2 pbdZMQ_0.3-14 \n", + "[61] ragg_1.5.0 zoo_1.8-15 coda_0.19-4.1 \n", + "[64] evaluate_1.0.5 viridisLite_0.4.3 rstantools_2.6.0 \n", + "[67] rlang_1.1.7 isoband_0.3.0 glue_1.8.0 \n", + "[70] jsonlite_2.0.0 R6_2.6.1 systemfonts_1.3.1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Clean up Stan temp files\n", + "temp_patterns <- c(\"lavExport*\", \"*.stan\", \"tmp_*\", \"*.bak\")\n", + "for (pat in temp_patterns) {\n", + " files <- Sys.glob(file.path(OUT_DIR, pat))\n", + " if (length(files) > 0) file.remove(files)\n", + "}\n", + "# Also clean in working directory\n", + "for (pat in temp_patterns) {\n", + " files <- Sys.glob(pat)\n", + " if (length(files) > 0) file.remove(files)\n", + "}\n", + "\n", + "cat(\"\\n=== SESSION INFO ===\\n\")\n", + "sessionInfo()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_caa_4gp_SEM_mediation.ipynb b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_caa_4gp_SEM_mediation.ipynb new file mode 100644 index 0000000..13b2132 --- /dev/null +++ b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_caa_4gp_SEM_mediation.ipynb @@ -0,0 +1,3296 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parallel Mediation Analysis: APOE Region SNP -> Gene Expression -> CAA Pathology\n", + "\n", + "## Aim\n", + "\n", + "This notebook tests whether the effect of the APOE-region SNP **chr19:44954310 T>C** on\n", + "**cerebral amyloid angiopathy (CAA, 4-group ordinal)** is mediated through gene expression\n", + "of four genes in a **parallel (joint) mediation model**:\n", + "\n", + "- **APOC4/APOC2** (anterior cingulate cortex)\n", + "- **APOC2** (anterior cingulate cortex)\n", + "- **APOC1** (microglia, DeJager)\n", + "- **APOE** (microglia, Mega)\n", + "\n", + "All four mediators enter the model simultaneously, so we estimate the **specific (unique)**\n", + "indirect effect through each mediator while controlling for the others.\n", + "\n", + "### Design\n", + "\n", + "- **Design 2 (Parallel Mediation)**: SNP -> {M1, M2, M3, M4} -> Y\n", + "- **Direction**: Unidirectional only (SNP -> M -> Y)\n", + "- **Covariate strategy**: Covariates-in-model (Strategy A)\n", + "\n", + "### Methods\n", + "\n", + "| Method | Purpose | Missing Data |\n", + "|--------|---------|-------------|\n", + "| FIML SEM (lavaan) | Primary analysis | FIML (all N=1153) |\n", + "| Bootstrap (1000 resamples) | Nonparametric CIs | FIML inside each replicate |\n", + "| MNAR Sensitivity | Robustness to informative missingness | Delta-shift imputation |\n", + "| Bayesian SEM (blavaan/Stan) | Posterior inference | Stan data augmentation |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n\n## Conclusion\n\n### Model Fit\nThe FIML SEM with 4 parallel mediators and residual covariances fits well: **CFI = 0.998**, **TLI = 0.992**, **RMSEA = 0.024**, **SRMR = 0.020**.\n\n### Main Finding: No Mediation\nThe SNP **chr19:44954310 T>C** has a **significant direct effect** on CAA pathology (c' = 0.140, p < 0.001), but this effect is **not mediated** through any of the four gene expression measures.\n\n| Effect | FIML | Bootstrap | Bayesian |\n|--------|------|-----------|----------|\n| **Direct (c')** | 0.140 [0.062, 0.219]* | 0.143 [0.058, 0.227]* | 0.139 [0.059, 0.216]* |\n| **Total indirect** | -0.005 [-0.033, 0.023] | -0.006 [-0.039, 0.023] | -0.006 [-0.035, 0.023] |\n| ind1: APOC4/APOC2 (AC) | -0.239 [-0.765, 0.287] | -0.261 [-0.847, 0.211] | -0.337 [-0.833, 0.057] |\n| ind2: APOC2 (AC) | 0.215 [-0.303, 0.733] | 0.236 [-0.233, 0.812] | 0.312 [-0.086, 0.797] |\n| ind3: APOC1 (Mic) | 0.012 [-0.015, 0.039] | 0.012 [-0.016, 0.042] | 0.011 [-0.013, 0.043] |\n| ind4: APOE (Mic) | 0.007 [-0.014, 0.028] | 0.007 [-0.014, 0.031] | 0.008 [-0.015, 0.030] |\n\n\\* significant at alpha = 0.05\n\n### Notes\n- **a-paths** (SNP -> mediators): All four are highly significant (p < 0.001), confirming the SNP strongly predicts gene expression.\n- **b-paths** (mediators -> CAA): None are significant in the 4-mediator model. ind1/ind2 show inflated SEs (~0.95) due to collinearity between APOC4/APOC2 and APOC2 (both AC cortex).\n- **MNAR sensitivity**: All indirect effects remain non-significant across the full delta grid (tipping distance = Inf). The null mediation finding is robust to informative missingness.\n\n### Sensitivity Analysis: 3-Mediator Model (excluding APOC4/APOC2)\nRemoving the collinear mediator resolves the inflated SEs (b1 SE drops from 0.945 to 0.039). APOC2 b-path becomes borderline significant (p = 0.049), but the overall conclusion is unchanged:\n\n| Effect | 4-Mediator | 3-Mediator |\n|--------|-----------|-----------|\n| Direct (c') | 0.140 (p < 0.001) | 0.136 (p < 0.001) |\n| Total indirect | -0.005 (p = 0.72) | -0.001 (p = 0.94) |\n| Total | 0.135 (p < 0.001) | 0.135 (p < 0.001) |\n\n### Interpretation\nThe SNP chr19:44954310 T>C affects CAA pathology through a **direct pathway** not captured by APOC4/APOC2, APOC2, APOC1, or APOE gene expression in this parallel model. All three methods (FIML, Bootstrap, Bayesian) and the sensitivity analysis converge on the same conclusion.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Directed Acyclic Graph (DAG)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:35:44.812661Z", + "iopub.status.busy": "2026-03-31T20:35:44.763082Z", + "iopub.status.idle": "2026-03-31T20:35:48.729656Z", + "shell.execute_reply": "2026-03-31T20:35:48.727497Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201c\u001b[1m\u001b[22mThe `label.size` argument of `geom_label()` is deprecated as of ggplot2 3.5.0.\n", + "\u001b[36m\u2139\u001b[39m Please use the `linewidth` argument instead.\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in annotate(\"label\", x = 3, y = direct_y - 0.35, label = \"c\u2032 (direct)\", :\n", + "\u201c\u001b[1m\u001b[22mIgnoring unknown parameters: `label.size`\u201d\n" + ] + }, + { + "data": { + "image/png": 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x8+3J6VBujHfzprAnRr3759GzduFBFPT08777O/efPmmjVrbt++Xa5cuaeeekr9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Z2r9i9t9NbelqfXJ6T0NHSi0SzT0amVok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\nyWQEQchksujo6IKCAhMTk+oiEGoUhB8Ms7Kyej8BKyuruLg4gUBQXFy8e/f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i6euc0quRGe61nXSPEEJIoSI2OmbuTyUh90Eup+iwnOct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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# DAG: Parallel Mediation Model\n", + "# ============================================================\n", + "suppressPackageStartupMessages({\n", + " library(ggplot2)\n", + " library(grid)\n", + "})\n", + "\n", + "# --- Node positions ---\n", + "# SNP left (0,3), 4 mediators center, Outcome right (6,3)\n", + "nodes <- data.frame(\n", + " label = c(\"chr19:44954310\\nT > C\",\n", + " \"APOC4/APOC2\\n(AC cortex)\",\n", + " \"APOC2\\n(AC cortex)\",\n", + " \"APOC1\\n(Mic, DeJager)\",\n", + " \"APOE\\n(Mic, Mega)\",\n", + " \"CAA\\n(4-group)\"),\n", + " short = c(\"X\", \"M1\", \"M2\", \"M3\", \"M4\", \"Y\"),\n", + " x = c(0, 3, 3, 3, 3, 6),\n", + " y = c(3, 5.8, 4.4, 1.6, 0.2, 3),\n", + " fill = c(\"#D4E6F1\", \"#D5F5E3\", \"#D5F5E3\", \"#D5F5E3\", \"#D5F5E3\", \"#FADBD8\"),\n", + " stringsAsFactors = FALSE\n", + ")\n", + "\n", + "# Box dimensions for arrow offsets\n", + "bw <- 0.78\n", + "bh <- 0.48\n", + "\n", + "# --- a-paths: X -> M1..M4 ---\n", + "a_paths <- data.frame(\n", + " x = rep(0 + bw, 4),\n", + " y = rep(3, 4),\n", + " xend = rep(3 - bw, 4),\n", + " yend = c(5.8, 4.4, 1.6, 0.2),\n", + " lab = c(\"a1\", \"a2\", \"a3\", \"a4\"),\n", + " # Offset labels: a1,a2 above midpoint; a3,a4 below midpoint\n", + " lab_nudge_y = c(0.28, 0.20, -0.20, -0.28),\n", + " lab_nudge_x = c(-0.15, -0.15, -0.15, -0.15),\n", + " stringsAsFactors = FALSE\n", + ")\n", + "\n", + "# --- b-paths: M1..M4 -> Y ---\n", + "b_paths <- data.frame(\n", + " x = rep(3 + bw, 4),\n", + " y = c(5.8, 4.4, 1.6, 0.2),\n", + " xend = rep(6 - bw, 4),\n", + " yend = rep(3, 4),\n", + " lab = c(\"b1\", \"b2\", \"b3\", \"b4\"),\n", + " lab_nudge_y = c(0.28, 0.20, -0.20, -0.28),\n", + " lab_nudge_x = c(0.15, 0.15, 0.15, 0.15),\n", + " stringsAsFactors = FALSE\n", + ")\n", + "\n", + "# --- Direct path: X -> Y, routed BELOW all mediators ---\n", + "# Three-segment path: down from X, across bottom, up to Y\n", + "direct_y <- -1.0 # well below APOE at y=0.2\n", + "\n", + "direct_segs <- data.frame(\n", + " x = c(0, 0, 6),\n", + " y = c(3 - bh, direct_y, direct_y),\n", + " xend = c(0, 6, 6),\n", + " yend = c(direct_y, direct_y, 3 - bh),\n", + " stringsAsFactors = FALSE\n", + ")\n", + "\n", + "# --- Build plot ---\n", + "p_dag <- ggplot() +\n", + "\n", + " # a-path arrows (blue)\n", + " geom_segment(data = a_paths,\n", + " aes(x = x, y = y, xend = xend, yend = yend),\n", + " color = \"#2471A3\", linewidth = 0.7,\n", + " arrow = arrow(length = unit(0.12, \"inches\"), type = \"closed\")) +\n", + " # a-path labels\n", + " geom_label(data = a_paths,\n", + " aes(x = (x + xend)/2 + lab_nudge_x,\n", + " y = (y + yend)/2 + lab_nudge_y,\n", + " label = lab),\n", + " size = 3.3, fill = \"white\", label.size = 0, fontface = \"bold.italic\",\n", + " color = \"#2471A3\") +\n", + "\n", + " # b-path arrows (red)\n", + " geom_segment(data = b_paths,\n", + " aes(x = x, y = y, xend = xend, yend = yend),\n", + " color = \"#C0392B\", linewidth = 0.7,\n", + " arrow = arrow(length = unit(0.12, \"inches\"), type = \"closed\")) +\n", + " # b-path labels\n", + " geom_label(data = b_paths,\n", + " aes(x = (x + xend)/2 + lab_nudge_x,\n", + " y = (y + yend)/2 + lab_nudge_y,\n", + " label = lab),\n", + " size = 3.3, fill = \"white\", label.size = 0, fontface = \"bold.italic\",\n", + " color = \"#C0392B\") +\n", + "\n", + " # Direct effect: 3 segments (down, across, up), only last has arrow\n", + " geom_segment(aes(x = direct_segs$x[1], y = direct_segs$y[1],\n", + " xend = direct_segs$xend[1], yend = direct_segs$yend[1]),\n", + " color = \"#7D3C98\", linewidth = 0.9, linetype = \"dashed\") +\n", + " geom_segment(aes(x = direct_segs$x[2], y = direct_segs$y[2],\n", + " xend = direct_segs$xend[2], yend = direct_segs$yend[2]),\n", + " color = \"#7D3C98\", linewidth = 0.9, linetype = \"dashed\") +\n", + " geom_segment(aes(x = direct_segs$x[3], y = direct_segs$y[3],\n", + " xend = direct_segs$xend[3], yend = direct_segs$yend[3]),\n", + " color = \"#7D3C98\", linewidth = 0.9, linetype = \"dashed\",\n", + " arrow = arrow(length = unit(0.12, \"inches\"), type = \"closed\")) +\n", + " annotate(\"label\", x = 3, y = direct_y - 0.35, label = \"c\\u2032 (direct)\",\n", + " size = 3.3, fill = \"white\", label.size = 0, fontface = \"bold.italic\",\n", + " color = \"#7D3C98\") +\n", + "\n", + " # Residual covariances: subtle brace on the right of mediators\n", + " annotate(\"segment\", x = 3.6, y = 5.55, xend = 3.6, yend = 0.45,\n", + " linetype = \"dotted\", color = \"grey60\", linewidth = 0.4) +\n", + " annotate(\"text\", x = 4.15, y = 3.0, label = \"residual\\ncovariances\",\n", + " size = 2.4, color = \"grey50\", fontface = \"italic\", lineheight = 0.9) +\n", + "\n", + " # Node boxes (drawn last so they sit on top)\n", + " geom_label(data = nodes,\n", + " aes(x = x, y = y, label = label, fill = fill),\n", + " size = 3.5, fontface = \"bold\",\n", + " label.padding = unit(0.45, \"lines\"),\n", + " label.r = unit(0.2, \"lines\"),\n", + " color = \"grey20\", lineheight = 0.85) +\n", + " scale_fill_identity() +\n", + "\n", + " # Legend\n", + " annotate(\"text\", x = 3, y = -2.0,\n", + " label = \"Blue = a-paths (SNP \\u2192 Mediator) Red = b-paths (Mediator \\u2192 Outcome) Purple dashed = direct effect (c\\u2032)\",\n", + " size = 2.8, color = \"grey40\") +\n", + "\n", + " # Title\n", + " labs(title = \"Parallel Mediation: SNP \\u2192 Gene Expression \\u2192 CAA Pathology\") +\n", + " coord_fixed(ratio = 0.7, xlim = c(-1.5, 7.5), ylim = c(-2.5, 7)) +\n", + " theme_void(base_size = 12) +\n", + " theme(plot.title = element_text(hjust = 0.5, size = 14, face = \"bold\",\n", + " margin = margin(b = 8)),\n", + " plot.margin = margin(10, 10, 10, 10))\n", + "\n", + "options(repr.plot.width = 10, repr.plot.height = 8)\n", + "print(p_dag)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Input Specification\n", + "\n", + "- **Data file**: `APOE_ind_set_27_caa_4gp_mediation_all_input.txt`\n", + "- **Exposure**: `chr19_44954310_T_C` (APOE-region SNP)\n", + "- **Mediators** (all 4 in parallel):\n", + " - M1: `APOC4_APOC2_AC_exp` (covariates: msex_u, age_death_u, pmi_u, ROS_study_u)\n", + " - M2: `APOC2_AC_exp` (covariates: msex_u, age_death_u, pmi_u, ROS_study_u)\n", + " - M3: `APOC1_DeJager_Mic_exp` (covariates: msex_u, age_death_u, pmi_u)\n", + " - M4: `APOE_Mega_Mic_exp` (covariates: msex_u, age_death_u, pmi_u)\n", + "- **Outcome**: `caa_4gp` (covariates: educ, apoe4_dose, apoe2_dose, msex_u, age_death_u)\n", + "- **Direction**: Unidirectional (SNP -> M -> Y only)\n", + "\n", + "---\n", + "\n", + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:35:48.746587Z", + "iopub.status.busy": "2026-03-31T20:35:48.743852Z", + "iopub.status.idle": "2026-03-31T20:36:02.399114Z", + "shell.execute_reply": "2026-03-31T20:36:02.396833Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setup complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exposure: chr19_44954310_T_C \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mediators: APOC4_APOC2_AC_exp, APOC2_AC_exp, APOC1_DeJager_Mic_exp, APOE_Mega_Mic_exp \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Outcome: caa_4gp \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All covariates: msex_u, age_death_u, pmi_u, ROS_study_u, educ, apoe4_dose, apoe2_dose \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# 1. Setup: Libraries, Paths, Parameters\n", + "# ============================================================\n", + "suppressPackageStartupMessages({\n", + " library(lavaan)\n", + " library(blavaan)\n", + " library(ggplot2)\n", + " library(gridExtra)\n", + " library(reshape2)\n", + "})\n", + "\n", + "# --- Paths ---\n", + "DATA_FILE <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_4gp/APOE_ind_set_27_caa_4gp_mediation_all_input.txt\"\n", + "RESULT_DIR <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_4gp\"\n", + "\n", + "DIR_FIML <- file.path(RESULT_DIR, \"main_SEM_FIML\")\n", + "DIR_BOOT <- file.path(RESULT_DIR, \"bootstrap\")\n", + "DIR_MNAR <- file.path(RESULT_DIR, \"MNAR_sensitivity\")\n", + "DIR_BAYES <- file.path(RESULT_DIR, \"bayesian_blavaan\")\n", + "DIR_SUMM <- file.path(RESULT_DIR, \"summary\")\n", + "\n", + "for (d in c(DIR_FIML, DIR_BOOT, DIR_MNAR, DIR_BAYES, DIR_SUMM)) {\n", + " dir.create(d, showWarnings=FALSE, recursive=TRUE)\n", + "}\n", + "\n", + "EXPOSURE <- \"chr19_44954310_T_C\"\n", + "MEDIATORS <- c(\"APOC4_APOC2_AC_exp\", \"APOC2_AC_exp\", \"APOC1_DeJager_Mic_exp\", \"APOE_Mega_Mic_exp\")\n", + "MED_LABELS <- c(\"APOC4_APOC2_AC\", \"APOC2_AC\", \"APOC1_DeJager_Mic\", \"APOE_Mega_Mic\")\n", + "OUTCOME <- \"caa_4gp\"\n", + "N_BOOT <- 1000\n", + "\n", + "MED_COVS <- list(\n", + " APOC4_APOC2_AC_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\"),\n", + " APOC2_AC_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\"),\n", + " APOC1_DeJager_Mic_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\"),\n", + " APOE_Mega_Mic_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\")\n", + ")\n", + "OUT_COVS <- c(\"educ\", \"apoe4_dose\", \"apoe2_dose\", \"msex_u\", \"age_death_u\")\n", + "ALL_COVS <- unique(c(unlist(MED_COVS), OUT_COVS))\n", + "\n", + "cat(\"Setup complete.\\n\")\n", + "cat(\"Exposure:\", EXPOSURE, \"\\n\")\n", + "cat(\"Mediators:\", paste(MEDIATORS, collapse=\", \"), \"\\n\")\n", + "cat(\"Outcome:\", OUTCOME, \"\\n\")\n", + "cat(\"All covariates:\", paste(ALL_COVS, collapse=\", \"), \"\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Data Loading and Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:36:02.411804Z", + "iopub.status.busy": "2026-03-31T20:36:02.408226Z", + "iopub.status.idle": "2026-03-31T20:36:02.543896Z", + "shell.execute_reply": "2026-03-31T20:36:02.541613Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Raw data dimensions: 1153 x 57 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working data dimensions: 1153 x 14 \n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 \u00d7 14
SIDchr19_44954310_T_CAPOC4_APOC2_AC_expAPOC2_AC_expAPOC1_DeJager_Mic_expAPOE_Mega_Mic_expcaa_4gpmsex_uage_death_upmi_uROS_study_ueducapoe4_doseapoe2_dose
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1MAP153874211NANA1.7786686 1.0499480NA 092.402468.833333NANANANA
2MAP266378670NANA NA 0.6535373NA 091.244357.450000NANANANA
3MAP296298490NANA NA NANANA NA NANANANANA
4MAP333326461NANA NA NANANA NA NANANANANA
5MAP347260400NANA NA 1.3685606NA 172.826834.666667NANANANA
6MAP462466040NANA0.4611292-0.5313924NA 089.448323.750000NANANANA
\n" + ], + "text/latex": [ + "A data.frame: 6 \u00d7 14\n", + "\\begin{tabular}{r|llllllllllllll}\n", + " & SID & chr19\\_44954310\\_T\\_C & APOC4\\_APOC2\\_AC\\_exp & APOC2\\_AC\\_exp & APOC1\\_DeJager\\_Mic\\_exp & APOE\\_Mega\\_Mic\\_exp & caa\\_4gp & msex\\_u & age\\_death\\_u & pmi\\_u & ROS\\_study\\_u & educ & apoe4\\_dose & apoe2\\_dose\\\\\n", + " & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & MAP15387421 & 1 & NA & NA & 1.7786686 & 1.0499480 & NA & 0 & 92.40246 & 8.833333 & NA & NA & NA & NA\\\\\n", + "\t2 & MAP26637867 & 0 & NA & NA & NA & 0.6535373 & NA & 0 & 91.24435 & 7.450000 & NA & NA & NA & NA\\\\\n", + "\t3 & MAP29629849 & 0 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n", + "\t4 & MAP33332646 & 1 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n", + "\t5 & MAP34726040 & 0 & NA & NA & NA & 1.3685606 & NA & 1 & 72.82683 & 4.666667 & NA & NA & NA & NA\\\\\n", + "\t6 & MAP46246604 & 0 & NA & NA & 0.4611292 & -0.5313924 & NA & 0 & 89.44832 & 3.750000 & NA & NA & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 \u00d7 14\n", + "\n", + "| | SID <chr> | chr19_44954310_T_C <dbl> | APOC4_APOC2_AC_exp <dbl> | APOC2_AC_exp <dbl> | APOC1_DeJager_Mic_exp <dbl> | APOE_Mega_Mic_exp <dbl> | caa_4gp <dbl> | msex_u <dbl> | age_death_u <dbl> | pmi_u <dbl> | ROS_study_u <dbl> | educ <dbl> | apoe4_dose <dbl> | apoe2_dose <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | MAP15387421 | 1 | NA | NA | 1.7786686 | 1.0499480 | NA | 0 | 92.40246 | 8.833333 | NA | NA | NA | NA |\n", + "| 2 | MAP26637867 | 0 | NA | NA | NA | 0.6535373 | NA | 0 | 91.24435 | 7.450000 | NA | NA | NA | NA |\n", + "| 3 | MAP29629849 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n", + "| 4 | MAP33332646 | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n", + "| 5 | MAP34726040 | 0 | NA | NA | NA | 1.3685606 | NA | 1 | 72.82683 | 4.666667 | NA | NA | NA | NA |\n", + "| 6 | MAP46246604 | 0 | NA | NA | 0.4611292 | -0.5313924 | NA | 0 | 89.44832 | 3.750000 | NA | NA | NA | NA |\n", + "\n" + ], + "text/plain": [ + " SID chr19_44954310_T_C APOC4_APOC2_AC_exp APOC2_AC_exp\n", + "1 MAP15387421 1 NA NA \n", + "2 MAP26637867 0 NA NA \n", + "3 MAP29629849 0 NA NA \n", + "4 MAP33332646 1 NA NA \n", + "5 MAP34726040 0 NA NA \n", + "6 MAP46246604 0 NA NA \n", + " APOC1_DeJager_Mic_exp APOE_Mega_Mic_exp caa_4gp msex_u age_death_u pmi_u \n", + "1 1.7786686 1.0499480 NA 0 92.40246 8.833333\n", + "2 NA 0.6535373 NA 0 91.24435 7.450000\n", + "3 NA NA NA NA NA NA\n", + "4 NA NA NA NA NA NA\n", + "5 NA 1.3685606 NA 1 72.82683 4.666667\n", + "6 0.4611292 -0.5313924 NA 0 89.44832 3.750000\n", + " ROS_study_u educ apoe4_dose apoe2_dose\n", + "1 NA NA NA NA \n", + "2 NA NA NA NA \n", + "3 NA NA NA NA \n", + "4 NA NA NA NA \n", + "5 NA NA NA NA \n", + "6 NA NA NA NA " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# 2. Load Data\n", + "# ============================================================\n", + "dat_raw <- read.delim(DATA_FILE, stringsAsFactors=FALSE)\n", + "cat(\"Raw data dimensions:\", nrow(dat_raw), \"x\", ncol(dat_raw), \"\\n\")\n", + "\n", + "keep_cols <- c(\"SID\", EXPOSURE, MEDIATORS, OUTCOME, ALL_COVS)\n", + "missing_cols <- setdiff(keep_cols, colnames(dat_raw))\n", + "if (length(missing_cols) > 0) {\n", + " cat(\"WARNING: Missing columns:\", paste(missing_cols, collapse=\", \"), \"\\n\")\n", + "}\n", + "dat <- dat_raw[, intersect(keep_cols, colnames(dat_raw))]\n", + "\n", + "for (col in setdiff(colnames(dat), \"SID\")) {\n", + " dat[[col]] <- as.numeric(dat[[col]])\n", + "}\n", + "\n", + "cat(\"Working data dimensions:\", nrow(dat), \"x\", ncol(dat), \"\\n\")\n", + "head(dat)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:36:02.550754Z", + "iopub.status.busy": "2026-03-31T20:36:02.548967Z", + "iopub.status.idle": "2026-03-31T20:36:02.624400Z", + "shell.execute_reply": "2026-03-31T20:36:02.620411Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Variable N_observed N_missing Pct_missing\n", + "1 chr19_44954310_T_C 1153 0 0.0\n", + "2 APOC4_APOC2_AC_exp 593 560 48.6\n", + "3 APOC2_AC_exp 593 560 48.6\n", + "4 APOC1_DeJager_Mic_exp 419 734 63.7\n", + "5 APOE_Mega_Mic_exp 733 420 36.4\n", + "6 caa_4gp 1050 103 8.9\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Total subjects: 1153 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Subjects with SNP observed: 1153 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Pairwise overlap (N with both variables observed):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chr19_44954310_T_C APOC4_APOC2_AC_exp APOC2_AC_exp\n", + "chr19_44954310_T_C 1153 593 593\n", + "APOC4_APOC2_AC_exp 593 593 593\n", + "APOC2_AC_exp 593 593 593\n", + "APOC1_DeJager_Mic_exp 419 311 311\n", + "APOE_Mega_Mic_exp 733 514 514\n", + "caa_4gp 1050 564 564\n", + " APOC1_DeJager_Mic_exp APOE_Mega_Mic_exp caa_4gp\n", + "chr19_44954310_T_C 419 733 1050\n", + "APOC4_APOC2_AC_exp 311 514 564\n", + "APOC2_AC_exp 311 514 564\n", + "APOC1_DeJager_Mic_exp 419 417 387\n", + "APOE_Mega_Mic_exp 417 733 677\n", + "caa_4gp 387 677 1050\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# 3. Missingness Summary\n", + "# ============================================================\n", + "key_vars <- c(EXPOSURE, MEDIATORS, OUTCOME)\n", + "miss_df <- data.frame(\n", + " Variable = key_vars,\n", + " N_observed = sapply(key_vars, function(v) sum(!is.na(dat[[v]]))),\n", + " N_missing = sapply(key_vars, function(v) sum(is.na(dat[[v]]))),\n", + " Pct_missing = sapply(key_vars, function(v) round(100*mean(is.na(dat[[v]])), 1))\n", + ")\n", + "rownames(miss_df) <- NULL\n", + "print(miss_df)\n", + "\n", + "cat(\"\\nTotal subjects:\", nrow(dat), \"\\n\")\n", + "cat(\"Subjects with SNP observed:\", sum(!is.na(dat[[EXPOSURE]])), \"\\n\")\n", + "\n", + "cat(\"\\nPairwise overlap (N with both variables observed):\\n\")\n", + "overlap_mat <- matrix(NA, length(key_vars), length(key_vars),\n", + " dimnames=list(key_vars, key_vars))\n", + "for (i in seq_along(key_vars)) {\n", + " for (j in seq_along(key_vars)) {\n", + " overlap_mat[i,j] <- sum(!is.na(dat[[key_vars[i]]]) & !is.na(dat[[key_vars[j]]]))\n", + " }\n", + "}\n", + "print(overlap_mat)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Model Specification\n", + "\n", + "### Parallel Mediation with Covariates-in-Model (Strategy A)\n", + "\n", + "Each mediator equation includes its own covariates. The outcome equation includes its own covariates.\n", + "Residual covariances between mediators are freely estimated (lavaan default)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:36:02.632315Z", + "iopub.status.busy": "2026-03-31T20:36:02.629211Z", + "iopub.status.idle": "2026-03-31T20:36:02.709729Z", + "shell.execute_reply": "2026-03-31T20:36:02.706054Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model specification:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "APOC4_APOC2_AC_exp ~ a1 * chr19_44954310_T_C + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC2_AC_exp ~ a2 * chr19_44954310_T_C + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC1_DeJager_Mic_exp ~ a3 * chr19_44954310_T_C + msex_u + age_death_u + pmi_u\n", + "APOE_Mega_Mic_exp ~ a4 * chr19_44954310_T_C + msex_u + age_death_u + pmi_u\n", + "caa_4gp ~ b1 * APOC4_APOC2_AC_exp + b2 * APOC2_AC_exp + b3 * APOC1_DeJager_Mic_exp + b4 * APOE_Mega_Mic_exp + cp * chr19_44954310_T_C + educ + apoe4_dose + apoe2_dose + msex_u + age_death_u\n", + "APOC4_APOC2_AC_exp ~~ APOC2_AC_exp\n", + "APOC4_APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC4_APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC1_DeJager_Mic_exp ~~ APOE_Mega_Mic_exp\n", + "ind1 := a1 * b1\n", + "ind2 := a2 * b2\n", + "ind3 := a3 * b3\n", + "ind4 := a4 * b4\n", + "total_indirect := ind1 + ind2 + ind3 + ind4\n", + "total := cp + total_indirect\n", + "prop_med := total_indirect / total\n", + "diff_1_2 := ind1 - ind2\n", + "diff_1_3 := ind1 - ind3\n", + "diff_1_4 := ind1 - ind4\n", + "diff_2_3 := ind2 - ind3\n", + "diff_2_4 := ind2 - ind4\n", + "diff_3_4 := ind3 - ind4 \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# 4. Build Parallel Mediation Model String\n", + "# ============================================================\n", + "n_med <- length(MEDIATORS)\n", + "\n", + "med_eqs <- character(n_med)\n", + "for (k in 1:n_med) {\n", + " cov_str <- paste(MED_COVS[[MEDIATORS[k]]], collapse=\" + \")\n", + " med_eqs[k] <- paste0(MEDIATORS[k], \" ~ a\", k, \" * \", EXPOSURE, \" + \", cov_str)\n", + "}\n", + "\n", + "y_med_terms <- paste0(\"b\", 1:n_med, \" * \", MEDIATORS, collapse=\" + \")\n", + "out_cov_str <- paste(OUT_COVS, collapse=\" + \")\n", + "y_eq <- paste0(OUTCOME, \" ~ \", y_med_terms, \" + cp * \", EXPOSURE, \" + \", out_cov_str)\n", + "\n", + "# Residual covariances between mediators (NOT estimated by default in lavaan\n", + "# for endogenous variables -- must be specified explicitly)\n", + "med_covs <- c()\n", + "for (i in 1:(n_med-1)) {\n", + " for (j in (i+1):n_med) {\n", + " med_covs <- c(med_covs, paste0(MEDIATORS[i], \" ~~ \", MEDIATORS[j]))\n", + " }\n", + "}\n", + "\n", + "ind_defs <- paste0(\"ind\", 1:n_med, \" := a\", 1:n_med, \" * b\", 1:n_med)\n", + "total_ind <- paste0(\"total_indirect := \", paste0(\"ind\", 1:n_med, collapse=\" + \"))\n", + "total_def <- \"total := cp + total_indirect\"\n", + "prop_def <- \"prop_med := total_indirect / total\"\n", + "\n", + "contrasts <- c()\n", + "for (i in 1:(n_med-1)) {\n", + " for (j in (i+1):n_med) {\n", + " contrasts <- c(contrasts, paste0(\"diff_\", i, \"_\", j, \" := ind\", i, \" - ind\", j))\n", + " }\n", + "}\n", + "\n", + "model_str <- paste(c(med_eqs, y_eq, med_covs, ind_defs, total_ind, total_def, prop_def, contrasts),\n", + " collapse=\"\\n\")\n", + "\n", + "cat(\"Model specification:\\n\")\n", + "cat(model_str, \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Method 1: FIML SEM (lavaan)\n", + "\n", + "Full Information Maximum Likelihood handles missing data by using ALL available observations.\n", + "With `fixed.x=FALSE`, the SNP is treated as a random variable, allowing subjects with only\n", + "the SNP observed to contribute to parameter estimation." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:36:02.716337Z", + "iopub.status.busy": "2026-03-31T20:36:02.713999Z", + "iopub.status.idle": "2026-03-31T20:36:08.437056Z", + "shell.execute_reply": "2026-03-31T20:36:08.434245Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting FIML SEM...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FIML converged: TRUE \n", + "N used: 1153 \n", + "\n", + " label est se ci.lower ci.upper pvalue N method\n", + "1 a1 0.279 0.056 0.169 0.389 0.000 1153 FIML\n", + "6 a2 0.276 0.056 0.166 0.386 0.000 1153 FIML\n", + "11 a3 0.226 0.064 0.100 0.351 0.000 1153 FIML\n", + "15 a4 0.213 0.049 0.117 0.309 0.000 1153 FIML\n", + "19 b1 -0.858 0.946 -2.711 0.996 0.364 1153 FIML\n", + "20 b2 0.780 0.945 -1.072 2.632 0.409 1153 FIML\n", + "21 b3 0.054 0.058 -0.060 0.167 0.355 1153 FIML\n", + "22 b4 0.033 0.051 -0.066 0.132 0.515 1153 FIML\n", + "23 cp 0.140 0.040 0.062 0.219 0.000 1153 FIML\n", + "89 ind1 -0.239 0.268 -0.765 0.287 0.373 1153 FIML\n", + "90 ind2 0.215 0.264 -0.303 0.733 0.416 1153 FIML\n", + "91 ind3 0.012 0.014 -0.015 0.039 0.379 1153 FIML\n", + "92 ind4 0.007 0.011 -0.014 0.028 0.519 1153 FIML\n", + "93 total_indirect -0.005 0.014 -0.033 0.023 0.721 1153 FIML\n", + "94 total 0.135 0.038 0.061 0.210 0.000 1153 FIML\n", + "95 prop_med -0.038 0.107 -0.248 0.172 0.723 1153 FIML\n", + "96 diff_1_2 -0.454 0.533 -1.498 0.590 0.394 1153 FIML\n", + "97 diff_1_3 -0.251 0.268 -0.776 0.273 0.348 1153 FIML\n", + "98 diff_1_4 -0.246 0.269 -0.773 0.280 0.359 1153 FIML\n", + "99 diff_2_3 0.203 0.266 -0.318 0.724 0.445 1153 FIML\n", + "100 diff_2_4 0.208 0.264 -0.310 0.726 0.431 1153 FIML\n", + "101 diff_3_4 0.005 0.023 -0.039 0.049 0.821 1153 FIML\n", + "\n", + "FIML results saved.\n", + " measure value N\n", + "1 chisq 26.5020 1153\n", + "2 df 16.0000 1153\n", + "3 pvalue 0.0474 1153\n", + "4 cfi 0.9976 1153\n", + "5 tli 0.9924 1153\n", + "6 rmsea 0.0239 1153\n", + "7 srmr 0.0202 1153\n", + "8 aic 34160.9094 1153\n", + "9 bic 34605.3202 1153\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# 5. FIML SEM\n", + "# ============================================================\n", + "cat(\"Fitting FIML SEM...\\n\")\n", + "\n", + "fit_fiml <- tryCatch(\n", + " sem(model_str, data=dat, missing=\"fiml\", fixed.x=FALSE, estimator=\"ML\"),\n", + " error = function(e) { cat(\"FIML Error:\", e$message, \"\\n\"); NULL }\n", + ")\n", + "\n", + "if (!is.null(fit_fiml)) {\n", + " cat(\"FIML converged:\", lavInspect(fit_fiml, \"converged\"), \"\\n\")\n", + " N_fiml <- lavInspect(fit_fiml, \"ntotal\")\n", + " cat(\"N used:\", N_fiml, \"\\n\\n\")\n", + "\n", + " pe <- parameterEstimates(fit_fiml, ci=TRUE)\n", + " labeled <- pe[pe$label != \"\", c(\"label\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"pvalue\")]\n", + " labeled$N <- N_fiml\n", + " labeled$method <- \"FIML\"\n", + " print(labeled)\n", + "\n", + " write.csv(labeled, file.path(DIR_FIML, \"fiml_all_paths.csv\"), row.names=FALSE)\n", + " cat(\"\\nFIML results saved.\\n\")\n", + "\n", + " fm <- fitMeasures(fit_fiml, c(\"chisq\",\"df\",\"pvalue\",\"cfi\",\"tli\",\"rmsea\",\"srmr\",\"aic\",\"bic\"))\n", + " fm_df <- data.frame(measure=names(fm), value=round(as.numeric(fm), 4), N=N_fiml)\n", + " write.csv(fm_df, file.path(DIR_FIML, \"fiml_fit_measures.csv\"), row.names=FALSE)\n", + " print(fm_df)\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:36:08.444174Z", + "iopub.status.busy": "2026-03-31T20:36:08.442314Z", + "iopub.status.idle": "2026-03-31T20:36:10.110655Z", + "shell.execute_reply": "2026-03-31T20:36:10.106744Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FIML forest plot saved.\n" + ] + }, + { + "data": { + "image/png": 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ho1aqTValNTU5OTk81ms06nmzNnjvUl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\nhviU1t6de7pWF7tHXVR+T56rLKVrBwCcOgLhgN1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UxK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\nUFlZ2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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# 5b. FIML Forest Plot\n", + "# ============================================================\n", + "if (!is.null(fit_fiml)) {\n", + " plot_df <- labeled[!grepl(\"^diff_\", labeled$label), ]\n", + " plot_df$label <- factor(plot_df$label,\n", + " levels=rev(c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\")))\n", + "\n", + " plot_df$effect_type <- ifelse(grepl(\"^a\", plot_df$label), \"a-path (SNP->Med)\",\n", + " ifelse(grepl(\"^b\", plot_df$label), \"b-path (Med->Out)\",\n", + " ifelse(plot_df$label == \"cp\", \"Direct (c')\",\n", + " ifelse(grepl(\"^ind\", plot_df$label), \"Specific indirect\",\n", + " ifelse(plot_df$label == \"total_indirect\", \"Total indirect\",\n", + " ifelse(plot_df$label == \"total\", \"Total\", \"Prop. mediated\"))))))\n", + "\n", + " p_fiml <- ggplot(plot_df, aes(x=est, y=label, color=effect_type)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(aes(xmin=ci.lower, xmax=ci.upper), size=0.6) +\n", + " scale_color_brewer(palette=\"Set2\") +\n", + " labs(title=\"FIML SEM: All Path Estimates\",\n", + " subtitle=paste0(\"Parallel mediation: SNP -> {APOC4/2, APOC2, APOC1, APOE} -> caa_4gp | N=\", N_fiml),\n", + " x=\"Estimate (95% CI)\", y=\"\", color=\"Effect Type\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + " print(p_fiml)\n", + " ggsave(file.path(DIR_FIML, \"fiml_forest_plot.png\"), p_fiml, width=10, height=8, dpi=150)\n", + " ggsave(file.path(DIR_FIML, \"fiml_forest_plot.pdf\"), p_fiml, width=10, height=8)\n", + " cat(\"FIML forest plot saved.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sensitivity Analysis: 3-Mediator Model (Excluding APOC4/APOC2)\n", + "\n", + "Due to high collinearity between APOC4/APOC2 (AC cortex) and APOC2 (AC cortex),\n", + "we re-run the FIML SEM excluding APOC4_APOC2_AC_exp to assess robustness of the\n", + "remaining mediator effects and the direct effect." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Sensitivity Analysis: 3-Mediator Model (excluding APOC4_APOC2_AC_exp) ===\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3-Mediator Model specification:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "APOC2_AC_exp ~ a1 * chr19_44954310_T_C + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC1_DeJager_Mic_exp ~ a2 * chr19_44954310_T_C + msex_u + age_death_u + pmi_u\n", + "APOE_Mega_Mic_exp ~ a3 * chr19_44954310_T_C + msex_u + age_death_u + pmi_u\n", + "caa_4gp ~ b1 * APOC2_AC_exp + b2 * APOC1_DeJager_Mic_exp + b3 * APOE_Mega_Mic_exp + cp * chr19_44954310_T_C + educ + apoe4_dose + apoe2_dose + msex_u + age_death_u\n", + "APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC1_DeJager_Mic_exp ~~ APOE_Mega_Mic_exp\n", + "ind1 := a1 * b1\n", + "ind2 := a2 * b2\n", + "ind3 := a3 * b3\n", + "total_indirect := ind1 + ind2 + ind3\n", + "total := cp + total_indirect\n", + "prop_med := total_indirect / total\n", + "diff_1_2 := ind1 - ind2\n", + "diff_1_3 := ind1 - ind3\n", + "diff_2_3 := ind2 - ind3 \n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converged: TRUE \n", + "N used: 1153 \n", + "\n", + " label est se ci.lower ci.upper pvalue N\n", + "1 a1 0.276 0.056 0.166 0.386 0.000 1153\n", + "6 a2 0.226 0.064 0.100 0.351 0.000 1153\n", + "10 a3 0.213 0.049 0.116 0.309 0.000 1153\n", + "14 b1 -0.077 0.039 -0.153 0.000 0.049 1153\n", + "15 b2 0.061 0.058 -0.052 0.174 0.293 1153\n", + "16 b3 0.031 0.050 -0.068 0.130 0.544 1153\n", + "17 cp 0.136 0.040 0.058 0.214 0.001 1153\n", + "78 ind1 -0.021 0.012 -0.044 0.002 0.069 1153\n", + "79 ind2 0.014 0.014 -0.013 0.041 0.323 1153\n", + "80 ind3 0.007 0.011 -0.015 0.028 0.548 1153\n", + "81 total_indirect -0.001 0.014 -0.028 0.026 0.943 1153\n", + "82 total 0.135 0.038 0.060 0.210 0.000 1153\n", + "83 prop_med -0.007 0.101 -0.205 0.191 0.943 1153\n", + "84 diff_1_2 -0.035 0.021 -0.075 0.006 0.091 1153\n", + "85 diff_1_3 -0.028 0.015 -0.056 0.001 0.058 1153\n", + "86 diff_2_3 0.007 0.023 -0.037 0.051 0.751 1153\n", + " method label_full\n", + "1 FIML_sensitivity_3med a1_APOC2_AC\n", + "6 FIML_sensitivity_3med a2_APOC1_Mic\n", + "10 FIML_sensitivity_3med a3_APOE_Mic\n", + "14 FIML_sensitivity_3med b1_APOC2_AC\n", + "15 FIML_sensitivity_3med b2_APOC1_Mic\n", + "16 FIML_sensitivity_3med b3_APOE_Mic\n", + "17 FIML_sensitivity_3med cp\n", + "78 FIML_sensitivity_3med ind1_APOC2_AC\n", + "79 FIML_sensitivity_3med ind2_APOC1_Mic\n", + "80 FIML_sensitivity_3med ind3_APOE_Mic\n", + "81 FIML_sensitivity_3med total_indirect\n", + "82 FIML_sensitivity_3med total\n", + "83 FIML_sensitivity_3med prop_med\n", + "84 FIML_sensitivity_3med diff_APOC2_vs_APOC1\n", + "85 FIML_sensitivity_3med diff_APOC2_vs_APOE\n", + "86 FIML_sensitivity_3med diff_APOC1_vs_APOE\n", + "\n", + "3-mediator FIML results saved.\n", + "\n", + "Fit measures (3-mediator model):\n", + " measure value N\n", + "1 chisq 21.8658 1153\n", + "2 df 13.0000 1153\n", + "3 pvalue 0.0575 1153\n", + "4 cfi 0.9793 1153\n", + "5 tli 0.9396 1153\n", + "6 rmsea 0.0243 1153\n", + "7 srmr 0.0207 1153\n", + "8 aic 36350.2748 1153\n", + "9 bic 36739.1343 1153\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201c\u001b[1m\u001b[22m`geom_errorbarh()` was deprecated in ggplot2 4.0.0.\n", + "\u001b[36m\u2139\u001b[39m Please use the `orientation` argument of `geom_errorbar()` instead.\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22m`height` was translated to `width`.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22m`height` was translated to `width`.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[22m`height` was translated to `width`.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Forest plot saved.\n", + "\n", + "\n", + "=== Comparison: 4-Mediator vs 3-Mediator Model ===\n", + "Measure 4-Mediator 3-Mediator\n", + "-------------------------------------------------- \n", + "CFI 0.9976 0.9793\n", + "TLI 0.9924 0.9396\n", + "RMSEA 0.0239 0.0243\n", + "SRMR 0.0202 0.0207\n", + "AIC 34160.9094 36350.2748\n", + "BIC 34605.3202 36739.1343\n", + "\n", + "--- Key path comparison ---\n", + "Parameter 4-Med est(p) 3-Med est(p)\n", + "-------------------------------------------------- \n", + "cp (direct) 0.1403(0.000) 0.1359(0.001)\n", + "total_indirect -0.0051(0.721) -0.0010(0.943)\n", + "total 0.1352(0.000) 0.1350(0.000)\n", + "\n", + "Comparison table saved.\n" + ] + }, + { + "data": { + "image/png": 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5DYAYAJOHr06JkzZwwdBQCAscNyJwBgAvLz80UikaGjAAAwduixAwAAAOAIJHYA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\nAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n# 5c. Sensitivity: FIML SEM without APOC4_APOC2_AC_exp\n# ============================================================\nDIR_SENS <- file.path(DIR_FIML, \"sensitivity_analysis\")\ndir.create(DIR_SENS, showWarnings=FALSE, recursive=TRUE)\n\ncat(\"=== Sensitivity Analysis: 3-Mediator Model (excluding APOC4_APOC2_AC_exp) ===\\n\\n\")\n\nMEDIATORS_3 <- c(\"APOC2_AC_exp\", \"APOC1_DeJager_Mic_exp\", \"APOE_Mega_Mic_exp\")\nMED_LABELS_3 <- c(\"APOC2_AC\", \"APOC1_DeJager_Mic\", \"APOE_Mega_Mic\")\nn_med_3 <- length(MEDIATORS_3)\n\n# Build model string for 3 mediators\nmed_eqs_3 <- character(n_med_3)\nfor (k in 1:n_med_3) {\n cov_str <- paste(MED_COVS[[MEDIATORS_3[k]]], collapse=\" + \")\n med_eqs_3[k] <- paste0(MEDIATORS_3[k], \" ~ a\", k, \" * \", EXPOSURE, \" + \", cov_str)\n}\n\ny_med_terms_3 <- paste0(\"b\", 1:n_med_3, \" * \", MEDIATORS_3, collapse=\" + \")\ny_eq_3 <- paste0(OUTCOME, \" ~ \", y_med_terms_3, \" + cp * \", EXPOSURE, \" + \", out_cov_str)\n\n# Residual covariances between 3 mediators\nmed_covs_3 <- c()\nfor (i in 1:(n_med_3-1)) {\n for (j in (i+1):n_med_3) {\n med_covs_3 <- c(med_covs_3, paste0(MEDIATORS_3[i], \" ~~ \", MEDIATORS_3[j]))\n }\n}\n\nind_defs_3 <- paste0(\"ind\", 1:n_med_3, \" := a\", 1:n_med_3, \" * b\", 1:n_med_3)\ntotal_ind_3 <- paste0(\"total_indirect := \", paste0(\"ind\", 1:n_med_3, collapse=\" + \"))\ntotal_def_3 <- \"total := cp + total_indirect\"\nprop_def_3 <- \"prop_med := total_indirect / total\"\n\ncontrasts_3 <- c()\nfor (i in 1:(n_med_3-1)) {\n for (j in (i+1):n_med_3) {\n contrasts_3 <- c(contrasts_3, paste0(\"diff_\", i, \"_\", j, \" := ind\", i, \" - ind\", j))\n }\n}\n\nmodel_str_3 <- paste(c(med_eqs_3, y_eq_3, med_covs_3, ind_defs_3, total_ind_3,\n total_def_3, prop_def_3, contrasts_3), collapse=\"\\n\")\n\ncat(\"3-Mediator Model specification:\\n\")\ncat(model_str_3, \"\\n\\n\")\n\n# Fit\nfit_sens <- tryCatch(\n sem(model_str_3, data=dat, missing=\"fiml\", fixed.x=FALSE, estimator=\"ML\"),\n error = function(e) { cat(\"FIML Error:\", e$message, \"\\n\"); NULL }\n)\n\nif (!is.null(fit_sens)) {\n cat(\"Converged:\", lavInspect(fit_sens, \"converged\"), \"\\n\")\n N_sens <- lavInspect(fit_sens, \"ntotal\")\n cat(\"N used:\", N_sens, \"\\n\\n\")\n\n pe_s <- parameterEstimates(fit_sens, ci=TRUE)\n labeled_s <- pe_s[pe_s$label != \"\", c(\"label\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"pvalue\")]\n labeled_s$N <- N_sens\n labeled_s$method <- \"FIML_sensitivity_3med\"\n\n # Rename labels for clarity: a1->a1(APOC2), a2->a2(APOC1), a3->a3(APOE)\n med_map_3 <- c(\"a1\"=\"a1_APOC2_AC\", \"a2\"=\"a2_APOC1_Mic\", \"a3\"=\"a3_APOE_Mic\",\n \"b1\"=\"b1_APOC2_AC\", \"b2\"=\"b2_APOC1_Mic\", \"b3\"=\"b3_APOE_Mic\",\n \"ind1\"=\"ind1_APOC2_AC\", \"ind2\"=\"ind2_APOC1_Mic\", \"ind3\"=\"ind3_APOE_Mic\",\n \"diff_1_2\"=\"diff_APOC2_vs_APOC1\", \"diff_1_3\"=\"diff_APOC2_vs_APOE\",\n \"diff_2_3\"=\"diff_APOC1_vs_APOE\")\n labeled_s$label_full <- ifelse(labeled_s$label %in% names(med_map_3),\n med_map_3[labeled_s$label], labeled_s$label)\n\n print(labeled_s)\n\n write.csv(labeled_s, file.path(DIR_SENS, \"fiml_3med_all_paths.csv\"), row.names=FALSE)\n cat(\"\\n3-mediator FIML results saved.\\n\")\n\n # Fit measures\n fm_s <- fitMeasures(fit_sens, c(\"chisq\",\"df\",\"pvalue\",\"cfi\",\"tli\",\"rmsea\",\"srmr\",\"aic\",\"bic\"))\n fm_s_df <- data.frame(measure=names(fm_s), value=round(as.numeric(fm_s), 4), N=N_sens)\n write.csv(fm_s_df, file.path(DIR_SENS, \"fiml_3med_fit_measures.csv\"), row.names=FALSE)\n cat(\"\\nFit measures (3-mediator model):\\n\")\n print(fm_s_df)\n\n # Forest plot\n plot_labels <- labeled_s[!grepl(\"^diff\", labeled_s$label), ]\n plot_labels$label <- factor(plot_labels$label_full,\n levels=rev(plot_labels$label_full))\n plot_labels$sig <- ifelse(plot_labels$pvalue < 0.05, \"p < 0.05\", \"n.s.\")\n\n p_sens <- ggplot(plot_labels, aes(x=est, y=label, color=sig)) +\n geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n geom_point(size=2.5) +\n geom_errorbarh(aes(xmin=ci.lower, xmax=ci.upper), height=0.25) +\n scale_color_manual(values=c(\"p < 0.05\"=\"#E74C3C\", \"n.s.\"=\"#2C3E50\")) +\n labs(title=\"Sensitivity: 3-Mediator FIML (excl. APOC4/APOC2)\",\n x=\"Estimate\", y=\"\", color=\"\") +\n theme_minimal(base_size=11) +\n theme(plot.title=element_text(face=\"bold\", size=12),\n legend.position=\"bottom\")\n\n ggsave(file.path(DIR_SENS, \"fiml_3med_forest_plot.png\"), p_sens, width=9, height=6, dpi=150)\n ggsave(file.path(DIR_SENS, \"fiml_3med_forest_plot.pdf\"), p_sens, width=9, height=6)\n print(p_sens)\n cat(\"Forest plot saved.\\n\")\n\n # Comparison table: 4-med vs 3-med\n cat(\"\\n\\n=== Comparison: 4-Mediator vs 3-Mediator Model ===\\n\")\n cat(sprintf(\"%-25s %12s %12s\\n\", \"Measure\", \"4-Mediator\", \"3-Mediator\"))\n cat(paste(rep(\"-\", 50), collapse=\"\"), \"\\n\")\n fm_4 <- read.csv(file.path(DIR_FIML, \"fiml_fit_measures.csv\"), stringsAsFactors=FALSE)\n for (m in c(\"cfi\", \"tli\", \"rmsea\", \"srmr\", \"aic\", \"bic\")) {\n v4 <- fm_4$value[fm_4$measure == m]\n v3 <- fm_s_df$value[fm_s_df$measure == m]\n cat(sprintf(\"%-25s %12.4f %12.4f\\n\", toupper(m), v4, v3))\n }\n\n # Compare key paths\n cat(\"\\n--- Key path comparison ---\\n\")\n paths_4 <- read.csv(file.path(DIR_FIML, \"fiml_all_paths.csv\"), stringsAsFactors=FALSE)\n cat(sprintf(\"%-25s %12s %12s\\n\", \"Parameter\", \"4-Med est(p)\", \"3-Med est(p)\"))\n cat(paste(rep(\"-\", 50), collapse=\"\"), \"\\n\")\n\n # Direct effect\n cp4 <- paths_4[paths_4$label == \"cp\", ]\n cp3 <- labeled_s[labeled_s$label == \"cp\", ]\n cat(sprintf(\"%-25s %7.4f(%.3f) %7.4f(%.3f)\\n\", \"cp (direct)\",\n cp4$est, cp4$pvalue, cp3$est, cp3$pvalue))\n\n # Total indirect\n ti4 <- paths_4[paths_4$label == \"total_indirect\", ]\n ti3 <- labeled_s[labeled_s$label == \"total_indirect\", ]\n cat(sprintf(\"%-25s %7.4f(%.3f) %7.4f(%.3f)\\n\", \"total_indirect\",\n ti4$est, ti4$pvalue, ti3$est, ti3$pvalue))\n\n # Total\n t4 <- paths_4[paths_4$label == \"total\", ]\n t3 <- labeled_s[labeled_s$label == \"total\", ]\n cat(sprintf(\"%-25s %7.4f(%.3f) %7.4f(%.3f)\\n\", \"total\",\n t4$est, t4$pvalue, t3$est, t3$pvalue))\n\n # Save comparison\n comp_df <- data.frame(\n parameter = c(\"cp\", \"total_indirect\", \"total\"),\n est_4med = c(cp4$est, ti4$est, t4$est),\n p_4med = c(cp4$pvalue, ti4$pvalue, t4$pvalue),\n est_3med = c(cp3$est, ti3$est, t3$est),\n p_3med = c(cp3$pvalue, ti3$pvalue, t3$pvalue)\n )\n write.csv(comp_df, file.path(DIR_SENS, \"comparison_4med_vs_3med.csv\"), row.names=FALSE)\n cat(\"\\nComparison table saved.\\n\")\n} else {\n cat(\"3-mediator FIML model failed to converge.\\n\")\n}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### FIML Interpretation\n", + "\n", + "The FIML SEM provides maximum-likelihood estimates for all paths in the parallel mediation model.\n", + "\n", + "- **a-paths (a1-a4)**: Effect of the SNP on each mediator (gene expression)\n", + "- **b-paths (b1-b4)**: Effect of each mediator on CAA, controlling for the SNP and other mediators\n", + "- **c' (cp)**: Direct effect of the SNP on CAA, controlling for all four mediators\n", + "- **Specific indirects (ind1-ind4)**: Product a_k * b_k -- unique mediated effect through mediator k\n", + "- **Total indirect**: Sum of all specific indirects\n", + "- **Total**: Direct + total indirect\n", + "- **Proportion mediated**: Total indirect / total\n", + "\n", + "---\n", + "\n", + "## Method 2: Bootstrap (FIML inside each replicate)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:36:10.117941Z", + "iopub.status.busy": "2026-03-31T20:36:10.115557Z", + "iopub.status.idle": "2026-03-31T21:00:29.818377Z", + "shell.execute_reply": "2026-03-31T21:00:29.815694Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running 1000 bootstrap replicates with FIML inside each...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Replicate 200 / 1000 ( 200 converged)\n", + " Replicate 400 / 1000 ( 400 converged)\n", + " Replicate 600 / 1000 ( 600 converged)\n", + " Replicate 800 / 1000 ( 800 converged)\n", + " Replicate 1000 / 1000 ( 1000 converged)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bootstrap complete: 1000 / 1000 replicates converged.\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# 6. Bootstrap\n", + "# ============================================================\n", + "cat(\"Running\", N_BOOT, \"bootstrap replicates with FIML inside each...\\n\")\n", + "\n", + "param_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\")\n", + "\n", + "boot_mat <- matrix(NA, N_BOOT, length(param_labels),\n", + " dimnames=list(NULL, param_labels))\n", + "\n", + "set.seed(42)\n", + "n_success <- 0\n", + "\n", + "for (i in 1:N_BOOT) {\n", + " idx <- sample(nrow(dat), replace=TRUE)\n", + " boot_dat <- dat[idx, ]\n", + "\n", + " fit_b <- tryCatch(\n", + " sem(model_str, data=boot_dat, missing=\"fiml\", fixed.x=FALSE,\n", + " se=\"none\", test=\"none\"),\n", + " error = function(e) NULL,\n", + " warning = function(w) {\n", + " tryCatch(sem(model_str, data=boot_dat, missing=\"fiml\", fixed.x=FALSE,\n", + " se=\"none\", test=\"none\"),\n", + " error = function(e) NULL)\n", + " }\n", + " )\n", + "\n", + " if (!is.null(fit_b) && lavInspect(fit_b, \"converged\")) {\n", + " pe_b <- parameterEstimates(fit_b)\n", + " for (lab in param_labels) {\n", + " row_b <- pe_b[pe_b$label == lab, ]\n", + " if (nrow(row_b) == 1) boot_mat[i, lab] <- row_b$est\n", + " }\n", + " n_success <- n_success + 1\n", + " }\n", + "\n", + " if (i %% 200 == 0) cat(\" Replicate\", i, \"/\", N_BOOT, \"(\", n_success, \"converged)\\n\")\n", + "}\n", + "\n", + "cat(\"\\nBootstrap complete:\", n_success, \"/\", N_BOOT, \"replicates converged.\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:00:30.013808Z", + "iopub.status.busy": "2026-03-31T21:00:30.011864Z", + "iopub.status.idle": "2026-03-31T21:00:30.110502Z", + "shell.execute_reply": "2026-03-31T21:00:30.107327Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label boot_mean boot_se ci_lower ci_upper\n", + "a1 a1 0.281782714 0.05550201 0.17550243 0.39299885\n", + "a2 a2 0.278635307 0.05565842 0.17336369 0.38852366\n", + "a3 a3 0.223237935 0.06371455 0.09809953 0.35067986\n", + "a4 a4 0.212213238 0.04537182 0.11865467 0.29645833\n", + "b1 b1 -0.923905707 0.91097343 -2.78476849 0.75742884\n", + "b2 b2 0.844422749 0.90858852 -0.84491552 2.70657248\n", + "b3 b3 0.054219971 0.06472397 -0.07144015 0.17624593\n", + "b4 b4 0.033688170 0.05374446 -0.06843109 0.13993094\n", + "cp cp 0.142829853 0.04227618 0.05811169 0.22685244\n", + "ind1 ind1 -0.260607770 0.26677357 -0.84679260 0.21080194\n", + "ind2 ind2 0.235504131 0.26249354 -0.23254922 0.81172188\n", + "ind3 ind3 0.011676119 0.01501737 -0.01566676 0.04242190\n", + "ind4 ind4 0.007312507 0.01177654 -0.01444887 0.03124174\n", + "total_indirect total_indirect -0.006115012 0.01618337 -0.03924512 0.02329425\n", + "total total 0.136714841 0.03829100 0.06413311 0.21541122\n", + "prop_med prop_med -0.047373937 0.13937524 -0.33889706 0.21142617\n", + " pvalue N n_replicates method\n", + "a1 0.000 1153 1000 Bootstrap\n", + "a2 0.000 1153 1000 Bootstrap\n", + "a3 0.000 1153 1000 Bootstrap\n", + "a4 0.000 1153 1000 Bootstrap\n", + "b1 0.326 1153 1000 Bootstrap\n", + "b2 0.366 1153 1000 Bootstrap\n", + "b3 0.422 1153 1000 Bootstrap\n", + "b4 0.560 1153 1000 Bootstrap\n", + "cp 0.000 1153 1000 Bootstrap\n", + "ind1 0.326 1153 1000 Bootstrap\n", + "ind2 0.366 1153 1000 Bootstrap\n", + "ind3 0.422 1153 1000 Bootstrap\n", + "ind4 0.560 1153 1000 Bootstrap\n", + "total_indirect 0.720 1153 1000 Bootstrap\n", + "total 0.000 1153 1000 Bootstrap\n", + "prop_med 0.720 1153 1000 Bootstrap\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrap results saved.\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# 6b. Bootstrap Summary\n", + "# ============================================================\n", + "boot_summary <- data.frame(\n", + " label = param_labels,\n", + " boot_mean = apply(boot_mat, 2, mean, na.rm=TRUE),\n", + " boot_se = apply(boot_mat, 2, sd, na.rm=TRUE),\n", + " ci_lower = apply(boot_mat, 2, quantile, 0.025, na.rm=TRUE),\n", + " ci_upper = apply(boot_mat, 2, quantile, 0.975, na.rm=TRUE),\n", + " stringsAsFactors=FALSE\n", + ")\n", + "\n", + "boot_summary$pvalue <- sapply(param_labels, function(lab) {\n", + " vals <- boot_mat[, lab]\n", + " vals <- vals[!is.na(vals)]\n", + " if (length(vals) < 10) return(NA)\n", + " p_pos <- mean(vals > 0)\n", + " 2 * min(p_pos, 1 - p_pos)\n", + "})\n", + "\n", + "boot_summary$N <- N_fiml\n", + "boot_summary$n_replicates <- n_success\n", + "boot_summary$method <- \"Bootstrap\"\n", + "\n", + "print(boot_summary)\n", + "write.csv(boot_summary, file.path(DIR_BOOT, \"bootstrap_results.csv\"), row.names=FALSE)\n", + "cat(\"Bootstrap results saved.\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:00:30.122078Z", + "iopub.status.busy": "2026-03-31T21:00:30.119373Z", + "iopub.status.idle": "2026-03-31T21:00:31.643410Z", + "shell.execute_reply": "2026-03-31T21:00:31.640976Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrap forest plot saved.\n" + ] + }, + { + "data": { + "image/png": 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Jdffjl+/PgJEyYsXbrUeRBOf39/l3sy58+fb7VaBw4cWNwyN2/ebLfb+/Tp42a9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HH3xQ1/UVK1a88sorDz30UF1d3TPPPDNc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\nHSfxul1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Edt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\nThHRvn37FBQUiIjD4Xh5ee3du1ddXb1PPsLat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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# 6c. Bootstrap Forest Plot\n", + "# ============================================================\n", + "plot_boot <- boot_summary[!grepl(\"^diff_\", boot_summary$label), ]\n", + "plot_boot$label <- factor(plot_boot$label,\n", + " levels=rev(c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\")))\n", + "\n", + "plot_boot$effect_type <- ifelse(grepl(\"^a\", plot_boot$label), \"a-path (SNP->Med)\",\n", + " ifelse(grepl(\"^b\", plot_boot$label), \"b-path (Med->Out)\",\n", + " ifelse(plot_boot$label == \"cp\", \"Direct (c')\",\n", + " ifelse(grepl(\"^ind\", plot_boot$label), \"Specific indirect\",\n", + " ifelse(plot_boot$label == \"total_indirect\", \"Total indirect\",\n", + " ifelse(plot_boot$label == \"total\", \"Total\", \"Prop. mediated\"))))))\n", + "\n", + "p_boot <- ggplot(plot_boot, aes(x=boot_mean, y=label, color=effect_type)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(aes(xmin=ci_lower, xmax=ci_upper), size=0.6) +\n", + " scale_color_brewer(palette=\"Set2\") +\n", + " labs(title=\"Bootstrap: All Path Estimates (Percentile CIs)\",\n", + " subtitle=paste0(\"N=\", N_fiml, \" (FIML), \", n_success, \" resamples\"),\n", + " x=\"Estimate (95% Percentile CI)\", y=\"\", color=\"Effect Type\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + "print(p_boot)\n", + "ggsave(file.path(DIR_BOOT, \"bootstrap_forest_plot.png\"), p_boot, width=10, height=8, dpi=150)\n", + "ggsave(file.path(DIR_BOOT, \"bootstrap_forest_plot.pdf\"), p_boot, width=10, height=8)\n", + "cat(\"Bootstrap forest plot saved.\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:00:31.653420Z", + "iopub.status.busy": "2026-03-31T21:00:31.650208Z", + "iopub.status.idle": "2026-03-31T21:00:34.288747Z", + "shell.execute_reply": "2026-03-31T21:00:34.286308Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrap distribution histograms saved.\n" + ] + }, + { + "data": { + "image/png": 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16ooVKzIyMgYMGKDT6eoYE0ZXA/CbJNOXP512Tb+1Zws0CP2UkWGL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\nVM3m3hMCcvhupwdpvtxoa9FulxERi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0Zs0aIgoPD2cfV65cydJGuQGw9tv//d//sY8sbSwvslIAACAASURBVPj6+goHpaLs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\nNvfzzz9XKIxKX6sZKWlOYm6SmOlqeruQg8DdyPgq98kGh0tNT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Y6XBL4OXPtiWWXzBm4Pk1A3IfD0DOpJsQzpo167XXXquu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\nqHZ1MyH86aefvLy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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# 6d. Bootstrap Distribution Histograms\n", + "# ============================================================\n", + "ind_labels <- c(paste0(\"ind\", 1:n_med), \"total_indirect\")\n", + "ind_names <- c(MED_LABELS, \"Total Indirect\")\n", + "\n", + "hist_plots <- list()\n", + "for (k in seq_along(ind_labels)) {\n", + " vals <- boot_mat[, ind_labels[k]]\n", + " vals <- vals[!is.na(vals)]\n", + " if (length(vals) < 10) next\n", + "\n", + " df_h <- data.frame(value = vals)\n", + " med_val <- median(vals)\n", + "\n", + " p_h <- ggplot(df_h, aes(x=value)) +\n", + " geom_histogram(bins=50, fill=\"steelblue\", alpha=0.7, color=\"white\") +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"red\", linewidth=0.8) +\n", + " geom_vline(xintercept=med_val, linetype=\"solid\", color=\"darkblue\", linewidth=0.8) +\n", + " labs(title=paste0(\"Bootstrap: \", ind_names[k]),\n", + " subtitle=paste0(\"Indirect effect | median=\", round(med_val, 4)),\n", + " x=\"Indirect Effect (a x b)\", y=\"Count\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + " hist_plots[[k]] <- p_h\n", + "}\n", + "\n", + "if (length(hist_plots) > 0) {\n", + " g <- do.call(grid.arrange, c(hist_plots, ncol=2))\n", + " ggsave(file.path(DIR_BOOT, paste0(\"bootstrap_distributions_\", EXPOSURE, \".png\")),\n", + " g, width=12, height=10, dpi=150)\n", + " ggsave(file.path(DIR_BOOT, paste0(\"bootstrap_distributions_\", EXPOSURE, \".pdf\")),\n", + " g, width=12, height=10)\n", + " cat(\"Bootstrap distribution histograms saved.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bootstrap Interpretation\n", + "\n", + "Bootstrap provides nonparametric inference for the indirect effects.\n", + "Percentile confidence intervals that exclude zero indicate significant mediation.\n", + "The bootstrap does not assume normality for the product of coefficients (a*b).\n", + "\n", + "---\n", + "\n", + "## Method 3: MNAR Sensitivity Analysis\n", + "\n", + "Missing Not At Random (MNAR) sensitivity analysis tests robustness to potential\n", + "informative missingness. We systematically shift imputed values by delta * SD units." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:00:34.297586Z", + "iopub.status.busy": "2026-03-31T21:00:34.294790Z", + "iopub.status.idle": "2026-03-31T21:01:13.390935Z", + "shell.execute_reply": "2026-03-31T21:01:13.387863Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running MNAR sensitivity analysis...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Grid point 30 / 121 \n", + " Grid point 60 / 121 \n", + " Grid point 90 / 121 \n", + " Grid point 120 / 121 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR sensitivity complete: 847 results.\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# 7. MNAR Sensitivity\n", + "# ============================================================\n", + "cat(\"Running MNAR sensitivity analysis...\\n\")\n", + "\n", + "delta_range <- seq(-2, 2, length.out=11)\n", + "grid <- expand.grid(delta_med=delta_range, delta_out=delta_range)\n", + "\n", + "med_means <- sapply(MEDIATORS, function(m) mean(dat[[m]], na.rm=TRUE))\n", + "med_sds <- sapply(MEDIATORS, function(m) sd(dat[[m]], na.rm=TRUE))\n", + "out_mean <- mean(dat[[OUTCOME]], na.rm=TRUE)\n", + "out_sd <- sd(dat[[OUTCOME]], na.rm=TRUE)\n", + "\n", + "mnar_params <- c(paste0(\"ind\", 1:n_med), \"total_indirect\", \"cp\", \"total\")\n", + "mnar_results <- data.frame()\n", + "\n", + "for (k in 1:nrow(grid)) {\n", + " dat_imp <- dat\n", + "\n", + " for (m_idx in 1:n_med) {\n", + " m <- MEDIATORS[m_idx]\n", + " miss_m <- is.na(dat_imp[[m]])\n", + " if (any(miss_m)) {\n", + " dat_imp[[m]][miss_m] <- med_means[m_idx] + grid$delta_med[k] * med_sds[m_idx]\n", + " }\n", + " }\n", + "\n", + " miss_y <- is.na(dat_imp[[OUTCOME]])\n", + " if (any(miss_y)) {\n", + " dat_imp[[OUTCOME]][miss_y] <- out_mean + grid$delta_out[k] * out_sd\n", + " }\n", + "\n", + " for (cov in ALL_COVS) {\n", + " miss_c <- is.na(dat_imp[[cov]])\n", + " if (any(miss_c)) {\n", + " dat_imp[[cov]][miss_c] <- mean(dat_imp[[cov]], na.rm=TRUE)\n", + " }\n", + " }\n", + "\n", + " fit_m <- tryCatch(\n", + " sem(model_str, data=dat_imp, estimator=\"ML\", fixed.x=FALSE),\n", + " error = function(e) NULL\n", + " )\n", + "\n", + " if (!is.null(fit_m) && lavInspect(fit_m, \"converged\")) {\n", + " pe_m <- parameterEstimates(fit_m, ci=TRUE)\n", + " for (param in mnar_params) {\n", + " row_m <- pe_m[pe_m$label == param, ]\n", + " if (nrow(row_m) == 1) {\n", + " mnar_results <- rbind(mnar_results, data.frame(\n", + " delta_med = grid$delta_med[k],\n", + " delta_out = grid$delta_out[k],\n", + " param = param,\n", + " est = row_m$est,\n", + " se = row_m$se,\n", + " pvalue = row_m$pvalue,\n", + " ci_lower = row_m$ci.lower,\n", + " ci_upper = row_m$ci.upper,\n", + " N = nrow(dat_imp),\n", + " stringsAsFactors = FALSE\n", + " ))\n", + " }\n", + " }\n", + " }\n", + "\n", + " if (k %% 30 == 0) cat(\" Grid point\", k, \"/\", nrow(grid), \"\\n\")\n", + "}\n", + "\n", + "cat(\"MNAR sensitivity complete:\", nrow(mnar_results), \"results.\\n\")\n", + "write.csv(mnar_results, file.path(DIR_MNAR, \"mnar_grid_results.csv\"), row.names=FALSE)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:01:13.403868Z", + "iopub.status.busy": "2026-03-31T21:01:13.401106Z", + "iopub.status.idle": "2026-03-31T21:01:13.496337Z", + "shell.execute_reply": "2026-03-31T21:01:13.493889Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR Tipping Point Summary:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(ref_significant = significance at delta=0; tipping = where conclusion flips)\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " param ref_significant tipping_delta_med tipping_delta_out\n", + "1 ind1 FALSE NA NA\n", + "2 ind2 FALSE NA NA\n", + "3 ind3 FALSE NA NA\n", + "4 ind4 FALSE NA NA\n", + "5 total_indirect FALSE NA NA\n", + " tipping_distance N\n", + "1 Inf 1153\n", + "2 Inf 1153\n", + "3 Inf 1153\n", + "4 Inf 1153\n", + "5 Inf 1153\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "MNAR tipping summary saved.\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# 7b. MNAR Tipping Point Analysis\n", + "# ============================================================\n", + "tipping_summary <- data.frame()\n", + "\n", + "for (param in c(paste0(\"ind\", 1:n_med), \"total_indirect\")) {\n", + " param_data <- mnar_results[mnar_results$param == param, ]\n", + "\n", + " ref_row <- param_data[param_data$delta_med == 0 & param_data$delta_out == 0, ]\n", + " ref_sig <- if (nrow(ref_row) > 0) (ref_row$pvalue[1] < 0.05) else NA\n", + "\n", + " if (is.na(ref_sig)) {\n", + " tipping_summary <- rbind(tipping_summary, data.frame(\n", + " param = param, ref_significant = NA,\n", + " tipping_delta_med = NA, tipping_delta_out = NA,\n", + " tipping_distance = NA,\n", + " N = NA, stringsAsFactors = FALSE\n", + " ))\n", + " next\n", + " }\n", + "\n", + " if (ref_sig) {\n", + " # Reference IS significant: find closest point where it LOSES significance\n", + " flip_points <- param_data[param_data$pvalue >= 0.05, ]\n", + " } else {\n", + " # Reference is NOT significant: find closest point where it BECOMES significant\n", + " flip_points <- param_data[param_data$pvalue < 0.05, ]\n", + " }\n", + "\n", + " if (nrow(flip_points) > 0) {\n", + " flip_points$dist <- sqrt(flip_points$delta_med^2 + flip_points$delta_out^2)\n", + " closest <- flip_points[which.min(flip_points$dist), ]\n", + " tipping_summary <- rbind(tipping_summary, data.frame(\n", + " param = param, ref_significant = ref_sig,\n", + " tipping_delta_med = closest$delta_med,\n", + " tipping_delta_out = closest$delta_out,\n", + " tipping_distance = round(closest$dist, 2),\n", + " N = closest$N, stringsAsFactors = FALSE\n", + " ))\n", + " } else {\n", + " # No flip found in the grid: robust\n", + " tipping_summary <- rbind(tipping_summary, data.frame(\n", + " param = param, ref_significant = ref_sig,\n", + " tipping_delta_med = NA, tipping_delta_out = NA,\n", + " tipping_distance = Inf,\n", + " N = if (nrow(ref_row) > 0) ref_row$N[1] else NA, stringsAsFactors = FALSE\n", + " ))\n", + " }\n", + "}\n", + "\n", + "cat(\"MNAR Tipping Point Summary:\\n\")\n", + "cat(\"(ref_significant = significance at delta=0; tipping = where conclusion flips)\\n\\n\")\n", + "print(tipping_summary)\n", + "write.csv(tipping_summary, file.path(DIR_MNAR, \"mnar_tipping_summary.csv\"), row.names=FALSE)\n", + "cat(\"\\nMNAR tipping summary saved.\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:01:13.504561Z", + "iopub.status.busy": "2026-03-31T21:01:13.501419Z", + "iopub.status.idle": "2026-03-31T21:01:16.648454Z", + "shell.execute_reply": "2026-03-31T21:01:16.645346Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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3fSaxQAAECrtNBl1LaAgIAff/zx9u3bo0aNkraMGDHi4cOH5uFbUl6aMGHCsWPH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nT58+cOBARERETU1NeHh47969U1JSevXq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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR contour plots saved.\n" + ] + }, + { + "data": { + "image/png": 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fLl559/Xr6EEhIS7rrrrj179qxbt04IcdNNN8ltvA+Edq9P6/r16+UiOqGhocnJ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\nvIcQg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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# 7c. MNAR Contour Plots\n", + "# ============================================================\n", + "ti_data <- mnar_results[mnar_results$param == \"total_indirect\", ]\n", + "\n", + "if (nrow(ti_data) > 0) {\n", + " p_cont_ind <- ggplot(ti_data, aes(x=delta_med, y=delta_out, z=est)) +\n", + " geom_contour_filled(bins=12) +\n", + " scale_fill_viridis_d(option=\"viridis\") +\n", + " geom_point(data=data.frame(x=0, y=0), aes(x=x, y=y, z=NULL),\n", + " shape=4, size=4, color=\"red\", stroke=2) +\n", + " labs(title=\"MNAR Sensitivity: Total Indirect Effect\",\n", + " subtitle=paste0(\"Exposure: \", EXPOSURE),\n", + " x=expression(delta[mediators]), y=expression(delta[outcome]),\n", + " fill=\"Indirect\\nEffect\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + " print(p_cont_ind)\n", + " ggsave(file.path(DIR_MNAR, paste0(\"mnar_contour_indirect_\", EXPOSURE, \".png\")),\n", + " p_cont_ind, width=8, height=6, dpi=150)\n", + " ggsave(file.path(DIR_MNAR, paste0(\"mnar_contour_indirect_\", EXPOSURE, \".pdf\")),\n", + " p_cont_ind, width=8, height=6)\n", + "\n", + " ti_data$neg_log10_p <- -log10(pmax(ti_data$pvalue, 1e-16))\n", + "\n", + " p_cont_pv <- ggplot(ti_data, aes(x=delta_med, y=delta_out, z=neg_log10_p)) +\n", + " geom_contour_filled(bins=12) +\n", + " scale_fill_viridis_d(option=\"plasma\") +\n", + " geom_point(data=data.frame(x=0, y=0), aes(x=x, y=y, z=NULL),\n", + " shape=4, size=4, color=\"red\", stroke=2) +\n", + " labs(title=\"MNAR Sensitivity: -log10(p-value) of Total Indirect\",\n", + " subtitle=paste0(\"Exposure: \", EXPOSURE),\n", + " x=expression(delta[mediators]), y=expression(delta[outcome]),\n", + " fill=\"-log10(p)\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + " print(p_cont_pv)\n", + " ggsave(file.path(DIR_MNAR, paste0(\"mnar_contour_pvalue_\", EXPOSURE, \".png\")),\n", + " p_cont_pv, width=8, height=6, dpi=150)\n", + " ggsave(file.path(DIR_MNAR, paste0(\"mnar_contour_pvalue_\", EXPOSURE, \".pdf\")),\n", + " p_cont_pv, width=8, height=6)\n", + "\n", + " cat(\"MNAR contour plots saved.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:01:16.655748Z", + "iopub.status.busy": "2026-03-31T21:01:16.653938Z", + "iopub.status.idle": "2026-03-31T21:01:22.391255Z", + "shell.execute_reply": "2026-03-31T21:01:22.385286Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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OnRo8czzzyj0/3/r2OWLFmycuXKGTNmSJI0Z86c2NjYAwcOiHx9vgDgPPrBkt8P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\nxZ2FU6xWa1JSUnp6enEngizQDxYe+sFCQj9YeOgHC0nR9INuhbr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6JZS/0HRYCOSg3SUcE6KjVIL9Ua8AdC87/YZo6DAABE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\nl2nR2Go3aDlQIdFeDAQAvo2CEABwzizS8KR2z9/WIzzYLunI8dI/frzyy9V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0crV668bt06sVeGXC6fMmXKzp07//rrr6NHj/bq1QvAmjVrVqxY0b179y+++CJn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XZmqLWAk8lQA0eKqf2Wo/kFX8w9G8Y+fL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\nW6uqqmpqagr0I3audDqdyWRKJW0qlUomk126dKmsrCx2lrwkEomqqqpSSRu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Pm6GHY2zcwBUm9DlWUFBgaWlpKk//A4ksqftyiu5FRHRqFjV6nWw8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\nr7CQ0xdAlG2vbvYFQpKGMaMMkDA4DgIp4oTfaOM/1LxLO97V2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kyDpsmRHB0DaCeiQjhmzJhY5wDSU7gQ2qQhvbnQC0hcHAcBdGD0/2jt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\ngtMo2bNH8uabnEb9xInatDROo/jMGW+rqEzPn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Hqnck5JkvdbNBIroVQdZNqPuiSkQODQUSQgBDYln22N8pPx65k1NY\nwpU4Wskm9/XrH+Bu2MAqhGlIAQCgvuu1lGya0uUVlHWfiMjMkVpNou6LKpcNUrWmY32RMcZ8\nRs/v6t9YbEGKwlpshNQZCSmxpkdH9G8Z+y21epesPYgR8D14jchLpuvrKD2WFPnk0IpajiP3\nXnUagMnDNzYAg7mXmr360K27qdncU5GAGdTRc1IfPyNKpcqahvTY3ynjg5qFtvPANKQAAGA4\nDAW8RwHv5T5LUsrz7d1b1tFpSyeQBWl05D09W0ptaWo8yex1mhkLnidbCEvqejrWkjz6yYuo\nko2QMge+keiVHEN7h5A858XTlDN0Yz11nU89/q9ah4XKMJrvnQANyfN8+U/H7nAzdnICvR0/\nCGvl4WhpyLCqqvQ0pJl5xd8d+DvqQiKmIQUAAINjJdZqprxumLWuzbuUeJAe7H2tUCil0J9I\nZk+km0PK7Z5b2Jc7W1ulp2OtrZGQDJGDUEpSOzKz5zsdq0WjF42QJXm0f9SrbFDj/GJy7Ure\nb/INGKoHCSFAnVKq2f2XH245ea9QruRKGtuZvx/aqkszZ8MGVn2aaUg3HI278UhrGtILdlP7\nt2jlbnfwatLJm6mPM/LMpWI/V5sR3Xx8G2HxJQAAMAGMkIbsphvr6c52yrxFUltq1Ik6zSGX\nDlU8YGV7sapKSJ5N8hzKT6adIcQq9QUpIMfWVJJH8uxKJZCMSk6FaVSYVvGmHIGIpLYktSFW\nRYXp+rc5/28yb0QyW5LaktS2jnqxsmq68SPd2UYZt0hqTc6B1GkOuXavi1MbFMOyvH8tMG0q\nlSorK0smk1laGkcbjlwuVyqVFhbGMetjUVFRQUGBtbW1RFJ3q65XR15enkwmE4srNwjh+sPM\ntdG3Hj7N455KxcKR3ZqO7uErEdXux1xOTo5CoXB0dKzVs2jTnoaUY2cpzcqXa28jFgq+eDuw\nR4tGpXd//vy5ffm/jNYbLMtmZmZKJBJra+NIbhUKhVwuN6LPsby8PEtLS5lMZuhYAPVg7TKR\netBQ6r4erKbarQcPTaTbW/SUB7xHA9a/eirPIXkOleSQPOdFMvnqQfbL/3JInqMuyhIocklZ\nVFsBE5HEiqQ2L9JIiQ3JbEli86JEZkdSG91ycZn/8MusB1k1/fEO3dupW/7GZvIPr+nr4atu\n6kG0EALUMJWaPXUr9cbj5xm5RY1szTs0derq55KZV/zz8bhjN1I0m3Vp5vxBWGsXW4P2YKk1\n3DSkx/5O+fn43cy8YiLSyQaJSKFSf7P/RoCng5WZcXyfAAAAaAj6rqKsu/TkwmuFbj0peOVr\nJVyWVRGWZZ9rfhgtazpW1cthkFUeCVmSRyV5lJfMd3sqcySkgJGISUQ2jXSnY43boScbJKKj\nM8lzAFkYaPyLWiHITyJzXyIkhABGokCunL/j4q2kLE3J/suPPBwtn+YUyRUqrqSJk+XM0FaB\n3kbzO2XVMAzTP8C9Z8vGu88nbDt1X62vM0J+seL8vfQBbev7lKoAAAANh9SG3jlNtzbT4+OU\n+4hsvMmzP7UcT4Jq5wWV7cgat4MOjNX/km0zcgnUbZysVCNkGSMhhUT6F1ss6/IVBXRyNvkO\nfdE+yTVFSm1JbF6JYKog4286+Yk0OUaqKiGBmNx6UO8VVe9jXC4khAA1afWhm9rZICcpI597\nYC4VjQ9qNrSzt8hk5t6UiYXjejX74/Kj56VaCDmpWQV1HBIAAICpE4ipzVRqM9XAYTQfQef/\nTZm3dMuFUhr8GzkH6parSqgkh4qzX/xfq9vqiweaV7mSYt2vZOVR6xtXyYnboWflRoH4VS9W\nmd3rHVltXmWPLx7YkdSmEiMh06/Qb71Jkf8yNgUlnaRfe9HIY+TarRIXxQ8SQoAak1tUcvJm\nalmv9glwm96vpZ2lKS7abiYREelPCGVifAoBAACYJIGY3vqdogbR8zuvCsUWFLpRTzZIREIJ\nmTmRmVMlTlFqOlZVYYYy/5mULdTt2pp1vxJdWIlIraCiDCrKqMQuOmtCljMd65Fpr7JB7Ws5\n+j6FX6vEGXnGVeNHBDBZj5/l6+0YyQnv1cw0s0Eiat3EPuW5/pbANp7GMXkMAAAA1DybphR+\njeL3UXosyXPJqQ35vkUWeiacq6JSvVjVCoVCLpeWnlTm+D/p6vf6DxIwg8wd9TdFVqoRsjLr\neej37DrlJJBN06ofQR8khAA1p9x+oAxjKt1ESxvV3efUrdTil6MoNTr5Ovm72xkkJAAAAKgX\nhBJqPpKajzRwGB0/pdtbSZ6tW+7ZnwasLe9LXq2uCVlaXgoSQoD6y8vJSsgwKn2NhNZmEmeb\nhjmhKB/uDhaLx3RatudaRl6xdnmQP+9x5wAAAAC1x7oJjYimA2MpO/5Voe9QCt1UwU/+lV4T\nUv5qSY/ibJJnvb6kRw4VPKFHR8rc3bwyPWb5QUIIUGMu3H/KlvGTz9DObfC7qQAAIABJREFU\nXkKTmUhGrwBPh4h/9Pn70fNHGXlJz/IPXHlMRL+dfdCvjZuJ3xkAAACoFxp1pslxlHqOMm+R\nxIpcOpB9y5o/i1BK5s5k7lzeNpsDKONvPeU2Tcner8YjQkIIUDP2XXr4Q/RtvUMI+7ZxG9PT\nt84jqnfEQkH7po7tmzqyRHGp2fFpucmZBcf/TsGyEwAAAFAvCETkHkTuQQYOI/gb2jOIVKUm\n5Ov7XQXNlVWChBCgBkSei994LI573Kd1417+rrceZz3NKWpsZ97Rx6mtl4Nhw6tvGKIJQc0X\nRl4moq0x9/q0dhUJeU/EDAAAANCwefanYb/TsQ8o+8GLEhtv6vM/ajqoNs6GhBCgWlii9dG3\n91xM5J4O7uj5QVgrhmF6+NXcBFkNUTc/lxZutnEp2enZRdHXkgZ28DR0RAAAAAD1hlcITblX\n8uxeUdptmUsLqXOL2mgb5OBXeYCqU6rUS6OuarLBUd19PnyjtSnPJlop4cHNuQe/nH4gV+pO\nQAoAAABg2hjW2kvRuBdr7V172SAhIQSoMrlCtTDy8qlbqUTEEL03oOWUfi0MHZQx6dDUKcDT\ngYgy84oPxD42dDgAAAAApggJIUBVFMiV83+LvfTgGREJGObjwQHDu9bwmjCmYGKfF42Ev555\nUFSiNGwwAAAAACYICSFApWUVyBfuvnE7OZuIxELBv0a0D23nYeigjFJrD/uOPk5ElFNYsu/S\nQ0OHAwAAAGBykBACVE56dtHsiL8Sn+YTkZlEtHhMpx4tMH9M1U3q48d1it95LiG/WGHgaAAA\nAABMDBJCgEp4/Cz/k4hzKc8LiMjKTPyf8V0CvR0NHZRxa9bYpqufCxHlFyuizicaOhwAAAAA\n04KEEICve6nZn275KyOvmIgcraQrJnRp4WZr6KAagknBftzUrFEXErMLSgwdDgAAAIAJQUII\nwMu1h5lztl7IKSwhIg9HyyWj2rrZmxs6qAbCy9mqt39jIioqUe76K97Q4QAAAACYECSEABU7\ndzdt/o6L3DSYzRrbrJzYzdFKZuigGpTw4OZCAUNE+y495NpgAQAAAKAOICEEqMCR68lLdl0p\nUaqJKMDTYfmErjbmEkMH1dC42Vv0C3AnohKl+rezaCQEAAAAqCNICAHKE3kufuXv11Vqloi6\nNndZMraTuVRk6KAapglBzcRCAREdvPL4aS4aCQEAAADqAhJCAP1Yoo3H4jYei2OJiKhfG7ev\nRnaQioQGDqvhcrYxCwv0ICKlSr03NsXQ4QAAAACYBCSEAHqoWfa7A39HnnvRd3FIJ6/Phrbl\nBrlB7Rnbq5lULCSiU3eeJWXkGzocAAAAgIYPCSGALqVKvTTq6qErj7mno7r7fBDWilsXAWqV\nvaV0UAdPIlKz7C+n7xs6HAAAAICGDwkhwGuKFaqvfr0cc/sJETFEM0L8p/RrYeigTMjoHj5m\nEhERnbz1JCE919DhAAAAADRwSAgBXskvVny+7UJswjMiEgqY2UPavtXF29BBmRYbc8lbXbyI\niGXZbTFoJAQAAACoXUgIAV54ni//dMv5O8lZRCQVCxeN7jSgrbuhgzJFI7r5WMpERHQ2Lu1u\narahwwEAAABoyJAQAhARpWUXfhJxLjE9l4gsZeKvx3Xu5Otk6KBMlIVUNLCdK/d4y8l7hg0G\nAAAAoGFDQghAD5/mfRLx15OsQiKys5Aun9C1tYe9oYMyaW+0bWxrISGiy/HPbjzKNHQ4AAAA\nAA0WEkIwdXEp2Z9u+Sszr5iIXGzNVk7q5tPI2tBBmTqpSDCquw/3ePMJNBICAAAA1BYkhGDS\nLt5/Omfr+bwiBRE1cbT8ZmJ3N3sLQwcFRESDO3o5WsuI6GbS86uJGYYOBwAAAKBhQkIIpuvE\nzdRFkZflChURNXe1XTGxG5eBQH0gEQne6eHLPf75WBxr2GgAAAAAGigkhGCi/oh9tGzvNaWa\nJaK2Xg7LJ3SxMZcYOih4zRvtmzS2Myeie09yLtxLN3Q4AAAAAA0QEkIwRZHn4r8/eJNlWSLq\n5ueyZExnbjF0qFdEAmZszxeNhBEn7nLvFwAAAADUICSEYFpYoh+P3Nl4LI572j/Aff6IDhIR\n/iHUU/3bujdxtCSixKd5p++kGTocAAAAgIYG34PBhKhZ9tv9N3afT+CeDuvs9enQtkIBY9io\noBwChhkX1Ix7vPnkXZUajYQAAAAANQkJIZgKhUq9ZNeV6GtJRMQQTenX4v3QVsgF67/erVy5\nhUCSMwuO/51i6HAAAAAAGhQkhGASikqU83dcOhuXRkQChvloYBvNMndQzzFEE4Kac4+3xtxT\nqtSGjQcAAACgIUFCCA1fXpHi820XuLXsRELBF28HvtG+iaGDgkro5ufSws2WiNKzi7g2XgAA\nAACoEUgIoYF7mlM0a9PZuJRsIpKJhYtGdwzyb2zooKDSwoNfNBL+cvqBXKkybDAAAAAADQYS\nQmjIkjLyZ2/+KzmzgIgsZeKl47t09HEydFBQFR2aOgV4OhBRZl7xwdjHhg4HAAAAoIFAQggN\n1v0nObM3//U0p4iI7CylK8K7+rvbGTooqLqJfV40Eu4486CoRGnYYAAAAAAaBizGDQ2EXKm6\n+fj544x8a5mkuatNVoF8wW+XC+VKImpka750XGdXewtDxwjV0trDvqOP0+X4ZzmFJfsuPXyn\nh6+hIwIAAAAwekgIoSG4mpixbO+1rHy5pkTAMGqWJSJPJ6ul4zo7WMkMFx3UmEl9/GLjn7FE\nO88lDOrgaSkTGzoiAAAAAOOGLqNg9BKf5i349bJ2NkhEXDbo52q7YmJXZIMNRrPGNl39XIgo\nv1gRdT7R0OEAAAAAGD0khGD0fj1T5rSTM0L8rc0kdRwP1KpJwX4MwxBR1IXE7IISQ4cDAAAA\nYNyQEILRu/n4eVkv3XuSU5eRQB3wcrYKbtWYiIpKlDv/ijd0OAAAAADGDQkhGL1iRZmr0hUr\nMBdlAzQ+qLlQwBDR/kuPnr/eVRgAAAAAKgUJIRi9xnbmZb3kaoeZRRsgdweLfgHuRCRXqnac\nvm/ocAAAAACMGBJCMHpuDvqzPhtzSSdfLEPfME0IaiYWCojo4NWktOxCQ4cDAAAAYKyQEIJx\nO3Yj5dStJ6XLJSLBp0PbmkmwsErD5GxjFhboQURKlXrHmQeGDgcAAADAWCEhBCN2Ni5t5f7r\nLLfChJttKw87S5m4sZ15n9auq97t0dnX2dABQi0a26uZVCwkoj+vJSdnFhg6HAAAAACjhPYT\nMFZ/3U3/evcVlZolop4tG817uz030QiYCHtL6aAOnrvPJ6hZdlvMvc/fCjR0RAAAAADGBy2E\nYJRiE559HXVFqWaJqLtfoy+QDZqk0T18uF7BJ289SUjPNXQ4AAAAAMYHCSEYn6uJGYt+iy1R\nqomoQ1OnecMDRcgGTZKNuWRYZy8iYll2WwymGwUAAACoNCSEYGRuJ2ctioyVK1VEFOjtuHB0\nR262STBNI7v7WJmJiehsXNrd1GxDhwMAAABgZPBNGoxJXEr2l9svFpUoicjf3W7BqA4SEf6G\nTZqFVDS8a1Pu8dZT9wwbDAAAAIDRwZdpMBoJ6bn/2nGxUK4kopZutl+P64xVJYCI3uribWsh\nIaJLD57deJRp6HAAAAAAjAkSQjAOiU/zPt92Ia9IQUT/z96dx0dV3/sf/0xmyb6HkJWEnUAW\ndhDZZBH3HaRSbbX2p7f2/ur1WqtWsS73urTX3lZ6tZui1Z8Lrq2CJoBsVnZC2EJAICEhC9n3\nWc/vj4ORCyQMJDNnzsnr+Ucfx++cmb4nxvnmM+d7vp+hSVFPf49qEKeEWM2Lpg1Vj1//kouE\nAAAAF4CCEDpQUd/26FtbmtodIjI4MfK5709RbxsDVNdOzEyIChGRvcfrdx2t1ToOAACAblAQ\nItBVNrQ/9Mbm+la7iKTFhz+7ZEpUqE3rUAgsNkvQ4kuHqcevrilWtE0DAACgHxSECGgnmzse\nfnNLbUuniKTEhb9w+9TYiGCtQyEQXTl+UHJsmIiUVDZtKanWOg4AAIA+UBAicNW2dP78jc1V\nje0ikhgd+tySKfGRIVqHQoCyBJlum37qIuHyLw8qCpcJAQAAzo+CEAGqsc3xyFtbKhvaRSQh\nKuSF26cOjAnVOhQC2ry8tEEJESJytKZl44EqreMAAADoAAUhAlFTu+MXb24uO9kqIjHhtmeX\nTFFXAwI9CDKZlswcrh6/vu6g28NFQgAAgPOgIETAabO7Hvt/W4/VtIhIdJjthdunqpd9gPOa\nNSZlyMAoESmva1u7p0LrOAAAAIHOrwVhZWXlwYMHW1paLuKc7sZbWloOHjxYVcXyMINot7se\neXNLSWWTiESEWP/jtskZAyK1DgXdMIncMWuEevy3DSUut0fbPAAAAAHOT62929vbn3nmmeLi\n4ri4uLq6uttuu23hwoVentPDcz/44IO33norPj6+rq4uNzf30UcftdloSKBjdqf7qQ92HjzR\nKCJhwZb/XDJ5eHK01qGgM5eMHDgqNaa4orG6seOLwuNXT8jQOhEAAEDg8lNB+MYbb7S0tLz+\n+uuRkZFFRUVPPPFEdnZ2VlaWN+d0N75r164PP/zwxRdfzMzMbGpqevXVV8vKyoYNG+afd4Q+\n53B7nn9/V1FZg4gEW81PLZ40MiVG61DQpTtmj3j0ra0i8tbGw/Py0oItZq0TAQAABCg/LRnd\nuHHjTTfdFBkZKSK5ubl5eXnr1q3z8pzuxj///PMFCxYkJyeXlJS4XK5/+7d/oxrUL5fb87sv\nDp2qBi3mpxdPyhkUp3Uo6NWEIQNyM+JFpK6lc+WOMq3jAAAABC5/XCFsaGhoaWnJyPhu4VZm\nZmZxcbE35/Tw3EOHDkVHR//rv/5rREREWVnZtGnT7r///qCgc5e4brfb4+nV3UTq0z0ej9Pp\n7M3r+I36lnWR1uVRnv9kT2Fpo4hYzEEP35A7OjUqwJN7PB6Xy6V1Cm+pTfkC/Ed6hl6mXTJj\nSFFpnYi8venw3OykUJuvPuvUn62iKHr58bpcLr18MoiI2+1W/7f3ga1Wa18kAgDAaPxRELa1\ntYlIWNh3bQPCwsLUwfOe08NzGxoaiouL//u//zssLOzEiRP3339/QUHBggULzpmho6Ojs7Oz\n9+/F4XA4HI7ev47f2O12rSOch0dR/mf1N9uO1ImIJcj0fy8fNmKArampSetc56eXP6m76OKn\n2qWXadMig3LTY4qONza1O1Z8VXL9+NS+CnZOTqdTXz9efX2OdXR0dHR09OYVzGZzbGxsX+UB\nAMBI/FEQWiwW+faLXpXb7VYHz3tOz8+dPXu2WiumpKRMnTq1sLCwu4LQarWaTKbevAtFUTo7\nOy0Wi16+ZlavEAZ4WkVRfv958deH60QkyGT66YKR07OStA7lFYfDYbFYursiHWjsdrvH4wkN\nDdU6iLc6OztDQkJ6+SK3zxz60Fs7FJHPCquunZgZHuyrj7uOjg6z2ayXHa3Ui9t6Set2ux0O\nh81mM5t7dSOoXv5TBQDA//xREMbFxQUFBdXW1qamnvqS/uTJk4mJid6c08Nz4+PjT79EExoa\n2tra2l2G4ODg4ODg3rwLt9utFoTh4eG9eR2/sdvtLpcrkNMqIi99tmf9gWoRMZlM984ZMjc3\nXS9/p3o8npCQkACvt7uoqwQD+ZfhDHa7vfdpc4eETx058OuD1W121+d7qrvaUfQtRVHUglAv\nP16n09knP17/sNvtakHY+y8IAADAOfnjS1ObzZaVlbV161b1Hx0Ox65du8aOHevNOT08Nzc3\nt2tcRIqLiwcNGuSHt4M+oYj8z6q9n+0sExGTyL/MH3npiAStQ8Fofjh7pLo04MPNRxvb9LRI\nEgAAwD/81HbitttuW7p0qdlsHjRo0Nq1a8PCwubOnSsiGzdufPfdd5ctW9bDOd2N33LLLQ88\n8MDzzz9/ySWXbN269eTJk9dff71/3g5679U1xX/fXioiJpH7rsyeNybxjNtKgd7LTIycNTp5\n3b4THQ7X+19/c/e8rPM/BwAAoD/x020VOTk5zzzzTGNj48aNG0eMGPHss8+qKwODg4NjYmJ6\nPqe78ZSUlN/85jcRERFr166Njo7+3e9+x54BerH8y4Pv/fMb9fiuuaOunUjrcPjKHbNHmINM\nIvLJtmO1LX2wsxQAAICR+OkKoYiMGTNmzJgxZwxOnjx58uTJPZ/Tw3haWtp9993Xtznha29t\nPPT2psPq8Q8vG7lo2lBt88DYUuPC5+am5Rced7g87371zX1XnOOTBAAAoN9i4zX41Udbjr6x\nrkQ9/v7M4d+bPkzbPOgPbp853GoOEpGVO8uqGtu1jgMAABBA/HeFEPj7tmOv5O9Xj2+aMvh2\n3+z6CJwhMTr0inHp/9he6nJ73t50+N+uydU6EfojRVHeeeed/Pz85ubm9PT0O++8My8vz8tz\nenjunj17Xn311bKysujo6Kuvvvrmm2/29xsDAOgcVwjhJ/mFx//ni1PV4PWTMu+5fLS2edCv\n3DZjeLDVLCL5heXldWxfBA188skn//jHP37yk5+8/PLLkydPfvrpp6urq708p7vxioqKZ555\nZt68ea+88so999zz2Wef7du3T4P3BgDQMwpC+MOaoooXP92jKIqILBib/i/cxwX/iosIvmZC\nhoh4FOXNDSVax0F/tHLlyltvvXXSpEmJiYm33XbboEGDvvjiCy/P6W78448/njZt2tVXXz1g\nwIApU6a8+uqr57zfHgCAHlAQwuc2Haj6r3/sVqvBeblp/3ZNjknrSOiHbr10aKjNIiLr9lUe\nqW7WOg76l9bW1qqqqlGjRnWNZGVlHTp0yJtzenjunj17kpKSfvWrX91yyy333HNPfn6+798K\nAMBouIcQvvXPg1XPfrjT7VFEZEZW8gPX5qqNwgE/iw6z3TA58+1NhxVFeXPDoaULJ2idCP1I\nU1OTiERFRXWNREVFqYPnPaeH59bW1ubn5z/wwAMPP/zw5s2bf/vb3yYmJo4dO/acGdra2hwO\nR+/fi8PhaGho6P3r+IH6RWSfvGs/UNO2trbqZZb0eDxOp1NHaUVEL7+6IqIoio7SiojT6dRL\nYEVRFEVxOp1aB/GK+snQ3t7e0dHRm9cJCgqKjo7u7lEKQvjQjiMn//ODXS6PIiLTRiY9fNM4\ntSMcoImF04Z+uqO0pcP5VXHVwRONI1NitE6E/uWMP53P+Zd0d+ecc9zlcs2ZM0ddJjp79uwN\nGzZ8+eWX3RWE8u3fxL2k/i3V+9fxD0VR9FKxqHT041Vz6ittn/wn4B+KougorUpHgXX34+39\nJ0PPn4QUhPCVXUdrf/XudqfbIyIThw549OZxFqpBaCo82HLz1CHLvzwoIn9bX/LM9yaf9ylA\nn4iNjRWRxsbGpKQkdaSxsTEmJsabc3p4blRUVFhYWNcrDBw4sKKiorsM4eHh4eHhvXkXbre7\noaEhODg4IiKiN6/jN3a73eVy9fJd+01HR0dbW1tkZKTNZtM6i1daWlpCQkKsVqvWQbzS1NTk\ndDrj4+O1DuKt+vr6uLg4rVN4RVGUuro6q9V6+kKGQOZ0Ou12u44+x1paWsLDw0NCQnz3/8I9\nhPCJ/eUNT763w+HyiMi4wQlPLJqoNoIDtHXjlMEx4TYR2Xb4ZFFpndZx0F+EhYWlpqbu37+/\na2Tfvn2n3xbYwzk9PHf48OHFxcVd4xUVFQMHDvTh2wAAGBF/o6PvFVc0PvrW1g6HS0TGpMc+\nsWiCzcJvGgJCiNW8aNpQ9fj5DwuffG/HnwoObD1co481T9Cz66677r333tu6dWt1dfXy5ctr\namoWLFggIvv27XvllVd6PqeH8S1btnz22WcNDQ1ffPFFUVGROg4AgPdYMoo+9k1V82Nvn6oG\ns1Jj/uO2yerWjkCAuHxs+vIvSxwud21rZ+3BKhH5YPORaSOTHr15HNex4TtXXnllW1vbyy+/\n3NTUNGTIkCeffFJdD1ZWVvb555/fe++9PZzT3XheXt7999//zjvvvPbaaykpKY888siwYcO0\nfZsAAN0x6eVuYM2p906EhIToaM2x/++dOFrT8tAbm5s7HCIyNCnqhdunRoR4dXeBeu9EVFQU\n9074gnrvREJCgtZBvOXTeyeWrdr7j+2lZ4/fPHXI/5mfdaGvpt47YbPZuHfCF9R7JyIiInx6\n7wS8xDzoU8yDPsU86DvMgz7ln3mQr8PRK26Pcry29XBVs9PtKa9re/StLWo1OHhg1HPfn+Jl\nNQj4TYfD9UXh8XM+tHJnmXrXKwAAQP/BWj5cJJfb87f1hz7eerTT6RYRc5DJag5SjwclRDy3\nZEpUqD6+40S/cryurbuqr8PhqqhvG5wY6edIAAAAGqIgxEX69Se71+070fWPbo/i9rhFJCUu\n/Pnbp6obOQIAAAAIZCwZxcXYU1Z/ejV4uvm5aXERwX7OA3gpPT68u51jQqzmlLiwcz4EAABg\nVBSEuBhbD9V099D+8np/JgEuSKjNcvnY9HM+dNX4QcEWs5/zAAAAaIuCEBejsd3R3UMNbd0+\nBASCe+ZnjR9y5kZzw1Oi75wz6pznAwAAGBj3EOJixHZ/iyDrRRHggq3m/7xt8j8PVhcerd1X\n3vBNVbOIJESG2ix8QQYAAPod/gDCxbhkxMDuHprW/UNAgDCZTJeOSrrvyuzf3jlNbY6y7VB1\nU/fXvQEAAIyKghAXo67FLmI6e3xMeuz8bm7QAgJQsMU8Y3SyiLg8ytq9FVrHAQAA8DcKQlyw\nQ5VNL3xSKKKISLD11CYcIVbzdZMy/+O2yZagcxSKQMCan5umHqzeXa5tEgAAAP/jHkJcmLqW\nzl+9u93udIvIpGEDnlo8qba50+n2JMeGBZkoBaE/Y9Jj0xMijte2Hq5qPlLdPGRglNaJAAAA\n/IcrhLgAHQ7XY29vq23pFJGMAZGP3jQ+yGRKjA5NjQunGoR+zc1JVQ8KuEgIAAD6GQpCeEtR\nlOc/LjxS3SwisRHB/3HbpLBgrjDDCObnpanfaKzdW+HyKFrHAQAA8B8KQnjrTwUHvj5YLSLB\nFvOvFk0cEBWqdSKgbyREhuRlxotIY5tj2+EareMAAAD4DwUhvPL5ruMfbjkqIiaRB67LHZUa\no3UioC/Nzzu1tQyrRgEAQL9CQYjzKyqte2nVXvX4h3NGzh6Tom0eoM9Nz0pSGxJuOVRDQ0IA\nANB/UBDiPI7Xtj753g6X2yMi83LTFl86TOtEQN8LtphnZCWJiMvt+XLvCa3jAAAA+AkFIXrS\n3OF44t3trZ1OEclOj7v/mhytEwG+Mq9r1WgRq0YBAEB/QUGIbrk8yjPv76yobxORpJiwpYsm\nWM38wsCwstPj0uLDReRwZZO6my4AAIDh8fc9urVs1d7dx+pEJCzY8tTiidFhNq0TAb41N+fU\nRcLVRRXaJgEAAPAPCkKc29ubDq/aWSYiliDT0oUTMgZEap0I8LnLv21IuGZPOQ0JAQBAf0BB\niHPYdKDq9XUl6vG/XDFm3OAEbfMA/pEQ9V1Dwu00JAQAAP0ABSHOdLiy6defFCqKIiI3Tx1y\nzYQMrRMB/jM/l61lAABAP0JBiP+lrqXzife2dzrdIjJp2IC7543SOhHgV9NHJ4UHW0RkcwkN\nCQEAgPFREOI7HQ7XY29vq23uFJGMAZGP3DRevZ8K6D+CLeYZo5NFxOX2rNtHQ0IAAGBwFIQ4\nRVGU5z8uVHfbj40IfuZ7k9TrJEB/892q0d2sGgUAAAZHQYhT/lRw4OuD1SISbDH/atHExOhQ\nrRMB2sgedKoh4aHKpqM0JAQAAIZGQQgRkS8Kj3+45aiImET+7drcUakxWicCtDQnJ1U9WL2H\nhoQAAMDIKAghRaV1v1+5Vz3+wWUjL8tO0TYPoLn5uWkmtSFhUQUNCQEAgIFREPZ35XVtT763\nw+X2iMi83LTvTR+mdSJAe4nRoXkZcSLS0Gbf8c1JreMAAAD4CgVhv9bc4Vj6zrbWTqeIZKfH\n3X9NjtaJgEAxP4+tZQAAgPFREPZfLo/yzPs7K+rbRCQpJuzxhROsZn4fgFOmZyWH2iwi8nVJ\nNQ0JAQCAUVEA9F/LVu3dfaxORMKCLU/eOjEm3KZ1IiCAhFjNM79tSLiehoQAAMCgKAj7qbc3\nHV61s0xEzEGmx2+ZkJkYqXUiIOCwahQAABgeBWF/9FVx1evrStTjn1wxZvyQBG3zAIEpe1Bc\ncmyYiJRUNh2tadE6DgAAQN+jIOx3Dlc2vfBxoaIoInLz1CHXTMjQOhEQoEwi83JPXSRcU8RF\nQgAAYEAUhP1LXUvnE+9t73S6RWTSsAF3zxuldSIgoF2ed6oh4eqiCjcNCQEAgOFQEPYjHQ7X\n4+9sq23uFJGMAZGP3DQ+yGTSOhQQ0BKjQ3NpSAgAAIyLgrC/UBTl+Y8Lv6lqFpHYiOBnvjcp\nPNiidShAB+Z/u2q0gFWjAADAcCgI+4s/ry7++mC1iNgsQU8smpgYHap1IkAfZow+1ZDwnwdp\nSAgAAIyGgrBf+KLw+Aebj4iISeSBa/OyUmO0TgToRojVPOPbhoQb9ldqHQcAAKAvURAa356y\n+t+v3Kse/+CykZdlp2ibB9Cd+bmp6gENCQEAgMFQEBpceV3br97d7nJ7RGTWmJTF04dpnQjQ\nn5yMeLUh4cETjTQkBAAARkJBaGStna6l72xr7XSKSHZ63M+vz2NTUeAimETm5py6SLh2T4W2\nYQAAAPoQBaFhuTzKC//YW1HfJiJJMWGPL5xgNfOvG7hIl+elqw0JC3aX05AQAAAYBhWCYb1S\nULz3eKOIhAVbnrx1Yky4TetEgI4NjAnNGfRtQ8IjNCQEAAAGQUFoTO98dTh/d4WImINMj98y\nITMxUutEgO7Nz/u2ISFbywAAAKOgIDSgr4qrln9Zoh7/y4Ix44ckaJsHMIaZpzUkbO6gISEA\nADACCkKjOVzZ9MLHhYqiiMi149OunZihdSLAIEKs5hlZSUJDQgCfscsBAAAgAElEQVQAYCAU\nhIZS19L5xHvbO51uEZkwJP4Hs4ZqnQgwFFaNAgAAg6EgNA670/2r93bUNneKSMaAyAevzQky\n0WYC6EtdDQmLKxqP0ZAQAADoHwWhQSiK8uxHu0pONIpIbETwM9+bFB5s0ToUYDSnNyRcQ0NC\nAACgfxSEBvHn1cVfH6wWEZsl6IlFExOjQ7VOBBjT5Xnp6pX3NXsqPAoNCQEAgL5REBrBF4XH\nP9h8RERMIg9cm5eVGqN1IsCwBsaE5mTEi0hdS+eOb2q1jgMAANArFIS6t6es/vcr96rHP7hs\n5GXZKdrmAQxvfu6pVaMFRWwtAwAA9I2CUK/sLreIlNe1/erd7S63R0RmjUlZPH2Y1rkA45s5\nJkVtSPj1warWTqfWcQAAAC4e+47ozLGaltfXlewtq2/ucMRHBnc63G12l4iMSY/9+fV5bCoK\n+EGI1XzpqKTVReUOl2f9vhNTMyO0TgQAAHCRuEKoJ/vLG3726lf/PFjV3OEQkboWu1oNJkaF\nLl040Wrm3ybgJ10NCVcXsdcoAADQMUoI3VBEfvuPIrXp/BkGDYiMCbf5PxLQb+VlnmpIeKCi\nsaKhQ+s4AAAAF4mCUDeOVTeX1bae86Gi0lqn2+PnPEB/ZhKZ821Dwo0H2WsUAADoFQWhbtQ0\nd3b3kMPlaWi1+zMMgPm5aepdu1+V1NKQEAAA6BQFoW6EB3e7A5BJJDzE6s8wAJJjw7IHxYlI\nfZujqKxR6zgAAAAXg4JQN0akxIR1UxMOT4npoVwE4CNdW8us21+tbRIAAICLQ0GoGzZLUFr8\nOXa3t5qDfjR3lP/zAJg5OlltSLjtSB0NCQEAgB5REOrGB5uPlJw4c1lafGTIL28ZPzYzXpNI\nQD8XarNMGzVQRBwuz/r9lVrHAQAAuGCsM9SHfccbXl1TrB7fd8WYIQOjapo60uLDBw+Mov0g\noKF5OalriipEpGB3+dXjB2kdBwAA4MJQEOrAyeaOp1Zsd3kUEblq/KDrJmVqnQjAKWMHJwyI\nDD7ZYj9Q3nC8tjU94RzrugEAAAIWF5cCndPteXrFzsY2h4iMSo35yRVjtE4E4DsmkekjEtTj\n1UUV2oYBAAC4UBSEge4Pq/YePNEoIjHhtsdvmcACUSDQzMoaoDYkXF1UTkNCAACgL1QXAe2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wu9xaxwEAAEZAQQgA+hAdZps0LFFE2uyu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\nbLnwwpzFmjlz6r/xjZzF5JFHNv3udyGEZDKZTCazi+WvvTbgs5/N2TKqq1v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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR 1D slice plots saved.\n" + ] + }, + { + "data": { + "image/png": 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IiIry6K93hY4GAAD+g4QQAKqI9X8mKlUaIuoR6VffFu63DKvjOeCF\nQGNzLyRjyBmocrosIakHEdGNnZR8UOhoAACACAkhAFQNNzKyj11JJyKZg3hs51Chw7GUskhj\nbFa+AgNvQJXjUovafPLf5z+nk1ohaDQAAECEhBAAqgCeaPXvCewhvGFtg6u7yQQOyGI+Hk7G\nZtXyNDoLwIY1e5u8I4mIspPo7FdCRwMAAEgIAcD2nUi4fy0ti4hquMkGt6kndDil0DLE21la\n8mjPnRrVqeBgACqCSELdVhBxRESnF1HOHaEDAgCwd0gIAcC2qTXaDcdusM/jujaQOYiFjadU\nXGUO0/pElDgroIZrBQcDUEF821HEWCIitZyOThU4GAAAu4eEEABs2+7TyRlPC4gopJZ718a2\n16vWrbHv4tdaNw2s7uQo4TjOVebApi89eCUrXylsbADlpeNX5FSDiCjlMCXtEToaAAC7hhfT\nA4ANyyks+ulkEvs8uVcjjqtcb6K3UGRg9cjA6jxRkVojEYlmbvo34V5WdkHR1/suRY9oZZNF\nAjDNqTq1j6Yjk4mI/nyb6nYnB3SJAwAIAz2EAGDDtsTdLFCqiah9eK3GAV5Ch/NcOCKpRCwW\ncXMGN2P9hGdvP95zOlnouADKR+MJVLs1EVFeGp36XOhoAADsFxJCALBVaZn5h86nEpFELBrX\ntYHQ4ViNt7vTjP6N2ef1fyQmPcgVNh6AcsGJqNsK4sREROe+pScJQgcEAGCnkBACgK1afSRB\nreWJaEDLQF8vF6HDsaYO4bV7RvoRkUqj/TzmvLxILXREAOWgZhRFTiYi0hTR0clEvNABAQDY\nIySEAGCTLqY8OZP0mIhcZQ6vtg8ROhzre7NPhF91FyLKeFqw+nd0nkAV1WEhudQmIrp3gq5v\nFzoaAAB7hIQQAGyPludXHb7GPo/uHOrm5CBsPOVB5iD+8OVmErGIiA5dSPvrWobQEQGUA0d3\n6vjlf5//eo+U2YJGAwBgj5AQAoDtOXQhLflRHhH5VXfp17yu0OGUl5DaHq93CWOflx28+jBb\nLmw8AOWi4WsU0JWIqPAh/f2J0NEAANgdJIQAYGPkReotcTfZ5wndwyWiqvxehsFt6rWq70NE\n+QrV4j0XtTwesoKqqOsyEjkQEV1cQQ/ihY4GAMC+ICEEABvz08nb7I3tkYHVW4fWFDqc8sUR\nvfdiZDVXKRFdTXu67fgtoSMCKAfVG1Lzd4iIeC39MY14rdABAQDYESSEAGBLMnMVsfHJRMRx\n3ITu4UKHUxE8XRxnvhTJukG3nUi6lPJE4IAAykObeeQeSET04AxdWSdwMAAA9gQJIQDYknV/\nXFeqNETUq6lf/doeQodTQVoEew98IYiIeJ5fvPdinlwldEQA1ubgTJ2/+e/ziQ9J/ljQaAAA\n7AgSQgCwGYnp2X9dzSAiJ0fJmM5hQodTocZ3axBSy52IMnMV3+2/LHQ4AOWg/iCq14+ISPGU\njn8gdDQAAPYCCSEA2Iy1R66zMVWGtQ32cpUKHE3FchCLPhoc5eQoIaK/Ex8cupAmdEQA5aDL\nEpLIiIiubqB7cUJHAwBgF5AQAoBtiLuWcTXtKRHVcJMNbh0kdDgCqOPlMqlnQ/Z55eFrqZn5\nwsYDYH2ewdSK9Q3y9Mc00uLuaACAcoeEEABsgFqj3XDsBvs8rlsDqYNY2HiE0qeZf+dGdYhI\nqdJ8HnO+SI3BGKHKafUBeYUREWVepQsrhI4GAKDqQ0IIADZg9+nk+1mFRBRSy71rRB2hwxHS\nW30jano6EVHKo7wf/0wUOhwAaxNLqeuy/z7/8wnlpwsaDQBA1YeEEAAqu5zCop9OJrHPU3o1\n4riq/CZ6s1xlDu8PbCriOCLaczr51M2HQkcEYG11e1DoECKiojyKmyl0NAAAVRwSQgCo7Db/\ndbNAqSaiDuG1IwK8hA5HeBH+XiM6hBART/Ttr5ef5iuFjgjA2jp/Rw6uRESJP1HyIaGjAQCo\nypAQAkCllpqZf+hCKhFJxKLXu9rXqyZMGNWxfpO61Ykop7Do672XeJ4XOiIAq3Lzo7bz/vt8\nbDppcNUDAKC8ICEEgEptzZEEjZYnooGtAn29XIQOp7LgOG72wKZuTg5EdO7O492nk4WOCMDa\nomaQdyQRUdYtOvu10NEAAFRZSAgBoPK6mPLkTNJjInJzcnilXYjQ4VQuNdxlM/o1YZ9//CMx\nMT1b2HgArEwkoW7LiTgiolOfU84doQMCAKiakBACQCWl5flVh6+xz6M7hbLeMNDXPrxW72b+\nRKTW8l/uuSgvUgsdEYBV+banRqOJiNRyOvqm0NEAAFRNSAgBoJL649rD5Ed5RORX3aVv87pC\nh1NJTenVyL+GKxFlPC344XCC0OEAWFunb8ipOhFRym90e5/Q0QAAVEFICAGgMpIXqXedTmWf\nJ/ZoKBHZ9asmTJA5iD8aHOUoERHR7xfTjl3NEDoiAKtyqk5tP/vv859vQLSlswAAIABJREFU\nkapA0GgAAKogJIQAUBn9/M+d7IIiIooMrP5CfR+hw6nUgnzcXu/agH1edvDKg+xCYeMBsLLI\nyVT7BSKi3FQ6vVDoaAAAqhokhABQ6TzOle89c5eIOI6b2D1c6HBswMsvBLG0uUCpXhhzQa3F\nWyigCuFE1G0FcWIiorNf09PrQgcEAFClICEEgEpn3dFEpUpDRL2b+oXU9hA6HBvAEb33UqSX\nq5SIbmRkbz9+S+iIAKyqZnNqMpGISFNERyYT4ZIHAIDVICEEgMolMT077loGEckcxKM74030\nlvJwdpw5IJLjOCLafjLpYsoToSMCsKoOi8ilNhHRveOU+JPQ0QAAVB1ICAGgEuGJ1hxJYBf/\nBzT3Y11eYKHm9bwHvRBERDzPf7X3Yq68SOiIAKxH6kEdFv33+a93SYkXbwIAWAcSQgCoROKu\nZVxLyyKiGm6yF6N8hQ7H9ozr1qCBrycRZeYqvt9/RehwAKyq0Wjy70JEVPCA/pkvcDAAAFUF\nEkIAqCzUGu3GYzfY57FdQtmrFKBUJCJu9sCmTo4SIvo78cGB86lCRwRgRRx1W0YiByKiC8vp\n0QWh4wEAqArwewsAKouYU8n3swqJKKSWe5dGtYUOx1bV8XKZ2qsh+7zq8LXkh7nCxgNgTdUb\nUdR0IiJeQ0cmEa8VOiAAAJuHhBAAKoWcwqKf/05in6f0asQGR4Gy6dnUv0tEHSIqUmsXxV5U\nqjVCRwRgPW0/Jfe6REQPztDVH4WOBgDA5iEhBIBKYdNfNwuUaiLqEF47IsBL6HBs3lt9G9fy\ndCaiu4/z1h9NFDocAOtxcKZOX//3+fhskmcKGg0AgM1DQggAwkvNzP/tQioRScSicV3xqgkr\ncJFK5gxuJhFxRLTvTMq/Nx8KHRGA9YQOoaC+RESKp3RyjtDRAADYNiSEACC81b8naLQ8Eb3c\nKrCOl4vQ4VQRYXU8R3SsT0Q80Xe/Xn6SpxA6IgDr6bqEJDIioivr6cIyurqB0o6RKl/osAAA\nbA8SQgAQ2IXkzLO3HxORm5PD8HYhQodTpYxoH9I0sDoR5RQWfRF7UcvzQkcEYCWeIdRyNhER\nr6U/36bD42hnV1rtR1fWCx0ZAICNQUIIAELS8vzq3xPY59Gdw9ycHISNp4rhOG7WgKbuTo5E\ndPnuk1/+vSN0RADW41JsLGJlDv3+BiX+JEQ0AAC2CgkhAAjp0PnU5Ed5RORfw7VvVIDQ4VRB\nNdxlM/o3Zp83HrtxPT1b2HgArIPX0D/zSp514kMidIYDAFgKCSEACEZepN5y/Bb7PLF7OBsB\nBayuXYNaLNnWaPkvYy/Ii/AWCrB9T65ToZGhknJTKCe5YqMBALBhSAgBQDA7TiZl5SuJqGlg\n9Vb1fYQOpyqb0qtRkI8bEd3PKlzzx02hwwF4bkW5puYqcyoqDgAAm4eEEAAqWqFSfTMjO+He\n0z3xKUTEcdyE7uFCB1XFOUpEH7zcTCoRE9FfCQ/iEh4IHRHA82Hvpi8RJzY1FwAA/pdE6AAA\nwI7czypcdvDquTuP9Sf2buYfUttDqJDsR6CP27huDVYevkZEK4/ciAj09sUbPgSVnp5+/vx5\nlUoVERERGhpaqmWMTb93797ly5dVKlW9evUaN25c7mUQkKsv+XWie3ElzArsRTKvCg8IAMBW\niefPny90DDapqKhIo9E4OTlxnA089aRWq3med3CwjfEb5XK5WCyWyWRCB2IRpVIplUptpRkU\nFRU5OjpKJMJcCcrMU8z48e87Dw1v9GoR7N0sqIbBRJ7ni4qKbKUZKBQKrVbr7OwsdCBmhPl6\n3n6Qe+9JgVqjvZ6e3bOpv6hyN12e5xUKhUQikUqlQsdiZSdOnPjkk0+IKC8vb8uWLRzHNWrU\nyMJljE0/cODAwoULOY7Ly8vbvn17enp669aty68Iwp8H67xAN34mdeH/TOQ46rmueA8hzoPl\nB+fBcoLzYPlRqVQcx9lKM6iA86ANVAQAVA3bjt96mq8sPj3m3zsDWwVWc6lqv/grIY7onReb\nJK6KyyooupmRvTXu5tguYUIHZY80Gs3q1atHjx49cOBAIoqPj1+0aFGXLl1q1Khhdplq1aqV\nON3V1fXHH3+cPHlyz549iejChQvz5s175ZVXatWqJVApy59XOI06R//Mo7RjlJtKji5UlE88\nT+e+Jb+OQgcHAGAz8AwhAFSQ+FuPSpyu0mgv3Mms4GDsloez44y+Ddm1/J/+vn0p5YnQEdmj\nmzdv5uXlscyNiFq1auXu7n7u3DlLljE23cHB4bvvvuvSpQubHhAQQES5uSZHXqkC3AOo9waa\nkELvqmh8Ekk9iYiS9tLtX4WODADAZqCHEAAqSHZhkbFZWQUl9BxCOWkSUO3llgG74+/yPL94\nz8WVkzqwN9dDhbl//76bm5v+vVW1atW6f/++Jcs4ODiUOF0sFrMkkPn9999r1aoVHBxsLAaF\nQsHzz/WyPo1Gw7ZTWe4V5NwlrT5xOPEuEfF/vKnwbkMO//+ULLtlVC6XCxefpdhx0Wq1NhEt\nEWm12krUDExSq9VEVFRU9JyNv2JotVrbagZEZCvRqtVqjuNspRkQkUajec665TjOxO3HSAgB\noIJ4uUof5ZT858zL1TaekagyRnYIvnov52ZGdmae4ttfL88b1sIGfspVIUql0tHxf5JwqVSq\nVCotWcaSdffv379///7PPvtMLBYbi6GwsJD9znhOhYWF5heqMEEjPa9vlWSe5/LS+NOLCpt9\naDC/qMjoZanKRqPRFBQUCB2FpSpXMzCnqKjIhlqCDTUDsrVoDf5yVmZqtZpdzigz048lIyEE\ngArSOrTmvjMpxadLHcRR9QwHlYFyJRFxcwY1m7r2RKFS/e+Nh/vP3n2xBYbprzhSqdTg96hC\noTA4VRtbxvS67MnDS5cuffHFF/7+/iZicHV1fc6r4wqFQqVSubq6VqquIW3XZbSrI/Ea52vL\nJY3H8NX+e0pWpVJptVqbGJ2I5/n8/HyxWGwr43MUFhbayhh7KpWKfV9sYngh1vVqK82goKBA\nq9W6ubkJHYhFlEqlSCSylWZQUFAgkUicnJyeZzumv6FICAGggozoEHLq5sPinYTjujbwcMYt\nixWtdjXnN3s3+mrvJSJacyShkX+1ejXdhQ7KXvj6+ubm5ubn57u6urIp9+/f1z0WaHoZE+uq\n1epFixYplcpvvvlGN9cYg27GMmB5qaOjo0hUmcYj8G9Ljd+gy6tJU+R48j0acoRN5nnethJC\nkUhkE9ESkUKhqHTNwAh2EcRWBi7WaDRFRUU2ESoRyeVyW/mKEZFGo7GVrxi7WUAsFpdrtDbw\n7QWAqqGai/S7sW2DfP7/8mHtas7vD2w6sFWgcEHZte5N/Lo29iWiIrV2UewFpVojdET2on79\n+l5eXr/99hv778mTJ+VyeYsWLYiosLAwPz/fxDIm1l27dq1CoZg3b57ZbLCK67CInH2IiO4e\npRs/Cx0NAEBlhx5CAKg4Ndxl9et4Jj/KI6K5Q5u3b1B1B8S3EW/3jbiRnp3+tCD1cf7aI9en\n9YkQOiK7IBKJpkyZsnjx4uvXrzs6Op45c+b111/39PQkohUrVuTm5i5YsMDEMiVOT01N/e23\n36KiolatWqXbUefOnav46+lLJKtGHb6gw+OIiI7NoMDeJPUQOiYAgMoLCSEAVCjdi+kj/L2E\njQSIyMlR8sHLTd/Z8I9ay/969m7zet5twmoKHZRdeOGFF5YtW3b27FmN5v/Yu/P4qKr7f/yv\nO2smmawkIQQSSBBEdhAVEJFFraWuFRTr0rr0pxbEpWjdtVJFW3GFun4UaetSK/ZLrQvIokhZ\nBGRfNZOENRvZJrPe5ffHhIA3C5NkJic383r+4eP6zp3JK8PN3HnnnHuuMm3atPz8/FB97Nix\nDescNLdPk3WbzTZ9+nTddzHEhKioGPwb7FqEA6tQdxRr/4gJz4sORETUebEhJKKOo6hacVkt\ngFSnPSWB1w12Cv2zU64/v//ClXsBPLdk66v/33mZye26cp3ClJ2dfdlll+mK55577in3abKe\nlZV17bXXRjykYUmYPB+LRkANYvPLGHgjkgaIjkRE1EkJawgPHTq0efPmYDA4ePDg/v37t2qf\n5uoHDx7ctm1bMBjMz8+PxUkyRJ3eoYq6gKwC6Mv1SzqT6ef23VpY8b2r3O0LPrP4+19POr28\n2tc91dEvK9lubfa+BUSdWrdBGHEnNj0PTcGKmbhimehARESdlPmJJ57o+O+6evXqxx57DEBt\nbe3f/vY3SZIGDRoU5j7N1f/73/8+/fTTkiTV1ta+9957hw4dGj16dPR+hEAgoCiKUdZZDt2Q\n1xCr6wLwer0t3yylU/H7/Xa73SiHQSAQsNlsFouwvwRtdpV/u+cogHMHZI3Mz2hhT03TAoGA\nUQ4Dn8+nqqpRFgcPBoOSJJ18GEiSNDwvffm2Q/6gUlbjW7b14Jq9R5duOfjppqKUBHvfLGHd\nu6ZpPp/PKEsCxhoDnAezx2LXIgRqUXtAS8xR04fxPBgNPA9GCc+D0dP4PNhpdcx5UMALEbpL\n0o033njFFVcA2LBhw9y5cydOnJienn7KfVJTU5usO53Ot99++/bbbw8tvf39998//vjj06dP\nz8rikhVEnYirtP4CwjyOEHYy6YlxFwzt+fE618nFWm9w3pKtcVbz+IE9RAUjajtbIiY8j0+v\nAWD530Ny7ykwyKdVIqKOJOC2E/v27autrW244dLZZ5+dlJS0adOmcPZprm61Wl944YWJEyeG\n6rm5uQBqamo66EciovC4SmpDG7zlXWejadqqnUea/NJby3e36/7lRAKdfjXyfg5A8h2zrn9C\ndBoios5IwAjhkSNHEhMTTx5TzsrKOnLkSDj7WK3WJutmsznUBIYsXbo0Kyurb9++zWUIBAKh\nm5O2maqqoecxyhwJVVUbVq7rzEL/LkZJC0BVVQMdBqH/CnxtfyypBmAxmzKdlpZjqKpqrMMA\ngFHSyrJsMpl0aQ9U1FXU+prcv6TKW1xSlZUiYKWZ0AurKEo7X1tJktp/H3Yyqkkv490hkH2W\n3Qsx7FZkjxEdiIiocxHQEPr9ft2J2W636072ze0TzmM//fTTTz/99MknnzSbm10Lwe12hz5n\ntFPo9sFGEQgEREcIl6IotbW1olOEy1iHgc/n8/ma/twfbW6fXFHrB9AjJc7rqQvnIQY6DGC0\ntLrDoKyypcO49Fh1glmOcqJmybLcztfWbDazIYxdKadh1H1YNweaiq9ux/WbYDLAhUNERB1G\nwHui3W7XdSY+n093yWxz+7T82NCVh1u3bn3mmWdycnJayBAfH9/OEUK/3y/Lcnx8vFGGhoyy\nqIymaR6Px0AX0/t8PgNdTB/6k4qoI8F1rDK00TcrKSEhoeWdQ0OvRjkMvF6vqqqn/KE6iSYv\nps/NbPZ0IElSbvfUBIeAw0ZVVa/X2/6L6Q3xG0pRdM6D2q6/SzUulG3D1tcwYqboQEREnYiA\nhrBnz541NTVut9vpdIYqR44cabgssOV9WnisLMtz5871+/3z5s1r+Gpz2v8pU5ZlWZbj4uJM\nJgHXYbZWaOknh8MA9xYLNYQmk8kQaQGEmhajHAahhlBUl3Ww8mhoo3926in/fRVFkWXZKIeB\n3+83yq8YAE3TTCaT7jDo6XAMyU3bXnys8f5De6d1TxNzzaeiKKHlFo3y2lInZXEEz3/J9p/L\nAODbh9Hvl3Bmi85ERNRZCPgU269fv7S0tC+++CL0v99++63X6x01ahQAj8cTmn3X3D4tPPbN\nN9/0+XyPP/74KbtBIhKioLR+1l9eJleU6YxmTRmS2NQwoAZwURkyOjX3QiXvUgAI1OCbP4iO\nQ0TUiQgYITSZTHfcccef//zn3bt322y277777qabbkpJSQGwYMGCmpqaOXPmtLBPk/Xi4uIv\nvvhi5MiRr732WsM3mjBhAm9PT9R5uEoa7jmRKDYJNSk3w7ng1vMWrtq7tbCiotaXmmCv9QVl\nRd1WWPHR/368emyzy3QRGUJg3HOOgysRdGP33zH4JuROEp2IiKhTEHNd9TnnnPPKK69s3LhR\nUZRp06bl5+eH6mPHjm1YIaa5fZqs22y26dOn674Lb2RM1HmomlZc5gaQ6rSnJvB3s5PqnuL4\nwxXDAQQV1Wo2fb3z8NOLvwfwzoq9/Xokj8hLP9UTEHVemrMXRj+M1Q8CwFd34NfbYOZ7ERGR\noIYQQHZ29mWXXaYrnnvuuafcp8l6VlbWtddeG/GQRBQpB8vr/LICIJ/zRY3AajYBOH9Q9vbi\nY//ZWKRq2rOfbFnw23HdEo2xzA9R0868FzsX4dhuVO7DphdxNueOEhGJuIaQiGJQQWn9fNF8\nzhc1lNt/NuiMXqkAKuv8z3yyRW3f+sxEgpltuPA1QAKAdU+iplBwHiKiToANIRF1BFfJ8RVl\nunOE0EgsJumhX45IctgAbCuqWLRqn+hERO3TazzO+BUABD1Yda/oNERE4rEhJKKOUHB8RZn8\nTI4QGkxmsuO+y4eFbuX3wbc/rN1bIjoRUftMmAd7CgDs/wQFn4pOQ0QkGBtCIuoIoSmjFrMp\nJ503hjGes/tlXj02H4AGPLdk69Eqj+hERO0Q3x1j/1i/vXwmgjyeiSimsSEkoqir9QbLa3wA\nctOdFjPfdgzpNxNPD60y6vYFn/r4+6Ciik5E1A4jZiBzBADUFOG7Z0WnISISiZ/MiCjqCngH\nQuMzSdIfrhweWmV03+GqN5btFp2IqB0kMy58HZIJADY8g2N7RQciIhKGDSERRd2JJUZ5zwkj\nS02wP3DlcLNJArDku8Ll2w+JTkTUDllnYcgtAKAEsOJO0WmIiIRhQ0hEUXfSEqMcITS2ob27\n3Tihf2j7lc92FJe7xeYhapfznoEjAwCKlmHfR6LTEBGJwYaQiKLuxBKjvOeE8V1z7mljT88C\n4A3IT3282RdURCciaqu4NJw3t357xV3wVwtNQ0QkBhtCIoouVdOKy9wAkuNtqQl20XGovSTg\n95cNzUqJB1BYWvvip9tEJyJqhyE3o9f5AFB3BOvmiE5DRCQAG0Iiiq6DFXV+WQHQN4vDg12E\nM8768FUjrGYTgJU7Dn++uVh0IqI2kzB5PkxWANj8Esq2is5DRNTR2BASUXRxvmiX1D875f+7\naGBo+69f7tx/hHPtyLDSB2PEDABQZSyfCWiiAxERdSg2hEQUXQ0rynCJ0S7mslG9Jw/tCSAg\nq09+tKnWGxSdiKitxj4JZ08AOPQtdr4rOg0RUYdiQ0hE0dVwzwkuMdr1zJoyJDfDCaC02vvc\nkq0cWCGjsiXi/Ofqt7+eDW+50DRERB2KDSERRVdoyqjFJOWmO0VnoQiLs5ofnzbKYbMAWLev\nZPG6AtGJiNpqwHT0uRgAvBVY86joNEREHYcNIRFFUa03WF7jA5CTkWgx8w2nC+rVLeHuS4aE\ntv9v+Z4dxcfE5iFqu0kvw2wHgG1v4Mg60WmIiDoIP58RURS5js8Xzc/kfNEua8Kg7F+MzAWg\nqNqfPt5c6faLTkTUJqn9MGo2AGgqls+ExntsElFMYENIRFFUcHxFmTwuMdql3XHxoH49kgFU\nuv1zP/le1Xg5IRnT6IeRnA8AJZuw9TXRaYiIOgIbQiKKohMjhFxRpkuzmk2PTTszyWEDsLWw\n4u/f7BediKhNLA5csKB+e/VDqDsiNA0RUUdgQ0hEUVRwtGHKKEcIu7jMZMfsy4dJAID3Vv+w\n8ccywYGI2qbPxeh7GQAEavDNA6LTEBFFHRtCIooWVdOKyt0AkuNtqU676DgUdef0y5w6Jh+A\npmnPfPJ9SZVXdCKiNpn0CqwJALDrbziwUnQaIqLoYkNIRNFyqKLOH1QA9M3i8GCsuGnSgCG5\naQBqvcGnPt4sK6roREStl5SLcx4CAGhYPhNqUHAeIqJoYkNIRNESugMhgDzOF40ZZpP08NSR\naU47gL2Hq95avkd0IqI2GTUbaWcAQMUubHpRdBoioihiQ0hE0eIqrV9ilCvKxJTUBPsDV44w\nSRKAT9a7Vu/mshxkQGYbLnwVkABg7ROoKRSch4goatgQElG0NIwQ5vOeEzFmWJ9uN5zfP7Q9\nb8m2A+VusXmI2qLX+RgwHQCCHqyaLToNEVG0sCEkomgJ3YTQYpJy052is1BHu3Zc37NOywDg\nDchPfbw5dDUpkcFMeB72FADY/zEK/is6DRFRVLAhJKKocPuC5TVeADnpTouZbzUxR5KkB64c\n0T3FAcBVWvvif7eLTkTUeglZGPtE/fbKuyD7RIYhIooOfkojoqgoKKnRAHC+aAxzxlkfvmpk\n6M8BK7Yf+nLLAdGJiFpvxExkjgCAqh+x4RnRaYiIIo8NIRFFRWi+KIA8NoQx7PTslN9ecEZo\ne/7nO344WiM2D1GrSWZc+DokEwBseAbH9ooOREQUYWwIiSgqXKUN95zgEqMx7Yqz+0wa0hNA\nQFaf+nhznV8WnYiolbLOwuCbAUDxY+Us0WmIiCKMDSERRUXDCGFfjhDGvFlTBocWFjp8rO4v\n/96iic5D1Grjn4UjHQAKl2L/x6LTEBFFEhtCIoo8VdOKymoBJMfbUp120XFIMIfN8vBVI+1W\nM4C1+0r+vd4lOhFRK8WlYdzT9dsr70aQd1Ihoq6DDSERRd6hirrQbQa4ogyF9MlMvOeSoaHt\nt77avfNApdg8RK025BZkjwGA2oNY+6ToNEREEcOGkIgiz1VaP1+UDSE1mDg4++IROQBkVXt6\n8eZqT0B0IqLWkEy48HWYLACw6QWUbRUdiIgoMtgQElHkFZTUryiT350rytAJMy4efFqPZADl\nNb65i79XNV5OSIaSPgTDfwcAqozlMwEewETUFbAhJKLIa2gI8zI5Qkgn2Cymh385IsFuAfC9\nq/z91T+ITkTUSufOgTMbAA59i11/E52GiCgC2BASUeSFpoyaTVJuhlN0FupcstMS7rtiuAQA\n+Ns3+zf+WCY4EFGr2JIw/i/121/fBx+vhiUiw2NDSEQR5vYFy6q9AHLTnVYz32RIb0z/7r8c\nnQ9A07Q//3tLea1PdCKi1jjjV8idDACeUqx5RHQaIqL24mc1IoqwgpKa0IU1eVxRhppxy+QB\ng3PSAFR7An/6aJOsqKITEbXGBa/CbAeAra/hyHrRaYiI2oUNIRFFWMMSo3mZXFGGmmY2SQ/+\nckRyvA3A7kNVb6/YKzoRUWuk9sOoewFAU7F8BjRFdCAiorZjQ0hEEeY6scQoRwipWelJcQ9d\nNdIkSQAWryv4dvdR0YmIWmP0o0jOA4CSTdj2hug0RERtx4aQiCLsxxLehJDCMrxPt1+ddxoA\nDXhuydaDFXWiExGFzeLAhBfqt1c/iLojQtMQEbUdG0IiiiRV04rKagEkx9vSnHbRcaizu358\nv1F9MwB4A/KfPt7slzn1jozjtMvR91IA8Fdj9YOi0xARtREbQiKKpMPHPP6gAg4PUngkSXrg\nyhGZyQ4ArpKaV7/cJToRUWtMmg9rAgDsXIQDqwSHISJqEzaERBRJBbyAkFop0WF9+KqRFrMJ\nwOebi5duPSg6EVHYknJx9gMAAA3LZ0INCs5DRNR6bAiJKJIaGkIuMUrhG9Az5ZbJA0Lbr3y2\n/cejNWLzELXCWfcjbQAAVOzEN/djz/so+greCtGxiIjCxYaQiCLpxD0nOEJIrXHlOXnnndED\nQEBWn/p4s8cvi05EFB6zDZNeqd/e9CL++yv860K8no2v74PKw5iIDIANIRFFUmiE0GySctOd\norOQkUjA7y8bmpPuBHDoWN0Ln24TnYgobM5sSJafVJQANj6HVfcKCkRE1ApsCIkoYty+YFm1\nF0BOutNm4dsLtY7DZnn4qpF2qxnAN7uOLPmuUHQiovCsfRJaU4OBW19FTWFHhyEiaiXLqXch\nIgqPq7RWA8ALCKmt8jITf/ezQaHhwdeW7lyzt6Sy1pMcbx+U2+3ys/qk8kYm1DkVr2i6rso4\nsAqDftOhYYiIWokNIRFFDJcYpfa7eETOzoPHlm45qKjY4ioHAHi2FVd+trn46V+dfVqPZMH5\nuoqqqipN09rzDKqqAqiuro5QoujSNE3TNL/fH40nTw00+yJ4jh32V1a24TllWa5s0wM7nqqq\nBjoMANTV1Xm9XtFZTi100BrlMFAUBYBR0oZeW0McBiGBQKCdr63JZEpObvYEyoaQiCLGxYaQ\nIuG0rOSl0N98otoTeGrx92/ePj50gwpqpxY+GYTJ7Xb7/f6kpCSTyQD/Ij6fT1XV+Pj4qDx7\nYi6qfmjyK47uAxwpKa16Mk3Tjh07ZrFYkpKM8UZaU1PjdDqNchjU1dXFx8fHxcWJznJqiqLU\n1dUZ5TCorq6WZTmllUe7KF6vV5IkoxwGVVVVNpvN6Yzi0gxsCIkoYgpKji8xyimj1A7LmrkV\n4eFjdVsKK0b1zejgPF2SJEmRep5IPVVUScdF5dlPvwbrn2qi7ugm5V2Etn5TQ7ywIQY6DGDA\ntKKDtIKB0hrrMECUX1sD/DmHiAxB07SisloAyfG2bokG+KsbdVrF5e42fIlImLP/gMwRTdQH\n3wIr11smos6ODSERRcahYx5fUAGHB6ndrM1PCm3hS0TC2BIx/RucdT9S+kIyweKor+95H0H+\nCYOIOjueWYkoMriiDEXKgJ7NXoXSwpeIRLI6Mf5Z3PIDZrkxy43sMQBQewDr/iQ6GRHRKbAh\nJKLIcJXWN4R5bAipfa4e27fJ+qi+Gf24yih1chYHJBMueA0mCwBsnIeybaIzERG1hA0hEUWG\n6/iKMhwhpHYa1qfbvZcOtVl+coYa2rvbA1c2dZkWUSeUMRTDbgcAVcaKmUC77vBBRBRVXGWU\niCLjx5IaAGaTlJvORRSovX42POes0zLX7jmy/1BFamLcsPysYX26GWA9OKIG457C/sVwH8bB\n1dj9D5xxvehARERNY0NIRBFQ55fLqr0AenVz6gZ2iNomzWm/eETNW6hiAAAgAElEQVTOOX2c\ndrs9MZErFZHR2JIw/ll8dgMArJqN/Etg5xWwRNQZ8XMbEUWAq6QmNCMqvzs/uBMRAQDOuB65\nkwDAU4I1j4pOQ0TUNDaERBQBXGKUiKgJF7wKsx0AtvwVRzeITkNE1AQ2hEQUAQWl9SvK5GWy\nISQiOi61P868GwA0FV/NgKaKDkREpMeGkIgiwHVihJBTRomITjL6MST1AYCSjdj+puAwRESN\nsCEkovbSNK2ozA0gyWHrlhgnOg4RUWdijceE5+u3Vz8IT6nQNEREemwIiai9Dh3zeAMyODxI\nRNSkflci/xIA8FVi9QOi0xAR/QQbQiJqL1cpV5QhImrR5PmwxgPAjoU4+LXoNEREJ7AhJKL2\ncpUcX1GGDSERUZOSeuOsPwAANCyfCTUoOA8R0XFsCImovU7ccyKTU0aJiJpx9gNIOx0Aynfg\n+/mi0xAR1WNDSETtVVBaA8BsknIz2BASETXDbMOkV+q3//c43IeEpiEiqseGkIjapc4vl1Z5\nAfTq5rRZ+JZCRNS83hei/zQACNRi1e9FpyEiAtgQElE7uUpqNABcYpSIKByTXoI9GQD2fgjX\n56LTEBGxISSi9ikoPb6iTCZXlCEiOpWEHhj9aP32ilmQfULTEBGxISSi9nGV8J4TREStMfIu\nZAwDgKofsPE50WmIKNaxISSidmlYYjSPU0aJiMJhsmDyfEACgPVPo7pAdCAiimlsCImo7TRN\nKypzA0hy2NIT40THISIyiJ7jMOjXACB78dUM0WmIKKaxISSitjtc6fEGZHBFGSKi1jr/OTi6\nAUDhF/jh/4lOQ0Sxiw0hEbXdSfNFeQEhEVFrOLrh3Dn12ytnIVgnNA0RxS42hETUdq6S+iVG\n8zM5QkhE1EpDb0OP0QBQU4z1T4lOQ0Qxig0hEbVdQSmXGCUiaivJhMnzIZkBYOM8HNstOhAR\nxSI2hETUdqEpoyZJyslwis5CRGRA3c/EsNsAQAlg2e2AJjoQEcUcNoRE1EYev1xa5QXQKz3B\nbjGLjkNEZEznzUVCDwA4+A12vy86DRHFHDaERNRGBaU1oT9l9+V8USKiNrMlYfwz9dur7oW/\nSmgaIoo5bAiJqI0Kjq8ok5fJhpCIqB0G3oCciQDgKZHWPiE4DBHFGDaERNRGrhP3nOASo0RE\n7SFh8nyYrADw/QLLse2i8xBRDGFDSERt1NAQ5nOEkIionboNxJl3A4CmONf+HpoqOhARxQo2\nhETUFpqmFZa5ASQ6rOlJcaLjEBEZ35gnkNQHgKX8e9vev4lOQ0Sxgg0hEbXF4UqPNyCDdyAk\nIooUazwmPBfajPvuCXjLxMYhohjBhpCI2sJ1fEUZzhclIoqYflch/xcAJH8lVj8kOg0RxQQ2\nhETUFgWlXFGGiCjytAkvamY7AGz/Pxz+n+g4RNT1sSEkorYoaFhRhlNGiYgiKKWvd/AsAICG\nZbdDDQrOQ0RdHRtCImqL0JRRkyTlZjhFZyEi6lK8Q+9Wk08DgPLt2PJX0XGIqItjQ0hEreYN\nyCVVHgC9uiXYLWbRcYiIuhTNZPOO+XP9/6x5FO5DQuMQURfHhpCIWq2gpFYDwPmiRETRIfea\nhH5XAUCgFl/fJzoOEXVlbAiJqNUaLiDkijJERNEy8UVYnQCw530ULxedhoi6LDaERNRqroYV\nZXjPCSKiKEnshTGP1W9/dQcUv9A0RNRlsSEkolYrKD1+E0JOGSUiip4z70HGUACo3I+N80Sn\nIaKuiQ0hEbWOpmmFpbUAEh3W9KQ40XGIiLoukwWTFwASAKz7E6pdogMRURfEhpCIWudIpccb\nkAHkcb4oEVG09RyHgTcAgOzFyrtFpyGiLogNIRG1juv4fNG+nC9KRNQBzv8L4lIB4Mcl+PE/\notMQUVfDhpCIWodLjBIRdaj4TJw7p357xUwE64SmIaKuhg0hEbVOQUn9CGEeRwiJiDrGsDvQ\n4xwAqCnG+rmi0xBRl8KGkIhax1VaA8AkSb3TnaKzEBHFBsmEyQsgmQFg419wbI/oQETUdbAh\nJKJW8Abko5UeAL26JditZtFxiIhiRvczMfS3AKAEsOx2QBMdiIi6CDaERNQKBSW1oc8gnC9K\nRNTRxj2N+EwAOPg19nwoOg0RdREW0QHECAQCmtauP62pqhp6HkmSIhQqimRZVlXV7/eLDnJq\noX8Xo6QFoKqqgQ6D0H/b89ruP3wstJGT5ojqv5GqqsY6DAAYJa2iKEZ5bUMvrKIo7UwrSZLN\nZotQKCJx4lIx/ll8cRMArLoHeT+HPVl0JiIyvBhtCGVZjkhDKMuyIToBRVE0TQv1A51c6N/F\nKGlxPKpRDoPQf9uTtmFFmd7d4qP6b6RpmoEOgxCjpA29fRnioI3UG4LJxOkw1FUM+jV2vosD\nq1B3FGufwIQXRAciIsOL0YYwPj6+nc+gqqqiKPHx8Yb4nOHz+VRVbf9P3QE0TfP5fGazOSEh\nQXSWsMiybKDDIBgM2u32uLi4Nj9JcYUntHFG74yEBEeEojVBURRFUYxyGASDQVVVjZLW4/GY\nTKb2HAYdRlEUn89nsViM8toSRZ+EyfOxaATUIDa/goG/RuZw0ZGIyNgM8CmWiDoJDSgqqwXg\njLOmJ0WxGyQiomZ1G4SRswBAU7DsNmiq6EBEZGxsCIkoXEcrPR6/DCC/e5IBphsSEXVVYx5H\nYi8AOLoBO94RnYaIjI0NIRGFq6CkJrSR3z1RbBIiophmS8SE5+u3v7kf3nKhaYjI2NgQElG4\nGhrCvEzec4KISKj+05A3BQB8x/Dtw6LTEJGBsSEkonC5SuuXGM3PYkNIRCTapJdgiQOA7W/h\n8FrRaYjIqNgQElG4QiOEJknqne4UnYWIKOalnIaz7gcATcWy26Aa4843RNTZsCEkorB4A/LR\nSg+Ant0S7Faz6DhEhqeqqsvl2r9/fyAQaO0+LTy2srJyx44dUUlMndDZDyA5HwDKt2Prq6LT\nEJEhxeh9CImotVyltRoAIL8754sStZfL5XrqqafcbrfNZpNlefbs2SNHjgxznxYeu2LFijff\nfNPr9f773//u6B+JhLA4cMFf8fHFAPDtI+h3FZzZojMRkcFwhJCIwnLSijJcYpSovebNmzdw\n4MD3339/0aJFl19++bx587xeb5j7NFf/8MMPP/vss2nTpgn4eUigPj9DvysBIFCDb+4XnYaI\njIcNIRGFpaDk+IoyHCEkap+ioqLi4uIbb7xRkiQAU6dOlWV548aN4ezTwmOHDx/+7LPPZmdz\ngCj2THwJVicA7P4HileITkNEBsMpo0QUFhdvQkgUIUVFRfHx8enp6aH/NZvNOTk5xcXF4eyj\naVpzjz399NPDz6CqqqZp7fkpQg9XFKWdz9MxVFVVVVVRFNFBTi30emqa1oq08dnS2Q+a1jwM\nAF/doVz/Pcz2qAXUC0U1ymEQ+q8hjoTQq2qIqDjpDUF0kLCEjgRDpA2FbP+RIEmSydTsQCAb\nQiI6NQ0oLKsF4Iyzpic5RMchMra6urr4+PiTKw6Hw+12h7NPOI8NR1VVVegjUTtVV1e3/0k6\njM/nEx0hXLIsV1ZWtuIB+Ten7Py7pWo3Kvf51sz1DrkratGaYKzDwOPxeDwe0SnC1brDQDRj\npTXQYRAIBFpYfiwcZrM5NTW1ua+yISSiUzta6fH4ZQD53ZMk0WGIjM5qter+1qsoitVqDWef\ncB4bDpvN1s4hHVmWFUWx2ztuJKo9Qi+a2WyAFZI1TQsEAiaTqZX/rPbAeS9Y/vNzQIvfNg+n\nX6Mm9o5WxJ8KBoMWiyU0h7mTUxRFlmWLxWKUI0GW5Tb8dgsRCAQ0TeMbQsS19Q1Br+XfUDaE\nRHRqBZwvShQ56enp1dXVwWCw4QRfVlY2ZsyYcPYJ57HhcDrbezfR2tpaRVESEhJamIbUefh8\nPlVVdYOrnZOmaRUVFWazOTGxle+3iT/DGb/C7n9Isjdh7WyMvAuSCelDor3uaHV1tdPpNMph\n4Ha74+Li4uLiRGc5NUVR3G53qw8DQaqqqmRZNkpaj8djMpmMchgEAgGr1RrV19YAv71EJFxD\nQ9gnkyvKELXXgAEDLBZLwyoyhYWFJSUlw4cPD2efcB5LsWvCPNiTAaDwSyyego8vxus9seQq\neCtEJyOizosjhER0aq7S+iVG+3KEkKjd4uLipk6d+vLLL5eUlNhstsWLF0+ePDknJwfA22+/\n7Xa7Z82a1cI+zdXXrVvn8/l++OEHTdNWrVoF4LTTTuvVq5fQn5U6lj0FlgT4f3pF3/7FqD2A\na/8HEz/1EVET+NZARKcWGiE0SVLvDDaERBFwzTXXZGZmrl27VlGUyy+/fMqUKaH6yXMam9un\nufrq1atDKzoMGjRo6dKlAKxWKxvC2LLzXdQdbqJ+9Dvs/RBnXNfhgYjIANgQEtEpeAPy0Sov\ngOy0eLvVAFdgE3V+kiRNmjRp0qRJuvr06dNPuU9z9fvuuy8aUclIir5q/kvL2BASUZN4DSER\nnYKrtDa0GiFvSU9E1Kn5q5r9ks9I9wMgoo7EhpCITuGkW9KzISQi6sQSc5r9UlJuB+YgIiNh\nQ0hEp1BQUr+iTF4mLyAkIurETr+62S/1b/5LRBTb2BAS0SkUcISQiMgQ+vwMA29sop4xDL3O\n6/A0RGQMbAiJqCUaUFhWC8AZZ81IdoiOQ0RELbr4HUx8CWlnQDLDZAYkAKjYibKtopMRUSfF\nhpCIWnK00uPxywDyMhMl0WGIiOgUJBNGzsJNuzDLjVl1OPNuAFBlLJ8BaKLDEVFnxIaQiFri\nKuV8USIiA7LEwWzH2D/C2RMADq3BzndFZyKizogNIRG15MSKMmwIiYgMx5aI85+r3/56Nrzl\nQtMQUWfEhpCIWnLSijJcYpSIyIAGTEfezwHAW4E1j4pOQ0SdDhtCImqJq7QWgCRJvTPYEBIR\nGdPEl2CJA4Btb+DIOtFpiKhzYUNIRM3yBZUjlR4APdPi46xm0XGIiKhNUvvhzN8DgKZi+Qxo\niuhARNSJsCEkoma5Smo0TQNXlCEiMrrRDyM5HwBKNmPra6LTEFEnwoaQiJpVUHp8RZlMNoRE\nREZmceCCBfXbqx9C3RGhaYioE2FDSETNcnFFGSKiLqPPxTjtcgAI1OCbP4hOQ0SdBRtCImrW\nSUuMcoSQiMj4Jr4MawIA7PobDqwUnYaIOgU2hETUNO34EqPOOGtGskN0HCIiarekXJzzUP32\n8plQg0LTEFGnwIaQiJpWUuXx+GUAeZmJkugwREQUGaNmI+0MAKjYhU0viE5DROKxISSipjXM\nF83jfFEioi7DbMOFrwESAKz9I2oKBechItHYEBJR01wl9UuM5mdyRRkioi6k13gMmA4AQQ9W\nzRadhogEY0NIRE0rKD2+okwWRwiJiLqWCc/DngIA+z9GwX9FpyEikdgQElHTCkpqAUiS1DuD\nI4RERF1LQhbGPlG/vfIuyD6RYYhIKDaERNQEX1A5UukB0DMtPs5qFh2HiIgibcRMZI4AgKof\nseEZ0WmISBg2hETUBFdpraZp4B0IiYi6KsmMC1+HZAKADc/g2F7RgYhIDDaERNSEE0uMZrIh\nJCLqorLOwuCbAUDxY+Us0WmISAw2hETUBNeJe07wAkIioq5r/LNwZABA4VLs+5foNEQkABtC\nImpCwz0n+nLKKBFRFxaXhvOert9edQ+CbqFpiEgANoREpKcBhWW1ABLsloxkh+g4REQUTYNv\nRvYYAKg9iLVPik5DRB2NDSER6ZVUedy+IIC87kmS6DBERBRdkgkXvg6TBQA2vYCyraIDEVGH\nYkNIRHoN80W5xCgRUUxIH4LhMwBAlbF8JqCJDkREHYcNIRHpNSwxmp/JFWWIiGLDuXPgzAaA\nQ99i199EpyGijsOGkIj0CkoblhjlCCERUWywJWL8X+q3v74PvkqhaYio47AhJCK90JRRSZJ6\nZzhFZyEioo5yxq+QOxkAPKVY84joNETUQdgQEtFP+ILK4UoPgJ5p8Q6bRXQcIiLqQBe8CrMd\nALa+hiPrRachoo7AhpCIfsJVWqtpGoC8TM4XJSKKMan9MOpeANBULJ8BTREdiIiijg0hEf2E\nq6ThAkKuKENEFHtGP4rkPAAo2YRtb4hOQ0RRx4aQiH7CdWKJUY4QEhHFHosDE16o3179IOqO\nCE1DRFHHhpCIfqKANyEkIopxp12OvpcCgL8aqx8UnYaIoosNIRGdoAGFZbUA4u2WzBSH6DhE\nRCTIpPmwJgDAzkU4sEpwGCKKJjaERHRCaZXX7QsCyM9MkkSHISIiYZJycfYDAAANy2dCDQrO\nQ0RRw4aQiE4oaLiAkCvKEBHFuLPuR9oAAKjYic0viU5DRNHChpCITigobVhilBcQEhHFNrMN\nk16p3/7f46gpEpqGiKKFDSERneA6vqIMG0IiIkLvC3D6NQAQ9ODr+0SnIaKoYENIRCe4SmsA\nSJLUJ8MpOgsREXUCE1+EPRkA9n0E12ei0xBR5LEhJKJ6vqBy6JgHQHZqvMNmER2HiIg6gYQs\njHm8fnvFXZB9QtMQUeSxISSieoWltZqmAcjjijJERNRg5CxkDgeAqh/w3Z9FpyGiCGNDSET1\nXA1LjGbyAkIiIjpOMmPSfEACgPVPo3Kf6EBEFElsCImoXkFp/Yoy+VxRhoiITtbzXAy+CQAU\nP1bMEp2GiCKJDSER1Wu4CSGnjBIRkd74P8ORDgCFX2L/J6LTEFHEsCEkIgDQgMLSWgDxdkv3\nlHjRcYiIqJNxdMO4p+q3V96FoFtoGiKKGDaERAQAZdVety8IID8zSRIdhoiIOqMhtyJ7DADU\nHsC6P4lOQ0SRwYaQiADgR84XJSKilkkmXPAaTBYA2DgPZdtEByKiCGBDSEQA4CqpX1EmjyvK\nEBFRczKGYtjtAKDKWDET0EQHIqL2YkNIRADgKj1+zwk2hERE1IJxT8GZDQAHV2P3P0SnIaL2\nYkNIRMDxJUYlSeqT4RSdhYiIOjFbEsY/W7+9arYUqBaahojaiw0hEcEvK4ePeQD0SI132Cyi\n4xARUed2xvXInQQAnpK4TU+dam8i6tTYEBIRCktrVU0DkM8VZYiIKBwXvAqzHYBt11s4ukF0\nGiJqOzaERISCo8cvIMzkBYRERBSG1P44824A0FTTijuhqaIDEVEbsSEkIhSUNiwxyhFCIiIK\nz+jHkNQHAEo2YvubgsMQUVuxISQiuEq4xCgREbWSNR4Tnq/fXv0gPKVC0xBRG7EhJIp1GuAq\nrQXgsFm6p8SLjkNERMbR70o592IA8FVi9QOi0xBRW7AhJIp1ZdVety8IIL97oiQ6DBERGYt3\n7F9gjQeAHQtx8GvRcYio1dgQEsW6As4XJSKitlKdOdqo+wEAGpbPhBoUHIiIWok3HCOKdQUl\nDSvKsCEkihU+n0/TtPY8g6IooeeRJAPMLZBlWVVVr9crOsiphf5djJIWoahD7nLseU+q3Ify\nHcENL8jD7hQdqmmyLAMIBALtPPg7hqqqxjoMABglbehIMMphAEBRlHa+tpIkxcXFNfdVNoRE\nsc5V2nDPCS4xShQrItXFSZJkiIYQhooaYqC0ksUePO9525JLAFi/+5Pab6qWkC06VLOMciSE\nQhoiagMDpTXWYYB2v7YtP5wNIVGsC00ZlYDeGWwIiWKF3W5v5zMEg0FZlu12u8lkjMtPVFVt\n4Q/knYemaXV1dSaTyRBpAfj9frvdbur3C/Sfhn0fIVBrX/sQLvlAdK6m+f1+q9VqiNdWUZRg\nMGiIqAB8Pp9RfsUAqKpqlF8xRVE8Ho/ZbI5qWmO8iRNRlPhl5fAxD4AeaQnxdv6FiIiI2mrS\nS7AnA8DeD+H6XHQaIgoXG0KimFZYWqtqGoA8zhclIqL2SOiB0Y/Wb6+YBdknNA0RhYsNIVFM\na1hRhkuMEhFRe428CxnDAKDqB2x8TnQaIgoLG0KimOY6cc8JjhASEVH7mCyYPB+QAGD906gu\nEB2IiE6NDSFRTGu4CWFeJkcIiYio3XqOw6BfA4DsxVczRKcholNjQ0gU01yltQAcNktWarzo\nLERE1CWc/xwc3QCg8Av88P9EpyGiU2BDSBS7Squ9bl8QQH73RAPci4eIiAzB0Q3nzqnfXjET\nB1bC9RmqXUIzEVGzuMo8UezifFEiIoqKobdh57s4sh61B/HPSfXFrLNx8dvoNkhoMiLS4wgh\nUewKzRcFV5QhIqLIkkxI6q0vHt2ADyeg9oCIQETULDaERLHrxAgh7zlBREQRVPUD9v6zibq3\nHOue6vA0RNQSNoREsSvUEEpAnwyOEBIRUeQUfdXslwq/7MAcRHRqbAiJYpRfVg4f8wDISo2P\nt/NyYiIiihxfZfNfOtaBOYjo1NgQEsWootJaVdMA5HO+KBERRVZir+a/lNOBOYjo1NgQEsWo\nH0vqV5TJy+R8USIiiqi8i2FNaPpL/ad2bBQiOgU2hEQxynV8RRmOEBIRUYQ5MjBhXhN1SzxG\n3dvhaYioJWwIiWLUSfecYENIRESRNvQ2XLEE3UfBZAUAyQwAsgd7PhSbi4h0uJIEUYwqLK0F\n4LBZslIcorMQEVFX1PdS9L0USgCBGhz8GkumAsDqB9DvCjgyRIcjonocISSKRWU13hpvAEBe\nZqIkSaLjEBFR12W2wZGOflch/xcA4DuG1Q+JzkREJ7AhJIpFBQ0rynC+KBERdYyJL8ESBwDb\n/w+H/yc6DRHVY0NIFIsKTqwowyVGiYioQ6T0xVl/AABoWHY71KDgPEQEgA0hUWziijJERCTA\nOQ8itT8AlG/Hlr+KTkNEABtCotgUGiGUgD4ZHCEkIqKOYrZj0iv122sehfuQ0DREBAhsCFVV\ndblc+/fvDwQCrd2ntXUiOplfVg5V1AHISo2Pt3OpYSIi6kB9LkK/qwAgUIuv7xOdhogE3XbC\n5XI99dRTbrfbZrPJsjx79uyRI0eGuU9r60SkU1TmVjUNnC9KRERCTHwRhV8i6Mae9zHkFuRO\nFh2IKKaJGSGcN2/ewIED33///UWLFl1++eXz5s3zer1h7tPaOhHpFBytX1EmL5PzRYmIqMMl\n9sKYx+q3v7oDil9oGqJYJ6AhLCoqKi4uvvHGG0N3P5s6daosyxs3bgxnn9bWO/6nI+r8XKUN\nS4xyhJCIiEQ48x5kDAWAyv3YOE90GqKYJmDKaFFRUXx8fHp6euh/zWZzTk5OcXFxOPtomtaq\nenMZ5JUr4df/OUobNQrJybqitGMHSkr0j+/fX+3WDYAsyw039ZYOHMC+ffo9MzO1IUP0xepq\nqXGzardr48Y1jiqtWAFN00cdOxYOh37PTZtQVaXfc9AgZGUpiqJpWjBYv76z9OOPKCzUf6ec\nHK1/f32xrEzatk1fTEzUzj5bXwwEpNWrG+fXJk6ESf93B2ntWng8+j1HjEBamqZpABrSSrt2\n4cgR/ZP27av16aN/zkOHsGePfs/0dG3YMH3R7ZbWr9cXrVZt/PjG+aVVq6Ao+qijRyMhoX5b\n00KHgbRlCyoq9I8fOFDr0UP/nC4XCgr0e2Zna2ecoS9WVEhbtuiLCQna6NH6oqJIq1Y1zq+N\nHw+r9fguSui/8pq1I4pLAPTfqciFtvo9hw3D8d+gE1H37sXBg/onzcvT8vP1xSNHpF279MXU\nVK3xzG2PR1q7Vl80mbSJE08uqKqqaZq8YgUaXRKsnXUWkvStrLRtG8rK9E97+ular176PYuL\nsX+/fs+sLG3QIH2xqkratElfjIvTzj1XH0lVrV9/LR8/Kk7Ux42D3a4PsHEjqqv1ew4ejO7d\n9Xvu34/G72O5uVq/fvpiaam0fbu+mJysjRqlL/r90sqVkCTZ8pP3f23SJBx/NzsRYM0a+Hz6\nqGeeiZQU/Z47d+LoUf336tdPy83V73nwIPbu1e+ZkaENHaov1tRgwwarx2OyWOSGl9Fm0847\nT78nIK1cCVXVRx0zBvHxoW3r8V8EIuoUTBZMXoAPxgMa1v0JA65Fcp7oTEQxSkBDWFdXF3/8\nDB3icDjcbnc4+7S23lwG03XXmRq1GVWffy43+vCU+MQT9iVL9D/Ck08G77gDQE1NzYnv+N57\nCY8+qtvTf+mltW+/rStaNm1K+fnPdUU1K+tY489zQPovftG4IancvFnJydEVk++5x7puna5Y\n+9pr/quuCm37jn+wS3j1VcfLL+v29N16q3vuXF3Rtnx50o036ory0KHVy5friqaKirRGPxSA\nioMHtUYfiFNvusncqCOqXrw4ePxznizL1dXVABKfecb+wQe6PT0PPui5915dMe5f/3Lep782\nPXDhhTXvvacrWnbtavz6aykpFY2bBKDblVdKdXW6YuWaNcpJzXPoMEj+wx+sK1fq9nS/+KLv\nuut0xfi33or/y190Rd9117lffFFXtK5enXz11bqi0r9/1Zo1uqJUV9etydd//37tp5/dvV7v\nZW893fuICwDePVGvee+9wIUX6h7unDcvbuFCXdFzzz2ehx7SFe3/+U/izJm6YnDcuOpPPtEV\nzS5XauPX32arONTEWnOma64xlZfrilVffSU36vOTHnvM9tlnumLd3LneW2/VFR2LFiU8+aSu\n6P/lL2tff11XtG7YkHzJJbqimpNzbPNmfVBVTZ06tXH+Y9u2qY3+IpAya5al0Z+Eav/v//yX\nXaYrJsyf73j1VV3Re/vtdXPm6Ir2pUsTb75ZV5TPPLP6iy90RdPRo2mNvhGA8qNHYTbriqk3\n3mg+cEBXrP7Pf4KN/iSROGeOffFiXbHusce8d96pKzo+/DDhwQd1xcCUKTXvvqsrWrZuTZky\nRfdXOjU9/dju3Y3zd7v0UqnR3w4qN2xQ8vIAmM3m1NTUxo8iIpF6jsPAG7BrEWQvls/AL/Xv\n4UTUMQQ0hFarVflph6Moiu5vt83t09p6cxmU225TTurlQmx9+lgbDbvh8suDp52mq5lHjzab\nzYqixMXFNYwQmkePDjbqUjBokKPxUF6fPo331BITG+8JIPuIMksAACAASURBVHjPPY3/7G3P\nzNQa7axOnx4cO1ZXtAwdanI4ZFnWNK3hBZEmTAhaGv3Tn3124wCmgQObiJqd3UTU1NQmfnwg\nzulEo++l3HKL2mgwzdavn8Xh0DTN5/OZTCZ7qI2cMiWYna1PNW5cE1FHjWoiav/+Tbz+vXo1\nsWdcXJOvv3znnY1HqOzZ2Q2vv9/vt9lskiSpU6cGR4zQ7WkeMaKJqOPHBxs1+dKZZzax5+mn\nNxE1I6OJqGZz069/SkrDYLIsy8FgsMavLh1+QdpplWlO+/lnnBiSsg4YYG78tBddFExLa5y/\niajDhzcOoOblNfH6Z2U1EdVi0e2paVogEJDvuKNxQ27LzW38q6pdcUVwwAB9qrPOahzAPHZs\nEwGGDGkial5eE69/cnLjPf0+n3fWLEujQz0uI6OJX9Xrrgs2Go62DB5sahxg8uRg4+KYMU28\n/oMGNRG1V68mfqj0dP/ddwMw/XTo3hEf33gwX/ntb9VG8w6s+fmWxofKpZcGG43bm8eObSLq\nWWc1EXXAgCai5uYG7rlHURRJkszHm1UtIaHpX9W774Ys64r2rKzQ629q9KMRUadw/l9Q8B/4\nKuH6HD/+B30vFR2IKBZJWqPpiNG2ZcuWJ5544qOPPmroT377299edtlll1566Sn3ycnJaVX9\n5OeMrNraWr/fn5aWZojPGT6fT1VV3SBq56RpWkVFhdVqTW40fbdzqq6uTkxMNMph4Ha795T4\n5izeCuAXZ/aeNWWw6FDNUhTF7XYb5TCoqqqSZTm90Zzbzsnj8ZhMpri4ONFBTk1RlMrKSrvd\nnpjIBZA6HZ4HoyTmzoNbFmD5TABIysVvdsGqn3sfQaHzoNPpNMobIM+DUcLzoI6AN/EBAwZY\nLJaGFV8KCwtLSkqGDx8ezj6trXfUz0RkGK6y+qnU+d35CZuIiEQbdgd6nAMANcVYr790hYg6\ngIApo3FxcVOnTn355ZdLSkpsNtvixYsnT56ck5MD4O2333a73bNmzWphn9bWiehkhccbwrxM\nLjFKRESiSSZMXoB/nANNwca/YOB1SGu0xBoRRVNYDeHatWu7d++e32hdwerq6jfffHP27Nmt\n/a7XXHNNZmbm2rVrFUW5/PLLp0yZEqqfPJejuX1aWyeikxWVuQFIvAkhUWtE/DxIRCd0PxND\nf4utr0EJYNkduGYloF/0mIiiJ6xrCCdMmHDJJZc0PuFVVFSkp6d3/FWInQGvnYiSmLt2ogP5\nfL5j1TW3vrVRUbUeqfELZ0489WPE4bUT0cNrJ9qA58HGeB6Mkhg9D/oq8c4AeEoB4BfvY8D0\niGTTfxNeQxg1PA9GScecB08xQrhw4cLCwsLCwsKlS5fq7uKgaRrv/E5kLAePeRVVA+eLEoWN\n50GijhCXivHP4oubAGDVPcj7OezGaISIuoBTNIQHDhxYtGhRUVFRUVHRsmXLGu9w5ZVXRicY\nEUXegQpPaIMryhCFiedBog4y6NfY+S4OrELdUax9AhNeEB2IKFacoiF89NFHH3300fPPP3/8\n+PG33Xab7qspKSlOpzNq2YgoworLGxpCjhAShYXnQaKOImHyfCwaATWIza9g4K+RyeXiiTpC\nWIvKvPrqq6mpqVlZWaqqNtwduKamhmdBImMpOj5CyBVliFqF50GijtBtEEbOwsZ50BQsuw2/\nWgvJAFeoEhldWL9mAwcOXLduXe/evXfs2NFQHDFixLRp02pqaqKWjYgi7OAxL4A4q7lHqgFW\nViDqPHgeJOogYx5HYi8AOLoBO94RnYYoJoTVEH7//fdTp05NS0s7+U+h11133WeffXbrrbdG\nLRsRRczBirpP1hfV+oIAenZzShJX9CZqBZ4HiTqILRETnq/f/uZ+eMuFpiGKCWFNGX3jjTeG\nDRu2YcMGi+XE/k8++eSQIUOuvvrqqGUjogjQNO2NZbsXr3c1VApKqt9Zsfc3k05nU0gUJp4H\niTpO/2nImwLXZ/Adw7cP48LXRQci6uLCGiF0uVznnXfeyWfBkPHjx0chEhFF0gdrfjy5GwSg\nafhgzQ///mmRiFrA8yBRh5r0EixxALD9LRxeKzoNURcXVkOYlpa2Z8+exvUtW7ZEOg8RRZKs\nqB/978cmv/T+tz+oMXk3baI24HmQqEOlnIaz7gcATcWy26DKogMRdWVhNYRXXXXV0qVLH3vs\nsaKiIk3TVFUtLS39xz/+cdNNNw0ZMiTaEYmozYrL3XX+ps+j1Z7A4WOeDs5DZFA8DxJ1tHMe\nQmp/ACjfjq2vik5D1JWF2xDOmDFjzpw5ffr0sVgsVqu1e/fu119/vSRJf//736MdkYjaLCCr\nLXzVH1Q6LAmRofE8SNTRzHZMerl++9tH4D4sNA1RVxbWojIA5s+ff8MNN3zyySf79+8PBAIZ\nGRmjR4+ePn16UhJvb03UeWWnxZskqcmpoRazKTuNN58gChfPg0Qdrc/P0O9K7P8EgRp8cz+m\n8I8vRFERbkMI4JxzzjnnnHOiF4WIIi7JYRtzevc1e442/tL4gT0ctla8AxARz4NEHW3iSyhc\nhqAbu/+BwTcjd5LoQERdUFhTRgEoivLOO+9cccUVQ4YMee211wAcPHjw888/j2Y2IoqAO38+\nuFe3BF2xT2bi7RcNFJKHyKB4HiQSIDEHox+p3/7qDih+oWmIuqawGsJgMPizn/3s5ptv/uab\nbwoKCqqqqgAsX758ypQpb731VpQTElG7pDrt828d1zerflZb3+5Jt0we8PLN5ybH28QGIzIQ\nngeJhBn1e6QPAYDKfdj0gug0RF1QWA3hwoULv/nmm3/+858VFRUNy6ndeOONt99++8MPPxzN\neEQUAQ6bxSTV34X+iWnDrx7b1241i41EZCw8DxIJY7Jg8gJAAoB1c1DNm+gSRVhYDeGyZcuu\nu+66adOmScc/UwKQJOmRRx4pLS2NWjYiigwNOFhRB8AZZ0lyWEXHITIengeJROp1Hs64DgCC\nHqy6V3Qaoq4mrIawqqoqLy+vcT0tLS3SeYgo8sprvN6ADCA7JU50FiJD4nmQSLAJz8GeAgA/\n/BsFn4pOQ9SlhNUQ9uzZc/369Y3rK1asiHQeIoq84nJ3aKNnqkNsEiKD4nmQSLD47jj3yfrt\n5TMRrBOahqhLCashvOaaaz777LPZs2cXFxeHKmVlZe+8885NN900duzYaMYjogg4UF5/4sxm\nQ0jUJjwPEok3fAayzgaAmiJseFZ0GqKuI6yG8OKLL37ggQfmzZvXu3fv9evXP/TQQ5mZmTff\nfHNiYuKiRYuiHZGI2qlhhJANIVHb8DxIJJ5kwgULIJkA4LtncWyP6EBEXUS4t6WeO3futGnT\nPvroo3379gWDwczMzHHjxl199dXx8fFRzUdE7XegoSFMYUNI1EY8DxKJ130UhtyKbW9ACWDF\nnZi6THQgoq6gpYZwwYIFaWlp11577TPPPDN8+PCLL7545MiRHZaMiCIl1BDaLKb0RN57kKgV\neB4k6nTOm4v9n8BbhqKvsPefOP1q0YGIDK+lKaNLlixZtWoVgC+++GLHjh0dlIiIIsrtC1bW\n+QH0TEswnbRiPhGdEs+DRJ1OXBrGP1O/vfIu+KuFpiHqCloaIezTp8/bb7+9e/fu7du3Hz58\n+Isvvmhyt6+++io62YgoAhrmi/ZM48Q2otbheZCoMxp8E3YuwsGvUXcUa5/EhHmiAxEZW0sN\n4UMPPXTw4ME9e/Z4vd7y8vJAINBhsYgoUhpWlOnFhpColXgeJOqUJEyej7+NhBrE9y9j0I3I\nGCY6EpGBtdQQ9u7d+7///S+ACRMmXHLJJbNnz+6oVEQUMQ0jhDndEsQmITIcngeJOqn0wRgx\nE5tegCpj+QxMXw3wmgiiNmrpGsIFCxa8//77AHr27JmamtpRkYgokjhCSNRmPA8SdV5j/whn\nTwA4tAY7FgoOQ2RkLY0QLlmypE+fPtdee+2hQ4cqKys7LBMRRdCBijoAkiT1SIsP+ryi4xAZ\nSfTOg263+4033vjuu+9kWR48ePAdd9yRmZkZ5j6trRN1TbZETJiHT6cDwDf3oe+lcKSLzkRk\nSFxUhqgrCypqSZUHQFaKw24xB0XnITKW6J0HX3zxxfLy8jlz5sTFxS1cuPDJJ598+eWXTSZT\nOPu0tt7GH56o8zv9Gux8F67P4a3AmkdxwauiAxEZEheVIerKDlbUKaoGICfdKToLkfFE6TxY\nXl6+YcOGF198MT8/H8Ddd999ww03bN26dcSIEafcJycnp1X1k5+TqAua+BIOrITsw7Y3MOjX\n6DFadCAi4+GiMkRd2UkryrAhJGq1KJ0H9+/fb7PZ8vLyQv/rdDpzcnL2799/cvPW3D4+n69V\ndTaE1MWl9sOo2Vj3J2gqls/AdRsgmUVnIjKYlhrCBq+88kpaWlq0oxBRxJ1oCDlCSNQOkT0P\n1tTUJCYmStKJRRGTk5Orq6vD2Sc5OblV9eYyyBddJJWV6YqeBQuUgQN1Rccf/2hZtUpX9P/u\nd8GrrgrlbCja/vUv+4IF+m90/vneJ57QFc27d8f/7ne6otqtW92//tU4auLkyVBVXbHu/ffV\nrCxdMf6uu8zbtumKvkceCU6erKoqgIYxXvsbb9jef1+3Z+CKK/x33aUrWtascTzyiK6o9Ovn\neeMNXVGqqnJeeWXj/LVffgmbTVdMuP5606FDuqJn3jxl5EhN0wDIslxVVQUg7plnrF9+qdvT\nf/PNgRtu0BWtS5bEvfCCriiPHu2dO1dXNP34Y8Ktt+qKWmKie8mSxvmdU6ZIXv3153ULF6q9\ne4e2FUUJHQaO++6zbNyo29N3333BKVN0RdvChfZ339UVg1Om+O67T1e0bNzoaFRUe/euW7iw\n4X+lATMSd/7ddKwQD25Wn+qjxf3kSkL3kiVaYmL9A1UVgMfjMV13nbmgQPe03rlz5dH6Aca4\nF16wNnpZAtdf77/lFl3RunRpXKOXWhkxwvP887qi6dChhOuv1xU1q9W9dOlPKpqmqqo8aZLU\n6AJmz+uvK/3764qORx+1fPutrui7887gL3+pK9o++MD++uu6YnDSJN+jj+qK5u3b42fN0hXV\n7t3rPvhAV5QDgZSLLlIk/Vqv7n/+U8vI0BXjZ8ww79qlK3off1yeMEFXtP/1r7aPPtIVA1On\n+mfM0BUtX3/taPRWo5xxhuevf9UVpfLyhKlTAejS1i5fjkZz7BOmTzeVlOiKnpdeUoYO1RXj\n5syxrlihK/pvuy0wfbquaF28OO6VV3RFedw475w5uqJ53774225L0bST02qpqe7Fi9GI86KL\npKD+2qC6v/9d7dkTgMlkSkpKavyokJYawvnz5zudzt/85jdDhgwJVQKBgMlksljqH7V79+4x\nY8aE3rCIqBM6UFHfEOayISRqveidB6WffhAJ9QD/P3t3Gt9Wded//KvFu53ES/aVkNCsrCFh\nzQoNhXaAFggDhUKhAwxbKTAtBUobStkKLSRsU/5QQju0wDAzbVlKSMhW1rCVJEBC4tgyWb1b\nsiVLV/f/QMYJiRc5lnR05c/7Aa/royvxlXKlo5/uuefEuU9P2zvkXrfOvX37Po1WY2MkEtl3\n1y1bPB9+uE+bvWNH7Lv13vtn7dix/56RkSM7eMzGxv33dA0Z0sGekuejj2RZ+0ZtabH229m1\nceP+Dxutrm5/2OiXhWXOF190EGDatP0DuGtrO3j60WgHe4ZC++8pyQqH7f2+Zbo/+WT/giTa\n0ND+sLZtx7ZdFRUdPOz27fsH8O7atf+e1sCBHewZCHTwpAYM6Pj1/+c/XYHAvg8bCOz9+sfu\n6N68uYOH3b17/4fN3rZt/z3Dkyfvv6errq6Dp9/c/NU9vf5j7u73twWqkltVUtXe+0ZCITsv\nb++WaDTq/vTT/QuSaF1dB69AZWUHT2rOnP339Oze3cHhV1jYwZ7NzR08ZnZ2h6+/++OP3dXV\n+zRaTU0dvFYdvVW1a9f+e2Zt397BW3Xs2A4es8O3aodvasn70Uf7N1rBYHT/N0tHb1W7tnb/\nh82pqurgSR1zTAevaodvVY+ng7dqMNhh1Eg4LM++p5fdGzZ4fL59GqMdfVS6tm7tIEBHb9Ws\nDt+qw4Z18Ko2NXVwUJWVdfZR6drvugaruTn2VvXs99S+Er6LDmP27NllZWXP7/Vz3YQJE844\n44y77ror9ue6deumTp3adZeTqZqamkKhUElJiSOu1w8Gg9FoND/fAasO2LZdU1OTlZXVv39/\n01ni0tDQUFRUlLaHwb//bvXmHY2Snrvh5GxX1O/3FxYW5ubmms7VPcuy/H6/Uw6D+vr6SCRS\nVuaMCe6am5vdbrdTDoO6urqcnJyiL3/gT7Ek9YNvv/32vffe+9xzz7WXcFdfffWsWbPOOuus\nbvcZOXJkj9r3fszEoh9MEvrBA/R/Z+jz/5OkSRfoG0s63CUYDNIPJgn9YJKkph80/e4FkDS2\nbVfVBCT1z8/ul7fvgCUAphxyyCHhcPjzzz+P/dnQ0ODz+SZMmBDPPj1tT9VzAkyb86CyCiRp\nw9Oq3HfkHoAuUBACGWtXQzAUtsR4USDNFBcXH3/88YsWLfr88899Pt/9998/bty4yZMnS1q6\ndOlf//rXLvbpabvp5wqkSr9RmnFz2/byqxVloSUgXhSEQMaqrOYCQiBNXXXVVQcffPAtt9xy\n/fXX5+bm3nzzzbGhnh9++OE777zT9T49bQf6imnXq2SiJNVs0Hv7zq8DoDNxzTIKwInaZ5Rh\nilEg3eTn51977bXX7jen5Y17TajY2T49bQf6Ck+2Tn5Uf54t2XrzF/raOeo3xnQmwAE4Qwhk\nLNacAAD0LSNmauK/SlK4WSuuN50GcAYKQiBjMWQUANDnzL5fOQMkadML2vKi6TSAA3QzZHTN\nmjWnnHJK+59VVVXPPvvsh18uiOH3+5MYDUDvxM4Q5mR5BvZzwMTKQHqiHwQcJn+wjvu5Xv+h\nJL1+rUbNk5dOEOhKNwXhzp07//73v+/dUl5eXl5ensxIABKgsaW1oblV0sjSAiaWAA4Y/SDg\nPEdcpfVPadcHqt+sd+7ScT83HQhIa10NGV2xYoUdh5RlBRC/Si4gBHqNfhBwJJdHJz8ml1uS\n3rlLtZ+ZDgSkNa4hBDKTbzcFIQCgrxpytKZ8X5KskJZfbToNkNYoCIHM5KsJxDaYUQYA0BfN\nvFt5AyWpYqk2Pm86DZC+KAiBzLRnzYlSCkIAQN+TW6ITf9W2veI6hZkCCugYBSGQmWLXELpd\nruGlBaazAABgwtRLNOw4SWqq0hu/MJ0GSFMUhEAGao1EdzW0SBpanJ/l4W0OAOibXDr5Ubm9\nkvT+b93lL2ZtW+Fq2CI7ajoYkEbi+qb45ptvbtmyZf/2hoaGX//614mOBKC3qmr8UdsWM8oA\nCUI/CDhV2VQdfqUkRSPZL53Vf+nZOX+YrP83TuUvmU4GpIu4CsKbbrrphRde2L89EonceOON\niY4EoLfa15xgRhkgIegHAQcbesy+LQ3l+r9va+urJtIAaaebhel///vfb926devWra+++qrf\n/5WLcW3bXrt2bTKzAThAe80owwWEQK/QDwIOZ2vNTztotkJacZ0uWp/yPEDa6aYg9Pl8S5Ys\nqaioqKioWLp06f47nHnmmckJBuDA7VmVfmCR2SSA09EPAs5Wu1EN5R3fVLNBjZXqNyq1gYC0\n001BeOutt956662zZs2aOXPmZZddts+tAwYMKCxkQBqQdtoXIeQMIdBL9IOAswVrurq1pZqC\nEOimIIx55JFHiouLhwwZEo1GPR5PrLGxsZFeEEhDtm1vqwlIKi7MKczNMh0HyAT0g4BTFQ7v\n/DaXirq4Fegr4ppUZtKkSW+99dbo0aPXrVvX3njEEUecffbZjY2NScsG4EDsqG8JRSwxowyQ\nOPSDgFP1G63BR3V80/DjlT84tWmAdBRXQfjBBx+cddZZJSUle/8Uev7557/00kuXXnpp0rIB\nOBB7zShDQQgkBv0g4GAnPays/P1aXTru56nPAqShuIaM/ud//udhhx32zjvveL179l+4cOHU\nqVPPOeecpGUDcCD2rDkxkIIQSAz6QcDBhkzXuf/Qqv9Q1UpZrXK5ZUclWxue1qh5psMB5sVV\nEJaXl5944ol794IxM2fOTEIkAL3iq+EMIZBg9IOAsw06XGe9Gmz2N+/eXJCbk/PnaQoHtH6J\nJn9PI+eYDgcYFteQ0ZKSkk8//XT/9g8//DDReQD0VuVuVqUHEox+EMgEbm+0YLjdf4xm3CRJ\nsrXsakXDhlMBpsVVEH7nO9959dVXf/azn1VUVNi2HY1Gd+3a9cc//vHiiy+eOnVqsiMC6JGq\nmoCkvGxvab9c01mADEE/CGSUaTeqZIIk1azX+w+YTgMYFm9BeOWVV95+++1jxozxer1ZWVmD\nBw/+7ne/63K5/vCHPyQ7IoD41QdaG1taJY0sLXCZDgNkDPpBIKN4sjV3Udv2G7epscJoGsCw\nuK4hlLR48eILLrjgf/7nfzZt2tTa2jpw4MBjjjnm3HPP7devX1LzAeiRyuqm2MZIZpQBEop+\nEMgoo0/ShHP16Z8UbtbKG/St50wHAoyJtyCUNGPGjBkzZiQvCoDe89UEYhvMKAMkHP0gkFFm\n/0blLyvUoI3Pq/wlHXSq6UCAGXENGZVkWdaTTz55xhlnTJ069dFHH5VUVVX18ssvJzMbgB7z\nMaMMkBz0g0CmKRiiY29r215+rSJBo2kAY+IqCMPh8Pz587///e+vWrVqy5Yt9fX1kpYtW3bq\nqac+/vjjSU4IoAf2rDlBQQgkDv0gkJmOvEaDDpek+s/17t2m0wBmxFUQ/v73v1+1atWzzz5b\nU1PTPp3ahRdeePnll998883JjAegZ2Kr0nvdrmHF+aazAJmDfhDITC6PTn5MLrckvX2n6jaa\nDgQYEFdBuHTp0vPPP//ss892ufZMW+hyuW655ZZdu3YlLRuAngmFrd2NQUlDSwq8nngHhAPo\nFv0gkLGGTNfkiyTJCmn5NYbDACbE9ZWxvr7+oIMO2r+9pKQk0XkAHDhftd+2bTGjDJBo9INA\nJpt5j/LKJGnr37XpBdNpgFSLqyAcPnz422+/vX/78uXLE50HwIGLjRcVM8oAiUY/CGSyvFKd\ncEfb9us/VNhvNA2QanEVhAsWLHjppZduuOGGysrKWMvu3buffPLJiy+++LjjjktmPAA9sNeM\nMgVmkwAZhn4QyHBTL9WwYyWpyac3bzedBkipuArCU0455Sc/+cl99903evTot99++6c//emg\nQYO+//3vFxUVLVmyJNkRAcTJV/3lIoScIQQSin4QyHAut056VG6vJL13v3b/03QgIHXiXZj+\nzjvvPPvss5977rmNGzeGw+FBgwadcMIJ55xzTn4+MxkC6aJ9yOgIriEEEo1+EMhwAw/VYVfo\ng0WKRrT8Ki1YKbm6vxfgfHEVhKtXry4sLDzyyCOPPPLIZAcCcGCsqL2tNiCprCi3ICfe33oA\nxIN+EOgTTvilNv23/NtUtVob/qBJF5gOBKRCXENGFy5cuGjRomRHAdAb2+uaw1ZUjBcFkoB+\nEOgTsvtp5j1t2ytvVKjeaBogReIqCE899dSVK1fW1dUlOw2AA7bXjDIUhECC0Q8CfcXE8zVq\nriQ179Q/bjWdBkiFuMaVzZ0799NPP502bdoZZ5wxduzYgoKvTGB40UUXJSUagJ7wVVMQAslC\nPwj0ISc9oqcOlRXShw9r0gUaMt10ICC54ioIr7322pUrV0q6//7797+VjhBIBz4WIQSShn4Q\n6EOKD9FR1+mdu2RH9dqVOv8tuTymMwFJFFdB+Otf/7qxsTErK8vlYrYlIE3tOUNYyiKEQILR\nDwJ9yzG36tM/qXGrdq7Vx4/r0MtMBwKSKK6CcNq0acnOAaCXfDUBSfk53pKiXNNZgExDPwj0\nLVn5mn2//vJtSVp9k8adqfxBpjMBydJVQbh8+fJwODx//vzly5fv2rWrs93OPffcJAQD0AO1\n/pA/GJY0qqyQ8xdAotAPAn3X+DM19pva8jcF67T6J5r/hOlAQLJ0VRAuXLiwvr5+/vz5Cxcu\njF070SE6QsC4Si4gBJKAfhDo0+Ytlm+5ws1a93tN/p5GzDIdCEiKrgrCe+65JxKJxDZqa2tT\nFQlAjzHFKJAM9INAn9ZvtI7+sd64TbK17Cpd8L7cWaYzAYnXVUE4ffr0fTYApKe9ZpShIAQS\nhn4Q6Oum/0Sf/pdqP1P1On2wSEf9yHQgIPG6Kgj//Oc/+3y+ru8fiUR+8pOfJDQSgB6r5Awh\nkAT0g0Bf58nW3EV6/uuS9MbP9bUFKhxuOhOQYF0VhI888kgXl0y0oyMEjPPV+CV5Pe6hxfmm\nswCZg34QgEafrEPO1sbn1NqkFT/SN/9sOhCQYF0VhEuWLGlubpZk2/ZPf/rTUCh0ySWXHHzw\nwV6vd+fOna+99tpzzz335JNPpioqgI61tEZqGoOShpXke9xMMgokDP0gAEma+4AqXlWoQZ89\nq8kX6aBvmA4EJFJXBeGoUaNiG0888cSOHTvWrFnj8XhiLVOmTJk3b94hhxxyySWXbNy4Mekx\nAXTOV+23JTHFKJBo9IMAJKlgqI65VStvkKTl1+h7H8vLkr/IHO54dvrb3/528sknt/eC7b71\nrW9t2rQpCakA9EAlM8oASUY/CPR1R16rgYdJUv3nWnuv6TRAIsVVELa0tFRUVOzfXlVVleg8\nAHrMVx2IbXCGEEgS+kGgr3N7Ne8hySVJb9+phi2mAwEJE1dBOGPGjD/84Q933313ZWWlbduS\nAoHAihUrLrjggry8vCQnBNCN2IwyYopRIGnoBwFo+PGa/D1JirTotStNpwESJq6C8Prrr58+\nffpPfvKT0aNHe73e7OzswsLCOXPmfPLJJw888ECyIwLoWmzIqEsaUVpgOguQmegHAUjSrF8r\nr0yStr6iz//XdBogMbqaVKZdUVHRmjVr/vKXvyxbsyrEXgAAIABJREFUtqyysjIUCvXv33/K\nlCnnnHPOhAkTkh0RQBciUXt7bUDSwP55edlxvaMB9BT9IABJyivV8bfrtSskafk1Gn2Sshib\nA8eL9+ujx+M588wzzzzzzKSmAdBT22sDkagtZpQBkox+EIAkHfpvWv+Utr+lJp/eukMn3mk6\nENBb8RaEO3fufPHFF7dt2xaJRPa56ec//3mCQwGIW/sUo8woAyQV/SAASXK5NW+x/jhDtqX3\n7tfkC1Uy0XQmoFfiKgg3btw4bdq0pqamDm+lIwQM2rPmRBkXEALJQj8IYI/BR+mwy/XhQ7Ja\ntfRyLVjRNvso4ExxFYS//e1vBw4c+F//9V+TJ0/OyspKdiYA8WOKUSAF6AcBfMWJv9KmFxTY\nrqpV+uQZTTzPdCDgwMVVEG7ZsuX666//5je/mew0AHqqfRFCCkIgeegHAXxFdj/NvEsvf0+S\nVvxIY09VzgDTmYADFNeyEwMHDowtuwQgrdhSVY1fUmFuVnFBjuk4QMaiHwSwr0kXaOQcSWre\nqTduM50GOHBxFYQXXXTRkiVLWltbk50GQI/UNAabQxExowyQZPSDAPbj0rzFcmdJ0gcPadcH\npvMAByiuIaMlJSWTJk2aMmXK+eefP2LECI/Hs/etF110UVKiAegOFxACqUE/CKADpZN01A/1\n7r2yLS29TOe9JVdc51qAtBJXQXjdddetXLlSnUykRkcImLLXFKMUhEASZV4/GI1GezkINnZ3\ny7IcMZg2Go1Go1HLskwH6V7s9bRt2xFp9WVUpxwGsf8m8rWdfqvns+fUuFU73o1+9Dt76qWJ\neuDYq+qgw0CSg9I65QMhFrL3R4LL5XK7O/21Iq6C8De/+Y3f73e5mFEXSC8+FiEEUiLz+sFA\nIBD7cnzAYt9OmpubE5QouWIFsCO+/7V/sfb7/aazxCUajTroMJAUCoXC4XACHzZr+sL81y6U\n5Fpzk3/oSXZuWUIeNnbEOugwkOSstIk9DJIk9oEQDod7+dq63e6ioqLObo2rIDziiCN6kwBA\nkrAqPZAamdcPdvHNIE5NTU2hUKioqKiLX53TRzAYjEaj+fn5poN0z7btmpoar9fbv39/01ni\n0tDQ4KDDwO/35+Xl5ebmJvJxD7tAW/6sLS+6QnX9PrpTX388IY8aqwadchjU19dHIhGnpG1u\nbna73Qk+DJLDsqy6urrs7Ozef2h3wQHvXgCdiZ0hzPK4Bw/IM50FAIC+as4D8uZK0sdPaNsb\nptMAPdPVGcJvf/vbb7zR/TG9Y8eOxOVJkd6fI46da45EIo4YQWRZVjQaddCZcdu2HZFWkm3b\npg4DfzBc6w9JGl6Sb0Ui3Q6Eio2VsizLEa9tbHyXI6JqrxEdpoPExUGvbfsFP71Pe2CryWdw\nPwggkQYcrKN/rDd/IdlaerkueK9t9lHACboqCEtKSoYMGZKyKKkUCoV6efVz+zD0BCVKrth1\nycFg0HSQ7sX+XaLRqCPSSopGo6YOgy07mmIbw4rz4nm5YgVhOBzu5YVDqRG74NtBh4Ekp6SN\nHQlOOQwkWZbVy9fW5XIdWEGYwf0ggASbcZM++5NqP1P1x/rgIR31Q9OBgHh1VRA+/nhixkCn\nocLC3l5w1dTUZFlWQUGBUwbNO+vaCY/Hk9Sh0gnU0NBg6jCo2dIQ2zhoyIB4Xq7YtRO5ublO\nGTTv9/udchjErp1wSlpnXTvR2tqalZVl6rXN4H4QQIJ5cjTnQf33fEl642f62tkqHG46ExAX\nBxQzADrUPsXoyFJmlAEAwLQxX9f470hSa5NW3mA6DRAvCkLAqVhzAgCA9DLnt8oqlKRP/6St\nr5hOA8SFghBwqtiaEy6Xa0RpgeksAABAKhqhY3/Wtr38GlnOmGwCfRwFIeBIESu6o75Z0uD+\neTlZHtNxAACAJOmo6zTwUEmq26S195lOA3SPghBwpC9qA1bUljSyjNODAACkDbdX8x6SXJL0\n1i/VUG46ENANCkLAkSr3XEDojJktAQDoK4afoEkXSFKkRcuuNJ0G6AYFIeBIvupAbGMkFxAC\nAJBuZt2r3GJJKn9Zm/9iOg3QFQpCwJF8NV+uOcEUowAApJv8QTr+l23by69WOGA0DdAVCkLA\nkdqHjFIQAgCQjg67XENnSFJjpd6+03QaoFMUhIDz2NIXNQFJ/fKy++dnm44DAAD243Jr3kNy\neSRp7b2q/cR0IKBjFISA8+xuaGlpjYgl6QEASGeDj9Kh/yZJVquWXiHZpgMBHaAgBJxnz3jR\ngRSEAACksRPuUP4gSapaqU//bDoN0AEKQsB5fO0FIVOMAgCQznKLNfPutu0V1ynUYDQN0AEK\nQsB5fCxCCACAU0z+nkbOlqTADr35c7NZgP1REALO46v5chHCMs4QAgCQ5lyat1juLEl6f5F2\nfWA6D/AVFISA81RWN0nK8XoG988znQUAAHSndLKOvEaSbEtLL5MdNR0I2IOCEHCYppZwfaBV\n0oiyApfLZToOAACIw3EL1W+0JO14V+8v0hdrVPuZohHTsQB5TQcA0DO+mvYZZZhiFAAAh8jK\n16x79ddzJGnFD9sa80p1zK068hqJX3hhDGcIAYep3N0+owwFIQAAzjFkhjzZX2lpqdHrP9Rb\nvzQUCJAoCAHH2XOGkIIQAAAHeWuhrNaO2n+pwPaUpwHaUBACDrNnVXoKQgAAHKT85Y7brVZV\nvJbaKMAeFISAw8QWIXS5XMNLWHMCAADnaKnu/KbdKcwBfAUFIeAkrZHozvoWSUMG5GV7ef8C\nAOAcBUM7v2lYCnMAX8EXSsBJvqjxR21bzCgDAIDjjDuj4/asQo0+ObVRgD0oCAEn8dUEYhtc\nQAgAgMMcc4v6j+2gfeqlyitNeRqgDQUh4CTtM8pwhhAAAIfJK9O//kMTz5M37yvt5S/JChnK\nBFAQAo7iY4pRAACcq2CITv2jrmnSpZt1ZY3KpkpS3Ua99xvTydB3URACTtJ+hnBEKVOMAgDg\nTC6P+o9VbonmPSS5JOmt29VQbjoW+igKQsAxbNv+ojYgqbggp19etuk4AACgd0acqInnS1K4\nWSt+ZDoN+igKQsAxdja0hMKWGC8KAEDGmP1r5QyQpM//V5v/ajoN+iIKQsAxKrmAEACADJM/\nWMff3ra9/CqFA0bToC+iIAQcY68ZZbiAEACATHH4v2vIdElqrNQ7d5tOgz6HghBwjD0FYSln\nCAEAyBQut056SC63JL17t2o/NR0IfQsFIeAYvuq2YSQsQggAQEYZPE1TL5Ukq1XLrzadBn0L\nBSHgGL4av6TcLM/A/nnd7gwAAJzkxDuVN1CSKl7TZ8+aToM+hIIQcIaG5taG5lZJI8sKXabD\nAACABMst0cy72rZfv1ahBqNp0IdQEALO4GOKUQAAMtuUizViliQFdrjf/qXpNOgrKAgBZ6hk\nRhkAADKcS/MWy50lyfXhIk/tOtN50CdQEALO0H6GkBllAADIWGVTdMRVkhSN5L1xg2SbDoTM\nR0EIOEMlixACANAXHPcLFQ6X5Nnxltb93nQaZD4KQsAZYlOMetyuYSUUhAAAZK7sIs2+r217\n1Y1qqTaaBpmPghBwgFDY2tUQlDS0OD/Lw9sWAICM9rUF9phTJKmlRmtuMZ0GGY5vloAD+GoC\ntm2LKUYBAOgborN+I2+uJH38O21/y3QcZDIKQsABfEwxCgBAn1I8PjT1akmyo1p2pWzLdCBk\nLApCwAFiFxCKM4QAAPQZocOvV/+xkrTzfX34iOk4yFgUhIADsOYEAAB9je3J1UkPtf2x5mYF\nthuNg4xFQQg4QHtBOKKUKUYBAOgzxpyicadLUmujVv3YdBpkJgpCIN1FbfuL2oCkksKcwtws\n03EAAEAKzXlQWQWStOFpVS43nQYZiIIQSHc76ppbI1ExXhQAgD6o3yjNuLlte/nVioaNpkEG\noiAE0l0lFxACANCXTbteJRMlqWaD3vuN6TTINBSEQLrz1QRiG0wxCgBAX+TJ1smPSi5JevMX\natxqOA8yCwUhkO72LEJIQQgAQN80YqYm/qskhZu14nrTaZBRKAiBdFdJQQgAAGbfr5wBkrTp\nBW150XQaZA4KQiDdxc4Q5mV7S4tyTWcBAACG5A/WcT9v2379WkWCJsMgg1AQAmmtLhDyB8OS\nRpYVukyHAQAAJh1xlQYdIUn1m/XOXabTIENQEAJpzccUowAAIMbl0cmPyeWWpHfuUu1npgMh\nE1AQAmltrwsIC8wmAQAA5g05WlO+L0lWSMuvNp0GmYCCEEhrnCEEAABfMfNu5Q2UpIql2vi8\n6TRwPK/pAAC64qv+chHCUgpCIKN88cUX77//fjgcnjJlyiGHHNKjfbq473vvvbdp06Zzzz03\nuekBGJRbohN/pVd/IEkrrtNBpyiLLwk4cJwhBNJabMio1+0aWsKQUSBzrF69+uqrr/7ggw82\nb9588803P/fcc/Hv01l7c3Pzgw8+eO+99/7pT39K3TMBYMTUSzTsOElqqtIbvzCdBs7GGUIg\nfQXDVnVji6RhJQVeN5OMAhnCsqzHHnvswgsvPOOMMyS98847d95555w5c8rKyrrdp7i4uLP7\nPv300/n5+VdeeeV9991n6qkBSBWXTn5UTx+paETv/1aTvquBh5mOBKfiDCGQvnzVflsSS9ID\nmWXjxo1NTU1f//rXY39Onz69X79+7733Xjz7dHHf884779JLL83KykrhUwFgTtlUHX6lJEUj\nWnaVZJsOBKfiDCGQviqZUQbIRNu3by8qKsrPz29vGTJkyPbt2+PZJysrq7P7FhUVxZ8hFArZ\ndq++PlqWFXscl8sB4xfC4bBt28GgAxbyjv27RKNRR6SVFI1GHXQYtP83/UWj0e4Pg6Nuzvns\nOVdgm75YE/7w/1kTvpuqdPuKRqOSnHLQRiIRRxyx+vKFtSyrl6+ty+XKycnp7FYKQiB9tU8x\nyowygKP5fL433ngjtj1v3rxQKJSdnb33Djk5OaFQaO+WzvaJ577xCAQCse8ZvRQIBHr/ICnT\n2tpqOkK8LMvy+/2mU8TLWYdB7H1kOkW8ujsMXOGjbitadZkk7z9+3Fg2084tSU2wDjnooJXk\noMMgEon08rX1eDwUhIAj7VmEcCAFIeBgfr+/vLw8th0MBnNycvapTILBYG5u7t4tne0Tz33j\nUVhY2MszhMFgMBwOFxYWOuKH9nA4HI1Gu/g+lD5s2/b7/R6PZ+/zwOmsubk5Ly/PKYdB7P3i\niJHVsdOD3R8Gh34vuuVP7qrXXcHaARvuj8x6ICXp9hX7jalH4xQMCoVCbrfbKYdBIBDwer15\neXm9eZyu36EUhED68tX4JbmkEUwxCjjZxIkTJ06c2P5nIBBobGz0+/2FhW2/9Wzfvr39ssCY\n4cOHd7hPZ+09jbTPacYDEKtLs7Oz3W4HzEdg27azCkK32+2ItJKCwaCDDgNJXq/XEa+tZVmt\nra1xRf36Y3pqqqyQZ93vPFMv1tAZyU+3r5aWFqe8xSRZluWUt5hlWYFAoOvze73ngHcv0DdZ\nUXt7bbOk0n65+Tn8dgNkjvHjx5eUlLzyyiuxP9esWdPS0jJt2jRJzc3NsXFBne3TxX0B9FHF\n4zXtekmyo1p2pWzLdCA4DN8ygTS1va45bEXFjDJAxnG73VdcccU999zzySefZGdnv/vuuxdf\nfPGAAQMkPfTQQ42NjbfffnsX+3TW/tRTTzU2Nu7atcu27UWLFkmaOXPmYYcxEz3QBxxziz59\nRg3l2vme/vmfOuwK04HgJBSEQJpiRhkgg82YMWPRokVr1661LOvss88eO3ZsrP24445rn+eg\ns306ay8uLs7Ozi4rK5s0aVKs5QCuLQTgSN48zf6N/u8MSVp9k8adoYKhpjPBMSgIgTS1Z0YZ\nzhACmWjYsGH/8i//sk/j8ccf3+0+nbV3uCeAvmLc6Tr4W9r8V4UatPomnfJ704HgGFxDCKSp\n2IwyYsgoAACIx9zFyiqQpPVL5HvddBo4BgUhkKYqd1MQAgCAuPUbpRk3SZJsLbta0bDhPHAI\nCkIgTVXV+CUV5mYVFzpgWmQAAGDetBtVMkGSatbrfTNrEsJxKAiBdFTTFAyEIpJGlrICIQAA\niI8nW3MXtW2/cZsaK4ymgTNQEALpaM+MMgOLzCYBAABOMvokTThXksLNWnmD6TRwAApCIB35\nagKxDc4QAgCAnpn9G+X0l6SNz6v8JdNpkO4oCIF05GPNCQAAcGAKhujY29q2l1+rSNBoGqQ7\nCkIgHbUXhEwxCgAAeuzIazTocEmq/1zv3m06DdIaBSGQjmLXEHo97iED8k1nAQAATuPy6OTH\n5HJL0tt3qm6j6UBIXxSEQNppDkVqm4KSRpQWeNwu03EAAIADDZmuyRdJkhXS8msMh0EaoyAE\n0o6vxm9LkkaWMl4UAAAcqJn3KK9Mkrb+XZteMJ0GaYqCEEg7lbu5gBAAAPRaXqlOuKNt+/Uf\nKuw3mgZpioIQSDu+GqYYBQAAiTD1Ug07VpKafHrzdtNpkI4oCIG046tmEUIAAJAILrdOelRu\nryS9d792/9N0IKQdCkIg7VRWN0lyScMpCAEAQC8NPFSHXSFJ0YiWXyXZpgMhvVAQAuklErV3\n1DVLGtg/Ly/bazoOAABwvhN+qcJhklS1Whv+YDoN0gsFIZBettUEIlFbzCgDAAASJbufZt7T\ntr3yRoXqjaZBeqEgBNILM8oAAIDEm3i+Rs2VpOad+setptMgjVAQAumlspqCEAAAJMFJj8iT\nI0kfPqwd75hOg3RBQQikF181ixACAIAkKD5ER10nSXZUr10p2zIdCGmBghBIL+0F4chSCkIA\nAJBQx9yqfmMkaedaffy44TBIDxSEQBqxJV9NQFJRXtaAgmzTcQAAQGbJytfs+9u2V9+k5l1G\n0yAtUBACaaS6saWlNSLGiwIAgCQZf6bGflOSgnVa9R+q/ljV6xQNm44FY1jlDEgjzCgDAACS\nbt5i+ZYr3Kz1T2n9U5LkzdWhl+mEXykr33Q4pBpnCIE04qsOxDY4QwgAAJKlaITyB32lJRLU\n+w/oL9+RbEOZYAwFIZBGmFEGAAAk3WfPqmFrB+1bX9Gm/011GJhGQQikEYaMAgCApNvyYqc3\nlXd+EzIUBSGQRnw1fknZXvfgAXmmswAAgAzVsrvTmwI7U5gDaYGCEEgX/mC4zh+SNLy00O1y\nmY4DAAAyVP6QTm8qHJrCHEgLFIRAumi/gJAZZQAAQBKN+5dObzr49BTmQFqgIATSxV4zyhSY\nTQIAADLZ+G/roG900F46UWNPS3kaGEZBCKSLSs4QAgCAVHDpX17Q0f+hnP5faW7YqoYthiLB\nGApCIF34atoWIWSKUQAAkFzeXM28W1fV6dLN+kG5Jl8kSZEWvXal4WBIOQpCIF3EzhC6XK7h\nJQwZBQAAKeBS/7HqN0azfq28Mkna+oo+ZynCvoWCEEgLESu6s75Z0pABeTlZHtNxAABAX5JX\nquNvb9tefo3CfqNpkFLGCsJoNFpeXr5p06bW1tae7tNZu2VZPp9vy5YtwWAwWbmB5KiqCVhR\nW9LIUsaLAgCAlDv03zT0GElq8umtO0ynQep4jfxfy8vL77jjDr/fn52dHYlEbrjhhiOPPDLO\nfTpr37Bhw/3339/S0pKdnR0IBC655JL58+cbeG7AAWmfUYYLCAEAgAEut+Yt1h9nyLb03v2a\nfKFKJprOhFQwc4bwvvvumzRp0jPPPLNkyZLTTz/9vvvua2lpiXOfDtuj0ehdd9119NFHP/30\n008++eR555336KOPNjQ0mHhywIHw1VAQAgAAowYfpcMulySrVUsvl2zTgZAKBgrCioqKysrK\nCy+80OVySTrrrLMikcjatWvj2aez9mAwuGDBgnPPPdftdkuaNWuWZVnbt29P/bMDDgyr0gMA\nAPNO/JUKhkpS1Sp98ozpNEgFA0NGKyoq8vPzy8rKYn96PJ6RI0dWVlbGs49t2x22n3jiiaed\ntmcZzXXr1uXl5Y0aNaqzDNFo1LZ79ZtH7O6WZfXycVIjGo1Go1HLskwH6V7s9bRt2xFp9WXU\n3h8GlbubYhvDinOT9Nyj0Wjsv454bWOvqiOiaq8PBNNB4hI7EhyRNhay90eCy+WK/VwIAOhG\ndj/NvFsvXyhJK36ksacqZ4DpTEiuVBSEjY2NVVVVse0xY8YEAoH8/Py9d8jLy/P7vzKXUWf7\nxHPfjRs3Pvzww5dffvk+e+6tvr4+9pWol5w1KtVBc+1EIpG6ujrTKeLV+8PAtu2qmoCkolyv\nFQzUBQOJyNWx5ubm5ubm5D1+YjnoMJDT0jroMGhtbe1i+rF4eDye4uLiROUBgAw36bta96R8\nr6t5p964TXMeMB0IyZWKgnD9+vWLFy+Obd92221ZWVn7/NZrWVZWVtbeLZ3t0+19V61a9eij\nj15xxRUzZ87sIlJ2dnYvT+lEIhHLsnJycnrzICkTe9E8HgcsZmDbdmtrq9vt3ueQSFvhcNjr\n9cbGMB+w3Y2hUCQqaWRpQfIOKsuyIpGI1+t1ypEQiUScchi0trbats0HQsIl6gOhl+9QAOhj\nXJq3WEsOVzSsDx7S5Is06AjTkZBEqSgIjz322GOPPbb9z+bm5oaGhnA43N7B7969e+8dJJWV\nlXW4T2ftse3nn3/+xRdfXLhw4bhx47qOVFjY28u0mpqaLMsqKChwxDCkYDAYjUa7OGWaPmzb\nrqmp8Xg8RUVFprPEpaGhobCwsJeHwWe72k7ejhncP3lPPBgM+v3+3Nzc3NzcJP0vEsiyLL/f\n75TDoL6+PhKJOCVtc3Oz2+12ymHQ2tqalZXllNcWADJE6SQd9UO9e69sS0sv03lvyeWAb7w4\nMAb+aSdMmOD1ettnkdm6devOnTsPP/zwePbp4r6vvPLKyy+/fM8993RbDQLphjUnAABAejn2\n5+o3RpJ2vKuP/5/hMEgmA5PK5ObmnnXWWQ8++ODOnTuzs7NfeOGFefPmjRw5UtITTzzh9/uv\nueaaLvbpsL22tvaJJ56YN2/e+vXr2/9H48ePHz58eOqfINBTTDEKAADSS1a+Zv9afzlLklb/\nROPPUN5A05mQFGYWpl+wYMGgQYPefPNNy7JOP/30U089Nda+95jGzvbpsL22tnbcuHEVFRUV\nFRXtj5CTk0NBCEfYc4awlIIQAACkh/Hf0djTtOVFBWu1+iZ9/XHTgZAUZgpCl8s1d+7cuXPn\n7tN+7rnndrtPh+3jxo371a9+laS0QLLFzhDmZHkG9XfAZV0AAKCvmPOAKpcpEtTHT2jK9zXs\nONOBkHhcHgoY1tjS2tDcKmlkaQFzIQIAgDQy4GAd/WNJkq2llysaNpwHSUBBCBjGjDIAACB9\nzbhJJV+TpOqP9cFDptMg8SgIAcN81W3L0FMQAgCAtOPJ0ZwH27bf+Jn8XxhNg8SjIAQM8zGj\nDAAASGdjvq7x35Gk1iatvMF0GiQYBSFgGGtOAACAdDfnt8oqlKRP/6Str5hOg0SiIAQM89X4\nJbldrmElBaazAAAAdKRohI79Wdv28mtkhYymQSJREAImtUaiO+tbJA0pzs/28n4EAADp6qjr\nNPAwSarbpLX3mU6DhOELKGBSVY0/attivCgAAEhzbq/mLZZckvTWL9VQbjoQEoOCEDCpkgsI\nAQCAUww/QZMukKRIi5ZdaToNEoOCEDBprylGuYAQAACkvVn3KrdYkspf1ua/mE6DBKAgBEzy\n1bAIIQAAcI78QTr+l23by69WOGA0DRKAghAwqX3IKAUhAABwhsMu19AZktRYqbfvNJ0GvUVB\nCBhj2/a2moCk4sKcwtws03EAAADi4HJr3kNyeSRp7b2q/cR0IPQKBSFgzI76llDEkjSylNOD\nAADAOQYfpUP/TZKsVi29QrJNB8KBoyAEjPExxSgAAHCoE+5Q/iBJqlqZveV/TKfBgfOaDgD0\nXVxACMCUxsbGaDTam0eI3b2xsTFBiZIrlra1tdV0kO7Zti0pEonU19ebzhIXy7KcdRg0NzcH\ng0HTWbpn23Y0Gk3vw8CVPe22/FVXSsp966aWIbPr651RWcSOBKccBpJaW1t7eSS43e5+/fp1\ndqsz/tmAjOSr4QwhADOKiop6+Qh+vz8UChUVFbndDhhtFAwGo9Fofn6+6SDds227trbW6/V2\n8e0trTQ2NhYWFjrlMAgEAnl5ebm5uaazdM+yrEAgkO6HwbTLVf6cfCvcLbvyP7on95RHTAeK\nS0tLi8vlcsphUF9fn52dXViYxO+KFISAMXsWISxjEUIAKeVyuRL1OIl6qKRyfcl0kB5wUFqn\nvLaxkI5LazpI11yat1hLjlA0nPfJ4zrqMg06wnSkuDjrMFCSjwQH/JwDZCpfdUBSXra3rF+e\n6SwAAAA9VzpZR14jSbalpZfJ7tVYdBhBQQiYUR9obWxplTSytMABv1ABAAB06LiF0cKRkrTj\nXa170nQa9BgFIWBG+wWEzCgDAAAcLCu/ZfrCtu1V/6GWaqNp0GMUhIAZTDEKAAAyQ/igM1qH\nnyRJwVqt+anpOOgZCkLADBYhBAAAGSMw41fy5krSx/9P2940HQc9QEEImOHjDCEAAMgUVtFB\nOvo/JMmOaullikZMJ0K8KAgBM2JDRj1u17BiB6yLBQAA0I0ZP1XxIZJU/bE+fNh0GsSLghAw\nIBS2djcGJQ0tzvd6eBsCAADn8+Ro7oNt2/+4Vf5tRtMgXnwTBQzwVftt25Y0qqzIdBYAAIAE\nGTNf48+UpNZGrfoP02kQFwpCwABfTSC2MbKswGwSAACARJrzgLIKJemTP6pyuek06B4FIWBA\nZXVTbGNkKTPKAACADFI0Usfc0rb92hWyQkbToHsUhIABvuq2M4SjBlIQAgCAzDLtepVNlaS6\njXrvN6bToBsUhIAB7avSj+AMIQAAyDBur+Y9JLkk6a3b1VBuOhC6QkEIpFrUtrfVBiSVFeUW\n5HhNxwEAAEi0ESdq4vmSFG7Wih+ZToOuUBATG6GjAAAgAElEQVQCqba9rjlsRcWS9AAAIIPN\n/rVyBkjS5/+rzX81nQadoiAEUq19vCgFIQAAyFj5g3X87W3by69SOGA0DTpFQQikmo+CEAAA\n9AWH/7uGTJekxkq9c7fpNOgYBSGQansWISxlEUIAAJC5XG6d9JBcbkl6927Vfmo6EDpAQQik\nmm932yKEozhDCAAAMtvgaZr6A0myWrX8atNp0AEKQiDVYmcI83O8JUW5prMAAAAk2Yl3Km+g\nJFW8ps+eNZ0G+2LK+x6rqgk8/9aWT6tqm1paxwzqN3vy8JMOG+EynQpOUesP+YNhSaPKCjls\nAABA5sst1sy79PdLJOn1azVmvnL6m86EPSgIe+aD8urb/rw2FLZif1Y3Va/dXP3O57tu+vYR\nbhdf79E9ZpQBAAB9zpSLtX6JqlYqsENvLtTs+0wHwh4MGe2BltbI3f/zYXs12G7Vhu0vf+Az\nEgmOs2fNiVIKQgAA0Ee4NG+x3FmS9MGD2v2R6TzYg4KwB9Zu3l0XCHV406sfUhAiLu1nCJlR\nBgAA9CFlU3TEVZIUjWjZlZJtOhDaUBD2wBe1na6nWVXDUpuIi6+GIaMAAKBPOu4XKhwuSV/8\nQ+t+bzgMvkRB2APZXk9nN+VkdXoTsDdfdUCS1+MeUpxvOgsAAEAKZRftuXpw1Y1qqTaaBm0o\nCHtg8sjiA7gJaNfSGqlubJE0rCTf62YWIgAA0Md8bYEO+oYktdRozS2m00CiIOyRrw0bMO3g\ngfu3e9yuBcePS30eOI6v2h8bL88FhAAAoI+a84C8uZL08e+0/S3TaUBB2EM3ffuI/WvCaNSO\nWFEjeeAssfGiYopRAADQZxWP17QbJMmOatmVsvedwB8pRkHYM4W5WXecN33RJcdfOmfcWdNH\nHHvIYEm2tPjldVGbuZLQDWaUAQAA0Iyfqv9YSdr5vj58xHSavo6C8EAcMmzAKYcNO/Oo4T8+\n47AhA/Ilbdre8Ne1FaZzId1VsuYEAACAN08nPdS2veZmBbYbTdPXURD2Sk6W56pvTIltP7n8\ns+qmoNk8SHOxRQhd0ojSAtNZAAAAzBlzisadLkmtjVr1Y9Np+jQKwt46etzA4ycMkdTSGnns\n1Q2m4yB9RaL2trpmSWX98vKyvabjAAAAGDXnQWUVSNKGp1W53HSavouCMAGumD8p9v1+1Ybt\nb2/aZToO0tT22kBs8iHGiwIAAKjfKM24uW17+dVqrFD1Olkho5n6IgrCBBjYL++CWeNj24tf\nXhcMM1cSOtB+AeHIMsaLAgAASNOuV8lESarZoN+N0VNT9WCh/vId+b8wnawPoSBMjDNnHDRu\nSD9Juxpa/rTmc9NxkI7apxjlDCEAAIAkebI14vivtEQj2vSCnjlBLdWGMvU5FISJ4Xa5rj1t\nqtvlkvTsG5vLdzaaToS0s2cRQgpCAAAASf4vtH5JB+2NW/X2nSlP00dRECbMIcMGnHrkKElW\n1F788noWJcQ+9hoySkEIAAAgbf27rNaOb9r8l9RG6bsoCBPpknkTSotyJa3z1S790Gc6DtJL\nVY1fUmFuVnFBjuksAAAAaSCws/ObdqQwR59GQZhI+TneH5w0Mbb9n6990tDcyQ8e6Huqm4LN\noYg4PQgAANCuYHDnNw1JYY4+jYIwweZMGTZ93CBJTS3hx5d9ajoO0oWvmhllAAAAvmrMfHmy\nO77p4G+lNkrfRUGYeFfMn5TtdUta+qHvw601puMgLfi4gBAAAGAfhcN1zK0dtHvyNOOnKU/T\nR1EQJt6wkoJzjx8nyZYWvfRx2IqaTgTzKjlDCAAAsL9jbtFJD6tg6FcarRb5VpjJ0/dQECbF\nguMPjn3vr6oJPP/mFtNxYN6eM4SlrEoPAACwl8Ou0OXb9IOtuuADnfRwW+Pya9TKQm6pQEGY\nFF6P+5rTprokSc+s/nx7XbPhQDAtdoYwy+MePCDfdBYAAID002+0Bh2uwy7XsOMkKbBdb95u\nOlOfQEGYLFNHlcw9dLikUMRa/PI603FgUnMoUusPSRpRWuBxu0zHAQAASFsunfyo3F5Jev+3\n2v2R6TyZj4IwiS7/+qT++dmS1m7evWrDdtNxYAxL0gMAAMSrbKoOv1KSohEtu0qyTQfKcBSE\nSdQvL/viOV+LbT/89/X+YNhsHphCQQgAANADx9+uwuGS9MUarV9iOk2GoyBMrlOOHDV5ZLGk\nOn9oyYqNpuPAjL1mlKEgBAAA6E52kWbd27a98nq1sJBbElEQJpdLuubUqV63S9Jf1lZ88kW9\n6UQwgFXpAQAAembCv2rUPElqqdE/OlqrEAlCQZh0YwYVffuYsZJs237gxY+tKMOg+xxfjV+S\ny+UawZoTAAAAcTrpEXlyJOmfj2n726bTZCwKwlT47szxQwbkSyrf2fjXtRWm4yClIlY0tu7I\noP65OVke03EAAAAconi8pl0vSXZUy66UbZkOlJkoCFMhJ8tz1TemxLZ///pn1U1Bs3mQSl/U\nBmKnhRkvCgAA0DPH3KL+B0nSzvf00WOm02QmCsIUOXrcwOMnDJHU0hp59O8bTMdB6viqA7EN\nZpQBAADoGW+e5vy2bXv1TQqwkFviURCmzlXfmFKQ45W0+pPtb2/aZToOki5iRVeu3/Y/b2+J\n/dkvP9tsHgAAAOc5+F908LckqbVRq28ynSYDURCmTklhzndnHRLbXvzyumCYYdCZbHdjy1WP\nr/nVCx+s89XFWp5euYkrSAEAAHps7mJlFUjS+iXyvW46TaahIEypM6aPGTe0v6RdDS3PrP7c\ndBwki23bC597v3xX096NVjS6+OV1H5RXm0oFAADgSP1GaUbs3KCtZVcrGjacJ7NQEKaU2+W6\n9rSpbpdL0nNvbt6ys9F0IiTFx5W1G7d1vObkf7+1JcVhAAAAHG/ajSqZIEk16/X+A6bTZBQK\nwlQ7ZGj/044aJcmK2g+9vJ5FCTPSpu0NB3ATAAAAOubJ1smPSi5JeuM2NXIZTsJQEBpw8dwJ\nJYU5ktb5av/+gc90HCRetPNC3+riNgAAAHRmxCxNWCBJ4WatvMF0msxBQWhAQY73sq9Pim0/\nvuyThuZWs3mQcGMGFnV200GD+qUyCQAAQOaY/Rvl9Jekjc9ry4um02QICkIzZk8eNn3cIElN\nLeHHX/vEdBwk2BEHlQ4vKejwpm9NG53iMADSUzQaLS8v37RpU2trpz8LdrZPZ+2WZfl8vi1b\ntgSDwWTlBgCDCobo2J+3bb9+rSJ81iWA13SAvuuK+ZM+2loTilhLP6qad+iIw8eUmk6EhPF6\n3LeedeR1v3+jpfUri4ucdezYmZOGmkoFIH2Ul5ffcccdfr8/Ozs7EonccMMNRx55ZJz7dNa+\nYcOG+++/v6WlJTs7OxAIXHLJJfPnzzfw3AAgqY68Whue0q4PVb9Z796tY28zHcjxOENozLCS\nggUnHCzJlha99HHYippOhEQ6aHC/0n55se3xQ/p/86jR9154zA9Ommg2FYA0cd99902aNOmZ\nZ55ZsmTJ6aefft9997W0tMS5T4ft0Wj0rrvuOvroo59++uknn3zyvPPOe/TRRxsamMUKQMZx\neXTyY3K5JentO1W30XQgx6MgNGnBcQePKiuUVFUTeP5NViPIKJt3NFZV+yWNKitc/IMTrj51\nyqGjOQkMQJIqKioqKysvvPBCl8sl6ayzzopEImvXro1nn87ag8HgggULzj33XLfbLWnWrFmW\nZW3fvt3E8wOAJBsyXVMuliQrpOVXm07jeAwZNcnrcV9z2tQbn3rTlp5Z/fnsycOGFuebDoXE\nWLF+W2xj7tThZpMASDcVFRX5+fllZWWxPz0ez8iRIysrK+PZx7btDttPPPHE0047rf3u69at\ny8vLGzVqVGcZbDsxMx7btp2oh0oBR0RtD+mItDFOOQxiIR2X1nSQHkhp2hPvdn3+f2qp1tZX\n7Y3/rfHf7ukDOOK1TeAHQuxnxA5REBo2dVTJvENHvPbPqlDEWvTSul+dP910IiSALa3c0FYQ\nzuKiQaDPa2xsrKqqim2PGTMmEAjk53/l57+8vDy/3793S2f7xHPfjRs3Pvzww5dffvk+e+6t\nrq4uGk3ApQp1dXW9f5CUaW5uNh0hXuFwuKamxnSKeDnrMAgEAoFAwHSKeDnoMFDK0+YeflPh\nm9dLspdfW9dvmu3teD6/zuzzyZnOQqFQKBTqzSN4PJ7i4uLObqUgNO+yr0989/NdDc2t723Z\nvXL9tlmTh5lOhN5aV1m7s75F0sQRxcM6mW4UQN+xfv36xYsXx7Zvu+22rKwsy/rKjFOWZWVl\nZe3d0tk+3d531apVjz766BVXXDFz5swuImVnZ/fy9+ZIJGJZVk5OTm8eJGViL5rH4zEdpHu2\nbbe2trrd7n0OibQVDoe9Xm8XJx/Sh2VZkUjE6/U65UiIRCJOOQxaW1tt207xB4I95RJry589\nO99xB74oWvfb0IyFcd6xD34gdP0OpSA0r19e9sVzJ/z2b/+U9MirG446eGBhrjPe/OjM6+va\nTg/OmUJ5D0DHHnvsscce2/5nc3NzQ0NDOBxu7+B379699w6SysrKOtyns/bY9vPPP//iiy8u\nXLhw3LhxXUcqLCzs5ZNqamqyLKugoCB21WKaCwaD0Wi0i1Om6cO27ZqaGo/HU1TU6ZK2aaWh\noaGwsNAph4Hf78/Nzc3NzTWdpXuWZfn9fqccBvX19ZFIxEDaUx7X00cqGsn+eHH2YRdr4KHx\n3Km5udntdjvlMGhtbc3Kykrqa+uAd29fcMoRI2MzjtT5Q0+tYK4kZ4tE7TWfbJfkdrlYZALA\n/iZMmOD1ettnkdm6devOnTsPP/zwePbp4r6vvPLKyy+/fM8993RbDQJAhiibqsOukKRoRMuu\nlBxwWWAa4gxhWnBJV31j8r//5+pI1P7r2oq5U4dPHD7AdCgcoPc2725obpV05Niy4gJnDKYC\nkEq5ublnnXXWgw8+uHPnzuzs7BdeeGHevHkjR46U9MQTT/j9/muuuaaLfTpsr62tfeKJJ+bN\nm7d+/fr2/9H48eOHD2deKwAZ7YRfatN/y79NX6zRhj9o0gWmAzkPBWG6GD2w6NvHjH32jc22\nbT/w4scPXXqCx+2A4fjY3+vrvohtzOZyUACdWLBgwaBBg958803Lsk4//fRTTz011r73mMbO\n9umwvba2dty4cRUVFRUVFe2PkJOTQ0EIIMNl99PMe/TSdyVp5Y0a+03ldjp7CjrkcsSMq2mo\nqakpFAqVlJQkcNB8KGxd9tiq7XXNki6fP+nM6Qcl6pEdd+1EVlZW//79TWeJS0NDQ1FRUfth\nEApbC+5/raU1ku11/+lHJxfkpNFvLrFrJwoLC50yaN7v9zvlMIhdO9G+EkCac9a1E3V1dTk5\nOU65iqZPSUY/mDz0g8mzTz+YzugHk8d8P/jcPFUul6TDr9S8xV3vSz+4Dwe8e/uOnCzPVd+Y\nEtt+6vWN1Y1Bs3lwAN74bGdLa0TSMYcMTqtqEAAAIGOd9Ig8OZL00SPa8Y7pNA5DQZheph08\n8PgJQyS1tEYeeXV9t/sj3TBeFAAAINWKD9FR10mSHdVrV8q2ursD9qAgTDtXfWNK7MzSmk92\nvLVxp+k46IGmlvB7W6ol5ed4jx43yHQcAACAPuOYW9X/IEnauVb//J3pNE5CQZh2SgpzLph1\nSGz7oVfWB8P8wuEYqzZsi1hRSTMnDc328uYCAABIlax8zb6/bXvNT9W8y2gaJ+E7azo6ffqY\ncUP7S9rV0PJfqzeZjoN4Lf9yPXrGiwIAAKTauDM09puSFKzTqh+bTuMYFITpyO1yXXvaVLfL\nJen5N7ds2dloOhG6t7uxZb2vTlJJYc5hY0pNxwEAAOh75i1WVoEkrX9KvhWGwzgEBWGaOmRo\n/28eNUqSFbUfenk9a4Okv9fXbYst4jJ78rBYMQ8AAICU6jda02PnBm0tu0rRsOE8TkBBmL4u\nnjuhrChX0jpf7d8/8JmOg268/uV40TlTWQYaAADAkKN/rJIJklSzXh8sMp3GASgI01d+jvff\nTp4Y23582Sf1gVazedCFymp/bGTvsJKCQ4Y6Yw1ZAACADOTJ1twv68A3fi7/F0bTOAAFYVqb\nNXnY9PGDJDW1hB9f9onpOOjU8o/bPmvmTmE6GQAAAKNGn6SvnSNJrU1a8SPTadIdBWG6u+qU\nKblZHkmvfVT14dYa03HQsZUbtsc25kxhvCgAAIBpcx5QTn9J+uxZlb9kOk1aoyBMd4MH5C04\nfpwkW1r00sdhK2o6Efb1SVXdttqApEOG9h9RWmA6DgAAQJ9XMETH/qxte/m1igSNpklrFIQO\ncM5xY0cNLJRUVRN47o0tpuNgXyvWt50enM14UQAAgDRxxDUaeJgk1X+utfeaTpO+KAgdwOtx\nX3vq1Ng6Bv+1elNVTcBwIOwlaturP9khyeVyzWI9egAAgDTh9mreQ5JLkt6+Uw2cVumY13QA\nM6LRaGzJuAMWu7tlWb18nDhNHN5/3qHDX/vnF2Er+tDL6375r9N6dPdoNBqNRi3LSlK8BIq9\nnrZtOyKtpI8q6uoCIUmHjiouzs9K59jRaDT233QO2S725nJEVO31gWA6SFxs23bQYaBEfCC4\nXC63mx9AAaDvGX68plykdU8q0qLX/l3fecV0oHTURwvCQCAQ+3J8wGLfTpqbmxOUqHv/esyI\ndzbtamwJv19e/er75ccdMjD++8YKYEd8/2v/Yu33+01nics/Nu6ObRwzrjTNM8eO+VAoFA47\nYJHW2BGb5i9pu9hr66y0TjkMJIXD4V6+tm63u6ioKEGhAACOMvNebf6rWqq19e/6/H817gzT\ngdJOHy0Ie//NoKmpKRQKFRUVpexX5/79dcm8ib/52z8lPbVm64lTRxfmZsV532AwGI1G8/Pz\nkxkwMWzbrqmp8Xq9/fs7YDW/1kh07ZZaSV6P++QjDirKi/dfxIhgMOj3+/Py8nJzc01n6V6s\nGnTEYSCpvr4+Eok4JW1zc7Pb7XbKYVBXV5ednU05BwA4QHmlOuGXWnq5JC2/RqNP4qK5ffBy\nOMn8I0YeOrpUUp0/9NTrn5mOA721cWdzqyVp+rhBaV4NAsD/b+++A6Mo8z+Of2dLeiEkJISE\n3kOCdKSJFEHkh/pTUcDT81RUTrHrqXeKv1NUFD0RPfUOxXLqoZ5HOYJIC0hEOpIEAgk1hIT0\nXnd3fn8sxhwmEEiyz5b366/Z2dmdz24m8+SbeeZ5AMBDxc2WyMtFREoz5Mf5qtM4HQpCV6KJ\nPDCln8mgiciq3ScPZhapTuTpElJO2xfGMb4oAACAc9IMMuEd0YwiIrvfMBSmqg7kXCgIXUzn\ndoE3jugmIrquL1qdZLU5YkgbNKii2rIzPUdEfL1Mw3uGq44DAACARkQMksvuExGx1nhteVCE\nP6F/QUHoem69omdkiJ+IHDtTsnLncdVxPNeWA1k1FpuIjOwd4W02qo4DAACAxo15SfwjRcRw\nOtF4eJnqNE6EgtD1eJuMD0yJtS9/nHD4eE5ZenZJWZULDBjoZugvCgAA4DK8guSKBfZF09Y/\nSDX3Xp3loaOMuroh3duN7tt+68HsyhrLve9vtq/sGRn8+6v7xUSHqM3mIQrKqn86ni8iQb7m\nAV1CVccBAADAhcTcJikfycmNWmWOJD4n499SHcgpcIXQVY2N6aD995q0rOKnPt2eklGoJpCH\nSUg5bdN1ERnZq53RoF1wewAAAKg3frEYzCIi+96R7B2q0zgFCkJX9cnmw7++GbbaYn13bYqC\nNJ5nU/LZ/qKjeoWpTQIAAICmCo2p7f+AiIhukw0PiG5THUg9CkKXdLqgPCOvrMGn0rKK80ur\nHJzH02QWlB8+XSQi7dv49YoMUh0HAAAATVU75Bk9sLOISPZOSVqiOo56FIQuqbC85pKfRfPV\nXR4cF3tux10AAAA4NbOfZfTZ0WXk+6elMldpGvUoCF1SiL/XeZ5tG+DtsCSeaVNypn3hyn6M\nLwoAAOBirN2uk25TRUSqCmTLU6rjKEZB6JI6tPXv1C6gwad6dWhDQdiqDmcVn8ovF5GuEUFd\nwgNVxwEAAMDFG7dITD4iIslL5dRm1WlUoiB0VfdfHetlOvfHp4ncMrq7kjyeI+Hn/qLjmX4Q\nAADARbXpLsPs1wZ12fCA2Dx3Tm8KQlc1oEvo/FnDerT/rxFNdJFv95xUFckT6Lq+OeW0iGgi\nY2MoCAEAAFzWsKekbW8Rkbxk2fu26jTKMDG9C+vfOfSd2WPKqmpziiu9zcYHP0gsq6rdmZ67\n5UDWFTGRqtO5p59OFOSVVolIv05tI9r4qo4DAACAS2X0lvGL5etJIiI/zJPeN0tAlOpMCnCF\n0OUF+Ji7RQRFtfW/c3xv+5q/rk0pq/Lcq96tKuHn4WTG0V8UAADA1XW+SnrdJCJSUyqbH1ed\nRg0KQvdxzaBOsR3bikhhWfUHG1JVx3FDFqtta2q2iBgN2ug+XIMFAABwfePfEq8gEZHUf8qx\nNarTKEBB6D40TXtkWn/7SDNr9pzcdzxfdSJ3syM9p7SyVkQGd2/X5rwzfwAAAMA1+EfKiGfP\nLm96SCxVStMoQEHoVqJD/W8e2V1EdJHF8Uk1FpvqRG7ll/nomX4QAADAbQx6WNpdJiJSmCa7\nX1edxtEoCN3NzNE97FMUnsovX5aYrjqO+6issWxPyxERb7NxRO8I1XEAAADQQgwmmfC2iCYi\n8uN8yU+WgoNSW6E6loNQELobk9Hw0DVxmoiI/DPxyMncMsWB3EVianZ1rVVERvaO8PVieF4A\nAAA3EjVa+t0uImKplI/iZGmMLA6Uf46WM7tUJ2t1FIRuKLZT26sHdRIRi9X2xqqfdF1Xncgd\n1PUXvZL+ogAAAO6n53TRtF8e6jbJTJRlYyVru7pMjkBB6J5mT+wbFugjIgczi+L3ZqiO4/KK\nK2r2HssTkQAf85Du7VTHAQAAQEvb+rT8+jpKbYVsnKsijeNQELonf2/TvZNi7MtL1h/ML6tW\nm8fVbU45bbXpIjI2JtJk5LcGAADAvRQfk7ykhp/K3illpx2bxqH409ZtXRETOaJXhIhUVFuW\nbDisOo5r+6W/KPPRAwAAuJ/y7PM+m+WoHApQELqz31/dzz78yQ+Hc3ak56mO46pyiisPnioU\nkdBAn7hObVXHAQAAQEvzO+89QX7hjsqhAAWhOwsP9v3tuF725fc3HC6vtqjN46I2JGXau5OP\nj+2g1b/VGAAAAO6hTQ9p27vhp9r1l8COjk3jUBSEbu76oV36RoeISEFZ9ScJh1THcUkJKXX9\nRaPUJgEAAEBrGfdWAys1Tca85PAoDkVB6OY0TXtoapzJoInIip0n7F0f0XTHzpQczykVkY5h\nAT3aB6mOAwAAgNbRZZLcuFba9vmvlboux9cpCuQgFITur2t44LVDOomIruuLVidZbExLeBE2\n/Xx5cBzDyQAAALi3LpPkdwfl3kyZsVVuSRCDWURk79uSs1d1slZEQegRZozqGhniJyLHckq/\n3nZEdRyXodfvL8p89AAAAJ4goINEjZLosTLoIRER3Srr7hXdpjpWa6Eg9AjeJuP9k3rbh0P5\nx+a0U/nligO5iJSMgjNFlSLSJ6pNVFt/1XEAAADgQCP/T4I6i4hk75TkD1WnaS0UhJ6iX3Sb\nCf2jRaTWalu0Ooluo02xKYnpBwEAADyV2U/GLjy7vOUPUpmrNE1roSD0IPdO6tvG30tE9p/I\nX//TKdVxnJ3Vpm9NzRIRTdOu6BupOg4AAAAcrtdN0m2qiEhVgXz/jOo0rYKC0IME+XrdMzHG\nvvz+ugNF5TVq8zi53Udy7V/RwK6hoYE+quMAAABAhXGLxOQjIpL8oZzepjpNy6Mg9CwT+kcN\n7tZOREora/+27oDqOE6t3viiTD8IAADgqdp0l6F/EBHRbbLuXrFZVAdqYRSEHuehqXE+ZqOI\nbEjK3JGWozqOk6qutW47dEZEvEyGUX3aq44DAAAAdYY/LSG9RETykmTfO6rTtDAKQo8T0cb3\n1it62pcXr0muqrWqzeOcfjh0prLGIiLDe0b4e5tUxwEAAIA6Rm8Zv/jscuKzUnZaaZoWRkHo\niW68vFuPyGARySmu/Mfmw6rjOKME5qMHAABAnS6TpOcNIiI1pbLlCdVpWhIFoScyGrSHpsYZ\nNE1Evtl+LD2rWHUi51JWVbvrSK6I+HmbhvRopzoOAAAAnMC4N8UcICJy8HM5uUF1mhZDQeih\nekUGXzesi4hYbfrrq/ZbbUxM+IvNB7IsVpuIjOkb6W0yqo4DAAAAJxDYUUY8e3Z5/RyxVitN\n02IoCD3X78b1bt/GT0SOnilZsfO46jhOJCGZ/qIAAAD4lcGPSrv+IiKFabLrDdVpWgYFoefy\nNhsfmBJrX/5o06Gswgq1eZxEfmlV0skCEQkJ8L6sS6jqOAAAAHAaBpOMf1tEExH58QUpPqY6\nUAugIPRoQ3u0uyImUkSqa61vr0lWHccpbEw+reu6iFzZr4P9NksAAADgrOgxEvMbERFLpSQ8\nojpNC6Ag9HS/v7pfgI9ZRHYdya0bWtOTJSRn2hfoLwoAAIAGjH1NfEJERNJXyJFVqtM0FxOs\neboQf++7J/R5c3WSiLy39sCQ7u3s9aFnysgrS88uEZHIEL9eHdqojgMAraWqqsreG+KSWa1W\n+/tortCZwmKx2Gy2yspK1UEuzP5zcZW0ImKz2VzoMBCRmpqaZh78jmGz2VzrMBARV0lrPxKa\ndRhoQaZhz5m3PCIi+ob7q9qNELN/S8Wrz/7FWq3WZn63mqb5+Pg09iwFIeTqQZ0SUk7vO55f\nWF69ZP3Bh/+nv+pEymz6eTiZ8XFRLtCyAcClaqk/3zVNc4lKQFwqqp0LpXWt79ZV0tpDukTU\nOi6UtvmHgTX2HmPqZ4acXVpphnnv65bh81oqW311IZuZ9vwvpyCEaCJzr4mb87ctNRbbt3sz\nxvbrMLBrmOpQatR1mr0yhv6iANyZt2aHvmkAACAASURBVLd3M9+htrbWYrF4e3sbDK5x+4nN\nZjvPP8idh67r5eXlBoPBJdKKSHV1tQsdBtXV1Waz2SW+W6vVWltb6xJRRaSqqspVfsVExGaz\ntcyv2KR35bPLRbea9r5hivuttO3TEun+i9VqraioMBqNrfrdusZvL1pbdKj/jFE9REQXWRyf\nXGOxqU6kQGpmUWZBuYj0iAzu1C5AdRwAAAA4sYgh0n+2iIi1RjbOVZ3m0lEQ4qxbRnXv3C5Q\nRDILyv+5NV11HAV+mX6wH5cHAQAAcCGjXxK/cBGRE+vl0DLVaS4RBSHOMhkNj07rb+9hvOyH\nIydyS1Uncihd17cczBIRTdOupCAEAADABfmEyJhXzi5veliqi5WmuUQUhPhFn6g21wzqJCIW\nq+2NVftdYgyulrL3WH5+aZWIxHVqGxbkGj3gAQAAoFjsHdLxShGR8mzZ9n+Kw1wSCkL8l7sn\n9AkL9BGR1Myi/+w+qTqO42z6eTgZph8EAABAk2ky4W0xmEVE9rwlOftU57loFIT4L37epjmT\n+9mXP9yYmldSpTaPY9RYbD+kZouIyaCN7ttedRwAAAC4jtB+MnCuiIhulY0PiLhYJzsKQpxr\ndN/2I3u3F5GKastf16aojuMIO9JyyqpqRWRoj/AgXy/VcQAAAOBSRj4vAVEiIpmJkrxUdZqL\nQ0GIBjwwpZ+/t0lEElOzfziUrTpOq9uYnGlfuJL+ogAAALhYXoFy5Rtnl7c8KZV5StNcHApC\nNCA00OeOcb3ty4vjk+1Xz9xVRbVlV3quiPiYjZf3ilAdBwAAAC6o983SdYqISGW+bP2j6jQX\ngYIQDZs2pHNMdIiIFJRVf7zpkOo4rej7g1nVFquIjOrT3sdsVB0HAAAArmn8W2LyERFJWiKn\nt6lO01QUhGiYpmkPTY0zGTQRWbX7ZEpGoepErWVT3Xz0sVFqkwAAAMCFtekhQ54QEdFtsv4+\nsVlUB2oSCkI0qkt44PSR3UVE1/W34pMsVpvqRC2vsKz6p+P5IhLs5zWwW5jqOAAAAHBlw5+W\n4G4iIrn75ad3VadpEgpCnM+tV/TsGBYgIsdzSr/adlR1nJaXkHLapusiMjYm0n45FAAAALhE\nJl+Z+Nezy1v/JGWnlaZpEgpCnI/ZaHjwmlh7nfTZlrSTeWWKA7W0uv6iV9JfFAAAAM3XZbL0\nuF5EpKZEtvxBdZoLoyDEBfTvHHrVgI4iUmu1LY5PdrGJNs8rq7Di8OkiEQkP9o3pGKI6DgAA\nANzC+LfEHCAicvAfcnKj6jQXQEGIC7tnYt82/l4isv9E/nf7MlTHaTEbkzLt9e342Ch6iwIA\nAKBlBHaUy3+eeWL9HLFWK01zARSEuLBAX/O9V8XYl/++/mBhuVMf002XcKCuvyjz0QMAAKDl\nDH5U2vYVESk8LLvfVJ3mfCgI0STj46KG9wwXkdLK2ve/O6A6TgtIzyo+mVsmIp3aBXQND1Qd\nBwAAAG7E6CVXvSeiiYj8+GcpOa44T+MoCNFUD0yJ9fUyicim5NM/Hj6jOk5zbUo5e3lwQhzD\nyQAAAKClRV8hfWeJiNRWSMKjqtM0ioIQTRUe7PubK3ral9/5NqWyxjWm2myQrusJKadFRBO5\nsh/9RQEAANAKrnxdvNuIiKT9W47+R3WahlEQ4iLccHnXnpHBIpJTXPnp5jTVcS5d0smCvJIq\nEYnpGNK+jZ/qOAAAAHBHfhEy8v/OLm94QGorlKZpGAUhLoJB0x6d1t9o0ETk39uPpWUVq050\niZh+EAAAAI4w8H4JHygiUnJCdi5QnaYBFIS4ON0igq4f1lVEbLr++qr9FpvrTUxoselbD2aL\niNGgjenbXnUcAAAAuC/NKFe9L5pBRGTHK1JwSHWgc1EQ4qL99spekSF+InLsTMny7cdUx7lo\nO9NzSiprRGRQt7AQf2/VcQAAAODW2g+VuLtERKw1snGu6jTnoiDERfM2Gx+YEmtf/mTz4axC\nZ+wMfR4JP/cXHdeP/qIAAABofWNeEd92IiIn1snhr1Sn+S8UhLgUQ7q3GxfbQUSqa61vr0lW\nHeciVNVa7XNmeJkMI3pHqI4DAAAAD+DTVsa8fHZ540NS7UQjcVAQ4hLNmdwv2M9LRHYdya0b\no8X5JaZmV9VaRWRE7/Z+3ibVcQAAAOAZ4u6U6LEiIuVZ8uMLqtP8goIQlyjYz+uuCX3sy++u\nTSmuqFGbp4nq9Rdl+kEAAAA4jCYT3haDWURkzyLJ/Ul1nrMoCHHpJg3oOLBrmIgUV9Qs2ZCq\nOs6FFVfU7DmaKyIBPuYhPdqpjgMAAABPEhYrA+8XEbFZZMMDIk4xXD8FIS6dJjL3mlgvk0FE\n1u3L+DHtTHp2SU5xpepcjdpyIMs+T8aYmEizkYMfAAAAjjXyzxIQJSKSuVVSPladRkSEe6jQ\nLFFt/WeO7vlxwiFdZN4/d9lXhgb63Da215SBHdVm+zX6iwIAAEAlr0AZu1BWzxQR2fy4dJsm\nvqFqE3GRBM01oX+U0aDVX5NfWvXmf/Z/ve2oqkgNyimuTMkoEJHQQJ+4zm1VxwEAAIBH6jND\nulwtIlKZL4nPqk5DQYhm++fWdKutgQ7QHyccKip3opFmNiZn2lNe2a+DQdMusDUAAADQSsa/\nJUZvEZH970vWj2qzUBCiuXak5TS4vsZi23ssz8FhziMhJcu+YJ9BEQAAAFAjpKcMeVxERLfJ\nhgdEtyrMQkGI5ioqr27sqSNnShyZ5DyO55QeO1MiItGh/j0jg1XHAQAAgGe7/I8S3FVE5Mxu\n+el9hUEoCNFcbQK8G3vqqx+OPPxh4pq9GZU1FkdG+rWElJ+Hk4mNUpsEAAAAEJOvTHjn7PL3\nT0t5lqogFIRorhG9Is7z7MHMojf/s//m19fN/9eevcfylEy2oots+nl80SsZXxQAAADOoOsU\n6X6tiEhNiWx5SlUKCkI0161jeoYF+fx6/fBe4ZEhfvblGotty4Gsp/6xffa7m7/84UhxhUMH\nmzmQUZhdVCEivTu0iQ71d+SuAQAAgEaNXyxmfxGRA59KxiYlEZiHEM0VEuD9lztG/vXblB3p\nOfbhRsODfW8f2+uqy6J1Xd93PH/d/lNbD2RXW6wikpFX9sGG1I8TDo/sHTEhLmpYz3AHDPiZ\nkJxpX2A4GQAAADiRoE4y/BnZ+kcRXTY8ILfvE4PZwREoCNECwoN9n79lSK3Vdiq/vI2fV8jP\ndxVqmjawa9jArmG/n1y75UDWql0njp4pERGL1bblQNaWA1lhQT7jY6OmDekcHuzbStmsNv37\ng9n2MGNiIltpLwAAAMClGPK4HPiHFByU/AOy+00Z+oSD909BiBZjNhq6hgc2+FSAj/maQZ2u\nGdQpLas4fs/JTcmn7cPM5JVUffnDka+2HR3QJfSaQZ1G9mlvbOnrhXuO5hWWV4vIgC6hYYEN\ndG0FAAAAlDF6yVXvyrJxIrpse156T5egLo7cPwUhHKpnZPBDU+PuuapvYmr2+v2Z9okKdV3f\neyxv77G8kADvsTGRl3cJ7Na+xWaG2JRCf1EAAAA4seix0meGpH4htRWS8Lhc+7Ujd05BCAV8\nvUwT+0dP7B99Mq9s3U+n1u7LsA8zU1hWvXzH8eU7pFt4wLShXcfHRfmYjc3ZUY3Ftu3QGREx\nGw2j+rRvmfQAAABAy7ryDTkWL9XFkvYvObpauk112J4ZZRQqdQoLuGtCn88envDHGwcN7BpW\n1130aE7ZotVJs/6yftHqpPSs4kt+/22HsiuqLSIyrGd4gI+j79AFAAAAmsS/vYx4/uzypofE\nUuWwPXOFEOqZjYYrYiKviInMLancmJS5cufxvNJqESmvtsTvORm/52SndgFX9Y+eMrBToO/F\nFXWbfpmPnv6iAAAAcGKD5sqBTyRnrxQdkR2vyMjnHbNbrhDCibQL8r15ZPc3b73s2f+NvSIm\n0mQ4e8nwZG7ZBxtSZ725/qJmty+rqt2Vnisift6mYT3DWy01AAAA0GyaUa56XzSDiMiOV6Tw\nsGN2yxVCOB1N0+I6thkd27mgrHr9/lNr9macLiiXn2e333IgKzrUf/KAjpMu69jG3+s87/P9\ngaxaq01ERvdp721q1r2IAAAAQKtrP1RifydJH4i1WjbOlevjHbBPCkI4r7YB3jeP7H7zyO72\nySo2JGVW11pF5FR+uX12+8Hdwib2jx7Vp73RcO5sFYVl1ev2nx1f9Er6iwIAAMAlXPGqpK+Q\nyjw5/p2W9i+DT0/N0F6k4andWgQFIVyAfbKKuyf23ZxyevXuE+nZZ2e3356Wsz0tJyzQZ3xc\n1NTBndq38bNYbZ9/n/6f3Sfsw5aKiK+XMa5TqNL4AAAAQNP4tJXRL8m6e0TEED+jra6LaBIa\nIyOfl143tcYOKQjhMvy9TXWz26/ff2pDUmZpZa2I5JX+Mrt9eVXt4f8elbSyxvr6yp+evmGg\notQAAADAxehxnWx6UCxVotuHztAlP0VWTZeJf5XL5rT43hhUBq6nZ2TwnMn9Pn1w/KPT+sdE\nh9hX2me3P9zQHBUJKad3H811bEYAAADgkux4ueFpJzY/IZV5Lb43CkK4Kl8v0+QBHf/yu5F/\nv2/sDcO7Bvudb4CZxNQzDgsGAAAAXLq05Q2vry2XE+tafG8UhHB5ndoF3Dsp5rOHJ3SNaPR2\n2/xSx03uCQAAAFy68qxGnyrLbPG9URDCTZiNhi7tGi0Iz3/9EAAAAHAWvmGNP9WuxfdGQQj3\nMbR7o7PPD+vBxPQAAABwBV2nNLze6CWdJ7b43igI4T6ujO1QN8ZMfQO7ho3qE+H4PAAAAMBF\nG/Gc+DV0MWPY0xIQ1eJ7oyCE+zAatBdnDbvqsui6eepNBu2aQZ3m3TxY086duR4AAABwRoEd\n5ZYtEj3mlzU+ITJ2oYyc1xp7UzYPoc1mO3HihMVi6dy5s5dXw/d3NbbN+V9bU1Nz6NChzp07\nBwUFteIHgFPy9zY9fu1l91/d70RumUGTTu0CfcxG1aEAAACAi9G2t9yyxVqaVXZylykwwj96\noBjMrbQrNQXhsWPH5s+fX1ZW5uXlZbFYHn/88UGDBjVxmwu+9vPPP//mm2+eeeaZyy+/3HEf\nCc7E18vUJ6qN6hQAAABAM/iF10aMMHh7t141KKq6jL7++usxMTFffPHFJ598ct11173++uuV\nlZVN3Ob8r01PT9+0aZOfn59DPw8AAAAAuCAFBeGJEydOnjx5++2322/ruummmywWy65du5qy\nzflfa7VaFy9efNtttzXWBxUAAAAAUEdNQejn5xcWdnZ6DaPR2LFjx5MnTzZlm/O/9uuvvw4K\nCpo4seUHYwUAAAAA9+OIewhLSkpOnTplX+7SpUt5efk5XTp9fX3Lysrqr2lsm/O8NiMjY+XK\nlW+88UZTIhUUFNhstkv4LL9+n+a/icNUVFSojtBUtbW1eXl5qlM0lWsdBmVlZef8ujkzFzoM\nxNXSutBhUF1dXV1d3Zx3MBqNISENzEmjkK7r69ev37lzp8ViiY2NnTZtmtl87v0hjW3T2Pqq\nqqo1a9akpqZardZu3bpNnTo1ODhYwWcDALgURxSEKSkpb7/9tn153rx5ZrPZarXW38BqtZ7T\nEDa2TWPrdV1fvHjxjBkzIiKaNN2c2WxuZkFotVptNtuv22/nZLPZdF03Gl1gvE1d1y0Wi6Zp\nJpOyIXAvisViMRqNLjGthc1ms1qtRqPRYHCB+WZ0XbdarS50GOi67ionBKvVqmmaqxwGLXJC\ncMIP+8knn6xdu3b69Ok+Pj7Lly8/dOjQ008/3cRtGlyv6/q8efMqKiqmTp1qMBhWr16dmJj4\n1ltvucSZHwCgkCP+2BoxYsSIESPqHlZUVBQXF9fW1tb98ZSbm1t/AxEJCwtrcJvG1sfHx5eW\nlsbFxZ04cUJErFZrbm5uTk5OeHhDUzqKBAYGNvNDlZaWVldXBwYGOuHfGb9WVVVls9lcYqwd\nXdfz8/NNJpOr/GO7uLjYhQ6DsrIyX19fHx8f1VkuzGq1lpWVucphUFRUZLFYXCVtRUWFwWBw\nlcOgsLDQy8ur+Sdtp1JRUbFixYqnn3566NChItK/f/85c+Y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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# 7d. MNAR 1D Slice Plots\n", + "# ============================================================\n", + "for (param in c(paste0(\"ind\", 1:n_med), \"total_indirect\")) {\n", + " param_data <- mnar_results[mnar_results$param == param, ]\n", + "\n", + " slice_m <- param_data[param_data$delta_out == 0, ]\n", + " slice_y <- param_data[param_data$delta_med == 0, ]\n", + "\n", + " p_left <- ggplot(slice_m, aes(x=delta_med, y=est)) +\n", + " geom_line(color=\"steelblue\", linewidth=0.8) +\n", + " geom_point(color=\"steelblue\", size=2) +\n", + " geom_hline(yintercept=0, linetype=\"dashed\", color=\"red\") +\n", + " labs(title=paste0(param, \": vs delta_mediators\"),\n", + " x=expression(delta[mediators]), y=\"Indirect Effect\") +\n", + " theme_minimal(base_size=11)\n", + "\n", + " p_right <- ggplot(slice_y, aes(x=delta_out, y=est)) +\n", + " geom_line(color=\"darkorange\", linewidth=0.8) +\n", + " geom_point(color=\"darkorange\", size=2) +\n", + " geom_hline(yintercept=0, linetype=\"dashed\", color=\"red\") +\n", + " labs(title=paste0(param, \": vs delta_outcome\"),\n", + " x=expression(delta[outcome]), y=\"Indirect Effect\") +\n", + " theme_minimal(base_size=11)\n", + "\n", + " g_slice <- grid.arrange(p_left, p_right, ncol=2,\n", + " top=paste0(\"MNAR Sensitivity: \", param, \" (\", EXPOSURE, \")\"))\n", + "\n", + " fname <- paste0(\"mnar_1d_slices_\", param, \"_\", EXPOSURE)\n", + " ggsave(file.path(DIR_MNAR, paste0(fname, \".png\")), g_slice, width=12, height=5, dpi=150)\n", + " ggsave(file.path(DIR_MNAR, paste0(fname, \".pdf\")), g_slice, width=12, height=5)\n", + "}\n", + "cat(\"MNAR 1D slice plots saved.\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MNAR Interpretation\n", + "\n", + "The tipping point analysis identifies how large the systematic difference between observed\n", + "and missing subjects must be (in SD units) before the mediation finding becomes non-significant.\n", + "A large tipping distance (> 1 SD) suggests robustness; a small one (< 0.5 SD) suggests vulnerability.\n", + "\n", + "---\n", + "\n", + "## Method 4: Bayesian SEM (blavaan/Stan)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:01:22.403155Z", + "iopub.status.busy": "2026-03-31T21:01:22.399511Z", + "iopub.status.idle": "2026-03-31T22:58:46.198898Z", + "shell.execute_reply": "2026-03-31T22:58:46.195920Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting Bayesian SEM via blavaan/Stan...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This may take a long time for model compilation and MCMC sampling.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 1).\n", + "Chain 1: \n", + "Chain 1: Gradient evaluation took 0.007789 seconds\n", + "Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 77.89 seconds.\n", + "Chain 1: Adjust your expectations accordingly!\n", + "Chain 1: \n", + "Chain 1: \n", + "Chain 1: Iteration: 1 / 1500 [ 0%] (Warmup)\n", + "Chain 1: Iteration: 150 / 1500 [ 10%] (Warmup)\n", + "Chain 1: Iteration: 300 / 1500 [ 20%] (Warmup)\n", + "Chain 1: Iteration: 450 / 1500 [ 30%] (Warmup)\n", + "Chain 1: Iteration: 501 / 1500 [ 33%] (Sampling)\n", + "Chain 1: Iteration: 650 / 1500 [ 43%] (Sampling)\n", + "Chain 1: Iteration: 800 / 1500 [ 53%] (Sampling)\n", + "Chain 1: Iteration: 950 / 1500 [ 63%] (Sampling)\n", + "Chain 1: Iteration: 1100 / 1500 [ 73%] (Sampling)\n", + "Chain 1: Iteration: 1250 / 1500 [ 83%] (Sampling)\n", + "Chain 1: Iteration: 1400 / 1500 [ 93%] (Sampling)\n", + "Chain 1: Iteration: 1500 / 1500 [100%] (Sampling)\n", + "Chain 1: \n", + "Chain 1: Elapsed Time: 1051.11 seconds (Warm-up)\n", + "Chain 1: 2486.21 seconds (Sampling)\n", + "Chain 1: 3537.32 seconds (Total)\n", + "Chain 1: \n", + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 2).\n", + "Chain 2: \n", + "Chain 2: Gradient evaluation took 0.00245 seconds\n", + "Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 24.5 seconds.\n", + "Chain 2: Adjust your expectations accordingly!\n", + "Chain 2: \n", + "Chain 2: \n", + "Chain 2: Iteration: 1 / 1500 [ 0%] (Warmup)\n", + "Chain 2: Iteration: 150 / 1500 [ 10%] (Warmup)\n", + "Chain 2: Iteration: 300 / 1500 [ 20%] (Warmup)\n", + "Chain 2: Iteration: 450 / 1500 [ 30%] (Warmup)\n", + "Chain 2: Iteration: 501 / 1500 [ 33%] (Sampling)\n", + "Chain 2: Iteration: 650 / 1500 [ 43%] (Sampling)\n", + "Chain 2: Iteration: 800 / 1500 [ 53%] (Sampling)\n", + "Chain 2: Iteration: 950 / 1500 [ 63%] (Sampling)\n", + "Chain 2: Iteration: 1100 / 1500 [ 73%] (Sampling)\n", + "Chain 2: Iteration: 1250 / 1500 [ 83%] (Sampling)\n", + "Chain 2: Iteration: 1400 / 1500 [ 93%] (Sampling)\n", + "Chain 2: Iteration: 1500 / 1500 [100%] (Sampling)\n", + "Chain 2: \n", + "Chain 2: Elapsed Time: 1025.97 seconds (Warm-up)\n", + "Chain 2: 2454.23 seconds (Sampling)\n", + "Chain 2: 3480.2 seconds (Total)\n", + "Chain 2: \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cThere were 2000 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See\n", + "https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cExamine the pairs() plot to diagnose sampling problems\n", + "\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cThe largest R-hat is 1.55, indicating chains have not mixed.\n", + "Running the chains for more iterations may help. See\n", + "https://mc-stan.org/misc/warnings.html#r-hat\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cBulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.\n", + "Running the chains for more iterations may help. See\n", + "https://mc-stan.org/misc/warnings.html#bulk-ess\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cTail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.\n", + "Running the chains for more iterations may help. See\n", + "https://mc-stan.org/misc/warnings.html#tail-ess\u201d\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing post-estimation metrics (including lvs if requested)...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian SEM fit complete.\n", + "N used: 1153 \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# 8. Bayesian SEM (blavaan)\n", + "# ============================================================\n", + "cat(\"Fitting Bayesian SEM via blavaan/Stan...\\n\")\n", + "cat(\"This may take a long time for model compilation and MCMC sampling.\\n\\n\")\n", + "\n", + "N_CHAINS <- 2\n", + "N_BURNIN <- 500\n", + "N_SAMPLE <- 1000\n", + "\n", + "fit_bayes <- tryCatch(\n", + " bsem(model_str, data=dat, fixed.x=FALSE, target=\"stan\",\n", + " n.chains=N_CHAINS, burnin=N_BURNIN, sample=N_SAMPLE, seed=42),\n", + " error = function(e) {\n", + " cat(\"blavaan error:\", e$message, \"\\n\")\n", + " NULL\n", + " }\n", + ")\n", + "\n", + "if (!is.null(fit_bayes)) {\n", + " cat(\"Bayesian SEM fit complete.\\n\")\n", + " N_bayes <- lavInspect(fit_bayes, \"ntotal\")\n", + " cat(\"N used:\", N_bayes, \"\\n\")\n", + "} else {\n", + " cat(\"Bayesian SEM failed. Will skip Bayesian results.\\n\")\n", + " N_bayes <- NA\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T22:58:46.994482Z", + "iopub.status.busy": "2026-03-31T22:58:46.989643Z", + "iopub.status.idle": "2026-03-31T22:58:47.700932Z", + "shell.execute_reply": "2026-03-31T22:58:47.698284Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Posterior draws dimensions: 2000 x 101 \n", + "Draw column names:\n", + " [1] \"a1\" \n", + " [2] \"APOC4_APOC2_AC_exp~msex_u\" \n", + " [3] \"APOC4_APOC2_AC_exp~age_death_u\" \n", + " [4] \"APOC4_APOC2_AC_exp~pmi_u\" \n", + " [5] \"APOC4_APOC2_AC_exp~ROS_study_u\" \n", + " [6] \"a2\" \n", + " [7] \"APOC2_AC_exp~msex_u\" \n", + " [8] \"APOC2_AC_exp~age_death_u\" \n", + " [9] \"APOC2_AC_exp~pmi_u\" \n", + " [10] \"APOC2_AC_exp~ROS_study_u\" \n", + " [11] \"a3\" \n", + " [12] \"APOC1_DeJager_Mic_exp~msex_u\" \n", + " [13] \"APOC1_DeJager_Mic_exp~age_death_u\" \n", + " [14] \"APOC1_DeJager_Mic_exp~pmi_u\" \n", + " [15] \"a4\" \n", + " [16] \"APOE_Mega_Mic_exp~msex_u\" \n", + " [17] \"APOE_Mega_Mic_exp~age_death_u\" \n", + " [18] \"APOE_Mega_Mic_exp~pmi_u\" \n", + " [19] \"b1\" \n", + " [20] \"b2\" \n", + " [21] \"b3\" \n", + " [22] \"b4\" \n", + " [23] \"cp\" \n", + " [24] \"caa_4gp~educ\" \n", + " [25] \"caa_4gp~apoe4_dose\" \n", + " [26] \"caa_4gp~apoe2_dose\" \n", + " [27] \"caa_4gp~msex_u\" \n", + " [28] \"caa_4gp~age_death_u\" \n", + " [29] \"APOC4_APOC2_AC_exp~~APOC2_AC_exp\" \n", + " [30] \"APOC4_APOC2_AC_exp~~APOC1_DeJager_Mic_exp\" \n", + " [31] \"APOC4_APOC2_AC_exp~~APOE_Mega_Mic_exp\" \n", + " [32] \"APOC2_AC_exp~~APOC1_DeJager_Mic_exp\" \n", + " [33] \"APOC2_AC_exp~~APOE_Mega_Mic_exp\" \n", + " [34] \"APOC1_DeJager_Mic_exp~~APOE_Mega_Mic_exp\" \n", + " [35] \"APOC4_APOC2_AC_exp~~APOC4_APOC2_AC_exp\" \n", + " [36] \"APOC2_AC_exp~~APOC2_AC_exp\" \n", + " [37] \"APOC1_DeJager_Mic_exp~~APOC1_DeJager_Mic_exp\"\n", + " [38] \"APOE_Mega_Mic_exp~~APOE_Mega_Mic_exp\" \n", + " [39] \"caa_4gp~~caa_4gp\" \n", + " [40] \"chr19_44954310_T_C~~chr19_44954310_T_C\" \n", + " [41] \"chr19_44954310_T_C~~msex_u\" \n", + " [42] \"chr19_44954310_T_C~~age_death_u\" \n", + " [43] \"chr19_44954310_T_C~~pmi_u\" \n", + " [44] \"chr19_44954310_T_C~~ROS_study_u\" \n", + " [45] \"chr19_44954310_T_C~~educ\" \n", + " [46] \"chr19_44954310_T_C~~apoe4_dose\" \n", + " [47] \"chr19_44954310_T_C~~apoe2_dose\" \n", + " [48] \"msex_u~~msex_u\" \n", + " [49] \"msex_u~~age_death_u\" \n", + " [50] \"msex_u~~pmi_u\" \n", + " [51] \"msex_u~~ROS_study_u\" \n", + " [52] \"msex_u~~educ\" \n", + " [53] \"msex_u~~apoe4_dose\" \n", + " [54] \"msex_u~~apoe2_dose\" \n", + " [55] \"age_death_u~~age_death_u\" \n", + " [56] \"age_death_u~~pmi_u\" \n", + " [57] \"age_death_u~~ROS_study_u\" \n", + " [58] \"age_death_u~~educ\" \n", + " [59] \"age_death_u~~apoe4_dose\" \n", + " [60] \"age_death_u~~apoe2_dose\" \n", + " [61] \"pmi_u~~pmi_u\" \n", + " [62] \"pmi_u~~ROS_study_u\" \n", + " [63] \"pmi_u~~educ\" \n", + " [64] \"pmi_u~~apoe4_dose\" \n", + " [65] \"pmi_u~~apoe2_dose\" \n", + " [66] \"ROS_study_u~~ROS_study_u\" \n", + " [67] \"ROS_study_u~~educ\" \n", + " [68] \"ROS_study_u~~apoe4_dose\" \n", + " [69] \"ROS_study_u~~apoe2_dose\" \n", + " [70] \"educ~~educ\" \n", + " [71] \"educ~~apoe4_dose\" \n", + " [72] \"educ~~apoe2_dose\" \n", + " [73] \"apoe4_dose~~apoe4_dose\" \n", + " [74] \"apoe4_dose~~apoe2_dose\" \n", + " [75] \"apoe2_dose~~apoe2_dose\" \n", + " [76] \"APOC4_APOC2_AC_exp~1\" \n", + " [77] \"APOC2_AC_exp~1\" \n", + " [78] \"APOC1_DeJager_Mic_exp~1\" \n", + " [79] \"APOE_Mega_Mic_exp~1\" \n", + " [80] \"caa_4gp~1\" \n", + " [81] \"chr19_44954310_T_C~1\" \n", + " [82] \"msex_u~1\" \n", + " [83] \"age_death_u~1\" \n", + " [84] \"pmi_u~1\" \n", + " [85] \"ROS_study_u~1\" \n", + " [86] \"educ~1\" \n", + " [87] \"apoe4_dose~1\" \n", + " [88] \"apoe2_dose~1\" \n", + " [89] \"ind1\" \n", + " [90] \"ind2\" \n", + " [91] \"ind3\" \n", + " [92] \"ind4\" \n", + " [93] \"total_indirect\" \n", + " [94] \"total\" \n", + " [95] \"prop_med\" \n", + " [96] \"diff_1_2\" \n", + " [97] \"diff_1_3\" \n", + " [98] \"diff_1_4\" \n", + " [99] \"diff_2_3\" \n", + "[100] \"diff_2_4\" \n", + "[101] \"diff_3_4\" \n", + " label post_mean post_sd ci_lower ci_upper P_positive\n", + "1 a1 0.282759677 0.05452581 0.17573770 0.39306147 1.0000\n", + "2 a2 0.279477624 0.05462738 0.17194479 0.38932622 1.0000\n", + "3 a3 0.225379395 0.06480895 0.10277104 0.35491314 0.9990\n", + "4 a4 0.214799201 0.04981432 0.11606274 0.31248405 1.0000\n", + "5 b1 -1.202062910 0.72991439 -2.82674864 0.20792764 0.0600\n", + "6 b2 1.126765124 0.72914989 -0.28203646 2.75752044 0.9285\n", + "7 b3 0.048287700 0.05714316 -0.05990779 0.15844060 0.7990\n", + "8 b4 0.036596357 0.05075441 -0.06314732 0.13396175 0.7605\n", + "9 cp 0.138752831 0.03936252 0.05885248 0.21562798 1.0000\n", + "10 ind1 -0.337053351 0.21771764 -0.83314449 0.05728272 0.0600\n", + "11 ind2 0.311964844 0.21425310 -0.08564675 0.79650353 0.9285\n", + "12 ind3 0.011370002 0.01409484 -0.01308862 0.04258820 0.7990\n", + "13 ind4 0.007776618 0.01126849 -0.01457686 0.03046644 0.7605\n", + "14 total_indirect -0.005941887 0.01483892 -0.03539606 0.02281522 0.3520\n", + "15 total 0.132810944 0.03699750 0.05977100 0.20351101 1.0000\n", + "16 prop_med -0.052994842 0.14783645 -0.36033854 0.18150569 0.3520\n", + " pvalue N method\n", + "1 0.000 1153 Bayesian\n", + "2 0.000 1153 Bayesian\n", + "3 0.002 1153 Bayesian\n", + "4 0.000 1153 Bayesian\n", + "5 0.120 1153 Bayesian\n", + "6 0.143 1153 Bayesian\n", + "7 0.402 1153 Bayesian\n", + "8 0.479 1153 Bayesian\n", + "9 0.000 1153 Bayesian\n", + "10 0.120 1153 Bayesian\n", + "11 0.143 1153 Bayesian\n", + "12 0.402 1153 Bayesian\n", + "13 0.479 1153 Bayesian\n", + "14 0.704 1153 Bayesian\n", + "15 0.000 1153 Bayesian\n", + "16 0.704 1153 Bayesian\n", + "Bayesian results saved.\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# 8b. Bayesian Results Extraction\n", + "# ============================================================\n", + "if (!is.null(fit_bayes)) {\n", + " pt <- parTable(fit_bayes)\n", + " labeled_bayes <- pt[pt$label != \"\", c(\"label\", \"est\", \"se\")]\n", + "\n", + " draws_list <- blavInspect(fit_bayes, \"draws\")\n", + " draws <- do.call(rbind, draws_list)\n", + "\n", + " cat(\"Posterior draws dimensions:\", nrow(draws), \"x\", ncol(draws), \"\\n\")\n", + " cat(\"Draw column names:\\n\")\n", + " print(colnames(draws))\n", + "\n", + " bayes_summary <- data.frame()\n", + "\n", + " target_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\")\n", + "\n", + " for (lab in target_labels) {\n", + " if (lab %in% colnames(draws)) {\n", + " vals <- draws[, lab]\n", + " ci <- quantile(vals, c(0.025, 0.975))\n", + " pp <- mean(vals > 0)\n", + " bayes_summary <- rbind(bayes_summary, data.frame(\n", + " label = lab, post_mean = mean(vals), post_sd = sd(vals),\n", + " ci_lower = ci[1], ci_upper = ci[2],\n", + " P_positive = pp, pvalue = 2 * min(pp, 1 - pp),\n", + " N = N_bayes, method = \"Bayesian\", stringsAsFactors = FALSE\n", + " ))\n", + " } else {\n", + " pt_row <- labeled_bayes[labeled_bayes$label == lab, ]\n", + " if (nrow(pt_row) > 0) {\n", + " bayes_summary <- rbind(bayes_summary, data.frame(\n", + " label = lab, post_mean = pt_row$est[1], post_sd = pt_row$se[1],\n", + " ci_lower = pt_row$est[1] - 1.96*pt_row$se[1],\n", + " ci_upper = pt_row$est[1] + 1.96*pt_row$se[1],\n", + " P_positive = NA, pvalue = NA,\n", + " N = N_bayes, method = \"Bayesian\", stringsAsFactors = FALSE\n", + " ))\n", + " }\n", + " }\n", + " }\n", + "\n", + " rownames(bayes_summary) <- NULL\n", + " print(bayes_summary)\n", + " write.csv(bayes_summary, file.path(DIR_BAYES, \"bayesian_results.csv\"), row.names=FALSE)\n", + " cat(\"Bayesian results saved.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T22:58:47.725914Z", + "iopub.status.busy": "2026-03-31T22:58:47.724048Z", + "iopub.status.idle": "2026-03-31T22:58:54.222226Z", + "shell.execute_reply": "2026-03-31T22:58:54.219781Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No id variables; using all as measure variables\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian posterior plots saved.\n" + ] + }, + { + "data": { + "image/png": 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GLxvTk/sDJ80+Pn369M6dOw8ZMkRVkQA5tfzBEgkdHZGZGWombyUB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\ny8tLg8A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VXlvaOvx5i0Zg\nGBYZGQlqzSKodiRJvnnzJi8vTy6X17LZ58+f//Of/5SXl6ssMKgpKi8vd3NzO3/+vG3/Tj//\nEqal1/DS/BravLnbx2rp8mfMmJGdna3AIJsuytKy/MgRfPJk0IFA4KBo+ZEjklWrQMcBhlwu\nDwwMfPv27YBoHzu/IQ3eD8pAPZdP6envcv/+fQ8PD7qcHgR9QV2KyigP1bYtpqfH09ICHQik\nahiGjRkz5ty5c/a9zddvHqupqfRlZAa7WA0cYnnl4vX9+/eHhYUpuzmoXt68ebNixQqxWMzh\ncHAcnz17tr29/RfbYBiWnJyck5Ojp6dXUlIyevTokJAQINFCak4oFLq5ud2+fdvR1Xr6+tEs\ndmOXKWtlpjd9vd+qyQcCAgLu379vZGSkkDibMB0dbNAgFp8POg7VIUkyPz8fx/F27drVUmHo\n8+fPZWVlJiYmzb9MCIpigwYhLbVCZlxc3I0bNyyG9ho4bXQjd4UyUK/EKTKx5EbmDbqXnMVq\n/s//UL3AAwJqniiKGj9+/KlTp3o5tFud7Mvnq+iOEjfX7ea1lwsXLvTz8wO2mC/0LcnJyTY2\nNjNnzkRR9LfffktOTt69ezf/fx83Dx8+XFRUtG/fPk1NzefPn2dmZlZVVcHvEfqCUCgcNmxY\nbm6us2e36CSfxhemonV37ugXM/j3TRfDw8NPnz6tkH1CTQXssYJqOnLkyJYtWww6tB6xYjKi\niDqLKJPhmxwjKavIyMiIioratWtX4/cJNSdNMiEkSbLu1djoLQmCAFX+EcdxkiTBFp+kKApg\nABRF0R+CKhtdvHjxwYMHrWxM16z343AYFEWpJgDT1jr+Yx0P7LuRnJw8f/58BB4A/7bOYCh3\ngDqLxaqlOnF+fn5BQUFCQgK9jZ+f359//pmbmztgwICacZ47d27WrFkEQeTl5ZmZmc2aNUup\nMUNNET1SNDc3d8DIHlGrvRlMRZan8okc8OTmm8zMzNTU1NDQUAXuGVJzsMcKqlZQUDBlyhQW\njzN68wyOhsIGNzE57IBts9MCl+zevbtTp070IwoE0ZpkQkgQhFQqrfvGCIJgGAaqojedvtY9\nYIWj57MBDIAkSZlMpsqlRA4cOLBmzZrWbXTXbRrD47Hor0BlGWloeJ8Tx+6tW7du3Lhxenp6\nJEmSJAnw80cQBGwAqjkABAIBk/ndYXv5+fkCgcDQ0JD+kclkmpmZFRQU1NymsLBQJBI9fPhw\n27ZtfD7//fv348eP9/T0/N4+CYJQ6mRReuc4jiuvidrRJw6oAOgrNkmSoAKgKIruzKr5S7FY\n7OHhcfv27X4e3SJWjkRQiiQVfAxErR41b9SOefPmOTk5dejQAcdxUOswtfADgFav1lEUreUq\nVAvYYwVVI0kyLCysrKxsxNKJrbqYyWQyBe6cqyUI2jN/t+/CRYsWWVhYjB7d2MGoULPRJBNC\nNpvNrvOYcolEguM4j8fjcrlKjep7MAyTSqVa4CYxyuVyBoMBMIDy8nINDY2G3SYbIDs7e8aM\nGZqa3E1bg01M9HAcl8vlLBZLZQEYGHLCxjunbvpr27Ztq1atksvlcrlcU1NTNa1/TSaTMZlM\ngAeAUCjU1NRU9hvC2lVWVn7Rj87n88Vicc3fCIVCBEFEItHOnTtRFL169WpycrKtra2Zmdk3\n9ykWi1Xw3pWOCpTaq++ogFQqBduZUvPzp1eYuH79eu+fLCcsdZNjMkQJ379AlxM8b+jORRlT\np049deoU8OJGVVVVAFuXyWSKfSCur3qdgEwmU09PrwGtKKPHiiTJxvdY0R2aYFfIpCgKYACq\nb339+vWXL1+2GNyzd9AwgiSRfzunFLV/LWP9gO1z9wUljBs3rn379nZ2drVsTB9FYD9/BEEI\nggDYL4aiKMBXSojiDkIURWt5EmuSCSEEfc+7d+/8/PxwHFu3KahDR0NQYQQGO6UfuLl58+aZ\nM2fq6uqCCgOqxmazv7ieEgTxzX4ld3d3+sYzYMCAvXv3Pnz48HsJIYfDUWovg1wupygKVE8W\ngiAYhjGZTFCZPD3On8ViASx+IJfLq2t74DgeHh5+9epVu4Gdo5NGNb6KTC36j+xx56+8nL9z\ndu3aFRsbC+pJiH45qbKutC9QFCWTycAeADKZrF4nYINPFiX1WCmqQwdspwyO42AXSxCJRCpr\nKy8vb/HixXxdzWFLwiX/fuwYhim281Gvs+nw5RNPztnm7e19/vz56p6I7wG+WAXYjlEEdL8Y\nhmEK+Qpq77Fq/gkh4/JlzfR0dPJkpH9/0LFAyiWVSn19fQsLC2PjhvXrX7/loRWLz2ePm+C8\nYW3WunXrEhMTAUYC0QwNDcvLyzEMq04Ci4qK+vbtW3MbfX195H9HiPH5/FreTvCVXP9QKBQS\nBAHw3TJd36KWaodKJZfLMQzjcDigpkjR40Xpz5+iqIkTJ2ZkZFg7tJu1OYDDU/qtc/Iyr+e5\nqatWrQoKCurYsaOym/umyspKFosFqkuCePmSvXw5OnQoF1zFZpUN7lBGj1Xt06rriI4KVKcA\nQlHsadPIDh2IuDgwASAI3S2lmk4ZkiRnzpwpk8lGrZykY2KA/PtqiMFgKLxjrqtHv5JXH65t\nOz5p0qSTJ09+b9gdfRms+6A8hcMwjCRJgB2jYE8BiqLoUX4K+QpqP4xbQEL49Cn711+xIUNg\nQtjsRUZG5uTkuI3oFhreD3QsyGj/3vv2Xtu2bduMGTO0tbVBh9PSWVlZsVis3NxcOgn8559/\nCgsLvxgq06pVKxMTk9u3b9vY2CAIUlZW9unTp3bt2oGJGFInixcvTktLa2dlPGfbWBVkgwiC\n6BppBswasjfhzNSpU8+cOaOCFtXO58+8X3/FNDWRFrCEjzJ6rBTSkyKVSkmSBFa3hiSRX38l\nnJyYCQlgAlDtnJeUlJRbt25Z/uRg5z2I/g1BEARBKOk9+dBZY4vz3l27cC0hISElJeWb25Ak\nKRKJwM45IklSU1MT1EAJiUSCoiiPp/R1y76JJMnS0lIWi6WCr6D5L0wPtRCbN2/et29fFyuT\nxUtHgo4FQRCEx2OHhvcTi8Xfu85CqsTj8fz8/FJSUo4fP56ZmZmYmDh06FC6Z33v3r3V31Fo\naOjx48f3799/6dKlxMTEzp07f135HWpp9uzZs2LFCqM2ugt2hwi0VNdRPdDH1qq32dmzZ3//\n/XeVNQoBUd1jRf9Ye48V/SPssWpmCgoKFi1axNUSeCybpJoWUQbqsy7GoGPrzZs3Hzx4UDWN\nQmqr+b8hhFqCy5cvx8XF6ejy120K4PHUZRFbvwCHX/de27Zt29SpUwEO/INoAQEBrVq1unHj\nBkEQo0aNGjFiBP37mp3fAwYM0NDQ+Ouvv/Lz8x0dHX18fED1SkJq4u+//46KitLQ5s3bGaRr\npNKzGEWR0EWuS/x/mTlzpru7Oxxo0IxV91gVFhZyOJw///yzZo+VWCyePn06giChoaHJycl0\nyZlTp07BHqvmJCYmRiwWeyZO1jJuSF2ihuFqCQK2zd7tszAiIsLW1rZbt24qaxpSNzAhhJq8\nf/75Z8yYMSRJrFo3tnVrNargwuezx4b22Zry97Zt21auXAk6nJYORVEXFxcXF5cvfh8YGFjz\nR3t7e/iMBdHevHkzZswYkiJmpoxt29lI9QGYttf3mtTv2Lar8fHxGzduVH0AkMrAHquW7Nix\nY6dOnTLrZWkf8JOKmzbq3NZrZcTRGZv8/f1v374NO69bLDVKCHEcP3Xq1KNHj9hstpOT09fP\nbRD0tYqKCi8vr6Kiotnz3R2dOoAO50v+Yx33/3J9+/btCxYsADgKH4Kg+qqsrAwJCSktLZ2Y\n4NGtL7Bri3fEgOyTj1JTUydOnNi9e3dQYUDKBnusWiyxWBwbG8tgMT0Tp6AMABl+Ny/ngjvP\nc/afi4qK2r9/v+oDgNSBGs0hXL169ZUrV/r06WNlZbVz585Tp06BjghSdziOBwQEPH782MfP\nPjDYCXQ436CpyfUPdCgrK0tNTQUdCwRB9RAREfH06VOXMfbDxvYGGAaXzx63yB3H8ZiYGAWu\nRQZBkJpISEh4+/Zt3wmerbp8u2CsCrgtDDPt2uHAgQP79u0DFQMElrokhI8ePXry5MmKFStc\nXV19fHy2bt06ZMgQheyZ7NlTMn06ZWOjkL1BaiUmJubMmTOOTh3mLfIAHct3BQQ5CgScDRs2\ngF3HBoKgutuxY8ehQ4fa25iMXzwcdCxI76GWtgM6X7lyJT09HXQsqkOZmkqmTycGDwYdCAQO\nikqmT5f7+4OOQ4keP36ckpKi09pw0DQ/gGEwOezRm2ZwBLyYmJiXL18CjAQCRV0SwpycHHt7\n+7t3765YsWLt2rV5eXmKGsdM9ulTuXgxZWurkL1B6iMhIWHHjh0dOxut2eDPYqnLkfw1HV2+\nr7/958+ft2/fDjoWCIJ+7PHjxzNnztTQ5k1d583mqsXEivCf3Vls5ty5c79YrLw5MzOrXLyY\n+HcqHdQSoWjl4sXyiRNBx6EsFEXFxMRgGOb28zi2ANhSezSDDqYjlk4Ui8UhISE1VzeBWgi1\nuNUhCPLhw4fXr18zmUxXV9f8/PykpKSYmJjvvSSUy+USiaSOe6bXlKyqqpJKpQoLtz4oiiJJ\nsry8HEjr6hAAjuMVFRWKnfu+ffv2pUuXtjLWTt40hsNBa1mLiR5khWEYqAscRVEBY3sf+e1O\nUlJScHAwkNVsCIIAeAAQBCESiZRd/EBTUxPY6slQMyKTyUJCQiQSSewqP6M2OqDD+S/T9gYj\nwvuc3HUtMTFx9erVoMOBIEgBDh06dPny5c4D7azd1GLOi63voBd/37l15ubKlSvj4+NBhwOp\nlLokhFKplM/nz5w5E0VRBwcHsVh85MiR7yWEJEliGFav/dOLeyoi0gYiSRJg6xRF1fcTUyzF\nJmO//PLLggUL9PQE6zePMTDUqMs3C/bz19Hle/vaHT6Ys2vXrsjISNUH0MwOgG+C06sghYiP\nj3/w4MEgX7u+w7uC6kb8Jt/ogdknH27YsGHixIkWFhagw4EgqFHKy8tnz57N5LCHJ0wAHcv/\n80ycXJD7PDExceTIkV+shAk1b+qSEPL5fD6fX/0OwcLC4uTJk9/bmMfj1f01i0Qiqays1NLS\n4nLBvI7HMEwqlQKsMFlSUsJgMPT0VLeyzRfKy8sV+PZm69atc+fO1dHlb9szrrNFqx9uj+O4\nXC7ncrmgXh/RnRETpgw68ef9LVu2zJw5U0NDQ5UBFBcXs1gsXV1gC3IIhUJtbW0GQ32H9UIQ\n7caNG8nJyYatdcIXuYOO5Us8ASdozrAts/+cOXPm6dOnQYcDQVCjLFmy5NOnTwNjRuu3MwEd\ny//j62p5rYhIn7ImPDw8JycHjrtpOdTlEa1du3bv37+v/lEkEgF8foXU1rp162JiYnR0+Vt3\nhdUlG1Qf+voa/mMdCwsLYblRCFJPcrl80qRJJElErBjJ1wQ8n+ebnD27WdqbZWRkZGRkgI4F\ngqCGe/jwYWpqqq5ZqwFRPqBj+VKXob16+Ax88OBBUlIS6Fgg1VGXN4SDBg06cuTI+fPnhw0b\nVllZmZmZ6ejoCDooSL3Ex8cvX77cwFBz667QTp2bUjZICxvvfOT33KSkpIiICB0ddZmbBDVY\nRUWFUofC0uOcy8rKlNfEDwOQy+WgVr6mxwBLpdJaZggr1po1a54+fTrQp4eFfWt6sChFUQBH\njVZ/AjV/GTRv6NKx+6ZPn96rVy9lD3shSRJFUbDlkWUymVwuB9U6SZL1OgEZDIb6XNsVMoqe\n3gnwAflgA6AoSrEBUBQVHR2N47j7z+EsHqf2LRXYbt25/zzu1ZUHiYmJvr6+pqamLfkAAHsK\nVLerqABquaGrS0JoZmYWHR29Y8eOP/74o7y83NLSMiQkRCF7Zjx7xrt0CXV3R+DKE00WSZKx\nsbFbtmwxba2TujPMvJ0+6IgaQkeXHzKu747US0lJSStWrAAdDtRYyh4HLhQKCYIAONhbLBZz\nOBwOp7bnFeWRy+UikYjH4wkEAhU0l5eXt3HjRh1DjXELh1dPSZBKpUCqQNFkMhlBEF8EYGnX\nbqh/r/OHc/fu3btw4UKlBlBZWclisUDNtsA/fJAePsy0teUPHQokAARBSktLAZ6AjVRZWdn4\nqeN0p4DKOmW+RFG8X3+lWrUSegBbWYokyYqKCsXu89ChQ9euXes0yK5d/261dzmBqoqH8tgu\nc8eemrc9KirqyJEjQqFQla3XRBeJAFuXEfmqY07FMAxTyFdQe48VCjzvr6mqqurt27f6+vpG\nRkaK2ie2fj07Lg5LS2OHhytqn/ULAM4hbNwcQhzHx48ff+DAgfYdDFN3hhqbaNf3z9VhDiH9\nVF1VKff2SJFKqBcvXrRp00Y1AcA5hE0UnRAaGBiACkAdEkKBQKCahNDd3f3cuXNT1/oMGNmj\n+pfqkBB+/c+vEEpmuW8h5ejTp0/Nzc2VFwDYhJC4fp3p7IxFR7PBDbMvLS3V12+S/Y+KIpVK\nSZJUzTn4DSSJMJmEkxPz5k0wASi6CAKCIKWlpVZWVmUV5VPPrtc1+8FYJ4IgZDIZh8NhsQC8\nv/k1dPmb64/27NkzYQKwsjfl5eUYhhkYGIAaqyKRSFAUBXUjIEmytLSUw+Foa9fv0bcB1OsR\nTSAQWFpaKjAbhJo6uVzu7+9/4MABaxvTXfvG1zcbVDcCDc6UqMFVVVWLFi0CHQsEQf914sSJ\nc+fO2Ti26+/VHXQsP6alyw+cNbSysnLWrFmgY4EgqH7mzZtXVFQ0MNr3h9kgcB5LJzLZrJ9/\n/rkFLX/agqlXQghBNUmlUh8fn2PHjtnZm2/bM05PD1AnpUL5+Nl37GS0f//+nJwc0LFAEITI\nZLK4uDgGEw1fPAJUJ3R9ufj17NyjzdGjR8+ePQs6FgiC6io7O3vPnj2Gndr0mzISdCw/ZtCx\ndZ8JHh8/fly5ciXoWCClgwkhpKZkMpmvr29mZqaDU4fN20M01bLoXwMwmYy4ee4kScbExIBd\nHRGCIARBNm/e/OrVq6H+vcy7qHuHfTWUgU5I8GAw0ZiYGIlEAjocCIJ+TCaTRUREUAjiuXwy\nk60uJTxqNyDaR9NId8OGDW/evAEdC6RcMCGE1BGGYf7+/mfOnHHs03FjahCfzwYdkSI59e3o\nMsz69u3b27dvBx0LBLVoJSUlK1asEGhxx0wfAjqW+unY1dQ1yOHVq1fLly8HHQsEQT+2cuXK\np0+f2vu7tHNqMjUOORr8gbF+Uql0/vz5oGOBlAsmhJDaIUly3LhxJ0+etO/dbv3mQC63aXSk\n1UvcXHeBBmfhwoUfPnwAHQsEtVyJiYlCodAPX2ZzAAAgAElEQVQ7coC2ftMbkR4ww0XfWGvd\nunWPHj0CHQsEQbV59OjR6tWrNVvpDZuvmBL6KtNt1AATmw5//PHHTXClfSAVaP4JIdW2LTZo\nEGJqCjoQqK5mzJiRnp7etXubjVuCeLxm9W6wmrGJ9tTpQ8vLyyMiIkDHAkEt1Js3b7Zt22Zg\nqj081Al0LA3B1+SOjx+BYdikSZPo4uzNio4ONmgQZWEBOg4IHBTFBg0ievYEHUdjYRgWHh4u\nl8s9lk7kaWuADqd+UAbquiCEoqi5c+eCjgVSoib57oUgiLqvriN3d5e7uPB4PALQKiL0qgNg\n1zABu7wySZIymayOqw4kJydv3ry5XXuD5BR/DpfR+LV36Hl6JEmCWmGFbvrrf4iPX8/z5x6f\nPn16+/bt4UpeE4UkSeAHgLLLdXA4HLiyBVQvS5Yskclk4dPc2E12GILDT1Z93G1unr2dnJzc\nzB7XKEvL8iNH+Hw+mGVPIHWAouVHjrDZ7KZ+DKxcufLu3bvdPJ2tXB1Bx9IQHfp17zzQ7uqV\nq6dPn/b09AQdDqQU6rUOYR3hOF73ZVJxHMcwjMPhgFqGjiRJHMdBreWFgF5EBUEQmUzGZrPr\n8rD+xx9/TJgwwcBQY9cv40xMv7t6Zr2QJEkQBJPJBJUt0AnhNw+/9++FYQG7UZSdnZ1tobSO\ncIlEwmAwQC0mhiAIvYySshNCPp//w6/4/fv3d+/exTCsW7duXbp0qWXLkpKSrKysAQMGtG3b\nVqFh1gNch1Cp6xA+fvzYzs7OtIN+0skoBvPbB6d6rkP4BVFJ5WzPrbJKIjc3t1u3bgoMAPDC\n9DguFAr5fL6GBrA3KnAdQsDrECJIcXExm82uZTVtZWv8OoS3b992dnbmG2hFZa7j69ZvSWqw\n6xAiCEJRlEwm4/F4n57+s3PUvO7dut+7d0+VT1NwHUKVrUPYJLtFWSxW3c8NiUSCYRiXywV1\nV6MXpgd4S5NKpQwGA2AAOI4LBIIfXk+zs7MjIyP5Avam1GAzc0MFtk4QBIvFArswPZv9jbGv\n7dsbzf/ZI37hsfDw8Bs3bvD5fGUEQCeEAA8ADMMEAgHw13dXr17dsGGDnZ0dn89PT0/39/cf\nM2bM9zbetm3b7du3O3ToADAhhJQqPj6eIAj/2CHfywabCm0DjYkJHhum/xEWFnbz5k2AnY9Q\nY4jF4p07d+bk5OA43q1bt6ioqFatvlv29q+//tq0adPChQv79OmjyiCh+qqoqAgKCsIJYtTq\n6Ppmg2rFxKZ9V49+D09dS09PDw4OBh0OpHhwhBWkFl69euXj44Nh8lVr/SytTUCHozojvHp4\njrJ98OBBVFQU6FiaM4IgduzYERYWFh8fP2fOnDlz5hw6dKi4uPibG1+5cqWwsBBgnzSkbLm5\nucePH+/Y1dRhmBXoWBTAyc1mwKge9+7dg5UAm66NGzcWFBQsX758w4YNTCZz2bJl31uXSCgU\n7tu3D2b+TUJERMSrV6/6jB/RaaAt6Fgaa8gMfwaTmZCQUPdJW1ATAhNCCDyhUOjl5VVcXBw3\nz915QIsrITD/Z48uVib79u3btGkT6FiarRcvXlRUVLi6utI/Ojo6amtr37lz5+stRSLR7t27\no6Ojm8oa5VADxMfHUxQ1JnZIs/mWJ8SPMDbX27hx46lTp0DHAtVbcXHx7du3p0+f3rlz57Zt\n286YMeP9+/cPHjz45sbbt293cXEBOIwTqqPU1NT09PTW3Tv9NCcIdCwKoN/e1M5v8MuXL/ft\n2wc6FkjxmuSQUag5wXE8ICDg2bNn/mMdAoKa5HzrRuLx2Os2BYwL3BUXF2dhYTFixAjQETVD\nHz9+1NLSqvkIZWJi8vHjx6+33LlzZ//+/a2trX+4T5lM9r0ufIWgdw5w2XGCIORyOajylXS7\nOI4r/BO4devWmTNnLOzadHfuUEvZKoqivlkOSmXoGf4YhtUla2XzmDHrfJaG7AsLC7t27VqH\nDh0aHwCO4yRJKvUgrwXdrjIOgLqjKKperTd4rlFeXh6Hw6n+1jQ1Nc3MzPLy8np+VWDzxo0b\nr1+/njVr1t9//92AhiCVuXLlysyZM/m6mmO2zGJymkm99EHT/B4cu7J8+fLQ0FCAhQkgZWgB\nCaFIxHz3DmnfHoHHrlqKjY3Nysrq069T3Dx30LEA07q17rpNgVGT9gUEBFy+fNne3h50RM0N\nPS+/5m+4XO7Xtalyc3OfP3++ZcuWuuxTKpWqYORMZWWlspuoBfChQXK5XC6XK3afCQkJCIKM\ninSuy54V3np91f0raGNhEDTH5dcV5/38/DIzMzU1NRUSQN1LuCkWKpczP37EtbQwoGVd6nUC\nMpnMhiWEIpFIS0urZuavo6NTXl7+xWZisXj79u2zZ8+uy3jRysrKxp+/dFoO8Cxg5ueTPJ4Q\nXPlDgiBEIlF9hxK8evXK19eXIEnfdVN5hlqNrPKN4zjYnqnq+Dl6GrZ+g+8eOr9ly5aJEyeq\noHW6Z/Drc0Fl6FMA7EoBGIYJhcLG74fBYNRSnKb5J4Ssgwf14uKwtDREyZX9oQZITU3dunVr\nh46Gq9b5MZktegCzbU+zhETvRfOOenh4ZGdnd+rUCXREzQqXy/3imebrApJVVVVbt26dNm1a\nHR/pNDQ0lFqlWSwWkySpgtpi3yORSNhsNqjqdhiGVVVV8Xg8xfZDZ2dnX7582dqhnf0gy9q3\npCiKrlCtwNbrRS6XkyTJ5XLr/jDqHtLn3YuSv/+4GxUV9eeffzbyu5NKpUwm85sFsVSAunmT\nM3CgPCoKBTeWvqKiQkurHoVAGjMC+Yu//ea1Zc+ePY6Ojt27d6/LDgmCUFQWAeotMUKShr17\nYw4O5ZmZYAJAEOTfnKTuPn/+7OfnV1JS8tPCUHMn68Z/esA+/3/VPBqdJno8PHo5OTk5MDBQ\nZS8JAebDNLBfgaLGqtReW7H5J4SQ2srKypoxY4aOLn/DliAtLWC13dWH6/BuRUUVG9Zmubm5\nZWdnm5i0oOI6ytamTRuRSCQWi6tfm3z8+LF6SiHt999/53K5Hz58+PDhA4IgMpnszp07KIo6\nOX171XJlZ0r0AyKox3EEQWQyGch8gKIQBGEwGIoNYNmyZQiCjJk+pC5lbzEMA1gdlz4A6lse\neWKCx+d3ZWfPnp06deqePXsak6LI5XKABwDBZCIIgqIowFMAUdUJqKurKxKJKIqq/r7Ky8v1\n9PRqbnP//v1Hjx6lpKTUcZ8K6UsCvOwESSIIwmAwDA0VVni8vuq77ERxcXFgYOA///zTb/JI\n54lejWxdfZadqP6NoL2gd9Cwm2kZR44ciY2NVXYAcNkJlS07oXbvZDAMi46OVsFBBoH19OlT\nf39/FKXWbghoa6b34z9oGYLD+oaG93v16pW7u7tCRghANAsLC319/bNnz9I/ZmdnSySS3r17\nIwhSVVUlFosRBDE1NbWxsXn9L4IgPn369M15hlATdfHixUuXLnXr28HGsR3oWJSFyWLM2hzQ\n3tokLS0tJiamKS413AJ16dIFw7CXL1/SP5aXl799+9bK6n9K4J4/f14oFE6ePDk4ODg4OLi8\nvHzDhg2rVq0CES/0bR8/fhwyZMijR4/sA4f+NK/Zrs3QP9KbzeeuXr26qqoKdCyQwqjdG8L0\n9PTS0lJjY2PQgUBKVFRU5OXlVV5eHr98lH3vZvtk1jDTZ/1ULqw6efz+yJEjz507p6TFCVsa\nBoMRFRWVlJT07NkzDoeTk5Mzfvx4XV1dBEFSU1NFItHy5cvd3Nzc3Nyq/yQnJ8fDwwMu89Wc\nLF68GEGQMdOHgA5EuQRa3IV7QpaN27d161aJRLJz505QrxegOtLT03N2dt68efP06dO5XO7u\n3bs7d+7ctWtXBEHOnz8vlUq9vLwiIyPHjx9f/SczZ84MCwv73vgFSPWePn3q6en55s2bXmOH\neSyb1GwqGH9Nw1DHIcTt+q6TW7dunT17NuhwIMVQrzeEr1+/Pnfu3MiRI0EHAimRTCbz9fV9\n/fp12ATnkd52oMNROyiKLkrwGjTE8urVq2PHjgVV47H5cXJy2rx5s62tbZcuXZKSkjw8POjf\n9+vXb8iQb2QIPj4+cFX65uTcuXPXrl2z7d/J0t4MdCxKp22gEb9vXDsr47S0tFGjRolEItAR\nQT8QExPTqVOnn3/+OS4ujsfjLVq0iM4o7t+/f/v2bQRBtLS0DGtAUVRLSwvgHGOopqNHj/bt\n2/fNmzf9o3w8lk9CGc02G6Q5R4ziCHhJSUn0+BqoGVCjXkOCIFJSUsLCwoBPn4WUh6KoyZMn\nZ2dnD3axiokdCjocNcVkMlat9YuJOHDixIno6OgdO3aAjqiZaN269df9Tc7Ozt/c2MfHR/kR\nQSpCUdTixYtRFPGf4QI6FhXRNtCI3x++Pua3zMxMBweHP/74o0ePHqCDgr5LIBDExsZ+PV9m\nzpw539z+119/VX5Q0I+Vl5fPnj179+7dLB7He91UW59BoCNSBYGeltP4EVdT/9y0adOiRYtA\nhwMpgBolhMeOHdPQ0HB1da2e5/M9Mpms7gOXOQwGS1dXRlHisrJGx9gQ9GJWZYBapwMgCAJg\nACRJVvdPr1+/fv/+/V2sjH9OGCGXq6KaOT2FRi6Xgxq/QQfQgJrFK9f6RE06sHPnThMTkxkz\nZjQmBuAHgApqRmtpacFxcdD3HDt2LCcnx+Enq07dW4OORXU0tHkL94buX33u7P7bjo6OCQkJ\ncXFxYGu01AOLRenqInDMfMtG6epSClpAReEIgti3b9+iRYs+ffpkZGE2euN0Y6sWNAWm30Sv\nnP3n1q1bFx0d/UUBJKgpUpcho+/fvz927Ni0adMU/tQuDQ0tycuTjxql2N1CDXDq1KmVK1ca\nGmmuWT+ax28iDyXgaGpxk1PGGBppLl++/MSJE6DDgaCmiiCIxYsXowzUf0Yznz34NSaLEf7z\n8Fmb/TkC5oIFC+zs7M6cOQM6qDqh7O1L8vLkS5eCDgQCh8EoycurPHoUdBxfksvl+/bt6969\n+8SJE4vLSgZN85tyYnWLygYRBOHpaPSbPFIoFCYlJYGOBVIAtehQpyhq8+bNgYGBdayzz+Vy\n6774iUQiqays1NDQUNl6KV/AMEwqldZrLSPFKikpYTAYAPtv6KrNDx8+jI6OZrMZ6zePNTMz\nUlnrOI7L5XIOh1Pf6u2KQhAEQRANW83MvB1vw5agyePSpk6d2r179169ejVgJ8XFxUwmky6g\nAoRQKNTW1gZYvh9q4fbt2/f06dMBI3uYWbQCHQsYjq7WVr3bHVx7/sqx+yNGjOjXr9/cuXO9\nvLzgWQlB9ZKXl5eWlpaWlvbp0yeUyejhM3DIjADdtqp7pFErfcJH3NqXmZKSMn36dFNTU9Dh\nQI2iFjeDDx8+PH369PDhw3Qx5bS0tPz8/ODg4BcvXoAODVKMT58+jRo1qqqqMmGFt03XFjRk\nq/GsbUyXrfSRSKp8fHw+f/4MOhwIamIkEsmSJUvYHOaY2Bb3erAmbX1B1KpRK45OsRvY+fr1\n697e3p07d16xYkVBQQHo0CBI3RUVFaWmpjo7O3fp0mXVqlVl4nLHsOHTLmzyWRfTYrNBBEHY\nAu7AqaOrqqqWwjf5TZ9avCE0MTHZu3dv9Y+XLl26ePHi0qVLAb7TgBRIJpP5+fm9fft2SvTg\nYW5dQYfT9LgMs54UMWjX9suBgYFZWVlwphwE1d2GDRvevXvnMb5vq7bwhoJ07Go6f1fwP88+\nZf5y88aZJz///HN8fPzAgQMDAgJGjx5tZNRyH20h6GsYhp0+ffq33347e/YshmEoA23nZGM3\nerDN8D4cAZiVytVNr7E/3UzL2LNnT2xsrLW1NehwoIZTizeETCazZjFlDQ0NFotlaGgIH3yb\nh1mzZt28eXOoq83kyIGgY2mqJkcNch5gcfHiRXohNQiC6uLjx4+rVq3S0uX7RsOLz/9rb20S\nvcZ7e3bchPgRnXq0vnTpUlRUVOvWrd3c3H755Re4RgUEffr0KT4+3tzcPCQk5NSpU7rtTYbO\nCYq9nBp+KMFu9GCYDVZjslk/zQnCcRwuSNjUqWPGNXz48OHDh4OOAlKMlJSUgwcPWlgaJyR6\nN+N1WpWNwUCXr/IJ8d+5Zs2a/v37V6+hB0FQLebPny8Wi8MXuWtowwe4L2lo81yDHVyDHT6/\nE17PeHwj83FWVlZWVlZ0dLSPj8+ECRPgoudQC5Sfn79q1apffvlFJpNxNPg9A1x6jx3Wunsn\n0HGpL+vhfcwdrDMzM8+cOQOf3psulK6J34zRRWW0tLRgURnVN33x4kVXV1cNTfb+w5NbtwFT\n1YYuKsPlcptiUZkvPHn0ftK4NB0dvfv377dp06aOf1VcXMxisWBRGYUjCEKp18+KigqSJHV0\ndJTXRO2qqqrYbDaoVQowDKusrOTxeDxeA3O569evDx48uE0nw1XHpzCZDTn86HpUDWu98eRy\nOUmSDf7nN8CH18XXTj/OPvWo6J0QQRALC4vIyMgJEyZogqj7TxBERUUFl8vlg1t5QiQS1Wvl\ndxRFQd1olEQqlZIkKRAIQAVQXFzMZrNVcxksKipaunTprl275HK5blujPhM8e/oNplgMDocD\nqjubIAiZTMbhcECNmKMoSiaT/fAq9PHx610+Cyw6Wzx8+FCxD9vl5eUYhhkYGID6CiQSCYqi\nqrwO10SSZGlpKYfDqdeFqGGaf0KIbd3KWrQIT01lBwWBCaClJoT5+fkODg6lpSUbUwP69LMA\ndTI3p4QQQZCDv97YsDZryJAhFy5cqGOKBRNCJamqqiIIQnn7l8vlCIIATEhwHGcwGKC+OJIk\nMQxjsVgNO3MxDBs4cODTp08W7A226m3esBgIggD4fE/3ODCZTBVfPCkKeXY7/+KRe7kX/kPg\npJ6eXmRkZFRUlIqvIYx79wSjRsnCw7Fly1TZbk317RFgMBgaGhrKi6deFHKBovcA7CwgSc32\n7XF7e+nx40ptB8OwHTt2rF69WiQS6bQxco7y7j6qP4PFRBCEIAgGgwFwHWM6AFDXYYqiSJKs\nywFwZsne+7//vXjx4jlz5igwAAzDSJIE9UYHAX0KUBQll8sZDIZCemZRFK2ld08dh4wqmFSK\nCoWIXA46jpalqqrKx8enqKgobp67fe+WtTiPUgWF9rl14/XFixfXrFmzYMEC0OG0aMruNRcK\nhQRBAOxLEovFHA4HVEYql8sxDONwOA37nBMTE588eTLIx7ZHv84NjkEqlQJMyGUyGUEQQJ6E\n7AZY2A2w+Py+7K/Ddy4cvrNq1arU1NRZs2bNmjVLZQckwWKhQiFTLueBOwVKS0sBnoCNxOVy\nG9/jr/rX1P+DJFGhEBWLlfqW+MaNG1FRUU+ePOFqCX6aF+IY5sbk/P/DN0mSLBYL4BtCulsK\nYEJCd8z9cMuf5ox9cSF37dq1Y8eOtbCwUFQABEHQRyCor0Amk6EoCupGQJKkXC5nMpkKOQVq\n/wxbQEIIgTBp0qR79+55jLQdG+Ikk8lAh9N8oCiakOgdOHrbkiVLhg0b1rt3b9ARQZDaefjw\nYWJioo6hRsh8N9CxNGF6rTT9Zwzxjhhw7mBOxt7rCQkJW7duXbJkyZQpU2DJN/WnkBQCx3EE\nQYB93SSJ0ANxlRNARUXF/Pnzt2/fTlKUre+gYfNDNAy+HJuKoijYN4TVMQAMoC6ta+jruC8O\n/3NmyuTJk69cuaKogOlPHmBOjmEYiqKgTgHy31NABQE0t0FckDpYu3Ztenq6TbfWi+I9QcfS\nDOkbaCQkeuM4FhISUllZCTocCFIvEokkODhYJpNNXualpQts+lmzwdfkekf033Qh1jd6YHlF\n2dSpU21tbbOyskDHBUGNkpWV1a1bt61bt+qaG487GO+9durX2SBUL91H9rf8qfe1a9fWrl0L\nOhao3mBCCClYZmbmggULDAw1120M4HBhL7JS9OvfeUyg43/+85+4uDjQsUCQeomNjX38+LHL\nGPveQy1Bx9J8CLS4/rFDNmZNG+Rr9+z5Mzc3N29v79evX4OOC4LqTSQSTZ482d3d/e37d85T\nRkVlrG3vBFdIVgzPxCkCfe34+Phbt26BjgWqnyb5vI7jeN1HITJwnE1XFgH0LoUkSRzHAb7J\noecEqyaA58+fBwYGMpjIqrW+evp8DMOQf4tDgHrdT79wx3Gc/g8gAdCj8BW72+jpg3Nuvd6x\nY8fQoUNHjBjxwxgAHoEkSVZVVSn7AODz+c2vbg1UX7t27dq1a1dbC6Nxi9xBx9IM6bXSilo1\nyjXI4ZfEMydOnDh37ty8efPmzZsHsBAoBNXLmTNnIiIi3r59a9S57aik6Da2DZ9jDH1N00jX\ne+3UQ5NW+/v75+bmGhkZgY4IqqsmmRDWr7Izitb7TxQNeOuqCaC4uHjMmDEVFRWLl3r1sDP7\nIgBQCSHdLsA5AOi/R6Bid8vjsZeuGDUx7Jfo6Ojc3FxjY+PatwdbDF31lRKhFigrK2vq1Kka\n2ry4LQFcPpjVMlqCTt1bLzs84cqxB4eSLyxdunTfvn0bNmzw9vYGHRcE1aakpGTmzJn79+9n\nMJn9o3wGT/erWTwGUhSLwT0HTvW9suWoj4/PhQsXgFUkguqpSSaE9Sq4JA0IENrb821suIAO\nSrpmLsBTorKyUgWLqEgkEn9//zdv3oyb4DzK177m/yIIAuCEYHpOPIPBALvshDImBNt0axsV\nMyRlw4XIyMjMzMzvfcJisZjBYAA8AqVSKZfLha/vIKXKzs729fWlEHLW5rGm7Q1Ah9PMoSg6\nyNfOYZj17ykXzx/M8fHxcXV13bhxo7W1taKaoLp2FV64wGnbFj6zt1wMhvDCBaaOTiMrvVIU\ndeDAgbi4uKKiImOrdiNXR8KF5pVqcKx/Ud67a+eujR079vfffwe1mC1UL83/EY0yMsJtbRF9\nfdCBNGcEQQQFBd24ceMnV5upsUNBh9OChIT36+3Y/uzZsykpKaBjgSBgzp07N3z4cKlUMi3Z\nt2ufDqDDaSkEWtzwRe6rjkd0dWqflZXVo0eP6dOnl5SUKGbvGhq4rS3Vtq1i9gY1TbitLdm4\nNQyePHni4uISFhZWVlHuMitwyonVMBtUNpSB+q6f1s7R+vjx4wEBAbDUfJPQ/BNCSNkoioqK\nijp+/LidvfmylT4MBhwZqDoMBrpspY+OLn/evHn3798HHQ4EqRpFUevXr/f09JTKJNPWj3Zy\nswEdUYtj3qXV4l/HzUwZo2esuXnz5s6dO69Zs0YikYCOC2rpiouLp02bZmdnd+nSpU4DbaPP\nJA+Y6ksvNw8pG4vHCdo937y31bFjx4YOHVpYWAg6IugHYEIINdbs2bN37drV2aLV+s1jYVlR\n1WtlrL146UiZTBYYGCgWi0GHo77EYvH69evHjh07ZsyYpUuXfv78+ettSktLk5OTQ0NDAwMD\nFyxY8OLFC9XHCdXdy5cv3dzc4uLiBNqchXtD+7jDbBAYJzeb9WemBs35CSOl8+fPt7Cw2LZt\nm1wuBx1XU/L+/ftTp079+eeftVx53r17l5mZeeLEiUePHqkytqalqqpq9erVFhYWW7Zs0TTV\n998aF5K2SM/8BzPtIcXiaPBDfllk7eZ07do1Ozu706dPg44Iqg0zISEBdAzKheM4hmFcLhfg\nspI4jnO5XCCtIwgikUhQFFVSFbh58+atW7fOvJ3B9j3j9PQE39yGIAiANUVIkqSn8AFc15Wi\nKKXOYGzfwVAolFy59LCgoMDX1/eL/1tVVaUOcwiBF5VJSkr6+PHj/PnzPT09Hz58mJGR4e7u\n/kVUixYtksvlcXFxnp6eeXl5f/zxx4gRI0BdOqRSKUVRAsG3TysVkMvl9ZqwrVgEQchkMjab\n/c35J0+fPv35558nT56cl5fXtU+HhXtCzS1bKTwGHMcBrsBOEARFUQCn35AkWa8VsZkshqW9\nuYt/L5IgH956cerkqf3793O53O7duzfgYyRJUiqVstlsDodT379VFIlEorICqlevXo2Pj0cQ\npKKiYv/+/SiKdu365VoIGRkZK1euRFG0oqLi0KFD79+/79Onj1KjwnEc7EFYVVXFZDLrfv+S\nSqVbt2719/c/fvw4wmEOnuHvs26asaV5gwMA+wBDURQdAMBJ+I0pgsBks2xG9GWy2U8u5hw6\neDA3N9fa2trU1LTue5DJZCRJCgQCgHUoAC5MT1GURCJhMpkqSCLg+xyogSiKmjVr1saNG9ua\n6W3fE2ZgqAk6ohZtxmzXxw/fHTx4sF+/ftHR0aDDUTvFxcW3b9/euHFjx44dEQSZMWNGaGjo\ngwcPevbsWb1NRUWFsbFxSEhImzZtEAQJDw+fOHFiQUFBly5dgMUN/YsgiIKCgidPnty4cePM\nmTP37t1DEMSojW7ATBdnz27Auxugalq6/ND5rh7hfY7vyL545G50dPSyZcumT58eERGhDyfz\nfwdBEDt27AgLC6OLtd6+fXvVqlVDhgwxNDSs3kYqle7duzcyMtLV1RVBkHv37i1ZsiQwMNDE\nxARY3OpELBbv3LkzOTn5w4cPbD633+SR/SO9+brwyQQwFEUHRPtYDO6ZEb87IyMjIyNjwIAB\nwcHBnp6e9K0WUhMwIYQaAsfxKVOmpKWlmbcz2LY7rJWxNuiIWjoOh7k6eUxIwM6ZM2f27Nmz\nb9++oCNSL3l5eRwOp0OH/5Yb0dTUNDMzy8vLq5kQamlpzZs3r/rHkpISFEXhI6wKFBYWPn78\n+NWrV+/evSsuLhYKhVKptLy8XCqVSiQSqVQqFAqLioroisEIgjCYaHfnjoN9e/Zxt2Gy4MQH\ndaRvoj1hyQjvyAGn91z/+4+7CxcuXLFiRUhISHR0dI8ePUBHp3ZevHhRUVFBZ3oIgjg6Ompr\na9+5c8fNza16GzabvWHDhuq3K+bm5giCiEQimBB+/vw5NTU1NTW1pKSEzef2meDhPGWUppEu\n6Lig/2di037CH8vz/r57fc+pq1evXqvMgtwAACAASURBVL16FUGQDh069OzZ08rKytzcvHXr\n1kZGRvr6+vr6+np6emAXymqZ1CghvHjx4p9//vnx40cdHZ1BgwYFBwcr5IBgnjqls3UrsmAB\nUuPCCjWGWCwOCAjIzMzsYmWyZXuIvoEG6IggBEGQ1m10E1f7zph6aPTo0Tk5ObDvrSaRSKSl\npVXzPZKOjk55efn3tq+oqNi8ebOXl1fNHvoviMXi6hRFGQiCQBBEKBQqr4nakSSJYVhVVZUy\ndl5RUXH+/Pnz589nZ2e/e/euli01tHk8DY65lZGBqbZpB4MOXU272LfV0OYhCILhckyJ3wBC\nUZRUKlViAz9qHUEQsAGgKIphWIP3INBh+88a5DHR6eKR+38dvrtjx44dO3Y4ODiEhoZ6e3tr\nadW2mgDjxQuduXPlHh7CyZMbHEAjkSRZrxOQyWTW/o/6no8fP2ppadUcH25iYvLx48cvdk4n\ngbSsrCwTE5NOnb5bMJMea9eAYGqih4wCKxFEUTp+fqSVlWTDhm/+/2fPnm3ZsiU9PV0qlfK0\nBc6RoxzChmsYaCP/rjiliBAoetCgQvZWX/Q3SM88AhIAPedFUa13HGTbcZBtWX7hf87nvL7+\n6MPDV2/+/PObW+rp6RkbG7du3bpNmzYdOnSws7Pr2bPnD9dbVgYMw1AUpa/Gqke3SxCEQs7B\n2pegU5eEMCcnJyUlJTIy0t7e/p9//lm3bp2mpubXs6EaAH33jn35MhYe3vhdQQiCFBQUjBw5\n8sGDBw5OHdZuDNDUBDY3Evpav/6do6e7bNn416hRo65cuQJw+pka+uKOXsv1/d27d8uXL7ez\ns5s4cWItO6Snpyosvq9U3wmU18QPA1D4YxBFUVeuXDlw4EBmZiZdi1ygxe3Wt71Zl1Ym7fT1\njLV0DDX4Ghy+JhdBEYEml8H89gtA1dyeQT0E1GwdYAyKalqgzfWY4OQe5nD3Yt6lI/dzb+fk\n5OTMnTt3+PDh3t7eQ4cO/eYzCioUsi9fJqyspOBOAURVJ6BMJvtiqiSXy62lWP/p06dPnz69\nbNmyWvrNZTKZoor6ACsORJKGly9jVVUVlZU1f00QRFZW1p49e65cuUJRlLapQd8Q7x6jB3M0\neMqItvF5dSOBygarKfYj1TDVsw9ztQ9zRSiq/EOx8O1n0adScWGZRCiWlouloippuVgiFL9+\n+8/z589r/mH79u379+8/dOjQIUOGaGio9FUE2JUzCIKo/N9ToGFqn46rLglhaWmpv78/PTrC\nyMjI2dn54cOHCkkIIQW6evXqmDFjCgsLR3rbLYj3ZLPhO321M26C8+uXRZmn7wQFBR09ehSO\nu6Dp6uqKRKKaGU55ebment7XWz548CApKSkoKMjDw6P2fWprK3ektFAoJAjCwADYGutisZjD\n4SiqpIdMJjtw4EBycvKzZ88QBDE21+vn4djLxbJj19YM5jfSTrqoDIvFAljQQiqVAqzGJJPJ\nCIJQWVGTr2EYxmAwFHgNGTjSbuBIu0/5pVeOP8g++fDYsWPHjh0TCATDhg0bPnz4sGHD6Cm+\nNEJTE0EQJpMJ8BQoLS1VzaBxLpf7xTP39449erbhgwcPVq9ebWZmVss++Xx+4wtRYBhGkiSw\nqngkiSAIiqLV713fvn27b9++tLS09+/fIwjS1r6LY5i7lZsjQ2l3OgzDWCwWwDeEdACgbuUU\nRWEYprzCTq06tm3V8btrjcrEkuJ/PpS8/lDy8v2Hh6/f3887cODAgQMHeDyem5tbQEDAiBEj\nlH2JlsvlKIqCug1RFCUWi1kslkJuBLUfxuqSELr973jOkpKSVq0UXzIOaoyUlJTZs2cTBB4b\nNyw0vB/ocKBvQ1H056UjP34QnjhxIiYmZtu2baAjUgtdunTBMOzly5cWFhYIgpSXl799+9bK\nyuqLzZ4+fZqUlBQXF2dvbw8izOZJIpHs3Llz7dq179+/ZzIZfYd3HRbkYO1gDivBtEwm7fT9\nY4eMmT447/77W+ee5lx4fuLEiRMnTiAIYmZm1r9/fycnJ3t7+x4SiQ7oUFWmTZs2IpFILBZr\nav63CMrHjx+rpxRWw3F81apVMpksOTm5esvvUcgjLEVR6pAQVlRUHDt27NChQ1euXCFJki3g\n9vR3cQhxM+3aQdkh4DgOsMooTbH9MvVCjxcF1bpAR9PYqp2hRVuBjwBBEJIg3t198eLi3Wfn\nbtMXDT09veDg4MmTJytvZjJdbBnUKUC/nWYwGC20yuiZM2fy8vKmTp36vQ2kUmnd11vjy+Vs\nBJFKpeXFxQoKsCGAv24ubsQ/XywWz5o169ixY9o6/IRE796O7es7rQj4IsVgP39E5UM+lq8Z\nFTPl0Pbt2wUCwYIFC3Acb8wB0HilpaXKbkJXV7eWwtB6enrOzs6bN2+ePn06l8vdvXt3586d\n6aru58+fl0qlXl5ecrl848aNI0eONDc3r/64NDU1Ab4jauqqqqp27NiRlJT06dMnLp/tHuro\nOaGfYeuW85wPfReKol16tu3Ss23ofNd3L4seZr96fPPNf+4UpKenp6enIwjihCA3EeTkyZPn\nCaJjx44dO3bs1KlTp06dlP1mHggLCwt9ff2zZ8/6+fkhCJKdnS2RSHr37o0gSFVVFUmSdPq3\na9cuqVSakJAA8LW5yshksjs5Of0Q5OnTpz1NTOixu217Wtj6Du7u5czVghMiWhwGk2nuYG3u\nYP3T3OCPj18/OHbl0YmrW7Zs2bJlS58+faZMmRIQEABnyjQYsImS30RR1KFDh7KysuLj42uZ\nKi2Xy+ueYLC3bxfMn1+5YwceEKCgMOuH7mADOHKPnhHb4EVUnjx5Eh4enpeXZ21juiLJx9ik\n3jdjun8F4DI+JEkyGAyAAVAUpfpFhD5/roiedODjh/JFixbNnDkT4HJqqulh1dTUrP0sq6qq\n2rVr140bN0iS7NmzZ2RkJD1kdO3atSKRaPny5Q8ePFi8ePEXfxUREfHDsaNK0qSHjIpEom3b\ntq1fv/7z589cPnvY2N5eE511DOsx66P2dQhVQx2GjAJ8vlH4kNEfokjq3aui148+vHn2SfdO\nQfqTj5sQZMb/bmNsbGxjY9OtWzd7e/u+fftaWloqLx6VDRlFEOTWrVtJSUl2dnYcDicnJ2f8\n+PH0laf6AlVQUDBt2jR7e/uaIQ0ePLh79+7Ki0oqldKrwClqh3SdHqFQiGFYdc9+RUUFhmFl\nZWVFRUWfPn169erVs2fPnjx5QmAYgSDXEcSvW0drN6euI/rot6/HEnYKQU/vBPX8QF8GORwO\nwHXwZDKZ2l4GCQx/npVz97cLr68/RihKR0cnMDBw/PjxTk5OigqAXsob1CdAkmRpaSmHw1FB\nR5gaJYQ4jq9du7aoqGjhwoW1VParL2z9enZcHJaWxgZUVwbDMKlU2rDKYwpRUlLCYDC+OV3q\nh3bt2hUbGyuRSPzHOs6Y7crhNOTJAOz1FMdxuVzO5XIBrq9NEASQtZXfvS2bMv6Xz4Uiuua7\n6gOgCYVCbW1tgOvqNlFNNCF8/fp1amrq7t27RSIRT8AZFtTba6Kztn69HyhhQtgCE8Ka9O6/\ndQ3Y+zjA/nRA78K3ZZ/flhW+LfuUX/opv6Tko6h6s9atW7u7u/v4+Li5uSn8UFFlQoggyIcP\nH3JzcwmCsLW1rZ5Oee3aNZlM5uLi8unTp4sXL37xJ7169VLqQqmNTwgLCwsvXLhw48aNR48e\nvXz58tOnT3Wp0cJks4ws2pp173T7t7/edOu478TqBgfQSDAhVOeEsFpp/qe7v/11/+ilyuJy\nBEEsLS0DAwP9/f1tbGwaGQBMCFWNoqgVK1YQBLFgwQLFPjrL7t7FLl3iuLtzGn1YNEwTTQgr\nKioiIiLS09M1NLmLl478ybXhnx5MCEElhAiCvC0ojZjwy+fCijlz5qxZswbItwATwoZpWglh\nRUXF8ePH9+/f/9dff5Ekqa0vcA12dA911NRp4FR4mBC28ISQU1RhfPZxhZWJ0OHLeWISsSz/\nP4WvH394nlvw9NY/4nIJgiCtWrUKDw+PiYmpvdRKvag4IVRDDU4IJRLJ4cOH09LSrl27Vp0B\nahnr6bQ25OtqsflcnpaAvh+x/4+9+45r6ur/AH4yCBD2BgERBBRExQHIEHEPBEURZ927uG0d\nTx1VHK1bXBVra21V3LMOnHUrDlQc4GLJFpJA5h2/P+5THn4oGDDJCeT7/sMXiZfcLzd3fe49\n9xxDfQ6Pq2fA4xkZ8i1NTGwszJ1szJ1t2RwOoumWe8+Kbczf9MbWbQEEwnoRCBkUQaZfffj4\nyLX0q49IuQIh5O7uHhER0bNnz44dO9ZtXwqBUNPOnj2bmJi4YsWKiucmORxO3W5qVSGRSMrL\ny01MTHA9ElofA2FKSkpMTExaWpqXt8PqdYMcnb7qi4BAiDEQIoTevM6dFXvwQ07pyJEjExIS\nNF8JBMK60f5ASFFUamrq5cuXz58/f+XKFWbEPPdWjt2GtA/u66On/1VnMBAIdTwQUhQllUq/\nuAJQJP3qYeatM89unXlWLpTyeLzRo0cvWrTIyanarguVB4GwDoFQIpFs3rx53bp1hYWFiMVy\n8vXw7NzWxd/LvoUrj1/rrUksFnM4HGy92uA+gYFAWLfdoFRY/jLp/otzd9/eekpI5QghHo/n\n5+cXEhISFBQUGBhoY2Oj5EdBINS0+fPnP3/+vPI7JiYmf/3119d/MgTC2gbCX375ZebMmTKZ\ndMjwgOmzu3/92BIQCPEGQrFYXFoi+X7WkZcvcsPCwg4fPqzhjAGBsG60MBBKJJIXL148f/78\nyZMnDx8+TE5OFggEzH81crMO6OkVHN7SyUPZA23NIBBCIFQmEFaQSRTXTzw5uetmQVaJoaHh\nvHnz5s2b95VfHwTC2gbCI0eOzJ49OzMzk2dk2G5ot/bDulu62H9NARAIIRB+zW5QIZG9v/v8\n7Y0n724/K0jLpKn/Rp6mTZt26NChQ4cOgYGBrVu3rmHxQiBsOCAQKh8Iy8rKJk2atG/fPhMT\ng8XL+3XuWrVT/rqBQIg9ELLZbIpi/+f7I9evpbm5uR0+fLhNmzYaKwACYd1gD4QCgeD9+/dM\n/EtNTU1NTX3//n3l539sHM092zp7tXdpGeRm11gFDToqg0AIgbBWgZBBEtTlQw8PxV8VFpc3\na9Zs9+7dQUF1b20IgVD5QFhQUDBp0qTjx4+zuZyAUb07To0yNFfBaQ8EQgiEqtoNSgXlWY/S\nsh+mZT18lfPkjbz8v51TGhsbBwUFhYaGdu3a1c/Pr8oeDwJhwwGBUMlA+Pz584EDB758+dK7\nRaNVa6O/sploZRAItSEQGhgYUBS9ddOlP367qa9vsG7duilTpmjmS4FAWDdYAqFIJLp+/fr1\n69dv3759//79ygPMGJkaNHKzdvawdXK3cfa0beJlb2KhxqwCgRACYR0CIaNcKN2/7uKlxAds\nNmfBggVLliyp2/k0BEIlA2FSUtLIkSPz8vKc2nhGrp5s466C9roMCIQQCNWxG6QpuvB1dvbD\ntMwHL7MevPqYkce8b25u3q1bt969e4eHh9vZ2SEIhA0JBEJlAuH+/fsnTJhQXl4+aIjfrO96\n1q030epAINSSQMi8vHLp5bJFJ0QiaZ8+fRISEho1aqTuAiAQ1o3GAiFFUQ8ePDh79uz58+fv\n3bvHjJnJYiG7xpZuLRu5ejm4NLdz9rS1sNXoTgwCIQTCOgdCRuqdd9vmHy/OFXbs2PHAgQN1\n2NdBIPxiIKRpOi4ubunSpYjFCpsZEzKpH4ujyl09BEIIhBrYDYryS97fefb21rM311NE+R8R\nQmw2OyAgoF+/fr179/b09IRA2BBAIKw5ECoUijlz5sTHxxsa6i1cEtE7XPUjGkEg1KpAiBDK\n/SBY8p9jD5MzzM3NV65cOWnSJLWmNQiEdaPuQFheXp6UlHT69OkzZ87k5eUhhFhslquXfYtA\nV6/2Lp5tnXmGHIaaCqgZBEIIhF8ZCBFCZQLJ9vknHlx+ZW9vf+jQoZCQkFr9OgTCmgNhWVnZ\nN998c/z4cRM7y0Hxs5zbqX5MSAiEEAg1vBvMf5GRfu3Rq4vJOSnpzDOHLVq0GDBgQFRUlCaf\ntWFAIFQl2a1bxLlzegMG8Hx9sRSgzYHww4cPMTExN2/edGlitWbDYDd31fQGUQUEQm0LhAgh\niqIP7r+3bfNlsVjepk2bNWvWdO3aVU0FQCCsGzUFwrS0tPPnz//9999Xr15l+gU1NjNsHere\ntpNHq5CmlVuByuVyCIQ6dSZUBd5AqJ8ncDrysLSVU3FHj6/5HJqmTybcTNx4mcvR27Zt27hx\n45T/XQiENQTCzMzMyMjIlJQU53bNBm+ba2RtpvrZ07T/1sNl9lbPo7uo/sOVA4FQ1wJhhbLC\n0leXkp+fvZNx9zmpIBBCLi4uffv2DQ8P79Spk2ZKgkCoSjAwfXWB8Nq1a4MHD87Pz+/SzWvJ\n8n5Gxuq6AgeBUAsDIaMgX7hx7YWk86k0jbp06bJo0aKwsDCVF9BQA6FUKiVJUn2fL5PJaJr+\n+iOxTCZLTU198ODB3bt3b9y4kZ2dzbzv0MSqTZh7mzAPz7bObPZnNk+SJFksFq4vjqIokiQ5\nHA7GNYcgCFznYczcaZrmcrkYT0YxrgBWKTm9h+95Mbz9gwU9vv7Tntx4u/W742KRdNq0aXFx\ncUoeDmp7RYDNZhsa1nHgTZVTKBTKDAH/xQ+hafrT49eDBw8GDhyYl5fXemBYnx/HcnhquWrD\nouhlzYdl+HrsOrhMHZ+vDIVCgXEbpCiKKQDXCQxN0wqFAuMJDLMa483kUpH47bWUVxfvv73+\nRC6WIoT09fUDAgI6duzo5+fXtm1bW1tbNc2dpumysjIul6uSHQuLxarhq8R2qAMY0TS9fv36\n+fPnUxQ5fVa3b8YE4drSAF62dqYr10QPGdFh2+ZLly9fvnz5sp+f38yZMwcOHIixiU59weFw\n1LrhyOVyJg8oM/HHfxUVFRUXF+fl5X348OH9+/fp6env379nHgtECPFNDNp18WwZ5NaqY1M7\n5y90HEVRFJvNxpjH8AYSmqYxzh0hxGKxaJpms9kYT0YxLoGKixQqKcA31P3H/WPWTk2Mj49/\n+/btnj17jI2NlfnFWl0R0KojKXMt8us/BCFUsQNhnD59ety4cWKJuPOcIYETIhBCX588P4v1\n7yABavp8ZdA0zWwIWObO/OFMDVgKYG4a4V3+zL+4bl/RNK1vbNgiIqhFRBApV2Tef/nmesq7\nm0//uX79n3/+YaaxsrLy8PBo0qSJvb29jY2Nqampubm5vr6+oaGhoaGhvr6+qampiYmJpaVl\nba8wVvz5VbbBuql5X1ovA6FCoZDJZEpOzCEIPeZXysrUWlV1mOvcZZjmjv7dlVQUIBKJJk+e\nfPLkSQtL/vJV/du2d1EoFGotgLnEpdZZ1IDZnAiCUOvNnJoLoGlaLpdjmXtFDTUU0NzLdvP2\noSmPsv747fadW/eHDx8+Y8aMoUOHjhgxwtvb++vnTlFUeXm5ug+ofD5fw2eu6m7KKJFIEEJV\nkjlFUS9evHj8+HFqamp6enpWVlZ2dnZ+fn51RwtDY30XL9smXvZNvB082zg39rRlfe5m4GeR\nJImxxSCDxWJhLIAgCLxzR7UMJKrFXBHAtQRYbDZS6Qrg7GEbd2j8hmmJZ86c6dat26lTp744\neH15eXn9vTSmkmZ+nzYZ3bBhw9y5c9k8bvTmWd69O3z9LGrw30DIUvvOtgYURenp6WG8PcU0\nlMDYZJRZAljmjv49hcZYQOX9sJ6enmdYW8+wtgih8mJB1oNXOSmv856/L3ydfefu3Tt37tT8\nUSwWy97evkmTJt7e3q1btw4MDGzTpk3N+zfmUWoOh2NkZKS6v+nz6mUgrNUTxhSbjRDicrks\nTLt1ptkP3keiWSwWU8CjR4+GDRv25s2bVr5OK38eaGun9kbJCCGKotR9L6UGzMaMseEZSZIU\nReFteMZisb5YQDs/13Z+rhnviw8nJv996kl8fHx8fLyvr+/w4cNjYmIcHBzqXADT4ETdy1+r\nrs2r3Lt3706dOpWUlHT9+vWKseARQhwO28zaqHEzG1MrIxNzvpG5oakF38SCb2FrYmFrYtPI\nzNRK7UcRAOoLE3PDhbu/SVh06tqxxwEBAcePH/fz88NdVL2hUCimTZv2yy+/GFmZDfnle6c2\nX/VsJwD1mpGVWfMe/s17+DMvCZlC8KGorLBUXCKUlJbJxVJSTsjKJRRBykRihUQuFZWXFwuF\nucV37t65ffs281sWFhbdu3cfMGBAREQExsfFGfUyENaqFZPi36uMeC8wYJw7QojJA5s3b543\nb55cLhv2TYfps7tzuRoKSEyjI1zn6+x/VwCMDc+Ydl9Y5l5ByQJc3Wy+W9B7xuzuVy69PH3y\n8d3bKY8fP54/f37Xrl1HjBgxYMCAOlymYrY+7EugPsrPz//tt9/27dv36NEj5h3rRmbBoS1d\nvR0ae9o6uFpZ2ZuxOQ05CQOgWlw9zpTV/R1crRM3Xu7UqdPu3buHDBmCu6h6oLi4OCYm5vLl\nyzYezsMS5pk7q+uhKQDqI66+npWrg5Xrly+dk3JF4evs3GfvMu6/eHvzycGDBw8ePGhiYjJ4\n8OBJkya1b99eA9V+Vr0MhKC2cnNzhw4dev78eTNzw1VrhoR2Vn3f0KAh4elze/bx6dnHp6iw\n7MLZZ2fPPLlw4cKFCxe+/fbbQYMGjR8/PjAwEHeNDRlFURcuXIiPj79w4QJBEGwOyyfQ1a9b\nc99QD7vGX3jwDwDwRf0nhTRytdr6/bFhw4Y9evRo5cqVeJtGa7lHjx4NHDjw3bt3HmFtBm6a\nqW+sLR3nAFDvcHh69t6u9t6ubWK60DSd++xt6plbT0/e2LVr165duwIDA2fNmjVgwADN75E4\nS5cu1fAsNYwQCAgOh9W9O6dxYywFUBRFEASuJqM0TSckJHzzzTepqal+Aa5bfhnh7eOo4RqY\nFvAYn8kmSZLL5eK9Q4jxbEOhUCjTZPSz+Ea8lq2dBgxq161nCz6f9+5d/s0bd3/99ddjx44h\nhJo3b67Mii2VSvX19Rt2k05VKSsr++WXX0aOHLl58+b09HQHN8uoyR2nrOrfY5ifeytHYzON\nnocxzxBi3HCYXQfep/gwNvYmSZKmabxP7+DsVEZOIrGs0K+xqJm9Oj7fsalNu86eKddfXzh7\n6fr16z169Pi0M3CJRKI9vYZiQRDE7t27Bw8eXFRcHDypX+SqyVwDjXY4afihKLONZ46flyZn\nWhneE5iK3SDezr10fDeIVNS11adYLJaJnWXTkNYdRoc7+LiKS8ue3n546NChv/76S19f38fH\nh8PhSCQSzQzF2fCHndDlgekfP348bdq0GzduGBjqTZvZbdAQv892Lq9uMOyE1g47UVsURd+6\n8frY4QfXr6VRFG1qajp27NjY2NimTZvW8FsNddgJ1crMzIyPj09ISBAIBFw9TkAv77DoVu6t\nHTGej8I4hDAOYX0fmP6LygSSLXOPPv7nta2t7e7du8PDwyv/r46PQ1hcXDxhwoRjx47pm/D7\n/TTFq2eA5muAgelhHEK8u0ENd+5VkJZ1e9eppydvkArCwcFh1qxZMTExFhYWMA6hCuhmIHzz\n5s2yZcv+/PNPiqICg5vO/r67q5udJguoDAJhgwmEFQryhYcPJh8//PDjx3I2mx0RETFr1qxO\nnTp9dmIIhDW7devWpk2bjh49ShCEiQW/+9D2PYb5mdsYS6VSmqYhEEIgxFWALgRChBBN06d+\nvZW44TJF0mPHjl2zZk3FsL26HAgPHz48bdq0vLy8Rq3dozfNsHDGcwoBgRACoU4FQoYwr/hW\nwqmHBy8pxDILC4sJEybMnTvXxsZGrTOFQKh2Gg6EN2/ejI+PP3z4MEmSbk1tps3q1t7fmcVi\n4d2eIRA2sEDIkMvJ82ef7v/zbtrLPISQr6/v9OnThw4dWmVeEAg/SywWHzhwYNu2bQ8ePEAI\nOTa17jOqQ8d+rXkG/z3wQCCEQAiBUGMrwNvU3G3zjmWnF9rZ2a1evXrkyJFsNls3A+GLFy/m\nzJlz9uxZjh43ZGpU4MQIfXxbAQRCCIQ6GAgZ4o/CO7//fX/veamw3MDA4JtvvpkxY0aLFi3U\nNDvtCoSlpaV5eXmWlpa2tirrwEpHAmFKSsqJEyf279//8uVLhJCrm/WY8R17hbdks1kSiQQC\nIQRCta4Ayffe7/vzzo1raRRFW1lZjR49ety4cV5e/33qQ3sCIUVRGRkZBEG4uLhU940oM81X\nunfv3u+//75///7S0lIWm+Ub6t7rm4BWwW5VthEIhBAIIRBqcgVQyMkTO2+c2HlDISNat269\nbNmy4OBgKysrzcwdacEO6s2bN6tWrdqzZw9BEC7+XuHLxlu4OuB9ggsCIQRCnQ2ECCGapgVF\nJU+P/vPgzwuCD0UsFissLGzChAlRUVEq/1K0KBAmJCScOXPG2tq6uLjYz8/vu+++U8k+qKEG\nwvLy8qdPnyYnJ9++ffvKlSu5ubkIIT09TsdOnlHR7ToE/e/kEgIhBEJ1B0JGTnbJoQP3Tx57\nJBRKEUIBAQFDhw6Njo42MjLShkD47t27FStWlJWV8Xg8giDmzp3btm3bOkxTNzRNP3r06Nix\nY4mJienp6QghUyujsCjfroPbVddxKARCCIQQCDW/AhRkl+5fd/HO2VSaRs2bNz916pS7u7sG\n5ot3ByUQCMaNG3f8+HGSJC2bOHSdM8SrdwcWi8UMpAyBEAIhlrkjLdgNYg+ETKcyelzui3N3\n7+09n3n/BULIwsIiOjp68ODBnTp1UlVt2hIIb968uWnTppUrV7q7uxcXF3/33Xe9evWKiYn5\n+k9uGIGwsLAwLS3t1b9SU1Pfvn3L9H2EEDI1M/QPcA0J9Qzt3MzUtOp2C4EQAqFmAiFDLiMu\nXnh+8vijh8kZFEW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4eDhz\ntxOAOmOnp3NTUpB2dCqhy8rLy0eMGDF+/HiSzRmw7rcBa3frm2hXZ9q2ni1ituybeOS6a2DY\n5cuX27dvP23aNKbhPajXtOhY2KNHjz/++GPOnDlbtmxZtmxZfek1CFTgHDhg3q0b+9Klinc2\nbtx44cIFt2D/4EnfYCxMSTy+Yc8fZsjl8unTp+OuBeDx6tWrBQsWmJmZhYaG5uXlzZs3T5kO\njYH2ys0179btfvfusbGxUpLs8Z8fpl255jdyJI+P4cFXlfONHhQaOy0zM3PAgAGwooKvYTh9\nunm3boggcBei0zIyMoKDg//66y+HFr6Tjt9qGRGDu6JqOfi0GbnnzNAdh8ycmmzZsqVFixZn\nzpzBXRT4KloUCBFCfD6/WbNmNjY2uAsBKpCamrpw4UJDC7P+65bUl8vw3n26uga1v3jx4tGj\nR3HXAjD4/fffBw4cOHr06C5duixcuHDlypXwmGK99vTpU4RQQUFB046hU5MuBo6fwGlYX2jY\nrNlevXvfvn172rRpuGsBANRdcnJyQEBASkqK78Bvxh64pO7eRFXCs0ufKafvdZzyfW5+Qd++\nfadOncr09wPqo/pxmg7qHYVCMXLkSKlU2jdunold/RjaiNF7yRw2hzNnzhym03OgO8Ri8fPn\nz1u0aJGQkLBs2bK9e/caGxvXl57NwafOnj07dOhQhJCNp+fw3/eY2jvgrkj1WCxW/7XrbD2b\nJSQk/P7777jLAQDUxeXLlzt37lxQWNhj/qp+q3Zw9etHB6oIIa6+QZdZS8Yfumrt5rl9+/ag\noKD379/jLgrUhbb0MgoamOXLlz98+LBV/97efbrirqV2bDzd/L6Jvvt74rp163744Qfc5QDN\nyc3NpWn6999/79Gjh4+Pz4kTJ+bNm7d58+bqxuZihhvRcJEVmA7WZDKZ8kONaQBBECKRSBt6\nUTt9+vTYsWOdaBohZNXUXa5NS4miKIqiVLbycLj9N8f/PjBq6tSpXl5ezZs3r9vHkCSpVQ8C\nMWPNyeVy3IX8D9OJdw1LicPh1O1pl/Ly8rdv31Z5s1GjRlXGPHzz5o1YLK54yWazW7RoUYfZ\nAa1y8eLFyMhImYIYsG63T/gg3OXUhUML34nHbpxeMuPR8f3+/v4nTpwIDAzEXRSoHQiEQPWS\nk5NXrVplYm/Te+kc3LXURdjMCU9PnF+9evXo0aOdnJxwlwM0hGnr0r9//44dOyKEfHx8xowZ\nc/fu3eDg4M9OTxAE9jBGkiQzzo32wBiSKyQlJY0dO5Zms3v/uBzN+w4hWtuWEq3S3jvMnJ17\nLFl6+vvvxowZk5SUpK9fl9F9aJrGvj5/Stu+OIRQDUuJCbF1kJOTEx8fX/mdwsLCiRMn9u7d\nu/Kb69evFwqFFUMcGxgYbN68uW5zBFri1q1b/fv3lxNkTPxfzbrWehhk7aFnaBT18y4Hb98L\nPy3s2rXroUOH6jCqM8BIqUCoUCjgQRqgJIIgRo8eTZBk5KqFBmb1Y5TbKgzMTDrPmXTmh5++\n//77ffv24S4HaAhzmuXo6Mi8NDExsbOzy83NrW56U1Ocnb8pFAqhUMjn8yvODrWBUCg0MjLC\nOyrU9evXx40bR7FYw3btburighBisdhaNXKxXC7ncDiqXUrtBsVk37//+NDBn3/+eePGjbX9\n9eLiYi6Xa2ZmpsKSvpIWDj5eWlpKUZSlpaXKP9nT03Pnzp0VL1NSUtatWxcSElJlMpFINHHi\nROaKFWgAXr58GRERIZZKB23aW6/TYIUOo2PNnVyOzBodFRWVmJgYFRWFuyKgLKUej4mMjBw8\nePDff/+thdfqgBYxMKDNzQ8eP56amtomJsI9LAh3QXXXbkh/u+buBw4cuHHjBu5agIY4Ojpy\nudycnJyKd0QiUQ2nyCzctKSMyrDXk5aWFhUVJZMrBm3d5hocTLPZElNTuTZlZvXpvXSppUuT\n+Pj4S5cu1Xa5MZ+gjm+kzuppSV//Pcrl8i1btowfP97EpOoV1bKyMhMTE5FIlJeXp9qbzMjE\nhDY3V+UHgi8pLi7u27fvx48fw5du9OrRD3c5KtO8W8SwnUcQV2/IkCF///037nKAspS6Q+jr\n67t58+aDBw/a29sPHz581KhRLVu2VHdloN4hJk681KzZuL59zRrZ9/xhJu5yvgqLw+7949zf\nh0yZPn36/fv38d7xAJqhr68fFBR06NChNm3aGBsbX7x4USwW+/r64q4LKOvjx4/MCVbflas8\nu3ZDCAkcHJbcvsvlcnm4a9MAHt8oav2G3THRY8eOffr0qVbd7gPKO3LkiJWVVWhoaJX3pVIp\nQRB//PFHVlYWi8XS09ObOnVqdQ3a0b9PPCo507LERIIgLNhsVB+u+zNNc+vRLQqartpqnSTJ\nmJiYN2/eBI2f1TZmjIrj/Veg//U1H9KkQ6ch2w/unxQdHR2dlJTUoUMHVZVXGbMafLpstRZT\nqkouG9UNi8WqoZ88lpLfulgsPn36dGJi4tmzZyUSia+v76hRo4YNG2Zra6u6UlWJefLbHMcV\nL5IkS0pKDAwMsAylKJPJCIIwMsIwytbHjx8DAgJev3nzzd7NbsH+Gp67XC7ncrmq7RPyyIxF\nz05e2LZt25QpU2qYTCAQKBQKKysrLNv5x48f1dGESRlFRUV6enpYzjsJghCLxSpvtCkUCuPi\n4t6/f29ubi4QCL799ttPT8u0hEKhEAgEfD5fqxpDCgQCY2NjLBdQCILo2bPn5cuXAydM6LHw\nv91B0TQtkUi4XC6Pp0WRUB1NRitc+vmnG9u3jR079tdff1X+t4qKirhcLpYjZnWYfp61qkV0\nSUkJRVFVOnpRLZFINH78+KVLl3p5eVX5r7Kysu3bt7do0aJXr14sFmv//v1HjhyJj49v1KjR\nZz9KKBRqVZc8oLIVK1Zs3LjRNaTrwM376su4XLWVfvnMibnjrCwtzp8/37hxY9zlAMThcCws\nLKr7X2UDYYWysrJTp04dPHjw7NmzJEn26tVr7NixERER1XXEhwsEQs3Petq0aVu2bGk3fEDf\nuHman7s6AqEwr2Br1xgjfYOXL1/WcO0DAqHmZ62mQMjIzs6WyWTOzs5alSKqgEBYxezZszds\n2OAeFjZs127WvwXoYCAk5fJfIsKL0tPPnz/fvXt3JX8LAqEyNBAIDx06dPv27fXr139xSpqm\nR40aFR0dHRkZ+dkJJBKJ8ndO5HI5RVFa9cRmDZieq7TttLM6UqmUzWZX3gVdvHhx4MCBJvaO\nYw5dMzSr9gQdC4qiVNX4GSGU/NcvSavme3t7X758WeXnpRRFMfvS+tLLiUwmq1uPX6rCZtf0\nOH2tAyEjPz8/ISEhLi5OJpMhhJycnOLi4kaNGlX3MmtDLpdX7nm5OiRJ4mrpRxAEm83GMoIZ\nc69f87O+ceNGv379TB3txxzfzeNjOITTNK2OPHZv94Era7YPGTJk+/bt1U1DkiRN07gOTgRB\nYJw1i8XCspXRNE1RVOVZm5iY6FTLXgiElR08eHDw4MGWLk0mnDxlUOkygQ4GQoRQzuNHv0YP\nbNK48dOnT5U8CYNAqAwNBMKZM2eGhoYOGDBAmYmnTJkSFhY2ePDgr58vc1nT2rp+DBqshetG\nDapsXAUFBa1atSos/jh2f5Jjaz+8tX1K5dfWT/0Q+/DgbzExMYmJiar6TAZBEKWlpbjuvtRB\nSUmJubk5xiajNavdeaREIjl27NiePXsuXrxIUVT79u3Hjh3bqlWrDRs2jB49+tmzZ2vWrFFT\noZXxeLwvXg+gaVooFGK5d0GSZGlpKY/Hw3KbTiaTkSSp4XNEoVAYGxtLs1DvlfP5piZY8oma\nzrFCJo54fiopMTFx/PjxXbp0+ew0QqFQoVCYmppi2c5LSkpwPSzE9EyIpbNNgiAkEknlThe0\ndicL1C0tLW38+PF6hoYxO3YYYO36VUs4+rYJGD3mzq+7Fi9evG7dOtzlAGV9/Pjx7du3sbGx\nn/3fV69erV+/fvXq1Uyjr/z8/Ly8PDc3N83WCL7WxIkT8/Pzu875UQvToDr0Wbwu/9WzgwcP\nhoaGfvvtt7jLAdVS9sT95s2be/bsSUxMFAqF1tbW06dPHzdunI+PD/O/wcHBy5cvX7p06cyZ\nMys6bVcrJU/+sJwjVu6LDNfcNTzrGTNmZGRkdBg/zKltS7zPy6r8M9lcbt+VC3ZHT5g8eXJK\nSkpNd9tV18qitvBmId1Zz4EWkkgkgwYNEolE/deus2te9bErndVl9pyX589t2rRp2LBh7dq1\nw10OUEpWVhZCqMrgt7t37y4rK5s+fXrTpk0NDQ2XLFkSGRlJEMTx48dbtGjRvn17TMWCuvjj\njz9OnDjh3DYwePws3LVoCIenP2jjHzv6B86dOzc0NBT6pNRaSt0UHjhwYEhIyO7du4ODgw8d\nOpSTk7Nhw4aKNMiYNm0aRVHM7gzolMOHD+/Zs8fOyyNs1kTctaiFUxsf/5GDXr9+vXDhQty1\nAAD+n5kzZz558sR3UEzrgdG4a9Eienx++PIVJElOmjSpvnTBBxBCrVq1qvIgH5/PZ1rEcbnc\nuLi44ODgO3fupKSkhIeH//jjj3BRrB7Jzc2dOXOmniG//0+/sHTpAQczx8aRcVulUunw4cOZ\nB82AFuIsXbr0ixMxz7/u2bNn8uTJ3t7en22Vx2azHR0dO3XqpD2tumUyGZYnpGmalkqluJ5a\nIUmSoiiNzTo7O7tPnz4ETY3Ys6nHyQtTh04tdG9S1KypZuZeGUmSbDZbTUfHxv6+qaeT/rl4\nqWPHjq6urlX+VyaTURTF5/OxHJslEgmujU4sFnM4HCxbGUVRCoUC7/PZeFEUJZPJ9PT0tOp5\neplMxuPxNPYM88GDBxcsWGDj4TFkx07O55aDeU7O4vZtbd6+fRGuRYM+MzsrdS8lyyZNCtPT\nn/xzzcbGxt//C90+i8ViNputVX2KMB2HaNXqLZVKaZpW3xMZ9vb2nz6Y4OPj06ZNG+ZnHo/n\n4+MTGhoaEhLSrFkzFa5C3C5djKdPR//5D6oPQUUL140aVGxco0aNevToUffv4zzCeuEuqlpq\nOpWycW8u+JD15GqSXC5XvrOrmlEUhfFkuw6kUqmBgYHWXsRRam/CZrMbN278adfG+fn5xsbG\nBQUFCCEejzd58uQa+jMFDQ9Jkt98801JSUn3BdNsPTGEQI3h8Q2j/o+984xrYmn78KaH3jsi\niNiwoIBiQbGhiB1RioKA6LFhPyqWo2KvKGCvWPCoR+xdFMWDYkFQsIsIiCgtCSSk7O77YZ+H\nhxclBEgyGzLXB38mGXb+2ezOzj1zl21/4RQkKCiouLgYtBwIBILk5ORMnTqVzmaPi41jkCmz\nDnkYuvIvlrb28uXLv3//DloLBKLWnD9/PjEx0bprjx5BM0BrAcPQZVv0LG22bdv25MkT0Fog\nv0Emg/Ddu3e/Pk5wHM/Ozq6srMzNzVWAMIgKsG7duvv37zsM6O0a5Atai8Jp4dKl76zQgoKC\nSZMmEeVQIRAIKCQSSWBgIIfDGbpipWmbtqDlkBQdM7P+8xdwOJwFCxaA1gKBqC88Hi8iIoJK\nZ4yIim2uVQfrhaWtM3JdHIphU6ZMgRUySUg9SWU8PDySk5MRBElOTl60aNGvDTQ0NFq2bKkQ\naRByc//+/TVr1uiYGY/esoK0O+Dypd/ssK9PX964cWP58uXr168HLQcCGMJVGFTvRGAYkW0V\nlIZfIXx4lOAyumrVqtTU1HZDhnYZP4HwH/st//kIx6W0UT7EZdO4mk8NpWtAYPqZMwkJCcHB\nwe7u7tJVkepaEovFoCXUhvjJpJwlCoVCKrdbCElYt25dQUGB+x+LTNt0AK0FJK16D3AaO/Hl\nP8c3b968fPly0HIg/496DMLjx4/fu3dvxYoV5ubmv6YG0tfX9/X1lVKwG9JcKSoqCggIwBDc\nZ2eUpqG6+AlTaNRxMWsPjArZuHFj69atQ0NDQSuCgARgXlkEdDZjKSjhtCQnJ2/dulXX0tJ7\nXT3rMmQ7OQTKVEWl0YauWn3Mf8L8+fMfP34svSYQqU4XhUJRUHXZJiJFEgnVQoCTmZl58OBB\ngxZ2fWcsBq0FPJ5LNny4f2PdunUTJkxwcHAALQfyP+oxCFu0aBEUFPT69esePXr4+PgoRxOE\n5KAo6u/vX1hYOGDBHy17dAMtR6loGhoEHNp+2HfqtGnTdHV1x42DiQ3VF7CB7GKxmCi5Tqod\nCaFQyGKxFFqYvqSkJCwsDKdQfKJ3atdXIvw/SigUIMVR6wLDMIUWpq+FbY8eXcb6ZPxz7sCB\nA/Pm/T7ZfUVFBdmSyhDbcaSSJBAIcBwnlSQIycEwbNGiRSiKDvtrO51NlpyLANHQM/BcsjFx\nUdjMmTNv3boFWg7kf8jk2LN582ZoDUKqWbZs2b1791p79OozIxi0FgCYtGkVcGgblcnw9/eP\nj48HLQcCUS/CwsIKCgr6zppt41pP5kxINYOXLGXr6q5atQpml4FAlMnBgwdfvHjRZtDw1n09\nQWshC51H+dm69b19+/a5c+dAa4H8D4qUMIagoCAej5eYmBgUFJSWllZXs/j4+HpTWsuOWCye\nM2cOg8HYuXNnU46D4ziHw9HX15eXMNlBUbSsrIzNZhO1g5SMUCiUSCRaWloKOv4///zj6+ur\nZ2U+9XK8hr5uzY/oZeX0nyViSzMUxBcXiUR0Ol1p+e6/Pn15KnS+qJK/ZMmShQsXYhhmZGQE\nxF+otLTU0NBQ+f0iCFJcXMxgMPT09JTftUQi4fP5urq69TdtpojFYg6Ho6mpqbgk+I2Aw+Fo\na2srbu9r9+7dM2fOtHFxDU44TZVh048iEbM+f8Z0dEUWFgqS1AhEIpEydwgJnhw5fGPN6kmT\nJv12Gau4uJhOpwN5YtYFEapHnkJWCIKUlZURQz1oITLB5/NlL0GJ5uXhAgHdwQFRBa9X4nsp\n+Q5qBCUlJc7OzjxBVVjivwbWqpFuQ6EVvKop/vTukI+7pbnZs2fPGv0II6pP0Wg0UjmASEEk\nEoF1LKJQKFIME2knsfrPtLW1pTwn5PtLJCQklJaWmpmZyfGYEHnx+vXryZMn01jMCXs31bIG\nEQSp0tEWsZgsFovsg7Q8sHF1Cjm7P2HKgg0bNty7d2/Hjh2qMkuAQFSU169fL1y4kK2rO3bn\nLlmsQQRBMBq91LoFnU5XjTJVisR1UtCL06dPnDgxbdq03r17g5YDUThMJlP2xEWVlpaoRKJN\nJvNbCkRxc/LXoY2KiiotLe0bsVzPsoWqGC04jtNoNEWvrZu3dew+afrjI7t27ty5evXqxh0E\nRVHCIFQVL26ieBHMjcIAACAASURBVDJpI42l7RAqn8+fP69YscLb2/vJkydwh7BxKG6HsLS0\ntHv37p8+fRq7Y3Wn0b8pqyqRSEQikaIjiOpCyTuEBIIyzsXFa9/dfsBkMmfPnr148WITExNl\nCkDgDqG6om47hAKBwNXVNSsry3f3ng5ew2T8KxzHiUhLUlUuBrJDiCDIl8ePj/lP6Nq169On\nT2v1DncIZUG1dggbBIfDEYvFxsbGoIXIBAmvjV958uRJr169DG1bB/19j8Fiq4rRorSplLCC\nFzuki4THyc7OtrOza8QRJBJJeXk5qMl2IygrK9PX1yetQSjrT45hWE5OTvXLa9eubd68+f79\n+3KUgqLorl27goKCYHV7EiIWi319fT99+tQrPPC31qB6omGg57d/y8htK5kGutu2bbOzs5s1\na9bbt29B64JAmhsLFizIysrqNsFPdmsQUgtbNzfH4SPS09P3798PWgsE0pxBUXTmzJkYhnmt\n3E5jkGg1ijywtHUGLlhTVVX1559/gtYCQZB6s4wSVFZWDh48WFdX98aNGwiCLFq0aOvWrcRH\nsbGxM2fOlIuUxMRELS0tT09PohcpCIXCioqKeg+I43hJSYlctDUCoVBIeDUoHxzHq6qq5HvM\nRYsWJSUltXLv0TsirK4qTMRus1AoBLL+geO47PES8qXt0P6tPHplnL749NjZuLi43bt39+7d\nOyQkxNvbWwleImCvc4lEAqr3Wl9cT09PVXxyIA0lMTFxz549xq1bD135F2gtqo3nsmUfkpKW\nL1/u6+urKttBEIjKsW/fvufPnzsO82nVqz+fzwcth6R0GR3w9OT+c+fOJScn9+vXD7QcdUem\nHcLDhw+/fv16yZIlCILk5eVt3749LCzs69evM2fOjIqKkkv12IKCgsTExNmzZ8toS1BlgEKh\nyNJMESAIAqp3RfR78ODBI0eOGNvbjtz+F5VOo9QN8t8qZEAA1TuCIAw2q3uo3x93To/YssLS\nyTElJSUsLMzZ2Xnfvn1CoVDuv0hNKECvcxlvRrlDnPlfxcgLkUj06tUrLpcr38NCGkFeXt6U\nKVPoLNa4XbEMMvnHqiK65hZ9Z88uLS2NjIwErQUCaZ4UFRUtX76cqaU9ZOlG0FpIDYVKHbps\nE0KhzJ07F8Mw0HLUHZkW1FNSUvz8/Dw8PBAEuXz5MoVC2bBhg4mJSWRkZFxcXG5ubuvWrZsi\nAsfxmJgYPz8/c3NzWdqzWKx6g4mBxxAymczmEUN4/fr1ZcuWaRroBxzermcsLXaCiCFkMpnq\nE0NIIBQKURRls9kIm91t3PBu44YXvf34NP5sRuL1ZcuWxcbGrlmzJjQ0VEHaSktLQXlZE3FH\nzTKG8NSpU+fPn4+MjHRzc1NQFxBZQFF04sSJpaWlXqtWm7VvD1pOc8AtbEr62TOHDh2aOnWq\ni4sLaDkQSHNj4cKFZWVlnks36JhZgtZCdlp06+no5fPy2rmjR4+GhoaClqPWyDRDLS8vt/hv\nzu6bN286OzsTmTOIf4uLi5so4tu3b9nZ2adPnw4MDAwMDDxy5Ehubm5gYOD79++beGRIE3n1\n6pWfnx9Co03Yv9nAxkp6Y8eb98OnLLRNS1eONjJj1q718PVL56Zc7DV14s+y0vDw8H79+n38\n+BG0LohMfPz48d69e6RK1qK2rFu37sGDB20GDHQNakzVU+3i4vApof337Ja7MNWFxmB4/bUK\nwzAixgm0HAgpYEdG6o0bh0gkoIWoPHfv3j1x4oRZ2449gmaA1qIaDF4URWexly9fLkssGERx\nyGQQmpmZff78GUEQDodz9+5dLy8v4v28vDwEQZqeccvc3Pzw4cM7/8v48eOtrKx27tzZqlWr\nJh4Z0hQKCwuHDx/O5fFGboq0celSb3v9gu8Oqc+1i0uVoE0l0DIyGLx09qw7Z9oMdE9JSenW\nrdvZs2dBi4LUA4qiMTExkyZNIlVeSvUkJSUlKipKx8xs1NatlEZFJtOFQofUVDO4tvj/se/b\nr/3QoWlpaYcOHQKtBUIKaOnpjORkhExp51WRqqqq6dOnU6jU4Wt2UWkwpl0m9Kxs3CbPLCws\n3Lx5M2gtao1M16unp2doaKiRkdGzZ88EAkFAQACCIEKhcM2aNRYWFk0322g0Ws3odi0tLTqd\nDuPdwcLn80eOHPn169d+EWGdR3uBlqPC6FmZ+x/cmn7m8vVVWydMmJCdnf3XXzAxBnk5d+6c\nrq7uoEGDflu/uyZga/ZU906q0kEIguA4LhdJZWVlgYGBKIaN2R6taQCmsEozZsiKvz4+eLB0\n6dLRo0dXP23Jdi0hKiipcSsXCIJERER8+fKl+iWbzT5z5kytNhUVFfv373/69KlEIunYseP0\n6dNNTU0b1x1EEURFRX348MElINy6aw/QWlSJPtMWpf9zfNu2beHh4S1atAAtR02RySD08/O7\nc+dOXFwclUrdt29fmzZtEAQ5cuRIfHz8sWPH5B4w5uXlVb0JCQEChmETJ0589uxZp9FD+80N\nBy2nOdB1/AjLTu1OhS1YtWpVUVFRXFxco+cNEMWRl5d36dKl7du3y9KYy+XKJaVWU+Dz+WRL\nYVdeXi6X44SEhHz9+tUtfKqZk1OjvyOrqgpBEBzHyHaWJKB98xj6+m5Tpz2M3rFgwQLiggeY\nMVgKlZWVoCXURspZotFojY7orqiomDp1anXQ8m9jzqOjo4uLi6Oioths9tGjR9esWbNr1y4g\nkfOQX8nIyNiyZYuOmeWgBWtAa1ExWNo6/SOWX1kZERkZefz4cdBy1BSZDEI6nX706NG9e/di\nGFYdVzNs2LCXL1927txZkfIgYFi8eHFiYqKNS5dRm5ZBu0VemLV3mHL+0PGg2Xv27MEwbM+e\nPfDckorq7FZmZmaytAdb5QLHcYlEQqPRSDUdJCQ1/cI+evTolStXLLs49Z0zl9qENUcqjTg5\nFCCZruoCw7DqBMUAcQubkn3p4smTJ4OCgpycnCgUCqkKtxDxjWS7vHEcZzAYdTVoymXG4/HM\nzc2l+EYVFxenpaVFR0cTbllz586dNGlSRkZG165dG90pRF5IJJKwsDCxWOyzKpqlo6hsZ82Y\nbr6T007sPXnyZEREhKurK2g56kgDRn82m13zpY2NjY2Njbz1QMBz4MCBrVu3Gra0nrB/Mw2G\nUckVHXOTyX/vPeY/Y9++fZqamjLuREGUw7Vr13g8XqdOnXJzcxEEQVH058+fP378qMspS465\nfBuBWCzmcDgsFotUyW84HI62tnYTra/s7OzIyEiWjo5vTKxG004yk8FEEIRCodSbmFqZiEQi\nGo0G3kZlsbyj1sUH+i9cuPDGjRtsNhtIxuC6IAreamhogBbyP8rKyjAMU8RZEovFQqEwNTX1\n2LFjlZWV9vb2ISEhlpb/L0flhw8fmEymnZ0d8VJbW7tFixYfPnyABiEZ2Lhx4/PnzzsOH992\noDdoLSoJhUbzXLLxROjI+fPnP3z4ELQcdUQmgxDDsKVLlyYkJHz79u3X2t/37t0jKlJAmgFJ\nSUkzZ85k6+kEHN6uaQCgaEezR9NAP+hE7GHfqTt27LCyslqwYAFoRZD/QIxv69evJ15WVlae\nO3fu06dPc+fOBStMraiqqvL39+fz+T67YvRhMImCsevVq9Po0RkXLhw4cGD27Nmg5agvfD5f\nX1+fz+fPnDmTSqUmJCQsXbp09+7dNVeduFyujo5OzY1lPT09DodT1zG5XK5IJJJRgB6OI0TS\n+Lr3P8kGedyJMzMzo6KitIxNPRZG/dY7HcNI57UuBVAO7RbderbqMygl5c7hw4dHjhwp419V\nVVVVVVUpVJgcAeuWL92nXSaDcN++fZs3b3Z2du7fv/+vzhLVFSkgqs779+/HjRuH4rj/7o1G\nrVo29M/f9e9dYmL4vWtHRWhrTmgZG06K33XIZ8qff/5pZ2c3duxY0IogCIIg4eHh4eH/i5gN\nCgqaPn06rEOoZBYtWpSZmek0zrfjCFknBFKoNDQ8sX0Hz8q66YdqrgxZtuLDvXubNm0aO3Ys\nkMq9EARB9PT0amaxWrx4cXBwcEpKypAhQ2o2q+VmLD29DY1Gk90HWLBkSVVJCZ3FQsjko1sX\npHInrqqqmjFjhkgkGvlXtJbhbzx+SeIiLiM4jgOUOmBh1JfU+6tXrx42bFgtt8RfwXEcRVEK\nhVwRAVJAURSsVOm3jEyDRVJS0rRp0/bu3SsnSRAyUl5ePnLkyLKysuHrltj1aky14mK7Ft+s\nzFgslmrcmkDRb2Hpf3DrkQl/TJo0yc7ODvr8kJD27dsrrvA95LdcunQpLi7OyK6V1+rVcjmg\nWEMjc8hQOp0Ofd/rQsvYeNCfi68si1y0aNG1a9dAy4EgCIKw2WxjY+Namwn6+vpcLrfmfJ3D\n4UhZ72+QTzvHw0MkFhsbqkY6X1K5E8+cOfPdu3fOE0Idh4z6bQM+n0+hUOo1b0iCSCSi0+mg\njG2rDp1dAsLTju85fPhwZGSk9MYSiaS8vJzFYmlraytHXhMpKyvT09Mj7dKATD85n8/v2bOn\noqVAAIKiaEBAwLt373pMnuAcMAa0HLXAsnOH0VtX8gWCMWPG/Pz5E7QcSG2WLl3aoUMH0CrU\niIKCgrCwMCqDMS4mlqkJMj5T3ejm52/l1PX69evnzp0DrUVNyc3NjY2Nrc5aLBAIfvz4Ucv9\nqk2bNmKx+OPHj8RLDoeTl5fXrl07ZWuF1OD8+fO7d+82btVmSORG0FqaCR4RyzT0DTds2PDt\n2zfQWtQLmQxCFxeXV69eKVoKBCDLli27fv26XW9Xz2VzQGtRIxy9B/WdGZKbmzthwgTgaegh\nEICgKDpx4sTi4uJBfy42d3QELUe9oFCpnqvX0BiM2bNnl5WVgZajjhgaGqampsbFxX3//r2g\noCA6OlpXV5dYiL99+/bly5cRBDEwMOjdu3dMTMzHjx/z8vK2b9/eunVrR3izgOPjx4+hoaF0\nFntcdDxDA65hyQcNPYP+c5ZXVFQsXrwYtBb1QiaDcMGCBY8fPz5y5AicszZLzp49u3nzZv0W\nlr4x66h06O+pVDzmhbf26HXv3r16vSMgkGbMhg0b7t+/39rDo0doGGgt6ohx69ZuU6d9//59\n4cKFoLWoIzo6OqtXry4pKZk7d+6SJUsQBFm/fj3hZPjy5cu0tDSi2axZs+zt7ZcvX75gwQI2\nm71sGawLBQw+nz9u3DgOh+O1cptZu06g5TQrnP3CTNs4njx5MiUlBbQWNYIiPSiZYM6cOUlJ\nSa9fv6bT6ebm5rViIk+fPq3kvAtEIGm9bSoqKnR0dJQjqSYYhnG5XCaTCSQdvEgkQlFUdt/6\nN2/e9OrVS4hKQs7uN2vv0JSuURQVi8UMBgNI1KxYLAZVk00kEmEY1ugIgSoO7+CokPL8b6dP\nn25Eghkulwsq1K28vJxOpwNx30dRtKqqqmaQjFzK36kQRNkJTU3NZlB2IiUlpX///mxDw+nX\nb2gaGslRD47jAoGATqczyVRBhyxlJ2rA5/NxieSYr0/xhw83b94cPHgwaEXkihMjIMpOGBnJ\n8xIlCRwORywWS6mCSCqAXxs4jvv7+//9999OPpNGbagnvwafz6dSqTCGsEHkpj08Osmrc6dO\nz58/rys3EhFDyGazVSiGUF9fn7QTFVkzUJmamg4cOPC3Hyn/npRIJLIkmSWmAkrQ82u/CIKg\nKAqkdwzDMAyTseuKigpfX9+KioqRW1cYOdg1cQeYSPyFoqgsqwxyh+id+FfJEN+30WePrqUx\nJiYq3m/6lClTWrdu7eDQMLMc1HVOIPvFJl+IVaGaXWtqapJqhg2RkZKSkoCAABTDxu7YKV9r\nENIgaEzmqE2bD43zCQ8Pz8zMhBmVIJC6WLdu3d9//23V2cV7VTRoLc2Tlt3dO40Yn3np79jY\nWFj5STnItEOoiuA4zuFwgCTRRlG0rKwM1KKFUCiUSCQyphcLCAhISEhwmejjHfVn07u2fvy8\n9Z2HWWO8fjq2bfrRGgrAZS2hUIiiaBM3al6eu3JxUZSjo+OTJ08alB2utLTUEFBquOLiYgaD\nAaSYtUQi4fP56jxnbR47hDiOjxo16vLly+6zZg9YIH9nRRaH0zNmV1H7Dm98fOR+8EZDzh1C\nYhPj9ob1/+7fFx4evn//frCSgO8C/Uoz3iEU7NiBfPmisX07QqbLsi7AXht///23v7+/tqlF\n+D8PdEzrr7sGdwgbR8XPotihTiwKkp2dbWVl9WsDuEMoXxrwk2MY9vLly3/++ef169fEy+Zq\nTKoJ+/btS0hIsOzcYegK+ay+WGW+8TiUYPw5Vy5HUzecxg3v5jc6KyurZik8CKR5s23btsuX\nL7fs3sNjjkLWgNk8nsehg+2T7iri4M2S/vMXmDg4HDx4EJagUCsYZ89q7NqFgHCxUS0ePnw4\nefJkhoaW/96zsliDkEajbWI2cP5qLpc7a9Ys0FrUAlkNwps3b9rb23ft2nXcuHFXrlxBEOTO\nnTs9e/YsKipSpDyIonj16tW8efPYujrjYtbSyBRdo854rVpg2blDQkLCzp07QWuBQBTOo0eP\nIiMjNQ2NfHbuospcQRuiUOgs1phtOyg0WlhYGCyHA4HUJDs7e/To0SKxZFz0MQtHJ9Bymj8u\n/lOsu/a4cOECrIijBGR6BmdlZY0cObJ9+/bR0dG7d+8m3jQ0NMzLywsODr5x44YiFULkD5/P\nnzBhgkAg8N22wcDmNxvxECDQWczxezbsHx68aNEiJyenfv36gVYEkUZFRQXAxMuEg0ZVVZVI\nJAKl4VdQFOVyubK4xPz48cPX11eComO3bGHo68sSFt4INEQiBEFwHFfQ8RsHjuMYhlUXnSMJ\n1WfJwMGh94yZD3ftDA4OPnnyJCgHJyIgXCgUAun9txDZ7MrLy+tqQKPRgKSygyiBvLy8oUOH\nlpaWjlgb6+AxFLQctYBCpY5cG7dvTO+ZM2d6eHioStIjFUUmg/DAgQNt27Z9+vQpg8FISEgg\n3nRxcTly5MjQoUN//PhhamqqSJEQOTNv3rw3b944B4zp4DUAtBbI/0PP0nxczNoTwXPGjx//\n9OlTGxsb0IogddKgUE+5IxaLuVwum80mVZAVl8vV0tKqNzpOIpFMnTq1sLCw//wF7Qb8Pl2Z\nXCCSi1IoFFIF8JAzhrDmWeofMSc3NfX69evHjx+fPXs2EEkkjCEsLy/HMAxI1DQELMXFxUOG\nDMnLy/OIWN5tfAhoOWqEiUP7fjOXJO1YPWPGjDNnzoCW05yRySB8+/btyJEjGQxGrff79euH\n4/iXL1+gQahCJCYm7t+/36RNq6Er5oHWAvkNdr1dBy+dfXNt9KhRo1JSUsBaHRApgA0Nr+6d\nbBHqFAqlXkkLFy5MTk5uM2Cg+0wYHEJGKDTa2Oid+7y9/vzzz169erm4uADQQKEg5Lu8EVJK\n+i1CoVD2tNvE9E4gECCqUG5aybvrFRUVXl5eb968cQkI7/3HokY4huA4rip1vDEMk0gkZEgq\nU02P0Ii3d66cPXv20KFDAQEB1e8Tl7dEIgGYaL1BEDnhAQ4g0tdGZfrJGQzGb083EUD4q6EI\nIS0FBQXh4eE0JtMneg2dzQItB/J73ML8u/h4v3z5ctKkSUAKaUAgiuPw4cO7du0ysms1Zkc0\nhUzTDkhN9CwtR23ZKhSJfH19S0pKQMuBNBhKQ2jEnwBEmVJFIpGfn9/z588dvcd5Rm4G+pMq\nCQrJljyoNPqoTfsZGprz58/PyckBdSU0HTKolXKeZdoh7N69e2xs7Pz582smfkVRdOXKlTo6\nOh06dGjabw1REjiOT548uaSkZMjyuU2sQf9bvrezf+I7vBQGJcqDEeuXlObmJyYmLl68eMuW\nLaDlQCDyITk5efr06SwdHb8DB9mKrxoi0tR84jv+W+fOiu6oWdJ20OA+f0xP2bPb39//2rVr\ndZWHhpATZkPSxVV5eUkcHNiamipRdoKIoFaCHziGYUFBQUlJSa37Dh675SCV3pj9D5FIRKFQ\nVOX2wTCMRqORaocQQRDT1u28lm+9tGxGcHBwSkoKi8VC/lt9ikajkSoiQAoCgYDNZku3ygBC\nW7VqVb2NHB0d9+/fv2PHjg8fPqSnp/N4vCdPnkRERNy/f3/t2rV9+/ZVvM7GIBQKgVwlRGg+\nnU5v0HAsL1AUxTDst13v3Llzz549rfp0H7ZmkSKuyBIri9fuPQSW5kCGEhRFqVQqkDsNRVEc\nx+W7VU6l0doO6vPm5v2k6zdMTEy6d+9eV0uBQAAqzAbgWEzk5CCeCuoJhmFCoZDBYJDKR0Mo\nFDKZzLpGgDdv3nh6egqqhH77D1g7dVWCHjGbndmnz/dOnUkVsEcMVqSaconF4t/OWe3ceua/\nfPnsXlJ5ebmXl5cyJREudqS6vKuqqnAcJ1XlT3lR6eRUNWiQporUc1PatTF79uyjR49aO3UP\n2P8PndXIJ11dNxc5ATiVko6Fo1Ppl48v79/++fPn8OHDEQTBMAzgZLsRVFVVkdkglOmBZGxs\nnJKS4uHhER8fX1BQcOfOnd27d+M4vnfv3kWLFslLCoqi//777/nz5+/cucPlcuV1WAjB69ev\nly5dqmGgN3rLStJejpCaaBoaBB7ZoWmgHxERcfbsWdByIJAmUVBQ4OXlVVZW5r12rb07SZcR\nIbWg0GjjdsUY29vHxMRs374dtBwIRHmsW7cuLi7OpHW7gAPnGRowmB88w6NiTBza79u3b9++\nfaC1NENkXaG0s7O7ePEih8PJzMx8/vx5Xl5eTk7OtGnT5KWDx+NFREQcO3YsPz//2rVr4eHh\nOTk58jo4RCgUBgYGVlVVDV+3RMfcBLQciKwY2dn4H9pGY7MmTZp069Yt0HIgkEZC5OjLzc3t\nO2t2Nz9/0HIgDYCtpxdw+KiWkdGiRYtOnDgBWg4Eogzi4+NXrFihY2YZePCChp4BaDkQBEEQ\npqa2X9xptp7+7Nmz7969C1pOc0NWg7C4uPj27dtXr14tKirS19e3traWr44LFy5IJJKYmJiI\niIht27a1bNkyMTFRvl2oM5GRkZmZmU7jhsM6EyqHddeOE/ZukmDYmDFjHjx4AFoOBNJgysrK\nPD09s7KyXCcF9V+wELQcSIMxsLEJPHKMoakZEhICK0RDmj1JSUnh4eFMbZ3Ag4l6li1Ay4H8\nD0Pb1uN3nURxxMfHJzMzE7ScZkX9BuHbt2+HDRtmamrq6ek5YcKEwYMH29vbOzk5ydeHbfjw\n4VFRUdU1o2xsbKDXqLy4ffv2jh07DGysvFYtAK0F0hjs3XuMi1lbJRQOHz48JSUFtJxmDuGk\ncPHixVevXoHW0hwoLi4eOHBgenq60zhfr1WrQcuBNBKLTp0CDh+hMln+/v7Hjx8HLQcCURRv\n3rzx8fGRYPj4mFNmbTuClgOpjV1Pj5Hr4jhcrre39+fPn0HLaT7UE+T6/PnzAQMGVFRUeHt7\n9+3b19jYuKysLCMj4+LFi+PHj58zZ050dLRcdBgY/G9Hvry8PC0tLSgoqK7GKIrWW9EFx3Ei\n6YJc5DUIok4AiqJAeheLxTW/eElJSXBwMIVKHbV1JY3NQlFUcV1Xf3HFdSG9dyK5i/K7JjqV\nSCSKC850GOQ+evtfF+av9vLySkxMdHd3r9k7kCuNANRdRiRPqtk1k8ls+vm/evXqoUOHnJ2d\nNTQ0Tp482atXr7lz5zbxmOpMQUGBp6dndnZ2F59xIzdugkUmVBob1+6BR4+eCg0JDg7+8ePH\nggVwhRHS3Pj586e3t3d5efnIdbtb9eoPWg7k93QZE8gvK7m1cenYsWNv3LjRsSO02+UARcoE\nGkXR9u3bczicK1euuLq61vyopKQkNDT00qVLp06d8veXZ0BIcXFxVFRUq1at5syZU1ebqqqq\niooKOXbaXMFxfNKkSTdv3uw9K6TX9DoNbHlh8iXf4t3Hr106lJubKrov9eTdjftXFq9jMRjH\njh3r3x8+q/4f+vr6TUzjVlVVFRgYOG3aNE9PTwRB0tPT//rrr/3795ubm8tJozwRi8UcDkdT\nU5NUOQ85HI62tjaR0jM7O3vYsGG5ubkuEycNW70GiDVI5/Ntb97gWVkX1Z2nV/mIRCIajUaq\nxKd8Pp9KpcqSMbjw9etToZMrfv6cMmVKbGys4jL9EtWPQaVQ/i1lZWUYhhkZGSni4KWlpUeO\nHHn58qVYLLazswsJCWnTpk2tNhEREV++fKl+yWazz5w5I5feK69cwX780Jk8GVGFVRsFXRtC\noXDgwIGPHj3qPXX+oIVR8jqs7DcXGRCJRHQ6nVQ5kOsiOW7j/Z1RlpaWd+7cad++PWg59VNW\nVqavr0/atI7SDMILFy6MGTPm0qVLI0aM+PXTqqoqFxcXGo2WkZEhLzXv379fv379oEGDAgMD\npZwyiUQiFoulH4qo/QDkWYJhmEAgYDAYQDLhSiSS6rITcXFxixYtsnF1mng8hkJT+O3d8+Ap\n701xCdGrs7wHKbqvX5FIJKAyuRPnnMFgKOE+f3crOXHuXzQK5ciRI2PGjEEQhM/ngzIJKisr\nyVN2gsViNfHXR1G0oKDAwsKCyGZeUlISEhKydevWX6dlZIDkBuHt27fHjx9fXl7eb85cj7nz\nQOnRy8+f5947c5j3+bjdoDT8ikobhAiClOfnJ4SF/nj/rnv37n///betra0iJKmbQTh//nwW\nixUeHq6hoXHixInMzMwDBw7U+kVCQ0PHjh3r5uZGvKRSqYaGhnLpXdKrFz01FRGJEDLV+agL\nBV0bwcHB8fHx7QaPHB9zUo4LWNAgVBAYht2P2/gwZp2xsfGVK1d69OgBWlE9kNwglLagnpSU\nZG1t/VtrEEEQNps9d+7c8PDw79+/y2UFPSMjY9OmTTNmzOjTp4/0lnQ6vd6tABzHRSIRkGcJ\niqICgYBGowHpXSgUSiQSDQ2Np0+fLlu2TMNAz2dXFIOlDNOUGEGoVCqQejsYhoEaxQgvWeXU\ny3IcNpCtLJmtmwAAIABJREFUo/33H4uDgoJ27949depUgHUIKysrqVQqkN4lEgmKovLtmkaj\n2djYVL+8deuWubm5vb19Xe1FIhHhJg0E4qqTSCRVVVWgNPwK4ce7Z8+eJUuWYAgyfMPGLuN8\n6/XwVxz/8WDHcYAafgXHcVD+7VLAZT5L2ubmwWfPXY1cknb1qpOT07Zt2wIDA+WuhxBDqsub\n+MmkSKJQKI3bMuXxeGZmZhMnTrSyskIQZPLkyWFhYV+/fq21GsXj8czNzY2NjRvRBUQ6mzZt\nio+PN+/QZezWg9C5XVVwC5uroat/e8PigQMHnjhxYvTo0aAVqTDSJu5fvnxxcnKS0qBr164I\ngnz9+rXpBmFRUdGGDRv+/PPPbt26NfFQEARBSkpKxo8fLxKL/bdu0IUOnM0Le/ceQcdjToXN\nnzZtWmFh4ezZs0EraoZcuXLlypUra9askbKNIxAI6nVVUDQikUgkEoHVUBOBQPDHH3+cOXNG\nw8Bg1I7oFq7dwcojfiAcwUl1lsgJsYoqa2s63XvzVmsX13ubNk6ZMuXs2bNbtmyxsLCQuyqA\nAdJ1ISVihUajNc4g1NHRWbx4cfXLkpISCoVSa/dPLBYLhcLU1NRjx45VVlba29uHhIRYWlo2\nojtILS5evBgZGaltYua/5wwsOahaOPtP0Te3PL9oio+Pz/r16//880/SbsGRHGkGIY/Hkz7W\n6OnpIQjC5/ObruPYsWNMJvPRo0ePHj0i3tHQ0JgyZUrTj6yGoCgaGBj45cuXPtOD2wyoZ7sV\noopYd+sUcmb/ieA5q1at+vTp0+HDh4HsyjZLUBTdt29fRkbGxo0bW7SQlnBcQ0NDcQFU9UJ4\nIjCZTCCu6b/l/fv3/v7+2dnZ5o6O4+L26FlZgVb0n317CkIhz1lCEARFUQqFQiqnLJFIRKFQ\nGurm4Dpxkn0f9ytLF9+8eTM1NXXVqlXTpk2T1/cijHnleF7ICJ/Px3FcS6tOg0EuM1EejxcT\nEzNixIhaO4F8Pl9fX5/P58+cOZNKpSYkJCxdunT37t116amoqJB9xUobxxEEKSsrUwmX0Xq3\nahvE69evAwMDqQzGmOjjTANjRWxKE0FMcj+sIiD8F1TCpiIuAxRF7foN9T90MXHupCVLlqSl\npe3atYtUkRTVYBhWXl4OUACVSiUMt98ibRKJ47jSrolu3brVmnsBnGmpOqtXr75582ar3t0H\nLPgDtBaIojBxsJty/tCJyXOOHz9eVlZ2+vRpKdMUiIxIJJINGzYIhcJt27Zpa2tLbwzWxhCL\nxQKBgE6nkyQ05dSpU3/88QePx+s6fsKwNVF0cgzg/9ngpVBItWKCYRjZYggJg7ARZ8m0deuQ\nv88+PXE8acvm+fPnJyQk7Nmzx9nZuemSiNkeSS5vAoFAgOO4QiXl5+dHRUU5OTmFhYXV+khP\nTy8+Pr765eLFi4ODg1NSUoYMGfLbQxG51hvUO4ZhCDg3eNkhrg25TFCLior8/f0r+fwRG/ZZ\ndOymIEdusvmHS4GY+auWYARBLDp2Czp1J3Fe8Pnz59+8eXP06FEp4R6gaMQtKV+k3zL1jP7F\nxcWPHz+u69P8/PxGivqFQYMApCFplpw8eXL79u0GNlbjYtYqIZEMBCA65iYhZ/YlTF105cqV\n/v37X7lyxdQUugc3iQMHDlRVVa1atYpU+xIkh8/nR0REHDp0iKGpOXzj5m7jx6vE6jJEXlCo\n1O5Bwe09h9xYs/rp9Ws9evSYMWPG2rVrdXV1QUtTMTIyMjZv3hwQEODt7V1vYzabbWxsXFJS\nUlcDHR0d2buWUCgIghgZGanEDqG8kspUVlYGBwcXFBT0mx3Zbaz842AJYFIZBYFhWFVVFZ1O\nJxZnNVq2Ck24fT1q4YszRwYNGnTgwIEJEyaA1vj/UOGkMgiC3Lx58+bNm8qRAmk6SUlJ06dP\nZ2lr+R3YomFQ576wgqgwNsx3bCPQbcBDCNJE2Lo6vvs23/pr69OLN3v16nXt2jVypsRUCb5+\n/Xrjxo1u3brt3bu3+k0PD49OnToBVEVysrOzx48fn5WVZdqmrW/cbh2pTrbKB2Uw8h0dy6yt\nQQtp/uiYm/vu3vMx+f61v1bGxMT8888/MTExY8eOBa1LZcjOzt68efOCBQvqyqSQm5t7+fLl\nadOmEctVAoHgx48f8orbxBwcJHw+naxTVUWAoqi/v/+zZ886jZzQb1YkaDkQOUBnsUesjW3R\nze3a6nl+fn7Jycnbt29XFVMcONIMwsmTJ3t4eNR7CAWlnIY0lPT09DFjxkgwbMKuKNM2APbK\nM0Z6Ph3qwWKxSOQIpQbQmIwxO1brmps+2ne8d+/ely5d6tmzJ2hRKgmTyfTz86v1JvRdl8Kh\nQ4ciIiL4fH63CX5eq1bT2WyypQDhmZruOnOOTqeTKIKwWdO6n8eMG7cexMU+2rvHx8dnzJgx\ncXFxikg208wQiUTR0dEjR460sbEpLi4m3tTW1maz2bdv366qqhoxYoShoWFqaqpEIvHz80NR\nND4+XldXV16jvWDXLrFYbEwmz2qFguP49OnTL1++3LK7+6j1e0i7aQNpBE5jJ1p27HZ2zsQ9\ne/akpqaePn26bdu2oEWpAKrkKNwgcBzncDj6+vrK7xpF0bKyMjabXW8MkhzJzs728PD4WVw8\nautKxxGDgTi8SSQSkUjEYrGAxMYA9HMQCoUoioIKYq4uO/H0+Lnrq7exmayTJ08SJQoVTXFx\nMYPBkBKjrDgkEgmfz1dnnzSwdQh5PN6MGTNOnDjB1NIasX5Dx5GjiPeFQiGTySTP7ArHcSLS\nklRJZVS9DqEs/Hz//tKSP/PT0w0MDKKjo4OCghp6BLWqQ5iRkbFixYpab06bNs3b23vLli1c\nLjcqKgpBkI8fPx47duzDhw8MBqNDhw6hoaFmZmZyEcDhcMRisaoUtGj6tREZGblhwwbTNo4h\np26zdRX7CIMuowqilstoLUT8imur5mVcOKWtrR0TEzN58mSlC6wNyV1GoUEof5RvEL5+/XrQ\noEFFRUVeqxc6B44liqQrp+uaQINQ+V0jNQxCBEHe3X7wz5wVqFC0ffv2OXPmKLpraBACBKBB\n+PLlywkTJrx//96iY8dxMbGGtnbVH0GDUBbUwSBEEATHsCdHjyRt3SIWCEaMGLF///4GVahS\nK4MQOGplEK5evXrVqlUGLexCEm7rmCp8+xoahApCukFIkHkh4erquaLKCn9//z179gCZrlRD\ncoNQBX5yiHSePHni4eFR9OPHkBXzugf5gpYDAUnbwX2DT+3WMNCbO3fuvHnzwOazgjRL4uLi\nevbs+f79++5BwWH/JNa0BiGQmlCoVLfQsD+u37Bxcb18+XLHjh3PnDkDWhRE3Vm5cuWqVat0\nLayDjl1RgjUIAUvn0f7TEh9ZduyWkJDg5OSUkpICWhF5gQahanPp0qWBAweWlpV5r1nkFlo7\n/Amihlg5OYb9c9DIziY6OtrHx6eyshK0Ikgzobi4ePTo0bNmzUJYrPF79nmtXkMj07YbhJwY\ntrSd/PcZz8jlnIqKCRMm+Pv7S0mMCYEoDhRFZ82aFRUVpWdpM/nEDX1rW9CKIMrA0LZ16N93\ne4fPy/361cPDIzIyUiQSgRZFRqBBqKrgOL5p06axY8cKJeJxsetcJvqAVgQhCwYtrcP+OWjj\n6nThwoW+ffsWFBSAVgRRea5fv965c+eLFy/auLj+ce1G+6FDQSuCqAwUKrVnePjUy1ctO3U+\nffp0x44dL126BFoURL3gcDijRo2Ki4sztm8bcuqWQQvo2qBG0BjMQYvWBh29qmVqsWHDhu7d\nu2dkZIAWRTqgQaiSlJaWjh49esmSJRpGBsGndnfwGgBaEYIgCF0o0uDyaGIxaCEQRMNAL+hE\nTOcxXi9evHB1dZVSTRQCkU55eXl4eLi3t3fRj58e8+ZPPv23nqUlaFGyQsEwDS6XKRCAFgJB\nTBwcws4n9p+/4Edx8ahRoyZOnFidThMCHEpFBaW8HLQKRfH8+XNXV9erV6/auvUNPX1Xz5Jc\n1XEgysHWre/0y2ldRgdkZGS4urquXLmSbGmxwaKSSWXEYrEsv6JIJAKSSADHcaFQSKPRFJTZ\n5e7duzNnziwoKLB27jwmerW26f+LAsdxHMdxIAHBvQ+f9t4Ul7Bj9athAAxUDMMoFAqQaF0U\nRXEcpwNK2I2iqJTsFKn7TyTvOMBkMDZt2hQWFibfrquqqqhUKqi7TCwW1+xaU1NTJeLg5YUS\nksrgOH769OkFCxYUFhaatGkzestWy85dpP8J2ZLK6OXnz3PvnTnM+3zcbtBa/oeaJJWpi6I3\nby4sWvA9K8vExGTbtm0TJ0787QUDk8ooE0mvXvTUVEQkamaF6QUCwbp16zZv3iyWSNyCZw5e\nvI5KU/aTGiaVURCyJJX5Le+Trl1ZGcH7UdiuXbs9e/bIUmBPLpA8qYxK1pyh0Wj1Fgcj5otA\naohhGEYYhHLvvbi4eOnSpfHx8RQq1X1WaN+IUOovUwqAxglxlVMoFCC9i8ViGo0GZBTDMAzH\ncRqNBsoclXLC3WdMNm/vcGH+qrlz5z558iQ2NlZHR0deXRMGIZC7DEVRFEVrdq38k0/87kru\ntGbvCILgOI6iqCKOn5qaunjx4n///ZfGYPSdHeE+cxaNyaz3++I4jmEYeSYT/xWMk23pk1i5\nA62iNsqRZNqu3ZTEi/8e2P8gZldQUNDBgwd37drVsWPHWs2IK1xBl3dTkCKJQqGQ5+JXc8Ri\n8YkTJ1atWvX161cdUwvfdXEO/YaAFgUhBW0GDJvh0vv25mUvzh4dMGBAQEDApk2brKysQOsC\njEoahFQqtd4xF8dxCoUCpPoC8bSgUqly7F0kEu3du3f16tWlpaWmbexHbl5m1cXxty0BTsj+\nYxBSwTwRiScxwC8OcL1f+rduO9B96qVjZ2dFJiQkPHny5NixY3369JFX16DuMuLnBtJ1NQKB\nQCKRgOqdmLuLRCL5asBxPDk5eefOnffu3UMQxN7DY+CSSENbWxRBUBkC8YmVOPKsgIolYgRB\ncBwhVRYBDMMwDCObqYPjuDLPUvewKQ6DB99as+bBgwfOzs7BwcFLliwxNTWtbkAsuAC8xX6F\nkFRRUVFXAxqNpsz6w9IRi8WyJ5omnl5CoRBRhdzUxFVRl6fYp0+fEhISDh06VFhYSGMwewTP\n7DtzKUtHF+wdR7b7vS6IoYmEy1W/0pRVUYaW9rDVOzuN8r8RteDkyZMXLlyYN2/evHnztLS0\nFKD0PxD+gwCfjxQKRcpuqkoahGoFscoVFRWVk5PD1NQYtHhmzykBVEDeiRAVhUgzc3fLnseH\nT/fr12/GjBlr164FW5CnGaDQJ0e9EC6jLBZLXi6jubm5p0+fPnr06Nu3bxEEsXHt3n/+Als3\ntwYdhGwuo0wGE0EQCoUCZB+7LsjpMqr8s2Tu0Cbo+Im3t27eXr/+8OHDZ86ciYiIWLBggaGh\nIUJil1FVGTkJNwoZGxPXokQiQUhz80qBsARqLhaUl5c/fvz44cOHd+7cycrKQhCEoaHp7D/F\nLSRCz8qm+k9AQazUAxTQIFRFLWG1NkWtlVP3kDP30s8cfRi3Ye3atQcOHFi8ePHkyZMVFAhD\nrHABfD5K3zyAdgV5+fHjx6FDh3bv3p2fn0+l010Cx/aLCKsVMQiByAiNyfRcNqftIPdLi9fF\nxsaePXt2zZo1oaGhoEIfIWRAIBCkpqYmJSVdv379xYsXCIJQ6fQOw7zdQkJbuLiAVgdRC9p5\nDnHoP+DZieMPYmPXr18fExMzffr0uXPn6uvrg5am2jQoaI0wrbS0tFQlhlAoFGZkZKSlpT19\n+vTp06cfP34kzAMag9mqV3/HYT6OXj4sHV3QShEEQQinCbD+LLJDxByphOczhmESiaSpvkIM\nhlvQdKfRASn7tz6J3zN//vzo6OilS5eGhITIfYFMJBJpaWmRZ8G0FnAuSDoqKiquXr16+vTp\na9euiUQihgbbZaJP72mT9K1hBVVIU2nZo9v0G6dS9hx7tP/EtGnTtmzZsnTp0sDAQFLtn0AU\nSn5+/r///puamvr48eMXL14QXoJUGt22Z88OQ4c5DvfWNGyGOTMgZIbGYPQICe06YULasWOp\nBw9u3rw5Ojp63Lhxf/zxh7u7O2h1ELKA4/iLFy+uXbt269atZ8+eVVVVEe8zNbVtXPtYO7m2\ndOndsnsfpiZZvHYhqgJbV2/QwqgeQTMe7tny4uzR6dOnR0VFzZ8/Pzw8XFeXFMsKSgAahKQA\nx/GsrKw7d+7cvHnz/v37xDBn3Nq22/iRTr4jNPTV5XKEKAE6m+Uxb2o3v1H3dx7M+OdaWFhY\nZGRkWFhYaGiovb09aHUQhVBQUHD79u2kpKTk5OSvX78Sb1JoNLO2bW1cu9u69bTr1YutNo89\nCDlhamr1mT6jx+SQ9LNnHh8+dOrUqVOnTnXt2jUsLMzf35/wI4WoJ+np6cePHz937lxeXh6C\nIAiFYmLfztG5p7VTd6vOLsat2lDI5IANUVF0TC2G/bXd/Y+Fjw5GvzhzZOHChVFRUVOmTJk1\na5atrS1odQpHJctOyAKO4xwOB4jPCYqiZWVlbDZbenA5hmGZmZnJyckPHjx4+PDhz58/ifeN\n7W3bDu7rOGygRad2jesdwzAwzgmVfJzHo+jrISDSKwPMlSwUClEUVVz2f+kIBIJGh9mU5xc+\nPpTw8twVYUUlhUJxc3Pz9fUdPXq0nZ1MRXuLi4sZDAaQiBqJRMLn89Vn6e5XZCk7kZmZee7c\nucuXL798+ZJ4h6WjY+PiYt3NuUU3Z6suXZjyDoMkWwwhgqL4z58UDQ2ETHFf5IwhJFVmfBzD\n3t698+LUyU8PH+IoymQyvby8/Pz8hg8fDjBrSzMuO8EtKBALBEatW4MW8v/g8XgnTpzYv38/\nMYIxtbQd+g1tO2BYS7d+mobGqhLvQLabSzrqUHZCFvilxWkn9z09uZ9fWkyj0UaMGDF9+vTB\ngwc35elG8rIT0CCUP9INwnfv3t26devu3bvJycnl/60Dq21i1LJ7V7teLvbuPfRbNKnoM0CD\nUCKRiEQiFosFZKIDDcJGI+ILsi7ffvnP1bznmTiGIQji6Og4bNgwT0/PPn36SHmMQYMQIFIM\nwq9fv8bHxyckJGRnZyMIQqXTbVxdW/fzaNWrt3mHDgpdSiebQYjjuEAgUNCModFAg1AWiJQh\n/J8/Ms6fz0w8X/zpE4IgbDZ78ODBI0aMGDZsmPLTxDdjg5DD4YjFYmNjsiQpeP/+fWxs7LFj\nx7hcLoVGa91nUJcxgW0HDKOzNZD/XhvQIFQE0CCsiaRKkHnpdNrxvUXvXiMIYm9vHxoaGhwc\n3LjBBxqEYCCVQSgUCu/fv3/58uWrV69++fKFeFPX3NTWzbmlW9eWrk5GrVrKsXdoECoZVTcI\nq+F9//nm1v13tx7kpqWjYjGCIBoaGn369BkwYMCAAQOcnZ1r/bLQIATIrwYhn8//559/jh49\nev/+fQzDaAyGfd9+jsOHt+k/gK2s3wgahLIADUJZqDXp/56VlXX1Svb166Vfcoh3OnXqNHjw\n4P79+7u7uytnFIIGoaLBMOz69euxsbG3bt3CMEzL2NR5fIjzhFBdC+uazaBBqDigQfhbvj57\n9CzhYPbNi6hISKPRBg4cGBAQMHr06AaNPNAgBAMZDEKhUHjt2rXLly9fv36dqFzE1NSw6+1q\n797D3r2HoW0LBfUODUIl02wMwmpEfMGX1GefHjz5/Ohp8acvxJt6enp9+/bt37+/h4dHly5d\nqFQqNAgBUm0QstnslJSU+Pj4s2fPcrlcBEGsu3btMtbHcfgIDaUPgNAglAVoEMpCXZP+4o8f\n39298zH5ft6zZ8S6FY1G69y5c+/evd3c3Hr06NFaYX6P0CBUHJ8/fz5x4sSRI0eIRXPLTs49\nJk13HDaWxvxNwjNoECoOaBBKQcApe3Xp9MvEk4Wv0xEEYbFYAwcOHD16tLe3t6Vl/c590CCU\nlcLCwosXLxYWFhoYGAwbNqxNmzZNORoogxDDsGfPnl26dCkpKSktLY2oAqRnZd52oHubQe62\nPbrSFHzhQoNQ+V03P4OwJrzvP3NSn+X8+yzn32ecb9+JNw0MDNzd3V1cXHr27NmvXz/lX28K\nMghxHL9z587Tp08lEknHjh1HjBhB2lzhQqEwKSnpxo0biYmJRKIFHTOzzqPHOI3zNQYXCAQN\nQlmABqEs1DvpF/P5uU/TvqSm5j5N+5aZif23Kp2hoaGLi0u3bt2cnJycnJwcHBzk9VxQqEEo\ny+CjuAEKiEGI4/irV6+uX7+emJiYlpaG4zidreHoNdY1INyqi6uUP4QGoeKABqEsFH969/rq\n2azr54s/vUMQhEKhdOrUaeDAgR4eHr17965riIAGoUyUlJREREQ4Ojq6ubm9e/fuzp07Gzdu\ndHBwaPQBlWkQlpSUpKenP3369N9//01JSflPZCCFYtmxXZtB7m0H9jF3bKsEGQTQIFR+183b\nIKxJWW5+zuPnXx6/+PL4Oe/7f9IgaWhoODs7d+/e3cXFpWvXrg4ODkq4ABRkEB47duzmzZu+\nvr5sNvvChQu2trZLly6VbxdN5N27d48ePUpKSrp16xaRiYqpqdXWc3DnUWPs3d2Bp9qDBqEs\nQINQFho06RcLBN9eZeanpxe8TP+Wmcn59q36I01NzQ4dOnTq1Kl9+/YdOnRo166dra1t406+\nQg1CWQYfxQ1QyjEIeTxeTk7O+/fvs7Kynj9/npqaWlxcjCAIhUq1ce7VacR4x2Hj2Lr1u5xA\ng1BxQIOwQRR/fv/u7tWPD27mvXiCikUIglAoFAcHB1dX165du3bp0qVjx47m5uZEY2gQysTR\no0dfvXq1detW4kxt3bq1qqpq+fLljT6g3A1CPp9fVlZWXFxcWFj448ePr1+/fvny5fPnz2/f\nvi0sLKxupmtuatvT2drVqXU/NwNLc3n1LjvQIFR+1+pjENakLDf/w6O0b+mvv2VkF3/KJbLR\nIAiioaHRrl27tm3bOjg4tGrVqmXLllZWVlZWVlpyTWipCIOQz+dPnDhx6dKlrq6uCIIUFBRM\nnz49Ojq6VatWcuxFdjgcTn5+fm5u7ufPn9+9e5eVlfXy5cuysjLiU01DQzt393aDPdsNHEQn\nzVQDGoSyAA1CWWjKpL+ypOR71uvCrKzv2dk/3r0t+ZyDoZLqT5lMZqtWrezt7e3s7GxtbW1s\nbKysrKytrc3MzKRXZFWcQSjL4KPQAaopBmFFRUVZWVlpaWnZfymvAfHRz58/CwsLidiZarRN\nzFu69rHvPcDBY6i2iZnsPUKDUHFAg7BxiAWVuU//zX2akpf+5NurF2JBZfVHBgYGbdq0cXBw\nsLKyat++va2tbYsWLaysrMhW/5kst1NWVla3bt2qpxEuLi579+6V18HfvXuXl5dHTKQqKirE\nYnFVVZVAIMBxnNjNEwgEVVVVKIpyudzqN8vKykQiUWVlZXl5OY/HE4vFvz24jplxq97dzTs4\nWHRqb921o761RfUURF76VQXnc1f6xR29umrhp4F9QGuBKByDltYdTQy7+HizWKwqLq/w1dtv\nr958z37/493nzNev09PTa7XX1NQ0NjY2MjIyMDDQ19fX0dHR0tLS0dHR0dFhsVi6urpMJlNL\nS4vNZmtoaDCZzA4dOijZf+nt27c4jnft2pV4aWVlZWFh8erVK3kZhC9fviwuLiZGGGK04fP5\nQqGwvLy8srKSx+Nxudzy8vLS0tKSkpKfP39Wl12uRt/auoNbzxbOzrZuPU3athWKRAwGg05W\np1YyoPv9+5zxvu/697+5Jgq0Fojy0DIysu/bz75vP+IlKhYXf/r488OH4o8fiz99KvmS8/nL\nl7dv3/76hwYGBmZmZsRIRQxWGzZsUMICqyyDj0IHKM3QUPTx4xeJiTyBQCwWE5MfiUTC4/GI\nfysrK/l8Po/H43A4XC6Xw+EQtl9paalIJKr3+Br6htrm1qamFnpWNoY29ib2bc07dNGzVEgm\nBQhE+TA0tFr3Hdy672AEQXAULf78/vubzKJ3r368zy7+/D7t2bMnT57U+hNTU1NTU1MTExNT\nU1NDQ0M9PT19fX1tbW0Gg0H8SzSj0Wh1rX3r6+t369ZNXtY7WYyWkpKSmpM/IyMjPp/P5/N/\nu+siEokEAkG9x0RRlMPhIAiye/fuXbt2NUIVW1ebxmQytDWMLEzYutosHW0NAz0tIwMtI0Nd\nS1Mdc1MDGyum1v9TKBQKiU1XFEWFQmEjOm0iOI7jOI79d7tGmTDLuYb5hTQuD8gXxzAMwzAg\nWxPE2QbyrREEwXEcVNcIgmAYJhQKKSympUtnS5fO/3kTRTkF38u+5JXnF3ILf1T8KOYV/eSX\nlJWUcQq+F6Ki36+t1GLv3r1+fn51fYrjePUNTqCtrd3ELZeSkhJdXd2aSzlGRkaER9NvISZM\nsh/fy8vr+/fv9TajMZkaenq6LVtamJhqm5joWVnpWVsb2toatbKvWTteLJEg//UIkF2DovnP\n9UCaHUKNKoFhfp5mcTHAe+RXiMGqQRePEgA7kvxK9ZNULkfTt2ulb9fKwXNI9TuVJcWc/Hzu\nt2/c79+5hYUVP39W/Cjil5bmFBRU24oUGm358uXV1zOGYYTzUV290Gi0xpVJlGXwUegApfnt\nG/vbN7cePWQanREEQRCWti5bV9/Avp2GrgFbT5+tZ8DW1Wfr6rN19di6+mwdPZaOHltHl62r\nz9bTp1B/Mzg3+nqT77WhBMh2c0kB4FSqoYCdbEtH16aVrk2rNkNGEy9Rsag8P7f062fut6/c\nwnzu9wJe0beKn0XvPn56/fp1UzoqKiqSfaeRSqXq6OjU9SlZDEKJRFJzMkcMeXXd7RiG1bVf\nVwuimbu7O5vNZrFYLBZLQ0ODQqEQeRE1NTUZDAaNRiNOkLa2Np1OJ3YnNDU1ybAHrVpoaJkh\nCDJLhlZ6AAAgAElEQVRC19r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AK1ExUVVVJSMshjKVslbQ4srsbEsEMsrsbcuXMr9lekP/mYMcI9e4jevakOAuoPny/cs0ey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wUIJgMhxOFwGJT32oBnCBvUqjdv8MwVv8XExsZu3bq13PLkGUKhUGiBZwiNOhkg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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# 8c. Bayesian Posterior Density Plots\n", + "# ============================================================\n", + "if (!is.null(fit_bayes)) {\n", + " plot_params <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\")\n", + " avail_params <- intersect(plot_params, colnames(draws))\n", + "\n", + " if (length(avail_params) > 0) {\n", + " draws_long <- reshape2::melt(as.data.frame(draws[, avail_params]),\n", + " variable.name=\"parameter\", value.name=\"value\")\n", + "\n", + " p_post <- ggplot(draws_long, aes(x=value, fill=parameter)) +\n", + " geom_density(alpha=0.5) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"red\") +\n", + " facet_wrap(~parameter, scales=\"free\", ncol=3) +\n", + " labs(title=\"Bayesian Posterior Distributions\",\n", + " subtitle=paste0(\"Exposure: \", EXPOSURE, \" | N=\", N_bayes,\n", + " \", \", N_CHAINS, \" chains x \", N_SAMPLE, \" samples\"),\n", + " x=\"Parameter Value\", y=\"Density\") +\n", + " theme_minimal(base_size=11) +\n", + " theme(legend.position=\"none\")\n", + "\n", + " n_panels <- length(avail_params)\n", + " plot_h <- max(6, ceiling(n_panels / 3) * 3)\n", + "\n", + " print(p_post)\n", + " ggsave(file.path(DIR_BAYES, paste0(\"bayesian_posteriors_\", EXPOSURE, \".png\")),\n", + " p_post, width=12, height=plot_h, dpi=150)\n", + " ggsave(file.path(DIR_BAYES, paste0(\"bayesian_posteriors_\", EXPOSURE, \".pdf\")),\n", + " p_post, width=12, height=plot_h)\n", + " cat(\"Bayesian posterior plots saved.\\n\")\n", + " }\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T22:58:54.231907Z", + "iopub.status.busy": "2026-03-31T22:58:54.229247Z", + "iopub.status.idle": "2026-03-31T22:58:55.783672Z", + "shell.execute_reply": "2026-03-31T22:58:55.781452Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian trace plot saved.\n" + ] + }, + { + "data": { + "image/png": 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YhXzLm/snkmAmXfVYCxPTW0jOopntUkQWbDYRcpAEjrhCWM1OxjkI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\nRrX0EDa5TYwuHYeMAsGlIC0u8tYN6A4+unsm3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dGjQjnth4xiBKV5GgrV0kiUIUwiAmjZCYrIfA5h8ecyKUzSyLcGDK2lOEF7aHe9Edlk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\n2rp2o3fO6xAKAByGTmoPoUXekiXBT4rJb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OwhIn0vd92dgyoFIK6EsKkoWMDye8em07qm/ui486TyL+Xd2/cFbd+16XInAhAEh2YhO1gD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\nvvARbJDAUNiGVDCQ0mxSDYCwB0KhswYGJky+E0abAz8mKJrd1QUE2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UAQ5uYV51J4YoKqJVLQo5SdN3MML12XASFyNDLX1FFQFJPAvaz9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\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EhlREN1sD2cITrz6nSKhCTLntcoEX5C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\nuuB5suq6aFY3hhBKLs1TC2X6Nr/fbz6B8/jTPx4ghA5hjtv3RRYOCy/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0AsQ5nJlD6MyllMIv2ij2Zv1RJ+hKDuFuz2MReSKyX9cUpg0I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\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nhvEfYHAU1QgEaWwBAAAMoKvu0LZpVZ\nC4XpdxJUiiq6ny7rgLmgyKP/v717D46qvv8//n5/dpPNbhIuFmO4eMGqgApaAa2/6vfbeoNp\nvU6lWuq9UFu04Gj79YoChY5S29GOY72gQeutXuuI1VFrHdsqiKMgtlKgIxeDNhOkAskmmz3n\n8/vjbDZLwDWSs7tn9zwff2j2nM2ez54cPruv87mV4p6ChnYAoYi43ZPKdCSdD/7hnYWYtyzh\nzuekNhKpKmkLYaEiYf9eNaJqij680hR0HY6SqlJlSEilIhAiHxWxtozmTykKNT0f0uWgmJPK\nVHBXmfJWXW2+/o1SFwLlzYiUJBBKGFel7+ky6i3pYFMp+e8Wb2uNMdbt/QF0+fChA6Il6/NV\nZTSYg+pN79U5ikEzM3tX4FUbZZbRykUgRD6MIdwNY6zr2kB++O1W9xjCovwFWZg+qLS2rtRF\nQHlTkWKPHuw+bjRsA5d69Qb1alTXZgNh0GrYKjWF+5LQn/a9+kjk3Ia9fSxMX1R2CyFDQipV\nyCpZfEnZWUapAHqoiuuWUUIu6iyjXgth2YRlAH1lVOKlSCKqGr4Wwm47d9HxKvMaY6x1JUgh\nOVrIONifzxMVOShe41th+qbaeAulFPmwxVClhhu+lYpJZZBPpssogTCXMSrWlk+XURHR4i7E\nVD4nBkBfGdWEKU0LYejGEHa/X+stQ68qItbrPuq1EFoJ1LIGBZxltJw+aTO+85W9/t+AAYOi\nFTgXUlSLvagjioZAiHwyXUbLsEYuIDXiuuU17Z0p7ph67iAClee4AfXDqqtLcuiyqm59kG1v\n8+7JqjFWRGxPl1Fr3UB9Kv/PoIF7FWYEo6pm1v0tH0bkK1WV+e26yhhTVrOso+8q85KFX7zp\nRQN2L7LE1BhxrS2r+5ZFu8XOwvRApRqbSFRFSjPLaNjGEGbHqFvXzXYZzdarNSYzzWipirer\n/WKxAr2yFcsdxuBg2YkKRiBEPmq9QFiZK+rsIaO2rJadEBEtXqdfFqYH4Cet0OFY+fQsTG9V\nux/aTDSqiUSstaGZ7VEZtBIcI2tibU5VqUuBgiAQIh9VsWKpjneiRqwV78ZtmdBizR/VvTA9\nAPgmErKPoNwuo2JFE7XmgAPd5k1eX49qVVt2dyX3lBZ9FUHksW/BmoJRcnxzQz5GNbPkXqlL\nEhzZGcDLqCHMFK2bB8tOAPBVCGcZzd5r9CaVsdGojtjP6+hR4y0FaW2gZhktKFs+916B8hWW\nCgV7RkUzLYQh+zzOxxgR0YCN6c+viF1GM4cDAF+EcJbR7NtVa6216nW+cK1qJhBaa0PSbmYl\nLO8UKC0CIfJRsVaUhelzqaqoSjktTV+8FsLMOoRFOBKA0IiGLhLkLDvh3ZFVY62rol4g1OyU\no5VO+7cOIYA+Ygwh8sksO8FX/F5UxbpldFKKNoaQLqMA/BXCFsJsBPKCn8387HUZ1e5nhOKc\n6Ff2lrr6UpcCqHwEQuSTmWWUMYS9GGPT6VIX4kughRBAmYqqVodmvJwn20nSeoP4xbsLafer\nicW6u4yW0ZiF/tAR+5W6CEAoEAiRT/c6hCH56OmrzDLB5XNSij2GsHzODICAO3ZAvRu2aUW0\n5//eIH41aq0dHInslYiKiFg3NMtOACiGcN11w5fVPakMYwh35t2uLp+vKMVbdiKzMH35nBoA\nwRZRrTLh+q7S3SjorXlrVTTTTSdbtdpgLUwPoNyFq5LFl2W6J0/hkyeXjRiRcjopWrxxfd0T\n4gEA9oxXg2p2hYmetekzTwjNLKMAioNAiHxUGEO4GxqrESmnLqNGi9e9qGxOCgAEUibseXdk\nJbM6u4jstEIhdS0A/xAIkY+KsA7hbtTES12CL0dFTFG+PWixZq8BgErlNQRa9W7JesM2VEQy\n+VBEXMsYQgA+IhAiHyPqWrESkhWP+kpraqR7vFxZMEUra1gWxwKAQsm0B6qRzDqEXhshN9sA\nFAqBEPkkIpF217VlFX6KwAuEZbQyvYoU524yX1kAoL+8atQYr4GwZxUK62aeYF367QDwEctO\nIJ/B0cinXV0MVujFxmqkrKZOUdHidBmtVh1URa0CAHsuM8uoMZJJgDkNhtlnhGxtRgAFVQlf\n3RzHcV33i59XYF4ZHMfp6uoqdVl8M0B1Y2cq7TiOk+7qKvHHj7U2nU4H4W9tI1Fx3a6uLomW\nx9/adRy3b1dmP6/eapHpew+ppH8Cfkmn09ZazkyBOI4jFVf9BopX/Za6FBUue/XadFpcV6wr\n6bSoOuku7eqyruukUmoiImK7usQY5WrvG8dxqH4Lx6t+Xe9LUQBEIhETsoVqfFEJgTCdTqdS\nqVKXQrwPy66uriAkFr8knPR/2tu7RDo7OjpK/b5c1+3s7AxCu5yqmnTa7UzZMrlH66TTXZ2d\nHV905qy1HR0dRSlR6Liu6zgOp7dAvOrXS92lLktlchwnlUqRCQvEu2576gfHiaTTNu2IOGKM\n7UzZjo6Ik3Y6Or3WQpNK2UjUUp/0jeu6rutS/RaIFwjT6XRAznAsFquuri51KcpPJQTCWCwW\ni8VKXQppa2tLJpM1NTU1NTWlLotv9o3H29s74sbU19fXl/of2H//+9/a2tpIJFLaYoiItdZN\nJEx9vZbJ37q+LTmovq4+b2m9sF1fX1+0UoVKV1dXMpnk9BZIe3t7e3t7LBaLx8tsBuBysW3b\ntng8XlVVVeqCVKZUKmWtzdYP1kk71dWaSIjjiDGmrk7r652aGlOb0EStiLjxuFRVGeqTvkmn\n0zt27KD6LZCOjo4dO3ZUV1fX1taWuizYc5UQCFE4AyORVGZSGfRQ1cg3/leqS38boo++3zCk\nOgAtqwCAPvAGDXYvRp9dpz53DCEA+Kc8OryhVCKqg6LRTtclTPRWPmlQRGLGBKGrLQDgC3Uv\nO6HdwU8z/+nJg0wqA8BPtBDiC0zZe8gnXakh9BQCAKDwMmsaGW8dQiu986Go0G8HgJ8IhPgC\nQ2PVQ2MMzwUAoBi0e50Ja62qeC2DNqeJ0Lqu0kIIwD9UKAAAAIHhNQkao5nGQhWvH2luCyEA\n+IdACAAAEBSZgYPGWGszTYOyU5dRm+1HCgB+IBACAAAERSbqqaqVbAvhTmMICYQAfEUgBAAA\nCIzuLqPWurudVAYA/EUgBAAACBarqtkZR6VXl1FXDd/fAPiGCgUAACBIVNUY61rdaQaZbJdR\noa0QgI8IhAAAAAGSnVe0p8uoMeLmtBAyhhCAfwiEAAAAAWLFG0NorfSMIbQ5y05YAiEA/xAI\nAQAAgkRVjOmeZbR7W/cP1rW0EALwEYEQAAAgQFRURK11paeF0Ih1u/cz3ygAPxEIAQAAAsSK\nFWPUaxXMLFTf02VUrFXl+xsA31ChAAAABImqN4ZQrM10FVXRbLNgdiMA+IFACAAAECAqqkZF\nvM6h2YXps11GhUQIwEcEQgAAgACxYkWNiFjX7c6DZqcuo4ZACMA3BEIAAIAgUc0sLNG9DqHN\nmWVULJPKAPATgRAAACBAVFWMERHNRj+jwqQyAAqDCgUAACBgvMiXXYxejXW7xxAyqQwAXxEI\nAQAAAsSKiKqaiP2cLqNCCyEA/1ChAAAABIiqqlEbiUhmkfqd1yEEAF8RCAEAAALEWhFRiUZE\nuledMDnLTtBlFICvCIQAAABB4oXASDT7wIrKTgvT8/0NgG+oUAAAAAJEVcWoRCIikp1VRugy\nCqAwCIQAAAAB0t1lNCrS3VqoPV1GrXW9RSkAwBdUKAAAAEGiIprpMqqaSYQ9O2kpBOArAiEA\nAECAqKqo8bqMZpsIxc0EQWvd3HwIAP1EIAQAAAiQTPLL6TKaO4ZQe7YCgA8IhAAAAIGiot2T\nyvTEwO4WQteKIRAC8A2BEAAAIEBURVTFW3YiO4awJxpapYUQgH8IhAAAAAGiX9lbYzVeC2Fm\ntKDpCYTWiiUPAvBPtNQFAAAAQA8z5nAR0WjPwvQiPZ1HxbrKpDIA/EMLIQAAQPB4s4xmuowa\nu9NoQgIhAN8QCAEAAALHepPKeHPJaG6XUatMKgPAPwRCAACAwPEWps8uM6Hds4xqtq0QAPxA\nIAQAAAieTJdRERFRY92eGKjK9zcAvqFCAQAACJ5IVLymQRHNdhm11tJCCMBXBEIAAIDAycwy\nqioitrulMBMGmWUUgH8IhAAAAIFjo1E1kcwDVbGut1lEmGQUgI8IhAAAAIGj0Wjkf07wGgPV\nZMYQaqaBkO9vAHxDhQIAABBIVVXe/+kyCqBwoqUuAAAAAPLyJpVpb7dt20tdFACVhkAIAAAQ\naKrGta77n4/t1i2qSgshAB/RZRQAACDQrIpYkc4OcRzWnADgLwIhAABAoHnrENrOTnFdZpQB\n4C+6jAIAAASbiYjrSMoVx7F0FwXgKwIhAABAsMXjtr1No1H6iwLwHYEQAAAg0DRRK45j02mt\nrhZDl1EAfqJOAQAACDZVTSTEWsukMgD8RiAEAAAIvESdiCiTygDwG3UKAABA0Gltraha16WF\nEIC/CIQAAABBpwMHa/0AEVHDNKMA/EQgBAAACDrdu8EcNk5ErBAIAfiJQAgAAFAG1ETEW6Qe\nAPxDIAQAACgHXmdRAiEAXxEIAQAAyoBlflEABUDNAgAAUA68JemZVAaArwiEAAAA5SATBQmE\nAPxEIAQAACgD3qQyjCEE4C8CIQAAQDlQJQ0C8B2BEAAAoEyoZkYSAoBPqFMAAADKg0YipS4C\ngEpDIAQAACgTaoTFJwD4ijoFAACgPFijTDIKwF8EQgAAgDJB8yAAv1GtAAAAlAeN0GUUgM+o\nUwAAAMqEGrqMAvAXgRAAAKBMGKO0EALwFXUKAABAmVC1pS4CgApDIAQAACgTxqihzygAPxEI\nAQAAyoOai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NmzZFo1Ev/mXNmjVLRJYs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\ngRAAAAAAQur/A5gJkK8JHllLAAAAAElFTkSuQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# 8d. Bayesian Trace Plot\n", + "# ============================================================\n", + "if (!is.null(fit_bayes)) {\n", + " a1_label <- \"a1\"\n", + " if (a1_label %in% colnames(draws_list[[1]])) {\n", + " trace_df <- data.frame()\n", + " for (ch in seq_along(draws_list)) {\n", + " chain_draws <- draws_list[[ch]]\n", + " if (a1_label %in% colnames(chain_draws)) {\n", + " trace_df <- rbind(trace_df, data.frame(\n", + " iteration = 1:nrow(chain_draws),\n", + " value = chain_draws[, a1_label],\n", + " chain = as.factor(ch)\n", + " ))\n", + " }\n", + " }\n", + "\n", + " if (nrow(trace_df) > 0) {\n", + " p_trace <- ggplot(trace_df, aes(x=iteration, y=value, color=chain)) +\n", + " geom_line(alpha=0.5, linewidth=0.3) +\n", + " labs(title=paste0(\"MCMC Trace: a1 (SNP -> \", MED_LABELS[1], \")\"),\n", + " subtitle=paste0(N_CHAINS, \" chains, \", N_SAMPLE, \" post-burnin samples each\"),\n", + " x=\"Iteration\", y=\"Parameter Value\", color=\"Chain\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + " print(p_trace)\n", + " ggsave(file.path(DIR_BAYES, paste0(\"bayesian_trace_a_\", EXPOSURE, \".png\")),\n", + " p_trace, width=10, height=4, dpi=150)\n", + " ggsave(file.path(DIR_BAYES, paste0(\"bayesian_trace_a_\", EXPOSURE, \".pdf\")),\n", + " p_trace, width=10, height=4)\n", + " cat(\"Bayesian trace plot saved.\\n\")\n", + " }\n", + " } else {\n", + " cat(\"a1 not found in draws columns. Skipping trace plot.\\n\")\n", + " }\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T22:58:55.796485Z", + "iopub.status.busy": "2026-03-31T22:58:55.793813Z", + "iopub.status.idle": "2026-03-31T22:58:57.002474Z", + "shell.execute_reply": "2026-03-31T22:58:57.000312Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian forest plot saved.\n" + ] + }, + { + "data": { + "image/png": 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EB73Rt0l/j0008JIeyhauYxYtBK5u/2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\n/PNPhmFUK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AAAQFIEQgAAAACQFIEQAAAAACRFIAQAAAAASREIAQCw\nlPHjx8+dO7e8vNzsQgAAGYAfpgcAwFImTpw4ZswYr9drdiEAgAxAIAQAAIAQQuinDulH9ohA\nu7A7lKIKZcpFIr/E7KIApBaBMFWWL1++YMGCq6666qxTm5qaNmzY0N7eXlJSct11102bNi3N\n5QEAAHzIf0Z7/dd6y4HeJ3QhxObfKxdcYfvYEuFwmVcZgNTiGsJUaWxsbGtrO+ukLVu23Hff\nfR0dHTU1Na2trd/85je3bduW5vIAAAD+puOk+vzyvmnwbzRN3/2m9vsfiUTMjLIApANHCE3w\n9NNPX3HFFffee6/x54MPPrh69ep58+aZWxUAAJCRmlBf+Zno6T7XdP3UYW3T72zXfT6dRQFI\nGwJhCimK8s4772zatCkWi9XV1d144402my2RSNx2223Tp0/vbTZt2rS//vWvJtYJAACkpe/Z\nLLpOD9bmr2LeJ0ThmPSUBCCdOGU0hbZv3/7CCy/U1NSUlpY+9dRTzz//vBDC4XBcf/31EyZM\n6G12/PjxsWPHmlcmAACQl35gCNet6JrexOUtgDVxhDCFWltbf/WrX7lcLiGEruvr1q27/fbb\n7XZ73zabN29+//33v/3tbw+8KL/fr+t6UmtXVVUIEQgEkivaQnRd9/l8ZldhJk3TpO0BXdcl\n33whRCwWk7YHNE0TQsTj8bSt0f32S462o2lb3cDUSEREowmPJ+52m12LOdy6rihK+v77R5ks\nXc8SQh9yD9g6Tw6lmfreen3fu+dTWNp4dV1VFNXsMsySp+tCiLiimF2IaXJ03fTNj150Q2L8\n9MHbjTRjAJBIJPoNAPLz85Vz9wmBMIXmzp1rpEEhxOzZs9evX9/a2jpu3LjeBu++++7jjz++\nZMmSiy66aOBFJRKJZANh74zDmMsyJN98Xdcl7wHJN58XgBEL08PjP2Nrb0nb6gaWLUS2ECIe\nFEGzS4GFKNGwEg2bXQWQGbRw0MSv4GQHAATCFCouLu59bPxAcHf3h1dsb9q0acWKFUuWLLnt\nttsGXVRBQUGyaw8EAqqq5ufn22ySnhjs8/mG0W/WYBwdtdvteXl5ZtdiDlVVw+Fwbm6u2YWY\nIx6Pd3d3u1yunJwcs2sxRzQaVVU1Ozs7fau84Uuj5zaMW7Zs2bFjx4IFC+rq6syuxRw9PT1O\np9PhkHSQEwqFVFX1er1DHQCs/anoah28We0CMX/RedaWHsFg0Ov1DnA8xNoCgYCiKNJ+A+q6\n3t3dbfrm52Tn5ThNOEdDVdVAIOBwOPr1wMBvB0k/K9Oj79lK0WhUCOF0Oo0/N23a9Pjjj//z\nP//zxz/+8aEsqt+JpkNnt9ulDYTiPPot0/UeT5a5BxRFkXbzjTPGZe4Bm82m63paNz+vKH3r\nGkyPy9ulOaKePHtRudm1mEMPBhW32+6S9KfzdLtfjceVggL70CKxNnm2vm3wQGib/g9Khryi\nNOGyFRRIO/5RNYeiKPai4sGbWpGmabrNYy8sNLsQcxgjwGQHAJK+VdKjubm59/GpU6cURSkr\nKxNC7N+/f8WKFXffffcQ0yAAAECK2OquHfR355WyKmXCzPTUAyDNCIQp1NDQsGfPHiFEKBTa\nsGHDzJkzvV6vruu/+tWvrrjiiuuuu87sAgEAgPRyi2xX3T5QA5dH+cSdQtYzMAHL45TRVNE0\n7aabblq5cmV3d3cgEPB6vffff78Q4vjx4wcOHDhw4MCmTZv6tn/22WcLZT26DQAATKTMusKm\n69qm54X6kRtR5Bba/vFupWTc2eYDYAUEwlR56KGHxo4de8cddxw/fjwWi02cONG4un3MmDGP\nPPLIR9ubfvErAACQlnLhlfbqWm37Bv1Igwh2CsWmlIxTps5V5lwtzLg3BoC0IRCmSm1trfGg\nqqqq7/Mej2fWrFlmVAQAkMIll1xSW1sr7W2WMXx5JbarPiOuMrsMAOnFNYQAAFiK0+n0eDzS\n/ugCACApBEIAAAAAkBSBEAAAAAAkRSAEAAAAAEkRCAEAAABAUgRCAAAAAJAUgRAAAEuJRCKB\nQCAajZpdCAAgAxAIAQCwlPfee2/VqlV79uwxuxAAQAYgEAIAAACApAiEAAAAACApAiEAAAAA\nSIpACAAAAACSIhACAAAAgKQIhAAAAAAgKQIhAACWUl5eXltbW1paanYhAIAM4DC7AAAAMJKm\nTp06btw4r9drdiEAgAzAEUIAAAAAkBSBEAAAAAAkRSAEAAAAAEkRCAEAAABAUgRCAAAAAJAU\ngRAAAAAAJEUgBADAUhobG9evX3/48GGzCwEAZAACIQAAltLe3n7w4MGuri6zCwEAZAACIQAA\nAABIikAIAAAAAJIiEAIAAACApAiEAAAAACApAiEAAAAASIpACACANKJhoalmFwEAGEUcZhdg\nWY2NjcXFxWVlZedq0NbW5vP5SktLCwsL01kYAMDa5s+fP3Xq1OLi4t5n9GN79R0b9eP7RCIm\nhKKUjlOmX6LUXSMcLhPrBACMBgTCVFm+fPnixYuXLFny0Umtra2PPfbYgQMH7Ha7qqoXXXTR\nfffd5/V6018kAMB6PB5PXl6e2+0WQgg1oW1cpe/d0me6rp9p0c/8QTS8Zf/kPaJ4rEllAgBG\nBU4ZNcGjjz6q6/rKlSvXrFnzwx/+cN++fc8884zZRQEALEjb8Ju/T4N9+NrUPzwmgvx+PQBI\njUCYWrquHzt2rKmpKR6PG88Eg8HS0tIvfvGL5eXliqJMnz594cKF+/btM7dOAID16Id36Y31\nA7UI+bU3n09XOQCA0YhTRlMoFAr967/+64kTJ6LRaEFBwcMPPzxp0qTc3NyHHnqob7MzZ84U\nFRWZVSQAwKr07RsGb9P0vvC3i/ySNNQDABiFCIQptH79+q997WuXXXaZz+f793//9yeffPJH\nP/pR79R9+/adPn1627Zthw8ffvjhhwdeVCKRSHbtuq4bM9ps8h4HHka/WYPxv6/rurQ9oKqq\n5Jsv5H4BaJqmaZq1N1/patXjsbNO0qNReyymBxz6if1DWZS6+y/65LpBGmXnCW9BskWaRdd1\nVVWt/QIYgPEVYHwOSEvy8Y+QeAikaZrMX3/nGgA4HAOFPgJhCtXU1CxYsEAIUVhYeOONN65c\nuTIYDObm5hpTn3vuud27d+fk5CxdunTy5MkDL8rv9xuf78kKBALDmMsyfD6f2SWYSdM0yXtA\n8s2PxWKx2NkDgySi0ajZJaRQ3vqn7WeOn3WSRwhPUst67zXlvdcGbtIz+9rI7OuSWqq5JH/x\nCyGCwaDZJZhJ8vGPruuSfwNKvvmJRKJfDxQXFyuKcq72BMIUqq6u7n1cUVEhhGhra+sNhMuX\nL49EInv27FmxYsXBgwe//vWvD7Aop9OZbCBMJBK6rjscjgH++60tHo87nU6zqzBNPB5XFGXg\nHUIWZhwfkHbzNU1TVdVms9ntdrNrMYexh9jam6+VVQnn2XOfqqqapjkU4Wg7MpRF6XmlmneQ\nH0CyFZRl0CeqqqqKokh7gIgBQDwel3zzhRAZ9IYdWcbBMck3P9kRoKSjpfTweD78qjbGJf3O\n3/B4PPPmzbv99tuffPLJL3zhC/n5+edaVF5eXrJr7+rqUlU1Ly9P2m/Ejo6OAbrU2nRd7+jo\nsNls0vZAIpEIhULSbn4sFgsEAk6ns3cPlGwikYiqqjk5OWYXkkpOGiQXAAAgAElEQVQf/6dz\nTXn99dfr6+uvu+7aS8I+0T34TUTt193hqKoduE1m/V5hMBh0u90uV2ZVPWL8fn88Hvd6vdLu\nFOvs7JR5/NPe3q4oirTfgJqm+f1+aTffODbocDiS6gFJ3yrp0fd0BeOx1+s9duzYT37yk87O\nzt5JBQUFQohQKJT+CgEA1qUoNRcP3ionXxlfk/piAACjFIEwhd5//31N04zHDQ0NXq+3vLw8\nJydn06ZNGzdu7G22bds2j8czZswYk8oEAFiT7eIbRNYgR4ltl39a2CU9jgQAEJwymjq6rufn\n5z/88MNXXHHFyZMnN2zYcPvtt9tstpKSkltuueW3v/3t8ePHKysrm5qa3n333bvuusva17oA\nAEyQlWtbfLf20k9ELHLW6cpF1yozL0tzUQCAUYVAmCo1NTXXX3+9rut/+ctf4vH4l7/85UWL\nFhmTPve5z82cOXPr1q2NjY2lpaXLly+/8MILza0WAGBJyripttv/P/1Pz+onD/7dhKxc28L/\nR5l1hUl1AQBGCwJhqvT+tOBll51l5+u8efPmzZuX3ooAADJSiscqt/2bfvqYOP6B3t0l3DlK\naaUy8QLhkPSeKwCAvgiEAABYnzKmSoypkvQ2/ACAc+OmMgAAWEpJScmUKVMKCwf5aUEAAARH\nCAEAsJgZM2ZUVVV5vV6zCwEAZACOEAIAAACApAiEAAAAACApAiEAAAAASIpACAAAAACSIhAC\nAAAAgKQIhAAAAAAgKQIhAACW0tTUtGnTpuPHj5tdCAAgAxAIAQCwlNbW1r179545c8bsQgAA\nGYBACAAAAACSIhACAAAAgKQIhAAAAAAgKQIhAAAAAEiKQAgAAAAAkiIQAgAAAICkHGYXAAAA\nRtL8+fOnTp1aXFxsdiEAgAzAEUIAACzF4/Hk5eW53W6zCwEAZAACIQAAAABIikAIAAAAAJIi\nEAIAAACApAiEAAAAACApAiEAAAAASIpACACApcTj8UgkkkgkzC4EAJABCIQAAFjK22+//dRT\nT+3atcvsQgAAGYBACAAAAACSIhACAAAAgKQIhAAAAAAgKQIhAAAAAEjKYXYBAAAglfxn9OON\nIuQTrmxRWqmMnyoUdgcDAP6GQJgqy5cvX7BgwVVXXTVwsxUrVvT09DzwwAPpqQoAIBF/u7b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l2OvNdMPoNyvRdV3aHtA0TdM0aTdfVVXj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\n9fVr1qxZvny5pqZmcnLyokWLLl68WFpampWVlZubW1xcLCM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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# 8e. Bayesian Forest Plot\n", + "# ============================================================\n", + "if (!is.null(fit_bayes) && nrow(bayes_summary) > 0) {\n", + " plot_bayes <- bayes_summary[!grepl(\"^diff_\", bayes_summary$label), ]\n", + " plot_bayes$label <- factor(plot_bayes$label,\n", + " levels=rev(c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\")))\n", + "\n", + " plot_bayes$effect_type <- ifelse(grepl(\"^a\", plot_bayes$label), \"a-path (SNP->Med)\",\n", + " ifelse(grepl(\"^b\", plot_bayes$label), \"b-path (Med->Out)\",\n", + " ifelse(plot_bayes$label == \"cp\", \"Direct (c')\",\n", + " ifelse(grepl(\"^ind\", plot_bayes$label), \"Specific indirect\",\n", + " ifelse(plot_bayes$label == \"total_indirect\", \"Total indirect\",\n", + " ifelse(plot_bayes$label == \"total\", \"Total\", \"Prop. mediated\"))))))\n", + "\n", + " p_bforest <- ggplot(plot_bayes, aes(x=post_mean, y=label, color=effect_type)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(aes(xmin=ci_lower, xmax=ci_upper), size=0.6) +\n", + " scale_color_brewer(palette=\"Set2\") +\n", + " labs(title=\"Bayesian SEM: Posterior Estimates (95% Credible Intervals)\",\n", + " subtitle=paste0(\"N=\", N_bayes, \", \", N_CHAINS, \" chains x \", N_SAMPLE, \" samples\"),\n", + " x=\"Posterior Mean (95% CrI)\", y=\"\", color=\"Effect Type\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + " print(p_bforest)\n", + " ggsave(file.path(DIR_BAYES, \"bayesian_forest_plot.png\"), p_bforest, width=10, height=8, dpi=150)\n", + " ggsave(file.path(DIR_BAYES, \"bayesian_forest_plot.pdf\"), p_bforest, width=10, height=8)\n", + " cat(\"Bayesian forest plot saved.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bayesian Interpretation\n", + "\n", + "- **P(indirect > 0)**: Posterior probability that the indirect effect is positive.\n", + "- **95% Credible Interval**: Excludes zero = credibly non-zero.\n", + "- **Trace plots**: Good mixing (chains overlap) indicates convergence.\n", + "\n", + "---\n", + "\n", + "## Cross-Method Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T22:58:57.009597Z", + "iopub.status.busy": "2026-03-31T22:58:57.007373Z", + "iopub.status.idle": "2026-03-31T22:58:57.058697Z", + "shell.execute_reply": "2026-03-31T22:58:57.056012Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " exposure method label est se\n", + "1 chr19_44954310_T_C FIML a1 0.279039981 0.05603136\n", + "2 chr19_44954310_T_C FIML a2 0.275817440 0.05611711\n", + "3 chr19_44954310_T_C FIML a3 0.225523125 0.06399825\n", + "4 chr19_44954310_T_C FIML a4 0.212803701 0.04913284\n", + "5 chr19_44954310_T_C FIML b1 -0.857552348 0.94555878\n", + "6 chr19_44954310_T_C FIML b2 0.779718372 0.94483110\n", + "7 chr19_44954310_T_C FIML b3 0.053666933 0.05799306\n", + "8 chr19_44954310_T_C FIML b4 0.032936786 0.05053026\n", + "9 chr19_44954310_T_C FIML cp 0.140334493 0.04015525\n", + "10 chr19_44954310_T_C FIML ind1 -0.239291391 0.26836798\n", + "11 chr19_44954310_T_C FIML ind2 0.215059926 0.26435836\n", + "12 chr19_44954310_T_C FIML ind3 0.012103134 0.01375153\n", + "13 chr19_44954310_T_C FIML ind4 0.007009070 0.01087676\n", + "14 chr19_44954310_T_C FIML total_indirect -0.005119261 0.01433813\n", + "15 chr19_44954310_T_C FIML total 0.135215232 0.03804226\n", + "16 chr19_44954310_T_C FIML prop_med -0.037860088 0.10696429\n", + "17 chr19_44954310_T_C FIML diff_1_2 -0.454351316 0.53259965\n", + "18 chr19_44954310_T_C FIML diff_1_3 -0.251394525 0.26778051\n", + "19 chr19_44954310_T_C FIML diff_1_4 -0.246300460 0.26877919\n", + "20 chr19_44954310_T_C FIML diff_2_3 0.202956791 0.26583415\n", + "21 chr19_44954310_T_C FIML diff_2_4 0.208050856 0.26431554\n", + "22 chr19_44954310_T_C FIML diff_3_4 0.005094065 0.02256915\n", + "23 chr19_44954310_T_C Bootstrap a1 0.281782714 0.05550201\n", + "24 chr19_44954310_T_C Bootstrap a2 0.278635307 0.05565842\n", + "25 chr19_44954310_T_C Bootstrap a3 0.223237935 0.06371455\n", + "26 chr19_44954310_T_C Bootstrap a4 0.212213238 0.04537182\n", + "27 chr19_44954310_T_C Bootstrap b1 -0.923905707 0.91097343\n", + "28 chr19_44954310_T_C Bootstrap b2 0.844422749 0.90858852\n", + "29 chr19_44954310_T_C Bootstrap b3 0.054219971 0.06472397\n", + "30 chr19_44954310_T_C Bootstrap b4 0.033688170 0.05374446\n", + "31 chr19_44954310_T_C Bootstrap cp 0.142829853 0.04227618\n", + "32 chr19_44954310_T_C Bootstrap ind1 -0.260607770 0.26677357\n", + "33 chr19_44954310_T_C Bootstrap ind2 0.235504131 0.26249354\n", + "34 chr19_44954310_T_C Bootstrap ind3 0.011676119 0.01501737\n", + "35 chr19_44954310_T_C Bootstrap ind4 0.007312507 0.01177654\n", + "36 chr19_44954310_T_C Bootstrap total_indirect -0.006115012 0.01618337\n", + "37 chr19_44954310_T_C Bootstrap total 0.136714841 0.03829100\n", + "38 chr19_44954310_T_C Bootstrap prop_med -0.047373937 0.13937524\n", + "39 chr19_44954310_T_C Bayesian a1 0.282759677 0.05452581\n", + "40 chr19_44954310_T_C Bayesian a2 0.279477624 0.05462738\n", + "41 chr19_44954310_T_C Bayesian a3 0.225379395 0.06480895\n", + "42 chr19_44954310_T_C Bayesian a4 0.214799201 0.04981432\n", + "43 chr19_44954310_T_C Bayesian b1 -1.202062910 0.72991439\n", + "44 chr19_44954310_T_C Bayesian b2 1.126765124 0.72914989\n", + "45 chr19_44954310_T_C Bayesian b3 0.048287700 0.05714316\n", + "46 chr19_44954310_T_C Bayesian b4 0.036596357 0.05075441\n", + "47 chr19_44954310_T_C Bayesian cp 0.138752831 0.03936252\n", + "48 chr19_44954310_T_C Bayesian ind1 -0.337053351 0.21771764\n", + "49 chr19_44954310_T_C Bayesian ind2 0.311964844 0.21425310\n", + "50 chr19_44954310_T_C Bayesian ind3 0.011370002 0.01409484\n", + "51 chr19_44954310_T_C Bayesian ind4 0.007776618 0.01126849\n", + "52 chr19_44954310_T_C Bayesian total_indirect -0.005941887 0.01483892\n", + "53 chr19_44954310_T_C Bayesian total 0.132810944 0.03699750\n", + "54 chr19_44954310_T_C Bayesian prop_med -0.052994842 0.14783645\n", + " ci_lower ci_upper pvalue ci_type n_eff\n", + "1 0.16922053 0.38885943 6.356200e-07 Wald N=1153 (FIML)\n", + "2 0.16582992 0.38580496 8.876760e-07 Wald N=1153 (FIML)\n", + "3 0.10008886 0.35095739 4.252524e-04 Wald N=1153 (FIML)\n", + "4 0.11650511 0.30910229 1.483049e-05 Wald N=1153 (FIML)\n", + "5 -2.71081350 0.99570880 3.644456e-01 Wald N=1153 (FIML)\n", + "6 -1.07211655 2.63155329 4.092318e-01 Wald N=1153 (FIML)\n", + "7 -0.05999737 0.16733124 3.547565e-01 Wald N=1153 (FIML)\n", + "8 -0.06610070 0.13197427 5.145154e-01 Wald N=1153 (FIML)\n", + "9 0.06163165 0.21903734 4.744207e-04 Wald N=1153 (FIML)\n", + "10 -0.76528296 0.28670018 3.725784e-01 Wald N=1153 (FIML)\n", + "11 -0.30307294 0.73319279 4.159219e-01 Wald N=1153 (FIML)\n", + "12 -0.01484938 0.03905564 3.787890e-01 Wald N=1153 (FIML)\n", + "13 -0.01430898 0.02832712 5.193108e-01 Wald N=1153 (FIML)\n", + "14 -0.03322148 0.02298296 7.210632e-01 Wald N=1153 (FIML)\n", + "15 0.06065377 0.20977670 3.789254e-04 Wald N=1153 (FIML)\n", + "16 -0.24750624 0.17178606 7.233758e-01 Wald N=1153 (FIML)\n", + "17 -1.49822744 0.58952481 3.936137e-01 Wald N=1153 (FIML)\n", + "18 -0.77623469 0.27344564 3.478293e-01 Wald N=1153 (FIML)\n", + "19 -0.77309799 0.28049707 3.594743e-01 Wald N=1153 (FIML)\n", + "20 -0.31806857 0.72398215 4.451823e-01 Wald N=1153 (FIML)\n", + "21 -0.30999807 0.72609979 4.312054e-01 Wald N=1153 (FIML)\n", + "22 -0.03914066 0.04932879 8.214276e-01 Wald N=1153 (FIML)\n", + "23 0.17550243 0.39299885 0.000000e+00 Percentile N=1153 (Bootstrap FIML)\n", + "24 0.17336369 0.38852366 0.000000e+00 Percentile N=1153 (Bootstrap FIML)\n", + "25 0.09809953 0.35067986 0.000000e+00 Percentile N=1153 (Bootstrap FIML)\n", + "26 0.11865467 0.29645833 0.000000e+00 Percentile N=1153 (Bootstrap FIML)\n", + "27 -2.78476849 0.75742884 3.260000e-01 Percentile N=1153 (Bootstrap FIML)\n", + "28 -0.84491552 2.70657248 3.660000e-01 Percentile N=1153 (Bootstrap FIML)\n", + "29 -0.07144015 0.17624593 4.220000e-01 Percentile N=1153 (Bootstrap FIML)\n", + "30 -0.06843109 0.13993094 5.600000e-01 Percentile N=1153 (Bootstrap FIML)\n", + "31 0.05811169 0.22685244 0.000000e+00 Percentile N=1153 (Bootstrap FIML)\n", + "32 -0.84679260 0.21080194 3.260000e-01 Percentile N=1153 (Bootstrap FIML)\n", + "33 -0.23254922 0.81172188 3.660000e-01 Percentile N=1153 (Bootstrap FIML)\n", + "34 -0.01566676 0.04242190 4.220000e-01 Percentile N=1153 (Bootstrap FIML)\n", + "35 -0.01444887 0.03124174 5.600000e-01 Percentile N=1153 (Bootstrap FIML)\n", + "36 -0.03924512 0.02329425 7.200000e-01 Percentile N=1153 (Bootstrap FIML)\n", + "37 0.06413311 0.21541122 0.000000e+00 Percentile N=1153 (Bootstrap FIML)\n", + "38 -0.33889706 0.21142617 7.200000e-01 Percentile N=1153 (Bootstrap FIML)\n", + "39 0.17573770 0.39306147 0.000000e+00 Credible N=1153 (Bayesian)\n", + "40 0.17194479 0.38932622 0.000000e+00 Credible N=1153 (Bayesian)\n", + "41 0.10277104 0.35491314 2.000000e-03 Credible N=1153 (Bayesian)\n", + "42 0.11606274 0.31248405 0.000000e+00 Credible N=1153 (Bayesian)\n", + "43 -2.82674864 0.20792764 1.200000e-01 Credible N=1153 (Bayesian)\n", + "44 -0.28203646 2.75752044 1.430000e-01 Credible N=1153 (Bayesian)\n", + "45 -0.05990779 0.15844060 4.020000e-01 Credible N=1153 (Bayesian)\n", + "46 -0.06314732 0.13396175 4.790000e-01 Credible N=1153 (Bayesian)\n", + "47 0.05885248 0.21562798 0.000000e+00 Credible N=1153 (Bayesian)\n", + "48 -0.83314449 0.05728272 1.200000e-01 Credible N=1153 (Bayesian)\n", + "49 -0.08564675 0.79650353 1.430000e-01 Credible N=1153 (Bayesian)\n", + "50 -0.01308862 0.04258820 4.020000e-01 Credible N=1153 (Bayesian)\n", + "51 -0.01457686 0.03046644 4.790000e-01 Credible N=1153 (Bayesian)\n", + "52 -0.03539606 0.02281522 7.040000e-01 Credible N=1153 (Bayesian)\n", + "53 0.05977100 0.20351101 0.000000e+00 Credible N=1153 (Bayesian)\n", + "54 -0.36033854 0.18150569 7.040000e-01 Credible N=1153 (Bayesian)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Cross-method summary saved.\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# 9. Cross-Method Summary\n", + "# ============================================================\n", + "all_summary <- data.frame()\n", + "\n", + "# FIML\n", + "if (exists(\"labeled\") && nrow(labeled) > 0) {\n", + " fiml_rows <- data.frame(\n", + " exposure = EXPOSURE, method = \"FIML\", label = labeled$label,\n", + " est = labeled$est, se = labeled$se,\n", + " ci_lower = labeled$ci.lower, ci_upper = labeled$ci.upper,\n", + " pvalue = labeled$pvalue, ci_type = \"Wald\",\n", + " n_eff = paste0(\"N=\", N_fiml, \" (FIML)\"), stringsAsFactors = FALSE\n", + " )\n", + " all_summary <- rbind(all_summary, fiml_rows)\n", + "}\n", + "\n", + "# Bootstrap\n", + "if (exists(\"boot_summary\") && nrow(boot_summary) > 0) {\n", + " boot_rows <- data.frame(\n", + " exposure = EXPOSURE, method = \"Bootstrap\", label = boot_summary$label,\n", + " est = boot_summary$boot_mean, se = boot_summary$boot_se,\n", + " ci_lower = boot_summary$ci_lower, ci_upper = boot_summary$ci_upper,\n", + " pvalue = boot_summary$pvalue, ci_type = \"Percentile\",\n", + " n_eff = paste0(\"N=\", N_fiml, \" (Bootstrap FIML)\"), stringsAsFactors = FALSE\n", + " )\n", + " all_summary <- rbind(all_summary, boot_rows)\n", + "}\n", + "\n", + "# Bayesian\n", + "if (exists(\"bayes_summary\") && nrow(bayes_summary) > 0) {\n", + " bayes_rows <- data.frame(\n", + " exposure = EXPOSURE, method = \"Bayesian\", label = bayes_summary$label,\n", + " est = bayes_summary$post_mean, se = bayes_summary$post_sd,\n", + " ci_lower = bayes_summary$ci_lower, ci_upper = bayes_summary$ci_upper,\n", + " pvalue = bayes_summary$pvalue, ci_type = \"Credible\",\n", + " n_eff = paste0(\"N=\", N_bayes, \" (Bayesian)\"), stringsAsFactors = FALSE\n", + " )\n", + " all_summary <- rbind(all_summary, bayes_rows)\n", + "}\n", + "\n", + "rownames(all_summary) <- NULL\n", + "print(all_summary)\n", + "write.csv(all_summary, file.path(DIR_SUMM, \"all_methods_summary.csv\"), row.names=FALSE)\n", + "cat(\"\\nCross-method summary saved.\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T22:58:57.065098Z", + "iopub.status.busy": "2026-03-31T22:58:57.062601Z", + "iopub.status.idle": "2026-03-31T22:58:57.154820Z", + "shell.execute_reply": "2026-03-31T22:58:57.152678Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Effect FIML_est_CI FIML_p Bootstrap_est_CI\n", + "1 a1 0.2790 [0.1692, 0.3889] <0.001 0.2818 [0.1755, 0.3930]\n", + "2 a2 0.2758 [0.1658, 0.3858] <0.001 0.2786 [0.1734, 0.3885]\n", + "3 a3 0.2255 [0.1001, 0.3510] <0.001 0.2232 [0.0981, 0.3507]\n", + "4 a4 0.2128 [0.1165, 0.3091] <0.001 0.2122 [0.1187, 0.2965]\n", + "5 b1 -0.8576 [-2.7108, 0.9957] 0.3644 -0.9239 [-2.7848, 0.7574]\n", + "6 b2 0.7797 [-1.0721, 2.6316] 0.4092 0.8444 [-0.8449, 2.7066]\n", + "7 b3 0.0537 [-0.0600, 0.1673] 0.3548 0.0542 [-0.0714, 0.1762]\n", + "8 b4 0.0329 [-0.0661, 0.1320] 0.5145 0.0337 [-0.0684, 0.1399]\n", + "9 cp 0.1403 [0.0616, 0.2190] <0.001 0.1428 [0.0581, 0.2269]\n", + "10 ind1 -0.2393 [-0.7653, 0.2867] 0.3726 -0.2606 [-0.8468, 0.2108]\n", + "11 ind2 0.2151 [-0.3031, 0.7332] 0.4159 0.2355 [-0.2325, 0.8117]\n", + "12 ind3 0.0121 [-0.0148, 0.0391] 0.3788 0.0117 [-0.0157, 0.0424]\n", + "13 ind4 0.0070 [-0.0143, 0.0283] 0.5193 0.0073 [-0.0144, 0.0312]\n", + "14 total_indirect -0.0051 [-0.0332, 0.0230] 0.7211 -0.0061 [-0.0392, 0.0233]\n", + "15 total 0.1352 [0.0607, 0.2098] <0.001 0.1367 [0.0641, 0.2154]\n", + "16 prop_med -0.0379 [-0.2475, 0.1718] 0.7234 -0.0474 [-0.3389, 0.2114]\n", + " Bootstrap_p Bayesian_est_CI Bayesian_p\n", + "1 <0.001 0.2828 [0.1757, 0.3931] <0.001\n", + "2 <0.001 0.2795 [0.1719, 0.3893] <0.001\n", + "3 <0.001 0.2254 [0.1028, 0.3549] 0.0020\n", + "4 <0.001 0.2148 [0.1161, 0.3125] <0.001\n", + "5 0.3260 -1.2021 [-2.8267, 0.2079] 0.1200\n", + "6 0.3660 1.1268 [-0.2820, 2.7575] 0.1430\n", + "7 0.4220 0.0483 [-0.0599, 0.1584] 0.4020\n", + "8 0.5600 0.0366 [-0.0631, 0.1340] 0.4790\n", + "9 <0.001 0.1388 [0.0589, 0.2156] <0.001\n", + "10 0.3260 -0.3371 [-0.8331, 0.0573] 0.1200\n", + "11 0.3660 0.3120 [-0.0856, 0.7965] 0.1430\n", + "12 0.4220 0.0114 [-0.0131, 0.0426] 0.4020\n", + "13 0.5600 0.0078 [-0.0146, 0.0305] 0.4790\n", + "14 0.7200 -0.0059 [-0.0354, 0.0228] 0.7040\n", + "15 <0.001 0.1328 [0.0598, 0.2035] <0.001\n", + "16 0.7200 -0.0530 [-0.3603, 0.1815] 0.7040\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Display table saved.\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# 9b. Summary Display Table (wide format)\n", + "# ============================================================\n", + "fmt_pval <- function(p) {\n", + " if (is.na(p)) return(NA)\n", + " if (p < 0.001) return(\"<0.001\")\n", + " return(sprintf(\"%.4f\", p))\n", + "}\n", + "\n", + "display_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\")\n", + "\n", + "display_df <- data.frame(Effect = character(), stringsAsFactors=FALSE)\n", + "\n", + "for (lab in display_labels) {\n", + " row_data <- list(Effect = lab)\n", + " for (meth in c(\"FIML\", \"Bootstrap\", \"Bayesian\")) {\n", + " r <- all_summary[all_summary$label == lab & all_summary$method == meth, ]\n", + " if (nrow(r) > 0) {\n", + " row_data[[paste0(meth, \"_est_CI\")]] <- sprintf(\"%.4f [%.4f, %.4f]\",\n", + " r$est[1], r$ci_lower[1], r$ci_upper[1])\n", + " row_data[[paste0(meth, \"_p\")]] <- fmt_pval(r$pvalue[1])\n", + " } else {\n", + " row_data[[paste0(meth, \"_est_CI\")]] <- NA\n", + " row_data[[paste0(meth, \"_p\")]] <- NA\n", + " }\n", + " }\n", + " display_df <- rbind(display_df, as.data.frame(row_data, stringsAsFactors=FALSE))\n", + "}\n", + "\n", + "print(display_df)\n", + "write.csv(display_df, file.path(DIR_SUMM, paste0(\"summary_display_table_\", EXPOSURE, \".csv\")),\n", + " row.names=FALSE)\n", + "cat(\"Display table saved.\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T22:58:57.161840Z", + "iopub.status.busy": "2026-03-31T22:58:57.159579Z", + "iopub.status.idle": "2026-03-31T22:58:58.460455Z", + "shell.execute_reply": "2026-03-31T22:58:58.458499Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary forest plot saved.\n" + ] + }, + { + "data": { + "image/png": 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JYqLyGkieX48ePppVyNRpOYmEgI+fjjjy0XtPTuu+8SQtq2bfvkyROGYUpKSho0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\nqSZ9G41V9I/9Za4mXa0Nxrf+OPkeQgckJCQIhUKhUEgP7MHBwTS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CgUll1CWhEIASDL3Pn9G5bdcF2CBWKxWE9Pj8fjKSgo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ALgIhAAAAACiKS0YBAMPm/T37l616QHYV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\njnXKVQ+BUGdnZ2dnZ9f0RjSo076np6fqtY6OjvpbQoiNjU2D00EFAoHqtkZ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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# 9c. Summary Forest Plot: All Methods\n", + "# ============================================================\n", + "plot_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\")\n", + "sm_plot <- all_summary[all_summary$label %in% plot_labels, ]\n", + "sm_plot$label <- factor(sm_plot$label, levels=rev(plot_labels))\n", + "\n", + "p_all_forest <- ggplot(sm_plot, aes(x=est, y=label, color=method, shape=method)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(aes(xmin=ci_lower, xmax=ci_upper),\n", + " position=position_dodge(width=0.6), size=0.5) +\n", + " scale_color_manual(values=c(\"FIML\"=\"#2C3E50\", \"Bootstrap\"=\"#E67E22\", \"Bayesian\"=\"#8E44AD\")) +\n", + " scale_shape_manual(values=c(\"FIML\"=16, \"Bootstrap\"=17, \"Bayesian\"=15)) +\n", + " labs(title=\"All Methods Comparison: Path Estimates\",\n", + " subtitle=\"Parallel mediation: SNP -> {APOC4/2, APOC2, APOC1, APOE} -> caa_4gp\",\n", + " x=\"Estimate (95% CI)\", y=\"\", color=\"Method\", shape=\"Method\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + "print(p_all_forest)\n", + "ggsave(file.path(DIR_SUMM, \"summary_forest_all_methods.png\"), p_all_forest,\n", + " width=11, height=9, dpi=150)\n", + "ggsave(file.path(DIR_SUMM, \"summary_forest_all_methods.pdf\"), p_all_forest,\n", + " width=11, height=9)\n", + "cat(\"Summary forest plot saved.\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T22:58:58.469631Z", + "iopub.status.busy": "2026-03-31T22:58:58.467310Z", + "iopub.status.idle": "2026-03-31T22:59:02.579438Z", + "shell.execute_reply": "2026-03-31T22:59:02.576703Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Faceted summary plot saved.\n" + ] + }, + { + "data": { + "image/png": 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kU3blepVGZkZEivMzMzMzMzDQZDZWUlz/PR0dFlZWXS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T849//ANvngAAgPr4mBBSFDVs2LCUlJTi4uKwsDBCyLFj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\n8zXe3t4RERFyuZztQNqEUCh01XVSIBC4ubm56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zMzKyurffv2hBCmc/XAgQMlJSUJCQkfPnwQiUTW1tZ2dnaH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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# 9d. Summary: Faceted by Effect Type\n", + "# ============================================================\n", + "sm_plot2 <- sm_plot\n", + "sm_plot2$effect_type <- ifelse(grepl(\"^a\", sm_plot2$label), \"a-path (SNP->Med)\",\n", + " ifelse(grepl(\"^b\", sm_plot2$label), \"b-path (Med->Out)\",\n", + " ifelse(sm_plot2$label == \"cp\", \"Direct (c')\",\n", + " ifelse(grepl(\"^ind[0-9]\", sm_plot2$label), \"Specific indirect\",\n", + " ifelse(sm_plot2$label == \"total_indirect\", \"Total indirect\",\n", + " ifelse(sm_plot2$label == \"total\", \"Total\", \"Prop. mediated\"))))))\n", + "\n", + "p_facet <- ggplot(sm_plot2, aes(x=est, y=method, color=label, shape=method)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(aes(xmin=ci_lower, xmax=ci_upper),\n", + " position=position_dodge(width=0.5), size=0.5) +\n", + " facet_wrap(~effect_type, scales=\"free_x\", ncol=2) +\n", + " labs(title=\"Estimates by Effect Type Across Methods\",\n", + " subtitle=\"Parallel mediation: SNP -> {APOC4/2, APOC2, APOC1, APOE} -> caa_4gp\",\n", + " x=\"Estimate (95% CI)\", y=\"\", color=\"Parameter\", shape=\"Method\") +\n", + " theme_minimal(base_size=11) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + "print(p_facet)\n", + "ggsave(file.path(DIR_SUMM, \"summary_faceted_by_path.png\"), p_facet,\n", + " width=12, height=10, dpi=150)\n", + "ggsave(file.path(DIR_SUMM, \"summary_faceted_by_path.pdf\"), p_facet,\n", + " width=12, height=10)\n", + "cat(\"Faceted summary plot saved.\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T22:59:02.585922Z", + "iopub.status.busy": "2026-03-31T22:59:02.584239Z", + "iopub.status.idle": "2026-03-31T22:59:03.748712Z", + "shell.execute_reply": "2026-03-31T22:59:03.746526Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Indirect effect focus plot saved.\n" + ] + }, + { + "data": { + "image/png": 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7eiKLm5uQ888IDcG5Z1OgysJIfD53BE7G9u5QjJYTMdxuxwlMuxaZRkv+EVMhVt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\nTe9T8/7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BT4p1p6wAULFvTo0YMQ0tDQ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\nAAAkFBJCAAAAAAAAC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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# 9e. Summary: Indirect Effect Focus\n", + "# ============================================================\n", + "ind_focus <- all_summary[all_summary$label %in% c(paste0(\"ind\", 1:n_med), \"total_indirect\"), ]\n", + "\n", + "med_name_map <- c(\"ind1\"=\"APOC4/APOC2 (AC)\", \"ind2\"=\"APOC2 (AC)\",\n", + " \"ind3\"=\"APOC1 (Mic, DeJager)\", \"ind4\"=\"APOE (Mic, Mega)\",\n", + " \"total_indirect\"=\"Total Indirect\")\n", + "ind_focus$med_name <- med_name_map[ind_focus$label]\n", + "ind_focus$med_name <- factor(ind_focus$med_name,\n", + " levels=rev(c(\"APOC4/APOC2 (AC)\", \"APOC2 (AC)\", \"APOC1 (Mic, DeJager)\",\n", + " \"APOE (Mic, Mega)\", \"Total Indirect\")))\n", + "\n", + "p_indirect <- ggplot(ind_focus, aes(x=est, y=med_name, color=method, shape=method)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(aes(xmin=ci_lower, xmax=ci_upper),\n", + " position=position_dodge(width=0.5), size=0.7) +\n", + " scale_color_manual(values=c(\"FIML\"=\"#2C3E50\", \"Bootstrap\"=\"#E67E22\", \"Bayesian\"=\"#8E44AD\")) +\n", + " scale_shape_manual(values=c(\"FIML\"=16, \"Bootstrap\"=17, \"Bayesian\"=15)) +\n", + " labs(title=\"Indirect Effect (a x b) Across Methods\",\n", + " subtitle=\"Specific indirect = unique mediation through each gene, controlling for others\",\n", + " x=\"Indirect Effect (95% CI)\", y=\"\", color=\"Method\", shape=\"Method\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + "print(p_indirect)\n", + "ggsave(file.path(DIR_SUMM, \"summary_indirect_effect.png\"), p_indirect,\n", + " width=10, height=5, dpi=150)\n", + "ggsave(file.path(DIR_SUMM, \"summary_indirect_effect.pdf\"), p_indirect,\n", + " width=10, height=5)\n", + "cat(\"Indirect effect focus plot saved.\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T22:59:03.755980Z", + "iopub.status.busy": "2026-03-31T22:59:03.753583Z", + "iopub.status.idle": "2026-03-31T22:59:05.070687Z", + "shell.execute_reply": "2026-03-31T22:59:05.065991Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Combined panel saved.\n" + ] + }, + { + "data": { + "image/png": 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hJCmlhOmjSJ/pSrVqtjY2MJITNmzDBf0NzkyZMJIe3atXv27BnDMCUlJY0bNxaJ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\nWZS1RV5enru7O2/Pf3J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RZbG8G1wsWl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\n0TE3N5d1RWRGVVWVx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JQkJCWVl53rx5SkpKU6dOFf49AAB8EbgAAADi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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 480, + "width": 600 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# 9f. Summary: Combined Panel\n", + "# ============================================================\n", + "g_combined <- grid.arrange(p_all_forest, p_indirect, nrow=2, heights=c(2, 1))\n", + "ggsave(file.path(DIR_SUMM, \"summary_combined_panel.png\"), g_combined,\n", + " width=11, height=14, dpi=150)\n", + "ggsave(file.path(DIR_SUMM, \"summary_combined_panel.pdf\"), g_combined,\n", + " width=11, height=14)\n", + "cat(\"Combined panel saved.\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Output File Inventory" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T22:59:05.245159Z", + "iopub.status.busy": "2026-03-31T22:59:05.242858Z", + "iopub.status.idle": "2026-03-31T22:59:05.301526Z", + "shell.execute_reply": "2026-03-31T22:59:05.297842Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Output files in: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_4gp \n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "main_SEM_FIML/\n", + " fiml_all_paths.csv\n", + " fiml_fit_measures.csv\n", + " fiml_forest_plot.pdf\n", + " fiml_forest_plot.png\n", + "\n", + "bootstrap/\n", + " bootstrap_distributions_chr19_44954310_T_C.pdf\n", + " bootstrap_distributions_chr19_44954310_T_C.png\n", + " bootstrap_forest_plot.pdf\n", + " bootstrap_forest_plot.png\n", + " bootstrap_results.csv\n", + "\n", + "MNAR_sensitivity/\n", + " mnar_1d_slices_ind1_chr19_44954310_T_C.pdf\n", + " mnar_1d_slices_ind1_chr19_44954310_T_C.png\n", + " mnar_1d_slices_ind2_chr19_44954310_T_C.pdf\n", + " mnar_1d_slices_ind2_chr19_44954310_T_C.png\n", + " mnar_1d_slices_ind3_chr19_44954310_T_C.pdf\n", + " mnar_1d_slices_ind3_chr19_44954310_T_C.png\n", + " mnar_1d_slices_ind4_chr19_44954310_T_C.pdf\n", + " mnar_1d_slices_ind4_chr19_44954310_T_C.png\n", + " mnar_1d_slices_total_indirect_chr19_44954310_T_C.pdf\n", + " mnar_1d_slices_total_indirect_chr19_44954310_T_C.png\n", + " mnar_contour_indirect_chr19_44954310_T_C.pdf\n", + " mnar_contour_indirect_chr19_44954310_T_C.png\n", + " mnar_contour_pvalue_chr19_44954310_T_C.pdf\n", + " mnar_contour_pvalue_chr19_44954310_T_C.png\n", + " mnar_grid_results.csv\n", + " mnar_tipping_summary.csv\n", + "\n", + "bayesian_blavaan/\n", + " bayesian_forest_plot.pdf\n", + " bayesian_forest_plot.png\n", + " bayesian_posteriors_chr19_44954310_T_C.pdf\n", + " bayesian_posteriors_chr19_44954310_T_C.png\n", + " bayesian_results.csv\n", + " bayesian_trace_a_chr19_44954310_T_C.pdf\n", + " bayesian_trace_a_chr19_44954310_T_C.png\n", + "\n", + "summary/\n", + " all_methods_summary.csv\n", + " summary_combined_panel.pdf\n", + " summary_combined_panel.png\n", + " summary_display_table_chr19_44954310_T_C.csv\n", + " summary_faceted_by_path.pdf\n", + " summary_faceted_by_path.png\n", + " summary_forest_all_methods.pdf\n", + " summary_forest_all_methods.png\n", + " summary_indirect_effect.pdf\n", + " summary_indirect_effect.png\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top-level files:\n", + " APOE_ind_set_27_caa_4gp_mediation_all_input.txt\n", + " APOE_ind_set_27_caa_4gp_SEM_mediation.ipynb\n", + " run_reexecute.sh\n", + " session_notes.md\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# 11. Output File Inventory\n", + "# ============================================================\n", + "cat(\"Output files in:\", RESULT_DIR, \"\\n\\n\")\n", + "\n", + "for (subdir in c(\"main_SEM_FIML\", \"bootstrap\", \"MNAR_sensitivity\", \"bayesian_blavaan\", \"summary\")) {\n", + " full_path <- file.path(RESULT_DIR, subdir)\n", + " files <- list.files(full_path, full.names=FALSE)\n", + " if (length(files) > 0) {\n", + " cat(paste0(subdir, \"/\\n\"))\n", + " for (f in files) cat(paste0(\" \", f, \"\\n\"))\n", + " cat(\"\\n\")\n", + " }\n", + "}\n", + "\n", + "top_files <- list.files(RESULT_DIR, full.names=FALSE, recursive=FALSE)\n", + "top_files <- top_files[!top_files %in% c(\"main_SEM_FIML\", \"bootstrap\", \"MNAR_sensitivity\",\n", + " \"bayesian_blavaan\", \"summary\", \"log\")]\n", + "if (length(top_files) > 0) {\n", + " cat(\"Top-level files:\\n\")\n", + " for (f in top_files) cat(paste0(\" \", f, \"\\n\"))\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T22:59:05.307821Z", + "iopub.status.busy": "2026-03-31T22:59:05.305614Z", + "iopub.status.idle": "2026-03-31T22:59:05.334753Z", + "shell.execute_reply": "2026-03-31T22:59:05.332638Z" + } + }, + "outputs": [], + "source": [ + "# ============================================================\n# 12. Save session_notes.md\n# ============================================================\nnotes <- paste0(\n \"# Session Notes: Parallel Mediation Analysis\\n\\n\",\n \"## Analysis Summary\\n\",\n \"- **Date**: \", Sys.Date(), \"\\n\",\n \"- **Design**: Parallel (joint) mediation (Design 2)\\n\",\n \"- **Direction**: Unidirectional (SNP -> M -> Y)\\n\",\n \"- **Exposure**: chr19_44954310_T_C\\n\",\n \"- **Mediators**: APOC4_APOC2_AC, APOC2_AC, APOC1_DeJager_Mic, APOE_Mega_Mic\\n\",\n \"- **Outcome**: caa_4gp (cerebral amyloid angiopathy, 4-group ordinal)\\n\",\n \"- **Total N**: \", nrow(dat), \" (all subjects with SNP observed)\\n\\n\",\n \"## Methods\\n\",\n \"1. FIML SEM (lavaan) - primary analysis, N=\", N_fiml, \"\\n\",\n \"2. Bootstrap (\", N_BOOT, \" replicates, FIML inside each)\\n\",\n \"3. MNAR sensitivity (delta-shift grid)\\n\",\n \"4. Bayesian SEM (blavaan/Stan) - \", N_CHAINS, \" chains x \", N_SAMPLE, \" samples\\n\\n\",\n \"## Covariate Strategy\\n\",\n \"Strategy A (covariates-in-model):\\n\",\n \"- M1 & M2 (AC): msex_u, age_death_u, pmi_u, ROS_study_u\\n\",\n \"- M3 & M4 (Mic): msex_u, age_death_u, pmi_u\\n\",\n \"- Y (caa_4gp): educ, apoe4_dose, apoe2_dose, msex_u, age_death_u\\n\\n\",\n \"## Sample Sizes\\n\",\n \"- SNP: 1153/1153\\n\",\n \"- APOC4_APOC2_AC: 593/1153\\n\",\n \"- APOC2_AC: 593/1153\\n\",\n \"- APOC1_DeJager_Mic: 419/1153\\n\",\n \"- APOE_Mega_Mic: 733/1153\\n\",\n \"- caa_4gp: 1050/1153\\n\\n\",\n \"## Key Findings\\n\",\n \"See all_methods_summary.csv and the conclusion section of the notebook.\\n\\n\",\n \"## Sensitivity Analysis\\n\",\n \"A 3-mediator model excluding APOC4_APOC2_AC_exp was run to address collinearity\\n\",\n \"between APOC4/APOC2 and APOC2 (both AC cortex). Results are in\\n\",\n \"main_SEM_FIML/sensitivity_analysis/. See comparison_4med_vs_3med.csv.\\n\\n\",\n \"## Recommended Follow-up\\n\",\n \"1. Test each mediator individually (Design 1) to compare specific vs marginal effects.\\n\",\n \"2. Consider serial mediation if biological chain is hypothesized.\\n\",\n \"3. E-value analysis for sensitivity to unmeasured confounding.\\n\"\n)\n\nwriteLines(notes, file.path(RESULT_DIR, \"session_notes.md\"))\ncat(\"session_notes.md saved.\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T22:59:05.341581Z", + "iopub.status.busy": "2026-03-31T22:59:05.339313Z", + "iopub.status.idle": "2026-03-31T22:59:05.843344Z", + "shell.execute_reply": "2026-03-31T22:59:05.840967Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Session Info ---\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in system(\"timedatectl\", intern = TRUE):\n", + "\u201crunning command 'timedatectl' had status 1\u201d\n" + ] + }, + { + "data": { + "text/plain": [ + "R version 4.4.3 (2025-02-28)\n", + "Platform: x86_64-conda-linux-gnu\n", + "Running under: Ubuntu 24.04.3 LTS\n", + "\n", + "Matrix products: default\n", + "BLAS/LAPACK: /mnt/lustre/home/yl4437/.pixi/envs/python/lib/libopenblasp-r0.3.30.so; LAPACK version 3.12.0\n", + "\n", + "locale:\n", + " [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 \n", + " [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 \n", + " [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C \n", + "[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C \n", + "\n", + "time zone: Etc/UTC\n", + "tzcode source: system (glibc)\n", + "\n", + "attached base packages:\n", + "[1] grid stats graphics grDevices utils datasets methods \n", + "[8] base \n", + "\n", + "other attached packages:\n", + "[1] reshape2_1.4.5 gridExtra_2.3 blavaan_0.5-10 Rcpp_1.1.1 lavaan_0.6-21 \n", + "[6] ggplot2_4.0.2 \n", + "\n", + "loaded via a namespace (and not attached):\n", + " [1] gtable_0.3.6 QuickJSR_1.9.0 inline_0.3.21 \n", + " [4] CompQuadForm_1.4.4 lattice_0.22-9 quadprog_1.5-8 \n", + " [7] vctrs_0.7.1 tools_4.4.3 generics_0.1.4 \n", + "[10] stats4_4.4.3 parallel_4.4.3 sandwich_3.1-1 \n", + "[13] tibble_3.3.1 pkgconfig_2.0.3 Matrix_1.7-4 \n", + "[16] RColorBrewer_1.1-3 S7_0.2.1 RcppParallel_5.1.11-2\n", + "[19] uuid_1.2-2 lifecycle_1.0.5 tmvnsim_1.0-2 \n", + "[22] stringr_1.6.0 compiler_4.4.3 farver_2.1.2 \n", + "[25] textshaping_1.0.4 mnormt_2.1.2 repr_1.1.7 \n", + "[28] codetools_0.2-20 htmltools_0.5.9 bayesplot_1.15.0 \n", + "[31] pillar_1.11.1 crayon_1.5.3 MASS_7.3-65 \n", + "[34] StanHeaders_2.32.10 parallelly_1.46.1 rstan_2.32.7 \n", + "[37] tidyselect_1.2.1 digest_0.6.39 stringi_1.8.7 \n", + "[40] mvtnorm_1.3-3 future_1.69.0 nonnest2_0.5-8 \n", + "[43] dplyr_1.2.0 listenv_0.10.0 labeling_0.4.3 \n", + "[46] fastmap_1.2.0 cli_3.6.5 magrittr_2.0.4 \n", + "[49] loo_2.9.0 base64enc_0.1-6 pkgbuild_1.4.8 \n", + "[52] IRdisplay_1.1 future.apply_1.20.1 pbivnorm_0.6.0 \n", + "[55] withr_3.0.2 scales_1.4.0 IRkernel_1.3.2 \n", + "[58] matrixStats_1.5.0 globals_0.19.0 igraph_2.2.2 \n", + "[61] pbdZMQ_0.3-14 ragg_1.5.0 zoo_1.8-15 \n", + "[64] coda_0.19-4.1 evaluate_1.0.5 viridisLite_0.4.3 \n", + "[67] rstantools_2.6.0 rlang_1.1.7 isoband_0.3.0 \n", + "[70] glue_1.8.0 jsonlite_2.0.0 plyr_1.8.9 \n", + "[73] R6_2.6.1 systemfonts_1.3.1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# 13. Cleanup and Session Info\n", + "# ============================================================\n", + "temp_patterns <- c(\"lavExport*\", \"*.stan\", \"tmp_*\", \"*.bak\")\n", + "for (pat in temp_patterns) {\n", + " files <- Sys.glob(file.path(RESULT_DIR, pat))\n", + " if (length(files) > 0) file.remove(files)\n", + " files <- Sys.glob(pat)\n", + " if (length(files) > 0) file.remove(files)\n", + "}\n", + "\n", + "cat(\"\\n--- Session Info ---\\n\")\n", + "sessionInfo()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_caa_neo4_APOE4_2_adj_SEM_mediation.ipynb b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_caa_neo4_APOE4_2_adj_SEM_mediation.ipynb new file mode 100644 index 0000000..3f37222 --- /dev/null +++ b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_caa_neo4_APOE4_2_adj_SEM_mediation.ipynb @@ -0,0 +1,3764 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "39cdad97", + "metadata": {}, + "source": [ + "# SEM Mediation Analysis: Parallel Design (4 Mediators)\n", + "## APOE Region SNP -> Gene Expression -> CAA Neuropathology\n", + "\n", + "**Aim:** Test whether the effect of the APOE-region SNP `chr19_44938650_G_C` on cerebral amyloid angiopathy \n", + "(CAA, neocortical score `caa_neo4`) is mediated through gene expression of four genes in a **parallel mediation** model:\n", + "\n", + "1. **APOC4/APOC2** (anterior cingulate cortex expression)\n", + "2. **APOC2** (anterior cingulate cortex expression) \n", + "3. **APOC1** (De Jager microglia expression)\n", + "4. **APOE** (Mega microglia expression)\n", + "\n", + "All four mediators enter one simultaneous model so we estimate **specific indirect effects** \n", + "(the unique mediation contribution of each gene, controlling for the other three) and the **total indirect effect**.\n", + "\n", + "**Design:** Parallel mediation (Design 2), unidirectional: SNP -> {M1, M2, M3, M4} -> Y\n", + "\n", + "**Dataset:** Set 27, N = 1153 subjects with partially overlapping phenotype availability.\n" + ] + }, + { + "cell_type": "markdown", + "id": "b5840a87", + "metadata": {}, + "source": [ + "## Directed Acyclic Graph (DAG)\n", + "\n", + "```\n", + " a1 b1\n", + " +--------> [APOC4_APOC2] --------+\n", + " | a2 b2 |\n", + " +--------> [APOC2] --------+ |\n", + " [chr19_44938650]--+ +---> [caa_neo4]\n", + " | a3 b3 |\n", + " +--------> [APOC1_Mic] --------+ |\n", + " | a4 b4 |\n", + " +--------> [APOE_Mic] ---------+\n", + " | ^\n", + " | c' (direct) |\n", + " +----------------------------------------------+\n", + "```\n", + "\n", + "**Specific indirect effects:**\n", + "- ind1 = a1 x b1 (via APOC4/APOC2 AC expression)\n", + "- ind2 = a2 x b2 (via APOC2 AC expression)\n", + "- ind3 = a3 x b3 (via APOC1 DeJager microglia expression)\n", + "- ind4 = a4 x b4 (via APOE Mega microglia expression)\n", + "\n", + "**Total indirect** = ind1 + ind2 + ind3 + ind4 \n", + "**Total effect** = c' + total_indirect\n", + "\n", + "**Covariates:** Each mediator equation includes its own covariates (Strategy A: covariates-in-model).\n", + "- M1 (APOC4_APOC2_AC): msex_u, age_death_u, pmi_u, ROS_study_u\n", + "- M2 (APOC2_AC): msex_u, age_death_u, pmi_u, ROS_study_u\n", + "- M3 (APOC1_DeJager_Mic): msex_u, age_death_u, pmi_u\n", + "- M4 (APOE_Mega_Mic): msex_u, age_death_u, pmi_u\n", + "- Y (caa_neo4): educ, apoe4_dose, apoe2_dose, msex_u, age_death_u\n", + "\n", + "**Residual correlations** M_i ~~ M_j are freely estimated for all mediator pairs.\n" + ] + }, + { + "cell_type": "markdown", + "id": "892bff60", + "metadata": {}, + "source": "## Key Findings & Conclusion\n\n### Parallel Mediation: chr19_44938650_G_C \u2192 {APOC4_APOC2_AC, APOC2_AC, APOC1_Mic, APOE_Mic} \u2192 caa_neo4\n\n**Overall result: No significant mediation detected through any of the four mediators.**\n\n| Path | FIML Estimate [95% CI] | Bootstrap [95% CI] | Bayesian [95% CrI] | Significant? |\n|------|----------------------|-------------------|-------------------|:---:|\n| **a1: SNP\u2192APOC4/APOC2_AC** | 0.250 [0.139, 0.362] | 0.250 [0.138, 0.365] | 0.249 [0.133, 0.361] | Yes (all 3) |\n| **a2: SNP\u2192APOC2_AC** | 0.247 [0.136, 0.358] | 0.247 [0.135, 0.362] | 0.246 [0.129, 0.357] | Yes (all 3) |\n| **a3: SNP\u2192APOC1_Mic** | 0.284 [0.161, 0.407] | 0.282 [0.167, 0.399] | 0.282 [0.161, 0.400] | Yes (all 3) |\n| **a4: SNP\u2192APOE_Mic** | 0.210 [0.113, 0.307] | 0.211 [0.118, 0.310] | 0.210 [0.118, 0.303] | Yes (all 3) |\n| **b1: APOC4/APOC2_AC\u2192CAA** | \u22120.791 [\u22122.900, 1.318] | \u22120.827 [\u22122.839, 1.133] | \u22120.878 [\u22122.801, 1.252] | No |\n| **b2: APOC2_AC\u2192CAA** | 0.731 [\u22121.377, 2.838] | 0.767 [\u22121.197, 2.779] | 0.819 [\u22121.318, 2.773] | No |\n| **b3: APOC1_Mic\u2192CAA** | \u22120.009 [\u22120.142, 0.124] | \u22120.009 [\u22120.154, 0.139] | \u22120.015 [\u22120.141, 0.121] | No |\n| **b4: APOE_Mic\u2192CAA** | 0.082 [\u22120.031, 0.195] | 0.081 [\u22120.052, 0.204] | 0.085 [\u22120.035, 0.194] | No |\n| **ind1: via APOC4/APOC2_AC** | \u22120.198 [\u22120.734, 0.337] | \u22120.209 [\u22120.797, 0.278] | \u22120.220 [\u22120.775, 0.325] | No |\n| **ind2: via APOC2_AC** | 0.181 [\u22120.347, 0.708] | 0.192 [\u22120.296, 0.767] | 0.203 [\u22120.336, 0.750] | No |\n| **ind3: via APOC1_Mic** | \u22120.003 [\u22120.040, 0.035] | \u22120.003 [\u22120.048, 0.042] | \u22120.004 [\u22120.040, 0.035] | No |\n| **ind4: via APOE_Mic** | 0.017 [\u22120.008, 0.042] | 0.017 [\u22120.012, 0.049] | 0.018 [\u22120.006, 0.047] | No |\n| **Total indirect** | \u22120.003 [\u22120.035, 0.029] | \u22120.003 [\u22120.039, 0.033] | \u22120.004 [\u22120.038, 0.029] | No |\n| **c' (direct)** | 0.154 [0.065, 0.243] | 0.156 [0.067, 0.246] | 0.155 [0.066, 0.246] | Yes (all 3) |\n| **Total effect** | 0.151 [0.067, 0.235] | 0.152 [0.066, 0.235] | 0.152 [0.066, 0.238] | Yes (all 3) |\n\n**Key takeaways:**\n1. **All a-paths significant**: The SNP significantly predicts all four mediators, with consistent estimates across FIML, Bootstrap, and Bayesian methods.\n2. **No b-paths significant**: None of the four mediators significantly predict caa_neo4.\n3. **No indirect effects significant**: All four specific indirect effects and total indirect are non-significant (0/3 methods).\n4. **Strong direct effect**: c' ~ 0.155 remains significant \u2014 the SNP effect on caa_neo4 is not mediated.\n5. **Proportion mediated ~ -2%**: Essentially zero.\n6. **MNAR sensitivity**: Robust to missing-not-at-random assumptions.\n7. **Cross-method consistency**: FIML, Bootstrap, and Bayesian estimates are highly concordant.\n\n### Sensitivity Analysis: 3 Mediators (Excluding APOC4_APOC2_AC)\n\nBecause APOC4_APOC2_AC and APOC2_AC showed collinearity (large opposing b-paths, wide CIs), we re-ran FIML excluding APOC4_APOC2_AC:\n\n| Path | 3-mediator FIML [95% CI] | vs 4-mediator |\n|------|-------------------------|---------------|\n| **b1: APOC2_AC\u2192CAA** | \u22120.060 [\u22120.146, 0.026], p=0.174 | CIs dramatically narrower (was [\u22121.4, 2.8]) |\n| **b2: APOC1_Mic\u2192CAA** | \u22120.002 [\u22120.135, 0.130], p=0.974 | Stable |\n| **b3: APOE_Mic\u2192CAA** | 0.080 [\u22120.033, 0.193], p=0.167 | Stable |\n| **ind1: via APOC2_AC** | \u22120.015 [\u22120.037, 0.007], p=0.192 | Reasonable CI; still non-significant |\n| **ind3: via APOE_Mic** | 0.017 [\u22120.008, 0.042], p=0.190 | Stable |\n| **Total indirect** | 0.001 [\u22120.029, 0.031], p=0.926 | Still non-significant |\n| **c' (direct)** | 0.149 [0.061, 0.238], p=0.001 | Stable |\n| **diff_1_3 (APOC2 vs APOE)** | \u22120.031 [\u22120.062, \u22120.001], p=0.046 | Marginally significant contrast |\n\n**Conclusion:** Removing the collinear mediator resolves estimation instability but does not change the substantive finding \u2014 no significant mediation. The direct effect is robust (c' ~ 0.15, p=0.001)." + }, + { + "cell_type": "markdown", + "id": "c384d11c", + "metadata": {}, + "source": [ + "## 2. Input Specification\n", + "\n", + "| Parameter | Value |\n", + "|-----------|-------|\n", + "| **Data file** | `/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_neo4/APOE_ind_set_27_caa_neo4_mediation_all_input.txt` |\n", + "| **Exposure** | `chr19_44938650_G_C` |\n", + "| **Mediators** | APOC4_APOC2_AC_exp, APOC2_AC_exp, APOC1_DeJager_Mic_exp, APOE_Mega_Mic_exp |\n", + "| **Outcome** | `caa_neo4` |\n", + "| **Design** | Parallel mediation (Design 2) |\n", + "| **Direction** | Unidirectional: SNP -> M -> Y |\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ef341e2c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:20:08.069300Z", + "iopub.status.busy": "2026-03-31T16:20:08.064255Z", + "iopub.status.idle": "2026-03-31T16:20:10.401485Z", + "shell.execute_reply": "2026-03-31T16:20:10.398876Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setup complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exposure: chr19_44938650_G_C \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mediators: APOC4_APOC2_AC_exp, APOC2_AC_exp, APOC1_DeJager_Mic_exp, APOE_Mega_Mic_exp \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Outcome: caa_neo4 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All covariates: msex_u, age_death_u, pmi_u, ROS_study_u, educ, apoe4_dose, apoe2_dose \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Setup: Libraries, paths, output directories\n", + "# ============================================================\n", + "suppressPackageStartupMessages({\n", + " library(lavaan)\n", + " library(ggplot2)\n", + " library(gridExtra)\n", + " library(reshape2)\n", + "})\n", + "\n", + "# Paths\n", + "DATA_FILE <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_neo4/APOE_ind_set_27_caa_neo4_mediation_all_input.txt\"\n", + "RESULT_DIR <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_neo4/APOE4_2_adj\"\n", + "\n", + "fiml_dir <- file.path(RESULT_DIR, \"main_SEM_FIML\")\n", + "boot_dir <- file.path(RESULT_DIR, \"bootstrap\")\n", + "mnar_dir <- file.path(RESULT_DIR, \"MNAR_sensitivity\")\n", + "bayes_dir <- file.path(RESULT_DIR, \"bayesian_blavaan\")\n", + "summ_dir <- file.path(RESULT_DIR, \"summary\")\n", + "\n", + "for (d in c(fiml_dir, boot_dir, mnar_dir, bayes_dir, summ_dir)) {\n", + " dir.create(d, recursive = TRUE, showWarnings = FALSE)\n", + "}\n", + "\n", + "# Variable names\n", + "SNP_COL <- \"chr19_44938650_G_C\"\n", + "MED_COLS <- c(\"APOC4_APOC2_AC_exp\", \"APOC2_AC_exp\", \"APOC1_DeJager_Mic_exp\", \"APOE_Mega_Mic_exp\")\n", + "MED_SHORT <- c(\"APOC4_APOC2_AC\", \"APOC2_AC\", \"APOC1_Mic\", \"APOE_Mic\")\n", + "OUT_COL <- \"caa_neo4\"\n", + "\n", + "# Covariates per mediator equation\n", + "MED_COVS <- list(\n", + " M1 = c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\"),\n", + " M2 = c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\"),\n", + " M3 = c(\"msex_u\", \"age_death_u\", \"pmi_u\"),\n", + " M4 = c(\"msex_u\", \"age_death_u\", \"pmi_u\")\n", + ")\n", + "OUT_COVS <- c(\"educ\", \"apoe4_dose\", \"apoe2_dose\", \"msex_u\", \"age_death_u\")\n", + "\n", + "ALL_COVS <- unique(c(unlist(MED_COVS), OUT_COVS))\n", + "\n", + "cat(\"Setup complete.\\n\")\n", + "cat(\"Exposure:\", SNP_COL, \"\\n\")\n", + "cat(\"Mediators:\", paste(MED_COLS, collapse=\", \"), \"\\n\")\n", + "cat(\"Outcome:\", OUT_COL, \"\\n\")\n", + "cat(\"All covariates:\", paste(ALL_COVS, collapse=\", \"), \"\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "ce8a6b44", + "metadata": {}, + "source": [ + "## 3. Data Loading and Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "44834433", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:20:10.460237Z", + "iopub.status.busy": "2026-03-31T16:20:10.407641Z", + "iopub.status.idle": "2026-03-31T16:20:10.563186Z", + "shell.execute_reply": "2026-03-31T16:20:10.560536Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded data: 1153 rows x 58 columns\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N with exposure observed: 1153 \n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Variable N_observed N_missing Pct_missing\n", + "1 chr19_44938650_G_C 1153 0 0.0\n", + "2 APOC4_APOC2_AC_exp 593 560 48.6\n", + "3 APOC2_AC_exp 593 560 48.6\n", + "4 APOC1_DeJager_Mic_exp 419 734 63.7\n", + "5 APOE_Mega_Mic_exp 733 420 36.4\n", + "6 caa_neo4 1050 103 8.9\n", + "7 msex_u 1140 13 1.1\n", + "8 age_death_u 1140 13 1.1\n", + "9 pmi_u 1140 13 1.1\n", + "10 ROS_study_u 1115 38 3.3\n", + "11 educ 1113 40 3.5\n", + "12 apoe4_dose 1106 47 4.1\n", + "13 apoe2_dose 1106 47 4.1\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Load data\n", + "# ============================================================\n", + "dat <- read.delim(DATA_FILE, header=TRUE, sep=\"\\t\", stringsAsFactors=FALSE)\n", + "cat(\"Loaded data:\", nrow(dat), \"rows x\", ncol(dat), \"columns\\n\\n\")\n", + "\n", + "# Keep only needed columns\n", + "keep_cols <- c(SNP_COL, MED_COLS, OUT_COL, ALL_COVS)\n", + "missing_cols <- setdiff(keep_cols, colnames(dat))\n", + "if (length(missing_cols) > 0) stop(\"Missing columns: \", paste(missing_cols, collapse=\", \"))\n", + "\n", + "dat_sub <- dat[, keep_cols]\n", + "\n", + "# Convert all to numeric\n", + "for (col in colnames(dat_sub)) {\n", + " dat_sub[[col]] <- as.numeric(dat_sub[[col]])\n", + "}\n", + "\n", + "# Require exposure to be observed\n", + "dat_sub <- dat_sub[!is.na(dat_sub[[SNP_COL]]), ]\n", + "N_total <- nrow(dat_sub)\n", + "cat(\"N with exposure observed:\", N_total, \"\\n\\n\")\n", + "\n", + "# Missingness summary\n", + "miss_tbl <- data.frame(\n", + " Variable = c(SNP_COL, MED_COLS, OUT_COL, ALL_COVS),\n", + " N_observed = sapply(c(SNP_COL, MED_COLS, OUT_COL, ALL_COVS), function(v) sum(!is.na(dat_sub[[v]]))),\n", + " N_missing = sapply(c(SNP_COL, MED_COLS, OUT_COL, ALL_COVS), function(v) sum(is.na(dat_sub[[v]]))),\n", + " Pct_missing = sapply(c(SNP_COL, MED_COLS, OUT_COL, ALL_COVS), function(v) round(100*mean(is.na(dat_sub[[v]])),1))\n", + ")\n", + "rownames(miss_tbl) <- NULL\n", + "print(miss_tbl)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a3613646", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:20:10.568541Z", + "iopub.status.busy": "2026-03-31T16:20:10.566972Z", + "iopub.status.idle": "2026-03-31T16:20:10.619354Z", + "shell.execute_reply": "2026-03-31T16:20:10.616753Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Sample Overlap Structure ===\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "APOC4_APOC2_AC : Both=564, M-only=29, Y-only=486, Neither=74\n", + "APOC2_AC : Both=564, M-only=29, Y-only=486, Neither=74\n", + "APOC1_Mic : Both=387, M-only=32, Y-only=663, Neither=71\n", + "APOE_Mic : Both=677, M-only=56, Y-only=373, Neither=47\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Overall: Any mediator observed=813, Outcome observed=1050, Both=751\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Full FIML sample (exposure observed): N=1153\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Sample overlap structure\n", + "# ============================================================\n", + "cat(\"\\n=== Sample Overlap Structure ===\\n\\n\")\n", + "\n", + "# For each mediator-outcome pair, show overlap\n", + "for (i in seq_along(MED_COLS)) {\n", + " m <- MED_COLS[i]\n", + " has_m <- !is.na(dat_sub[[m]])\n", + " has_y <- !is.na(dat_sub[[OUT_COL]])\n", + " cat(sprintf(\"%-30s: Both=%d, M-only=%d, Y-only=%d, Neither=%d\\n\",\n", + " MED_SHORT[i],\n", + " sum(has_m & has_y),\n", + " sum(has_m & !has_y),\n", + " sum(!has_m & has_y),\n", + " sum(!has_m & !has_y)))\n", + "}\n", + "\n", + "# Overall: subjects with at least one mediator or the outcome\n", + "has_any_m <- rowSums(!is.na(dat_sub[, MED_COLS])) > 0\n", + "has_y <- !is.na(dat_sub[[OUT_COL]])\n", + "cat(sprintf(\"\\nOverall: Any mediator observed=%d, Outcome observed=%d, Both=%d\\n\",\n", + " sum(has_any_m), sum(has_y), sum(has_any_m & has_y)))\n", + "cat(sprintf(\"Full FIML sample (exposure observed): N=%d\\n\", N_total))\n" + ] + }, + { + "cell_type": "markdown", + "id": "87019829", + "metadata": {}, + "source": [ + "## 4. Parallel Mediation Model Specification\n", + "\n", + "**Strategy A: Covariates-in-model.** Each mediator equation includes its own covariates. \n", + "The outcome equation includes its own covariates. FIML handles the different missingness patterns.\n", + "\n", + "**Residual correlations:** All 6 pairwise mediator residual covariances (M_i ~~ M_j) are freely estimated,\n", + "acknowledging shared variance not explained by the SNP.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "58ee013e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:20:10.625061Z", + "iopub.status.busy": "2026-03-31T16:20:10.623486Z", + "iopub.status.idle": "2026-03-31T16:20:10.696052Z", + "shell.execute_reply": "2026-03-31T16:20:10.693456Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Parallel Mediation Model ===\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "APOC4_APOC2_AC_exp ~ a1 * chr19_44938650_G_C + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC2_AC_exp ~ a2 * chr19_44938650_G_C + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC1_DeJager_Mic_exp ~ a3 * chr19_44938650_G_C + msex_u + age_death_u + pmi_u\n", + "APOE_Mega_Mic_exp ~ a4 * chr19_44938650_G_C + msex_u + age_death_u + pmi_u\n", + "caa_neo4 ~ b1 * APOC4_APOC2_AC_exp + b2 * APOC2_AC_exp + b3 * APOC1_DeJager_Mic_exp + b4 * APOE_Mega_Mic_exp + cp * chr19_44938650_G_C + educ + apoe4_dose + apoe2_dose + msex_u + age_death_u\n", + "ind1 := a1 * b1\n", + "ind2 := a2 * b2\n", + "ind3 := a3 * b3\n", + "ind4 := a4 * b4\n", + "total_indirect := ind1 + ind2 + ind3 + ind4\n", + "total := cp + total_indirect\n", + "prop_med := total_indirect / total\n", + "diff_1_2 := ind1 - ind2\n", + "diff_1_3 := ind1 - ind3\n", + "diff_1_4 := ind1 - ind4\n", + "diff_2_3 := ind2 - ind3\n", + "diff_2_4 := ind2 - ind4\n", + "diff_3_4 := ind3 - ind4\n", + "APOC4_APOC2_AC_exp ~~ APOC2_AC_exp\n", + "APOC4_APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC4_APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC1_DeJager_Mic_exp ~~ APOE_Mega_Mic_exp \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Build parallel mediation model string\n", + "# ============================================================\n", + "n_med <- length(MED_COLS)\n", + "\n", + "# Mediator equations: Mi ~ ai * SNP + covariates\n", + "med_eqs <- character(n_med)\n", + "for (i in 1:n_med) {\n", + " cov_str <- paste(MED_COVS[[paste0(\"M\", i)]], collapse = \" + \")\n", + " med_eqs[i] <- paste0(MED_COLS[i], \" ~ a\", i, \" * \", SNP_COL, \" + \", cov_str)\n", + "}\n", + "\n", + "# Outcome equation: Y ~ b1*M1 + b2*M2 + b3*M3 + b4*M4 + cp*SNP + covariates\n", + "y_med_terms <- paste0(\"b\", 1:n_med, \" * \", MED_COLS, collapse = \" + \")\n", + "out_cov_str <- paste(OUT_COVS, collapse = \" + \")\n", + "y_eq <- paste0(OUT_COL, \" ~ \", y_med_terms, \" + cp * \", SNP_COL, \" + \", out_cov_str)\n", + "\n", + "# Specific indirect effects\n", + "ind_defs <- paste0(\"ind\", 1:n_med, \" := a\", 1:n_med, \" * b\", 1:n_med)\n", + "\n", + "# Total indirect and total\n", + "total_ind <- paste0(\"total_indirect := \", paste0(\"ind\", 1:n_med, collapse = \" + \"))\n", + "total_def <- \"total := cp + total_indirect\"\n", + "\n", + "# Proportion mediated\n", + "prop_med_def <- \"prop_med := total_indirect / total\"\n", + "\n", + "# Pairwise contrasts\n", + "contrasts <- c()\n", + "for (i in 1:(n_med-1)) {\n", + " for (j in (i+1):n_med) {\n", + " contrasts <- c(contrasts, paste0(\"diff_\", i, \"_\", j, \" := ind\", i, \" - ind\", j))\n", + " }\n", + "}\n", + "\n", + "# Residual correlations between mediators (explicitly)\n", + "cov_terms <- c()\n", + "for (i in 1:(n_med-1)) {\n", + " for (j in (i+1):n_med) {\n", + " cov_terms <- c(cov_terms, paste0(MED_COLS[i], \" ~~ \", MED_COLS[j]))\n", + " }\n", + "}\n", + "\n", + "model_str <- paste(c(med_eqs, y_eq, ind_defs, total_ind, total_def, prop_med_def, contrasts, cov_terms), \n", + " collapse = \"\\n\")\n", + "\n", + "cat(\"=== Parallel Mediation Model ===\\n\\n\")\n", + "cat(model_str, \"\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "e0d94fb4", + "metadata": {}, + "source": [ + "## 5. Method 1: FIML SEM (Full Information Maximum Likelihood)\n", + "\n", + "FIML uses **all N subjects** who have the exposure observed, even if they are missing one or more \n", + "mediators or the outcome. This maximizes statistical power by leveraging partially overlapping samples.\n", + "`fixed.x=FALSE` ensures the SNP is modeled as a random variable, allowing subjects with only the SNP \n", + "observed to contribute information to the model.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c3b5e2f6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:20:10.702419Z", + "iopub.status.busy": "2026-03-31T16:20:10.700228Z", + "iopub.status.idle": "2026-03-31T16:20:15.608329Z", + "shell.execute_reply": "2026-03-31T16:20:15.605633Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting FIML SEM with N = 1153 ...\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FIML converged successfully.\n", + "N used: 1153 \n", + "\n", + " label est se ci.lower ci.upper z pvalue N\n", + " a1 0.250 0.057 0.139 0.362 4.417 0.000 1153\n", + " a2 0.247 0.057 0.136 0.358 4.350 0.000 1153\n", + " a3 0.284 0.063 0.161 0.407 4.513 0.000 1153\n", + " a4 0.210 0.049 0.113 0.307 4.245 0.000 1153\n", + " b1 -0.791 1.076 -2.900 1.318 -0.735 0.462 1153\n", + " b2 0.731 1.075 -1.377 2.838 0.680 0.497 1153\n", + " b3 -0.009 0.068 -0.142 0.124 -0.131 0.895 1153\n", + " b4 0.082 0.058 -0.031 0.195 1.418 0.156 1153\n", + " cp 0.154 0.045 0.065 0.243 3.379 0.001 1153\n", + " ind1 -0.198 0.273 -0.734 0.337 -0.725 0.468 1153\n", + " ind2 0.180 0.269 -0.347 0.708 0.671 0.502 1153\n", + " ind3 -0.003 0.019 -0.040 0.035 -0.132 0.895 1153\n", + " ind4 0.017 0.013 -0.008 0.042 1.341 0.180 1153\n", + " total_indirect -0.003 0.016 -0.035 0.029 -0.183 0.855 1153\n", + " total 0.151 0.043 0.066 0.235 3.509 0.000 1153\n", + " prop_med -0.020 0.108 -0.232 0.193 -0.182 0.855 1153\n", + " diff_1_2 -0.379 0.542 -1.441 0.684 -0.699 0.485 1153\n", + " diff_1_3 -0.196 0.272 -0.729 0.338 -0.719 0.472 1153\n", + " diff_1_4 -0.215 0.274 -0.752 0.321 -0.786 0.432 1153\n", + " diff_2_3 0.183 0.272 -0.349 0.715 0.674 0.500 1153\n", + " diff_2_4 0.163 0.269 -0.363 0.690 0.608 0.543 1153\n", + " diff_3_4 -0.020 0.030 -0.078 0.038 -0.667 0.505 1153\n", + "\n", + "Saved: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_neo4/APOE4_2_adj/main_SEM_FIML/fiml_all_paths.csv \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# FIML SEM fit\n", + "# ============================================================\n", + "cat(\"Fitting FIML SEM with N =\", N_total, \"...\\n\\n\")\n", + "\n", + "fit_fiml <- tryCatch(\n", + " sem(model_str, data = dat_sub, missing = \"fiml\", fixed.x = FALSE, estimator = \"ML\"),\n", + " error = function(e) { cat(\"FIML Error:\", conditionMessage(e), \"\\n\"); NULL }\n", + ")\n", + "\n", + "if (!is.null(fit_fiml) && lavInspect(fit_fiml, \"converged\")) {\n", + " cat(\"FIML converged successfully.\\n\")\n", + " cat(\"N used:\", lavInspect(fit_fiml, \"nobs\"), \"\\n\\n\")\n", + " \n", + " # Summary\n", + " pe <- parameterEstimates(fit_fiml, ci = TRUE)\n", + " \n", + " # Extract key labeled parameters\n", + " key_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\",\n", + " paste0(\"diff_\", apply(combn(n_med, 2), 2, paste, collapse=\"_\"), \"\"))\n", + " # Fix contrast labels\n", + " contrast_labels <- c()\n", + " for (i in 1:(n_med-1)) {\n", + " for (j in (i+1):n_med) {\n", + " contrast_labels <- c(contrast_labels, paste0(\"diff_\", i, \"_\", j))\n", + " }\n", + " }\n", + " key_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\",\n", + " contrast_labels)\n", + " \n", + " key_pe <- pe[pe$label %in% key_labels, c(\"label\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"z\", \"pvalue\")]\n", + " key_pe$N <- N_total\n", + " key_pe <- key_pe[match(key_labels[key_labels %in% key_pe$label], key_pe$label), ]\n", + " \n", + " print(key_pe, digits=4, row.names=FALSE)\n", + " \n", + " # Save\n", + " write.csv(key_pe, file.path(fiml_dir, \"fiml_all_paths.csv\"), row.names = FALSE)\n", + " cat(\"\\nSaved:\", file.path(fiml_dir, \"fiml_all_paths.csv\"), \"\\n\")\n", + " \n", + " # Also save fit measures\n", + " fm <- fitMeasures(fit_fiml, c(\"chisq\", \"df\", \"pvalue\", \"cfi\", \"tli\", \"rmsea\", \"srmr\", \"aic\", \"bic\"))\n", + " fm_df <- data.frame(measure = names(fm), value = as.numeric(fm), N = N_total)\n", + " write.csv(fm_df, file.path(fiml_dir, \"fiml_fit_measures.csv\"), row.names = FALSE)\n", + "} else {\n", + " cat(\"FIML did not converge!\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "335afb24", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:20:15.615051Z", + "iopub.status.busy": "2026-03-31T16:20:15.613323Z", + "iopub.status.idle": "2026-03-31T16:20:15.644192Z", + "shell.execute_reply": "2026-03-31T16:20:15.641434Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Residual Correlations Between Mediators ===\n", + " lhs rhs est se ci.lower ci.upper\n", + " APOC4_APOC2_AC_exp APOC2_AC_exp 0.896 0.052 0.794 0.998\n", + " APOC4_APOC2_AC_exp APOC1_DeJager_Mic_exp 0.198 0.053 0.094 0.302\n", + " APOC4_APOC2_AC_exp APOE_Mega_Mic_exp 0.052 0.042 -0.030 0.134\n", + " APOC2_AC_exp APOC1_DeJager_Mic_exp 0.201 0.053 0.097 0.306\n", + " APOC2_AC_exp APOE_Mega_Mic_exp 0.053 0.042 -0.029 0.136\n", + " APOC1_DeJager_Mic_exp APOE_Mega_Mic_exp 0.564 0.050 0.466 0.661\n", + " z pvalue\n", + " 17.212 0.000\n", + " 3.734 0.000\n", + " 1.241 0.214\n", + " 3.780 0.000\n", + " 1.267 0.205\n", + " 11.344 0.000\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# FIML: Residual correlations between mediators\n", + "# ============================================================\n", + "if (!is.null(fit_fiml) && lavInspect(fit_fiml, \"converged\")) {\n", + " rescov <- pe[pe$op == \"~~\" & pe$lhs %in% MED_COLS & pe$rhs %in% MED_COLS & pe$lhs != pe$rhs,\n", + " c(\"lhs\", \"rhs\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"z\", \"pvalue\")]\n", + " cat(\"\\n=== Residual Correlations Between Mediators ===\\n\")\n", + " print(rescov, digits=4, row.names=FALSE)\n", + " write.csv(rescov, file.path(fiml_dir, \"fiml_mediator_residual_covariances.csv\"), row.names = FALSE)\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "ebd57dab", + "metadata": {}, + "source": [ + "### FIML Interpretation\n", + "\n", + "**Path estimates:**\n", + "- **a-paths (a1-a4):** Effect of the SNP on each mediator (gene expression), controlling for mediator-specific covariates\n", + "- **b-paths (b1-b4):** Effect of each mediator on CAA outcome, controlling for the SNP, other mediators, and outcome covariates\n", + "- **c' (direct):** Direct effect of SNP on CAA not through any of the 4 mediators\n", + "- **ind1-ind4:** Specific indirect effects -- each represents the unique mediation contribution of that gene\n", + "- **total_indirect:** Sum of all specific indirect effects\n", + "- **total:** c' + total_indirect (total SNP effect on CAA)\n", + "- **prop_med:** Proportion of total effect mediated through all 4 genes combined\n", + "- **Contrasts (diff_i_j):** Differences between specific indirect effects (is one mediator stronger than another?)\n", + "- **Residual covariances:** Shared variance between mediators beyond what the SNP explains\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "48f2c782", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:20:15.650742Z", + "iopub.status.busy": "2026-03-31T16:20:15.648518Z", + "iopub.status.idle": "2026-03-31T16:20:18.096566Z", + "shell.execute_reply": "2026-03-31T16:20:18.093920Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "FIML forest plot saved.\n" + ] + }, + { + "data": { + "image/png": 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D+SnJycJB8uNjIyQv/9tRwTE4MQioyMTE9PJwvgVYmJiQih+Ph4hNDAgQMld+jl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\nTqc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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# FIML Forest Plot\n", + "# ============================================================\n", + "if (!is.null(fit_fiml) && lavInspect(fit_fiml, \"converged\")) {\n", + " # Prepare plot data: main paths only (not contrasts)\n", + " plot_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\")\n", + " plot_pe <- key_pe[key_pe$label %in% plot_labels, ]\n", + " \n", + " # Nice display names\n", + " display_names <- c(\n", + " a1 = \"a1: SNP->APOC4/APOC2_AC\", a2 = \"a2: SNP->APOC2_AC\",\n", + " a3 = \"a3: SNP->APOC1_Mic\", a4 = \"a4: SNP->APOE_Mic\",\n", + " b1 = \"b1: APOC4/APOC2_AC->CAA\", b2 = \"b2: APOC2_AC->CAA\",\n", + " b3 = \"b3: APOC1_Mic->CAA\", b4 = \"b4: APOE_Mic->CAA\",\n", + " cp = \"c': SNP->CAA (direct)\",\n", + " ind1 = \"ind1: via APOC4/APOC2_AC\", ind2 = \"ind2: via APOC2_AC\",\n", + " ind3 = \"ind3: via APOC1_Mic\", ind4 = \"ind4: via APOE_Mic\",\n", + " total_indirect = \"Total indirect\", total = \"Total effect\", prop_med = \"Prop. mediated\"\n", + " )\n", + " \n", + " plot_pe$display <- display_names[plot_pe$label]\n", + " plot_pe$display <- factor(plot_pe$display, levels = rev(display_names[plot_labels]))\n", + " \n", + " # Effect type for coloring\n", + " plot_pe$effect_type <- ifelse(grepl(\"^a\", plot_pe$label), \"a-path (SNP->M)\",\n", + " ifelse(grepl(\"^b\", plot_pe$label), \"b-path (M->Y)\",\n", + " ifelse(plot_pe$label == \"cp\", \"Direct (c')\",\n", + " ifelse(grepl(\"^ind\", plot_pe$label), \"Specific indirect\",\n", + " ifelse(plot_pe$label == \"total_indirect\", \"Total indirect\",\n", + " ifelse(plot_pe$label == \"total\", \"Total effect\", \"Proportion\"))))))\n", + " \n", + " p_fiml <- ggplot(plot_pe, aes(x = est, y = display, color = effect_type)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci.lower, xmax = ci.upper), size = 0.5) +\n", + " scale_color_brewer(palette = \"Set1\") +\n", + " labs(title = \"FIML SEM: Parallel Mediation Path Estimates\",\n", + " subtitle = paste0(\"SNP -> {APOC4/APOC2, APOC2, APOC1, APOE} -> caa_neo4 | N = \", N_total, \" (FIML)\"),\n", + " x = \"Estimate (95% CI)\", y = \"\", color = \"Effect Type\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + " \n", + " ggsave(file.path(fiml_dir, \"fiml_forest_plot.png\"), p_fiml, width = 11, height = 9, dpi = 150)\n", + " ggsave(file.path(fiml_dir, \"fiml_forest_plot.pdf\"), p_fiml, width = 11, height = 9)\n", + " print(p_fiml)\n", + " cat(\"\\nFIML forest plot saved.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "ls500zot0dq", + "source": "### Sensitivity Analysis: FIML with 3 Mediators (Excluding APOC4_APOC2_AC)\n\nBecause APOC4_APOC2_AC_exp and APOC2_AC_exp showed large opposing b-path estimates with wide CIs in the 4-mediator model (suggesting collinearity), we re-run the FIML analysis excluding APOC4_APOC2_AC_exp. This tests whether the remaining 3 mediators show clearer indirect effects when freed from collinearity.", + "metadata": {} + }, + { + "cell_type": "code", + "id": "dd2x418cniu", + "source": "# ============================================================\n# Sensitivity: FIML SEM with 3 Mediators (excl. APOC4_APOC2_AC)\n# ============================================================\nsens_dir <- file.path(fiml_dir, \"sensitivity_analysis\")\ndir.create(sens_dir, recursive = TRUE, showWarnings = FALSE)\n\n# 3-mediator setup\nMED_COLS_3 <- c(\"APOC2_AC_exp\", \"APOC1_DeJager_Mic_exp\", \"APOE_Mega_Mic_exp\")\nMED_SHORT_3 <- c(\"APOC2_AC\", \"APOC1_Mic\", \"APOE_Mic\")\nn_med_3 <- length(MED_COLS_3)\n\nMED_COVS_3 <- list(\n M1 = c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\"), # APOC2_AC\n M2 = c(\"msex_u\", \"age_death_u\", \"pmi_u\"), # APOC1_Mic\n M3 = c(\"msex_u\", \"age_death_u\", \"pmi_u\") # APOE_Mic\n)\n\n# Build model\nmed_eqs_3 <- character(n_med_3)\nfor (i in 1:n_med_3) {\n cov_str <- paste(MED_COVS_3[[paste0(\"M\", i)]], collapse = \" + \")\n med_eqs_3[i] <- paste0(MED_COLS_3[i], \" ~ a\", i, \" * \", SNP_COL, \" + \", cov_str)\n}\ny_med_terms_3 <- paste0(\"b\", 1:n_med_3, \" * \", MED_COLS_3, collapse = \" + \")\ny_eq_3 <- paste0(OUT_COL, \" ~ \", y_med_terms_3, \" + cp * \", SNP_COL, \" + \", out_cov_str)\n\nind_defs_3 <- paste0(\"ind\", 1:n_med_3, \" := a\", 1:n_med_3, \" * b\", 1:n_med_3)\ntotal_ind_3 <- paste0(\"total_indirect := \", paste0(\"ind\", 1:n_med_3, collapse = \" + \"))\ntotal_def_3 <- \"total := cp + total_indirect\"\nprop_med_3 <- \"prop_med := total_indirect / total\"\n\ncontrasts_3 <- c()\nfor (i in 1:(n_med_3 - 1)) for (j in (i+1):n_med_3)\n contrasts_3 <- c(contrasts_3, paste0(\"diff_\", i, \"_\", j, \" := ind\", i, \" - ind\", j))\n\ncov_terms_3 <- c()\nfor (i in 1:(n_med_3 - 1)) for (j in (i+1):n_med_3)\n cov_terms_3 <- c(cov_terms_3, paste0(MED_COLS_3[i], \" ~~ \", MED_COLS_3[j]))\n\nmodel_str_3 <- paste(c(med_eqs_3, y_eq_3, ind_defs_3, total_ind_3, total_def_3, prop_med_3, contrasts_3, cov_terms_3),\n collapse = \"\\n\")\n\ncat(\"=== 3-Mediator Parallel Model (excl. APOC4_APOC2_AC) ===\\n\\n\")\ncat(model_str_3, \"\\n\")", + "metadata": {}, + "execution_count": 6, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== 3-Mediator Parallel Model (excl. APOC4_APOC2_AC) ===\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "APOC2_AC_exp ~ a1 * chr19_44938650_G_C + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC1_DeJager_Mic_exp ~ a2 * chr19_44938650_G_C + msex_u + age_death_u + pmi_u\n", + "APOE_Mega_Mic_exp ~ a3 * chr19_44938650_G_C + msex_u + age_death_u + pmi_u\n", + "caa_neo4 ~ b1 * APOC2_AC_exp + b2 * APOC1_DeJager_Mic_exp + b3 * APOE_Mega_Mic_exp + cp * chr19_44938650_G_C + educ + apoe4_dose + apoe2_dose + msex_u + age_death_u\n", + "ind1 := a1 * b1\n", + "ind2 := a2 * b2\n", + "ind3 := a3 * b3\n", + "total_indirect := ind1 + ind2 + ind3\n", + "total := cp + total_indirect\n", + "prop_med := total_indirect / total\n", + "diff_1_2 := ind1 - ind2\n", + "diff_1_3 := ind1 - ind3\n", + "diff_2_3 := ind2 - ind3\n", + "APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC1_DeJager_Mic_exp ~~ APOE_Mega_Mic_exp \n" + ] + } + ] + }, + { + "cell_type": "code", + "id": "zmre0t3h31", + "source": "# ============================================================\n# Sensitivity: Fit FIML with 3 mediators\n# ============================================================\ncat(\"Fitting 3-mediator FIML SEM with N =\", N_total, \"...\\n\\n\")\n\nfit_fiml_3 <- tryCatch(\n sem(model_str_3, data = dat_sub, missing = \"fiml\", fixed.x = FALSE, estimator = \"ML\"),\n error = function(e) { cat(\"FIML Error:\", conditionMessage(e), \"\\n\"); NULL }\n)\n\nif (!is.null(fit_fiml_3) && lavInspect(fit_fiml_3, \"converged\")) {\n cat(\"3-mediator FIML converged successfully.\\n\")\n cat(\"N used:\", lavInspect(fit_fiml_3, \"nobs\"), \"\\n\\n\")\n\n pe_3 <- parameterEstimates(fit_fiml_3, ci = TRUE)\n\n contrast_labels_3 <- c()\n for (i in 1:(n_med_3 - 1)) for (j in (i+1):n_med_3)\n contrast_labels_3 <- c(contrast_labels_3, paste0(\"diff_\", i, \"_\", j))\n\n key_labels_3 <- c(paste0(\"a\", 1:n_med_3), paste0(\"b\", 1:n_med_3), \"cp\",\n paste0(\"ind\", 1:n_med_3), \"total_indirect\", \"total\", \"prop_med\",\n contrast_labels_3)\n\n key_pe_3 <- pe_3[pe_3$label %in% key_labels_3, c(\"label\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"z\", \"pvalue\")]\n key_pe_3$N <- N_total\n key_pe_3 <- key_pe_3[match(key_labels_3[key_labels_3 %in% key_pe_3$label], key_pe_3$label), ]\n\n print(key_pe_3, digits = 4, row.names = FALSE)\n\n write.csv(key_pe_3, file.path(sens_dir, \"fiml_3med_all_paths.csv\"), row.names = FALSE)\n cat(\"\\nSaved:\", file.path(sens_dir, \"fiml_3med_all_paths.csv\"), \"\\n\")\n\n # Fit measures\n fm_3 <- fitMeasures(fit_fiml_3, c(\"chisq\", \"df\", \"pvalue\", \"cfi\", \"tli\", \"rmsea\", \"srmr\", \"aic\", \"bic\"))\n fm_df_3 <- data.frame(measure = names(fm_3), value = as.numeric(fm_3), N = N_total)\n write.csv(fm_df_3, file.path(sens_dir, \"fiml_3med_fit_measures.csv\"), row.names = FALSE)\n\n # Residual covariances\n rescov_3 <- pe_3[pe_3$op == \"~~\" & pe_3$lhs %in% MED_COLS_3 & pe_3$rhs %in% MED_COLS_3 & pe_3$lhs != pe_3$rhs,\n c(\"lhs\", \"rhs\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"z\", \"pvalue\")]\n cat(\"\\n=== Residual Correlations Between 3 Mediators ===\\n\")\n print(rescov_3, digits = 4, row.names = FALSE)\n write.csv(rescov_3, file.path(sens_dir, \"fiml_3med_residual_covariances.csv\"), row.names = FALSE)\n} else {\n cat(\"3-mediator FIML did not converge!\\n\")\n}", + "metadata": {}, + "execution_count": 7, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 3-mediator FIML SEM with N = 1153 ...\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3-mediator FIML converged successfully.\n", + "N used: 1153 \n", + "\n", + " label est se ci.lower ci.upper z pvalue N\n", + " a1 0.247 0.057 0.135 0.358 4.345 0.000 1153\n", + " a2 0.284 0.063 0.161 0.408 4.523 0.000 1153\n", + " a3 0.210 0.049 0.113 0.307 4.246 0.000 1153\n", + " b1 -0.060 0.044 -0.146 0.026 -1.360 0.174 1153\n", + " b2 -0.002 0.068 -0.135 0.130 -0.033 0.974 1153\n", + " b3 0.080 0.058 -0.033 0.193 1.381 0.167 1153\n", + " cp 0.149 0.045 0.061 0.238 3.305 0.001 1153\n", + " ind1 -0.015 0.011 -0.037 0.007 -1.305 0.192 1153\n", + " ind2 -0.001 0.019 -0.038 0.037 -0.033 0.974 1153\n", + " ind3 0.017 0.013 -0.008 0.042 1.310 0.190 1153\n", + " total_indirect 0.001 0.015 -0.029 0.031 0.093 0.926 1153\n", + " total 0.151 0.043 0.066 0.235 3.507 0.000 1153\n", + " prop_med 0.009 0.101 -0.189 0.208 0.093 0.926 1153\n", + " diff_1_2 -0.014 0.025 -0.063 0.035 -0.564 0.573 1153\n", + " diff_1_3 -0.031 0.016 -0.062 -0.001 -1.994 0.046 1153\n", + " diff_2_3 -0.017 0.030 -0.075 0.041 -0.588 0.557 1153\n", + "\n", + "Saved: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_neo4/APOE4_2_adj/main_SEM_FIML/sensitivity_analysis/fiml_3med_all_paths.csv \n", + "\n", + "=== Residual Correlations Between 3 Mediators ===\n", + " lhs rhs est se ci.lower ci.upper\n", + " APOC2_AC_exp APOC1_DeJager_Mic_exp 0.197 0.053 0.093 0.301\n", + " APOC2_AC_exp APOE_Mega_Mic_exp 0.053 0.042 -0.030 0.135\n", + " APOC1_DeJager_Mic_exp APOE_Mega_Mic_exp 0.564 0.050 0.466 0.661\n", + " z pvalue\n", + " 3.706 0.00\n", + " 1.252 0.21\n", + " 11.342 0.00\n" + ] + } + ] + }, + { + "cell_type": "code", + "id": "avitiujptz", + "source": "# ============================================================\n# Sensitivity: Forest plot for 3-mediator model\n# ============================================================\nif (!is.null(fit_fiml_3) && lavInspect(fit_fiml_3, \"converged\")) {\n plot_labels_3 <- c(paste0(\"a\", 1:n_med_3), paste0(\"b\", 1:n_med_3), \"cp\",\n paste0(\"ind\", 1:n_med_3), \"total_indirect\", \"total\", \"prop_med\")\n plot_pe_3 <- key_pe_3[key_pe_3$label %in% plot_labels_3, ]\n\n display_names_3 <- c(\n a1 = \"a1: SNP->APOC2_AC\", a2 = \"a2: SNP->APOC1_Mic\", a3 = \"a3: SNP->APOE_Mic\",\n b1 = \"b1: APOC2_AC->CAA\", b2 = \"b2: APOC1_Mic->CAA\", b3 = \"b3: APOE_Mic->CAA\",\n cp = \"c': SNP->CAA (direct)\",\n ind1 = \"ind1: via APOC2_AC\", ind2 = \"ind2: via APOC1_Mic\", ind3 = \"ind3: via APOE_Mic\",\n total_indirect = \"Total indirect\", total = \"Total effect\", prop_med = \"Prop. mediated\"\n )\n\n plot_pe_3$display <- display_names_3[plot_pe_3$label]\n plot_pe_3$display <- factor(plot_pe_3$display, levels = rev(display_names_3[plot_labels_3]))\n\n plot_pe_3$effect_type <- ifelse(grepl(\"^a\", plot_pe_3$label), \"a-path (SNP->M)\",\n ifelse(grepl(\"^b\", plot_pe_3$label), \"b-path (M->Y)\",\n ifelse(plot_pe_3$label == \"cp\", \"Direct (c')\",\n ifelse(grepl(\"^ind\", plot_pe_3$label), \"Specific indirect\",\n ifelse(plot_pe_3$label == \"total_indirect\", \"Total indirect\",\n ifelse(plot_pe_3$label == \"total\", \"Total effect\", \"Proportion\"))))))\n\n p_sens <- ggplot(plot_pe_3, aes(x = est, y = display, color = effect_type)) +\n geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n geom_pointrange(aes(xmin = ci.lower, xmax = ci.upper), size = 0.5) +\n scale_color_brewer(palette = \"Set1\") +\n labs(title = \"Sensitivity: FIML SEM with 3 Mediators (excl. APOC4_APOC2_AC)\",\n subtitle = paste0(\"SNP -> {APOC2_AC, APOC1_Mic, APOE_Mic} -> caa_neo4 | N = \", N_total, \" (FIML)\"),\n x = \"Estimate (95% CI)\", y = \"\", color = \"Effect Type\") +\n theme_minimal(base_size = 12) +\n theme(legend.position = \"bottom\")\n\n ggsave(file.path(sens_dir, \"fiml_3med_forest_plot.png\"), p_sens, width = 11, height = 8, dpi = 150)\n ggsave(file.path(sens_dir, \"fiml_3med_forest_plot.pdf\"), p_sens, width = 11, height = 8)\n print(p_sens)\n cat(\"\\n3-mediator forest plot saved.\\n\")\n}", + "metadata": {}, + "execution_count": 8, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "3-mediator forest plot saved.\n" + ] + }, + { + "data": { + "image/png": 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Vb7upGVlZWTkwMATk5OktOZX9a0tLQO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\nNDQ0yM3Ll1utlvpEda3IyMiPH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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ] + }, + { + "cell_type": "markdown", + "id": "ve3s0p0j6vg", + "source": "### Sensitivity Analysis Interpretation (3 Mediators, excl. APOC4_APOC2_AC)\n\n**Comparison with 4-mediator model:**\n\n| Path | 4-mediator model | 3-mediator model | Change |\n|------|-----------------|-----------------|--------|\n| b (APOC2_AC\u2192CAA) | 0.731 [\u22121.377, 2.838], p=0.497 | \u22120.060 [\u22120.146, 0.026], p=0.174 | CIs dramatically narrower; collinearity resolved |\n| b (APOC1_Mic\u2192CAA) | \u22120.009 [\u22120.142, 0.124], p=0.895 | \u22120.002 [\u22120.135, 0.130], p=0.974 | Similar \u2014 unaffected by collinearity |\n| b (APOE_Mic\u2192CAA) | 0.082 [\u22120.031, 0.195], p=0.155 | 0.080 [\u22120.033, 0.193], p=0.167 | Similar \u2014 unaffected by collinearity |\n| ind (via APOC2_AC) | 0.181 [\u22120.347, 0.708], p=0.502 | \u22120.015 [\u22120.037, 0.007], p=0.192 | Reasonable CI now; still non-significant |\n| Total indirect | \u22120.003 [\u22120.035, 0.029], p=0.855 | 0.001 [\u22120.029, 0.031], p=0.926 | Similar, still non-significant |\n| c' (direct) | 0.154 [0.065, 0.243], p<0.001 | 0.149 [0.061, 0.238], p=0.001 | Stable |\n| diff_1_3 (APOC2 vs APOE) | \u2014 | \u22120.031 [\u22120.062, \u22120.001], p=0.046 | Marginally significant contrast |\n\n**Key takeaway:** Removing APOC4_APOC2_AC resolves the collinearity issue (APOC2_AC b-path CIs shrink from [\u22121.4, 2.8] to [\u22120.15, 0.03]). However, no indirect effects become significant. The direct effect remains stable, confirming the main finding: the SNP effect on caa_neo4 is not mediated through these gene expression measures.\n\n**Notable:** The contrast diff_1_3 (ind1 vs ind3, APOC2_AC vs APOE_Mic) is marginally significant (p=0.046), suggesting APOE_Mic may have a stronger (positive) indirect effect than APOC2_AC (negative), though neither is individually significant.", + "metadata": {} + }, + { + "cell_type": "markdown", + "id": "0da8b0db", + "metadata": {}, + "source": [ + "## 6. Method 2: Bootstrap (FIML Inside Each Replicate)\n", + "\n", + "We resample the **full dataset** (N = all subjects with exposure observed) and fit the same parallel \n", + "mediation SEM using FIML inside each replicate. This provides nonparametric confidence intervals for \n", + "the specific and total indirect effects.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8d4e63aa", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:20:18.102517Z", + "iopub.status.busy": "2026-03-31T16:20:18.100904Z", + "iopub.status.idle": "2026-03-31T16:43:04.428652Z", + "shell.execute_reply": "2026-03-31T16:43:04.426387Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running 1000 bootstrap replicates (FIML inside each)...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Replicate 200/1000 (converged: 200, elapsed: 277s)\n", + " Replicate 400/1000 (converged: 400, elapsed: 549s)\n", + " Replicate 600/1000 (converged: 600, elapsed: 821s)\n", + " Replicate 800/1000 (converged: 800, elapsed: 1095s)\n", + " Replicate 1000/1000 (converged: 1000, elapsed: 1366s)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bootstrap complete: 1000/1000 converged (100.0%)\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Bootstrap: FIML inside each replicate\n", + "# ============================================================\n", + "set.seed(12345)\n", + "B <- 1000\n", + "\n", + "# All labels to extract\n", + "param_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\")\n", + "\n", + "boot_mat <- matrix(NA, B, length(param_labels), dimnames = list(NULL, param_labels))\n", + "\n", + "cat(\"Running\", B, \"bootstrap replicates (FIML inside each)...\\n\")\n", + "n_converged <- 0\n", + "t0 <- proc.time()[3]\n", + "\n", + "for (i in 1:B) {\n", + " idx <- sample(nrow(dat_sub), replace = TRUE)\n", + " boot_dat <- dat_sub[idx, ]\n", + " \n", + " fit_b <- tryCatch(\n", + " sem(model_str, data = boot_dat, missing = \"fiml\", fixed.x = FALSE,\n", + " se = \"none\", test = \"none\"),\n", + " error = function(e) NULL,\n", + " warning = function(w) {\n", + " tryCatch(\n", + " sem(model_str, data = boot_dat, missing = \"fiml\", fixed.x = FALSE,\n", + " se = \"none\", test = \"none\"),\n", + " error = function(e) NULL\n", + " )\n", + " }\n", + " )\n", + " \n", + " if (!is.null(fit_b) && lavInspect(fit_b, \"converged\")) {\n", + " pe_b <- parameterEstimates(fit_b)\n", + " for (lab in param_labels) {\n", + " row_b <- pe_b[pe_b$label == lab, ]\n", + " if (nrow(row_b) == 1) boot_mat[i, lab] <- row_b$est\n", + " }\n", + " n_converged <- n_converged + 1\n", + " }\n", + " \n", + " if (i %% 200 == 0) {\n", + " elapsed <- proc.time()[3] - t0\n", + " cat(sprintf(\" Replicate %d/%d (converged: %d, elapsed: %.0fs)\\n\", i, B, n_converged, elapsed))\n", + " }\n", + "}\n", + "\n", + "cat(sprintf(\"\\nBootstrap complete: %d/%d converged (%.1f%%)\\n\", n_converged, B, 100*n_converged/B))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9ea7f27e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:43:04.617551Z", + "iopub.status.busy": "2026-03-31T16:43:04.613276Z", + "iopub.status.idle": "2026-03-31T16:43:04.689065Z", + "shell.execute_reply": "2026-03-31T16:43:04.686427Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label boot_mean boot_se ci_lower ci_upper p_value N n_converged\n", + " a1 0.250057 0.05666 0.13837 0.36477 0.000 1153 1000\n", + " a2 0.246599 0.05696 0.13468 0.36191 0.000 1153 1000\n", + " a3 0.282322 0.05907 0.16710 0.39887 0.000 1153 1000\n", + " a4 0.210625 0.04855 0.11828 0.30982 0.000 1153 1000\n", + " b1 -0.826887 1.01954 -2.83859 1.13333 0.418 1153 1000\n", + " b2 0.767364 1.02075 -1.19728 2.77923 0.450 1153 1000\n", + " b3 -0.008909 0.07678 -0.15363 0.13906 0.928 1153 1000\n", + " b4 0.080916 0.06426 -0.05160 0.20373 0.186 1153 1000\n", + " cp 0.155707 0.04712 0.06698 0.24603 0.000 1153 1000\n", + " ind1 -0.209439 0.26978 -0.79674 0.27799 0.418 1153 1000\n", + " ind2 0.191762 0.26594 -0.29602 0.76673 0.450 1153 1000\n", + " ind3 -0.002799 0.02227 -0.04791 0.04152 0.928 1153 1000\n", + " ind4 0.017117 0.01463 -0.01148 0.04894 0.186 1153 1000\n", + " total_indirect -0.003360 0.01779 -0.03883 0.03306 0.856 1153 1000\n", + " total 0.152347 0.04383 0.06617 0.23494 0.000 1153 1000\n", + " prop_med -0.107193 2.62635 -0.33658 0.23469 0.856 1153 1000\n", + " ci_type\n", + " Percentile\n", + " Percentile\n", + " Percentile\n", + " Percentile\n", + " Percentile\n", + " Percentile\n", + " Percentile\n", + " Percentile\n", + " Percentile\n", + " Percentile\n", + " Percentile\n", + " Percentile\n", + " Percentile\n", + " Percentile\n", + " Percentile\n", + " Percentile\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Saved: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_neo4/APOE4_2_adj/bootstrap/bootstrap_results.csv \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Bootstrap: Summarize results\n", + "# ============================================================\n", + "boot_summary <- data.frame(\n", + " label = param_labels,\n", + " boot_mean = apply(boot_mat, 2, mean, na.rm=TRUE),\n", + " boot_se = apply(boot_mat, 2, sd, na.rm=TRUE),\n", + " ci_lower = apply(boot_mat, 2, quantile, 0.025, na.rm=TRUE),\n", + " ci_upper = apply(boot_mat, 2, quantile, 0.975, na.rm=TRUE),\n", + " stringsAsFactors = FALSE\n", + ")\n", + "\n", + "# Two-sided p-value from bootstrap distribution\n", + "boot_summary$p_value <- sapply(param_labels, function(lab) {\n", + " vals <- boot_mat[, lab]\n", + " vals <- vals[!is.na(vals)]\n", + " if (length(vals) < 10) return(NA)\n", + " p_pos <- mean(vals > 0)\n", + " 2 * min(p_pos, 1 - p_pos)\n", + "})\n", + "\n", + "boot_summary$N <- N_total\n", + "boot_summary$n_converged <- n_converged\n", + "boot_summary$ci_type <- \"Percentile\"\n", + "\n", + "print(boot_summary, digits=4, row.names=FALSE)\n", + "\n", + "write.csv(boot_summary, file.path(boot_dir, \"bootstrap_results.csv\"), row.names = FALSE)\n", + "cat(\"\\nSaved:\", file.path(boot_dir, \"bootstrap_results.csv\"), \"\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ea983f88", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:43:04.699382Z", + "iopub.status.busy": "2026-03-31T16:43:04.696833Z", + "iopub.status.idle": "2026-03-31T16:43:06.077371Z", + "shell.execute_reply": "2026-03-31T16:43:06.074161Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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jAwIJOqX0GVdu3amZiY0H8mJCQAQFxcXHp6Ol1YUlICAImJiQBARnGbOXPmmjVr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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Bootstrap: Forest plot\n", + "# ============================================================\n", + "# Reuse display names from FIML section\n", + "bp_data <- boot_summary[boot_summary$label %in% names(display_names), ]\n", + "bp_data$display <- display_names[bp_data$label]\n", + "bp_data$display <- factor(bp_data$display, levels = rev(display_names[names(display_names) %in% bp_data$label]))\n", + "\n", + "bp_data$effect_type <- ifelse(grepl(\"^a\", bp_data$label), \"a-path (SNP->M)\",\n", + " ifelse(grepl(\"^b\", bp_data$label), \"b-path (M->Y)\",\n", + " ifelse(bp_data$label == \"cp\", \"Direct (c')\",\n", + " ifelse(grepl(\"^ind\", bp_data$label), \"Specific indirect\",\n", + " ifelse(bp_data$label == \"total_indirect\", \"Total indirect\",\n", + " ifelse(bp_data$label == \"total\", \"Total effect\", \"Proportion\"))))))\n", + "\n", + "p_boot <- ggplot(bp_data, aes(x = boot_mean, y = display, color = effect_type)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper), size = 0.5) +\n", + " scale_color_brewer(palette = \"Set1\") +\n", + " labs(title = \"Bootstrap: Parallel Mediation Path Estimates\",\n", + " subtitle = paste0(\"N = \", N_total, \" (FIML), \", B, \" resamples (\", n_converged, \" converged)\"),\n", + " x = \"Estimate (95% Percentile CI)\", y = \"\", color = \"Effect Type\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + "ggsave(file.path(boot_dir, \"bootstrap_forest_plot.png\"), p_boot, width = 11, height = 9, dpi = 150)\n", + "ggsave(file.path(boot_dir, \"bootstrap_forest_plot.pdf\"), p_boot, width = 11, height = 9)\n", + "print(p_boot)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a2e3ae2c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:43:06.084081Z", + "iopub.status.busy": "2026-03-31T16:43:06.081778Z", + "iopub.status.idle": "2026-03-31T16:43:08.564744Z", + "shell.execute_reply": "2026-03-31T16:43:08.562201Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201c\u001b[1m\u001b[22mUsing `size` aesthetic for lines was deprecated in ggplot2 3.4.0.\n", + "\u001b[36m\u2139\u001b[39m Please use `linewidth` instead.\u201d\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrap distribution plots saved.\n" + ] + }, + { + "data": { + "image/png": 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Eyq74qSfJwECBWcYwcQjJ07TZ07x2dm0ptvhjsUAJBQ1oalL0wa8sKkIWn7t4U7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\nypMkv9+vKEpOTk66g8SQkZFhhqNio3MtdrutTRJVVWVZttvtktT9JzyJ+aA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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Bootstrap: Distribution histograms for indirect effects\n", + "# ============================================================\n", + "ind_labels <- c(paste0(\"ind\", 1:n_med), \"total_indirect\")\n", + "ind_display <- c(paste0(\"ind\", 1:n_med, \": via \", MED_SHORT), \"Total indirect\")\n", + "\n", + "plot_list <- list()\n", + "for (k in seq_along(ind_labels)) {\n", + " lab <- ind_labels[k]\n", + " vals <- boot_mat[, lab]\n", + " vals <- vals[!is.na(vals)]\n", + " if (length(vals) < 10) next\n", + " \n", + " df_hist <- data.frame(value = vals)\n", + " p_h <- ggplot(df_hist, aes(x = value)) +\n", + " geom_histogram(bins = 50, fill = \"steelblue\", alpha = 0.7) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"red\", size = 0.8) +\n", + " geom_vline(xintercept = median(vals), linetype = \"solid\", color = \"darkblue\", size = 0.8) +\n", + " labs(title = ind_display[k],\n", + " x = \"Indirect Effect\", y = \"Count\") +\n", + " theme_minimal(base_size = 10)\n", + " plot_list[[k]] <- p_h\n", + "}\n", + "\n", + "if (length(plot_list) > 0) {\n", + " g <- do.call(gridExtra::grid.arrange, c(plot_list, ncol = 2))\n", + " ggsave(file.path(boot_dir, \"bootstrap_distributions_chr19_44938650_G_C.png\"),\n", + " g, width = 12, height = 10, dpi = 150)\n", + " ggsave(file.path(boot_dir, \"bootstrap_distributions_chr19_44938650_G_C.pdf\"),\n", + " g, width = 12, height = 10)\n", + " cat(\"Bootstrap distribution plots saved.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "c0dd7540", + "metadata": {}, + "source": [ + "### Bootstrap Interpretation\n", + "\n", + "The bootstrap provides **nonparametric percentile confidence intervals** for each path. \n", + "Unlike FIML which assumes multivariate normality for the indirect effect CI, bootstrap CIs \n", + "respect the actual sampling distribution of the product a*b, which is known to be non-normal.\n", + "\n", + "Key comparison: If bootstrap CIs are consistent with FIML Wald CIs, this increases confidence \n", + "in the FIML results. Discrepancies may indicate non-normality of the indirect effect distribution.\n" + ] + }, + { + "cell_type": "markdown", + "id": "f8dc3b15", + "metadata": {}, + "source": [ + "## 7. Method 3: MNAR Sensitivity Analysis\n", + "\n", + "We assess robustness to Missing Not At Random (MNAR) assumptions by applying delta-adjustment:\n", + "shifting imputed values for missing mediators and outcome by multiples of their observed SD.\n", + "\n", + "For the parallel model, we use a 2D grid: delta_mediators (applied uniformly to all 4 mediators) \n", + "vs delta_outcome. At each grid point we impute missing values, fit the model with ML, \n", + "and extract all indirect effects.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1b36325f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:43:08.571861Z", + "iopub.status.busy": "2026-03-31T16:43:08.569628Z", + "iopub.status.idle": "2026-03-31T16:44:16.410562Z", + "shell.execute_reply": "2026-03-31T16:44:16.407780Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR grid: 225 points ( 15 x 15 )\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running MNAR grid...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Grid point 50/225 (converged: 50, elapsed: 15s)\n", + " Grid point 100/225 (converged: 100, elapsed: 30s)\n", + " Grid point 150/225 (converged: 150, elapsed: 45s)\n", + " Grid point 200/225 (converged: 200, elapsed: 60s)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "MNAR complete: 225/225 grid points converged.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_neo4/APOE4_2_adj/MNAR_sensitivity/mnar_grid_results.csv \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# MNAR Sensitivity: Delta-shift grid\n", + "# ============================================================\n", + "delta_range <- seq(-2, 2, length.out = 15)\n", + "grid <- expand.grid(delta_med = delta_range, delta_out = delta_range)\n", + "cat(\"MNAR grid:\", nrow(grid), \"points (\", length(delta_range), \"x\", length(delta_range), \")\\n\")\n", + "\n", + "# Pre-compute observed means and SDs\n", + "med_stats <- lapply(MED_COLS, function(m) {\n", + " list(mean = mean(dat_sub[[m]], na.rm=TRUE), sd = sd(dat_sub[[m]], na.rm=TRUE))\n", + "})\n", + "names(med_stats) <- MED_COLS\n", + "out_mean <- mean(dat_sub[[OUT_COL]], na.rm=TRUE)\n", + "out_sd <- sd(dat_sub[[OUT_COL]], na.rm=TRUE)\n", + "cov_means <- sapply(ALL_COVS, function(v) mean(dat_sub[[v]], na.rm=TRUE))\n", + "\n", + "# Columns to store\n", + "mnar_labels <- c(paste0(\"ind\", 1:n_med), \"total_indirect\", \"cp\", \"total\",\n", + " paste0(\"ind\", 1:n_med, \"_p\"), \"total_indirect_p\")\n", + "\n", + "mnar_results <- matrix(NA, nrow(grid), length(mnar_labels),\n", + " dimnames = list(NULL, mnar_labels))\n", + "\n", + "cat(\"Running MNAR grid...\\n\")\n", + "t0 <- proc.time()[3]\n", + "n_ok <- 0\n", + "\n", + "for (k in 1:nrow(grid)) {\n", + " dat_imp <- dat_sub\n", + " \n", + " # Impute mediators\n", + " for (m in MED_COLS) {\n", + " na_idx <- is.na(dat_imp[[m]])\n", + " dat_imp[[m]][na_idx] <- med_stats[[m]]$mean + grid$delta_med[k] * med_stats[[m]]$sd\n", + " }\n", + " \n", + " # Impute outcome\n", + " na_idx_y <- is.na(dat_imp[[OUT_COL]])\n", + " dat_imp[[OUT_COL]][na_idx_y] <- out_mean + grid$delta_out[k] * out_sd\n", + " \n", + " # Impute covariates with column means\n", + " for (cov in ALL_COVS) {\n", + " dat_imp[[cov]][is.na(dat_imp[[cov]])] <- cov_means[cov]\n", + " }\n", + " \n", + " fit_m <- tryCatch(\n", + " sem(model_str, data = dat_imp, estimator = \"ML\", fixed.x = FALSE),\n", + " error = function(e) NULL\n", + " )\n", + " \n", + " if (!is.null(fit_m) && lavInspect(fit_m, \"converged\")) {\n", + " pe_m <- parameterEstimates(fit_m)\n", + " for (i in 1:n_med) {\n", + " lab <- paste0(\"ind\", i)\n", + " row_m <- pe_m[pe_m$label == lab, ]\n", + " if (nrow(row_m) == 1) {\n", + " mnar_results[k, lab] <- row_m$est\n", + " mnar_results[k, paste0(lab, \"_p\")] <- row_m$pvalue\n", + " }\n", + " }\n", + " for (lab in c(\"total_indirect\", \"cp\", \"total\")) {\n", + " row_m <- pe_m[pe_m$label == lab, ]\n", + " if (nrow(row_m) == 1) {\n", + " mnar_results[k, lab] <- row_m$est\n", + " if (lab == \"total_indirect\") mnar_results[k, \"total_indirect_p\"] <- row_m$pvalue\n", + " }\n", + " }\n", + " n_ok <- n_ok + 1\n", + " }\n", + " \n", + " if (k %% 50 == 0) {\n", + " elapsed <- proc.time()[3] - t0\n", + " cat(sprintf(\" Grid point %d/%d (converged: %d, elapsed: %.0fs)\\n\", k, nrow(grid), n_ok, elapsed))\n", + " }\n", + "}\n", + "\n", + "cat(sprintf(\"\\nMNAR complete: %d/%d grid points converged.\\n\", n_ok, nrow(grid)))\n", + "\n", + "# Save full grid\n", + "mnar_df <- cbind(grid, as.data.frame(mnar_results))\n", + "mnar_df$N <- N_total\n", + "write.csv(mnar_df, file.path(mnar_dir, \"mnar_grid_results.csv\"), row.names = FALSE)\n", + "cat(\"Saved:\", file.path(mnar_dir, \"mnar_grid_results.csv\"), \"\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b38b365d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:44:16.425337Z", + "iopub.status.busy": "2026-03-31T16:44:16.423557Z", + "iopub.status.idle": "2026-03-31T16:44:16.509850Z", + "shell.execute_reply": "2026-03-31T16:44:16.507032Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Tipping Point Summary ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " effect tipping_delta_med tipping_delta_out tipping_distance\n", + " ind1 (APOC4_APOC2_AC) NA NA NA\n", + " ind2 (APOC2_AC) NA NA NA\n", + " ind3 (APOC1_Mic) NA NA NA\n", + " ind4 (APOE_Mic) NA NA NA\n", + " total_indirect NA NA NA\n", + " fiml_significant N\n", + " FALSE 1153\n", + " FALSE 1153\n", + " FALSE 1153\n", + " FALSE 1153\n", + " FALSE 1153\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# MNAR: Tipping point analysis\n", + "# ============================================================\n", + "# For each indirect effect, find the minimum distance from (0,0) where it becomes non-significant\n", + "\n", + "tipping <- data.frame(\n", + " effect = character(),\n", + " tipping_delta_med = numeric(),\n", + " tipping_delta_out = numeric(),\n", + " tipping_distance = numeric(),\n", + " fiml_significant = logical(),\n", + " stringsAsFactors = FALSE\n", + ")\n", + "\n", + "for (i in 1:n_med) {\n", + " lab_p <- paste0(\"ind\", i, \"_p\")\n", + " lab_e <- paste0(\"ind\", i)\n", + " \n", + " # Check if FIML says it's significant at (0,0)\n", + " ref_idx <- which(abs(grid$delta_med) < 0.01 & abs(grid$delta_out) < 0.01)\n", + " fiml_sig <- FALSE\n", + " if (length(ref_idx) > 0 && !is.na(mnar_results[ref_idx[1], lab_p])) {\n", + " fiml_sig <- mnar_results[ref_idx[1], lab_p] < 0.05\n", + " }\n", + " \n", + " # Find non-significant points\n", + " ns_idx <- which(mnar_results[, lab_p] >= 0.05 & !is.na(mnar_results[, lab_p]))\n", + " if (length(ns_idx) > 0 && fiml_sig) {\n", + " dists <- sqrt(grid$delta_med[ns_idx]^2 + grid$delta_out[ns_idx]^2)\n", + " min_idx <- ns_idx[which.min(dists)]\n", + " tipping <- rbind(tipping, data.frame(\n", + " effect = paste0(\"ind\", i, \" (\", MED_SHORT[i], \")\"),\n", + " tipping_delta_med = grid$delta_med[min_idx],\n", + " tipping_delta_out = grid$delta_out[min_idx],\n", + " tipping_distance = min(dists),\n", + " fiml_significant = TRUE,\n", + " N = N_total\n", + " ))\n", + " } else {\n", + " tipping <- rbind(tipping, data.frame(\n", + " effect = paste0(\"ind\", i, \" (\", MED_SHORT[i], \")\"),\n", + " tipping_delta_med = NA,\n", + " tipping_delta_out = NA,\n", + " tipping_distance = ifelse(fiml_sig, Inf, NA),\n", + " fiml_significant = fiml_sig,\n", + " N = N_total\n", + " ))\n", + " }\n", + "}\n", + "\n", + "# Total indirect tipping\n", + "ref_idx <- which(abs(grid$delta_med) < 0.01 & abs(grid$delta_out) < 0.01)\n", + "ti_sig <- FALSE\n", + "if (length(ref_idx) > 0 && !is.na(mnar_results[ref_idx[1], \"total_indirect_p\"])) {\n", + " ti_sig <- mnar_results[ref_idx[1], \"total_indirect_p\"] < 0.05\n", + "}\n", + "ns_idx_ti <- which(mnar_results[, \"total_indirect_p\"] >= 0.05 & !is.na(mnar_results[, \"total_indirect_p\"]))\n", + "if (length(ns_idx_ti) > 0 && ti_sig) {\n", + " dists_ti <- sqrt(grid$delta_med[ns_idx_ti]^2 + grid$delta_out[ns_idx_ti]^2)\n", + " min_idx_ti <- ns_idx_ti[which.min(dists_ti)]\n", + " tipping <- rbind(tipping, data.frame(\n", + " effect = \"total_indirect\",\n", + " tipping_delta_med = grid$delta_med[min_idx_ti],\n", + " tipping_delta_out = grid$delta_out[min_idx_ti],\n", + " tipping_distance = min(dists_ti),\n", + " fiml_significant = TRUE,\n", + " N = N_total\n", + " ))\n", + "} else {\n", + " tipping <- rbind(tipping, data.frame(\n", + " effect = \"total_indirect\",\n", + " tipping_delta_med = NA, tipping_delta_out = NA,\n", + " tipping_distance = ifelse(ti_sig, Inf, NA),\n", + " fiml_significant = ti_sig,\n", + " N = N_total\n", + " ))\n", + "}\n", + "\n", + "cat(\"\\n=== Tipping Point Summary ===\\n\")\n", + "print(tipping, digits=3, row.names=FALSE)\n", + "write.csv(tipping, file.path(mnar_dir, \"mnar_tipping_summary.csv\"), row.names = FALSE)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ae08dc1f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:44:16.515656Z", + "iopub.status.busy": "2026-03-31T16:44:16.514039Z", + "iopub.status.idle": "2026-03-31T16:44:19.267312Z", + "shell.execute_reply": "2026-03-31T16:44:19.264653Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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GRFGy8pKXn58qVCh4qePXuKRCL5iw4AQIv4HeykUqmHh4dYLF6/fj0NFuWiXxif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ddEtvjndiFemk8lYsnHgC+NdlqkscvlNy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\n7Niff/5ZeqWYcQswtaSkpLCwsF9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9mFogAAIC8IdgAAAAAKgTF2AAAAAAqBYAcAAACgEAh2AAAAAAqBYAcAAACgEAh2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zs3vw4MFvv/1WWFi4YsWKDlMdQsja2nrF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\n9fT0Z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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# MNAR: Contour plots for total indirect effect\n", + "# ============================================================\n", + "mnar_plot <- cbind(grid, total_indirect = mnar_results[, \"total_indirect\"],\n", + " total_indirect_p = mnar_results[, \"total_indirect_p\"])\n", + "mnar_plot <- mnar_plot[!is.na(mnar_plot$total_indirect), ]\n", + "\n", + "if (nrow(mnar_plot) > 10) {\n", + " # Indirect effect contour\n", + " p_cont1 <- ggplot(mnar_plot, aes(x = delta_med, y = delta_out, z = total_indirect)) +\n", + " geom_contour_filled(bins = 12) +\n", + " scale_fill_viridis_d(option = \"viridis\") +\n", + " annotate(\"point\", x = 0, y = 0, shape = 4, size = 4, color = \"red\", stroke = 2) +\n", + " labs(title = \"MNAR Sensitivity: Total Indirect Effect\",\n", + " subtitle = paste0(\"SNP -> {4 mediators} -> caa_neo4 | N = \", N_total),\n", + " x = expression(delta[mediators] ~ \"(SDs)\"), y = expression(delta[outcome] ~ \"(SDs)\"),\n", + " fill = \"Indirect\n", + "Effect\") +\n", + " theme_minimal(base_size = 12)\n", + " \n", + " ggsave(file.path(mnar_dir, \"mnar_contour_indirect_chr19_44938650_G_C.png\"),\n", + " p_cont1, width = 9, height = 7, dpi = 150)\n", + " ggsave(file.path(mnar_dir, \"mnar_contour_indirect_chr19_44938650_G_C.pdf\"),\n", + " p_cont1, width = 9, height = 7)\n", + " print(p_cont1)\n", + " \n", + " # P-value contour\n", + " mnar_plot$neg_log10_p <- -log10(pmax(mnar_plot$total_indirect_p, 1e-20))\n", + " \n", + " p_cont2 <- ggplot(mnar_plot, aes(x = delta_med, y = delta_out, z = neg_log10_p)) +\n", + " geom_contour_filled(bins = 12) +\n", + " scale_fill_viridis_d(option = \"plasma\") +\n", + " annotate(\"point\", x = 0, y = 0, shape = 4, size = 4, color = \"red\", stroke = 2) +\n", + " labs(title = \"MNAR Sensitivity: -log10(p) for Total Indirect Effect\",\n", + " subtitle = paste0(\"SNP -> {4 mediators} -> caa_neo4 | N = \", N_total),\n", + " x = expression(delta[mediators] ~ \"(SDs)\"), y = expression(delta[outcome] ~ \"(SDs)\"),\n", + " fill = \"-log10(p)\") +\n", + " theme_minimal(base_size = 12)\n", + " \n", + " ggsave(file.path(mnar_dir, \"mnar_contour_pvalue_chr19_44938650_G_C.png\"),\n", + " p_cont2, width = 9, height = 7, dpi = 150)\n", + " ggsave(file.path(mnar_dir, \"mnar_contour_pvalue_chr19_44938650_G_C.pdf\"),\n", + " p_cont2, width = 9, height = 7)\n", + " print(p_cont2)\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ad19bd00", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:44:19.273495Z", + "iopub.status.busy": "2026-03-31T16:44:19.271843Z", + "iopub.status.idle": "2026-03-31T16:44:20.364899Z", + "shell.execute_reply": "2026-03-31T16:44:20.362791Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR 1D slice plots saved.\n" + ] + }, + { + "data": { + "image/png": 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AB8Gto6AB4gsRPe5MmTBw4caO9V67EuAQB8F9o6AB7gGTsAAAAAP4Fn7AAAAAD8\nBBI7AAAAAD+BxA4AAADATyCxAwAAAPATSOwAAAAA/AQSOwAAAAA/gcQOAAAAwE8gsQMAAADw\nE0js3KTRaJqamnirjs+61Gp1c3MzP3WZzWbe6jKZTGq1WqfT8VOd0WjU6/X81GUwGNRqtcFg\n4Kc6nU5nNBp5q0utVpvNZn6qg7bQ1nkE2jqPQFvnCiR2btLpdFqtls/qeKtLq9Xy9i1lGIa3\nryjDMFqtlrdvqclk4m3TTCaTVqs1mUz8VGc0Gnmry2AwaLVaJHYCQlvnEWjrPFUX2jqnkNgB\nAAAA+AkkdgAAAAB+AokdAAAAgJ9AYgcAAADgJ5DYAQAAAPgJJHYAAAAAfgKJHQAAAICfQGIH\nAAAA4CeQ2AEAAAD4CSR2AAAAAH4CiR0AAACAn0BiBwAAAOAnkNgBAAAA+AkkdgAAAAB+Aokd\nAAAAgJ9AYgcAAADgJ5DYAQAAAPgJJHYAAAAAfgKJHQAAAICfQGIHAAAA4CeQ2AEAAAD4CSR2\nAAAAAH4CiR0AAACAn0BiBwAAAOAnkNgBAAAA+AkkdgAAAAB+Qip0AADgyJVq9fcnL1+qaNQb\nTX1iQ6YOiR8QHy50UNA1XDtH2f8KLssis4G6Dae0JdTtJqFjAgAnkNgBdF6Hcsv/vu20zmhi\n/8y9Wrczq3jJrSl3j+0rbGDg/3K/pF0Pk1Fr+U+i8jidWUuTV9Owp4WNCwAcw61YgE7qWqP2\n7W+vZ3Wcz37J/b2kVpCQoKtouMxmda2X732GKk8JERAAuAqJHUAntf9cqdbQOqtj7cwq5jkY\n6Fp+/6+NrI519lN+QwGA9sGt2BuYzWaz2ezKmgzDEJHRaPRyRNfxWRfDMPxUZzabeavLZDKx\nNfJWXQfrKqpssP9So/Unsyctb5tmNptNJhNvdVHLsXNKJBJJJBIvR9Q11Jy3+9K133mMAwDa\nDYndDQwGg16vd2VNhmEYhmlubvZ2SFx1vNVFRGazmZ/qGIYxmUy81UVERqORn+rYHwkdqoth\n7L0iohvOBzbv0ev1LiZAHWQ0Gk0mEz91sbXodDqRSOR0ZZFIFBQU5P2gugCxzO5LEjmPcQBA\nuyGxu4FCoVAoFK6sWVtbazabg4ODvR0Sq6amhre6dDqdRCLhpzqTydTU1MRPXUajUa/Xy+Xy\nwMBAHqrT6XRGo7EjdaUlRO0+U2rzpdK65hotJURb9ptWq1Wr1UqlUqlUul2d6yXnByIAACAA\nSURBVJqamqRSqYvflA5Sq9Umk0mlUkmlaKx41G0kndtg9yUA6MTwjB1AJzUxtbtSbvvGYmOz\n4dn/HD7wexnPIUFXMfABCupuY7k8iIY8wXs0ANAOSOwAOqmLZfU6fevbndwdyWa98a9bsj74\n4azRbPeOLYCb5ME050cK6d16eY/xthM+AOg0cHcDoDMymsyrfzjLpWw3p8TFhgX0iQnu3yNs\n1XfZuVfr2OU/ZhUXV6v/cHsqvsngYdFD6KHzVPCdtvg3+YX/iXXVRETFe6mpjALjhA4OAOzC\nFTuAzujrwwXF1Wq2fFO/6D/PH/7obQNuGxLfKyroH4vHTB/ei1szp7jm+Y3H8sobBYoU/JdU\nSf3vbh7xiuamlZYlJh2dWiNoTADgBBI7gE6ntKbpq1/z2bJCJlk2fZD1qzKJePnMtD/cOUQh\ntTyBd02te337+a3HLvMdKHQNuj5zr1+ly/4nGZoEDQcAHEFiB9DpfLgzR2+0jKe4aGJytzBV\n23WmDI5/98ExsWEB7J8mM7MhM//tb0/r7IxpDOA+iZyGLbOUtTV2O8wCQCeAxA6gc9mXU5p1\nqZot94kJnj2qj701+8WFfrjk5uGJUdySX85efW794fI6jdejhK5myBMkaxm7J+t9YlwayB0A\n+IfEDqATadIZ/73HMui/SCR6ZmaaVOxoYN5QlfyvC0bOGZnArVRQ3vD0p4dOFFR5OVLoYpTh\nlLrYUq7No4IdgkYDAHYhsQPoRD7bc/5ao2WOzhnDew2MD3f6FrFItHhiv+emJwcqLF1jG5r1\nL39x7LNfchn7c1cAtNuIF0jUMrDiyVWChgIAdiGxA+gsLpTW/XjqClsOD1Q8PLm/6+9N7x3+\nzv039Yq2TKjFEG06XLDy6xN1Gv2pwurtx4t2ZhVfLKv3fNDQdYQmUt9ZlnLJQSo7Kmg0AGAb\nRr8C6BRMZuaDH85y19geyxgYpLQ/X6ctPSJUqx8et+q7MwfPW2akOJpXufD9PUbT9et2gxMi\n/zRnWHgQH7OBQWfW3Nys1WpdWZOdrre2tpaIpMmPBOd/yy7XH3m7adKnHg/MbDazdfHDaDTy\nVh3Pm6bT6Vyc+ryD2JnTeauLiDQaDW9TfotEIo2Gj6eWzWYzETU0NLgyL7ZYLA4NDbX3KhI7\ngE5h69HCgvIGtjw4IXLSIHfG9w+QS1+aN/zbY4X//vm8ycwQkXVWR0RnLl975avjHywZJ3ah\n7QA/5vrMwnV1dWazOSwsjIgobDqdGsVeq5MXbpfd8g6F2u3c457a2lpLXd537do1qVQaEhLC\nQ10mk0mj0fA2L3Z9fb1CoVCpbHSo9zidTsdO6MxDXVqttqmpKSAggJ95sTUajUQi4Wde7Kam\nJq1WGxwc3PF5sZHYAQivsr75f5kX2bJcKn7u9jS30y4R0V0j+yREBb+66YTNoU8ultX/dqFi\nXEo3d2sAf+DKVQHb66c/R9/fS0TEmESn19Akzz9s197YfKI6thY+6+K5Op7r4u0k4bMuT1WH\nZ+wAhLfmp3PaliTs3pv7dY8IdLy+U8MTo3pFBdl7Nae4poOfD11X8jwKTbSUz/6bdHhwE6Bz\nQWIHILBDueVHLlaw5Z5RQXeP7euRj2VvxdqkxSDG4DaRhIY9ZSnrGynnM0GjAYDWkNgBCKlZ\nb/x41zm2LCJ6ZsYgmcQz38ruEXYfeenR4SuC0KWlPUKKlge3T75HZoOg0QDADZDYAQhpw76L\n1Q2Wzom3DYkfnBDpqU+eMjje5nKZRDxhYJzNlwBcIg+mtKWWcmMJXdwiaDQAcAMkdgCCKaxo\n+O5EEVsODpAtnTLAgx8+Jjl25vBebZcnxYXGhAZ4sCLoioYvJ3HLcDwn/iFoKABwAyR2AMJg\nGOaDH85yT8I9MmVAqEru2SqemZn2x7uGpSdGRwQppC13eM9frbtU0eDZiqDLCY6n5HmWcsVJ\nKjkoaDQAcB0SOwBh7Dhx+fzVOrY8qGdExtCe3qjllkHd/7Zw5JfPTXnxziHsEoZh/puZ5426\noGsZ8cL1MmYYA+g0kNgBCKBWrduw3zJwnVQifnam+wPXuWhCavd+cZYH3g9fKD9fwt8I+OCf\nYtMpfoKlnP8d1eQKGg0AWCCxAxDAx7vOqbWWvoTzxyRyc7x6j4jowUnXJ5/97Bf8Nwwddv2i\nHUOnPhQyEgBogcQOgG8nCqoO/G6ZzjUmNODem/vxU+9N/aKH9Lb0uj1bXJN1qZqfesFv9Z1F\nESmW8rn11IwzCkB4SOwAeKUzmj7amcP9+fT0QUqZhLfal9yawt3z/c/eXLtDGAO4RETDn7EU\nDRo6s1bQYACACIkdAM8+P5BXVqthyxNTu49MiuGz9v7dw7ga88rqD50v57N28EMDF1NAlKWc\ntZqMWkGjAQAkdgA8ulzVuOW3S2xZpZA+epsnB65z0UOTU7hJptfty3Uw8xiAczIVDXncUtZU\nUu6XgkYDAEjsALxPazBdLK07klf57o6zxpZE6uHJKVHBSv6D6RMTPCnVMvNEybWmX86U8B8D\n+JVhT5O05Uw++S4RfioACEkqdAAA/owh+vJg/teH8rUGk/Xy5O5hM9NtTAvBjwdv6X/wfLnR\nZCai/x7IuyWth6cmqIWuSBVDKfdRzjoiouocKvqZemcIHRNA14XWHMCL1u+9sGH/hVZZHRHd\nd3M/scjbQ9fZ1S1MNbVlPOTK+ubvT14WKhLwE+nPE7WczxisGEBQSOwAvKVGrdv8W4HNlw78\nXspzMK3cPyFJ0dIb98uD+RqdUdh4wLdFDbp+la5oN1VlCxoNQJeGxA7AW3Ku1BntdE04WSDw\niF8RQYo7b+rNlus1+m1HCwUNB3xf+vPXy1kfCBcHQFeHxA7AW5rsXwZr1BoEf8L87rF9g5Qy\ntrz5yKWGZr2w8YBv651B0Zb5iOn8F9RUJmg0AF0XEjsAb4kIktt7KTpEKdgTdi2CA2TzxiSy\nZY3O+PUh23eNAVyVvtxSMOno1BpBQwHoupDYAXjL4F4R3CWxVsYPiOM5GJvuGtUnPEjBlr87\nXlTV0CxsPODbUhZQYMuJnf1PMjQJGg1AF4XEDsBbAuSSZdMHtV0eHxm4YDxP88M6ppRJFrTM\nVKs3mj8/mC9sPODbJHIa9pSlrK2h3zcKGg1AF4XEDsCLbhnU/YGJSdyfgQrpzOG93n9onL0r\nefybkZ4QF65iy7tOXblSrRY2HvBtQ54gWaClfPI9YsyCRgPQFSGxA/Au636xax+f8MzMtOCA\nzpLVEZFULLp/giX1NDPMfw/kCRsP+DZlBKUutpRr86hgh6DRAHRFSOwAvKugvIEthATIo0MC\nhA3GplvTeiTGhrDlA+dK88vqhY0HfNuIF0hkGSKRTr4raCgAXRESOwDv4hK7vt1ChI3EHpFI\ntGhiMltmiNbtuyBsPODbQhOp7yxLueQAlR0VNBqALgeJHYAXNTTrua6m/TprYkdEY/rHDogP\nZ8snCqqyi64JGw/4NuvBivc9Qxe/oTr0ywHgCRI7AC/iLtcRUd9uoQJG4tSSW1O48me/5Ao+\nfjL4sPjxFJVmKZcdox1302dJ9N080uEuP4DXIbED8KJ8q8SuM1+xI6K0XhHDE6PY8oXSumN5\nlcLGAz5MXUoNRa0X5m2hHfMFCAagi0FiB+BFBeWWSxQKmaRHZKDjlQX38OQUbj6MdXtzGQaX\n7cAtJ1aRvtHG8ss/U/Fe3qMB6FqQ2AF4EXfFLjE2RCwSfBYxJ5LiQm9umRKjsLJxX06psPGA\nryrJtPvSlf38hQHQJSGxA/AWncF09ZplVqVOfh+W8+At/SViSwK6fv8FowkDzEL72bxcZ3mp\nwe5LAOAJSOwAvKWoSm1uuZvZyXtOcOIjA6cMjmfLFXXNP52+Imw84JNC+9h/KZHHOAC6IiR2\nAN5SWHV9EvROO4hdW4smJSuklgFmPz+QpzOYhI0HfM+ABbaXSwMoaQ6/oQB0OUjsALylsNJy\nQ0oiFvWODhY2GNdFBStnpPdiyzVq3fbjRYKGAz5owP2UdJeN5UOXUXA879EAdC1I7AC85VKl\nmi30igqSS33pu3bfzf1UCilb/vpQQXWDVth4wMeIxDTrG5q8mrrdRFLl9eV1F4WLCaCr8KX/\nbAB8iMnMXGnpOeErD9hxQlXyu0ZZHpNSaw0LP/hl9t93/fF/R38vqRU2MPAZIgkNe5oWHqNn\nmyl+gmVh/ndUkytoWAD+D4kdgFdcqVbrjZYupb7SJdba9KE9ue6xRNSsN54qrH5x45HfLlYI\nGBX4pOszjDF06kMhIwHoApDYAXgFdx+WfKrnBGfLkUKTufUAxQaT+f3vz+iM6E4B7dHvDopo\nmbDu3HpqrhY0GgA/h8QOwCsutfScEBElxvpeYnfgfJnN5XVN+nNXMOMntIuIhj9jKRo0dGat\noMEA+DkkdgBecanCktjFhqmClDJhg2kvk5mpabTbYaKqoZnPYMAfDFxMAZaZiClrNRnRHQfA\nW5DYAXgeQ1RUZbkV64v3YSVikVIutfeqz+WpIDyZioY8bilrKunCV4JGA+DPkNgBeF55rUat\nNbDlpDgf6xLLGtYnyuZyqViUGh/GczDgD4Y9fX3okxOriFo/wQkAHoHEDsDzCsqvT4jpi1fs\niGjRpGSlTNJ2+V2jE8MC5fzHAz5PFUMp91nK1TlU9LOg0QD4LSR2AJ6XX369e0E/XxvEjtUn\nJvivC0f2iAhstTw2RGlzfQDn0p8nahlD5+S7goYC4LeQ2AF4HnfFLixQHhGkEDYYtw3qGfHZ\nkxP/9ej452cN4WbO2Hyk0MzgJhq4JWoQ9c6wlIt2UVW2oNEA+CckdgCex12x89HLdRyRSNQn\nNmTq0PhpQ3uyS8rrNEfzMQ4ZuOv6YMVEWR8IFweA30JiB+BhdU36GrWOLft6YseZOyaRm4hi\n2/ErwgYDPqx3BkUPsZTPf0FNtodLBAC3IbED8DDrB+x8tOdEW93CVONSurHl/PKGcyV1wsYD\nPix9uaVg0tHpjwUNBcAPIbED8DC/TOyIaP7Yvlx569Ei4QIBH5eygALjLOXTH5OhSdBoAPwN\nEjsAD+N6Tihlku7hKmGD8aDkuNDBCZFs+URB9eWqRmHjAV8lkdOwpyxlbQ39vlHQaAD8DRI7\nAA/Lb0ns+kQHiUQixyv7lvljEtkCQ7TlSKGwwYAPG/IEyVpG0jn5HjFmQaMB8CtI7AA8SaMz\nltVq2HKfmCBhg/G4m5JiEqKD2fLes1ev2Z9PFsARZQSlLraUa/OoYIeg0QD4FSR2AJ50qaKB\naRnmLdHvEjsR0bzRfdiywWTefrxI0HDAl414gUQtU5tgsGIAz0FiB+BJ+VaTifWJCRYwEi+Z\nPDg+Mtgy5PKOE5ebdEZh4wFfFZpIfW+3lEsOUNlRQaMB8B9I7AA8qaClS6xUIu4Z6T89JzhS\nsWjG0B5sWaMz7jqNMe3AXekvXC9nvS9cHAB+BYkdgCcVVFiu2CVEBUol/vn9mjakR6BCypa3\nHLlkNGOGMXBL/HiKG2UpX9xMDZcFjQbAT/jnfzwAgjCazJer1GzZL+/DsgLkktsGWy7aVTdo\nD5wrFTYe8GHpz1kKZiOd+lDQUAD8BBI7AI8pqlIbTZaBG/rG+m1iR0R3jujFXY/c9NslXLID\nNyXPo1BLdxw68wnp6h2uDQDOIbED8JgCqzknEmP9Z86JtiKDFRMHWiYPKKxoOHWpWth4wFeJ\nJDRsmaWsb6SczwSNBsAfILED8BiuS6xIJOodHeh4ZV83f2xfbvDlb34rEDIU8Glpj5Ai1FI+\n+R6ZDYJGA+DzkNgBeAx3xa5HhCpALhU2GG/rExM8PDGaLWddqrYe5wWgHeTBlLbUUm4skRdh\nsGKADkFiB+AZDMNcqrBMn9q3W6jjlf3D/LGJXHnLkUsCRgK+bfhyEsvYojJnjbCxAPg6JHYA\nnnG1RtOst4zW26+bPz9gxxnWJ6pfnCWFzTxXWlnfLGw84KuC4yl5LluUXjtNJQeFDQfApyGx\nA/AM654TfbtGYkdE80ZbLtqZzMy2o4XCBgM+bMT/u14+uUq4OAB8HhI7AM+wfsjMv7vEWpuY\nGhcXbplg48es4oZmvbDxgK+KTaf4CZZy/ndUkytoNAA+DIkdgGcUtCR2kcHK8ECFsMHwRiwS\n3TmyN1vWGkw7szDDGLgr/fmWEoPBigHchsQOwDMutUwm1nXuw7KmD+sVEiBny9uOFuqNZmHj\nAV/V7w6KSLGUz62nZgyOCOAOJHYAHlDdqK1t0rHlLtJzgqOUSWam92LLtU26fTlXhY0HfJaI\nhj9jKRo0dGatoMEA+CokdgAeUGD1gF2/rjHWibXZI/vIpS0zjB0uYBjMMQZuGbiYUUZaylmr\nyagVNBoAn4TEDsAD8rtkl1hOWKD81rQebLnkWtPRvEph4wFfJVNp+z9oKWsq6cJXQgYD4JuQ\n2AF4AHfFLkgpiw1TCRuMIOaN6SsSWeYY++Y3DFYMbtINfJSkSssfJ1YR4eovQPsgsQPwAC6x\nS4wNETle1U/FRwaOTophyznFNedLaoWNB3yUWRlFKfdZ/qjOoaKfBQ0HwPcgsQPoKLXWUFGn\nYctdreeEtXlWM4xtxgxj4Lb054lafh9tnUaf9KQdd9O1c4LGBOAzkNgBdNSligbudlEX7DnB\nGdQzYkB8OFs+lFtxtaZJ2HjAVwV1J1mgpcww1FhCF7+hz0dSyQFBwwLwDUjsADrKes6JLthz\nwtr8MZaLdgzDbD2CGcbALYdXkkHdeqFBQz89SGajEAEB+BIkdgAdxXWJlUvF8VFBwgYjrLH9\nY3tEWK617M6+wo3tB9AOuV/aXl5fSKW/8RsKgO+RClh3WVlZQ0ND9+7dg4OD27uOveUVFRX1\n9fWxsbGhoZY7YuXl5VVVVdbr9OvXLyAgwHPbAV0d13Oid0yIVNw1+05YiESiuaP7rP4xh4j0\nRvP3Jy4/MDFZ6KDAl4gMamq+ZvflhkKi8TyGA+B7hEnsNBrNG2+8kZubGxERce3atQULFsyf\nP9/Fdewtr6ur+9vf/lZYWBgWFlZdXT1lypQnn3xSJBJt27Zt//79XJ5HRC+99FJCQgKf2wt+\nTG80l1Rbbht15Z4TnNuG9PzvgbxatY6Ith8vmj+2r1ImEToo8BmMNIDEUru3XOX4igE4IUxi\nt3HjxsbGxg0bNgQHB585c2blypWDBg0aMGCAK+vYW/6vf/2LYZgNGzaoVKqLFy+uWLFi+PDh\nY8aMUavVEydOfPLJJwXZUvB7RZUNRrOl7wQSOyKSS8Wz0hM2Zl4kosZmw8/ZJbNG4HcUuEwk\noR7j6co+Gy9J5NTjZt4DAvAxwjxjd/DgwTlz5rB3UQcPHjxkyJD9+/e7uI695aNHj3788cdV\nKhURJScnd+vWraSkhIgaGxuDg4N1Ol1paaler+dxK6FLsO45kdiFu8Rau+Om3txVus2/XTJj\nhjFol3GvXx+j2NqIFyggivdoAHyMAFfsamtrGxsbrW+G9u7dOzc315V1HLx30qRJ3MKqqqqK\nioqUlBQiUqvVR48e3bFjh1QqbW5unjNnzgMPPGAvNrPZbDabXdkKdjZMo5G/Llp81sUwDD/V\nmc1m3uoymUxsjZ6t7mJpHVsQi0Q9IwK4DzeZTB6vyx72pOWzOpPJ5KCuAJloSlr377OuEFF5\nnWblV8dH9I0e3CuiZ1Sgvbc4qItajp1TIpFIIsFtX9/XYxzN+oZ2P0JN5Tcsjx0pUEAAvkSA\nxK6pqYmI2EtrLJVKxS50uo4r762vr3/jjTcmT56clpZGRAMHDpTJZPPnz1epVFlZWa+//nqP\nHj0mT55sMzatVqvRaFzflrq6OtdX7iA+6zIajf66aTqdTqfzZFfNvFLLFAtxYUptU2OrScs9\nW5djGo2mXWdvx6tz8OqtAyJ+yLrCXqk7ll91LL+KiKaldbt/XC9u5jHXNTY2urKaRCIJDw9v\n74dDZ5R4Oy0tpPLjVHqIfn2JGDMRUdZ7lDRb6MgAOjsBEjupVEo3/gQ3mUzsQqfrOH1vYWHh\nG2+8MWbMmCVLlrBLli5dyr06fPjw0aNH//bbb/YSO6lUqlTaugXQhk6nYxjGxZU7TqfTKRQK\nfurSarVisVgul/NQF8MwBoOBt7p0Oh13FnmEmWGu1DSz5cTYYOvzwWQyMQzjwbocMJlMBoNB\nJpPxc73KaDQ6vTZ2/Exl2/uvP50tjwwNmHNTL9frMhgMJpNJLpeLxc6fG3FlHfAZUiXFj6f4\n8VR2lPK/JSIqOUBlRylulNCRAXRqAiR2ERERYrG4urq6R48e7JKqqqqYmBhX1nH83uzs7L//\n/e9Lliy59dZb7dUeFBRUXl5u71W5XO5ikmEwGMxmc1AQT4OW6fV63urSarUSiYSf6kwmU1NT\nEz91GY1GnU4nk8kCA9t9Q9Ce4iq1zmD5mZESH2m9ITqdzmg0erAuB7RarcFgUCgU/PzSaGpq\nkkqlDn5pGM3MdydLbL60/UTJgokDXB8URq1Wm0wmlUrFT4oMnVH685bEjoiy3qeZdka5AwAi\nEqTzhFwuHzBgwLFjx9g/9Xr9qVOnhg4d6so6Dt576dKlN9988w9/+IN1VqfX659++mlufbPZ\nnJOT06dPH29vI3QRBRWYc8KGkmq1Wmuw+ZJaa+BGhwFwSfz461fpLm6mhsuCRgPQ2QnzI3jB\nggWvvPKKRCLp1avX3r17VSoVm40dPHjw66+//uijjxysY2/5Rx99lJSUVF9fz3WwjY6OTk1N\nHThw4OrVq+fNmxcSEpKZmdnU1DR37lxBthr8DzfnBBElxiKxszCYHPVAcvwqgA3pz9H39xIR\nmY106kOa+A+hAwLovIRJ7NLS0t54441du3Zdvnw5OTl5zpw57A1QhUIRFhbmeB2by81ms1Kp\nNJlMu3fv5moZNGhQamrqY489lpSUdPLkSYPB0K9fvxdeeCEkBP8Bg2fkl1kSu5jQgFAVH08K\n+oTuEYFSsYgb3s+aVCzqHsHH7WnwK8nzKLQP1RcSEZ35hEb/mRQYWgjANsEeW0lNTU1NTW21\ncOTIkSNHjnS8js3lYrH4b3/7m82KxGLxlClTpkyZ0uGQAVorrLT01sR9WGuBCun4gXH7ckrb\nvjQhtXugoms9LXfmzJk9e/Y0NDTEx8fPmTMnIiLC9XXsLT99+vT+/fvr6+u7des2c+bM+Ph4\nIvrhhx8OHTpk/bFPPfUU9ziybxNJaNgy2v8CEZG+kXI+o/TnhY4JoJNCJzIAN1XWN9drLENe\n98PQxDd6Ympqnzb3pkVE88ckChKPUE6cOPHKK6+EhYWNHz++uLj4xRdfbDtMjL117C3fs2fP\nX/7yl8jIyPHjx1dXV7/wwgsVFRVElJ+fzzBMhhW/ujuR9sj1q3Qn3yOz7Yc4AQCJHYCbCsrR\nc8KuUJX8g4fHPnrbgJH9YiKDLP1nGaLdp233lvVXX3311R133PHwww/feuutr7zyChH9/PPP\nLq5jb/m+ffvmz5//wAMPTJ48+Y9//KNcLmf7hzU2Nvbr12+SFXaGHj8hD6a0lrGrGkvo4hZB\nowHovJDYAbjJuucEZoltSyGVzB2d+Pp9N2189tbYsAB24Y+niuuausrMfnq9Pi8vb8SIEeyf\nUql06NChZ8+edWUdB+/961//et9997HLJRKJVCplB/BTq9VGo/Hzzz9ftWrV559/Xltby89m\n8mf4chLLLOUTqwQNBaDz6loPuwB4EHfFLiRAHh0SIGwwnZlULJo3pu+anTlEpDOYth0tfGhy\nf6GD4kNNTQ3DMJGRkdySyMjI/Px8V9Zx5b1E9N133xmNxgkTJhBRY2PjL7/8Mn369OTk5EOH\nDv34448ffPBBVJTtyVX1er2Lc2ez8/6p1TwNUuOoLlGYMvFOaf5mIqKKE815u01xYztYnclk\n4mfT2LkTeauLiAwGAz/VsYOx81YXtYwSykN1RqPRaDQaDHzc92dr0Wg0Lg7Gbj0FVytI7ADc\nlN+S2OE+rFPTh/X86tf8a41aIvruRNH8sYlBSpnTd/k69v8e6yk6JBJJq3lv7a3jynu3bNny\n7bffrly5kr3l+uc//1mlUrHP1c2YMePpp5/evHnz448/bi82rVZr8yWb2rVyBzmoy5jyeBib\n2BFJTr/fFD68g3WZzeZOsmkexyYlfFbHW10Gg4GfZIurjre6XPy5JZFIkNgBeFhDs76qwTKZ\nGO7DOiWTiOeM6vPvPeeJSKMzfnf88oLx/YQOyuvYmUise0u0nWfF3jqO32swGD788MPCwsJ3\n3nmnW7du7EKuQEQSiSQtLa24uNhebEql0sVZdhoaGhiGCQ3lqXtQQ0ODoz4fYROZHuNFVw8S\nkbz4p3Cmggl3/+pvXV2dVCrlZ+Ybs9nc3NzMz1Q0JpOpsbFRoVAEBPBxJ0Gv15tMJt7q0mg0\nKpWKn4kom5ubJRIJP3VpNBq9Xh8cHOzKzJCOZ9xGYgfgjht7TqBLrHO3j0jYdLiA7Ue87Wjh\nXaN6B8j9vP0JCwsLDQ29dOlSYqKlL7B12fE6Dt5rMpneeustk8n0zjvvcJPIGQyGI0eOpKWl\ncUOB1tXVhYeH24tNLBa7OLWuSCTibdZjlpO6bvp/dPUgERExkjMf061rOlKXSCTibUJn3upi\nicVi3jaNtzOEvS7I26aJxWL2MVZ+6qKWp2Y7+lGeiAegy8lHl9h2Usokd97Umy03NOt/zLJ7\nMcmf3HLLLdu2bWtoaCCiEydO5OTkTJ48mYjKy8t//fVXx+vYW75p06a6urqXXnrJempgqVS6\nYcOGdevWsbdrc3Jyjh07Nm7cOL43mAd9Z1FEiqV8bj01VwsaDUCn4+e/mAG8pKClS6xCJomP\nxFQKLpk9qs+WI5eadEYi+ua3S7ePSFBInd908GkLFiy4fPnygw8+GB4eXldXt3Tp0r59+xLR\nqVOn1q5de/PNNztYx+ZyhmE2b96sVCqfeuoprpb09PTHHnvsxRdffOed+lup1wAAIABJREFU\ndxYtWqRSqWpqau69996xYzvat6BTEtHwZ2jPk0REBg2dWUujXhI6JIBOBIkdgDu4K3aJMcFi\nh487ACdQIZ2ZnrDpcAER1ap1P2eX3J6eIHRQ3qVUKl977bWysrL6+vqePXtyj1iNGjWqZ8+e\njtext/zVV19tVQv7AFxycvLatWuvXr2q1+t79OhhfT3P3wxcTIdesVyry1pN6S+Q1H83FqCd\nkNgBtJvOYLp6rYkt4wG7dpk7OnH78SKdwUREXx8qmDa0p1Ti/w+ExMXFxcXFWS+JiIhoNbdY\n23VsLheJRGlpafYqEovFXL7oz2QqGvI4HXmDiEhTSblf0qCHhI4JoLPw/yYVwOMuVTSYGcsM\n9+gS2y5hgfLpwyyZR2V9s835ZAGcG/b09at0J98lYgSNBqATQWIH0G439JyIwxW79pk/pi93\nle6LX/O5FBmgHVQxlGKZfoOqc6io9URtAF0WEjuAduN6TkjEot7RfjQdJy+iQpS3De7Blktr\nmn49Xy5sPOCr0p8nanm89SRmGAOwQGIH0G7cFbteUUFyKb5E7XbPuH4SseW/5C9/zcclO3BH\n1CDqnWEpF+2mqmxBowHoLPB/EkD7mMzM5apGtoyeE+6JC1dNGGjpEHCpouFYXqWw8YCvSn/+\nejnrA+HiAOhEkNgBtE9xtVpvNLNl9Jxw24LxSdysOF8czBM2GPBVvTMoeoilfP4LaioTNBqA\nTgGJHUD7cA/YEeac6IBeUUFj+8ey5dyrdaeLrgkbD/iq9OWWgklHpzo0vRiAf0BiB9A+3AN2\nIqLEWCR27lswPokb2flLXLQD96QsoMCWcf6y/0mGJkGjARAeEjuA9iloSey6hauClDJhg/Fp\n/bqFpPeNZsuni66du1IrbDzgkyRyGrbMUtbW0LkNgkYDIDwkdgDtwBBdqrAkdrgP23H3T0ji\nyl8dyhcwEvBhQ54gWct8zVnvE2MWNBoAgSGxA2iH8lqNWmtgy/3QJbbDBsSHD+plmVnrWF5l\nXlm94/UBbFCGU+piS7k2jwp2CBoNgMCQ2AG0Q4H1nBO4YucJ993cjytvOlwgYCTgw0a8QCKJ\npYzBiqFrQ2IH0A751l1i0XPCE0b0jU7uHsaWD54v58YIBGiH0ETqO8tSLjlIZUcFjQZASEjs\nANqBu2IXFiiPDFY6XhlcdN/NfdkCwzDf/HZJ2GDAV90wWPH7wsUBIDAkdgDtwF2xwwN2HjSm\nf7c+MZYpd/edvVpepxE2HvBJ8eMpbpSlfOEbqi8UNBoAwSCxA3BVXZO+Rq1jy3jAzoNERPeM\nszxpZzQzmw7joh24Jf05S4Ex0amPBA0FQDBI7ABcZf2AHa7YedbE1LgeEZYRK3afvlLdqBU2\nHvBJyfMoNNFSPvtv0qGTNXRFSOwAXJWPycS8RiwSzR9redLOYDJvPYL7aNB+IgkNe8pS1jdS\nzmeCRgMgDCR2AK7iek4EyKXdw1XCBuN/bhsSHxMawJZ/OHm5XqMXNh7wSWmPkKLlavrJ98hs\nEDQaAAEgsQNwFTdLbN/YEJFI5HhlaC+pWDRvjOU+mtZg+vYYLtpB+8mDKW2ppdxYQltvpxP/\noKpsQWMC4BUSOwCXNOuNZbWW3pq4D+sl04f1ighSsOXtx4q4ST4A2mHoMhK1/Nd2eTdl/oE2\nDqW9T2OqMegikNgBuKSgooFhGLbcD4mdd8il4rtG9WHLTTrjjhOXhY0HfNLpj2zkcKc+ouPv\nCBENAN+Q2AE4V16n+TGrmPuzL7rEes2sEQkhAXK2vPVIodZgEjYe8DGGJjr9se2XTqwiBqcT\n+D8kdgCOMET/2Zv78Jr9v5y5yi08fKFcwJD8W4BcOuumBLbc0Kzfc7ZM2HjAx1TnkLHZ9kvN\nVRi1GLoCJHYAjnxzuODrQwUmM2O98H8H8nCX0HvuGtknQC5ly1uOXT5eWFte18w4fg8Ay3E3\nWBO6WoP/Q2IHYJfRZP76UIHNlz4/mMc9cgeeFRwgm5neiy03NBvf/+nikn8eeGLtgYtlGG8W\nnInoTyKJ7ZdkKgrtw280AAJAYgdg1+Uqtb2OmbVqXWktpjT1lrbPvhdWNr648ciVarUQ4YDv\nCIimpLtsvzRwEUkD+I0GQABI7ADs0hsdPWqtw3P93lHbpNt+oqjt8ma9cWPmRd7DAV8z5Z8U\nPbj1QomMxr0uRDQAfENiB2BXXHigvYGIpWJRtzBMPuEVZ4quGU22hxw7UVDFczDgewKiaMER\nuuUDSppDgd0sC00GKtghaFgAPEFiB2BXWKB8ZL9omy+NGxCnUkh5jqeLcDAusUZnNJrxaCM4\nIw2g4c/QHVtoUTZJlZaFJ98lwskD/g+JHYAjz8xIa3tlLj4y8MmpqYLE0xVEBivtvRQRpJCK\nMZkbuEwVQykLLOXqHCr6WdBoAPiAxA7AkagQ5ZpHbg4LtMxzpVJI75+Q9NHSm8MC5cIG5seG\n9okKDpDZfGn8gDiegwGfl/4cUcuPgZOrBA0FgA9I7ACcCFLKuEe+RiXFPDAxmRtlDbxBKZM8\nPX1Q2+U9IgLvn5jEfzzg26IGUe8MS7loN1VlCxoNgNchsQNwollv5J76ig1Fhwk+TEzt/tb9\nowbGh0slYiIKCZBPH9bzvYfGcrONAbTDiBeul0++L1wcAHzAhQcAJyrrr89QFB1q9/Ev8Kxh\nfaKG9Ymqa2isbWjq2S1KKkVjBe5KuI1ihlLlaSKi3C9o/N8oEPf0wW/hih2AExVWiV1MCAY4\n5ZVULApWIqWDDhv+rKVg0tOpNYKGAuBdSOwAnKiyTuxCkdgB+KABCyk43lLO/icZmgSNBsCL\nkNgBOFHVoOXKSOwAfJJYRkOesJS1NXRug6DRAHgREjsAJ7hbsUFKGQYlBvBVQ54gWZClnPW+\njQmJAfwCEjsAJ7jOE9G4XAfgu5ThlLrYUq7Nwwxj4K+Q2AE4wSV2sUjsAHzaiOdJJLGUMVgx\n+CkkdgCOmBmmptHyjF1MCMY6AfBloYnUd5alXHJQWnVS0GgAvAKJHYAj1Q1abtZ53IoF8HlW\ngxUHnF8rYCAAXoLEDsCRygaMdQLgR3rcTHGj2KKi6Dtx42VBowHwPCR2AI5U1WOsEwD/kv6c\npcCY5Oc+ETQUAM9DYgfgSCWmnQDwM8nzKDSRLcovbCBdvbDhAHgWEjsAR7jETioWRQQrhA0G\nADxAJKFhyyxFg5rOfipsOACehcQOwBHuGbvIEKVYJBI2GADwjLSlpAizlLPeJ7NB0GgAPAmJ\nHYAjVdcHsVMJGwkAeIw8mNKWWsqNJXRxi6DRAHgSEjsAR6ymncAgdgB+ZPizJJZZyif+IWgo\nAJ6ExA7ALrXW0KQzsmX0nADwK8HxuoSWwYorTlLJAUGjAfAYJHYAdlXVYxA7AL/VnPrk9T9O\nvitcIACehMQOwK7KBgxiB+C3jJFDjN3GWf7I/45qcgUNB8AzkNgB2FWBQewA/Jp+8LKWIkNZ\nq4UMBcBDkNgB2GV9KxadJwD8j6HXNIoYYPnj9w3UXC1oOAAegMQOwK6qlkHsQgLkAXKpsMEA\ngBeIaPgzlqJBQ9n/EjQYAA9AYgdgV0WdJbGLweU6AH+VupgCoizlUx+SUetwbYDODokdgF3c\ntBPR6DkB4K+kATTkcUtZU0m5XwoaDUBHIbEDsM1oZmoadWwZPScA/Nmwp0naclX+5LtEjKDR\nAHQIEjsA2641aM2MpX3HWCcA/kwVQykLLOXqHCraLWg0AB2CxA7ANu4+LCGxA/B7I54nElnK\nGKwYfBkSOwDbKuusxzpBYgfg1yJTqfdUS7loN1VlCxoNgPuQ2AHYZn3FLhaJHYDfG/H89fLJ\n94WLA6BDkNgB2FbZMjqxVCIOD5QLGwwAeF3CbRQz1FLO/YKaygSNBsBNSOwAbOMSu+gQpUgk\ncrwyAPiD4cstBZOeTq0RNBQANyGxA7CNuxWLnhMAXcWABRQcbymfXkPlx8mkFzQggHZDYgdg\nW1W9ZQB6PGAH0FWIZTTkCUtZV0efj6TVgbT9Lmq8ImhYAO2AxA7AhsZmQ7PeyJbRJRagC2HM\nN/xpNlL+t/TVzaSpECgggPZBYgdgQ0W91VgnmHYCoIvQVNLRv9pY3lBMR97gPRoAdyCxA7Ch\nqh5jnQB0PZf3kFFr+6WC7/gNBcBNSOwAbMC0EwBdkabc7ktqjH4CvgGJHYANlTfcilU6WBMA\n/Icqxu5Lgd14jAPAfUjsAGzgErtQlVwhkwgbDADwJOE2ktr5IZd4O7+hALgJiR2ADdwzdrgP\nC9CFqGJpzKs2lsuCaMwrfAcD4BYkdgA2VCCxA+iaRq6g29ZSYNwNC0UMSdEUgG9AYgfQmtFk\nrm2yDDePxA6gyxn8KD1eSo8UUfrzliX6Jjr7qaAxAbgKiR1Aa1UNWoZh2DISO4AuKiSBxr5K\nijDLn1nvk9kgaEAALkFiB9CadZfYGIxODNBlyYMpbaml3FhCF7cIGg2AS5DYAbR2Q2IXirFO\nALqw4c+SWGYpn/iHoKEAuEQqdACdi8lkMpvNztcjYm/VGQw8XZlnGIa3uviszmw281aXyWRi\na3RaXVmNmiuHqaTuhceeS3xumslk4u2o8VkXERmNRu7muGMymcz5SgCuC46n5HmU+yURUcVJ\nKjlA8ROEjgnAESR2NzAajXq93pU1GYZhGEartTP5jBfwWZfZbOanOoZhTCYTb3URkdFodFpd\nRV0TW5BLxUqJm0fZbDbzthvZxM5gMLj4s6SDjEajyWRiK/U2tha9Xi8SiZyuLBKJkNiB5414\nwZLYEdHJd5HYQSeHxO4GCoVCoVC4smZtba3ZbA4ODvZ2SKyamhre6tLpdBKJhJ/qTCZTU1MT\nP3WxWbtcLg8MDHS8Zq3GkrLEhAaEuBubTqczGo1O6/IIrVarVquVSqVSyceN46amJqlU6uI3\npYPUarXJZFKpVFIpGisQSGw6xU+kkkwiovzvqCaXIlKEjgnALjxjB9BaRb2GLUSj5wQAENGI\nlnFPiKGs1UJGAuAMEjuA1qoaLPdPMdYJABAR9Z1FEQMs5d83UHO1oNEAOILEDuD/s3fvcVHV\n+f/A32euDHe5iYo3VEJR8YKped20LBUr17K0otLW1GpJt22/62+r3bXa3VLJSi13K9zNrW+W\nfU37lqVfzdrKREBR8YoXvAECwwBzPXN+f8xhGObGAWbOmcvr+UePD5/5wHkP4Yc3n/P5vE8b\n2maT0dx6K1baYAAgMDA06mm+aW6m0k2SBgPgDRI7gDbaFrFDrRMAICKirDzSJPHt4jfIIt5p\nNoAOQWIH0IZjYpeMFTsAsFFoKPsJvt1c1XpOFiDAILEDaMMxseuOxA4A7EY+RYqWVfyitUSC\naisCiAwVBADasJ+cYIiScCoWQpTFYrFYLEJG2gpAilZHU+T6oB0rNimLVQ6aLz9RSERUU2Y6\ntcvaZ7rwC4lW2NJWz1K0EqG2wpbiXMtWF120cv220ugCq6N3kb1mp5B/mAzDeCk4hcQOoA17\nrZP4aLVKgSVtCE0d/XUlzu828a/V0ctZsp+Sn9hiW6tTlL5u7D2tQxcS563ZriL+5US4lv2K\nol0uGP+vIbEDaKNK21LrBMt1ELqUSqXAp3QYDAaO4zQakf456PV60a7V1NQkk8k6drleo6nf\n7XT+KyKSXfxG03iKkrOFfB7LshaLRZy3ZrFY9Hq9QqEQ53K2YuziXMtgMBiNRpVKJU4xdqvV\nKloxdttPiFqt7noxdixIALRR3bLHDrVOAMCNnJWt7cOvSxcHgHtI7ABamVlrfZPR1kZiBwBu\n9L2tdZXuxFZquippNADOkNgBtKrS6u27G5LjUMQOANwZnc83WCMVvyVpKADOkNgBtGpbnRgr\ndgDgTuYCiurBt0s3krlJ0mgA2kBiB9AKRewAoH1yFY18km8baulYoaTRALSBxA6gFR47AQCC\nZC8lZRTfPlxAnFXSaABaIbEDaFXVUp1YrZTHRaqkDQYAAldEN8rK49t1p+ns55JGA9AKiR1A\nK9Q6AQChclYSI+fbRWskDQWgFRI7gFb2W7HYYAcA7YhLpwG5fLvyAF39SdJoAHhI7AB4HFF1\nA5/YYYMdALRv9IrW9uEC6eIAaIXEDoBX32g0Wfgd0MmxKGIHAO1Jm0Q9xvLtkx+TtkLSaACI\nkNgB2FU1oNYJAHTQ6Gf4BsdS8ZuShgJAhMQOwK5NdWIkdgAgRMY8ikvn20c3k1EraTQASOwA\nWrQpYofHTgCAEIycRi7n2yYdlf1D0mgAkNgBtKjW8kXsGIbBHjsAEGrY46SO49tF68hqljQa\nCHdI7AB49j12CdFqhRz/NABAGFUMDVvMt3WVdOoTSaOBcIffXgC866hODACdMyqfZEq+feg1\nSUOBcIfEDoCHx04AQCfFpFHGPL59vYgqD0gaDYQ1JHYARERGM6ttNtnaKdhgBwAdlbOytY0n\njIF0kNgBEKHWCQB0UffRlDaZb5/ZQbXlkkYD4QuJHQBR2+rEeJ4YAHRG6xPGOCp+Q8pIIIwh\nsQMgIqqqd1ixQxE7AOiEgXMoIZNvl75NH02hfc9QdamkMUHYQWIHQERU3WCwt7vHI7EDgE5g\nqPcv+CbHUuW3VFRAH4yl0k2SRgXhBYkdAJHDrViNShEdofQ+GADAjaardPyfzp2skb5ZylSX\nSBEQhCMkdgBERNfrUesEALrmxAdkbnT7CnN0s8ixQNhCYgdARFTdYE/sUOsEADql5pjnl8pE\njAPCGhI7AOI4zr7HDicnAKCTZHLPLylEjAPCGhI7AKptNFpYq62NWicA0EndczrzEoBPIbED\nQHViAPCFwQspuqebfoXGmr1U9GggTCGxA2iT2HVHYgcAnaOKoXt2Ukxv5/60SRTbT4J4ICwh\nsQOgKocidlixA4DOSxlJj56gOwtpdD5FJPKdl/ZT01VJw4IwgsQOoHXFTsYwCTE4FQsAXaCM\noiEP09R1NPU1voc1yko3ShoThBEkdgBU3ZLYJcZEKGSMtMEAQIjIXEBRPWxN5sgmxtIsbTgQ\nJpDYAbQ+dgJF7ADAZ+QqGrmcbxtqlaf/LWk0EC6Q2AG03orFBjsA8KXspaSMsjVVR98izipt\nOBAOkNhBuNObLDq92dZGYgcAvhSRQFl5tqZMe5bOfi5tOBAOkNhBuHOsdZKMx04AgG/lrCSm\n5YkURWslDQXCAhI7CHdV2tZaJyhiBwA+FpdOA3L5duW3dPUnSaOB0IfEDsKd/eQE4XliAOAP\no1e0tg8XSBcHhAUkdhDuqvE8MQDwq7RJXOrNfPvkx6StkDQaCHFI7CDc2ffYRaoVUWqFtMEA\nQEjiRuW3tFgqeUvSWCDEIbGDcGdP7LrHR0obCQCEKm7QL60xffkPjrxDRq2k4UAoE5TYbdy4\ncf/+/a79NTU1s2fP9nVIAKK6bi9iF4vqxOEOcx34CyM3Zf2Kb5t0VPYPSaOBUCYosfvoo49+\n/vln1/7m5uZdu3b5OiQA8Vg5rlbHn4rFBjvAXAf+Y858hNRx/AdF68hqljQcCFnt7CjKz88v\nKSkpKSm5dOnSzp07HV/iOO706dP+jA3A727oDBYrZ2sjsQtnmOvA3zhlNA1bTIfWEBHpKunU\nJ5R5v9RBQQhqJ7GbNm1aQ0NDcXGxVqtlGOeHo/fs2fN3v/ud32ID8DvHInaoThzOMNeBGEbl\n0+H1/FrdodeQ2IE/tJPY5ebm5ubmVlZWzpw5Mz8/3/tggKBThVonQESY60AcMWmUMY/K/01E\ndL2IKg9Q2iSpY4JQI2iP3e7du/Pz80+fPq3T6eyde/bsaW5u9ltgAGJom9jh8ES4w1wHfpez\nsrVdtEa6OCBkCS138swzz2RkZJw8edLes2jRoiFDhhw7dsw/gQGIobrlsRMKGZMYg8QOMNeB\nn3UfTWmT+faZHVRbLmk0EIIEJXbbt28vKChYvnx537597Z0bNmxQq9V5eXl+iw3A7+y1ThJj\nI2QuO6sg3GCuAzG0LtpxVPyGlJFAKBJUZ//TTz+dNWvWm2++6dg5c+bM+Pj4CRMm+CcwADFU\ntxaxwwY7wFwHohiQSwmZ/Frdsffplj+SJknqmCB0CFqxq6qq6t+/v2t/v379fBwOgLjse+xw\ncgIIcx2IhKFRT/NNczMdeVvSYCDUCErs+vfvf+DAAYvF4tS/Y8cOP4QEIJJmo6XJyP9UI7ED\nwlwHohmS17pKd3g9WQxeRwN0gKBbsYsXL968efOMGTMefvhh21+u169f//rrrwsLC+fMmePf\nAAH85rrDkdhkJHaAuQ5Eo4yk7Cfox9VERM1VVP5vGvqo1DFBiBCU2OXk5GzdunXZsmV79+51\n7J89e3ZhYaF/AgPwuza1TrDHDjDXgZhGPkWHXuPX6orW0tBHiHB+C3xAUGJHRPPnz58zZ87+\n/ftPnz5tMpmSk5PHjRuXkZHh1+AA/MoxseuOFTsgIsx1IJrIFMp8gMreIyKqKaPzX1O/26WO\nCUKB0MSOiDQazR133DFmzBilUhkbG+u/mADEYS9iR0TJqE4MLTDXgUhGr6Cy94k4IqKiNUjs\nwCeEFiiurKzMy8vr1q1bUlLShg0biKikpOS5554zm83+DA/Aj+wrdjEapUbVgT9yIIRhrgPx\nJA1tTebO76bqUkmjgRAhKLGrqakZP378tm3bpk6dmpqaauusrKxct27dypUrvX8uQMBCrRNw\ngrkOxOb4hLHDr0sXB4QOQYndxo0bjUbj8ePHt2/fbi/IPnv27IKCgs2bN/szPAA/qtLyJQZw\ncgJsMNeB2PreRsnZfPvEVmq6Kmk0EAoEJXYHDx588MEHHZ+xY/Pggw8aDKi+A0GJtXK1jS2J\nHVbsgIgw14EkRufzDdZIxW9JGgqEAkGJndlsjoqKcu13LeMJECxqGgyslbO1UcQObDDXgQQy\nF1BUD75dupHMTZJGA0FPUGKXmZm5Y8cO173DhYWFcrncD1EB+F1VA2qdgDPMdSABuYpGPsm3\nDbV0DBUToUsEJXaPP/54eXn55MmTt2zZ0tDQUFFR8fHHH+fl5T377LMLFy70d4gA/lCFx06A\nC8x1II3spaRsWSo+XECcVdJoILgJKvGQlZW1bdu2RYsW5eXlEdGJEyfeeecdhmHmz5+/ceNG\nP0cI4BdtHzuBInZAhLkOpBLRjbLyqGQDEVHdaTr7OQ28S+qYIFgJrd2Vm5t76dKlffv2nTp1\nymw2p6SkTJgwoX///n4NDsB/qlsSO4VclhCtljYYCByY60AaOSup9G3iWCKiojVI7KDTvCV2\nS5Ys6dOnz6pVq+6///5p06Y9/vjjM2bMmDFjhmjBAfiPfY9dcmwEw+ARjWENcx1ILy6dBuTS\nmc+IiCoP0NWfqMdYqWOCoORtj93Fixd//PFHi8Vy7do1rVYrWkwAImgtYocNdmEPcx0EhNEr\nWtv7V9C5naSrlC4aCFbeErvRo0fv3LlTpVLt37//ueeeU3ggWqwAPoTHToAd5joICGmTKHk4\n3778H9qeS+/0pi8WkkknaVgQZLxNVatWrYqLiysvL9+1a1fv3r2zsrJECwvAr3R6s97EVybD\nYycAcx0EBH01NVx07jyxlfQ36JdfShEQBCVviZ1Go3n22WeJaNq0abNmzVqxYoWXwQBBBLVO\nwBHmOggIRQVkrHfTf/4ruriH+kwTPSAISt5uxS5ZsuSll14iouTk5JiYGLFCAvA7x+rEKXGo\ndRLuMNdBQLi41/NLe0SMA4KbtxW7ixcvXrlyBRuKIfS0LWKHFbtwh7kOAoKpweNLRvxYglA4\nPAHhqBq3YsEB5joICDF9PL4U20+8MCDI4fAEhKOqBr7WSVykKkKJZ4CGO8x1EBAy76fz7g5J\nyFWU8UvRo4FghcMTEI6q6pttDdQ6AcJcBwFiyIN0+hM6+7lz//DHKS5dioAgKAm6ubBnj/tt\nmwaDoby8fMSIET4NCcDv7Ct2SOzAEeY6kBIjpzmf0uECKnuf6k6SlS/JhDLF0CHe9tgtXrzY\n9les3XfffXfq1Cn7h2fOnBk5cqS/QgPwDwtrrW002to4OQGEuQ4Ch0xBOb+hR8ro6SZKm8x3\nntlBteWShgXBxFtid+bMmYqKCseexYsXv/vuu34OCcC/qhsMHMfZ2smodQKY6yAAyVWUs7Ll\nA44Or5cyGAgq3hI7gJBU3aaIHVbsACAgDcilhMF8+3gh6WskjQaCBhI7CDvX65HYAUDgY2jU\nU3zT3ExH3pY0GAgaUiZ2V69ePXnypE7n7fHGnsb4qh/CUBVW7AAgKAzJI00S3z68niwGSaOB\n4CBNyc3m5ubVq1eXl5cnJCTcuHFjwYIF9957r8AxvuqHsGWvTqyUy+Kj1NIGAwDgkTKSsp+g\nH1cTETVXUfm/aeijUscEgU6axG7Lli06na6wsDAmJubIkSMvvPDC0KFDBw8eLGSMr/oleeMQ\nCKq0rbVOGGlDAQDwbuRTdOg1fq2uaC0NfYQI8xZ4I82t2AMHDsydO9f2sO3hw4dnZ2fv27dP\n4Bhf9UPYst+KxX1YAAh0kSmUuYBv15TR+d2SRgNBoJ0Vu+3bt0dEtNaDMJlMr732WkFBge1D\ne82IDqmrq9PpdH379rX39OvXr7y8XMgYX/V7is1aWcldueLUySUmksNX4DU0KM6eZaOj23Rq\nNJzrWiDLMqWlrtfisrNJ7vwwK+bECdLrnUcOGEAMY7FY2vReuMDcuOE8MjWVevZ0vtKNG8yF\nC86dMTHcoEHOnQYDc/y4QqeTyeVsZCTfKZNx7uqyMkeOkFNIRFxmJtk/0T7y3Dmqr3ce2bs3\nJSdbrVaO41rf2rVrjMv3nxISuH79nDsbGxmHMmM8tZpzfRgUxzHFxURkZVlFczOjUsUePzLI\nYq3o3i8pRu30XWVOnqSmJucvkJ5O8fHOX/bSJaa62nlkSgqlpdnaLMtarVaLxUJ1dUzbUhpE\nRNHRXEaGc6fJxJSVOXcScaNGuXYyZWVkMvEfmM0Kg4EiIixZWeRwSxaeAAAgAElEQVT0M0nE\nVFRQXZ3z1+zVi7p3d/6iVVVMpUsp1Ph4Lr1N1Xur1co2NLCub0qp5IYNcxNqcTG5zBVcVhap\nne+DM6dPU9uNsIzBwPTowcbEOH/Ry5eZ69edO5OT5f37uwYghD/mOgDfyFlBZe8RcURERWup\n3wypA4KA5i2x81OZ9aamJiKKdPj1HxkZ2dT2t6mnMb7q9xQbu2GD8pVXnDoNDz3UuHatU6fq\np59i5893/vTMzPoDB5w6GZ0ucexY12vdOHuWi4116uw2f778xAmnzoYPP7ROm1bfNjeK/tOf\nIrZscRrZvHJl8+9+59QZ8fHH0U895dRpnjxZ+8knTp3ys2e7jRvnlMJwERE3Ll1yjT/htttk\ntbVOnfV791pcfrXH/vrXqi+dH4DY+Je/GBYt4j+r5a1p3n47avVqp5HGefN0Gzc6dSp/+CFu\nzhynTrZPn/qiIudAzeaksWOJSE6kIiIi2//L+3/7zxgVOX1X4x96SFFc7PQFGt57zzR7tlNn\n1F/+onnnHadO/fLlTS++2CZ4o1H9P/8T8/jjzkGNGaP94gunTtnlywnuflRqXDJIIkqYPVt2\n+bKtLSeypSTaL74wjxnjNDLmt79Vf/aZU2fTiy/qly936tS8/37UqlVOnabZsxvee8+pU1FS\nEnn77U6d1pSUumPHXENNmjCBzGanzrpDh1iXv5fiFi9W/uc/jj1RRNYNG3Qu+2KjCgo0LVmX\nnXHRIvnf/+4aQLvwSAkIaIlZ1O92Ov8VEdH53VRdSsnZUscEgctbYlfgMm/65pIKBRGxLGvv\nYVnW1tnuGF/1e4qNmTTJbDQ6d+bkaDTO9+zM/frpn37a6UtxKSmuI0kmM7t79GREXBxFOFfH\nZfPyrFVVTp3Km24yM0xE28HMHXeYk5KcRsomT3YNQDZihGsA1vR015FMjx7mFSssFgvDMHL7\naqJS6eZNEVmWLWOam506Vb17K10Gc/fcYx4yxKlTPmaMRqPhOM5kMqlbVm7kEya4+V4NH+4m\n1PR015Fct25uQlWpbCM5jmNZtkFv2V9eRUQGZUSPhGin8dYHHzT/4hdOX0CZlSV3DWD6dLPr\n2tiECfYvaFuxUyqVsmHD3ITap4+bN5WS4vZHxf33//HHmYYGPmyr1Wq1ymQyZb9+CtfBc+a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3kpx6TRQ9IJCMoMTuvffe279/v0wmKy0t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\nUTFkyBBra+vly5efP3+e76R0o6y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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# MNAR: 1D slice plots\n", + "# ============================================================\n", + "# Slice 1: vary delta_med, hold delta_out = 0\n", + "slice_med <- mnar_df[abs(mnar_df$delta_out) < 0.01, ]\n", + "# Slice 2: vary delta_out, hold delta_med = 0\n", + "slice_out <- mnar_df[abs(mnar_df$delta_med) < 0.01, ]\n", + "\n", + "if (nrow(slice_med) > 2 && nrow(slice_out) > 2) {\n", + " p_s1 <- ggplot(slice_med, aes(x = delta_med, y = total_indirect)) +\n", + " geom_line(color = \"steelblue\", size = 1) +\n", + " geom_point(color = \"steelblue\", size = 2) +\n", + " geom_hline(yintercept = 0, linetype = \"dashed\", color = \"red\") +\n", + " labs(title = \"Total Indirect vs delta_mediators\",\n", + " subtitle = \"delta_outcome = 0\",\n", + " x = expression(delta[mediators] ~ \"(SDs)\"), y = \"Total Indirect Effect\") +\n", + " theme_minimal(base_size = 11)\n", + " \n", + " p_s2 <- ggplot(slice_out, aes(x = delta_out, y = total_indirect)) +\n", + " geom_line(color = \"darkorange\", size = 1) +\n", + " geom_point(color = \"darkorange\", size = 2) +\n", + " geom_hline(yintercept = 0, linetype = \"dashed\", color = \"red\") +\n", + " labs(title = \"Total Indirect vs delta_outcome\",\n", + " subtitle = \"delta_mediators = 0\",\n", + " x = expression(delta[outcome] ~ \"(SDs)\"), y = \"Total Indirect Effect\") +\n", + " theme_minimal(base_size = 11)\n", + " \n", + " g_slices <- gridExtra::grid.arrange(p_s1, p_s2, ncol = 2)\n", + " ggsave(file.path(mnar_dir, \"mnar_1d_slices_chr19_44938650_G_C.png\"),\n", + " g_slices, width = 12, height = 5, dpi = 150)\n", + " ggsave(file.path(mnar_dir, \"mnar_1d_slices_chr19_44938650_G_C.pdf\"),\n", + " g_slices, width = 12, height = 5)\n", + " cat(\"MNAR 1D slice plots saved.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "20f14733", + "metadata": {}, + "source": [ + "### MNAR Interpretation\n", + "\n", + "The MNAR sensitivity analysis tests what happens to our mediation results if missing data are \n", + "**not missing at random**. The delta parameters represent how many SDs the missing values deviate \n", + "from the observed mean:\n", + "- delta = 0: MAR assumption (same as FIML)\n", + "- delta > 0: missing subjects have higher values than observed\n", + "- delta < 0: missing subjects have lower values than observed\n", + "\n", + "**Tipping point**: The minimum delta shift needed to make a significant result non-significant. \n", + "Larger tipping distances indicate greater robustness.\n" + ] + }, + { + "cell_type": "markdown", + "id": "850be40b", + "metadata": {}, + "source": [ + "## 8. Method 4: Bayesian SEM (blavaan/Stan)\n", + "\n", + "Bayesian SEM provides posterior distributions for all parameters, enabling direct probability \n", + "statements like P(indirect > 0). blavaan handles missing data via Stan data augmentation, \n", + "using the full dataset.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "20b045cf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:44:20.372140Z", + "iopub.status.busy": "2026-03-31T16:44:20.369870Z", + "iopub.status.idle": "2026-03-31T20:30:25.566166Z", + "shell.execute_reply": "2026-03-31T20:30:25.563322Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting Bayesian parallel mediation model...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N = 1153 (full data passed to blavaan with NAs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using 2 chains x 2000 samples (+ 1000 burnin) for efficiency with 4 mediators...\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 1).\n", + "Chain 1: \n", + "Chain 1: Gradient evaluation took 0.005988 seconds\n", + "Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 59.88 seconds.\n", + "Chain 1: Adjust your expectations accordingly!\n", + "Chain 1: \n", + "Chain 1: \n", + "Chain 1: Iteration: 1 / 3000 [ 0%] (Warmup)\n", + "Chain 1: Iteration: 300 / 3000 [ 10%] (Warmup)\n", + "Chain 1: Iteration: 600 / 3000 [ 20%] (Warmup)\n", + "Chain 1: Iteration: 900 / 3000 [ 30%] (Warmup)\n", + "Chain 1: Iteration: 1001 / 3000 [ 33%] (Sampling)\n", + "Chain 1: Iteration: 1300 / 3000 [ 43%] (Sampling)\n", + "Chain 1: Iteration: 1600 / 3000 [ 53%] (Sampling)\n", + "Chain 1: Iteration: 1900 / 3000 [ 63%] (Sampling)\n", + "Chain 1: Iteration: 2200 / 3000 [ 73%] (Sampling)\n", + "Chain 1: Iteration: 2500 / 3000 [ 83%] (Sampling)\n", + "Chain 1: Iteration: 2800 / 3000 [ 93%] (Sampling)\n", + "Chain 1: Iteration: 3000 / 3000 [100%] (Sampling)\n", + "Chain 1: \n", + "Chain 1: Elapsed Time: 2041.62 seconds (Warm-up)\n", + "Chain 1: 4699.42 seconds (Sampling)\n", + "Chain 1: 6741.05 seconds (Total)\n", + "Chain 1: \n", + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 2).\n", + "Chain 2: \n", + "Chain 2: Gradient evaluation took 0.00251 seconds\n", + "Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 25.1 seconds.\n", + "Chain 2: Adjust your expectations accordingly!\n", + "Chain 2: \n", + "Chain 2: \n", + "Chain 2: Iteration: 1 / 3000 [ 0%] (Warmup)\n", + "Chain 2: Iteration: 300 / 3000 [ 10%] (Warmup)\n", + "Chain 2: Iteration: 600 / 3000 [ 20%] (Warmup)\n", + "Chain 2: Iteration: 900 / 3000 [ 30%] (Warmup)\n", + "Chain 2: Iteration: 1001 / 3000 [ 33%] (Sampling)\n", + "Chain 2: Iteration: 1300 / 3000 [ 43%] (Sampling)\n", + "Chain 2: Iteration: 1600 / 3000 [ 53%] (Sampling)\n", + "Chain 2: Iteration: 1900 / 3000 [ 63%] (Sampling)\n", + "Chain 2: Iteration: 2200 / 3000 [ 73%] (Sampling)\n", + "Chain 2: Iteration: 2500 / 3000 [ 83%] (Sampling)\n", + "Chain 2: Iteration: 2800 / 3000 [ 93%] (Sampling)\n", + "Chain 2: Iteration: 3000 / 3000 [100%] (Sampling)\n", + "Chain 2: \n", + "Chain 2: Elapsed Time: 2059.27 seconds (Warm-up)\n", + "Chain 2: 4717.67 seconds (Sampling)\n", + "Chain 2: 6776.94 seconds (Total)\n", + "Chain 2: \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cThere were 4000 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See\n", + "https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cExamine the pairs() plot to diagnose sampling problems\n", + "\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cBulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.\n", + "Running the chains for more iterations may help. See\n", + "https://mc-stan.org/misc/warnings.html#bulk-ess\u201d\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing post-estimation metrics (including lvs if requested)...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian model fit complete.\n", + "\n", + "=== Bayesian Parameter Estimates (parTable) ===\n", + " label est se\n", + " a1 0.249 0.059\n", + " a2 0.246 0.059\n", + " a3 0.282 0.062\n", + " a4 0.210 0.048\n", + " b1 -0.878 1.058\n", + " b2 0.819 1.057\n", + " b3 -0.015 0.067\n", + " b4 0.085 0.058\n", + " cp 0.155 0.046\n", + " ind1 -0.219 0.277\n", + " ind2 0.201 0.273\n", + " ind3 -0.004 0.019\n", + " ind4 0.018 0.013\n", + " total_indirect -0.004 0.017\n", + " total 0.151 0.044\n", + " prop_med -0.026 0.204\n", + " diff_1_2 -0.420 0.550\n", + " diff_1_3 -0.215 0.276\n", + " diff_1_4 -0.237 0.278\n", + " diff_2_3 0.206 0.276\n", + " diff_2_4 0.183 0.273\n", + " diff_3_4 -0.022 0.030\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Bayesian SEM via blavaan\n", + "# ============================================================\n", + "suppressPackageStartupMessages({\n", + " library(blavaan)\n", + "})\n", + "\n", + "cat(\"Fitting Bayesian parallel mediation model...\\n\")\n", + "cat(\"N =\", N_total, \"(full data passed to blavaan with NAs)\\n\")\n", + "cat(\"Using 2 chains x 2000 samples (+ 1000 burnin) for efficiency with 4 mediators...\\n\\n\")\n", + "\n", + "fit_bayes <- tryCatch(\n", + " bsem(model_str, data = dat_sub, fixed.x = FALSE, target = \"stan\",\n", + " n.chains = 2, burnin = 1000, sample = 2000, seed = 42),\n", + " error = function(e) { cat(\"Bayesian Error:\", conditionMessage(e), \"\\n\"); NULL }\n", + ")\n", + "\n", + "if (!is.null(fit_bayes)) {\n", + " cat(\"Bayesian model fit complete.\\n\\n\")\n", + " \n", + " # Extract using parTable\n", + " pt <- parTable(fit_bayes)\n", + " labeled_pt <- pt[pt$label != \"\", c(\"label\", \"est\", \"se\")]\n", + " cat(\"=== Bayesian Parameter Estimates (parTable) ===\\n\")\n", + " print(labeled_pt, digits=4, row.names=FALSE)\n", + "} else {\n", + " cat(\"Bayesian model failed to fit.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f9d743bd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:27.099293Z", + "iopub.status.busy": "2026-03-31T20:30:27.093887Z", + "iopub.status.idle": "2026-03-31T20:30:28.092615Z", + "shell.execute_reply": "2026-03-31T20:30:28.089966Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Posterior draws matrix: 4000 x 101 \n", + "Column names: a1, APOC4_APOC2_AC_exp~msex_u, APOC4_APOC2_AC_exp~age_death_u, APOC4_APOC2_AC_exp~pmi_u, APOC4_APOC2_AC_exp~ROS_study_u, a2, APOC2_AC_exp~msex_u, APOC2_AC_exp~age_death_u, APOC2_AC_exp~pmi_u, APOC2_AC_exp~ROS_study_u, a3, APOC1_DeJager_Mic_exp~msex_u, APOC1_DeJager_Mic_exp~age_death_u, APOC1_DeJager_Mic_exp~pmi_u, a4, APOE_Mega_Mic_exp~msex_u, APOE_Mega_Mic_exp~age_death_u, APOE_Mega_Mic_exp~pmi_u, b1, b2 ...\n", + "\n", + "\n", + "=== Bayesian Posterior Summaries ===\n", + " label posterior_mean posterior_sd ci_lower ci_upper pp_direction\n", + " a1 0.249430 0.05861 0.133218 0.36066 1.0000\n", + " a2 0.245975 0.05867 0.129427 0.35745 1.0000\n", + " a3 0.281881 0.06153 0.160548 0.39951 1.0000\n", + " a4 0.209812 0.04774 0.117661 0.30266 1.0000\n", + " b1 -0.877716 1.05769 -2.801246 1.25238 0.2023\n", + " b2 0.818766 1.05737 -1.317939 2.77301 0.7825\n", + " b3 -0.015361 0.06721 -0.141271 0.12071 0.4060\n", + " b4 0.085313 0.05753 -0.034868 0.19426 0.9287\n", + " cp 0.155329 0.04646 0.066242 0.24586 1.0000\n", + " ind1 -0.220209 0.27733 -0.774943 0.32512 0.2023\n", + " ind2 0.202757 0.27316 -0.335725 0.74992 0.7825\n", + " ind3 -0.004103 0.01933 -0.040348 0.03539 0.4060\n", + " ind4 0.018006 0.01323 -0.006372 0.04652 0.9287\n", + " total_indirect -0.003550 0.01693 -0.037883 0.02859 0.4170\n", + " total 0.151779 0.04400 0.066246 0.23801 1.0000\n", + " prop_med -0.029101 0.20409 -0.305375 0.23421 0.4170\n", + " N n_chains n_samples\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + " 1153 2 2000\n", + "\n", + "Saved: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_neo4/APOE4_2_adj/bayesian_blavaan/bayesian_results.csv \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Bayesian: Extract posterior draws and compute CIs\n", + "# ============================================================\n", + "if (!is.null(fit_bayes)) {\n", + " draws <- tryCatch(\n", + " do.call(rbind, blavInspect(fit_bayes, \"mcmc\")),\n", + " error = function(e) {\n", + " cat(\"Draw extraction via mcmc failed, trying draws...\\n\")\n", + " tryCatch(do.call(rbind, blavInspect(fit_bayes, \"draws\")),\n", + " error = function(e2) NULL)\n", + " }\n", + " )\n", + " \n", + " if (!is.null(draws)) {\n", + " cat(\"Posterior draws matrix:\", nrow(draws), \"x\", ncol(draws), \"\\n\")\n", + " cat(\"Column names:\", paste(head(colnames(draws), 20), collapse=\", \"), \"...\\n\\n\")\n", + " \n", + " # Map labels to draw columns\n", + " # blavaan names draws by internal IDs; need to match via parTable\n", + " pt_full <- parTable(fit_bayes)\n", + " \n", + " # For defined parameters (:=), compute from constituent draws\n", + " # Get the regression parameters with labels\n", + " labeled_rows <- pt_full[pt_full$label != \"\" & pt_full$op %in% c(\"~\", \"~~\"), ]\n", + " \n", + " # Try direct column matching first\n", + " bayes_results <- data.frame(\n", + " label = character(), posterior_mean = numeric(), posterior_sd = numeric(),\n", + " ci_lower = numeric(), ci_upper = numeric(), pp_direction = numeric(),\n", + " stringsAsFactors = FALSE\n", + " )\n", + " \n", + " # For defined (:=) parameters, we need to compute from draws\n", + " # First get the raw path draws (a1, a2, a3, a4, b1, b2, b3, b4, cp)\n", + " raw_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\")\n", + " raw_draws <- matrix(NA, nrow(draws), length(raw_labels), dimnames=list(NULL, raw_labels))\n", + " \n", + " for (lab in raw_labels) {\n", + " # Find the parTable row\n", + " pt_row <- pt_full[pt_full$label == lab & pt_full$op == \"~\", ]\n", + " if (nrow(pt_row) > 0) {\n", + " # The column in draws corresponds to the row index in parTable\n", + " # blavaan uses \"sig_i\" or numbered columns\n", + " pidx <- pt_row$plabel[1]\n", + " # Try to find the column\n", + " if (pidx %in% colnames(draws)) {\n", + " raw_draws[, lab] <- draws[, pidx]\n", + " } else {\n", + " # Try by position - the free parameter index\n", + " free_idx <- pt_row$free[1]\n", + " if (free_idx > 0 && free_idx <= ncol(draws)) {\n", + " raw_draws[, lab] <- draws[, free_idx]\n", + " }\n", + " }\n", + " }\n", + " }\n", + " \n", + " # Compute derived quantities\n", + " derived_draws <- matrix(NA, nrow(draws), 0)\n", + " derived_names <- c()\n", + " \n", + " # Specific indirects\n", + " for (i in 1:n_med) {\n", + " if (!any(is.na(raw_draws[, paste0(\"a\", i)])) && !any(is.na(raw_draws[, paste0(\"b\", i)]))) {\n", + " ind_draws <- raw_draws[, paste0(\"a\", i)] * raw_draws[, paste0(\"b\", i)]\n", + " derived_draws <- cbind(derived_draws, ind_draws)\n", + " derived_names <- c(derived_names, paste0(\"ind\", i))\n", + " }\n", + " }\n", + " colnames(derived_draws) <- derived_names\n", + " \n", + " # Total indirect\n", + " if (ncol(derived_draws) == n_med) {\n", + " ti_draws <- rowSums(derived_draws[, paste0(\"ind\", 1:n_med)])\n", + " derived_draws <- cbind(derived_draws, total_indirect = ti_draws)\n", + " derived_names <- c(derived_names, \"total_indirect\")\n", + " }\n", + " \n", + " # Total\n", + " if (\"total_indirect\" %in% colnames(derived_draws) && !any(is.na(raw_draws[, \"cp\"]))) {\n", + " total_draws <- raw_draws[, \"cp\"] + derived_draws[, \"total_indirect\"]\n", + " derived_draws <- cbind(derived_draws, total = total_draws)\n", + " derived_names <- c(derived_names, \"total\")\n", + " }\n", + " \n", + " # Prop mediated\n", + " if (\"total_indirect\" %in% colnames(derived_draws) && \"total\" %in% colnames(derived_draws)) {\n", + " pm_draws <- derived_draws[, \"total_indirect\"] / derived_draws[, \"total\"]\n", + " # Clip extreme values\n", + " pm_draws[abs(pm_draws) > 10] <- NA\n", + " derived_draws <- cbind(derived_draws, prop_med = pm_draws)\n", + " derived_names <- c(derived_names, \"prop_med\")\n", + " }\n", + " \n", + " colnames(derived_draws) <- derived_names\n", + " \n", + " # Combine all draws\n", + " all_draws <- cbind(raw_draws, derived_draws)\n", + " \n", + " # Build results table\n", + " all_labels <- colnames(all_draws)\n", + " for (lab in all_labels) {\n", + " vals <- all_draws[, lab]\n", + " vals <- vals[!is.na(vals)]\n", + " if (length(vals) < 10) next\n", + " \n", + " bayes_results <- rbind(bayes_results, data.frame(\n", + " label = lab,\n", + " posterior_mean = mean(vals),\n", + " posterior_sd = sd(vals),\n", + " ci_lower = quantile(vals, 0.025),\n", + " ci_upper = quantile(vals, 0.975),\n", + " pp_direction = mean(vals > 0),\n", + " stringsAsFactors = FALSE\n", + " ))\n", + " }\n", + " rownames(bayes_results) <- NULL\n", + " bayes_results$N <- N_total\n", + " bayes_results$n_chains <- 2\n", + " bayes_results$n_samples <- 2000\n", + " \n", + " cat(\"\\n=== Bayesian Posterior Summaries ===\\n\")\n", + " print(bayes_results, digits=4, row.names=FALSE)\n", + " \n", + " write.csv(bayes_results, file.path(bayes_dir, \"bayesian_results.csv\"), row.names = FALSE)\n", + " cat(\"\\nSaved:\", file.path(bayes_dir, \"bayesian_results.csv\"), \"\\n\")\n", + " } else {\n", + " cat(\"Could not extract posterior draws.\\n\")\n", + " }\n", + "} else {\n", + " cat(\"Skipping Bayesian results extraction (model did not fit).\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "bad242b9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:28.134289Z", + "iopub.status.busy": "2026-03-31T20:30:28.131679Z", + "iopub.status.idle": "2026-03-31T20:30:29.546696Z", + "shell.execute_reply": "2026-03-31T20:30:29.543529Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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8sJgnj27Jm5uTmNRnv27Bnx3+8GPp9vbGysr6+PJxEEcePGDTqdPmDAAPzR3d1d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\ny7Gxserq6iYmJl3momMyme1fLd0q4fVQBlgH0NmzwAD4D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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Bayesian: Forest plot\n", + "# ============================================================\n", + "if (exists(\"bayes_results\") && nrow(bayes_results) > 0) {\n", + " # Filter to main effects\n", + " main_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\")\n", + " bfp <- bayes_results[bayes_results$label %in% main_labels, ]\n", + " \n", + " bfp$display <- display_names[bfp$label]\n", + " bfp$display <- factor(bfp$display, levels = rev(display_names[main_labels[main_labels %in% bfp$label]]))\n", + " \n", + " bfp$effect_type <- ifelse(grepl(\"^a\", bfp$label), \"a-path (SNP->M)\",\n", + " ifelse(grepl(\"^b\", bfp$label), \"b-path (M->Y)\",\n", + " ifelse(bfp$label == \"cp\", \"Direct (c')\",\n", + " ifelse(grepl(\"^ind\", bfp$label), \"Specific indirect\",\n", + " ifelse(bfp$label == \"total_indirect\", \"Total indirect\",\n", + " ifelse(bfp$label == \"total\", \"Total effect\", \"Proportion\"))))))\n", + " \n", + " p_bayes_f <- ggplot(bfp, aes(x = posterior_mean, y = display, color = effect_type)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper), size = 0.5) +\n", + " scale_color_brewer(palette = \"Set1\") +\n", + " labs(title = \"Bayesian SEM: Parallel Mediation Path Estimates\",\n", + " subtitle = paste0(\"N = \", N_total, \" (blavaan/Stan), 2 chains x 2000 samples\"),\n", + " x = \"Posterior Mean (95% Credible Interval)\", y = \"\", color = \"Effect Type\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + " \n", + " ggsave(file.path(bayes_dir, \"bayesian_forest_plot.png\"), p_bayes_f, width = 11, height = 9, dpi = 150)\n", + " ggsave(file.path(bayes_dir, \"bayesian_forest_plot.pdf\"), p_bayes_f, width = 11, height = 9)\n", + " print(p_bayes_f)\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "7d62177b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:29.552356Z", + "iopub.status.busy": "2026-03-31T20:30:29.550770Z", + "iopub.status.idle": "2026-03-31T20:30:35.814032Z", + "shell.execute_reply": "2026-03-31T20:30:35.811334Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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CeAHbtOfP36td27FKC8vb1/ZTC/huhV/hmNNnfuXNgdhCAIgiDB4KlYCIIgCIKg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\n/py0pUJSDOPArrCwcP369cHBwQMGiF/vgM1m1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nr1akhISHV1tew4\nTZv9e/Qs7dGBVwaDwYlxg6VOTZ8/PkrcHxUVdfDgQQ5PymAw6HS6oKAgfuXDYDDIZLz8HzRm\nu6CgIE720SXnbBxl4NgNN0pLSyMiIpSmTJy7yQe7NWkVRb+D4X9u3BkREXH79u3MzEwwExYw\n/Hjx4kV8fLzm3HmT7ZcPzAKBQDBYZj9+4aIHhw7mnD9///79jIwMVn1FAPzIy8ubOnXqokWL\nIAjS0tIqKiq6c+eOh4dH7yPFxfsJkkgkEvFbAoJTPzcKnU6nUqnCwsJYxp2amprWrl17/vx5\nsdFjl+w6OnXFyu4Dr52dnRw6Lgs2hte9fXXixAlTU1NnZ2dOfkKhUOh0upiYGE5dCeiqbVwv\nLp1Ox88+OuWDzcUFc+yGFXQ6ffXq1TCRsOxQBJFLj4SUovyq7CTTzb7/FBZOnjw5JSVl+A3f\n8wuNjY1v374Fsx65zpYtWyAi0SoqCqMdYUlJm/0HXVNPtdJoFhYWu3fvBg/LICMhIcFcyIIg\nSG1tLR6j2MOet2/fTp8+/fz581NXrAy4UWLo7M1mOh17CASC3f6TMqoaa9asefv2LXd1AvqE\nP3rsYBjmPLMbgiBY0rShsy7odDpGI11dXQPuRmZGOv1VDbGxscXFxYtCAqTHKdPpdCzRaBGY\nAUEQhPxfr+8c/1Xqc2ZcDo5es2ZNdnZ2YmLiuHHj+jXCYDBoNBqWdLEIgqCFOWALdDodY38+\nejkwasD+6nnmzBk0dkBlZeWKFSvs7OwwGgSg3Lp1q6CgYKa3zwAGYftk/Pz5ftdu5PqvDw8P\nLy8vP3XqFH6DMoAe2Nvb79q1a/PmzSoqKh8/fhw9erS5uTmvRfEZxcXFZmZmTW3tyw+n61uv\nwG5QWFJqxZHMVKcFTk5Ojx8/BgmN8IY/HDsEQdDwqlw/uDeoF4IxQD+qAaNjB8PwL/2R6urq\nvXv3yk/UnrHSEQ3+DsPwwP/F/3ljCNMtUzDQ9f7r1P0jKXfSc6ZNmxYdHb1mzRr29mEYxp6T\nAPsFJRAIWCyg+rFYwJ7vobS09Pbt2/Hx8dLS0pWVlffu3UMQBATo5wrh4eGCYmIm/v5ctCk+\ndqznuay8HdvPnTtXV1d36dKlfjL4AbiEhITEwYMHa2trm5ubZWVlwUzHX6W8vNzMzKy1s8vj\n1GVVw1ncMqswcYrZ73uuR28NCgpKSEjglllAn/CHY0cikTivFmk0GpY6FO2rExISwvJWgc4P\nwLIqtrOzU0BA4JdWxW7fvp3S2ekWs0NQWAiCIARBBAQEBt72k8ln/ohoVlPpPkNTQEDAImyT\nwdLFl7ft2bJlS15eXkZGBptoAthXxXZ1df3S1e8N9lWxaG8xFg1YevtQ7t+/v2DBAlFR0a9f\nvyorK3t5eWE0OPTpCgyEHR3xjnJ7/fr1p0+fzg0IFJWR5a5lkoCAzYGD4mPH3k5MsLS0vHbt\nWr+TugDcQl5evnfOt1+iNTmZpK4+0pZx1tXVWVlZNbd3uKf9h4teHcoMd7+Pjx8kJiYuXLjQ\n3t6eu8YB3eEPxw7QL9evX798+fJ0N3tFA13uWCQQXpibksnk3mNISpMn+uadvhd38u6JzClT\npmRlZaFTlQH48eXLFwqFEhISIioq+uHDh1WrVvU5wIQgSFtbW58W0B5HVnuxQKfT29rauN59\nSDM0hGEYam3lrlkIgmAYbv2v2aioKEExsWkenlQqFaNZtGe3hx2TjZtIgoL34w5bWFj8+eef\nIiIiGAVzEbQvnOuWMZrFnlKMK3QtXToUZAwmdDrd0dGx+tMnx/izXPfqIAgiEAg2exO/vSry\n8fGZPn26qqoq108BQAGO3XCASqVu3rxZREZqQZDf4JyRJCCwMHi9psnMC4GhlpaWx48f9/Hh\nwiJcACuoVGpra2tsbCyRSHz9+nV4ePiMGTNkZfvoZGLfO4i975CVPDzMQrgJRs3+888/T548\n+c3LW0BcHMtQe3d62/ltjS+DwXgUf9TFxSUzM3Ng8+1wKgcGg4FOouU6AxYMYirxioiIiHv3\n7s0L2K6z2AanU4hIySz/49Rpd0s3N7d79+6Ba40TwLEbDiQkJLx9+9YyaquIzKCmRFSbOc33\ncvrZ1Zt9fX0pFEpgYOBgnn1EIS0trauriwZH0NPTExcXr6qq6u3YEQiE0aNH92mB7wIUo7li\nR40axV2zULd4v0lJSSRBQZO1fgOLBN4DNjEIFm4JQqjUOyeSgoKCzp49+6tlBQIUA/Dm3r17\n+/btU585b17ADlxPpGo4a65/yL2ju3fv3h0REYHruUYs/Tt2K1asUFFR8fHx0dPTGwRBgF+l\nvr4+Ojp6zHj16a48mLUgqSC3OudEpkfgxo0bZWRk3N3dB1/DSEBTU/PTfxMcUanU9vZ2kNcc\nIy9evLhx48ZUJ+d+s4dxhYW/h3Q0NWZnZ48dO/bIkSODcEYAe1pbW9kv2KdSqfiFFsJjEJwJ\nOiuAQqFw2G/a1ta2cuVKQQkpqz2JVM4CUGCJPmHkvfH9o/zdu3fPmTNn+vTpvQ9Ar0trayt+\n68PodDquFxeCIPzso+UDwzCrN9L+HTtDQ8PDhw8fPnzY2NjYx8fHyckJLO8aUkRFRTU2Nrod\njiCSedOtLSwl4X76SNoKXx8fHy0trZkzZ/JExvDGysoqICDgr7/+0tHRycvLU1dX19DQ4LUo\n/iY2NhYiEGb5+g7O6QgEwtI9MR0NjUePHlVUVPz9998H57wAVrBfy4J2Z+K33qW1tVVMTAy/\nAMXNzc2cByjevn37p0+flv9xarSKGoen6OzsxDIHcXlsapLNzPXr1xcVFfX2TigUSkdHh7i4\nOH4BiikUCq4Xl06n49dpzYUAxSEhId++fbt9+7aenl5wcLCCgsLatWufPXvGTZmAgVJRUZGU\nlKRpYqQ1z5jLphFkfOHzsR+qOTlWREbKJSWWICTo6OjYiENyT8CoUaNiYmKqqqrOnz8vJycX\nFRU17GOdkMrKBP75Byfjnz9/zs7OnrBo8Sj1wfOPCSSSw9F4lenTt2/ffubMmUE7L6BPCGwR\nePCAWF7O/hgs9Ctg0Ow/fPgwKSlpwqIl+kscB63wpZXVzHccqKioCA0Nxah/YOBqn5MbjIsn\n6g1HrwskEmnRokUpKSm1tbXZ2dltbW3z589HkxDgN2kawAnBwcF0mGEWtpHrlokMeI3P1lmn\nz3N4vKyais2+0M+fP4OZdjihpqa2adOmnTt3enh4cGVO2BBHKCZGwtMTJ+OHDx+m0WizfNfi\nZJ8VZGFhl5OpozQ0vL298/PzB/nsAM6RcnISjIvjtQrcoVKpfn5+guIS1pGD/WenOnhqzTWL\nj4//B7f3txHLr/UDwzDc0dHR1dUFwzCFQtm0aZO+vn5paSlO4gDsuXPnzuXLl6c52ozV1uS1\nFgiCIF3L+ZPtrc6ePXvt2jVeawEAWNLU1HTy5EnV6TNUDA0H/+wi0tJu6RmC0tLLly8HlSeA\nt8TGxpaXly/cEikxVmHwz740Op4sIrp27VrOM0sBOIFTx+7ff//19/dXUFBwc3Mjk8nXrl2r\nqKj4+PHjhAkTrKys0BFfLFRUVGRnZ2M0MqJgMBhbtmwREhebv2Wwex3YYB62SVRWJjAwEPst\nAQDgxMmTJ9va2mavW8crAdLKyq6ppzoZjCVLlnz58oVXMgAjnE+fPu3Zs0dRf9p0Z94Eq5JU\nUF6wOeLVq1eHDx/miYDhSv+OXXp6ur6+/owZM65fvx4cHIzOTTE1NYUgaMyYMVlZWfX19Rjf\nOzs6Og4ePAgcu18iOTm5tLR0bqCX2GguR8zHgoiM1MJt6yorK8GDChiatLW1JSUlyenqjp+/\ngIcyFPT1VxxL/FZTu3TpUjyiRo9kvn79Ghoa6uzs7OXldfXqVV7LGbps2bKF0tlpFXmYwLt4\ncr+5rZXXnRQdHf3161deaRh+9O/YnTlzRkND49q1a+/fv9++ffvY/w0NIC4u7uDgMBZbvIDk\n5OQ+1zwDWFFfXx8eHj5KXdVotROvtfRk6oqlcrrj9+/fX19fz2stAEBPjh8/3tDQYLLen83U\n48FBy9TUMjKypKTE1dWVfdwNAOfQ6fRdu3ZNnz797NmzoaGh169fr6ur47WooUh+fv7Fixen\nLvdUmsTLxpdAIllF/NHW3r5t2zYeyhhm9O/YpaSk5OTkWFpa9liYXVBQgI6LnzlzRl1dfcAK\nCgsLv337ZmtrO2ALI5CQkJD6+nqLiC0kDGlYcYJAJC7atr65uXn//v281gIA/A9tbW0HDx4c\npampZ2XNay0QBEHT3T2MVq2+cuVKaGgor7UME4qLiyEIWrZsGYlE0tTUPHbs2JgxY3gtashB\np9M3bdokLCm1MCiK11oglWnGBksds7KyCgsLea1lmNB/HLtVq1YFBAQ4ODj02O7g4FBYWKij\no4Pl9M3Nzampqbt372YfzgdNRsmhTQaD0dTUhEUVBEEdHR1YZokxGAzswQk7Ozv7XHT8+PHj\n1NRU7cVzlY2mshGJIAiWbgACgpxMiW2RHzOAclCeOU3Z0CAhIcHHx2fMmDFYukYQBKHRaFgu\nKAzDBAKBQqEM2AKacwmLBgRBBi3vJKuooei/6Orq4npPFQzDVCqV62apv/+OrFkjxNVUWocO\nHaqrq7MJ2Q4jCIRPKq1fzdC1aPuOuvfv9u/fr6+v37uaRcESDJY9MAxz3TJa8wzYLJlMxhK9\n7P379yoqKgkJCcXFxaKiosuWLZs/f36fR7KfsN+Rk0NSUoJwm9SPZtTFqdsYvQkZDAar/5iQ\nkFBWVma2fZ+wtOyAmwkudjMv2BL15taVzZs3P3jwgEAgoPrpdDpOPdkwDMMwjN+KDVQ2fvaZ\n5UMm9+3CsXPsMjMzq6urq6urL168+ObNm+67Pn/+3NTU1NHRgVFffHy8g4ODvLz8jx8/MJoa\nIXR1dW3cuFFQTHTRjg24ngghEN4ZG7ILlcOWOQGrs1dvSUhIiIyM5K4wABvYePNoMHoEQdAP\nXD8p15soup4eg8EQ4F7N/vPnzz/++ENeT2+CuQXXCwHqVsK/9jMi0S7uSNoyOz8/Pz09vT5f\nlTG+pLEBD8voPYbFXcDi2LW1tb148SI8PNzf37+srCwiIkJRUXHChAm9j+wn88TcuTQI6sQt\neQCEZ8OPQqFQ+nyhbWhoiIqKGqWubbDcE0v/BRdXyAlKj5ruua4w+dDp06eXLVuGbsQvMwcK\nfpkhBsc+g8FgFQOZnWPX1NR08+bNb9++nT9/vketLSoq6u7uPmXKFCyyCgsL3759O2nSpMuX\nL7e2tiIIcvny5VmzZvVOdkkmkzlPlYgxryKdTm9qahIVFeUwZnefNDc3S0pKDripg2G4oaFB\nWFi4d7iysLCwiooK6+htY9RU2BuhUqkCAgJYmtuOjg4SiTSwnOUTTGerGE5KTU3dsWOHnJzc\ngDU0NDSQyWQsIbwpFAqRSMTSYYb21WG5qXDqbukNgUAQERHpcxf6hiosLMx1Dww1y/UY+jQa\nDYZhVn9nAOzbt6+lpcV2+w4iicTqTRcLaB6hAViWGDXaKSk51d7Ow8PjyZMnvVP7dHV1cbEc\nmKAPONctwzBMp9PxEMwJEhISWlpaBgYGEARNnDjR0NDw+fPnfTp24uLibLzw1tZWMpmM37+g\nUCh4PIwoMAy3t7cLCwsL9DVXJzQ0tKmpyeVAmojYwFMvUKnUgTUNrDBZu/XlpbN79uxxdHQk\nEoldXV34ZeZAe5RxvbgMBgO/zBboKwGbRo1dHRQYGBgYGGhqatrnUCx25OTkVqxYwXWzw5hn\nz57t379fbeY0QzcepIX9VUz8V5/z2pyQkLBr1y5eawGMdEpKSk6cOKG9YKHGHJMhGItHXk/P\nMjLqyvaQwMDAtLQ0XsvhY5SUlB4+fMj8iiAIq/4/9n5Ja2sriUTCbwZFV1eXoKAgfinFIAgi\nk8m99b948SI1NVV7vqW2qTmWUxAIBO7m+xKRkJy/KfxKqH9SUtKGDRvQ8sEvpRidTsf14sIw\njJ999IWkT68dpf+76t69e3h4dRAEaWho2PyXxYsXEwgEGxub3t11AJTOzs5Vq1YRBQVs9ofx\nfEEfJ2iZGo+doHn8+HGupjHgAAAgAElEQVTsQ/YAABYYDIafnx+BTDbfuZPXWlgyzdlFf6nN\nqVOnQOAnLBgZGbW0tOTn5yMI8vbt25KSEhByoTubNm2CiCSz7TG8FtIHU+095HQM9u7dCxYy\nY4Rlj939+/eFhYWNjIzu37/PqpTnzZvHrQVHYmJizs7OXDE1XAkLC3v9+rX17t9lVJV4rYUj\nCATCzDVul7fuSk1NBXnGADzk8OHDT548WRC0VXacGq+1sGPJnr1fS0v8/PyMjY3HjRvHazl8\niaCgYFhY2IkTJ1JSUmRkZAICAjQ1h0RinqFAbm7u3bt3Z/lsGqU2ntda+oBAIpnv2J/haRUR\nEXHw4EFey+FjCKwmGZiamsrLy6OxiO/fv9/nMXfv3kUjFQ8puDLHTlxcnOdz7ERFRZlz7B4+\nfGhqaqo+5ze39DgOzWKdY4cgWn9db1ZTqZtqMEALENTV2Zm02FmaLPTu3buBzWoaTnPsBm1h\nbJ+0t7dTKJRRo0Zxvbu3paVFXFyc64NK7bdvw9+/S7i7Y7RTUlIyc+ZMWW1tnz//IpLJEAR1\ndnZiebpZgY7wYrT8+fnzdCfHObNn3b17l1mkGOs0VtTX1wsKCkpISHDXLAzDbW1tWJ7ZoUDr\nqVMkdXVR3Bo4nJ4alD5bsY6ODl1d3QYKNfBWqSCG2XUoOD1EEATl+LtUFOTdvHlz/vz5+A3F\ndnR0cP3OZ9LS0kKn02Vl8cod0G9Vw/KuOnv27JEjR9AP71hgZGSEh2hADzo6Ory8vATFxWz2\nhw7aICyRAbtviZqRcxmTERLJyMu5qqoqNzeXW8IAIwSh+HhxzB29zc3Njo6OMIlkf/gIEYcF\nE1xHxdDQxN//wYMHoMeCt0j4+gokJ/NaBTfZu3fvp0+fFm/bg92rwxXz7TFEQaFt27aBqN0D\nhqVjp6SkhC5mVFJSUlZWFhER0dLS0tLSIhAI169ff/PmjZaWFq/WPY00wsLC3r9/bx6+SVIe\nU4YPnjB1xVIRGakDBw7gEWBiBNLc3JyVlQXmoHACg8FwdXV99+7dkj17R/PPeNzcwA2KkyZH\nRERgTNUIADB5+/ZtbGys6vTZBjZDLllRD6SV1eas3VpcXJyYmMhrLfxK//3AtbW1enp6J0+e\nhCCooqJi8uTJW7ZsWbp0KUgAMjg8ffr06NGjWvOMpzgs4bWWgSAgKvKbh0NJScmtW7d4rWU4\ncOzYsZycHODYcUJAQMC1a9eM16yZZLeM11p+ASKZvOyPwwwCwdPTc9AC5QCGN/7+/jQ6wzry\nMF8svJvptWG0lm5YWNjHjx95rYUv6d+xS0xMlJWV3bx5MwRB+/bt09DQaGxszM/Pj4+Pxzt+\nIIBOp/v6+pKEBJfsCeG1loHz2ypHAVGRffv28VoI35Ofn89gMMDKcU4ICQlJSkrStbRcHLKD\n11p+mdGamot+D3nx4gWI7w3AzpkzZwoKCmau8h+rPZHXWjiCJCBoGXWkg9K5cuVKMCA7APqf\ndFJSUuLh4SElJYUgyNWrVyMiIsTFxRcuXCghIVFZWTl58uRBUDliOXbsWGlpqdmODVJK8rzW\nMnBEZaSnOdrcS88pLCw0NjbmtRx+5cePH9nZ2QcOHACd5f3y+++/HzhwQHPuvOVxRwn4zE/H\nm99Wrnp7+9bBgwdtbGx0dXV5LWcY0tLSwsZpkIYgGIaxZ6dkBYPBaGlpwck4CpoYs76+fvPm\nzVKKqjN9t3IxgiOCIPjFg0QQRH7i1Jk+mx6eiI2MjNyyZQvXT8GV1KNsjEPYslCyB03uwmAw\negczR+nfsYNhGA3k+OLFix8/fpib/19UQwEBgT4zmQK4xY8fPyIjI8doaxitHuqzIvpllq/b\nv2f/3LNnT15eHq+18CUIghw5cmTlypXsV1ohCMIqjw3ahuGR5QanJkoUhkm/XjkyGIytW7em\np6drmJjYHTlKg2Far+YHpzYJnUXKRcsWu/ek2dp4enrevXuXWza7gyAIlUrFo/nB0moKCwvj\ntNyyB/2u2yUSiXgsRkYZhFWxaP6kDRs2/Pz50y3lkoQMNxdp4rcqFoIgOp1OpVIXbAz7/OxR\nTEyMmZnZnDlzuGh/cFbF4nfzDHxVLJPx48dfvXq1ra0tLi5OV1cXjQn04cOH2tpaFZV+sloB\nsBAZGdnc3GwVuZUnq/lgEvHo+RP3/Dy4Yk1SQW7ycutr164VFRVxxeBI4z//+Y+0tLSJiQmv\nhQweHVFRzZcu/dpPOjrc3d3T09N1LCzsE46TB8U/wA9JBcUFv29///59dHQ0r7WMOJpu3qSG\nhvJaBVZu3LiRmZlpsNRRa64Zr7X8MkQSefmhUwJiEs7OziCb/C/Rv8fg5+dnZGQkISFBIBAu\nXLgAQdCPHz/MzMwWLVokL8/H44NDnJcvX2ZmZupazFczNuSNAgLhy0RtMpnMrXSAc9Z5luRe\niYyMvHwZUwiVEUhDQ8PZs2ft7e3Rouvo6Pj777+JRGLvhPEEAoHVayIax05KSopf4ti1jB9P\no9FGcfzW+/XrVxsbm6KiIqPVXuZh4WxGYIdyHLse/Obm9r7gdkpKipub29y5c7loGQJx7NhC\nnzSJxNPAk9hpbW1du3atqMwoi1B+DZ0jpaS67GDKubUOrq6uN2/exCms3fCjf8duwoQJpaWl\nBQUF2traaHeolJSUpaXlYCYARVP2cn4whULBci4Igmg0GpbwHKiGAbegCILs3LmTQCYt2LYe\nzfo3MA10Oh1jK44awfJzBEHQJl9CUW6SvWXehby///572rRpHFpAZxJguaA0Go1AIGCZgYv+\nFosGOp2OJes8iUTy8OBO1+lw5dmzZ3Z2djXfv1tG7frNcyWv5XCTpTH7ExYv8vb2LikpYTWl\nBgDoTXh4+KdPn5b/cUpUlo+XW403tTBZu7Ug6WBUVBRIO84hHDU248aN8/LyYn4VEhI6duwY\nbpL6hvP+AAKBgL3zALsRIpE4YKfq2rVrjx49MvJyxpI9jPBfBmyBaYRbFkwCvF7+dTMqKurK\nlSu/ZAHLtUAFYLQA/cod2BssNwMEQVJSUjY2Nsyvly9fnj17du/uuhFLVlaWt7c3TCa7pqZp\nzTPltRwuIyEnt3D79ms7toeGhsbFxfFaDj/x7t07KSmpsWP5L/wndh4/fnzy5EntBVb6Sxx5\nrQUr8zeGfy55smfPnrlz5y5atIjXcviA/h27rq6usLCwq1ev1tbW9uj2yMvL4+6URlb8Ukoo\nCoWCJXcT2kFFJpOxGOns7BQSEhpYW85gMHbu3CksKTFrnSeWnmcGg0EikbC7ZVg0wDBMIpGY\nLpGsqtI0F9vbGRcKCws5TEbX3t6OMSEYDMMYLaB9dbxNCNYdGxsbbuVo5ncQBAkPD9+7d6/M\nODWXlFQ+ikL8Sxgss6+4dTM+Pn7FihWzZ8/mtRz+oKSkJCIiwtXV1cmJ7xef/SqdnZ2bN28W\nEBNfEnWE11q4AIFEWn7oVJLNTE9PzxcvXoB4T/3Sv2N3+PDhxMREe3t7BQWFHp0WioqKuAkb\nuWRkZLx69cp0q5+IFH9PUumTuQFeJReubt++/Z9//uGLUJlDkO69dyOZ9vZ2T0/PP//8U332\n7BXHEkVwW4M2FFi6NybRbLGPj09xcfHgLBrla9rb2xMTE0dsmJiYmJj3799bRsZJyA2TNlp8\njLzN3uNZfiv8/PzQuf4ANvTv2P3zzz9Hjhzx8fEZBDUACoUSEREhpSg/1ZXHsfIJMGyUm1c/\nXv3brBlcNCs+ZpSxl8uDY2kXL150cHDgomXAMIN85w7x61fI37/PvcylEjM8PC12RvBFHlgs\nSCooLvo95Gp42O7du3fv3s1rOUOdpKQkS0vL6urqAVsQPnOGOH48ZG3NRVWDQ1lZWWxsrPK0\nmdNWrOK1Fm6iPd9y2opVF8+fysnJGYG9sL9E/7UhmUwGYU0Gjfj4+M+fP9seCCcLcWs16gAh\nwMjyyENPnWy469hBEDRrrfvzrEvbt2+3sbFBQyQCAL0RTEsjPXzYp2NXWlpqbW39rbbWKip6\nhqfn4GvjCYZu7q/y8g4cOODo6Dhp0iReyxm6/P333z9//tyyZcuRI+wGIikUCps1VeLBwVQH\nh3bOZowMADqd3tHRwfVRCwRB1qxZw0Ags/BDDBhGaDTu2u9+IhpuxplLGHuUz4Kt0e8f3Nqw\nYcOcOXOwRIlDEIROp7e3t2MVygIGg4EgCH720QljBAKB1QSh/h27ZcuW/fnnn8y4xAD8aGxs\n3Ldv31htzUnLLDupwzZHpJC4mOmmNVfDDyQkJKCp6gAAzsnPz1++fHkng+FyMnX8/Pm8ljN4\nEAiEpTExSZYWPj4+hYWFIPRDnzQ2Np46dWrv3r39+kzsHTsxCEIQBMta+H7BI0r2uXPnCgsL\njddsGaWuzWAw0BQIOIGfY4fSOyADQVBo0fb9f250Dw0NjY2NxWgf14s7CPYxOXYTJ05MS0uz\nsrJavHixjIxM910WFhYglB0XiYmJaWxsdIkNI5D4MgkS50xztnuaeSE6Otrd3R2sAwBwTk5O\njqenp6C09Oq0dPmJ/JH4kouMUteYt3FTwYH9hw8f3rp1K6/lDEWOHTs2Z86ctv/S1NRUU1Oj\noKDQ+8gezVlviETiqFGj8JEJtba2iomJcTf6Y319fXR0tIyqhql/CB2CBAUFsURZYg+umSdo\nNBqNRhMWFu5dPgaWy8quLMnMzAwICDA0HGCQVzSElri4OGalfdPa2kqn0/u9wQYMGv2NzXq+\n/q/6li1b7t+/D0HQ9evXe+y6e/cucOy4xefPn+Pj48f9NlV7wRwsIfT4AiKZZB626Yznhp07\ndx4/fpzXcgD8QVpamq+vr5SyikfmGemROj9k1hrfsqt5ERERtra248eP57WcIYeoqOjXr1+z\nsrIgCKqsrKypqREREfHsa7yek2FQXBd4YQ8m1YMdO3b8/PnTLeUkWViEjlsi10EALRZWhWOx\nY/+Hh/kbN2589OjRwAqQvX1uwUP7/Tt258+fZzAYfZpgn7YS8EuEh4d3dnUtCgngtZBBQtPE\nSHvBnJMnT/r5+U2ePJnXcoYJbLKgouMaeIz+MBiMzs5OrtdiZASBug1npKenBwQEjNLSck3P\nEB8zBkvcbHSGDXdU/q9ZqK/xI65Y7m52Scy+NPtlq1evvnnzJsYuH4zRv/sEY1BxMpksICAw\n4LMHBQUxP8fFxSkoKIyQifZPnjxJTU3VNbPVmmuGJR770EdaWW2W98YHifuzs7NdXFx4LWco\n0n+lMHbsWAUFBXl5eSqVWlFRQafT5f8LmPnOLUpLSzMzM/Us5itP1ee1lsHDPHwTRCIFBgYO\n+x7KQQMNxdwnzJdUVgcMGDxsEv/rr6Cfs7KyAgICRmtre5zNkhg7ljCykdebOHvd+r///jsx\nMRFjCRPwuXYYzXLrcVBWVh4h0YkZDMb69etJQiLmO/bzWstgMNt3i8RYhe3bt+PxpjoM4GgA\n/vr161u2bHnz5g0EQcePH/fz87O0tNy7d+/UqVMxnp7BYNy4caO0tJRAIBgYGFhaWo7MGcHB\nwcEEEmnh731HduAJMJkUUXgZEhHBrzdZVk1l1hrXhwnp586dc3Nzw+08IwtWEy/QLp8Bx81m\nQ1dXl6CgIBfbY5TW5GQahSIrJHTlypW1a9fKamisPHtOVJYLc55oNBoe9Qw6lxwnyz3Mzgvc\n8K4gPyIiwsrKauJA5xq2tbURscXu7hMYhqlU6lAI6I0loFJ9ebmQhMTAew4Hl4SEhKKiokVb\no6UUR8QUBUFR8fmbdl7esS4uLi4kJITXcoYc/dfFz549s7Gx0dDQSEtLmzHj/yJfjBkzxt3d\nHfvpk5OTCwoKrKyszMzMrl69ik6MGGlcu3bt9u3b093tZccp81rL/0CRlKCJ4BsK1WT9KkkF\nueDg4ObmZlxPBOA7EDExRErqyZMnzs7OYmPHemSe4YpXNzwgCQgsOxxHg2E3NzfO82gDOAeR\nlkZERXmtgiO+fv0aHh4+ZryusVcgr7UMHlPs3eV0DGJiYn78+MFrLUOO/h275ORkBweHq1ev\nrl69mjmp7vjx45WVlR8+fMCuIDAwcMqUKYaGhjY2NiUlJdgN8hc0Gm3r1q0i0pLzNozEENAC\noiIWOzfX1NTs3LmT11oAQ44vX77Y2trCZLJbeoakfB8LG0cyY7UnLN6+o7S0tPusMsAIJCAg\noLWtbcmuo0Qyv/QwcgECkWgWEtPS0hIVFcVrLUOO/h276urq3kHsxMTE5OTksHvK69atU1dX\nRz9/+fJlBEZCTkpKKi8vn7fBR0R6GCYQ4wRdi/nj589KSEj4999/ea0FMISgUCju7u51P+sd\nE5PGgOWffTHDc6WOmXlCQsLIHOsAQBB04cKFv/76a5rjalXDWbzWMthozJqvPd8yOTm5rKyM\n11qGFv3PsZOVlS0tLe2xsamp6evXr1zMxfvo0aOHDx8ePny4z700Gq2lpYVDUwiC1NfXY9TT\n3t6OJWw0giANDQ39HlZfXx8RETFKY5y+g1XvRWR0Oh3j0j/s0SnpdDoWIxwuP1ywPbCqsGj1\n6tX5+fk9Ai8hCEKlUrFcUARBCARCW1sbFgsQBGG8qYSFhYfCrCM+IjAw8NWrV5ZRu9RB2nsW\nEAgE24OxP96+9fHx0dHRwT7peUTRb3xdGIZxTa5Ap9MxTnitq6vz9/eXlFdaGLSr+0pY9DOC\nILguj8XPOGoZhuF+y2dh8O73D/M3b9589erVX7KP98WF8AzgjDbKdDqdVZxCjjJPuLm5ycrK\nurq6omXx4sWLLVu2aGtrcyuK0oULFwoKCmJiYlhFgyQSiZyvwKXRaFhWy6OeBIlEwjIJmkMN\n+/bta2xsdI4NE+jV5KPPPJYJ6ehTgaXioNPpGBepcahBdpyyyQavOwcST5w40SMXBZVKJRAI\nWC4oGqwHy7+g0WgIgmBZAw7DMNfXFgxvEhMTc3Nz9e3sfvNcyWstQxphSUmn5ORU+2U2NjaP\nHz9WUlLitSK+gU6ns3dN0FUgOJ0dbWgwOnbr1q37UVfndPw8WVSs+xs4+i6Kq1fHlY4DNsah\n/1bd7I+UGadp6OJ9OzPpzz//XLJkCef28b646PXFyT6asoxMJrNy7AicRJoIDAw8duxY9y3K\nysp5eXnYw48hCBIfH19XVxcSEiImJobRGkpTUxOWLHJ0Or2pqUlcXBxLWO3m5mZJSUn2N+Wz\nZ89mzpypvcjE6cSBHrvQVDYCAgJYHBoqlSogIDDgioMAwzMSTv3Q065aaDJgDehqPk58GpjO\nSLX3bnj3sbi4WEdHh7m9oaGBTCZLSg58nJpCoWBc+tfU1ARBEJabqt9A4Zzw77//3rt3r6Oj\nQ01Nzc7O7lfLpL29nUKhjBo1iuurYltaWsTFxbnouT5//nz27Nkuo0fburiW+HM/siNOQfPR\nyAs4WWZv9t3du1lrvCfp69+/f19KSopDs/X19YKCghISEtzQ+P+BYbitrQ3LMzsUaI+MJOrr\ni2BYV8se7E9Namqqj4/PdBcf66ieKXFhGO7s7OTfzBN0Op1KpYqIiHBSWXW2NB8znzxGQqys\nrEyUs/UuDAajo6OD63c+k5aWFjqdjl+g336rGo7uqvj4+FevXh0+fDgkJCQyMvLixYvv3r3j\nSlDZ8+fPf//+PTIyklteHb/AYDDWrVtHEhI0Dx+iyVIJMGL1R7Le7QeDczoimWQbG06D4ZUr\nV+IR4pXfefjwYVxcnJGRkYODQ3V19Z49e3itCC9aW1udnZ0REnnDODXz5BO8lsMfjJ8/3zp6\nd2lpqY2NDd4ZKkcIYnv2kP/8k9cqWFJWVrZhw4bRmhPMQmJ4rYXHCEtKLQreXVVVtWvXLl5r\nGSpw6s5PnDhxwNGSWNHZ2Zmbm6uiohIdHY1uIRKJI2R1ZEJCwvPnzxcGr5dWBmv9/o+x2prz\nN6/J358QHR0NFjr1oLm52dfX18TEBIIgCQkJf3//rq6uYTlpb/369e/fv192OE7w2i9MmgEY\nurh21NffORRrb2//119/Dct7A4DS2tq6YsUKKox4xmUIiPBHTBZcmbzMrfSvs4cOHXJycgIz\nTaF+Hbv6+vqjR4/m5eVVVlbCMKygoGBqaurr6ztt2jTs5xYQEIiIiOi+heuDREOT6urqsLCw\nsdqaxmtcea1laDHL1/3d3X/27NmzcOHCuXPn8lrOEKL79JEvX76MHTt2WLbcZ8+ePXPmzKRl\n9pPslkHAsftFTAICu9rabpxIWrFixYULF0BmoGEJgiDe3t7l5eU2e4/LTRhBmYrYQCAQlkYf\nS7KZuXLlymfPng3LuvGXYOfYlZWVmZubf/v2bfr06XZ2dkQi8dOnT+np6SkpKVFRUaGhoRjP\nTSKRDAwMMBrhOxAE8fX1betod9ofSsIwhW5YQiAS7eOikqw9XFxcnj9/Li8vz2tFQ47Pnz8n\nJSVt2LChz71slmOjs2k5Waz9qyAI0tjYiN1OdXX1+vXrpVVUF+wIpVAoyP/miuUi6BxWrpvF\nzzLnZmdv3NTZ0XElM8POzi41NZW9b4cgSFdXFx5TvLGEJhAREeFwphQraDTa48ePf/z4MWbM\nmJkzZw4zB3f//v25ubnTHFdPdfDktZYhhOw4zcXbdl+L2vL777/HxcXxWg6PYenY0Wg0e3t7\nERGRoqKiKVOmMLc3NTVt3bo1LCxMS0trhCRX5i4nTpy4deuW8Ro3pSlcHtoeHkgqyNn/EXnO\nO8jBwaGgoIDXcoYWRUVFx44d8/f3NzQ0ZHUMq2YMjVyDRyNHo9HIZDLG7nY6nb5u3bq29naP\nlDSR/5t3T4DwydDFYDBwMksgEHgu2Cw0jABB1zMz1q5dm56ezmYBVldXF4lE4vr8ejTI0YAX\nfmEswPb29m3btsnJyenp6d28eTM3N/fQoUPDxre7cuVKaGioyrSZVhF/8FrLkGO6q++7+7eO\nHj1qYmKyfPlyXsvhJSwf6UuXLn348OHVq1fdlyhCECQtLZ2SktLQ0BAVFQUcu1/l3bt3wcHB\no7XUFgT58VrL0EXLdNaCIL+Cg4mrV6+Oj4/ntZyhQn5+/vnz5yMjI1VVVVkdQyAQWC31QlfF\niouLD81VseHh4U+fPl0QtFXtv3kLUZl4NMnogkE8zEJDQ7BV1C4igXAl47SPj09OTg4rH4tK\npZLJZJxWxeK35JA9RUVFUlJS4eHhBALBzs7O3d29vLycK0v9eE5ZWZmbm5u4nKJj/DmSwDBx\nVbkIgUBYduBk8rI5q1at0tLSGh4XfWCwdOwKCgoWLVrUw6tjEhISYmRk9Pnz5xGYK2LAdHV1\nOTs7U7q6XA/vIgsN9ccSJpO2ld0lk8k8ETpn/cr6qk9ZWVmioqKHDh3ihYShRVlZWWZm5h9/\n/MEq1iNfc/fu3ZiYGLWZM+esW8/ceCY+AYZhER7K4lsIBIJFZBQEQZcyTjs7O2dnZ2MJnMRf\nmJiYoGuMmAws5MLPmhohIaGhU2p1dXU2NjaddNjr+HnxMXK8ljNEEZGWdT6ek+a8yNra+tGj\nR2pqarxWxBtYOnafP39mMwEO3QUcu18iKCioqKjIPGyTgv4EXmvhA5bu3dHZ0paamspgMFJS\nUvAY4eIjMjMzBQQEuvdfenl5sem64yNqa2vd3NyEJKXsDx8hjOyrzEVQ3w5BkD8zM1xdXbOy\nsvALaTZkOXnypJ6enpaWVp97W1pa2IfwpVKpaAxLPGAwGJynU6JSqXZ2dpUfP9odOiWtMQHt\nG+4XGo2GX+goBEE4lDEw49B/w3/+KlJq420Ppf25wX3BggVXrlzpM2Q3GqAY14sL/TcAKh6g\n9y2DwWD10sLyUadQKGxedERERCAIwi+w8vAjIyMjISFBx2yekZczr7XwB0QyacWxPecDQtPT\n03/+/HnmzBnOI68OP7y9vXtUo1xM6MdD6HS6s7Nz7ffvrmmnJMBaGa5CIBAso3bBDMaFc2dJ\nJNKZM2dGjm9HpVKPHTvW0NCwY8cOXmvhAps3by4sLDQJ2DFh0VJea+EDNGYvXLrvxOXffS0t\nLS9evMitFFl8BMvnnJOMFAAOefz48dq1a0dpjLOL3TlCQrpwBSKZbHNo5z2FsXnp5w0NDTMz\nM42NjXktijcM17pp06ZN9+/fn7dxk9Y8U15rGYYQCATr3XsQBiMnJ5tIJGZmZo6Enu+2trbI\nyMjx48dv3LiRzf9lnxvj58+feKTlYML5zNSDBw+eO3fOYKnj/MAdHDYfaOYJAQEBvs48ISQk\nNODmcvJSRxEx8dxNnubm5jk5OYsXL+6+d3AyT2BJVsSefjNPsLvq58+ff/XqFfdFjTAqKytt\nbW0hIQGXkweFJMR5LYfPIBCJC0MCVKdNygvdZ2Ji4u/vv2vXrpHcdTeciIuLS0hI0DEzn7dh\nI6+1DFsIBMKSvTEIAmdlZREIhIyMjOHt2zEYjIiIiP/H3nmHRZFsDb8mkMMAkiRnEUEEE2bM\nYlZABFEQxbiyIEYMKOIqrlk/zCJBUcKKCcVVd1dBVsSEikrOOU+O/f3R986dlzCE6WEA6/fw\n8Ex3V5863V3VdfrUqaoxY8YMjLF99+/f37Vrl96IMQt/uwCdAt3CYtpcr6jk25vcnJycDh06\ntGvXrp/nBnZo2GlraxcVFWVmZnaUwNDQUHwG+4Churp6zpw59U1NnpFnBpkYSlqd/or1gpn6\nI20e7Q07e/bs7du3Q0JC1q5dO7DbpwFPREREYGCgru2IpadO47BbZxbSFhwev+BIGADg1q1b\nPB4vOjp6APfJPn78uKKigkAgJCQkoHusra07GgXYx3n37p2Hh4eStt7y8DtEGdjadhu9EWPW\n/ZEa/+vKoKCgly9fRkZGampqSlqp3qDD6n379u3e1EM4XC6369N+ooPte5wXP2xTlLBTLpdL\npVIbGxvnzZuXl5+/6MR+HXvr7oYkcrlcUTrEeTwem83u8ek4Hs/xWmyVpXneFAdRdODxeCJ+\nJ/F4PBaLJaeu5k2kqkkAACAASURBVHIxLOfZqxdh/2/Dhg2nT58ODQ2dPXt2p6ejcayi3Ao0\nUlWUQsXlcn+eYYld4dy5c/7+/upm5h4REVIdzEY77M8/SRXl79at72XdBiT/se1wuNu3bzOZ\nzNjY2IE6O7+2tvbChQsF6zv6BugucufP462tgYsLdqp1j+Li4gULFnBw+FWXExTUfwpzRBwo\na+uuvpny7Pj+JxHnbG1tb9y40ZVWo7+D6xexdAiCdL1ykslkUfrOuVwumUyWl5cXZT4qCoXC\nYrGcnJzef/gwL3SH/fLF3TodnRGeSCSK8mEt4rSxeC73gMXEjGULHx7Z3WMdOBwOHo8XZYYz\nJpOJx+MFrSIum/02KiE1/Aa9qWXWrFknT54cMkTYKGMmk4nD4UR5mmQyGQAgSqFisVh4PF6y\nHm50HrtBgwZJdh47Go3266+/Xr16dbCNjeeNSHm1DmdvcV231uTNm7BPn7HT9D+IKTyo08AX\nUSRjIhZBkMcH9r+NipoxY8bdu3eZTKY4wsjQT2vhEWz9ACkptqur1K1bYhIvvNbU1dVNmjQp\nJy/f43Ki6cTp3RWOxthJS0v36xg7OTk5DF9W+a+e3d3pS6uvDQgICA0N5XA44o6xU1NTE5N8\nkWLs+g44HK7rBbRbiTsCj8eLIqS+vt7FxeXjp09OBwJHeSzt7umotY3D4UQxidDTe27Y8RAA\nAMAB0XUQcepaAP6PDngZmfG+K+xcF/xz5uqz6AQ7O7vt27fv27evo1LOZrNFfJroPRRFQs98\nBgMMBEGSkpK2b9+en58/bN78Rcd+78hXBxETOBxu7sFDssqkZ+fPTZkyJSoqamDMmDPAaG5u\ndnJy+v7jx5JjV3tg1UHaxXTSjI333yTtWnfy5Ml//vnn+vXrw4cPl7RS4qJ/GHb9i6KiIicn\np7z8/HmHdoxa0W2rDtIV5FSU5wRvHem++NG+Y7/99ltiYuL169fHjx8vab0kDIIgVCq13UNo\naEFHR0UBDTwQ8glRWVl59+7dGzdufPv2TV5Nbd6RsOFLlyKdzZeE9iWIaSVTMYkF/UHhiVv8\n5NTU/jwcOnPmzMjISMxHmqNLivU4dEFaWnrArADWAxoaGpycnDIzM532nxi+CM6NhSUK6poe\nV+7+G3Hu2fH9kyZNun79+kBdeQwadhjz9u3bRYsW1dTVLTl5wGbRwO/LlywaFiZety98iLv/\n9PDZyZMnb9++/eDBgz9zq4DD4TryXNLpdA6HI8oMAh3B5XJlZGRa+WURBHn37l1ycvLjx4/f\nvXuHIIiSlpbj1sAxXt4yil0aG46qKY6+JB6PJyaxoJ8oPNbLW93YONHPb/78+aGhob/++qvo\nbnU+aGRtj/vpMNSk31FaWjp37twvX7/O2XNsjCdcdhJ7cDjcOB8/fXuHBP9Vrq6uAQEBYWFh\nA28s0UC7HskSExOzbt06HpHgeilsyNQJklbnpwCHw9m7LTKb7JC0/dDRo0f//PPPW7duWVhY\nSFovidHRSwptL0UJu+wINPiB3x6/e/cuOjo6ISGhvLwcAKCsPdje3WPonDkm4yf0YGEJMTXz\n4rMe+ovC5o5TV8UnPAjcumPHjkePHl26dEl4rGrXQcdL9f3Gkk6nC1l5QkGo/1t00KnUBCtj\nWlraqlWrauvq54Wcs13qKcp4L9R5LOLwu06zEEVD4aDPhc1mi2mCEk2rEd53/nq4Z9PJkyff\nvHkTFRWlpYXlKm3onRdr4UEQBIfDdTQEqq/Xvf4CjUYLCAi4fPnyIGOD5Zd/V9IfLGmNMICu\nrMTuJzPaKA/WWhl19t9rsc9/v2Bvb3/69Om1a9dKWqmfCwqFEhMTc+nSpY8fPwIABltbT3P3\nsJg+XctyaM8EsuTk6f09AL9vo2pgsOpOXPrFC68uXhg+fPgvv/wSFBQ0IBcjbhfhpiePRAKi\nDaETDpvNlpaWRg0XOp1++PDhEydOSCuRPK78YTx+qojCUcMIj8eLb04oLpcrPuGoPUogEMRk\n2CEIIqei5n4x/lV42MvwoxMnTrx58+bEiROxko+apOIrPGhghpD73z9GxXaLpqYmUWZ85nA4\nTU1NioqKXe9KSEtLW7NmzY8fP4Y6TV0UtldGSZHJZIoymwCCIHQ6XUpKSpQ5MlgslpSUlCgV\ng0ajEYlEUUonm80mEAiiOBvodDoej+/6zaz6+iPx1/11+UWLFy++dOmSpqZmdyW0BV3yT5RC\nhS56KNkJJsQ3KjY9PT0mJiYmJqalpUVBXd3OddkIF9dBJiYiimUymTweD129EFt+2lGxraDT\n6QQCQVpauibnx5ODBwpfv1ZWVvbz8/Pz89PQ0Oix2IExKraurk5GRkbcK08gCBIbG7tv376i\noiJjhymLwy4rD9YTXTgcFSsc1N2INm15L5/+EejDppJDQkJ27tyJiWtc4qNif95oBkyoqanx\n9fWdPHlyUXnZwqN7loUfhWtLSBbtYUPWPYgcvdIl6d69YcOGxcTEDLxPlz5CaWnp6dOnR48e\nPX78+PDwcFXLoc5nzwW8/nf6jp2iW3WQ3kTTYsiqm7ErIiIVjYxDQ0ONjIw2b96cm5srab0G\nMtXV1cePH7ewsFi5cmUdlbHoyMWVkY8wseog3cJs8qz1Sa+1htkFBQVNmzatoKBA0hphAPTY\ntaaLHjsymXz69Onjx4+3tLQMnTN1TvBWZe3/zSEJPXYove+x41OQmnF/1+Hm8qrJkyf//vvv\nY8aM6bEO0GPHh8PhvHnz5smTJ8nJyR8+fEAQREFd3Wr+glHuHppYxzVCj52gZLF67AR35r96\n+frSpYK0VDweP2fOnF9++WX27NndqsLQY9cRPB7v8+fPT58+ffToUWpqKpfLVdE1HOu1aaSb\nj5QcllP/QI+dcAQ9dig8Dvvvc7+lXT4pKyO9Z8+egIAAUd48EvfYQcOuNZ0adnV1deHh4WfP\nnq2vr9ceNmRWkJ/x+FGt0kDDDkWChh0AgE2j/3Pu+r/XYhEu183Nbe/evVZWVj2QAw27kpKS\nJ0+epKSkPH/+vLm5GQBA0tGxmD7DcvZsY4dxLA6HHy2EIdCwE5Tca4YdSvW3b/9GXP9y/x6H\nyTQyMlq9evWKFStMTU27IhYadoJQqdTMzMz09PS0tLTXr183NDQAAORU1MymzBm+0M10wjRx\nrKcHDTvhtDXsUCq+vH+436/yywcdHZ2tW7f6+Pioqqr2QP7PbtghCPLkyZPMzEw8Hj9hwgRH\nR0fRZYrPsPv333+vXLkSGxtLp9O1hppP3uIzdM7UdkseNOxQJGvYodQWFP9z+kr2o+c4AJyc\nnDZv3txdD0QfMezev3///PlzKpVqZWW1ePHi7j6X7hp2DAYjNTU1JSUlOTk5OzsbAECUlTUc\nM9Z00iSzKY4a5ub8lOgCBtCwG0iGHQqtseFjfPz727frCwsAAPb29vPnz58xY8aYMWOEFGaJ\nG3bl5eVJSUk1NTU6OjouLi49Gw7SY8OusbHxy5cvWVlZ7969y8zM/PbtGzqFpLSCot6IMYaj\nJ5qMn6prM1JMn0Mo0LATTkeGHQAA4fGy7t9++f+ONhTny8rKzp8/f8GCBdOnT9fV1e26/J/d\nsLt582ZaWtratWu5XO6FCxfc3d1nzpwpokxsDTsGg/Hvv/8+fvz4jz/+yMvLw+HxJhPHOPgs\nN53sIKTMQcMOpS8YdhwOB4fDNRaWpl2O/nzvKZfF0tfXX758+ZIlS8aMGdOVgV19wbD79OnT\nkSNHfH19tbS07ty5o6qqunXr1m5J6Iph19zc/Pbt27S0tJcvX6anp6MLNA8yNjGfOtVsyhTD\nMWOJ7b1KoGHHFwsGlmGHgiBI2fv3Xx7c//70aUtlBQBAWlp6+PDhdnZ2VlZWQ4cONTc3NzQ0\n5FclyRp2zc3NmzZtcnJyGjlyZGpqamZm5rlz53rwEuvUsEMQpKKiolCA/Pz8nJyc6upqNAGO\nQFA3sdC1GalrO1rfbqymuZXgdD9iqjUo0LATjhDD7j8JeLycvx5/SIjMe/WMy2ICAAwMDOzs\n7IYNG2Zubm5hYWFiYqKtrd3R6T+1Ycflcj09Pfft24d2kKWmpt68efPChQsiiu2xYUehUKqr\nq8vLy9HKWVhY+OnTp6ysLHRo8SATQ+sFM22XzlU16NxyHwCGHY7Hm/Lb2fIRw3Ln99zU7juG\nHdrq0BoaPyYmf0p8VPMjHwCgqqo6fvz4kSNHDh061NDQcPDgwSQSSVlZuZW11xcMu0OHDllY\nWLi5uaH6eHt7X79+vVsvjraGHZ1Oz8vL+/HjR3Z29ufPnz99+pSXl4e+EORV1QwdxppMmGg6\nabJqZ6tOiamJsoq7o1pSkrZtO7ZiATTs/ksXDTtBanJ+FKWnl75/X/n5c0NxEfLfeeCkpaWN\njIxMTEyMjY0NDQ0HDRpkYWGhpaWlra1NIpEw11wISUlJ7969O3ToELq5efNmNze3yZMnd1cO\nPSAAjBgh5+WFbtbW1ubk5Pz48QP9n5ubm5+fjz5xFByBQBqsr2Zoom4yRNN8qJaljdaQYVJy\nCh3Jh4adECRu2PFhUSmF6X8XvU2tyHpX9T2LRf3feiry8vLGxsYmJiZGRkZGRkYGBgZ6enoG\nBgba2toUCuXnXSu2qqqKRqOZ/7dPx8LCory8nE6nt/1GRxBEyEySAICsrKysrCzUwVBbW0sk\nEplMJp1O53K5LS0tAAAqlcpfk6elpQVduxPd2dzcTKFQGhsbWy3agyPgVfV1LWY76o8abjJx\nzCBjA74ynV4aqnCPCyU/C1HMblSHHhtVOB7P8VpshtvCnHkzRNEBpccS+HJEPBf9L6eqMm6t\nx7i1HnV5RTkvUgtfZz57+c+jR4/aPVFBQQGt+QiCSElJKSkpSUtLy8vLKysrKygoKCkpKSsr\nq6ioKCgoKCgo2NraTp3a4exTohQGlLy8vPnz56O/VVRU1NXV8/Ly2h0R0tG6tOgd+Pvvv8+f\nP19RUVFSUlJRUcE/SpSV1RoyxN7dXdd2hO6IERpm5uC/CnexwHf3ijpl2LM/Td68SQ3chrlk\nIB6FxSpZfGK7JVnD3ELD3GL0Ki8AAJtOr8vPqy8srC8oaCgqaiwpfpWZ+eTJk1anSElJDRo0\nSE1NjUQiqaqqWlpaHjt2TEgWoqxwDQDIy8szF4gTsLCwyMvLa9ew4/F4Qq5d9ty510ZGR+Lj\nS0pKiouL0XYERVpBcZCRuem0eaoGJqp6hip6Riq6hiQdPTyx9Xe48Hsr7kKIybu301zEJ1mU\n9ks46KPviv5S8goW0+dZTJ+HbjZXlDYU5zeWFDSUFDaVFjaUFRe8TKU/eCB4CpFI1NTU1P0v\n6urq6urqaPmXk5NDfcAyMjKonUMmk9E3NoPBQGfMRoOYAQA4HK6jFc9Qc0jI/ZGkYUehUASd\nUoqKigAAMpnc1rDjcDj8q22XDf/vWMa128Kzk1aUx+P/44mRkpcjEAkAABmSElFZTkFXfZCy\njayKsrwqSUF9kKKmOklXW0VfhyD9v4qKWo1dR/Bjrmew2WwRp/YWZe15PJcLAEB4vO5eeCvQ\n+BJR4HK5IurQFgVdLbuVznYrnQGCNJVXNRaWtFTWUOsbmWQKm0ZHEIRJFpg0HEEYZAoXgGYK\ntYHazKqpZJApbBqdy/7fpVkvnvPXiBFCchTx65ZCoSgo/O/rX1FRsd21OBEEaWxsFCKnqKjo\nwePHStraykZGthMmqhgYqBkbDzI1U9HVFewnonez9IpS0joCfe1i/uhRxCRWfJLFJFbEyqVi\naqZiaiY4pILNYLSUl7dUVVJraym1tbSGekZTE72pqam5uaqqipmTk1VdvVtoEZWXl5eX7/kQ\nUQqFYiIw246CggKZTG43ZVNTkxB/wSAA8vPzX9Q3KmnpaI2aNETPUNXARNXARM3YXFGjnT44\nJpsD2N1714mj1ggiegsiHPFVIhS0o0N89EB/aVV1bVV17RFjBXeyqJTmitKWylJyVTm5ugL9\ny6+r/vQ1m0Xr+eITODyhprpKSAIOh9NRwIMkDTsikchms/lWJ3qX2+18xOPxwkNtrv4aVLx4\nhaKiIpFI5HA46urqAAAlJSUikSgrK9utNpXH4zGZTCkpKVGc2KJ3xTIYDBF1EDXGjsMBANgp\nqFsbju6xDqJ3xTIYDDweL0qcn2BXbPsYASB0+beOOlKZTCaVSkU9vujXWEcSuFyuiJ+eqBOa\nv0mn0zsqGx2pwWazORzOsmXLli1bJoombRFTp5K0rCwRjw821MdWLBC5egoRC8Qz9llMCote\nudoFsTBjsVg9VliU+BMAAJFIFPyoZjAYQmqKEMMOh8O5uro6R0SIoowQxN0VK3orJhwxlUkU\nDofDZrNlZWUl3hXbNeSArQYA9vxtFouFLpdMo9Hq6upqa2ubmppaWlrIZDKHw6HRaILdg/L/\nXeAEjQLiO/NwOFxHL3PUXSLk/kvSsENXZ6upqUGDEKuqqmRlZduNZCIQCILuirbY2NjY2Nig\nv0UfPIEWWVFcLBwOR15evseFEo2QkJKSEuXLFQ087/mqLxwOAACHwwm/88KhUqnS0tKivKmZ\nTGanT184okfpoV+9bXVQUFBQU1PT1+/c8hD903Pw4MHV1dVoIWez2Q0NDYMHt7NsnZDnRaVS\nRSyWHcHlcuXl5THvNOHgcKC92y46bDZbTGJBv1IYNXowl8zj8Xg8njgU7gra2tr84QsAgKqq\nKnt7+3ZTdjouh0gkSontKsRUa1AwacWEI6YyiUKn09lstkjtl1DQtVbFpz9fvoKCgoaGxtCh\nPVxWsSPQTxchjZokV55QVFS0trZOSUlBN588eTJu3DgxWegQSL9m7Nixz549Q/tuXrx4QSKR\nzMzMJK0UBNLncHBwyMzMRKeLKykp+f79u4ODg6SVgkB6FUl67AAAGzduPHDgwKdPn9BFc0NC\nQkSXKfo3kOhrDw8EHXA4npERUFcXTQZO9KsQ8UIw0UGU01EdRJSwZMmSL1++bNiwQU1NrbKy\ncufOnd2VKb4VwcXkdUA0NXkGBuLQWEwKi0ms+CSLXrmESBaH2K5gbW09c+ZMPz8/Q0PDoqIi\nLy8vHR2dHsjhGRoCERbM7RRx3yLRWxDhiFX/ToJnsECs+ouvZqF0+mQlv/IEl8stLCwkEAhG\nRkbQXQeBCKG0tJROpxsZGWEeFwWBDCTq6+tra2t1dHT6+wIYEEgPkLxhB4FAIBAIBALBBEnG\n2EEgEAgEAoFAMAQadhAIBAKBQCADBGjYQSAQCAQCgQwQoGEHgUAgEAgEMkCAhh0EAoFAIBDI\nAEHC89j1Ed6/f//8+XMqlWplZbV48eK2c0lkZmb+/fffNBrNyMho8eLF4hhCX15enpSUVFNT\no6Oj4+LiMmjQoLZpPnz48Mcff6xcudLCwqKXc++KeiJCo9ESEhIKCgqUlZXnzp1raWnZNk1F\nRUVkZKS9vf3s2bMxV6ArOrS0tNy7d6+wsFBBQWHKlCmjRo0ShxqYgCDIkydPMjMz8Xj8hAkT\nHB0d26ah0WhRUVEIgmzcuLHXFfwfnaralWvpTfpCWe0WndbfkpKShw8f1tTUaGhoLFiwwMDA\nQCJ6SoS+8PYThU7bLwRBHjx48O+//4aEhIhvkbGe0WnV5nK5T548+fTpEw6Hs7GxcXJyEvcU\nd92i01dBY2PjvXv3iouLlZSUpk6damdn1wtaQY8d+PTp07Fjx+zt7V1cXD5//nz+/PlWCV69\nenX69OmxY8e6uLgUFxcfPnwYcx2am5t37NhBIpGWL1+Ox+ODgoIE15JDuXDhQkJCQm5ubrur\nv4s1966oJzqHDh0qKytzdXUdOnTogQMHiouLWyV4/vz54cOHKysrq6qELY0sPh24XO6ePXtq\na2tdXV3t7OzCwsI+fvwoJk1E59atWw8ePJg3b96sWbOioqL+/PPPVgny8vK2bdtWXl6en58v\nEQ35dKpqpwl6mb5QVrtOp/W3srJyx44dWlpay5cvV1ZW3rVrV3Nzs6S07WX6yNuvx3TafpHJ\n5KCgoKysrC9fvghZG1dSdFq1L1++/Pz587lz586aNevRo0exsbES0bMjhL8KmEzmzp072Wy2\nm5ubjY3N4cOHs7KyekMt5KcnJCTk9u3b6O/GxsZFixbV19cLJnjw4ME///yD/i4pKVmwYAGD\nwcBWh7t37+7du5e/uWnTJn6OfJKSkng83sqVK9+9e9fLuXdFPRHJzc11dXVlMpnoZnh4+Pnz\n51ulSUlJoVKpx44du3HjBra5d1GH2traEydOsNlsdDMsLCwiIkIcmogOh8NZvnz5169f0c1X\nr15t2LChVZq3b9+WlZUlJycHBgb2uoL/o1NVu3ItvUlfKKvdotP6+/Hjx5iYGP6mp6dnZmZm\n7+knUfrC208UOm2/SktL3759W1paumDBAn6h7SN0pWqHh4cXFBSgvyX+smpFp6+CsrKy8PBw\n/uZvv/0WGRnZC4pBjx3Iy8vj92yqqKioq6vn5eUJJpg/f/7kyZPR32VlZZqamqKsKN+RDubm\n5vxNCwuLVjoAABYtWiSmlTk6zb0r6olIfn6+4IIKQ4YMaZvFrFmz5OXlsc23Wzqoq6tv3boV\n7ctAEKSioqLP9lhVVVXRaDT+U7OwsCgvL6fT6YJpRo0apaurKwnt/g+dqtqVa+lN+kJZ7Rad\n1l9bW9sVK1agvxsbG+l0up6eXq+qKDn6wttPFDptv/T09PpsxEhXqvbGjRuNjY3R32VlZfr6\n+r2tZcd0+irQ1dXlR7nQaLTCwkL+tYgVaNgBCoWioKDA31RUVOyor7O0tPTixYviiEaiUCiK\nior8TQUFBTKZjHkuPc69F9Qjk8mCT6GX70B3dUAQ5MqVK3JychIP9uoICoUiJSUlJSWFbqKP\nr/dvaVfoVNW+di19oax2i67XXxqNduTIkYULF2ppafWWdhKmL7z9RKHr7VcfpFtVOzU19dWr\nV56enr2nX2d08VWQk5Oze/fuzZs3z58/f9KkSb2gWN+Ko+w1zp8/j5b+RYsWEYlEJpPJP0Sn\n09sNL33//v358+c3b948cuRITHS4f/9+dnY2AMDOzo5IJDIYDP4hBoPRmyGunebeC+q1yoJO\np/Nre6/RRR0YDMapU6fYbHZwcLAEFztvS0NDw+XLl9Hf8+fPZ7PZPB4P1RD9CO79W9oViESi\ncFU7TdDL9IWy2i26WH+rqqpCQ0PHjRvH9979DPSFt58odLH96pt0vWonJCQ8f/78yJEjfWrk\nShdfBTo6Oh4eHiUlJXfv3tXR0ekFB2q/KQHYMnr0aDQAVl1dffDgwdXV1TY2NgAANpvd0NAw\nePDgVumfPXsWFxd34MABDLveLCwsVFVVAQCDBw8uLS2trq7mH6qqqrK3t8cqo07R1tYWnnun\nCURn8ODBNTU1/M3q6uq2T0HcdEUHCoWyb98+W1tbLy8vMfWM9xg5ObkJEyagv3V0dAAANTU1\n2traAICqqipZWVkVFRVJ6tcBqHNIiKqdJuhl+kJZ7RZdqb8FBQWHDh1au3Ytvwj9JPSFt58o\ndKX96rN0pWojCHLu3Lna2trjx48Lusf6Ap2+Cmg0GoVC0dTUtLGxsbGxYTKZ8fHxvWDY9SF/\nQ28yduzYSZMmTZo0SUNDY+zYsc+ePeNyuQCAFy9ekEgkMzMzJpPJH8729evX6OjoI0eOYBtQ\nZWlpiepgZmbm4OCQmZnZ0NAAACgpKfn+/buDgwOCIJWVlb0wAqvT3NtNgK0Ow4cPp1Kp79+/\nBwDQ6fS///57/PjxAIC6urpe61noig5hYWEjR4709vbua1YdAEBOTm7Sf1FTU7O2tk5JSUEP\nPXnyZNy4cTgcjkwmo8+x76CoqChc1Y4SSErhvlBWu0WnFZxMJh84cMDPz+9ns+pA33j7iUKn\n7VdfptO6DwCIi4urrq4+cOBAX7PqQBdeBZ8/f/b3929qakLTFxYWot4ccYNDEKQXsunLMBiM\nQ4cO1dTUqKmpVVZW7ty5c9iwYe/fvw8JCUlKSgIA7Nq1q66uTjCa2MfHB/Oo+Rs3bjx79szQ\n0LCoqGj58uULFixgsVguLi5Hjx61srIqKSm5fv06ACArK8vIyEhZWXny5MnTpk3rndzbTYBV\n1nzS0tLCw8MNDAwqKyttbGwCAgLwePy2bdtGjx7t5uYGAAgJCeHxeIWFhdLS0rq6ugYGBj4+\nPr2pw6dPn1B3HX8iJRMTk1WrVmGrA1aUlZUdOHBAWVmZzWbjcLiQkBAVFZXIyMi8vLxDhw4B\nAKKiogoKCmpra+vr69Hpl4KDgyViMHWqarsJel9PPn2hrHYL4RX85s2bDx48EJyCa9q0afwR\nYwOevvD26zGdtl8vX7588eIFg8HIzs4eMWIEHo9ftWqViYmJpBX/D8LrPoPB8PT01NfXJ5FI\naHo8Hr9//37J6iyI8FcBOlT29evX5ubmtbW1PB5v3759aHeKWIGG3X8oLS2l0+n8ES5kMrm4\nuNja2hoAkJubK9iPDgAwNTUVx5C3+vr62tpaHR0ddAJkBEG+fPmC5kWj0VpNNqapqYltgLOQ\n3NtNIA5oNFpJSYmKigrqmQcA5OfnKykpaWpqAgC+fPkiWFwVFBTE8XoSokNzc3NJSYlgYkVF\nxd4Z5dQzuFxuYWEhgUAwMjJCLbaqqio6nY7qXFhY2MrDZG1tLSlPmHBV200gWfpCWe0WQip4\nRUVFfX29YGItLS30Qn4S+sLbTxSEtF81NTWCXckAAGNjY8HhIBJHSN3ncrloJDofHA6HXlff\nQfirAADQ3NxcXV0tLy+vq6vbO+8uaNhBIBAIBAKBDBB+0hg7CAQCgUAgkIEHNOwgEAgEAoFA\nBgjQsINAIBAIBAIZIEDDDgKBQCAQCGSAAA07CAQCgUAgkAECNOwgEAgEAoFABgjQsINAIBAI\nBAIZIEDDDgKBQCAQCGSAAA07CAQCgUAgkAECNOwgEAgEAoFABgjQsINAIBAIBAIZIEDDDgKB\nQCAQCGSA+qbakAAAIABJREFUAA07CAQCgUAgkAECNOwgEAgEAoFABgjQsINAIBAIBAIZIEDD\nDgKBQCAQCGSAAA07CAQCgUAgkAECNOwgEAgEAoFABgjQsINAIBAIBAIZIEDDDgKBQCAQCGSA\nAA07CAQCgUAgkAECNOwgEAgEAoFABghESSsgeWpra52dnZOTkxUVFTtNfOrUKW1tbXd3dwBA\nYWHh4cOHHR0dPT09xZopAOD58+eXLl3y9/cfP348f+fatWubmprQ32pqamPGjFm1apW0tDS6\np6qq6tatW1lZWVwuV19ff8mSJaNHj+afW11dHRMTk5WVxePxDA0NXVxcRowY0SrT/fv3EwiE\n4ODgvqNbQ0PDlStXvnz5QiKRlixZMn369B7o1sXLh4gbDw+PTZs2TZw4sdOUT58+ffnyZWho\nKIfDiY6OTk1NxeFw48ePX7VqFZHYvZdY1zMFAFRVVfn5+c2cOdPX15e/89SpU2lpaehveXn5\noUOH+vr6qquro3vodHpcXNzr16/JZLKGhsb06dPnz5+Px//nE5rBYNy5cyctLY1CoWhqas6c\nOXPevHnooe5eWru6Cc8C0h85duyYnJzcli1bOk1ZXl7+66+/3rlzh0AgPHr06OHDhzQazcbG\nxtfXl0QiiSlTlE2bNklJSZ05c4a/5+nTp5cvX0Z/S0lJGRgYeHl5WVlZoXsQBElJSUlJSamq\nqlJWVh49erSbm5uSkhL/9CdPnjx58qS6uppEIjk4OCxbtkxeXh49xOPxrl27lpKSEhsbKyUl\n1ZFKqAKbNm2aNm0af+ePHz/27Nnj7Ozs7u7e3WvsR0CPHSCRSGFhYbKysl1JnJOTU1JSAgCI\ni4tbvXp1UVERuinWTAEA4eHh9vb2/HqCkpmZOWnSpKCgoKCgoEWLFt25c+eXX35BD2VkZDg6\nOn7//n3x4sXe3t5qampoOUaPfvz40dHRMTs7e8mSJWvWrFFSUnJxcbl586ag8ISEhLi4uOzs\n7L6jG4fDcXV1LS8v9/PzmzJlyvr161++fNkD3bpy+ZBeYPv27fwXvXCqq6u/fPkCANi7d298\nfLyXl5eHh0dERMSJEyfElykAICYmxtra+sKFCywWi78zJydHRUUFLds+Pj6FhYXz58+nUqkA\ngLq6Oicnp1u3bjk6Oq5bt27EiBGhoaHu7u5sNhsA0NTUNG/evKioKEdHxw0bNgwfPjw4ONjP\nzw8V291La1c34VlA+iPOzs4LFizoSko6nZ6eno4gSExMzKFDhxYtWrRx48YPHz6sWbNGfJkC\nAF6/ft3Y2JiTk/P582f+zurq6tLSUrSa+Pn5qaurz507F63FPB5vw4YNO3futLS0XLdu3YwZ\nM5KTk6dPn442pgiCbNmyZdu2bZaWluvXr581a1ZiYuL8+fPJZDIAoLGx0dXVNS0tLT09ncfj\nCdGqurr669evV69eFdx569atL1++oBl16xr7GchPT11dnbOzM4VCaWxsdHZ2zs3N/fXXXz09\nPc+dO8flchEEyc3NDQgI8PT0jImJ2bBhw9GjRxEEuXnzZktLy4YNGw4fPswXtWvXrvj4+Fby\nAwMDk5OT+Zv79u1LSEjgZ4ogSEVFRXBw8IoVKzZv3pyamtpWw+/fv0+cOJHNZo8YMaK8vJy/\n39bWNikpib/56dMnHR2dpqYmNps9ZsyY4OBgQSEZGRm6uroZGRlcLnfSpEnbt28XPPr48eOD\nBw/yN8vLy8eOHXvmzJlly5YJv3u9qVt5efmWLVtYLBa6f/369aGhod3VrSuXD+kdNm/e/ObN\nGwRB/Pz8Xr9+fejQIQ8Pj7179zY0NCAI0tLScvDgQU9PzyNHjly+fHnRokUIguzcufPr16/o\n6ZGRkXPnzkUQJDo6+sCBA62EX79+/ffff+dv3r59G33E/EwZDMbZs2e9vLzWrFkTHR3N4/Fa\nSWCz2SNHjiwrK1uzZs0ff/zB379hw4YdO3YIJhs6dChaxzdv3jx37lw2m80/2tTUZGtre+bM\nGQRBtm3bNm3aNCaTyT9aUlKyfv169HrbvbSO6Eg34VlA+iPh4eHXr19HEOTSpUuxsbHXr1/3\n9PT85Zdfvn37hiAIj8e7fPnyqlWrAgMD//rrLx0dHTabHR4e/uLFC/T07OxsHR0dBoPx4cMH\nV1fXVsLfvHmzevVq/ubnz59XrFiBSkAzRRDkjz/+8PX19fT0PHbsWEtLS1sNfX19ExMTr169\nKvhejYqKmjRpkmCyVatW7du3D0GQmJgYCwuLiooK/iEul+vh4bF8+XIEQRITE42NjYuKivhH\n2Wy2n5/fhw8fEATJzc199uxZbm4uelFC7ltUVJSnp6elpWVpaSm6h8ViDR8+3MfHB23EBa/x\n6dOnmzdvXr169a1bt4TI7C9Ajx1gsVjp6elcLpfH46Wnp587d2737t2//fbb9evXk5OTmUym\ns7OzgYHBoUOHaDQa30Xk4eEh6DdGMTEx0dTUbLVTR0cnJiYG/d3Q0BAZGTlixAh+pgAAtGP3\nwIEDEyZMWLFiRVFRUSsJN27c8PDwIBKJLi4ufFFtQTuDWlpa3r9/X1ZW1srDPHr06HHjxiUl\nJX379i0/P5/vP0OZM2fO/v370d8IggQEBOzYsUNbW7vTu9ebuuno6Jw9exb1vfN4vIKCAgsL\ni+7q1unlQ3qN9+/fNzQ0AAA+f/4cGhq6ePHiixcvFhYW/v777wCALVu2FBcXHzhwYOTIkRER\nEegpR48e5fvb8vLy0AKgra1tbGzcSrilpeXVq1f53qyrV6+amJgIZhocHPzy5csdO3asXr06\nLCwsISGhlYTHjx8PHTpUV1fXw8MjKiqqo6sgEokqKipkMpnD4Tx+/HjTpk2CXagkEsnLywv9\nyHn48OG6dev4AQkAAH19/YsXL6qqqnZ0aR3Rrm6dZgHpj+Tn56ONQnFx8bFjxwYPHnzx4kUj\nI6ONGzcCAK5cuRIZGbl169aVK1eGh4ejp2zcuHHq1Kno7zdv3piamsrIyCgqKg4bNqyVcGtr\n69TU1Pfv36Ob8fHxJBKJSCTyM71//35ISIiPj8/u3btTU1Pbvierq6szMjLmzZvn4uKSnJyM\n+tXaRV1dvaWlBZXp4uIyePBg/iE8Hr9ly5ZXr17V19c/fPhwwYIFhoaG/KNEIvHMmTNotIyZ\nmVmnETiCYp2cnG7fvo1upqSkWFlZqamptbqxjx492r9///Llyzdv3nz58uVWTr7+CDTsWrN2\n7VotLS19ff3x48d//fo1NTWVw+H4+fkZGRn5+vrq6OgIOdfX13fy5Mmtdjo7O6empqJtycOH\nD21tbU1NTflHORxOUFDQtm3bzMzM3N3ddXR0Pnz4IHg6hUJ58ODBsmXLAAAeHh63b9/mcDjt\n5n737l1dXV09Pb3y8nISiTRo0KBWCUxNTcvKysrLy6WkpPT19Tu6iuvXr5NIpKVLlwq5Uknp\nhoIgSHBwsKKiohAlO9Kti1lAeplFixZZW1srKSk5OTllZ2dTKJRnz54FBgaamprOnDlz5syZ\nrdI/ePDg/v37O3fuBADMmDFj1apVrRI4ODiQSKS//voLAJCbm1tQULBo0SLBBIsXLz5+/LiV\nldWECRNmz5799u3bVhIiIyPR8FlHR8eKiorv37+3q3lmZmZFRcXIkSPr6+sZDAZqPgqClm0K\nhdLS0iJY9ztC8NI6ol3dup4FpJ8yfPjwOXPmKCgoLF26NDc3l81mJyUleXt729ra2tra+vj4\nCCbevXv39OnTk5KS0DgTMzOztgHT8vLyTk5O9+/fBwDweLz79++j70w+lpaW165dc3BwsLKy\nWrVqVdtqEhMTs3jxYhkZGRKJNHXq1MTExHY1b2ho+Oeff8aOHQsAKC8vb7eaIAhSXl5eXl6O\nVRlGEMTDw+POnTtop21sbKy7uzuCIK2SXb161cfHZ+LEiSNHjrx48eLIkSMxyV2CwMETrdHS\n0kJ/EIlEJpNZWVmpra3Nj302MDDorkBDQ0M7O7vk5GRPT8+7d++6ubkJHiUSiTo6OufPn6+u\nruZwOGhnpWCCxMRE1LJEN5uamlJSUvjR0GfPnr116xYAoLq6mkqlXrhwAYfDKSsrU6lUDofT\nKvi6qalJUVFRSUmJzWZTKJS2HkcAQH5+/rVr1x49etSVS+tl3VCoVKq/vz+TyYyOjiYQCN3V\nrStZQHoffr2TkpJiMpnV1dUIgvC/o/T19T99+sRPfO7cufj4+MTERCFOZRwOt3Tp0vv378+e\nPfvu3btOTk6tnrixsXF8fHxJSQmdTs/KyrKzsxM8mpOTk56eTiQSUX8Ym82Ojo4+fPgwevTZ\ns2doRaZQKPn5+aGhoaampjQaDQDAHzPEp6mpSUFBQV5enkAgNDY2Cr8PXbm0jnTrYhaQ/otg\n84QgCIvFqqys5FeTVs2Tp6fntGnT4uLidu7cGRUV1dFYHBcXF39//+Dg4NevXxMIhEmTJgke\n1dfXT0xMTExMJJPJFRUVrZonDodz8+ZNTU3NFStWAACqqqqys7O9vb3RoxUVFWg1YbFY3759\nmz9/PrqprKzcbjUBAKCtAIZleNSoUUpKSn///belpeXnz58jIiLaRmYXFRXp6uqiv4cMGYJV\n1hIEGnadICsry2Aw+JsUCqUHQlxcXO7fvz9t2rTPnz+36tPJzs5evHjxwYMHnZ2dpaSkPn78\n2OrcqKiovXv38vto0tPTo6Ki+MbTvHnz0PF9qqqqxsbGaNVFm6iXL18KjgZiMplv37719/e3\ntraWkZF5+vSps7Mz/yiZTE5JSVm6dOnJkyelpKQCAwMBAOXl5ZWVlT4+PlevXuWbthLUDY/H\nNzc3u7m5TZo0affu3e2q1KlunWYhRCak10DHFfGrHjo0AQCAIMi2bdvKy8sfPnyorKwsXIir\nq+ucOXPodPq9e/eOHDkieIjFYs2bN2/u3Lne3t7y8vKnTp1qdS7qEuN/hrW0tGzcuHHPnj3o\n0DxbW9sNGzYAAOTl5U1MTNCd8vLyVlZWz58/HzdunKCoV69eOTg4EAiEESNGpKSktPI+JiQk\nzJ49W0lJqeuXJkQ34VkIv12Q/ohgC8VvnoqLi9XV1YcNGzZs2LDp06dbWlqmp6e3stj4TJgw\nAYfDZWRkJCUlubq6tnoH+vv7NzY27ty5U01N7Z9//rlw4YLg0ZSUFG1t7dDQUP6eLVu2ZGRk\njBkzBgCgpqaGNiXoqFh+R83o0aNfvHixdetWHA7HP/HVq1daWlqGhoajRo16/Pjxnj17BC3R\n58+fm5mZCfbPdh13d/e4uDgrK6slS5YIRinwUVZWRr/KBgywGesEKyur0tLSsrIyAEBtbe27\nd++EJKZSqYIj1PgsXLjw48eP0dHRbd0Gnz59MjAwWLFihbm5eUtLS1lZGRp4h5Kenl5XV+fu\n7m7/X7y9vd+9e1dQUIAmMDU1HTNmzJgxY8zNzfnVQE1NzdvbOygoCB2CBACg0Wg7duyQl5d3\ndnZWUlJau3ZtSEhIeno6erSxsXHDhg13797F4/E7d+4MDw8PDAwMDAycM2eOgYFBYGBgu+ZO\n7+sGAFi3bt3UqVP37Nkj3AITolunWUD6AlpaWmpqauisIhwO5+nTp+j+M2fOlJSUxMTECJo+\nTCaz3feyiYmJhYVFeHg4i8VqNb9JZWVlZWWlv7+/lZUViUT68OGD4Ag7KpWamJi4evVqfvlx\ndHS0tLRMSkpCE2hoaKBl29ramj8LAwAgMDDw2rVr9+7dQzcRBImIiHjx4sWvv/4KAPD394+L\ni4uMjESPcjicsLCww4cPo0EC7V5aW4TrJjwLyMDDysrq9evX6O/Hjx+jP3x9fc+fP4/+Liws\npNPpGhoaHA6n3QA4PB6/dOnSR48eJScnt+qHBQC8e/fOy8tr5MiRBgYGb968EWyeAAARERGu\nrq72AixatCg6Oho9Kisri1YTOzs7wfAbX19f1M/NL5bp6eknT57ctm0bgUDw9vauq6vbsWMH\nnU5Hjz569GjDhg3V1dU9u0UuLi7p6en37t1Dw9nb4uDg8OTJE/R3WFjYtm3bepZR3wF67DrB\nysrK1dV14cKFo0aNqqqq4n+Lr1y5ksfjZWdnf/z48evXr0OGDNm/f7+bm9vMmTPRl7ggysrK\njo6Oly5d4r9w+UyePPno0aNr165FEERFRWXhwoXh4eEmJiZoLEJkZOSCBQsEOxwVFBSmTZsW\nHR0tfHq5ffv2ycjIuLi4aGhoKCsr5+fnjx49Oi4uDm2Edu7cKS0tvXr1ahKJpK6unp+fP3fu\nXPSrS9CZ//XrVwUFhbbxtii9r1tqampqaioAAHX7AwCsra13797dXd2EZAHpIxCJxL179+7Z\ns+fx48eVlZXm5uaFhYVUKvXs2bMWFhZeXl5oMjweHx0dfeLEiU+fPt25c6etHBcXl5CQkPXr\n17ey2vX09EaNGrVq1SpjY2P0G+Ds2bMxMTFo4FpiYqKurq6lpaXgKYsWLYqKivLw8BCi9pw5\nc06cOBESEhISEqKrq4s6TmJjY1FR06ZNCw8PDwkJOX78uIGBQUlJiYWFRVJSkqqqakeX1jYL\n4boJyaLTew7pj/j7+3t4eJSVlfF4PH5o2rFjx3x9fV+8eKGmpvbx40c/Pz9LS8u//vrLy8ur\n3fm5XFxc5syZM3z48LaDkJYuXRoSEvL8+fOioiIPD4/k5OSdO3eGhYUBAHJzc9++fdvKh7d4\n8eLZs2cfPHhQiM56enpxcXE7duywsbExMzOrq6tjMBi7du1CK5e2tnZCQsK2bduGDx9uZmbW\n2NiIx+OvXLmCegGTkpLi4+PRDzlvb288Hh8UFNRRI4Wiqqo6fvz4kpKSVrWGT2BgoJeX19y5\ncxUVFUtLSwfA1Fe4toGEPxssFuvdu3foBLlv374dNWoUOu4yLy9PSkoK9f2WlJQ0NDQMGzas\nvLwch8MZGhry/T0oSkpK1tbWnz9/VlVV1dPTa5tLZWVlUVHR2LFj0QaGnymRSKRSqTk5Oaqq\nqkZGRiwWKzs729zcXEFBAQCQmZlpZGTEn/uUL6q2tnb48OHtHhWEwWAUFBSwWCx9ff224xWY\nTGZ+fj6LxTIxMWnXSVBTU1NXV9fRpF+9r1t9fX1OTo5gMhKJ1K56wnXr4uVDxM2HDx8MDQ3R\ntkdPTw99XjU1NVVVVehjamhoKC4uNjEx4XK55eXlQ4cObRu7PW7cuOLiYiqV2m5JoFKpWVlZ\nQ4cOVVFRaZUpl8tFxxwMHToUj8d/+fJFS0tLQ0MDAJCTk0MkElvFd6OixowZk5+f3/aoIOiQ\n7ebmZk1NzbbDdBAEQY/q6OjwA+k4HE67l9ZWuHDd0I+ZdrOA9FPy8/MJBIKRkVFhYSGCIOij\nZzKZ79+/R584nU7PyclRV1fX0tJ6+/Yt2spwOJz8/Hw6nW5kZIQW/qampm/fvrVbqAAAGRkZ\naE9oq0wBAEVFRY2NjUOGDJGXly8uLuZyuagO1dXVlZWVbad2z8jIMDc3R+P/hE/8XlFRUVVV\npaioaGpq2jZgGnWrq6ioGBsb8ztty8rKSktLBZOhTve2wqurqxsbG1Fjrrq6msFgoFeXn5+P\ndg0LXiOPxysqKqLRaEOGDBEy6XF/ARp2EAgEAoFAIAME2BUL6YTi4uKjR4+23T99+nQXF5fe\n10eQvqwbBCIi6OjvVjv19fWDgoIkog8E0gcJDw8XXPGCz8mTJ+Xk5Hpfn74A9NhBIBAIBALp\nl1RWVvJHzQtiYmLy046Hg4YdBAKBQCAQyADhJ7VnIRAIBAKBQAYeYo+xy8vLO3XqlJyc3PHj\nx9sePXLkSEZGBt9fOn/+/NWrV4tbJQgEAoFAIJABiXgNu/T09Nu3b1tbW+fn57ebgEKhbN68\necaMGWJVAwKBQCAQCORnQLyGnY6Ozu+///78+XMhhh06YRsE8pPT1NR0/vz5jIyMhISEtuve\nPHz48MqVK/wFPIyNjdt1gUMgEAjkJ0e8hl2nK7tRKJTnz59HRkayWCwbGxsfH592ZxoEAAgO\n8kAQRHCNORFBJWMr8OdRD7BYCIWCU1YGHaww3QMG5A0Ufkp1dXVwcPDUqVMzMjLaTUChUBwd\nHQMCArqbbxfBuFT0mdveoTQ6HdDpOBIJtJkWtecyB2K5FS4Q9G0NRQHzqxOULCaxoH8p3NIC\neDzcf2cOx1LyT3+HJTyPnYODg66u7o4dO5hM5qlTp06ePNnuUiRsNru5ubn31YN0imxsrKKf\nX/ODB2wHB0nr0qdRUlKSkZHp6KiUlFRoaCiDwehoNRsymSxW3zaVSpWVlSViZJ1zudzGxkYF\nBQUM55Fqbm5Wwa4N4Jw4IbVvH/frV0IHC6t0WyCHw2AwFBUVMZEGAGhpaeHxeBguBUan04lE\nYruLoPcABEHq6+vl5OQwLJbYPuJOIZPJggsEtwJdxhSrGtFKsjjEcrlcHo8njlUTxKSw/Lx5\n+LIyyqdPmEsW3x1GEKTvFAkpKSnBhaoFkbBh5+vri/6QlpZeuXKlv78/g8GQlZVtlYxAICgp\nKfE3qVQqhm8TGo2GIAi2AuXk5LAywJlMJovFUlRUxFAgkUhsu35Lz0AUFBAVFRkFBVmBByQK\nbDYbQRCsmh8ul0uj0WRkZDAUyGaz2xbRThH+wlVTUwMAlJWVdZSASqWii9Y3NjYaGBisXr26\noyWt2k5p2xW4XC6LxWq1wnePQdtLDofTM2U6komhNCAtjaiosHk8DkYyuVwul8vF9noRBMFQ\nIIfDQRAEq/mtUDmYXzK2byfhCLfCyWQyh8MRx2KDzc3N4hDb7xTmKikBEqkfKYx+a/ULhSVp\n2LFYrB8/fgwbNgwdFcvhcPB4fLt2Kx6PF/R20Ol0Ic6P7sJgMHg8HrYCZWRksLLD0A9HbAVK\nSUlh9WHH8PCoX7iQRCJhJRBte7B6HBwOh0ajEYlEDAViW1q6iIWFhbq6+oIFCxQUFOLi4oKD\ngy9evNj2awRBEDKZ3LMs2Gy2yGr+H5hMJpamGAA9vrR28PEBPj6oUMxkiuEeYnnJYoDFYrFY\nLAwFkslkeXn5jvwQ2NKVN6qYuobF1+PcjxSm3r7N4/FU+4/CYpXc77tic3NzW1paRo4cCQAI\nCwtbuHChi4sLlUq9efPm2LFjxeHnhPSAsrKyx48ff/z4sbm52czMzM3NbejQoZJW6udl7ty5\n/N/u7u4PHz78/v07WokEweFwSj1ynTIYDCkpKaw8JTwej0qlYugoBVj76VksFpPJVFBQaDs3\n/ffv3xMSEvLy8lRVVceMGTN37tyOAn8F6bErtyMw70lgMpkEAgGrFyyCIBQKRVpaGsOPHPQR\nwyZgIJGbm3v58uXa2tqZM2e6ubnBh9s7iPcuHzx4MCsri8fj8Xg8Z2dnAEB8fPzr16/z8vJG\njhwpLS194MCBa9euJSUlSUtLjxo1ytvbW6z6QDqlrKzs3r17t2/fTktLE+y1CQ0N3b59e2ho\naO/0kkBakZOTo6WlxbcwuFxuRzZTzxpaNpstLS2NYYwdlUrF0FEKsPbTo71+0tLSguWZxWIF\nBARcvHiRH3p14cIFeXl5Hx+fHTt26OvrCxGIuSsX854ELpeLbYwdhUIhEAh99hFDJE5iYqKn\npyeDwQAAREZGnj59OiEhodMhlRDREa9hFxwc3Hanl5cX/7eZmdmRI0fEqgNEEDabff/+/Zcv\nXxYWFqLx8srKyqjfor6+Pjs7u6CgAAAgR1Ib6exrNX2pwYgJ0vKKVT8+ppzacfTo0ZqammvX\nrkn6In4W6urqPn78iM7yeOfOHTweHxAQICMjc+vWLUVFRXNzc0krOKBgsVgLFy5MSUmZOmnR\nRp8DQ8xsKdTm1xlP4+9dOn/+/JUrV7y9vf39/S0tLSWtKQTSD0hLS/Pw8NAdpBe5N85M1/xi\n0rkjMQfHjRv3559/Dhs2TNLaDXD65VqxTU1NGA6eam5uxnb0GRoIiVWXOZVKpdPpgwYNEl1g\nQkJCYGBgSUkJwOGUNXQI0jIIj8ekNNNbGnE4nLyqhqqusZ7NWItJc43HTCMQ/0/MHI/HTQxa\n+flx7JkzZ/z8/Pj7GQwGhULBMMaOwWAgCILVaEoOh9PU1ITh8EzMBz+iJCQkxMbGIgiCRkAC\nAA4cOMBms0NCQpKSkgAAzc3Nly9f/vjxIw6HMzMzW7NmjXAHUnehUCh9fFQstrWeTqdTqVRV\nVVW+x27dunVXrlzx9tgesDGsVV3L+vrvxYiQ1H8fAwDs7e0dHBx0dHQ4HE59fX1DQwObzdbT\n03NwcJg2bdqgQYOw0hDz9xIabNqXR8X24BG/ePEiPj6+sbFRVVV12bJlU6dOBQCkpqbGxMQ0\nNjZqaGj4+vra2tr2QJmWlhYOh4OOasIWbEsynz6lMJlMtrGxaWkgvzj7r5G2Mbrzr/fPPA+5\nKJEUX758aWFhgXkJF0XhrtCPFIYd3j8FXC7Xz88vPDxcWVPXaedZmzluimqa3ZKAxxOWHLxe\nW/Bt165d8+bNMzU1FZOqPycuLi4uLi5t96NWHQCARCJt3769d5X6iUhISLhy5crsacvaWnUA\ngOHDHMKPJ+fkZ919eO2ftIfh4eH8Q1JS0gQCkcGgAQB0dHROnjzp5ubWq6r/xHz9+vXSpUuH\nDh2ysLD4+PHjwYMHhwwZwuFwzp49u2fPnuHDh7958+bIkSOXLl3qSpQkBEOCg4OLi4uv7ozm\nW3UAgKn2M24fSFq2f+Hs2bPT0tLg2gTio3XgMGTgweFwli9fHh4ebjV96S93v45y3SCvot4D\nOUQZ2SUh15lM1tatW/k78f/+qxgYiOtgZREIpG9CePZMMTAQVFcDAMhksp+fn5amXvCOy0L8\n4hamw3f+eiY5Lv/NM0pyXH5KYvGbZ5R3fzEznlH/flC9f/slHoewfPnyTZs2YTVrDEQ4SkpK\n27Zts7CwAACMGDGCRCJVVla+fPly1KhRtra2OBzOwcHB2Nj49evXktb05+L79+/nz5+faj/D\n2XGtar6xAAAgAElEQVR5q0OTbB2v7YopLSldsGAB7uRJudBQiWg44IEeu4HPpk2bEhISRrus\nn78nHIfHizIjw2BLO/ula+4nXE5LS5swYQIAAJeTIxsVxfH2BjD2CNJ/wH36JBsVxd25E+jo\nHD16tLKy8vihOEXFLvl15GQV9HT+zySCaqqaSxesnTHFOeT3dRcuXCCTyZGRkW3H20KwxcDA\nwMDAAP2dk5PDZDItLCyePn1qZGTET6Onp1dSUtLu6UwmU0gkEjqPIBr4jy08Hk9MYvuIwtu2\nbUN4SMiaMHSurlbMGj03dO2x3ZcDS0pKhiooMA4fxk7T/zDg7zAKHo/vKLICGnYDnNOnT1+5\ncsV6thtq1Yku0HH9/o8PokJCQlJSUkSXBoFIlpqamjNnzthaj5s11VVEUfLySscPxe8/4hMT\nE6mhoXHy5ElMNIR0SmFh4dGjR7ds2aKkpMRgMARbOxkZGQqF0u5ZVCpVyMoTKB2dKyJiEis+\nyV0X+/r160ePHnnOWm2ibdbRHIerZq/9WvSl7mlEC5fLlbTCfURyD8RKS0tDw+5nJC0tbceO\nHbrDRi05FIGJVQcAUNbUtVvo/TT+4ocPH+zs7DCRCYFIihMnTlCp1F/WHsJEGh6PP7jralNz\n3alTp2xsbFavXo2JWIgQXr9+ffXq1S1btqCvI1lZWSqVyj9KoVA6GsEjfK5/KpXK5XLFtERE\nzyabFE5fUBhBkNDQUAU5xT2rDgqf0/H3zWfLUxOamppKvn5FO38wREx3mEKhiGnliZ4pLCRu\nBBp2A5ampqYVK1YQ5RTdjsdLyWA2OBEAMGFVYGbi5TNnzty4cQNDsRBIL0OhUC5evGhrPW7s\nqOlYySQQiEeDb3mud9i8ebO9vX3PhmRCusizZ88SEhJCQ0N1dHTQPUZGRvkCIb9FRUVOTk7t\nnit8DDjaaopjQl0cDicmsUDSCt+5c+ft27c7PPZqDxosPKWstKyF/tD63MyVK1d+/PgR28G8\n4rvD4pOMrVgYBTJg8fPzKy4uXrjvkoqOEbaS1QzMLCbNu337dl1dHbaSIZDeJDExsaWlxct9\nG7ZiFRWUT4QmAATv7u5Oo9GwFQ7hU1paGhERcfDgQb5VBwCYMmVKVlbW+/fveTzeX3/9VV1d\nPX78eAkq+fPAZDJ3796tqart5xLYlfQyUjKDlNVLS0t9fHz647RrfZn+4bFD5/cS3MRwTUZ0\ncVJsBbLZbKzmsUOjQLor8N69e9HR0bbzPIfOcG4VR4IgSKeRJZ0yetnGH/88uHLlylYDA66h\nIVdKCsHoBnK5XAwfBzo+EV3uCSuBPB6vB9IIBAKMpu9DqKhwDQ2jYmN1BxtPm7QIc/GmRlY7\nfz19IMw3MDDwwoULmMuHAACePHlCoVA2bdrE3+Pu7u7i4rJ169bLly/X1dXp6ent379fHL1y\nkLacOXOmsLDwzK+XFOS6NM0nT1VbBkHWTnK+eu/C6dOnAwICxK3hz0P/mKCYy+UKjhlhMpkY\nrjyDjo3CcJFHbNVjs9kcDqdbE702NjaOGjWKxsNviPskq9R62kMul4vH40W0OxEe7/xiSzVZ\n/Lt37zgcjoyMDFZWC2rBY+WXRleOkpKSwlAgl8vtwWzMMjIyfXmdxJ9wguJ79+65u7tv3XTM\n2wODCQJ5PB6Hw2kVyxwQtPTFq6Tk5OQ5c+Z0VyCcoFiy9Kn5fruCZBWurq62sLAwUDf6+1wG\nAd+lZSfRlhdHwDkFTvlc+OnJkyfTp2MTEQEnKO67LY0gBAJB8PXBZrMxfJugizxiK1BeXh7D\nlSe6K3Dz5s3V1dUrzt5XUtNoNwEm3qORS9c+P783IyPD3t5eVla2z648gS4Jiu3KE312ak0E\nQQQjx7sOm83m8XhYWefo5yKTycRwRjcej4fheDQulxsZGSktLTtvlmdHY/e6Ber4byVql/+5\nD1mpa9asefPmTXdf3KjrGsNL5nA4HA4Hk4vlw2azMdQQfcRCxvpB+ixBQUEtLS1H9pzsolXH\nR0ZKJmpv3HT/Cc7Ozi9evLC3txeThj8V/cOwg3Sdp0+fRkZGDp/rMWTKArFmZLfI+6/w4MjI\nSFgV+w44HK5nvmd0vXn++loiwneUYuu6xtCtXlhY+Oeff86aumyQmhYmAtFwkVYuTw31wfu2\nXwrYs3TPnj3dXWQZ/eDE8JIZDAaBQMDqAwyd0ItAIGCoIfqIYcRCv+Pdu3c3btxYOHHpxOFT\nenC6nqZBXMj9hbtmzpgx4969e5MmTcJcw58NaNgNKKhU6vr16+VV1J12nBZ3XsqaumYTZicl\nJR06dAiu2NN36Fl3Kh6PJxAIGHbFojIx7H3GduDYzZs3uVyu84K1WJkRPB4Ph8O1lTZ9ypK5\nMz2ioqKWLVs2b968rgvEfAgeto8Ydcr25UcM6TUCAgKkiNIha8J6LMHWzO6Pw4/d9i+cMWPG\nmTNnNmzYgKF6PyGwFg0o9u7dW1RU5PxbtIJq+52w2GK30DvnVfK9e/d++eWXXsgOAsEEBEGi\no6P1dExH2Tn2Qna7/M++efd8/fr1X7586TsxZBDhQ6D4o9YwzxfbsXqCYsUnWYjYpKSkV69e\n+btu19c06NawPFRh/il25iOfnkpdFeq6cePGt2/fnj9/vsdW/gC7wx0h5EMIGnYDh/T09LNn\nz5pPdLKd59k7OQ5xXCinrHb79m1o2EH6ES9fviwoKNjgHYxVIKxwVEiD9mz9f1v3ugQGBna3\nQxYiPtDO7o6Oths0iQliEoteSy8rzGazg4KC1EkaW5wDuxtQizp9Bc/S1zBIPvaX35n1169f\nr62tjY6O7pltJ7473KeKBJFIhIbdAIfBYKxZs0ZKTnHhvku9lilRWmaV/cTpf98vffrUpDvd\nTBCIBLlx48ZiHG7ftzfNdRUcDd1eyHGGo/PsacsiIiKWLVs2e/bsXsgR0inCB1Shg1fEMUwK\n28F/fCSi8MWLF/Py8o5tPKNGGtRdsfK3QnEt9awt5wV3SkmRIoJid170v3I/fPfu3eHh4dgq\nLAqYj7Pkg7nCMEx1gBAcHPzt27dZAWEkbf3ezNfBzMYVgD9jY3szUwikx5DJ5Pj4+Jm6Zjpv\nnuBp5F7Ld3fAOZLyoHXr1rW0tPRaphCI+GAwGKGhoYbaRl5z1/bgdKkvr2TetbPgOA6HC9tw\neuHEpRcuXIiLixNZzZ8RaNgNBF6/fn3ixAlThxmjXNb3ctYqusYAgKdPn4o+6TEE0gskJCRQ\nqdShQ3p7KLeaqmbQ1vMlJSXbt2MwbR4EInEuX75cXl6+w2OfNBHj6WlwONw5/8t6mgZbtmxp\nbGzEVvjPADTs+j0UCsXLy0tKTnHxgWu9EzPUltra2mfPnkkkawikW9y4cUNRkWRqZNX7Wc+Z\n7jZjytIrV648ffq093OHQDCEyWQeO3bMRMd02TQPcchXViD9vulMTU1NSEiIOOQPbMQeY0ej\n0aKiohAE2bhxY9ujCII8efIkMzMTj8dPmDDB0dFR3PoMPPz8/PLy8pwPR5EGG0hKBzyecOPG\njVmzZklKAQikKxQUFLx69cp5gS+RgM10bt1l77YL7z69WrNmzefPn+EIWUxobGx8+vTp5MmT\nBw8eDABoaWl59OiRYAJnZ2c44zHmREZGlpeXn/W/TCSIy4qYM3a+o930CxcuBAQEGBhIrHXr\nj4jXY5eXl7dt27by8vL8/Px2E9y6devBgwfz5s2bNWtWVFTUn3/+KVZ9Bh6xsbERERE2c5bb\nzl8pQTX0hjvcvXsX+sxFJDc3NzY2tqPBZU1NTY8fP05MTMzOzu5lxQYMN27cQBBk0VxvSSmg\npqq5b/vFsrIyOJAcE169ehUUFBQfH19ZWYnuqaqqSkpKkhJAUv0YAxgej3fixInBg3Tdpq8Q\na0Z7Vh1EXYNizWXgIV7Drqmpac+ePePHj2/3KJfLffjw4S+//GJvbz969GgfH58//vhDrPoM\nMH78+LF+/Xo1fdMF+y5KSgeayqBySzv9GUsZDMbNmzclpUZ/h8vl3rx58/fff+/IsKuoqNi8\nefOPHz9YLNbx48fv3r3b+0r2d3g8XlRUlInRUFvrcWw1Laq5HSKN2aoJXWfGlKUL5qy6efNm\nLBxyJDKpqam//fabvLw8fw+FQlFVVXURAKvFNiB87t+/n5OTs2HxFlGi67g65hzDTiIiRlmO\nnTJiWkRERH19fY8z+gkRr2E3atQoXd0OZxOoqqqi0Wjm5ubopoWFRXl5OZ1ObzcxIkCrTRFp\nK190gdhKa1dgS0vL0qVL6Sy22+9xsordW/iBL1Z0fkyefz4iVWrZJkV17WvXronvekURiKG0\nHgsUfhsLCgpYLFZQUFBHCeLj4ydMmODv7+/u7r53796bN292VFMgHfHXX38VFxcvnrsaANAw\nZ9WX/5fK0jaUiCa7A87pDjbeuHFjYWGhRBQYMOzevbvVouxUKlVRUfHbt2+PHz/OyMjAcLVi\nCJ/Tp08ryCl6Oa0RRUjLprONu+90mmzzUn8ajXb16lVR8vrZkOQ8dhQKBXWVo5uKiooAADKZ\n3HZ6ITab3dzcLLgHc/sdW4ENDQ0YSmsrkMfjeXl5ZWdnz91zgWRoSaPRsM2uu7C5POs57v/G\nnHr+/PmIESNEF9izlew7gkb7/+ydZ1wT29aHJ4UklNCbIk0pKiIgIoqAAgpSRATFgh0Fe0GP\nXQTF3hELiAo2LFiOokdREREV7AU7iopHipRAep33w9w3N5cSIEwylP184EcmO2v+s2dmz5pd\n1mKhW0UcDqelP6FSqVISp1paWlpaWv769auxAu/evQsP/08z2r17dxUVlc+fP9vZ2bVURmfm\n2LFjBALR30dB4buloKaqvi3mzLR57uPHj3/w4AGYAYYidDr969evf//9t6mpaVZW1pkzZ7Zt\n29bgrUej0aS4fTAM43A4eXQUwTAsp/4nOVmuY7agoOD+/fvT/SJIeHJrXi+R190mLbjYuJt1\n6X7w4MHp06c3M5l1e6/hZkIikahUaoNfYenYEYlEPp8vEomQBIvICW6w2xyPx0ummubxeCg2\nhTweD4ZhFLOVoytPIBAIBII6mbZXr1598+ZNp/Hz+gXNaKlBJKklWvNOYBgWCoUEAqFf8My8\n03vPnDkzcODA1hhEwmyilTJSJBLxeDwpEbplMCgUCmUY3Glmk9QYVVVVknPttbS0Gnx5gGFY\ntjBpQqFQIBCgOxuJw+GgGKVdKBTWebtrETQa7dKlSy4DfNTVtLlcLvJQ4XK5KN4IMAxzudxm\nlre2cJg7IzY+afX8+fN37NhRv4BAIIAgqDWHXAehUIjD4dDt6OVyuYhOVEBOMZlMrtPctQhP\nT8/Bgwcj2atDQ0MXLFiQmZk5cuTI+iWJRKKUu5LP58MwLA+fm8/ny2N0GMlJpQDBKSkpOBxu\nZsDcVrZpiFfdpBECRJjqMzM2ZXVubu7w4cNlEIwWCqvhZiLloYalY2dgYABBUHl5uaGhIQRB\npaWlFAqlwZViBAIB6c9DoNFokh9bSU1NjUgkQtegqqoqWg8MJpMpEAgkDcbHx+/fv9/Kzc9/\nxT48vsW3Fp/PJxAIaOU+FwgEiKNj2KN3dyeP9PT0+Pj41lQmh8OBYVh6UPgWyePxeGQyGUWD\nHA4Hxaul+Uj2Lkh5msr2oG3OeHFLEYlEKEY3hGG4NT7EuXPnOBzOSJ8pkpLQPWpYIvFlc5g4\nZtGrgkdHjhyxs7MbP358fWuQrGezMXk4HA7deJOtPCkNWmvlI5lCoYj9QgKB0L1799LS0gZL\nSr+La2trBQJBYz0irYFGo8nDrGIE02i0CxcuDHXw6m1u00qzyPtVc/ykMJ+pm0+tT0tLCw4O\nbo5lOdUw4iq0i0sCA8eOTqfz+XxtbW01NbU+ffrcunVr6tSpEATdvHlz0KBBYAWTdNLS0pYs\nWWJk0z90+zkZvDq54hgy68KKrLS0tFmzZmGtpaOhra0tXnQMw3B1dbWOTgM5fHA4XIPbm4TB\nYFAoFLT6NYVCYXV1tYqKClr+NARBNBqtNfFBzp07p6NtMGzoaCJRCfp/j59CoaDV4IhEIoFA\n0NK3+a3rT4dFOC9btszR0bFOVzfyFKkzgaw1sFgsIpGIVn8DMnhEoVBQTIXUylOMkJGRUV1d\nPXnyZAiChELh58+fQ0JC0FAHgCAIOnXqFJPJnO4Xocid6mnq+wzwv3btWnV1NYp3RAdGvo7d\niRMnvn379ufPn8rKypiYGAiC1q9ff+nSpcLCwo0bN0IQNGfOnJiYmNevX/P5fBwOB0IRSufG\njRtTp07VMraYdOAGSQWDTiPp9PIcraqll5iYCBw71LGzs3vy5MngwYMhCCooKBAKhdbW1liL\naje8fPnyxYsX0yb+hXh1bQc1VfX9265OnOU8evTovLw8U1NsFnO0UyoqKrKzsyEI4nA4ubm5\n375969WrV58+fVauXFlZWWlkZPTkyRNNTU0vLy+slXYckpKSDHW6+g5sYGhbrowfNinj0ZXz\n589HRio6u1J7RL6OnZubm4ODQ52NPj4+7u7uyP/dunVLTEwsKioiEAhmZmagu04Kjx49Gjt2\nrIqO4dTETFUtPazl/AeLx5mDUnfe/mtPhbUdkUR2GDUtN2XHs2fP+vfvj7W09sTHjx8LCgqQ\n6XGXL18mEAju7u5lZWX79u1DloONHTt26dKlu3fv1tfXv3379rRp01CcFdrhSU5OxuFwo/3/\nOyFVK+u8Vkby77WpAn2F5lauj0k3y10bL8xZ5hsQEPDw4UN1dXVs9bQjYBhGpj2NHj0agiA+\nny8UCs3MzBITE/Pz8xkMxtixY52cnMBjBS3y8vLevn27dPwqVIISU4+vxtX84a440ZzCw518\ntajap0+fBo5dc5CvY2dubl5/IzKjTgyBQLCwsJCrjA7Ap0+fAgMDIZLylEM3Nbu0odd66p8S\ni6f3HtBpyMf+IREPU3cmJiYCx65FCIVCPp+vrKw8YcIE0f+jq6s7bNgwpIC+vn5CQsKjR49Y\nLNaaNWusrKywFdyOYLPZZ86ccejram7aU7yRVPpD48W9Ujaai69lxrm/19qlB2O2zZowYcLV\nq1dbOSe986CnpzdhwoT62zU0NEAWHHmQnJyMx+En+7R4xV6DEL++JPwpbuZqIxKRFOQWkvJP\n8s+fP0EWiibBcvEEoJlUVVUFBATUMlhTk+7odccgx2Xz0Tax6O7slZaWtmvXLtD30HxsbGxs\nbBqYjCw5p15DQ8PX11eBojoI6enpNBoteF6rYm7Jm+CRM7///JSStnPZsmV79uzBWg4AUBc6\nnX7u3Dl3ew9TQzNMBIx2Dz1+48j58+eXLVuGiYB2hHwDFANaj1AojIyM/Pbt2+iNx03sG87h\n0aboPyaSyWSeOnUKayEAAARBUHJyspqahrfHWKyFNMHiOduGuATs3bv3xIlmDU4BAIrk7Nmz\nDAZj8gh0uutkwMXWzUC7y9mzZ7ES0I4Ajl1bZ/PmzdnZ2W4zVvbxGYe1lmbR02OUmq5hUlIS\n1kIAAOjz588PHjzwGz6RQlFpujSm4PH4LdGnzEys58yZ8/btW6zlAAD/Q3Jysra6jv+gUVgJ\nIOAJQW4hz58/byz1PEAMGIpt02RnZ+/YscOkn5vn/I1Ya2kuBKJSv1HTc45uyc/Pd3Z2xloO\noFODpLkLDmjT47Bi1NQ0dselT4xwHjduXFZWFlgfI1dqa2ulRPVDwkbSaDTU9ysUCuVhFpmY\nKyfBubm5T548iQicDwthjrDFeXcaBHnTalEWH/+BoxL/Tjhx4sSSJUukFJNTDSPB89vOJaGk\npNRYsCHg2LVdampqpk6dSqZqjYw51tZC1omh63UpdPLgUP8n+pRjyKwHx7clJiYCxw6AIXw+\nPzU1taelfW9rxzpf8QxNa/p5iJRRi8GGFhbd+6xctC9m26w1a9bs3LkTazkdGemTgJF4v62P\nq1cfVML11UeugtPS0nA4XHhAZGuSgtRB0MNBqNutRQZd7YYY6RlfvXo1NjZWSjE51TASWrJd\nXBJgKLbtsmTJkp8/f/qtSqDqdcVaS6MUDvI+Gp9RbmkruVHLyLzHwOHnzp1DMSESANBSMjIy\nysrKgkfOrP9VtWfox60ZfL1uilfVJMEjZ3q5jz5+/PidO3ew1gIAQEwm8/Tp04NsXK2MezZd\nutnQp2+uWdSyGTs4HG6Ua/CrV68+ffqEopKOB3Ds2ig3b948fvx4H59xvYe1y7Dp/cdEsFgs\nsIQCgCFHjx4lk5X9vcOwFtJiopcnamnoLl68GLwaATDn/PnzNTU14QFtIoDc6CGhEASdO3cO\nayFtGuDYtUXodHpkZKSqtr7/qv1Ya5ER6yEjqbpdEhMTsRYC6KT8+vXr5s2bw4eGUNXQHzqR\nN1qaeisWxf/+/RtEdgBgCwzDR44c0dcyDBg8GmstEARBjlZOpoZmYG2sdNrHHDuBQMBgMMQf\n0Z0aifokWaFQ2Mr37KioqJ8/fwbFnSQoU5Ec2y2aYSodGIYl08m33hoEQTwer/5XfUdOeXh8\nW2Zm5oABA5pvEJnOzOU2M25ls+Sx2WwUDco2f1ZVVbWV2c0BLSIlJUUoFDY4Dtsu8HQf7eU+\n+ujRo+PGjRPHqQbUB4bhDx8+GBsbS6ZRr6ys/PPnj6GhoTxmRHUq7t69++HDh5WToklEdLIM\ntxIcDhc8ZNyec9tevXplb2+PtZw2Cg558rUv0J1piHqy7ZqaGnV1dZnz2GRlZQ0bNqynx6gJ\ney5DEMTn8/l8vooKasEa+Hw+gUDA49HprBUnU69vkFbyY49f98mTJqWmpjbfIIfDgWEYreTx\nAoGARqOpqqqiaJDD4aiptblEvQgwDMv2UiESiXA4HIrJlwQCAR6PR+sygyBIKBQ2PyWDSCRy\ncHDAwZTzx141eFCIg46iPMQmihUIw3BVdfn4mf00taiPHj1qfQvQXk4xhUJp/oT60tLSffv2\nvX//fv369f369UM2JiYm5ubmmpmZFRUVBQUFjRkzRgYxyFoEbW1tGX4rHbkunkBdsL+//53b\nd9+mftXXMkDXMpfLhWFYhtUYH76/c5ljv2zZsh07djRYQK6LJ1B0FcSgLrh99Nh1Huh0enh4\nOEVda+SaQ1hraS2aXUwtB/teuHBhz5498mgfAfXB4XCyNRAMBoNCoRCJ6DQIQqGwurpaWVkZ\nLX8aamHbd+vWrZ8/f0bN3d6YAOSFhEwmo+XoiEQigUBAIqHWq8HlcnW0DZYv2rtm45StW7fG\nx8e30iCLxSISiWgphGG4srKSTCY3FnBBBmR4vMXFxc2aNWvXrl3iLS9fvnz48GF8fLyWllZp\naenChQudnZ2NjTHOCNxOefv27T///DPBazLqXl1r6GVm07eH/enTp7ds2YJWk9XBAHPs2haL\nFy/+/v17wKoENV3Dpku3AaxzMuZPdzX8+LLBb51CZ7PZ7GPHjilYFaCTk5iYqKRECvSd2lgB\n7Zsn+sxzJZX+UKQqGRjpM3mIS8CBAwfu37+PtZa2yPbt2+3s7CS3PH361NnZGelWMTQ0tLW1\nzc/Px0hdu2f79u04CDcnaJE8jKsfXKi1Rcao++OHTS4pKcnMzERXUocBeLttiPT09GPHjvXx\nGWfr20Bm67aJCq3S6ONLEovR4LeWrr5aRuaHDh2KiopCd9gLAGiM379/X7t2bdiQEG0t/cbK\nKFWVqX55ieOhNnVVfkQvTwyeYjtt2rTXr1+D/Mt1qD9CXVZWZm1tLf6or69fVlbW4G+RWR+N\nWUai0bLZbFR0SiISieRhFnXBRUVFZ8+e9RsU2KOrJTLVG11Ufn8h/CmWzXKwW2jM0VVJSUke\nHh71v5VTDYtEojZ1SeDx+MZimAPHrq3w/fv3iIgIzS6mI9e2+0FYMXg8wSl0Tuae5Tdu3AgI\nCMBaDqBTkJSUJBAIQkfPwVoIOujpdl0TdWB5zIQFCxa0aLpq50QoFEq+Q+JwuMZcBxaLJSXz\nBAKTyURTnJzNomt58+bNQqFwQfBSGIYbXB7XSqQsvGsSqrK69wC/Gzeuf/v2zcCggWHidlHD\nrTRLIpGAY9em4XK5oaGhtXTG9L0Zyuroz83EEMfR4fcOxezduxc4dgAFwOfzk5KSepjbONq5\nY60FNUYMG3//UcaJEyd8fHwmTpyItZw2DZVKra2tFX+k0+mNTfDV1NSU0mPHYDCEQqGGhgbq\nCmtra+XR84qu4G/fvp0/f36Es79Tb2culyuP7HbI9FaZp+HOHDkn49GV8+fPR0dH1/lKTjVM\np9NhGJaHZdkES5kfDBy7NsHixYufPn3qE7XDxN4Fay0oo6yh7RA4NevC4devX9eZDQMAoM6F\nCxdKSkrWLotGcflnW2DN0oNv3uXNnj3bycnJ0tISazltFysrq5ycHOR/GIbfvXsXHt5wpmDp\nk0OQ66f5C7GbDw6Hk5NZCD3BW7ZsEQgEyyeuQ8zK726S2bK7nUdP095JSUmrV6+u43fKr4Zh\nGG4XlwSY9oQ9J06cOHz4cC/P0S5TlmKtRS4MmrwEh8Nv374dayGAjs/u3bs11LVHjpiMtRCU\nUVNV3x57lsPhhoaGohjVsl3DZrM/ffr06dMnoVD469evT58+VVRUeHh4lJeXp6amFhQUJCQk\nqKioDBw4EGul7YzCwsKTJ0/6Dhxpb9kPay2NgsPhZgctLC0tPXPmDNZa2hzAscOYN2/ezJkz\nR8fUKjgupT32MVQbmb31DGZp6Uopo2Ni2XtYyLlz5758+aIwYYBOyL17954/fz52VKQypYkY\nHBxT6yr3YJEKVXqxNoVNz/5Rc3e8evUqKioKay1tAsSBS01NNTU1zcvLS01NffnypZqa2rZt\n2xgMxvnz55WVlTdt2iSPLpaOzYYNG4RC4apJ6+W6F34fN66jT2ssjPMM09cy3L59e5NzJTsb\ncg9Q/OPHj5cvX+Lx+AEDBhga1g3h8fDhw58/f4o/Wltbi4NMSqHDBCim0+n9+/f/XvxvxMDv\nudgAACAASURBVKk8fYs+DZZpvwGKJSn7/ObgOIewiRNPnjwp3SAIUIwJ8ohjh2K1Q8276728\nvB7mPrqZ/l1Hu4mwW8h1q6ys3Jbj2NUP3wrDcNSakLs5l8+fPz927NgWGZRHHDtlZWVs49jJ\nj04boPjDhw+2trYjXUYfX52GbOFwODKEEW4SmQMUS7L3wo7YY6vr3A4gQLF859g9fPhw//79\nvr6+QqFw8eLF0dHRvXv3lixw584dEokknjLS2V6tZs+e/fnz5+C41Ma8ug6DgVVfm+Fjz5w5\n89dff/Xt2xdrOW2RFy9e3L17l8lk9u7dOygoqM4DOC8v7+rVq+KPXbp0WbBggcI1tmlycnKy\nsrLCxi5s0qtrv+BwuNhVRz98eRkRETFgwABTU1OsFQE6GtHR0RAMrZos3+46tJgZMDv+ws4N\nGzaEhISAiFpi5OvYpaSkzJs3z83NDYIgHR2dEydObN26VbIAg8EICAgYMmSIXGW0TVJTU8+c\nOeMwapr9yClYa1EEXvM3fsi6vGTJkrt372Ktpc3x+vXr7du3z5o1y8DA4Ny5c79+/aoz3FZc\nXEwikSZM+E+AQ3m8QLdrYBheuXIlhaIyY9JKrLXIF3Wq1tbo09PnD5k0aVJ2dnZnexkGyJVn\nz55dvHhxvNdkK+OeWGtpFmrK1PnBSzamrktPTw8NDcVaTltBjh5uRUVFWVmZo6Mj8tHR0fHj\nx498Pl+yDIPBgGH43r17mZmZ//77r/zEtDWKiooWLFigY2Lpv3I/1loUhI6J5aCwRVlZWadP\nn8ZaS5vj6tWro0eP9vLy6tOnz9KlS3NycqqqqiQLMBgMAwMD6/8HdNXUIS0t7fHjx5PHLdHT\n6YK1Frljb+sSMXVtbm5uY7kyAQDZWLFiBYlIWjlpHdZCWkDkqPm6Gnrr168XCoVYa2kryLHH\nrqqqSklJSTw5TEtLSyQS1dTU6Or+d6I9g8E4ffq0m5sbh8M5duxYRESEp6dnfVMCgYDB+G9u\nA6FQSKPR0NKJXA3oGpSeiF0kEk2aNInJYofsOyrCE6WvcUMmQaK4Dg6GYRRvgBYFmRw4bfnb\nW+cXLFjQr1+/Ll0afgAj02C5XC6K8thsNooGYRiW4WpRVVVVUlJq7NvCwkJxnD9NTU1dXd3C\nwsIBAwaIC9DpdC6Xm5ycXFVVZWpqOmrUqMY67WQ7uTAMi0QitC4M5CSiaBCSet1WV1cvW7bM\nQK/bjLAVzZw0jBQTiURoDd8gBlGcsizd4Kwpqx88vrF+/foRI0bY2to2x6BIJELxjIjloX6K\n8Xi8YpaR8Xg8KecLSTOAVrshiZzMtl7wjRs3srKy5gQtNNI1rnNa5eEzIW1p6y1TSMoLxyyN\nProyJSVl0qRJUBuu4caQzSwej2/smSLfoVikIpC7tMHw3/v27VNTU0OmE1lZWSEZQtrj4tAW\nkZiY+OjRo8EzVna1ccJai0IhKasFRCedmec7c+bMK1euSHF0OhsMBkNyErqamprkmwwEQWQy\n+d9//3VxcVFTU7t8+XJ+fv6OHTvqD8PBMFxdXS2bBtQbLDabjW76ncYObdasWSUlJTtiz+Mg\nQov2iPoho555ScrhrFuWOGWuy5QpU27duoXVrcThcNCNvVJdXa2iooLiWjEpII+nxr4Vu/6o\n7xd5iZKHWagVgrlc7ooVK7Sp2lHjVtWpFsQDQ0FiQ6BiebpfxOG/92/cuHHMmDEkEklONYzQ\nLi4JOTp22traQqGQTqcjIZWrqqoIBEKdpR+SS3gsLS0ZDAaLxaq/zIpIJEr+UB6rYtE1KGVV\n7JcvX+Li4gyt7YbNjSUoNb1CDVkVi+KcKnRXxVrdPDc8ftXFbWmlts7NKW/t4j00MvreoZg1\na9YkJibWryV5rIpVVlZu46tiiUSipJPBZrPrLFCNjIwU/29tbT1p0qSPHz/a2NjUNyXbKkUe\nj0ckElHsvmKxWCQSCUWHg81mN3gSDx48eOXKlSD/6V5DRjffmvbfiYZndxXuusnr2h0VeUjT\njOKMN4FAAMOwlAq07NFn7ozYPYeWHzx4cPXq1U0a5PP5eDweRYVMJlNJSQnFhcDIKUZraXaT\nSG9U+Xw+ig2RJFwuVx5mWyl4x44dX7582T3/gK5m3dhVAoFAHidFbfsUfFUJa+c9FEwRqcsm\nrInaP/fUqVPz5s2TUw3zeDyRSNQuLgk53kK6urpGRkZPnjwZNmwYBEF5eXk2NjaS1weDwdi8\nefPixYv19fUhCPrw4YO2tjaKi+fbICKRaMaMGVweP3hjSnO8urYPmcXQ/reIyG3BW/vQiHV/\nvr4/cuSIqqrq7t27O3wHbXPo0qVLWVkZMqbG5/OrqqrqDFUj77VIXVEoFGVl5Qb7cnA4nGwN\nhFAoJJPJKIY7YbFYSkpKKLZWDbZ96enpK1as6GXVb/WShBaJV2LWkkuKiLBIhNIhI90/KD7/\nkCEq6QanTojKenB5+/btoaGhTQ7IIvJQDHfCZDKJRKK8TzFAAbx9+3bz5s39ezpP9ZupsJ3i\nq0sJFb/QsjbJe9r+9J1xcXHTpk1Dy2b7Rb7Lg2fMmJGcnHz8+PHExMS///4bqfH09HRkzq+a\nmpqBgcHatWvPnTt35MiRo0ePSnZLdEj279+fm5vrFr7K0Noeay2YgcPjQzaftHYP2Lt37/Tp\n0+WRXrrd4ezsfOfOHeRZnpWVpaGhYWFhweVyS0tLkQJ//fWXOMD606dPORyOhYUFZnLbBsnJ\nyRMmTOhqaJaw/RqFoojBu7YGHk/YsOoYDsJPnz4d9VFgQCeBxWJNnDgRB+MPRCXjce01YogS\nUWn15JjS0tK9e/dirQV75HsWnZycduzYoamp2bVr1/379yOPIktLS/FS2UWLFs2fP59EIhkb\nG+/Zs2fQoEFy1YMtnz9/Xr16taG13ZBZa7DWgjEEJdL43Rft/CelpqZ6eHj8+oXae1s7ZfTo\n0QQCYfbs2StWrDh9+vSSJUtwONy7d+9mz56NFIiMjLx7925UVNSqVavi4+MXL17cdkK5Kh4G\ngxERETFr1ixTY+tj+7P1dLtirQgzzE17zpu54fnz51u2bMFaC6D9AcPwrFmzCgoKNkfuai8h\nThojeOg42+52O3bsqKysxFoLxsh9NoOxsbGxsbHkljqZ4Pv27dsZItYKBIKpU6dy+YIOMwjb\nSghKpOBNJ/R69M5KWOfg4JCSkuLv74+1KMygUCibNm0qLi5ms9lmZmbIeJmlpWVcXBxSwNLS\n8siRI8XFxSKRqFu3bihObGp3XL58ecmSJT9+/PD2GBuzMllNVR1rRRgzZXzU3ZzLGzdu9PPz\nE78zd1rKy8vr9NlER0eDuI+NsXHjxjNnzkwcPmW63yystbQWPA4fG741eI3vli1bkpOTsZaD\nJe2137XdsXnz5ry8vKGR0Z15ELYOOBzOPXzV1KTbXBxp5MiRUVFRnXxY1tjY2MrKSuy0UanU\nPn3+m5KEQCCYmZl1796903p1Hz588Pb2Dg4OZtA5W9ef3rnxPPDqIAjC4wmb1p5QIpInTZrE\nZDKxloMxVVVVxcXFUyXotPdLkxw/fjwmJmawrfvuBQex1oIOHv2GeQ/wS0lJefXqFdZasAQ4\ndoogNzd348aNpg6ubuEdLSx+hanVk6AZDD3Zo8KaO3nMvfDK0tV3z549rq6uxcXFKMoDdAx4\nPN6WLVvs7e3v3cueMj7q2plPfsMntsYg29Ku3G+GUK2DDGebdLNYsXjfx48f58+fj7UWjGEw\nGOrq6tYSgExTDXLz5s2IiAhrk16notPJSmTFC+A6+XLcxqBudkvkbiJeKSIiojPPOiXExMRg\nraHFoJuTGElFjO7aLjKZLF7sWVZW5u3tzYMIUw7fUlZvcf5gJKwoimEjkKCsaK1FpekbvRvk\nw9fRb41BkrKqre9EkrLqwyunz5w57ezs3L07OkEoRCIRh8NBMe4G6rne2wiohztBsdqLiop8\nfX0vXLhg18clYUdGgPckEqm1tz+7i3mlkw9BXROtGwH1cCfNWRUrSS8rhx/FXy5cOmlkZNTg\ngCzq4U7YbDa64U5Qadg/ffr08eNHBoNx/fr1jx8/du3aVbbgRFwuV06xLdB9folpkeCCggJf\nX19NFe2MbXf0tZrIrSyncCcci/5c2yGoW9aiakMw7uw/p3E4nIeHB4qWUXcVxKB+SSgoYlCn\nhcfjhYaG/i4pmbjvb80uIA1Uo+BwONdpfxnZ9D+3LNTf3z8lJWX8+PFYiwJgT05OTnBwcE1N\n7aLIrdPD/gK9L1KIXp74qfD1ggULevXq5erqirUczBCJRKqqqn5+fk+ePFm8eHF8fDwSUasO\nVVVVTUaFraiokIdCOZltpuWampqRI0cKuIKU6LNaqjosFqvJnzSnjGzIw3Jk4PysF5lxcXEW\nFha+vr7oGm87lwSJREKCBNcHOHZyBIbhiIiInJwcz3kbrN0DsJbTDjB38ghPfXh6gX9YWFh5\nefnChQuxVgTAksuXL0+YMEFFWT15393e1k7Aq5OOirJa/Na/wyKcR48e/eDBg5492/ciR9nw\n8PAQ99P06dPny5cvWVlZDb4lkslkKWkPkIRjZDL6Y5Q8Hk8eXf58Pl8kEjVH8IwZM75//564\nLMXesl9zLAuFQhQ7eiXNQhAkD8s4IS7prxO+y4dGRkaeOXMGiaTbetraJSGlsxM4dnLkr7/+\nSk1NtfOfNGTWWqy1tBu0unWfcSwnbVHgokWL6HT6mjWdPTRMp+X8+fNhYWEGesaJezJNulmg\nm7qqo2Js1GPflisRS7y9vb1zcnLMzMywVoQBko6IpqZmY2nZpAfDr62tFQgEqOeYgSCIRqPJ\nw2xtbS0Mw01aTk5Ovnbt2gz/yFCvsGZaRqZVtFpgXZCRTXlY5nA43QyML2++OXLFsNDQ0ISE\nhIiIiNabRZJUtYtLArwBywUYhpcvX75r1y4rN7+g2KMguUKLUNHSm3bkrmk/t7Vr165fvx5r\nOQAMuHjxYlhYWFdDs5QDOSbdOnso5hbh0Nd118YLpaVlQ4cO/fLlC9ZyFE1qamp0dDQyxlpR\nUfH69WvJpeWdnO/fv0dFRVl2s9oUsQNrLXLHwsjy1q77lkbWkZGR4eHhnerNEDh26MPj8cLD\nw3fs2GHp6jt+10UQtU4GyKrqkw/+032A54YNG0CnXWcjIyNj4sSJXQxMj8bfM9DvhrWc9oe7\ni//uuPTSkjJXV9fHjx9jLUehBAcH8/n82bNnR0dHL1q0yMfHx8nJCWtRbQJkahCLyTq47DiF\n1ClSt5kYmGXufhDsHnrs2LEhQ4aIE/l0eHBSJhm0HQQCgWR8JnQX6SCLotEy+O+//06bNu3p\n06d9RowPWJfUeq8O9dV2IpEIh8Oh1YnY9+bZkTsWn9hzpbjvQFQMwjAMwzAym4rPYV1YNqbo\nSdbChQs3bNggmzWBQIDiYkCZT4eKigqKS5ulINuLKZfLVVJSQmsSm0gkYrFYZDJZhkO+e/fu\nmDFjNDX0k/fd7WpoJt7O5/NRrEDdC/FdUjd+OvyYh1J3IOr3qUAggGG4NYf8/HXOsnVjOVzm\nzp07Z86cyePx8Hg8Wg0dkitWSUkJxSlHLBZLRUWFSCS2XmRJSQmdTu/SpQuVSpXNAjIUq62t\n3Uol9aHRaPJIG9Ok4GPHjoWHhy8Iidowc1uLLMtpGa/yukB81W/mgWeoW64vOOHinphjq0xM\nTTIzM2VOxogMxWpptTi0RZOgfkm0jzl2RCJRcvVHTU1NY4tBZKC2tlYkErXeIAzDKSkpS5cu\nramlD1u42W3GSlScJ2RKLIlEQssVQzfqAUkoUKbTSDgIrfZd8nlGJpMnJWScXRIcHx8vEon2\n7t3bUs9DIBDU1NRQKBS01qgLBAIulyt9dk6DgOH45pCVlTV27Fiqmnbi7kxJrw518DwOkUHD\niYTy2wXmONq5px58uGzdmAULFty6dWvnzp1GRkZYi1IQXbp06dJF9uCaHY/fv38vXbq0h5HF\n6ikxWGv5DzgOA8+sUcy+5ocsMetiPnPb5CFDhty9e7fDrytqH44dVO+5iPpjspUGHz58uHz5\n8kePHmkb95i8/ZKFM5rhcyC0j7eNOxmS8pTIyhP2XrmwYnxCQkJ1dfWxY8daNNkWMYViD6XY\nICrW5IFs79ZIcD60unOEQiGLxSISiS0Sc/Xq1XHjxlFVtY7GZ5mZWNdXiGI/PXIGCQQCWjZF\nIhEMwygqbGkcuwbpbtbzzJEnuw78df7Koezs7OXLl0dFRcnwWlIfpMeupadYOnLqGQJAEBQR\nEVFbU3tq9cVOMghbnwCXoLT1lyfGBnt6et67d8/aum4L05EAc+xaBQzDmZmZw4cPd3V1ffr8\n5ZCItfPS3xjbu2Ctq0NBJJHH7bzgMGra6dOnR4wYARI8d0gOHToUEhKirWlw/MD9+l4dQGYo\nFJU1Sw+kHMgx6mIRHR1tbm4eFxcnvzhqgDbI4cOHr1+/Pitw7mBbd6y1YIlHv2FpMZerK2ke\nHh4fPnzAWo4cAY6djLDZ7KSkJFtbWx8fn/sPHjqPn7co44vXvI1KFBWspXVA8ARiUOwxj9nr\ns7Oz+/fv/+TJE6wVAVCDyWTOmDFj7ty5FuZ9Th5+ZNLNEmtFHRCHvq6nDudtiT6lQTVYt26d\niYnJzJkz3759i7UugNx5+fJlVFRULzObmBmbsdaCPUMdvNJiLtOqatzd3fPy8rCWIy/azVBs\n26G2tjY+Pj4+Pv7Pnz9U3S6ec2OdQueoaulhrauDg8PhPObEGFjaXo6e4erqunLlylWrVskj\nuwtAkdy6dWvevHlfv34dMWx87MpkZQoKo4SABsHj8SO8xvsNn/joya1T5/cdO3bs2LFjvr6+\n0dHRzs7OWKvDAGR2dWPfIkPhNBoN9f0KhUJ5mEWST9axXFpaGhgYSIAIiUtTIRFOtpVVSIZA\nlGT+F6QLRB6WpQse2Gvw2fVXpmwOHTp06LZt26ZOndpMs0KhEIbhtnNJKCkpNTaton2siq0D\nuktImr/Uhc/nHzx4cOPGjZWVlQaWtoOnLrMdMb7+ulckVyxa8vh8Pp/PV1FBrSOQz+cTCAS0\n1j/qv8nvmXn+1fh5td3Qye7a5CLl6l/fLq2d+uNlrqmpaWxs7MSJE6WsHBQIBDQaTVVVFcXF\nExwORx4xKrGFwWBQKBQU59hVV1dLr/bs7OxNmzbduXNHQ117+cI9I0dMkW4T3QlYlOdZqo+u\n0yatEGo1kGxKBlBPIoyEb0XxkOusmir68TH17K5rN0/w+bzg4ODt27f36NGj+dZgGK6srFRW\nVkZlxh6CnJaLykYHWBVbWlo6bNiwjx8+nopOH+Ese+ojOc19xF87hGPUCCesRN1ycwQX/vtl\n0oaQTz8/+Pj47Ny5sznBDtvRqljg2DX3bGVlZc2bN+/jx4963Xt5zdvYyyu4sRn0ncqxEwgE\nPB6PQqGgaBBqas44DMOv/k65e2Bdbfm/RkZG4eHhkyZNsrRsYAgPOHbNRGGO3Z8/f9LS0o4c\nOVJQUEAmUUICZ82eHq2podukTXSfLsh1q6ysjNY6mHbn2CGUlP08fCz2739SSCTS8uXLV61a\n1cw9AsdOZhTj2L1//z4wMPB70ff9S5ImDGvirUk6cnLsUL/CxTRTMJvL2piy7si1gyJY5Ofn\nN2PGjBEjRkh5UrQjxw7MsWua8vLyyZMne3l5ff+31H/l/nnpb3oPC2nL6yI7PDgcziFo+uKM\nwoA1B3lk9Q0bNlhZWTk6OsbFxRUUFGCtDtAAbDb77NmzAQEBRkZGixYtKiupjJy27saFbysX\nxzfHqwPIiS4GJrGrjp5NftbTst+GDRv69u17584drEUBWsuxY8ecnZ1//yo5tupMK726Dowy\nWWVz5K5Hh1+P85x0O/NOcHCwnp7eqFGjkpKSSkpKsFbXKkCPnTQ3XCAQJCYmrlu3rrq6uq9/\n2Iilu9R0DJo0CHrsWmkQamGUh+I3eQU3z767c7G27BcEQd27dw8KCho1atTgwYORKRGgx65J\n5NRjp6SklJWVdebMmUuXLtHpdApFxcN1VIDPpMHOPnh8yyIpgh67ViI9gCUMw5euJe89vLKm\ntio0NHT79u2mpqZSrIEeO5mRa49dVVXV/Pnzb926ZdnN6uiqM7bd7VpvuaP22ElCY1Rfe3jl\nxuOr919lsbksPB7v7u4eHh4eGhoqvqnbUY+d3B07oVBYVFSEx+PNzMwafPY3WaA+CnDsYBi+\ncuXK2rVr379/r2veM2BVQndnr2YaBI5dKw1CMoXvgmH49/tnH7KufMy+Wl5YAEGQrq6un5/f\n0KFD/fz8DAya9sibKU9+jt2vX79YLJaJiUljrVKTBWQGXceORqP9/fff2dnZGRkZFRUVeDzB\n2dHT3zvMa0iwqoqMmQCAY9dKmhOZvKq6fM+hFVf/SSWTybNnz162bFljMY3buGNXWVn5588f\nQ0NDmQ22O8fu69evu3fvPnr0qIAviAict27aRmUyOk+NzuDY/fe3PE7um+yruZf+zr1Uy6wx\nNDRcvHjx3LlzqVQqcOz+Q1FR0caNG7W1tUUiEZvN3rh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59zc3NXhEzT6HSdhbUiKJRs6V31fyLCfmzRqOnixYsrKirojxIA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\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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Bayesian: Posterior density plots\n", + "# ============================================================\n", + "if (exists(\"all_draws\") && ncol(all_draws) > 0) {\n", + " # Plot densities for key parameters\n", + " density_params <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\")\n", + " density_display <- c(\n", + " paste0(\"a\", 1:n_med, \": SNP->\", MED_SHORT),\n", + " paste0(\"b\", 1:n_med, \": \", MED_SHORT, \"->CAA\"),\n", + " \"c': Direct\",\n", + " paste0(\"ind\", 1:n_med, \": via \", MED_SHORT),\n", + " \"Total indirect\"\n", + " )\n", + " \n", + " avail <- density_params[density_params %in% colnames(all_draws)]\n", + " avail_display <- density_display[density_params %in% colnames(all_draws)]\n", + " \n", + " if (length(avail) > 0) {\n", + " df_dens <- data.frame()\n", + " for (k in seq_along(avail)) {\n", + " vals <- all_draws[, avail[k]]\n", + " vals <- vals[!is.na(vals)]\n", + " df_dens <- rbind(df_dens, data.frame(\n", + " parameter = avail_display[k],\n", + " value = vals\n", + " ))\n", + " }\n", + " \n", + " p_dens <- ggplot(df_dens, aes(x = value, fill = parameter)) +\n", + " geom_density(alpha = 0.5) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"red\") +\n", + " facet_wrap(~parameter, scales = \"free\", ncol = 3) +\n", + " labs(title = \"Bayesian Posterior Distributions\",\n", + " subtitle = paste0(\"chr19_44938650_G_C | N = \", N_total),\n", + " x = \"Parameter Value\", y = \"Density\") +\n", + " theme_minimal(base_size = 10) +\n", + " theme(legend.position = \"none\")\n", + " \n", + " ggsave(file.path(bayes_dir, \"bayesian_posteriors_chr19_44938650_G_C.png\"),\n", + " p_dens, width = 14, height = 12, dpi = 150)\n", + " ggsave(file.path(bayes_dir, \"bayesian_posteriors_chr19_44938650_G_C.pdf\"),\n", + " p_dens, width = 14, height = 12)\n", + " print(p_dens)\n", + " }\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cf36aec2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:35.821592Z", + "iopub.status.busy": "2026-03-31T20:30:35.819314Z", + "iopub.status.idle": "2026-03-31T20:30:37.568885Z", + "shell.execute_reply": "2026-03-31T20:30:37.566759Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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9etW3fHHXcoPejxeFavXn3w4MG6dev279+/d+/ewrvUsrVly5bNmzdnZWX169ev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0ymggAFDGlTuiz88XtzVunTRlEADgKJOr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\nkdI6OS/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3/ZToMGotUPt9Ne4uYJF2MX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dVgPn46rOz/US6vo6uj5Iwg/BsalS+9SWLQ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gD33EimADvAXWnfBNhyMy3UvA7H1W1SQ5k1ImZEZa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\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EPOks+QfIy6y9D1gl0BEGTUA0WL+WNT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hVRwiY90eEVUqlampqalogl2rulJfaRPR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\nS7rdLhE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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Bayesian: Trace plot for a-paths\n", + "# ============================================================\n", + "if (!is.null(fit_bayes)) {\n", + " mcmc_list <- tryCatch(blavInspect(fit_bayes, \"mcmc\"), error = function(e) NULL)\n", + " \n", + " if (!is.null(mcmc_list)) {\n", + " pt_full <- parTable(fit_bayes)\n", + " \n", + " # Find a1 parameter\n", + " a1_row <- pt_full[pt_full$label == \"a1\" & pt_full$op == \"~\", ]\n", + " if (nrow(a1_row) > 0) {\n", + " a1_plabel <- a1_row$plabel[1]\n", + " \n", + " # Build trace data from each chain\n", + " trace_df <- data.frame()\n", + " for (ch in seq_along(mcmc_list)) {\n", + " chain_mat <- as.matrix(mcmc_list[[ch]])\n", + " if (a1_plabel %in% colnames(chain_mat)) {\n", + " trace_df <- rbind(trace_df, data.frame(\n", + " iteration = 1:nrow(chain_mat),\n", + " value = chain_mat[, a1_plabel],\n", + " chain = paste(\"Chain\", ch)\n", + " ))\n", + " } else if (a1_row$free[1] > 0 && a1_row$free[1] <= ncol(chain_mat)) {\n", + " trace_df <- rbind(trace_df, data.frame(\n", + " iteration = 1:nrow(chain_mat),\n", + " value = chain_mat[, a1_row$free[1]],\n", + " chain = paste(\"Chain\", ch)\n", + " ))\n", + " }\n", + " }\n", + " \n", + " if (nrow(trace_df) > 0) {\n", + " p_trace <- ggplot(trace_df, aes(x = iteration, y = value, color = chain)) +\n", + " geom_line(alpha = 0.5) +\n", + " labs(title = \"MCMC Trace Plot: a1 (SNP -> APOC4/APOC2_AC)\",\n", + " subtitle = paste0(\"N = \", N_total, \" | 2 chains x 2000 samples\"),\n", + " x = \"Iteration\", y = \"a1 value\", color = \"\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + " \n", + " ggsave(file.path(bayes_dir, \"bayesian_trace_a_chr19_44938650_G_C.png\"),\n", + " p_trace, width = 10, height = 5, dpi = 150)\n", + " ggsave(file.path(bayes_dir, \"bayesian_trace_a_chr19_44938650_G_C.pdf\"),\n", + " p_trace, width = 10, height = 5)\n", + " print(p_trace)\n", + " }\n", + " }\n", + " }\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "d730cba1", + "metadata": {}, + "source": [ + "### Bayesian Interpretation\n", + "\n", + "Bayesian SEM provides **posterior distributions** for all parameters. Key advantages:\n", + "- Direct probability statements: P(indirect > 0) tells us the posterior probability that the indirect effect is positive\n", + "- No reliance on asymptotic normality for the product a*b\n", + "- Natural handling of missing data via data augmentation in Stan\n", + "\n", + "**Convergence:** Check that trace plots show good mixing (chains overlap) and Rhat values are close to 1.0.\n" + ] + }, + { + "cell_type": "markdown", + "id": "c36731f4", + "metadata": {}, + "source": [ + "## 9. Cross-Method Summary\n", + "\n", + "Combining results from all 4 methods to assess convergence of evidence.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "8d9b8004", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:37.576232Z", + "iopub.status.busy": "2026-03-31T20:30:37.573972Z", + "iopub.status.idle": "2026-03-31T20:30:37.630761Z", + "shell.execute_reply": "2026-03-31T20:30:37.628091Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== All Methods Summary ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " exposure method label est se ci_lower\n", + " chr19_44938650_G_C FIML a1 0.250397 0.05669 0.139281\n", + " chr19_44938650_G_C FIML a2 0.246957 0.05678 0.135677\n", + " chr19_44938650_G_C FIML a3 0.283800 0.06288 0.160554\n", + " chr19_44938650_G_C FIML a4 0.210032 0.04948 0.113055\n", + " chr19_44938650_G_C FIML b1 -0.791316 1.07599 -2.900215\n", + " chr19_44938650_G_C FIML b2 0.730878 1.07524 -1.376548\n", + " chr19_44938650_G_C FIML b3 -0.008900 0.06776 -0.141698\n", + " chr19_44938650_G_C FIML b4 0.081895 0.05774 -0.031279\n", + " chr19_44938650_G_C FIML cp 0.153605 0.04546 0.064506\n", + " chr19_44938650_G_C FIML ind1 -0.198143 0.27321 -0.733630\n", + " chr19_44938650_G_C FIML ind2 0.180496 0.26889 -0.346516\n", + " chr19_44938650_G_C FIML ind3 -0.002526 0.01921 -0.040171\n", + " chr19_44938650_G_C FIML ind4 0.017201 0.01283 -0.007940\n", + " chr19_44938650_G_C FIML total_indirect -0.002973 0.01626 -0.034851\n", + " chr19_44938650_G_C FIML total 0.150632 0.04293 0.066488\n", + " chr19_44938650_G_C FIML prop_med -0.019736 0.10829 -0.231977\n", + " chr19_44938650_G_C Bootstrap a1 0.250057 0.05666 0.138372\n", + " chr19_44938650_G_C Bootstrap a2 0.246599 0.05696 0.134682\n", + " chr19_44938650_G_C Bootstrap a3 0.282322 0.05907 0.167100\n", + " chr19_44938650_G_C Bootstrap a4 0.210625 0.04855 0.118281\n", + " chr19_44938650_G_C Bootstrap b1 -0.826887 1.01954 -2.838589\n", + " chr19_44938650_G_C Bootstrap b2 0.767364 1.02075 -1.197278\n", + " chr19_44938650_G_C Bootstrap b3 -0.008909 0.07678 -0.153633\n", + " chr19_44938650_G_C Bootstrap b4 0.080916 0.06426 -0.051603\n", + " chr19_44938650_G_C Bootstrap cp 0.155707 0.04712 0.066984\n", + " chr19_44938650_G_C Bootstrap ind1 -0.209439 0.26978 -0.796740\n", + " chr19_44938650_G_C Bootstrap ind2 0.191762 0.26594 -0.296015\n", + " chr19_44938650_G_C Bootstrap ind3 -0.002799 0.02227 -0.047911\n", + " chr19_44938650_G_C Bootstrap ind4 0.017117 0.01463 -0.011478\n", + " chr19_44938650_G_C Bootstrap total_indirect -0.003360 0.01779 -0.038832\n", + " chr19_44938650_G_C Bootstrap total 0.152347 0.04383 0.066172\n", + " chr19_44938650_G_C Bootstrap prop_med -0.107193 2.62635 -0.336577\n", + " chr19_44938650_G_C Bayesian a1 0.249430 0.05861 0.133218\n", + " chr19_44938650_G_C Bayesian a2 0.245975 0.05867 0.129427\n", + " chr19_44938650_G_C Bayesian a3 0.281881 0.06153 0.160548\n", + " chr19_44938650_G_C Bayesian a4 0.209812 0.04774 0.117661\n", + " chr19_44938650_G_C Bayesian b1 -0.877716 1.05769 -2.801246\n", + " chr19_44938650_G_C Bayesian b2 0.818766 1.05737 -1.317939\n", + " chr19_44938650_G_C Bayesian b3 -0.015361 0.06721 -0.141271\n", + " chr19_44938650_G_C Bayesian b4 0.085313 0.05753 -0.034868\n", + " chr19_44938650_G_C Bayesian cp 0.155329 0.04646 0.066242\n", + " chr19_44938650_G_C Bayesian ind1 -0.220209 0.27733 -0.774943\n", + " chr19_44938650_G_C Bayesian ind2 0.202757 0.27316 -0.335725\n", + " chr19_44938650_G_C Bayesian ind3 -0.004103 0.01933 -0.040348\n", + " chr19_44938650_G_C Bayesian ind4 0.018006 0.01323 -0.006372\n", + " chr19_44938650_G_C Bayesian total_indirect -0.003550 0.01693 -0.037883\n", + " chr19_44938650_G_C Bayesian total 0.151779 0.04400 0.066246\n", + " chr19_44938650_G_C Bayesian prop_med -0.029101 0.20409 -0.305375\n", + " ci_upper p_value ci_type n_eff\n", + " 0.36151 1.002e-05 Wald N=1153 (FIML)\n", + " 0.35824 1.364e-05 Wald N=1153 (FIML)\n", + " 0.40705 6.385e-06 Wald N=1153 (FIML)\n", + " 0.30701 2.187e-05 Wald N=1153 (FIML)\n", + " 1.31758 4.621e-01 Wald N=1153 (FIML)\n", + " 2.83830 4.967e-01 Wald N=1153 (FIML)\n", + " 0.12390 8.955e-01 Wald N=1153 (FIML)\n", + " 0.19507 1.561e-01 Wald N=1153 (FIML)\n", + " 0.24270 7.277e-04 Wald N=1153 (FIML)\n", + " 0.33734 4.683e-01 Wald N=1153 (FIML)\n", + " 0.70751 5.021e-01 Wald N=1153 (FIML)\n", + " 0.03512 8.954e-01 Wald N=1153 (FIML)\n", + " 0.04234 1.799e-01 Wald N=1153 (FIML)\n", + " 0.02891 8.550e-01 Wald N=1153 (FIML)\n", + " 0.23478 4.504e-04 Wald N=1153 (FIML)\n", + " 0.19250 8.554e-01 Wald N=1153 (FIML)\n", + " 0.36477 0.000e+00 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 0.36191 0.000e+00 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 0.39887 0.000e+00 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 0.30982 0.000e+00 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 1.13333 4.180e-01 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 2.77923 4.500e-01 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 0.13906 9.280e-01 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 0.20373 1.860e-01 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 0.24603 0.000e+00 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 0.27799 4.180e-01 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 0.76673 4.500e-01 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 0.04152 9.280e-01 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 0.04894 1.860e-01 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 0.03306 8.560e-01 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 0.23494 0.000e+00 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 0.23469 8.560e-01 Percentile N=1153 (Bootstrap FIML, 1000/1000 converged)\n", + " 0.36066 0.000e+00 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 0.35745 0.000e+00 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 0.39951 0.000e+00 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 0.30266 0.000e+00 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 1.25238 4.045e-01 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 2.77301 4.350e-01 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 0.12071 8.120e-01 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 0.19426 1.425e-01 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 0.24586 0.000e+00 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 0.32512 4.045e-01 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 0.74992 4.350e-01 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 0.03539 8.120e-01 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 0.04652 1.425e-01 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 0.02859 8.340e-01 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 0.23801 0.000e+00 Credible N=1153 (Bayesian, 2 chains x 2000)\n", + " 0.23421 8.340e-01 Credible N=1153 (Bayesian, 2 chains x 2000)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Saved: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_neo4/APOE4_2_adj/summary/all_methods_summary.csv \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Cross-method summary table\n", + "# ============================================================\n", + "main_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\")\n", + "\n", + "summary_all <- data.frame()\n", + "\n", + "# FIML\n", + "if (exists(\"key_pe\")) {\n", + " fiml_rows <- key_pe[key_pe$label %in% main_labels, ]\n", + " if (nrow(fiml_rows) > 0) {\n", + " summary_all <- rbind(summary_all, data.frame(\n", + " exposure = SNP_COL,\n", + " method = \"FIML\",\n", + " label = fiml_rows$label,\n", + " est = fiml_rows$est,\n", + " se = fiml_rows$se,\n", + " ci_lower = fiml_rows$ci.lower,\n", + " ci_upper = fiml_rows$ci.upper,\n", + " p_value = fiml_rows$pvalue,\n", + " ci_type = \"Wald\",\n", + " n_eff = paste0(\"N=\", N_total, \" (FIML)\"),\n", + " stringsAsFactors = FALSE\n", + " ))\n", + " }\n", + "}\n", + "\n", + "# Bootstrap\n", + "if (exists(\"boot_summary\")) {\n", + " boot_rows <- boot_summary[boot_summary$label %in% main_labels, ]\n", + " if (nrow(boot_rows) > 0) {\n", + " summary_all <- rbind(summary_all, data.frame(\n", + " exposure = SNP_COL,\n", + " method = \"Bootstrap\",\n", + " label = boot_rows$label,\n", + " est = boot_rows$boot_mean,\n", + " se = boot_rows$boot_se,\n", + " ci_lower = boot_rows$ci_lower,\n", + " ci_upper = boot_rows$ci_upper,\n", + " p_value = boot_rows$p_value,\n", + " ci_type = \"Percentile\",\n", + " n_eff = paste0(\"N=\", N_total, \" (Bootstrap FIML, \", n_converged, \"/\", B, \" converged)\"),\n", + " stringsAsFactors = FALSE\n", + " ))\n", + " }\n", + "}\n", + "\n", + "# Bayesian\n", + "if (exists(\"bayes_results\") && nrow(bayes_results) > 0) {\n", + " bayes_rows <- bayes_results[bayes_results$label %in% main_labels, ]\n", + " if (nrow(bayes_rows) > 0) {\n", + " # For Bayesian, \"p_value\" = 2*min(PP, 1-PP) as a two-sided analog\n", + " bayes_p <- 2 * pmin(bayes_rows$pp_direction, 1 - bayes_rows$pp_direction)\n", + " summary_all <- rbind(summary_all, data.frame(\n", + " exposure = SNP_COL,\n", + " method = \"Bayesian\",\n", + " label = bayes_rows$label,\n", + " est = bayes_rows$posterior_mean,\n", + " se = bayes_rows$posterior_sd,\n", + " ci_lower = bayes_rows$ci_lower,\n", + " ci_upper = bayes_rows$ci_upper,\n", + " p_value = bayes_p,\n", + " ci_type = \"Credible\",\n", + " n_eff = paste0(\"N=\", N_total, \" (Bayesian, 2 chains x 2000)\"),\n", + " stringsAsFactors = FALSE\n", + " ))\n", + " }\n", + "}\n", + "\n", + "cat(\"=== All Methods Summary ===\\n\")\n", + "print(summary_all, digits=4, row.names=FALSE)\n", + "\n", + "write.csv(summary_all, file.path(summ_dir, \"all_methods_summary.csv\"), row.names = FALSE)\n", + "cat(\"\\nSaved:\", file.path(summ_dir, \"all_methods_summary.csv\"), \"\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "418338c6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:37.636714Z", + "iopub.status.busy": "2026-03-31T20:30:37.635121Z", + "iopub.status.idle": "2026-03-31T20:30:37.686194Z", + "shell.execute_reply": "2026-03-31T20:30:37.683264Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Display Table: Estimate [95% CI] ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label Path FIML\n", + " a1 a1: SNP->APOC4/APOC2_AC 0.2504 [0.1393, 0.3615]\n", + " a2 a2: SNP->APOC2_AC 0.2470 [0.1357, 0.3582]\n", + " a3 a3: SNP->APOC1_Mic 0.2838 [0.1606, 0.4070]\n", + " a4 a4: SNP->APOE_Mic 0.2100 [0.1131, 0.3070]\n", + " b1 b1: APOC4/APOC2_AC->CAA -0.7913 [-2.9002, 1.3176]\n", + " b2 b2: APOC2_AC->CAA 0.7309 [-1.3765, 2.8383]\n", + " b3 b3: APOC1_Mic->CAA -0.0089 [-0.1417, 0.1239]\n", + " b4 b4: APOE_Mic->CAA 0.0819 [-0.0313, 0.1951]\n", + " cp c': SNP->CAA (direct) 0.1536 [0.0645, 0.2427]\n", + " ind1 ind1: via APOC4/APOC2_AC -0.1981 [-0.7336, 0.3373]\n", + " ind2 ind2: via APOC2_AC 0.1805 [-0.3465, 0.7075]\n", + " ind3 ind3: via APOC1_Mic -0.0025 [-0.0402, 0.0351]\n", + " ind4 ind4: via APOE_Mic 0.0172 [-0.0079, 0.0423]\n", + " total_indirect Total indirect -0.0030 [-0.0349, 0.0289]\n", + " total Total effect 0.1506 [0.0665, 0.2348]\n", + " prop_med Prop. mediated -0.0197 [-0.2320, 0.1925]\n", + " Bootstrap Bayesian\n", + " 0.2501 [0.1384, 0.3648] 0.2494 [0.1332, 0.3607]\n", + " 0.2466 [0.1347, 0.3619] 0.2460 [0.1294, 0.3574]\n", + " 0.2823 [0.1671, 0.3989] 0.2819 [0.1605, 0.3995]\n", + " 0.2106 [0.1183, 0.3098] 0.2098 [0.1177, 0.3027]\n", + " -0.8269 [-2.8386, 1.1333] -0.8777 [-2.8012, 1.2524]\n", + " 0.7674 [-1.1973, 2.7792] 0.8188 [-1.3179, 2.7730]\n", + " -0.0089 [-0.1536, 0.1391] -0.0154 [-0.1413, 0.1207]\n", + " 0.0809 [-0.0516, 0.2037] 0.0853 [-0.0349, 0.1943]\n", + " 0.1557 [0.0670, 0.2460] 0.1553 [0.0662, 0.2459]\n", + " -0.2094 [-0.7967, 0.2780] -0.2202 [-0.7749, 0.3251]\n", + " 0.1918 [-0.2960, 0.7667] 0.2028 [-0.3357, 0.7499]\n", + " -0.0028 [-0.0479, 0.0415] -0.0041 [-0.0403, 0.0354]\n", + " 0.0171 [-0.0115, 0.0489] 0.0180 [-0.0064, 0.0465]\n", + " -0.0034 [-0.0388, 0.0331] -0.0036 [-0.0379, 0.0286]\n", + " 0.1523 [0.0662, 0.2349] 0.1518 [0.0662, 0.2380]\n", + " -0.1072 [-0.3366, 0.2347] -0.0291 [-0.3054, 0.2342]\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Human-readable display table\n", + "# ============================================================\n", + "summary_all$formatted <- sprintf(\"%.4f [%.4f, %.4f]\", summary_all$est, summary_all$ci_lower, summary_all$ci_upper)\n", + "\n", + "display_wide <- reshape(summary_all[, c(\"method\", \"label\", \"formatted\")],\n", + " idvar = \"label\", timevar = \"method\", direction = \"wide\")\n", + "colnames(display_wide) <- gsub(\"formatted\\\\.\", \"\", colnames(display_wide))\n", + "\n", + "# Order rows\n", + "label_order <- main_labels[main_labels %in% display_wide$label]\n", + "display_wide <- display_wide[match(label_order, display_wide$label), ]\n", + "\n", + "# Add readable names\n", + "display_wide$Path <- display_names[display_wide$label]\n", + "\n", + "# Reorder columns\n", + "col_order <- c(\"label\", \"Path\", \n", + " grep(\"FIML\", colnames(display_wide), value=TRUE),\n", + " grep(\"Bootstrap\", colnames(display_wide), value=TRUE),\n", + " grep(\"Bayesian\", colnames(display_wide), value=TRUE))\n", + "col_order <- col_order[col_order %in% colnames(display_wide)]\n", + "display_wide <- display_wide[, col_order]\n", + "\n", + "cat(\"\\n=== Display Table: Estimate [95% CI] ===\\n\")\n", + "print(display_wide, row.names=FALSE)\n", + "\n", + "write.csv(display_wide, file.path(summ_dir, \"summary_display_table_chr19_44938650_G_C.csv\"), row.names = FALSE)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "02bfedb2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:37.691773Z", + "iopub.status.busy": "2026-03-31T20:30:37.690192Z", + "iopub.status.idle": "2026-03-31T20:30:39.026108Z", + "shell.execute_reply": "2026-03-31T20:30:39.023470Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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eX9+/e7du0iQ+9Zrl27BgA9evT4HKvGdPr06aNHjw4fPrxp06YcF8FqqqfFalaB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\nPn1q3GNFRQX9ggctLa22bdvSD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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Summary: All Methods Forest Plot\n", + "# ============================================================\n", + "sfp <- summary_all[summary_all$label %in% main_labels, ]\n", + "sfp$display <- display_names[sfp$label]\n", + "sfp$display <- factor(sfp$display, levels = rev(display_names[main_labels[main_labels %in% sfp$label]]))\n", + "\n", + "p_summ_forest <- ggplot(sfp, aes(x = est, y = display, color = method, shape = method)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper),\n", + " position = position_dodge(width = 0.6), size = 0.5) +\n", + " scale_color_manual(values = c(\"FIML\" = \"#E41A1C\", \"Bootstrap\" = \"#377EB8\", \"Bayesian\" = \"#4DAF4A\")) +\n", + " scale_shape_manual(values = c(\"FIML\" = 16, \"Bootstrap\" = 17, \"Bayesian\" = 15)) +\n", + " labs(title = \"All Methods Comparison: Parallel Mediation Path Estimates\",\n", + " subtitle = paste0(\"SNP -> {APOC4/APOC2, APOC2, APOC1, APOE} -> caa_neo4 | N = \", N_total),\n", + " x = \"Estimate (95% CI)\", y = \"\", color = \"Method\", shape = \"Method\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + "ggsave(file.path(summ_dir, \"summary_forest_all_methods.png\"), p_summ_forest, width = 11, height = 10, dpi = 150)\n", + "ggsave(file.path(summ_dir, \"summary_forest_all_methods.pdf\"), p_summ_forest, width = 11, height = 10)\n", + "print(p_summ_forest)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "bb8b1394", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:39.032112Z", + "iopub.status.busy": "2026-03-31T20:30:39.030405Z", + "iopub.status.idle": "2026-03-31T20:30:42.832530Z", + "shell.execute_reply": "2026-03-31T20:30:42.829608Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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sXFKt2DM2bM6Nevn9Ja8FFBXn+GS3B1dX3y5An1niJJpdKIiAg/P7/Zs2erv5S6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HEEJCoWD2n5FnX1gCwF2NooSzH0c2ehqQ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wiC0Ov1zc3NEydOPHbs2ODBgwEgLi5u8+bN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\n3NxcBoPxxpojAP5XB+fq4QX/70LaI/DQgZk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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Summary: Faceted by Effect Type\n", + "# ============================================================\n", + "sfp2 <- summary_all[summary_all$label %in% main_labels, ]\n", + "sfp2$effect_type <- ifelse(grepl(\"^a\", sfp2$label), \"a-path (SNP->M)\",\n", + " ifelse(grepl(\"^b\", sfp2$label), \"b-path (M->Y)\",\n", + " ifelse(sfp2$label == \"cp\", \"Direct (c')\",\n", + " ifelse(grepl(\"^ind[0-9]\", sfp2$label), \"Specific indirect\",\n", + " ifelse(sfp2$label == \"total_indirect\", \"Total indirect\",\n", + " ifelse(sfp2$label == \"total\", \"Total effect\", \"Proportion mediated\"))))))\n", + "\n", + "sfp2$short_label <- sfp2$label\n", + "sfp2$short_label <- gsub(\"a1\", \"APOC4/APOC2_AC\", sfp2$short_label)\n", + "sfp2$short_label <- gsub(\"a2\", \"APOC2_AC\", sfp2$short_label)\n", + "sfp2$short_label <- gsub(\"a3\", \"APOC1_Mic\", sfp2$short_label)\n", + "sfp2$short_label <- gsub(\"a4\", \"APOE_Mic\", sfp2$short_label)\n", + "sfp2$short_label <- gsub(\"b1\", \"APOC4/APOC2_AC\", sfp2$short_label)\n", + "sfp2$short_label <- gsub(\"b2\", \"APOC2_AC\", sfp2$short_label)\n", + "sfp2$short_label <- gsub(\"b3\", \"APOC1_Mic\", sfp2$short_label)\n", + "sfp2$short_label <- gsub(\"b4\", \"APOE_Mic\", sfp2$short_label)\n", + "sfp2$short_label <- gsub(\"ind1\", \"APOC4/APOC2_AC\", sfp2$short_label)\n", + "sfp2$short_label <- gsub(\"ind2\", \"APOC2_AC\", sfp2$short_label)\n", + "sfp2$short_label <- gsub(\"ind3\", \"APOC1_Mic\", sfp2$short_label)\n", + "sfp2$short_label <- gsub(\"ind4\", \"APOE_Mic\", sfp2$short_label)\n", + "\n", + "p_facet <- ggplot(sfp2, aes(x = est, y = method, color = short_label)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper),\n", + " position = position_dodge(width = 0.6), size = 0.5) +\n", + " facet_wrap(~effect_type, scales = \"free_x\", ncol = 2) +\n", + " labs(title = \"Estimates by Effect Type Across Methods\",\n", + " subtitle = paste0(\"Parallel mediation: chr19_44938650_G_C | N = \", N_total),\n", + " x = \"Estimate (95% CI)\", y = \"\", color = \"Parameter\") +\n", + " theme_minimal(base_size = 11) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + "ggsave(file.path(summ_dir, \"summary_faceted_by_path.png\"), p_facet, width = 14, height = 10, dpi = 150)\n", + "ggsave(file.path(summ_dir, \"summary_faceted_by_path.pdf\"), p_facet, width = 14, height = 10)\n", + "print(p_facet)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "eb49085b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:42.839772Z", + "iopub.status.busy": "2026-03-31T20:30:42.837529Z", + "iopub.status.idle": "2026-03-31T20:30:43.986586Z", + "shell.execute_reply": "2026-03-31T20:30:43.984541Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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YsWhBBbW1sbGxsHB4e1a9cqJ3aOjo6tW7em/3/06FHr1q0JIVZWVg4ODgKBoFmz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hISErV668hRXv4BbM3M7bAQAAdwrB7u7K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\ndCzRarVvvfWW6aXNQgiFQnH//feb3m22jgmKb/NmxMAd52Sol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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Summary: Indirect Effect Focus\n", + "# ============================================================\n", + "ind_labels_focus <- c(paste0(\"ind\", 1:n_med), \"total_indirect\")\n", + "ind_names_focus <- c(paste0(\"via \", MED_SHORT), \"Total indirect\")\n", + "\n", + "sfp_ind <- summary_all[summary_all$label %in% ind_labels_focus, ]\n", + "sfp_ind$ind_name <- ind_names_focus[match(sfp_ind$label, ind_labels_focus)]\n", + "sfp_ind$ind_name <- factor(sfp_ind$ind_name, levels = rev(ind_names_focus))\n", + "\n", + "p_ind <- ggplot(sfp_ind, aes(x = est, y = ind_name, color = method, shape = method)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper),\n", + " position = position_dodge(width = 0.5), size = 0.7) +\n", + " scale_color_manual(values = c(\"FIML\" = \"#E41A1C\", \"Bootstrap\" = \"#377EB8\", \"Bayesian\" = \"#4DAF4A\")) +\n", + " scale_shape_manual(values = c(\"FIML\" = 16, \"Bootstrap\" = 17, \"Bayesian\" = 15)) +\n", + " labs(title = \"Indirect Effect (a x b) Across Methods\",\n", + " subtitle = paste0(\"SNP chr19_44938650_G_C -> Mediator -> caa_neo4 | N = \", N_total),\n", + " x = \"Indirect Effect (95% CI)\", y = \"\", color = \"Method\", shape = \"Method\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + "ggsave(file.path(summ_dir, \"summary_indirect_effect.png\"), p_ind, width = 10, height = 5, dpi = 150)\n", + "ggsave(file.path(summ_dir, \"summary_indirect_effect.pdf\"), p_ind, width = 10, height = 5)\n", + "print(p_ind)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "42749d0d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:43.993711Z", + "iopub.status.busy": "2026-03-31T20:30:43.991474Z", + "iopub.status.idle": "2026-03-31T20:30:45.325400Z", + "shell.execute_reply": "2026-03-31T20:30:45.323324Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Combined panel saved.\n" + ] + }, + { + "data": { + "image/png": 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+8mU+9ZhIeHA4C/v/+XmDUmZ8+ePX78+IgRI5o0acLxEcymejSYzUqgqWzSLCsD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\nQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Summary: Combined Panel\n", + "# ============================================================\n", + "p_combined <- gridExtra::grid.arrange(p_summ_forest, p_ind, nrow = 2, heights = c(2, 1))\n", + "ggsave(file.path(summ_dir, \"summary_combined_panel.png\"), p_combined, width = 11, height = 14, dpi = 150)\n", + "ggsave(file.path(summ_dir, \"summary_combined_panel.pdf\"), p_combined, width = 11, height = 14)\n", + "cat(\"Combined panel saved.\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "aeed0a05", + "metadata": {}, + "source": [ + "## 10. Key Findings" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "e82aaa5b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:45.333542Z", + "iopub.status.busy": "2026-03-31T20:30:45.331821Z", + "iopub.status.idle": "2026-03-31T20:30:45.423254Z", + "shell.execute_reply": "2026-03-31T20:30:45.421100Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "========================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KEY FINDINGS SUMMARY\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "========================================\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- FIML ---\n", + " ind1 : est=-0.1981 [-0.7336, 0.3373] p=0.4683\n", + " ind2 : est=0.1805 [-0.3465, 0.7075] p=0.5021\n", + " ind3 : est=-0.0025 [-0.0402, 0.0351] p=0.8954\n", + " ind4 : est=0.0172 [-0.0079, 0.0423] p=0.1799\n", + " total_indirect : est=-0.0030 [-0.0349, 0.0289] p=0.8550\n", + "\n", + "--- Bootstrap ---\n", + " ind1 : est=-0.2094 [-0.7967, 0.2780] p=0.4180\n", + " ind2 : est=0.1918 [-0.2960, 0.7667] p=0.4500\n", + " ind3 : est=-0.0028 [-0.0479, 0.0415] p=0.9280\n", + " ind4 : est=0.0171 [-0.0115, 0.0489] p=0.1860\n", + " total_indirect : est=-0.0034 [-0.0388, 0.0331] p=0.8560\n", + "\n", + "--- Bayesian ---\n", + " ind1 : est=-0.2202 [-0.7749, 0.3251] p=0.4045\n", + " ind2 : est=0.2028 [-0.3357, 0.7499] p=0.4350\n", + " ind3 : est=-0.0041 [-0.0403, 0.0354] p=0.8120\n", + " ind4 : est=0.0180 [-0.0064, 0.0465] p=0.1425\n", + " total_indirect : est=-0.0036 [-0.0379, 0.0286] p=0.8340\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Significance Summary ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ind1 (APOC4_APOC2_AC): significant in 0/3 methods\n", + " ind2 (APOC2_AC): significant in 0/3 methods\n", + " ind3 (APOC1_Mic): significant in 0/3 methods\n", + " ind4 (APOE_Mic): significant in 0/3 methods\n", + " total_indirect (ALL): significant in 0/3 methods\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Key Findings: Data-driven summary\n", + "# ============================================================\n", + "cat(\"\\n========================================\\n\")\n", + "cat(\"KEY FINDINGS SUMMARY\\n\")\n", + "cat(\"========================================\\n\\n\")\n", + "\n", + "# For each method, report indirect effect significance\n", + "for (meth in c(\"FIML\", \"Bootstrap\", \"Bayesian\")) {\n", + " cat(sprintf(\"--- %s ---\\n\", meth))\n", + " meth_rows <- summary_all[summary_all$method == meth, ]\n", + " \n", + " for (lab in c(paste0(\"ind\", 1:n_med), \"total_indirect\")) {\n", + " row <- meth_rows[meth_rows$label == lab, ]\n", + " if (nrow(row) == 1) {\n", + " sig_marker <- ifelse(!is.na(row$p_value) & row$p_value < 0.05, \" *\", \"\")\n", + " cat(sprintf(\" %-20s: est=%.4f [%.4f, %.4f] p=%.4f%s\\n\",\n", + " lab, row$est, row$ci_lower, row$ci_upper, row$p_value, sig_marker))\n", + " }\n", + " }\n", + " cat(\"\\n\")\n", + "}\n", + "\n", + "# Count significant indirect effects across methods\n", + "cat(\"\\n--- Significance Summary ---\\n\")\n", + "for (lab in c(paste0(\"ind\", 1:n_med), \"total_indirect\")) {\n", + " meth_sig <- sapply(c(\"FIML\", \"Bootstrap\", \"Bayesian\"), function(m) {\n", + " row <- summary_all[summary_all$method == m & summary_all$label == lab, ]\n", + " if (nrow(row) == 1 && !is.na(row$p_value)) return(row$p_value < 0.05) else return(NA)\n", + " })\n", + " n_sig <- sum(meth_sig, na.rm=TRUE)\n", + " n_tested <- sum(!is.na(meth_sig))\n", + " med_name <- if (grepl(\"^ind\", lab)) MED_SHORT[as.integer(gsub(\"ind\", \"\", lab))] else \"ALL\"\n", + " cat(sprintf(\" %-20s (%s): significant in %d/%d methods\\n\", lab, med_name, n_sig, n_tested))\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "6f4d7797", + "metadata": {}, + "source": [ + "## 11. Output File Inventory" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "5142467e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:45.429581Z", + "iopub.status.busy": "2026-03-31T20:30:45.427873Z", + "iopub.status.idle": "2026-03-31T20:30:45.512984Z", + "shell.execute_reply": "2026-03-31T20:30:45.510398Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Output Files ===\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[main_SEM_FIML]\n", + " fiml_all_paths.csv 2,779\n", + " fiml_fit_measures.csv 275\n", + " fiml_forest_plot.pdf 6,876\n", + " fiml_forest_plot.png 108,228\n", + " fiml_mediator_residual_covariances.csv 926\n", + "\n", + "[bootstrap]\n", + " bootstrap_distributions_chr19_44938650_G_C.pdf 7,567\n", + " bootstrap_distributions_chr19_44938650_G_C.png 74,846\n", + " bootstrap_forest_plot.pdf 6,907\n", + " bootstrap_forest_plot.png 107,958\n", + " bootstrap_results.csv 1,816\n", + "\n", + "[MNAR_sensitivity]\n", + " mnar_1d_slices_chr19_44938650_G_C.pdf 7,197\n", + " mnar_1d_slices_chr19_44938650_G_C.png 75,479\n", + " mnar_contour_indirect_chr19_44938650_G_C.pdf 6,894\n", + " mnar_contour_indirect_chr19_44938650_G_C.png 112,215\n", + " mnar_contour_pvalue_chr19_44938650_G_C.pdf 7,852\n", + " mnar_contour_pvalue_chr19_44938650_G_C.png 141,244\n", + " mnar_grid_results.csv 58,545\n", + " mnar_tipping_summary.csv 287\n", + "\n", + "[bayesian_blavaan]\n", + " bayesian_forest_plot.pdf 6,949\n", + " bayesian_forest_plot.png 109,991\n", + " bayesian_posteriors_chr19_44938650_G_C.pdf 71,534\n", + " bayesian_posteriors_chr19_44938650_G_C.png 272,297\n", + " bayesian_results.csv 1,666\n", + " bayesian_trace_a_chr19_44938650_G_C.pdf 26,749\n", + " bayesian_trace_a_chr19_44938650_G_C.png 223,616\n", + "\n", + "[summary]\n", + " all_methods_summary.csv 7,997\n", + " summary_combined_panel.pdf 8,846\n", + " summary_combined_panel.png 180,698\n", + " summary_display_table_chr19_44938650_G_C.csv 1,788\n", + " summary_faceted_by_path.pdf 10,339\n", + " summary_faceted_by_path.png 111,226\n", + " summary_forest_all_methods.pdf 7,521\n", + " summary_forest_all_methods.png 110,804\n", + " summary_indirect_effect.pdf 5,625\n", + " summary_indirect_effect.png 51,816\n", + "\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Output file inventory\n", + "# ============================================================\n", + "cat(\"\\n=== Output Files ===\\n\\n\")\n", + "\n", + "dirs <- c(fiml_dir, boot_dir, mnar_dir, bayes_dir, summ_dir)\n", + "dir_names <- c(\"main_SEM_FIML\", \"bootstrap\", \"MNAR_sensitivity\", \"bayesian_blavaan\", \"summary\")\n", + "\n", + "for (i in seq_along(dirs)) {\n", + " files <- list.files(dirs[i], recursive = FALSE)\n", + " if (length(files) > 0) {\n", + " cat(sprintf(\"[%s]\\n\", dir_names[i]))\n", + " for (f in files) {\n", + " fsize <- file.size(file.path(dirs[i], f))\n", + " cat(sprintf(\" %-60s %s\\n\", f, format(fsize, big.mark=\",\")))\n", + " }\n", + " cat(\"\\n\")\n", + " }\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "8371f93f", + "metadata": {}, + "source": [ + "## 12. Session Notes" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "e5697c64", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:45.519419Z", + "iopub.status.busy": "2026-03-31T20:30:45.517206Z", + "iopub.status.idle": "2026-03-31T20:30:45.546871Z", + "shell.execute_reply": "2026-03-31T20:30:45.544133Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "session_notes.md saved.\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Save session_notes.md\n", + "# ============================================================\n", + "notes <- paste0(\n", + "\"# Session Notes: Parallel Mediation SEM Analysis\\n\",\n", + "\"\\n\",\n", + "\"## Analysis Summary\\n\",\n", + "\"- **Date:** \", Sys.Date(), \"\\n\",\n", + "\"- **Dataset:** Set 27, caa_neo4\\n\",\n", + "\"- **Design:** Parallel mediation (Design 2), unidirectional\\n\",\n", + "\"- **Exposure:** chr19_44938650_G_C\\n\",\n", + "\"- **Mediators:** APOC4_APOC2_AC_exp, APOC2_AC_exp, APOC1_DeJager_Mic_exp, APOE_Mega_Mic_exp\\n\",\n", + "\"- **Outcome:** caa_neo4 (cerebral amyloid angiopathy, neocortical)\\n\",\n", + "\"- **N (FIML):** \", N_total, \" (all subjects with exposure observed)\\n\",\n", + "\"\\n\",\n", + "\"## Methods Run\\n\",\n", + "\"1. FIML SEM (lavaan, missing='fiml', fixed.x=FALSE)\\n\",\n", + "\"2. Bootstrap (1000 replicates, FIML inside each)\\n\",\n", + "\"3. MNAR sensitivity (15x15 delta grid)\\n\",\n", + "\"4. Bayesian SEM (blavaan/Stan, 2 chains x 2000 samples)\\n\",\n", + "\"\\n\",\n", + "\"## Covariate Strategy\\n\",\n", + "\"Strategy A: Covariates-in-model. Each mediator equation has its own covariates:\\n\",\n", + "\"- M1 (APOC4_APOC2_AC): msex_u, age_death_u, pmi_u, ROS_study_u\\n\",\n", + "\"- M2 (APOC2_AC): msex_u, age_death_u, pmi_u, ROS_study_u\\n\",\n", + "\"- M3 (APOC1_DeJager_Mic): msex_u, age_death_u, pmi_u\\n\",\n", + "\"- M4 (APOE_Mega_Mic): msex_u, age_death_u, pmi_u\\n\",\n", + "\"- Y (caa_neo4): educ, apoe4_dose, apoe2_dose, msex_u, age_death_u\\n\",\n", + "\"\\n\",\n", + "\"## Key Findings\\n\",\n", + "\"See summary tables in summary/ directory and Key Findings section in notebook.\\n\",\n", + "\"\\n\",\n", + "\"## Recommended Follow-Up\\n\",\n", + "\"- If specific indirect effects are significant, consider testing serial mediation (Design 3) for causal chains\\n\",\n", + "\"- Compare with individual mediation tests (Design 1 loop) to see marginal vs specific effects\\n\",\n", + "\"- Consider additional SNPs in the APOE region as exposures\\n\"\n", + ")\n", + "\n", + "writeLines(notes, file.path(RESULT_DIR, \"session_notes.md\"))\n", + "cat(\"session_notes.md saved.\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "ea3242be", + "metadata": {}, + "source": [ + "## 13. Post-Analysis Sanity Check" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "9b410ee6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:45.553751Z", + "iopub.status.busy": "2026-03-31T20:30:45.551513Z", + "iopub.status.idle": "2026-03-31T20:30:45.923937Z", + "shell.execute_reply": "2026-03-31T20:30:45.921145Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "========================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "POST-ANALYSIS SANITY CHECK\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "========================================\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Check 1: Sign Consistency Across Methods ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ok] ind1: all methods agree (sign=-)\n", + " [ok] ind2: all methods agree (sign=+)\n", + " [ok] ind3: all methods agree (sign=-)\n", + " [ok] ind4: all methods agree (sign=+)\n", + " [ok] total_indirect: all methods agree (sign=-)\n", + " [ok] cp: all methods agree (sign=+)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 2: Magnitude Discrepancies ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ok] No large magnitude discrepancies detected.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 3: Significance Disagreements ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ok] ind1: all methods agree (not significant)\n", + " [ok] ind2: all methods agree (not significant)\n", + " [ok] ind3: all methods agree (not significant)\n", + " [ok] ind4: all methods agree (not significant)\n", + " [ok] total_indirect: all methods agree (not significant)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 4: Proportion Mediated Range ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [warn] Proportion mediated = -0.020 (FIML) -- outside [0,1], indicates suppression\n", + " [warn] Proportion mediated = -0.107 (Bootstrap) -- outside [0,1], indicates suppression\n", + " [warn] Proportion mediated = -0.029 (Bayesian) -- outside [0,1], indicates suppression\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 5: Very Large Standard Errors ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [warn] Large SE for ind3 (FIML): SE=0.0192 vs |est|=0.0025\n", + " [warn] Large SE for ind3 (Bootstrap): SE=0.0223 vs |est|=0.0028\n", + " [warn] Large SE for total_indirect (FIML): SE=0.0163 vs |est|=0.0030\n", + " [warn] Large SE for total_indirect (Bootstrap): SE=0.0178 vs |est|=0.0034\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 7: Bootstrap Convergence Rate ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ok] Bootstrap convergence: 1000/1000 (100.0%)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 8: MNAR Tipping Point Distance ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 10: Suppression Effects ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [warn] Indirect and direct effects have OPPOSITE signs -- suppression pattern\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 11: Component Path Check ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ok] Component paths checked.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "========================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SANITY CHECK SUMMARY\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "========================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Passed: 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ok] Sign consistency: all methods agree\n", + " [ok] Magnitude consistency: no >3x discrepancies\n", + " [ok] Bootstrap convergence: 1000/1000 (100.0%)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Warnings: 8\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [warn] Proportion mediated = -0.020 (FIML) -- outside [0,1], indicates suppression\n", + " [warn] Proportion mediated = -0.107 (Bootstrap) -- outside [0,1], indicates suppression\n", + " [warn] Proportion mediated = -0.029 (Bayesian) -- outside [0,1], indicates suppression\n", + " [warn] Large SE for ind3 (FIML): SE=0.0192 vs |est|=0.0025\n", + " [warn] Large SE for ind3 (Bootstrap): SE=0.0223 vs |est|=0.0028\n", + " [warn] Large SE for total_indirect (FIML): SE=0.0163 vs |est|=0.0030\n", + " [warn] Large SE for total_indirect (Bootstrap): SE=0.0178 vs |est|=0.0034\n", + " [warn] Indirect and direct effects have OPPOSITE signs -- suppression pattern\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Anomalies: 0\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Sanity checks\n", + "# ============================================================\n", + "cat(\"\\n========================================\\n\")\n", + "cat(\"POST-ANALYSIS SANITY CHECK\\n\")\n", + "cat(\"========================================\\n\\n\")\n", + "\n", + "checks_passed <- c()\n", + "checks_warned <- c()\n", + "checks_failed <- c()\n", + "\n", + "# 1. Sign consistency\n", + "cat(\"--- Check 1: Sign Consistency Across Methods ---\\n\")\n", + "for (lab in c(paste0(\"ind\", 1:n_med), \"total_indirect\", \"cp\")) {\n", + " signs <- sapply(c(\"FIML\", \"Bootstrap\", \"Bayesian\"), function(m) {\n", + " row <- summary_all[summary_all$method == m & summary_all$label == lab, ]\n", + " if (nrow(row) == 1) return(sign(row$est)) else return(NA)\n", + " })\n", + " signs <- signs[!is.na(signs)]\n", + " if (length(signs) > 1 && length(unique(signs[signs != 0])) > 1) {\n", + " msg <- sprintf(\"SIGN DISAGREEMENT for %s: %s\", lab, paste(names(signs), signs, sep=\"=\", collapse=\", \"))\n", + " cat(paste0(\" [!] \", msg, \"\\n\"))\n", + " checks_failed <- c(checks_failed, msg)\n", + " } else {\n", + " cat(sprintf(\" [ok] %s: all methods agree (sign=%s)\\n\", lab, \n", + " ifelse(length(unique(signs)) == 1, ifelse(unique(signs) > 0, \"+\", \"-\"), \"mixed/0\")))\n", + " }\n", + "}\n", + "if (length(checks_failed) == 0) checks_passed <- c(checks_passed, \"Sign consistency: all methods agree\")\n", + "\n", + "# 2. Magnitude discrepancies\n", + "cat(\"\\n--- Check 2: Magnitude Discrepancies ---\\n\")\n", + "mag_issues <- c()\n", + "for (lab in c(paste0(\"ind\", 1:n_med), \"total_indirect\")) {\n", + " ests <- sapply(c(\"FIML\", \"Bootstrap\", \"Bayesian\"), function(m) {\n", + " row <- summary_all[summary_all$method == m & summary_all$label == lab, ]\n", + " if (nrow(row) == 1) return(abs(row$est)) else return(NA)\n", + " })\n", + " ests <- ests[!is.na(ests) & ests > 1e-8]\n", + " if (length(ests) > 1 && max(ests) > 3 * min(ests)) {\n", + " msg <- sprintf(\"Large magnitude discrepancy for %s (range: %.4f to %.4f)\", lab, min(ests), max(ests))\n", + " mag_issues <- c(mag_issues, msg)\n", + " cat(paste0(\" [!] \", msg, \"\\n\"))\n", + " }\n", + "}\n", + "if (length(mag_issues) == 0) {\n", + " cat(\" [ok] No large magnitude discrepancies detected.\\n\")\n", + " checks_passed <- c(checks_passed, \"Magnitude consistency: no >3x discrepancies\")\n", + "}\n", + "\n", + "# 3. Significance disagreements\n", + "cat(\"\\n--- Check 3: Significance Disagreements ---\\n\")\n", + "for (lab in c(paste0(\"ind\", 1:n_med), \"total_indirect\")) {\n", + " sigs <- sapply(c(\"FIML\", \"Bootstrap\", \"Bayesian\"), function(m) {\n", + " row <- summary_all[summary_all$method == m & summary_all$label == lab, ]\n", + " if (nrow(row) == 1 && !is.na(row$p_value)) return(row$p_value < 0.05) else return(NA)\n", + " })\n", + " sigs <- sigs[!is.na(sigs)]\n", + " if (length(unique(sigs)) > 1) {\n", + " msg <- sprintf(\"Significance disagreement for %s: %s\", lab, \n", + " paste(names(sigs), ifelse(sigs, \"sig\", \"ns\"), sep=\"=\", collapse=\", \"))\n", + " cat(paste0(\" [warn] \", msg, \"\\n\"))\n", + " checks_warned <- c(checks_warned, msg)\n", + " } else if (length(sigs) > 0) {\n", + " cat(sprintf(\" [ok] %s: all methods agree (%s)\\n\", lab, ifelse(sigs[1], \"significant\", \"not significant\")))\n", + " }\n", + "}\n", + "\n", + "# 4. Proportion mediated range\n", + "cat(\"\\n--- Check 4: Proportion Mediated Range ---\\n\")\n", + "pm_rows <- summary_all[summary_all$label == \"prop_med\", ]\n", + "for (r in 1:nrow(pm_rows)) {\n", + " pm_val <- pm_rows$est[r]\n", + " meth <- pm_rows$method[r]\n", + " if (!is.na(pm_val) && (pm_val < 0 || pm_val > 1)) {\n", + " msg <- sprintf(\"Proportion mediated = %.3f (%s) -- outside [0,1], indicates suppression\", pm_val, meth)\n", + " cat(paste0(\" [warn] \", msg, \"\\n\"))\n", + " checks_warned <- c(checks_warned, msg)\n", + " } else if (!is.na(pm_val)) {\n", + " cat(sprintf(\" [ok] prop_med = %.3f (%s)\\n\", pm_val, meth))\n", + " }\n", + "}\n", + "\n", + "# 5. Large SEs\n", + "cat(\"\\n--- Check 5: Very Large Standard Errors ---\\n\")\n", + "for (lab in c(paste0(\"ind\", 1:n_med), \"total_indirect\")) {\n", + " rows <- summary_all[summary_all$label == lab, ]\n", + " for (r in 1:nrow(rows)) {\n", + " if (!is.na(rows$se[r]) && !is.na(rows$est[r]) && abs(rows$est[r]) > 1e-8 &&\n", + " rows$se[r] > 5 * abs(rows$est[r])) {\n", + " msg <- sprintf(\"Large SE for %s (%s): SE=%.4f vs |est|=%.4f\", lab, rows$method[r], rows$se[r], abs(rows$est[r]))\n", + " cat(paste0(\" [warn] \", msg, \"\\n\"))\n", + " checks_warned <- c(checks_warned, msg)\n", + " }\n", + " }\n", + "}\n", + "if (!any(grepl(\"Large SE\", checks_warned))) {\n", + " cat(\" [ok] No excessively large standard errors.\\n\")\n", + " checks_passed <- c(checks_passed, \"Standard errors: no SE > 5x|estimate|\")\n", + "}\n", + "\n", + "# 7. Bootstrap failure rate\n", + "cat(\"\\n--- Check 7: Bootstrap Convergence Rate ---\\n\")\n", + "fail_rate <- 1 - n_converged / B\n", + "if (fail_rate > 0.2) {\n", + " msg <- sprintf(\"High bootstrap failure rate: %d/%d (%.1f%%) failed\", B - n_converged, B, 100*fail_rate)\n", + " checks_failed <- c(checks_failed, msg)\n", + " cat(paste0(\" [!] \", msg, \"\\n\"))\n", + "} else {\n", + " msg <- sprintf(\"Bootstrap convergence: %d/%d (%.1f%%)\", n_converged, B, 100*n_converged/B)\n", + " checks_passed <- c(checks_passed, msg)\n", + " cat(paste0(\" [ok] \", msg, \"\\n\"))\n", + "}\n", + "\n", + "# 8. MNAR tipping point\n", + "cat(\"\\n--- Check 8: MNAR Tipping Point Distance ---\\n\")\n", + "if (exists(\"tipping\") && nrow(tipping) > 0) {\n", + " for (r in 1:nrow(tipping)) {\n", + " if (!is.na(tipping$tipping_distance[r]) && tipping$tipping_distance[r] < 0.5) {\n", + " msg <- sprintf(\"Close tipping point for %s: distance = %.2f SD\", tipping$effect[r], tipping$tipping_distance[r])\n", + " cat(paste0(\" [warn] \", msg, \"\\n\"))\n", + " checks_warned <- c(checks_warned, msg)\n", + " } else if (is.na(tipping$tipping_distance[r]) && tipping$fiml_significant[r]) {\n", + " cat(sprintf(\" [ok] %s: tipping point beyond grid (very robust)\\n\", tipping$effect[r]))\n", + " } else if (!is.na(tipping$tipping_distance[r])) {\n", + " cat(sprintf(\" [ok] %s: tipping distance = %.2f SD\\n\", tipping$effect[r], tipping$tipping_distance[r]))\n", + " }\n", + " }\n", + "}\n", + "\n", + "# 10. Suppression\n", + "cat(\"\\n--- Check 10: Suppression Effects ---\\n\")\n", + "fiml_total <- summary_all[summary_all$method == \"FIML\" & summary_all$label == \"total\", \"est\"]\n", + "fiml_ti <- summary_all[summary_all$method == \"FIML\" & summary_all$label == \"total_indirect\", \"est\"]\n", + "fiml_cp <- summary_all[summary_all$method == \"FIML\" & summary_all$label == \"cp\", \"est\"]\n", + "if (length(fiml_ti) > 0 && length(fiml_cp) > 0 && !is.na(fiml_ti) && !is.na(fiml_cp)) {\n", + " if (sign(fiml_ti) != sign(fiml_cp) && sign(fiml_ti) != 0 && sign(fiml_cp) != 0) {\n", + " msg <- \"Indirect and direct effects have OPPOSITE signs -- suppression pattern\"\n", + " cat(paste0(\" [warn] \", msg, \"\\n\"))\n", + " checks_warned <- c(checks_warned, msg)\n", + " } else {\n", + " cat(\" [ok] No suppression pattern detected.\\n\")\n", + " }\n", + "}\n", + "\n", + "# 11. Non-significant path with significant indirect\n", + "cat(\"\\n--- Check 11: Component Path Check ---\\n\")\n", + "if (exists(\"key_pe\")) {\n", + " for (i in 1:n_med) {\n", + " a_p <- key_pe[key_pe$label == paste0(\"a\", i), \"pvalue\"]\n", + " b_p <- key_pe[key_pe$label == paste0(\"b\", i), \"pvalue\"]\n", + " ind_p <- key_pe[key_pe$label == paste0(\"ind\", i), \"pvalue\"]\n", + " if (length(a_p) > 0 && length(b_p) > 0 && length(ind_p) > 0) {\n", + " if (!is.na(ind_p) && ind_p < 0.05 && ((a_p > 0.1) || (b_p > 0.1))) {\n", + " msg <- sprintf(\"ind%d significant (p=%.4f) but a%d (p=%.4f) or b%d (p=%.4f) non-significant\", \n", + " i, ind_p, i, a_p, i, b_p)\n", + " cat(paste0(\" [warn] \", msg, \"\\n\"))\n", + " checks_warned <- c(checks_warned, msg)\n", + " }\n", + " }\n", + " }\n", + "}\n", + "cat(\" [ok] Component paths checked.\\n\")\n", + "\n", + "# Final summary\n", + "cat(\"\\n========================================\\n\")\n", + "cat(\"SANITY CHECK SUMMARY\\n\")\n", + "cat(\"========================================\\n\")\n", + "cat(sprintf(\"Passed: %d\\n\", length(checks_passed)))\n", + "for (x in checks_passed) cat(paste0(\" [ok] \", x, \"\\n\"))\n", + "cat(sprintf(\"\\nWarnings: %d\\n\", length(checks_warned)))\n", + "for (x in checks_warned) cat(paste0(\" [warn] \", x, \"\\n\"))\n", + "cat(sprintf(\"\\nAnomalies: %d\\n\", length(checks_failed)))\n", + "for (x in checks_failed) cat(paste0(\" [!] \", x, \"\\n\"))\n", + "if (length(checks_failed) == 0 && length(checks_warned) == 0) {\n", + " cat(\"\\nAll sanity checks passed -- no anomalies detected.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "6ed7484c", + "metadata": {}, + "source": [ + "## 14. Cleanup and Session Info" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "a016a9fd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T20:30:45.929690Z", + "iopub.status.busy": "2026-03-31T20:30:45.928092Z", + "iopub.status.idle": "2026-03-31T20:30:46.880713Z", + "shell.execute_reply": "2026-03-31T20:30:46.878505Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No Stan/temp files to clean.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Session Info ===\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in system(\"timedatectl\", intern = TRUE):\n", + "\u201crunning command 'timedatectl' had status 1\u201d\n" + ] + }, + { + "data": { + "text/plain": [ + "R version 4.4.3 (2025-02-28)\n", + "Platform: x86_64-conda-linux-gnu\n", + "Running under: Ubuntu 24.04.3 LTS\n", + "\n", + "Matrix products: default\n", + "BLAS/LAPACK: /mnt/lustre/home/yl4437/.pixi/envs/python/lib/libopenblasp-r0.3.30.so; LAPACK version 3.12.0\n", + "\n", + "locale:\n", + " [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 \n", + " [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 \n", + " [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C \n", + "[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C \n", + "\n", + "time zone: Etc/UTC\n", + "tzcode source: system (glibc)\n", + "\n", + "attached base packages:\n", + "[1] stats graphics grDevices utils datasets methods base \n", + "\n", + "other attached packages:\n", + "[1] blavaan_0.5-10 Rcpp_1.1.1 reshape2_1.4.5 gridExtra_2.3 ggplot2_4.0.2 \n", + "[6] lavaan_0.6-21 \n", + "\n", + "loaded via a namespace (and not attached):\n", + " [1] gtable_0.3.6 QuickJSR_1.9.0 inline_0.3.21 \n", + " [4] CompQuadForm_1.4.4 lattice_0.22-9 quadprog_1.5-8 \n", + " [7] vctrs_0.7.1 tools_4.4.3 generics_0.1.4 \n", + "[10] stats4_4.4.3 parallel_4.4.3 sandwich_3.1-1 \n", + "[13] tibble_3.3.1 pkgconfig_2.0.3 Matrix_1.7-4 \n", + "[16] RColorBrewer_1.1-3 S7_0.2.1 RcppParallel_5.1.11-2\n", + "[19] uuid_1.2-2 tmvnsim_1.0-2 lifecycle_1.0.5 \n", + "[22] compiler_4.4.3 farver_2.1.2 stringr_1.6.0 \n", + "[25] textshaping_1.0.4 mnormt_2.1.2 repr_1.1.7 \n", + "[28] codetools_0.2-20 htmltools_0.5.9 bayesplot_1.15.0 \n", + "[31] pillar_1.11.1 crayon_1.5.3 MASS_7.3-65 \n", + "[34] StanHeaders_2.32.10 parallelly_1.46.1 rstan_2.32.7 \n", + "[37] tidyselect_1.2.1 digest_0.6.39 future_1.69.0 \n", + "[40] mvtnorm_1.3-3 stringi_1.8.7 listenv_0.10.0 \n", + "[43] nonnest2_0.5-8 dplyr_1.2.0 labeling_0.4.3 \n", + "[46] fastmap_1.2.0 grid_4.4.3 cli_3.6.5 \n", + "[49] magrittr_2.0.4 loo_2.9.0 base64enc_0.1-6 \n", + "[52] pkgbuild_1.4.8 IRdisplay_1.1 future.apply_1.20.1 \n", + "[55] pbivnorm_0.6.0 withr_3.0.2 scales_1.4.0 \n", + "[58] IRkernel_1.3.2 globals_0.19.0 matrixStats_1.5.0 \n", + "[61] igraph_2.2.2 pbdZMQ_0.3-14 ragg_1.5.0 \n", + "[64] zoo_1.8-15 coda_0.19-4.1 evaluate_1.0.5 \n", + "[67] viridisLite_0.4.3 rstantools_2.6.0 rlang_1.1.7 \n", + "[70] isoband_0.3.0 glue_1.8.0 jsonlite_2.0.0 \n", + "[73] R6_2.6.1 plyr_1.8.9 systemfonts_1.3.1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Cleanup Stan temp files\n", + "# ============================================================\n", + "stan_files <- list.files(RESULT_DIR, pattern = \"lavExport|tmp_|\\\\.stan$|\\\\.bak$\", \n", + " recursive = TRUE, full.names = TRUE)\n", + "if (length(stan_files) > 0) {\n", + " for (f in stan_files) {\n", + " if (file.exists(f)) {\n", + " if (file.info(f)$isdir) unlink(f, recursive = TRUE)\n", + " else file.remove(f)\n", + " }\n", + " }\n", + " cat(\"Cleaned up\", length(stan_files), \"Stan/temp files.\\n\")\n", + "} else {\n", + " cat(\"No Stan/temp files to clean.\\n\")\n", + "}\n", + "\n", + "cat(\"\\n=== Session Info ===\\n\")\n", + "sessionInfo()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_caa_neo4_APOE4_adj_SEM_mediation.ipynb b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_caa_neo4_APOE4_adj_SEM_mediation.ipynb new file mode 100644 index 0000000..1c9b725 --- /dev/null +++ b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_caa_neo4_APOE4_adj_SEM_mediation.ipynb @@ -0,0 +1,5158 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SEM Parallel Mediation Analysis: APOE4 SNP -> Gene Expression -> CAA Neuropathology\n", + "\n", + "## Aim\n", + "\n", + "This notebook tests whether four gene expression mediators (APOC4/APOC2, APOC2, APOC1, APOE) **jointly and independently** mediate the effect of the APOE4 region SNP (chr19_44954310_T_C) on cerebral amyloid angiopathy neuropathology (caa_neo4), using a parallel mediation SEM framework with partially overlapping samples.\n", + "\n", + "**Design:** Parallel mediation (Design 2) -- all four mediators enter the outcome equation simultaneously, allowing estimation of specific indirect effects (unique contribution of each mediator) and the total indirect effect.\n", + "\n", + "**Direction:** Unidirectional -- SNP -> Mediators -> caa_neo4 only.\n", + "\n", + "**Methods:** FIML SEM, Bootstrap (FIML inside), MNAR Sensitivity, Bayesian SEM (blavaan/Stan).\n", + "\n", + "**Covariate strategy:** Covariates-in-model (Strategy A). Each equation has its own covariates:\n", + "- APOC4_APOC2_AC_exp equation: msex_u, age_death_u, pmi_u, ROS_study_u\n", + "- APOC2_AC_exp equation: msex_u, age_death_u, pmi_u, ROS_study_u\n", + "- APOC1_DeJager_Mic_exp equation: msex_u, age_death_u, pmi_u\n", + "- APOE_Mega_Mic_exp equation: msex_u, age_death_u, pmi_u\n", + "- caa_neo4 equation: educ, apoe4_dose, msex_u, age_death_u" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Directed Acyclic Graph (DAG)\n", + "\n", + "```\n", + " a1 b1\n", + " +---------> [APOC4_APOC2] ---------+\n", + " | a2 b2 |\n", + " +---------> [APOC2] ---------+\n", + " [chr19_44954310]---+ a3 b3 +---> [caa_neo4]\n", + " +---------> [APOC1] ---------+\n", + " | a4 b4 |\n", + " +---------> [APOE] ---------+\n", + " | ^\n", + " +------------------ c' (direct) -------------------+\n", + "```\n", + "\n", + "- Specific indirect (APOC4_APOC2): a1 x b1\n", + "- Specific indirect (APOC2): a2 x b2\n", + "- Specific indirect (APOC1): a3 x b3\n", + "- Specific indirect (APOE): a4 x b4\n", + "- Total indirect = a1*b1 + a2*b2 + a3*b3 + a4*b4\n", + "- Total effect = c' + total indirect\n", + "\n", + "Covariates: msex_u, age_death_u, pmi_u, ROS_study_u (mediator eqs); educ, apoe4_dose, msex_u, age_death_u (outcome eq). Residual correlations among mediators freely estimated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Findings & Conclusion\n\n### Main Analysis (4-Mediator Parallel Model)\n\n**No significant mediation was detected** through any of the four mediators (APOC4_APOC2, APOC2, APOC1, APOE) for the effect of chr19_44954310_T_C on caa_neo4. This finding is consistent across all four methods (FIML, Bootstrap, MNAR Sensitivity, Bayesian).\n\n- **a-paths (SNP \u2192 M):** All four a-paths are significant (p < 0.001), confirming the SNP influences all mediators.\n- **b-paths (M \u2192 Y):** None of the b-paths reached significance. APOC4_APOC2 (b1 = \u22120.30, p = 0.46) and APOC2 (b2 = 0.68, p = 0.53) showed large estimates with extremely inflated SEs (~1.0), indicating **severe multicollinearity** between these two mediators.\n- **Indirect effects:** No specific indirect effect was significant. The total indirect effect was near zero (\u22120.005, p = 0.76).\n- **Direct effect:** The direct path (cp = 0.158, p < 0.001) is significant and accounts for essentially all of the total effect (0.153, p < 0.001).\n- **MNAR sensitivity:** All indirect effects had tipping distances of 0.00 SD, indicating they are fragile \u2014 even minimal departures from MAR would alter conclusions.\n- **Bayesian:** Posterior probability of direction for the APOE indirect effect was highest (P = 0.90), suggesting this is the most promising mediator, though still not credibly different from zero.\n- **Proportion mediated:** Total mediation is \u22123.1%, essentially zero.\n\n### Sensitivity Analysis (3-Mediator Model, Excluding APOC4_APOC2_AC_exp)\n\nRemoving APOC4_APOC2_AC_exp confirmed the **multicollinearity diagnosis**:\n\n- **APOC2 b-path stabilized dramatically:** SE dropped from 1.076 to 0.044 (a 24-fold reduction), and the estimate shifted from +0.678 to \u22120.067 (p = 0.13). This confirms the original APOC2 b-path was unreliable due to collinearity with APOC4_APOC2.\n- **APOE indirect effect remained stable:** 0.016 in both models \u2014 the most robust mediator signal.\n- **Direct and total effects unchanged:** cp = 0.153 (3-med) vs 0.158 (4-med); total \u2248 0.152 in both.\n- **Pairwise contrast:** The APOC2 vs APOE indirect difference became significant (diff_1_3, p = 0.040) in the 3-mediator model, driven by the APOE indirect being positive and the APOC2 indirect being negative.\n\n### Overall Conclusion\n\nThe SNP chr19_44954310_T_C has a **significant direct effect** on caa_neo4 (est \u2248 0.15, p < 0.001) that is **not meaningfully mediated** by any of the four molecular traits tested. The APOE-mediated pathway shows the most consistent positive signal (est \u2248 0.016, P(direction) = 0.90) but does not reach significance. The APOC4_APOC2 and APOC2 mediators are too collinear to be reliably distinguished in the same model; the sensitivity analysis excluding APOC4_APOC2 resolves this but does not change the substantive conclusion of minimal mediation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Input Specification\n", + "\n", + "| Parameter | Value |\n", + "|-----------|-------|\n", + "| Data file | `/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_neo4/APOE_ind_set_27_caa_neo4_mediation_all_input.txt` |\n", + "| Exposure | chr19_44954310_T_C |\n", + "| Mediators | APOC4_APOC2_AC_exp, APOC2_AC_exp, APOC1_DeJager_Mic_exp, APOE_Mega_Mic_exp |\n", + "| Outcome | caa_neo4 |\n", + "| Design | Parallel mediation (Design 2) |\n", + "| Direction | Unidirectional (SNP -> M -> Y) |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Methods Overview\n", + "\n", + "| Method | Purpose | Missing Data | Key Output |\n", + "|--------|---------|-------------|------------|\n", + "| FIML SEM (lavaan) | Primary analysis | FIML (all rows used) | ML estimates, SEs, CIs |\n", + "| Bootstrap (FIML inside) | Non-parametric CIs | FIML per replicate | Percentile CIs, p-values |\n", + "| MNAR Sensitivity | Robustness to MNAR | Delta-shift imputation | Tipping points |\n", + "| Bayesian SEM (blavaan) | Posterior inference | Stan data augmentation | Credible intervals, P(direction) |" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:30:39.219250Z", + "iopub.status.busy": "2026-03-31T16:30:39.214129Z", + "iopub.status.idle": "2026-03-31T16:30:49.030320Z", + "shell.execute_reply": "2026-03-31T16:30:49.028129Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setup complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exposure: chr19_44954310_T_C \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mediators: APOC4_APOC2_AC_exp, APOC2_AC_exp, APOC1_DeJager_Mic_exp, APOE_Mega_Mic_exp \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Outcome: caa_neo4 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All covariates: msex_u, age_death_u, pmi_u, ROS_study_u, educ, apoe4_dose \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 1: Setup -- Libraries, paths, output directories\n", + "# ============================================================\n", + "suppressPackageStartupMessages({\n", + " library(lavaan)\n", + " library(blavaan)\n", + " library(ggplot2)\n", + " library(gridExtra)\n", + " library(reshape2)\n", + "})\n", + "\n", + "# Paths\n", + "DATA_FILE <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_neo4/APOE_ind_set_27_caa_neo4_mediation_all_input.txt\"\n", + "RESULT_DIR <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_neo4/APOE4_adj\"\n", + "\n", + "dir.create(file.path(RESULT_DIR, \"main_SEM_FIML\"), showWarnings=FALSE, recursive=TRUE)\n", + "dir.create(file.path(RESULT_DIR, \"bootstrap\"), showWarnings=FALSE, recursive=TRUE)\n", + "dir.create(file.path(RESULT_DIR, \"MNAR_sensitivity\"), showWarnings=FALSE, recursive=TRUE)\n", + "dir.create(file.path(RESULT_DIR, \"bayesian_blavaan\"), showWarnings=FALSE, recursive=TRUE)\n", + "dir.create(file.path(RESULT_DIR, \"summary\"), showWarnings=FALSE, recursive=TRUE)\n", + "\n", + "# Variable definitions\n", + "EXPOSURE <- \"chr19_44954310_T_C\"\n", + "MEDIATORS <- c(\"APOC4_APOC2_AC_exp\", \"APOC2_AC_exp\", \"APOC1_DeJager_Mic_exp\", \"APOE_Mega_Mic_exp\")\n", + "MEDIATOR_SHORT <- c(\"APOC4_APOC2\", \"APOC2\", \"APOC1\", \"APOE\")\n", + "OUTCOME <- \"caa_neo4\"\n", + "n_med <- length(MEDIATORS)\n", + "\n", + "# Covariates per equation\n", + "MED_COVS <- list(\n", + " APOC4_APOC2_AC_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\"),\n", + " APOC2_AC_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\"),\n", + " APOC1_DeJager_Mic_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\"),\n", + " APOE_Mega_Mic_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\")\n", + ")\n", + "OUT_COVS <- c(\"educ\", \"apoe4_dose\", \"msex_u\", \"age_death_u\")\n", + "\n", + "ALL_COVS <- unique(c(unlist(MED_COVS), OUT_COVS))\n", + "\n", + "cat(\"Setup complete.\\n\")\n", + "cat(\"Exposure:\", EXPOSURE, \"\\n\")\n", + "cat(\"Mediators:\", paste(MEDIATORS, collapse=\", \"), \"\\n\")\n", + "cat(\"Outcome:\", OUTCOME, \"\\n\")\n", + "cat(\"All covariates:\", paste(ALL_COVS, collapse=\", \"), \"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:30:49.120560Z", + "iopub.status.busy": "2026-03-31T16:30:49.081614Z", + "iopub.status.idle": "2026-03-31T16:30:49.226393Z", + "shell.execute_reply": "2026-03-31T16:30:49.223739Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Raw data dimensions: 1153 rows x 58 columns\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Analysis sample (exposure observed): N = 1153 \n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Missingness Summary ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chr19_44954310_T_C : 0 missing ( 0.0%)\n", + " APOC4_APOC2_AC_exp : 560 missing ( 48.6%)\n", + " APOC2_AC_exp : 560 missing ( 48.6%)\n", + " APOC1_DeJager_Mic_exp : 734 missing ( 63.7%)\n", + " APOE_Mega_Mic_exp : 420 missing ( 36.4%)\n", + " caa_neo4 : 103 missing ( 8.9%)\n", + " msex_u : 13 missing ( 1.1%)\n", + " age_death_u : 13 missing ( 1.1%)\n", + " pmi_u : 13 missing ( 1.1%)\n", + " ROS_study_u : 38 missing ( 3.3%)\n", + " educ : 40 missing ( 3.5%)\n", + " apoe4_dose : 47 missing ( 4.1%)\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 2: Data Loading and Exploration\n", + "# ============================================================\n", + "dat_raw <- read.delim(DATA_FILE, header=TRUE, sep=\"\\t\", stringsAsFactors=FALSE)\n", + "cat(\"Raw data dimensions:\", nrow(dat_raw), \"rows x\", ncol(dat_raw), \"columns\\n\")\n", + "\n", + "# Select only needed columns\n", + "keep_cols <- c(EXPOSURE, MEDIATORS, OUTCOME, ALL_COVS)\n", + "missing_cols <- setdiff(keep_cols, colnames(dat_raw))\n", + "if (length(missing_cols) > 0) stop(\"Missing columns: \", paste(missing_cols, collapse=\", \"))\n", + "\n", + "dat <- dat_raw[, keep_cols]\n", + "\n", + "# Convert all to numeric\n", + "for (col in colnames(dat)) {\n", + " dat[[col]] <- as.numeric(dat[[col]])\n", + "}\n", + "\n", + "# Keep only rows with exposure observed\n", + "dat <- dat[!is.na(dat[[EXPOSURE]]), ]\n", + "N_total <- nrow(dat)\n", + "cat(\"Analysis sample (exposure observed): N =\", N_total, \"\\n\\n\")\n", + "\n", + "# Missingness summary\n", + "cat(\"--- Missingness Summary ---\\n\")\n", + "for (v in c(EXPOSURE, MEDIATORS, OUTCOME, ALL_COVS)) {\n", + " n_miss <- sum(is.na(dat[[v]]))\n", + " cat(sprintf(\" %-30s: %d missing (%5.1f%%)\\n\", v, n_miss, 100*n_miss/N_total))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:30:49.232025Z", + "iopub.status.busy": "2026-03-31T16:30:49.230354Z", + "iopub.status.idle": "2026-03-31T16:30:49.309936Z", + "shell.execute_reply": "2026-03-31T16:30:49.286962Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Sample Overlap (Mediator x Outcome) ---\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " APOC4_APOC2_AC_exp : Both=564, M-only=29, Y-only=486, Neither=74\n", + " APOC2_AC_exp : Both=564, M-only=29, Y-only=486, Neither=74\n", + " APOC1_DeJager_Mic_exp : Both=387, M-only=32, Y-only=663, Neither=71\n", + " APOE_Mega_Mic_exp : Both=677, M-only=56, Y-only=373, Neither=47\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Mediator Both Med_only Out_only Neither\n", + "APOC4_APOC2_AC_exp APOC4_APOC2_AC_exp 564 29 486 74\n", + "APOC2_AC_exp APOC2_AC_exp 564 29 486 74\n", + "APOC1_DeJager_Mic_exp APOC1_DeJager_Mic_exp 387 32 663 71\n", + "APOE_Mega_Mic_exp APOE_Mega_Mic_exp 677 56 373 47\n", + " Total\n", + "APOC4_APOC2_AC_exp 1153\n", + "APOC2_AC_exp 1153\n", + "APOC1_DeJager_Mic_exp 1153\n", + "APOE_Mega_Mic_exp 1153\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 3: Sample Overlap Structure\n", + "# ============================================================\n", + "cat(\"--- Sample Overlap (Mediator x Outcome) ---\\n\\n\")\n", + "\n", + "overlap_rows <- list()\n", + "for (m in MEDIATORS) {\n", + " has_m <- !is.na(dat[[m]])\n", + " has_y <- !is.na(dat[[OUTCOME]])\n", + " both <- sum(has_m & has_y)\n", + " m_only <- sum(has_m & !has_y)\n", + " y_only <- sum(!has_m & has_y)\n", + " neither <- sum(!has_m & !has_y)\n", + " overlap_rows[[m]] <- data.frame(\n", + " Mediator=m, Both=both, Med_only=m_only, Out_only=y_only, Neither=neither, Total=N_total\n", + " )\n", + " cat(sprintf(\" %-30s: Both=%d, M-only=%d, Y-only=%d, Neither=%d\\n\", m, both, m_only, y_only, neither))\n", + "}\n", + "overlap_df <- do.call(rbind, overlap_rows)\n", + "print(overlap_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:30:49.317131Z", + "iopub.status.busy": "2026-03-31T16:30:49.314490Z", + "iopub.status.idle": "2026-03-31T16:30:49.385428Z", + "shell.execute_reply": "2026-03-31T16:30:49.383296Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Parallel Mediation Model === \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "APOC4_APOC2_AC_exp ~ a1 * chr19_44954310_T_C + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC2_AC_exp ~ a2 * chr19_44954310_T_C + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC1_DeJager_Mic_exp ~ a3 * chr19_44954310_T_C + msex_u + age_death_u + pmi_u\n", + "APOE_Mega_Mic_exp ~ a4 * chr19_44954310_T_C + msex_u + age_death_u + pmi_u\n", + "caa_neo4 ~ b1 * APOC4_APOC2_AC_exp + b2 * APOC2_AC_exp + b3 * APOC1_DeJager_Mic_exp + b4 * APOE_Mega_Mic_exp + cp * chr19_44954310_T_C + educ + apoe4_dose + msex_u + age_death_u\n", + "ind1 := a1 * b1\n", + "ind2 := a2 * b2\n", + "ind3 := a3 * b3\n", + "ind4 := a4 * b4\n", + "total_indirect := ind1 + ind2 + ind3 + ind4\n", + "total := cp + total_indirect\n", + "prop1 := ind1 / total\n", + "prop2 := ind2 / total\n", + "prop3 := ind3 / total\n", + "prop4 := ind4 / total\n", + "prop_total := total_indirect / total\n", + "diff_1_2 := ind1 - ind2\n", + "diff_1_3 := ind1 - ind3\n", + "diff_1_4 := ind1 - ind4\n", + "diff_2_3 := ind2 - ind3\n", + "diff_2_4 := ind2 - ind4\n", + "diff_3_4 := ind3 - ind4\n", + "APOC4_APOC2_AC_exp ~~ APOC2_AC_exp\n", + "APOC4_APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC4_APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC1_DeJager_Mic_exp ~~ APOE_Mega_Mic_exp \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 4: Build Parallel Mediation Model\n", + "# ============================================================\n", + "\n", + "# Mediator equations (each with its own covariates)\n", + "med_eqs <- c()\n", + "for (i in seq_len(n_med)) {\n", + " m <- MEDIATORS[i]\n", + " cov_str <- paste(MED_COVS[[m]], collapse=\" + \")\n", + " med_eqs <- c(med_eqs, paste0(m, \" ~ a\", i, \" * \", EXPOSURE, \" + \", cov_str))\n", + "}\n", + "\n", + "# Outcome equation (all mediators + direct + outcome covariates)\n", + "med_terms <- paste0(\"b\", seq_len(n_med), \" * \", MEDIATORS, collapse=\" + \")\n", + "out_cov_str <- paste(OUT_COVS, collapse=\" + \")\n", + "y_eq <- paste0(OUTCOME, \" ~ \", med_terms, \" + cp * \", EXPOSURE, \" + \", out_cov_str)\n", + "\n", + "# Specific indirect effects\n", + "ind_defs <- paste0(\"ind\", seq_len(n_med), \" := a\", seq_len(n_med), \" * b\", seq_len(n_med))\n", + "total_ind <- paste0(\"total_indirect := \", paste0(\"ind\", seq_len(n_med), collapse=\" + \"))\n", + "total_def <- \"total := cp + total_indirect\"\n", + "\n", + "# Proportion mediated for each mediator\n", + "prop_defs <- paste0(\"prop\", seq_len(n_med), \" := ind\", seq_len(n_med), \" / total\")\n", + "prop_total <- \"prop_total := total_indirect / total\"\n", + "\n", + "# Pairwise contrasts\n", + "contrasts <- c()\n", + "for (i in 1:(n_med-1)) {\n", + " for (j in (i+1):n_med) {\n", + " contrasts <- c(contrasts, paste0(\"diff_\", i, \"_\", j, \" := ind\", i, \" - ind\", j))\n", + " }\n", + "}\n", + "\n", + "# Residual correlations between mediators (explicit)\n", + "cov_terms <- c()\n", + "for (i in 1:(n_med-1)) {\n", + " for (j in (i+1):n_med) {\n", + " cov_terms <- c(cov_terms, paste0(MEDIATORS[i], \" ~~ \", MEDIATORS[j]))\n", + " }\n", + "}\n", + "\n", + "model_str <- paste(c(med_eqs, y_eq, ind_defs, total_ind, total_def, prop_defs, prop_total, contrasts, cov_terms), collapse=\"\\n\")\n", + "\n", + "cat(\"=== Parallel Mediation Model ===\", \"\\n\")\n", + "cat(model_str, \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Method 1: FIML SEM (lavaan)\n", + "\n", + "Full Information Maximum Likelihood uses all N=1153 subjects with at least the exposure observed. FIML handles different patterns of missingness across the four mediators and outcome without listwise deletion, maximizing statistical power." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:30:49.392292Z", + "iopub.status.busy": "2026-03-31T16:30:49.390019Z", + "iopub.status.idle": "2026-03-31T16:30:54.048929Z", + "shell.execute_reply": "2026-03-31T16:30:54.046738Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting FIML SEM (N = 1153 )...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converged: TRUE \n", + "N used: 1153 \n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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A data.frame: 95 \u00d7 11
lhsoprhslabelexoestsezpvaluestd.lvstd.all
<chr><chr><chr><chr><int><dbl><dbl><dbl><dbl><dbl><dbl>
APOC4_APOC2_AC_exp ~ chr19_44954310_T_C a10 0.27722550990.056046493 4.946348937.561836e-07 0.2772255099 0.1982947649
APOC4_APOC2_AC_exp ~ msex_u 0 0.09732480720.081540339 1.193578652.326428e-01 0.0973248072 0.0470537419
APOC4_APOC2_AC_exp ~ age_death_u 0 0.02250595090.006027599 3.733817021.885995e-04 0.0225059509 0.1506595934
APOC4_APOC2_AC_exp ~ pmi_u 0 0.01158160280.007900552 1.465923311.426692e-01 0.0115816028 0.0872936269
APOC4_APOC2_AC_exp ~ ROS_study_u 0-0.16154776380.077152953-2.093863643.627213e-02-0.1615477638-0.0823146553
APOC2_AC_exp ~ chr19_44954310_T_C a20 0.27403273330.056130883 4.882031401.049985e-06 0.2740327333 0.1958129238
APOC2_AC_exp ~ msex_u 0 0.09630469980.081664436 1.179273442.382893e-01 0.0963046998 0.0465134922
APOC2_AC_exp ~ age_death_u 0 0.02223622160.006036591 3.683572472.299879e-04 0.0222362216 0.1487035267
APOC2_AC_exp ~ pmi_u 0 0.01221760490.007914006 1.543795221.226379e-01 0.0122176049 0.0919942746
APOC2_AC_exp ~ ROS_study_u 0-0.15964971550.077249162-2.066685393.876381e-02-0.1596497155-0.0812653133
APOC1_DeJager_Mic_exp~ chr19_44954310_T_C a30 0.22230199450.064176510 3.463915305.323741e-04 0.2223019945 0.1530354308
APOC1_DeJager_Mic_exp~ msex_u 0-0.16370421010.096991648-1.687817599.144625e-02-0.1637042101-0.0761729958
APOC1_DeJager_Mic_exp~ age_death_u 0 0.00914504030.006699585 1.365015931.722480e-01 0.0091450403 0.0589190344
APOC1_DeJager_Mic_exp~ pmi_u 0 0.00262025260.008662773 0.302472737.622917e-01 0.0026202526 0.0190076138
APOE_Mega_Mic_exp ~ chr19_44954310_T_C a40 0.21440288830.049140677 4.363043021.282657e-05 0.2144028883 0.1554473295
APOE_Mega_Mic_exp ~ msex_u 0-0.20645461690.073353668-2.814509814.885169e-03-0.2064546169-0.1011742024
APOE_Mega_Mic_exp ~ age_death_u 0 0.02199422780.005180132 4.245881542.177356e-05 0.0219942278 0.1492391336
APOE_Mega_Mic_exp ~ pmi_u 0 0.01085215750.005987077 1.812597006.989400e-02 0.0108521575 0.0829095455
caa_neo4 ~ APOC4_APOC2_AC_exp b10-0.74560228961.076453950-0.692646714.885313e-01-0.7456022896-0.6939264950
caa_neo4 ~ APOC2_AC_exp b20 0.67765632841.075514663 0.630076335.286446e-01 0.6776563284 0.6313277562
caa_neo4 ~ APOC1_DeJager_Mic_expb30-0.00076614790.067434915-0.011361299.909352e-01-0.0007661479-0.0007408807
caa_neo4 ~ APOE_Mega_Mic_exp b40 0.07642416440.057933406 1.319172641.871114e-01 0.0764241644 0.0701717585
caa_neo4 ~ chr19_44954310_T_C cp0 0.15750684310.045331187 3.474580135.116535e-04 0.1575068431 0.1048536907
caa_neo4 ~ educ 0 0.00159391010.008606436 0.185199808.530723e-01 0.0015939101 0.0054460791
caa_neo4 ~ apoe4_dose 0 0.73928545180.06373850111.598726750.000000e+00 0.7392854518 0.3367785468
caa_neo4 ~ msex_u 0-0.04480710370.066118022-0.677683674.979723e-01-0.0448071037-0.0201615410
caa_neo4 ~ age_death_u 0 0.02551801440.004994195 5.109534903.229528e-07 0.0255180144 0.1589836775
APOC4_APOC2_AC_exp ~~APOC2_AC_exp 0 0.88902018540.05166273017.208153430.000000e+00 0.8890201854 0.9992049265
APOC4_APOC2_AC_exp ~~APOC1_DeJager_Mic_exp 0 0.20475956390.053676552 3.814692931.363524e-04 0.2047595639 0.2167735573
APOC4_APOC2_AC_exp ~~APOE_Mega_Mic_exp 0 0.05273241290.041565782 1.268649612.045661e-01 0.0527324129 0.0598129609
\u22ee\u22ee\u22ee\u22ee\u22ee\u22ee\u22ee\u22ee\u22ee\u22ee\u22ee
apoe4_dose ~~apoe4_dose 0 0.23068900850.009811347 23.512471320.000000e+00 0.2306890085 1.000000000
APOC4_APOC2_AC_exp ~1 0-2.26805819520.553123947 -4.100452004.123440e-05-2.2680581952-2.311355590
APOC2_AC_exp ~1 0-2.24757382280.553948994 -4.057366014.962927e-05-2.2475738228-2.288165261
APOC1_DeJager_Mic_exp~1 0-0.96448302560.613193535 -1.572885181.157454e-01-0.9644830256-0.945970804
APOE_Mega_Mic_exp ~1 0-2.15909780240.470269561 -4.591191914.407219e-06-2.1590978024-2.230280371
caa_neo4 ~1 0-1.48043391490.485833787 -3.047202472.309821e-03-1.4804339149-1.404131664
chr19_44954310_T_C ~1 0 0.87510841380.020670493 42.336117320.000000e+00 0.8751084138 1.246798791
msex_u ~1 0 0.34208714090.014050996 24.346112590.000000e+00 0.3420871409 0.721072123
age_death_u ~1 088.95236533520.194552189457.215957260.000000e+0088.952365335213.541620792
pmi_u ~1 0 8.74443316900.219046858 39.920377030.000000e+00 8.7444331690 1.182307275
ROS_study_u ~1 0 0.49958537630.014970770 33.370721050.000000e+00 0.4995853763 0.999185389
educ ~1 016.36805978070.107900980151.695190470.000000e+0016.3680597807 4.543556087
apoe4_dose ~1 0 0.27225685160.014433873 18.862356400.000000e+00 0.2722568516 0.566846366
ind1 :=a1*b1 ind1 0-0.20669997490.301445210 -0.685696664.929044e-01-0.2066999749-0.137601991
ind2 :=a2*b2 ind2 0 0.18570001590.297275563 0.624672995.321857e-01 0.1857000159 0.123622134
ind3 :=a3*b3 ind3 0-0.00017031620.014986532 -0.011364629.909325e-01-0.0001703162-0.000113381
ind4 :=a4*b4 ind4 0 0.01638556160.013018679 1.258619362.081679e-01 0.0163855616 0.010908012
total_indirect :=ind1+ind2+ind3+ind4 total_indirect0-0.00478471360.015614549 -0.306426627.592798e-01-0.0047847136-0.003185226
total :=cp+total_indirect total 0 0.15272212950.043022423 3.549826283.854854e-04 0.1527221295 0.101668465
prop1 :=ind1/total prop1 0-1.35343827102.006669349 -0.674470005.000126e-01-1.3534382710-1.353438271
prop2 :=ind2/total prop2 0 1.21593390891.972859386 0.616330755.376762e-01 1.2159339089 1.215933909
prop3 :=ind3/total prop3 0-0.00111520320.098128362 -0.011364749.909324e-01-0.0011152032-0.001115203
prop4 :=ind4/total prop4 0 0.10729002820.089734450 1.195639232.318374e-01 0.1072900282 0.107290028
prop_total :=total_indirect/totalprop_total 0-0.03132953710.102882095 -0.304518857.607326e-01-0.0313295371-0.031329537
diff_1_2 :=ind1-ind2 diff_1_2 0-0.39239999090.598585725 -0.655545195.121168e-01-0.3923999909-0.261224125
diff_1_3 :=ind1-ind3 diff_1_3 0-0.20652965870.300550607 -0.687170994.919750e-01-0.2065296587-0.137488610
diff_1_4 :=ind1-ind4 diff_1_4 0-0.22308553650.302002311 -0.738688184.600964e-01-0.2230855365-0.148510004
diff_2_3 :=ind2-ind3 diff_2_3 0 0.18587033210.299108165 0.621415115.343265e-01 0.1858703321 0.123735515
diff_2_4 :=ind2-ind4 diff_2_4 0 0.16931445430.297199151 0.569700335.688810e-01 0.1693144543 0.112714121
diff_3_4 :=ind3-ind4 diff_3_4 0-0.01655587780.025815755 -0.641309075.213219e-01-0.0165558778-0.011021393
\n", + "
\n", + "
\n" + ], + "text/latex": [ + "\\begin{description}\n", + "\\item[\\$header] \\begin{description}\n", + "\\item[\\$lavaan.version] '0.6-21'\n", + "\\item[\\$sam.approach] FALSE\n", + "\\item[\\$optim.method] 'nlminb'\n", + "\\item[\\$optim.iterations] 254\n", + "\\item[\\$optim.converged] TRUE\n", + "\\end{description}\n", + "\n", + "\\item[\\$optim] \\begin{description}\n", + "\\item[\\$estimator] 'ML'\n", + "\\item[\\$estimator.args] \\begin{enumerate}\n", + "\\end{enumerate}\n", + "\n", + "\\item[\\$optim.method] 'nlminb'\n", + "\\item[\\$npar] 78\n", + "\\item[\\$eq.constraints] FALSE\n", + "\\item[\\$nrow.ceq.jac] 0\n", + "\\item[\\$nrow.cin.jac] 0\n", + "\\item[\\$nrow.con.jac] 0\n", + "\\item[\\$con.jac.rank] 0\n", + "\\end{description}\n", + "\n", + "\\item[\\$data] \\begin{description}\n", + "\\item[\\$ngroups] 1\n", + "\\item[\\$nobs] 1153\n", + "\\item[\\$npatterns] 24\n", + "\\end{description}\n", + "\n", + "\\item[\\$test] \\textbf{\\$standard} = \\begin{description}\n", + "\\item[\\$test] 'standard'\n", + "\\item[\\$stat] 18.7701729339465\n", + "\\item[\\$stat.group] 18.7701729339465\n", + "\\item[\\$df] 12\n", + "\\item[\\$refdistr] 'chisq'\n", + "\\item[\\$pvalue] 0.0942278583827429\n", + "\\end{description}\n", + "\n", + "\\item[\\$fit] \\begin{description*}\n", + "\\item[npar] 78\n", + "\\item[fmin] 0.00813971072590913\n", + "\\item[chisq] 18.7701729339465\n", + "\\item[df] 12\n", + "\\item[pvalue] 0.0942278583827429\n", + "\\item[baseline.chisq] 4343.57129642534\n", + "\\item[baseline.df] 45\n", + "\\item[baseline.pvalue] 0\n", + "\\item[cfi] 0.998425017879876\n", + "\\item[tli] 0.994093817049536\n", + "\\item[cfi.robust] 0.999078231378507\n", + "\\item[tli.robust] 0.996543367669401\n", + "\\item[logl] -16646.0633251899\n", + "\\item[unrestricted.logl] -16636.6782387229\n", + "\\item[aic] 33448.1266503798\n", + "\\item[bic] 33842.0362069607\n", + "\\item[ntotal] 1153\n", + "\\item[bic2] 33594.2831902032\n", + "\\item[rmsea] 0.0221204829440917\n", + "\\item[rmsea.ci.lower] 0\n", + "\\item[rmsea.ci.upper] 0.0404585446803905\n", + "\\item[rmsea.ci.level] 0.9\n", + "\\item[rmsea.pvalue] 0.995882357690177\n", + "\\item[rmsea.close.h0] 0.05\n", + "\\item[rmsea.notclose.pvalue] 1.56873050133425e-09\n", + "\\item[rmsea.notclose.h0] 0.08\n", + "\\item[rmsea.robust] 0.0237487835520414\n", + "\\item[rmsea.ci.lower.robust] 0\n", + "\\item[rmsea.ci.upper.robust] 0.0526428526851834\n", + "\\item[rmsea.pvalue.robust] 0.928615019776227\n", + "\\item[rmsea.notclose.pvalue.robust] 0.000161741074149594\n", + "\\item[srmr] 0.0199924392054754\n", + "\\end{description*}\n", + "\n", + "\\item[\\$pe] A data.frame: 95 \u00d7 11\n", + "\\begin{tabular}{lllllllllll}\n", + " lhs & op & rhs & label & exo & est & se & z & pvalue & std.lv & std.all\\\\\n", + " & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t APOC4\\_APOC2\\_AC\\_exp & \\textasciitilde{} & chr19\\_44954310\\_T\\_C & a1 & 0 & 0.2772255099 & 0.056046493 & 4.94634893 & 7.561836e-07 & 0.2772255099 & 0.1982947649\\\\\n", + "\t APOC4\\_APOC2\\_AC\\_exp & \\textasciitilde{} & msex\\_u & & 0 & 0.0973248072 & 0.081540339 & 1.19357865 & 2.326428e-01 & 0.0973248072 & 0.0470537419\\\\\n", + "\t APOC4\\_APOC2\\_AC\\_exp & \\textasciitilde{} & age\\_death\\_u & & 0 & 0.0225059509 & 0.006027599 & 3.73381702 & 1.885995e-04 & 0.0225059509 & 0.1506595934\\\\\n", + "\t APOC4\\_APOC2\\_AC\\_exp & \\textasciitilde{} & pmi\\_u & & 0 & 0.0115816028 & 0.007900552 & 1.46592331 & 1.426692e-01 & 0.0115816028 & 0.0872936269\\\\\n", + "\t APOC4\\_APOC2\\_AC\\_exp & \\textasciitilde{} & ROS\\_study\\_u & & 0 & -0.1615477638 & 0.077152953 & -2.09386364 & 3.627213e-02 & -0.1615477638 & -0.0823146553\\\\\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{} & chr19\\_44954310\\_T\\_C & a2 & 0 & 0.2740327333 & 0.056130883 & 4.88203140 & 1.049985e-06 & 0.2740327333 & 0.1958129238\\\\\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{} & msex\\_u & & 0 & 0.0963046998 & 0.081664436 & 1.17927344 & 2.382893e-01 & 0.0963046998 & 0.0465134922\\\\\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{} & age\\_death\\_u & & 0 & 0.0222362216 & 0.006036591 & 3.68357247 & 2.299879e-04 & 0.0222362216 & 0.1487035267\\\\\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{} & pmi\\_u & & 0 & 0.0122176049 & 0.007914006 & 1.54379522 & 1.226379e-01 & 0.0122176049 & 0.0919942746\\\\\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{} & ROS\\_study\\_u & & 0 & -0.1596497155 & 0.077249162 & -2.06668539 & 3.876381e-02 & -0.1596497155 & -0.0812653133\\\\\n", + "\t APOC1\\_DeJager\\_Mic\\_exp & \\textasciitilde{} & chr19\\_44954310\\_T\\_C & a3 & 0 & 0.2223019945 & 0.064176510 & 3.46391530 & 5.323741e-04 & 0.2223019945 & 0.1530354308\\\\\n", + "\t APOC1\\_DeJager\\_Mic\\_exp & \\textasciitilde{} & msex\\_u & & 0 & -0.1637042101 & 0.096991648 & -1.68781759 & 9.144625e-02 & -0.1637042101 & -0.0761729958\\\\\n", + "\t APOC1\\_DeJager\\_Mic\\_exp & \\textasciitilde{} & age\\_death\\_u & & 0 & 0.0091450403 & 0.006699585 & 1.36501593 & 1.722480e-01 & 0.0091450403 & 0.0589190344\\\\\n", + "\t APOC1\\_DeJager\\_Mic\\_exp & \\textasciitilde{} & pmi\\_u & & 0 & 0.0026202526 & 0.008662773 & 0.30247273 & 7.622917e-01 & 0.0026202526 & 0.0190076138\\\\\n", + "\t APOE\\_Mega\\_Mic\\_exp & \\textasciitilde{} & chr19\\_44954310\\_T\\_C & a4 & 0 & 0.2144028883 & 0.049140677 & 4.36304302 & 1.282657e-05 & 0.2144028883 & 0.1554473295\\\\\n", + "\t APOE\\_Mega\\_Mic\\_exp & \\textasciitilde{} & msex\\_u & & 0 & -0.2064546169 & 0.073353668 & -2.81450981 & 4.885169e-03 & -0.2064546169 & -0.1011742024\\\\\n", + "\t APOE\\_Mega\\_Mic\\_exp & \\textasciitilde{} & age\\_death\\_u & & 0 & 0.0219942278 & 0.005180132 & 4.24588154 & 2.177356e-05 & 0.0219942278 & 0.1492391336\\\\\n", + "\t APOE\\_Mega\\_Mic\\_exp & \\textasciitilde{} & pmi\\_u & & 0 & 0.0108521575 & 0.005987077 & 1.81259700 & 6.989400e-02 & 0.0108521575 & 0.0829095455\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & APOC4\\_APOC2\\_AC\\_exp & b1 & 0 & -0.7456022896 & 1.076453950 & -0.69264671 & 4.885313e-01 & -0.7456022896 & -0.6939264950\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & APOC2\\_AC\\_exp & b2 & 0 & 0.6776563284 & 1.075514663 & 0.63007633 & 5.286446e-01 & 0.6776563284 & 0.6313277562\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & APOC1\\_DeJager\\_Mic\\_exp & b3 & 0 & -0.0007661479 & 0.067434915 & -0.01136129 & 9.909352e-01 & -0.0007661479 & -0.0007408807\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & APOE\\_Mega\\_Mic\\_exp & b4 & 0 & 0.0764241644 & 0.057933406 & 1.31917264 & 1.871114e-01 & 0.0764241644 & 0.0701717585\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & chr19\\_44954310\\_T\\_C & cp & 0 & 0.1575068431 & 0.045331187 & 3.47458013 & 5.116535e-04 & 0.1575068431 & 0.1048536907\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & educ & & 0 & 0.0015939101 & 0.008606436 & 0.18519980 & 8.530723e-01 & 0.0015939101 & 0.0054460791\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & apoe4\\_dose & & 0 & 0.7392854518 & 0.063738501 & 11.59872675 & 0.000000e+00 & 0.7392854518 & 0.3367785468\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & msex\\_u & & 0 & -0.0448071037 & 0.066118022 & -0.67768367 & 4.979723e-01 & -0.0448071037 & -0.0201615410\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & age\\_death\\_u & & 0 & 0.0255180144 & 0.004994195 & 5.10953490 & 3.229528e-07 & 0.0255180144 & 0.1589836775\\\\\n", + "\t APOC4\\_APOC2\\_AC\\_exp & \\textasciitilde{}\\textasciitilde{} & APOC2\\_AC\\_exp & & 0 & 0.8890201854 & 0.051662730 & 17.20815343 & 0.000000e+00 & 0.8890201854 & 0.9992049265\\\\\n", + "\t APOC4\\_APOC2\\_AC\\_exp & \\textasciitilde{}\\textasciitilde{} & APOC1\\_DeJager\\_Mic\\_exp & & 0 & 0.2047595639 & 0.053676552 & 3.81469293 & 1.363524e-04 & 0.2047595639 & 0.2167735573\\\\\n", + "\t APOC4\\_APOC2\\_AC\\_exp & \\textasciitilde{}\\textasciitilde{} & APOE\\_Mega\\_Mic\\_exp & & 0 & 0.0527324129 & 0.041565782 & 1.26864961 & 2.045661e-01 & 0.0527324129 & 0.0598129609\\\\\n", + "\t \u22ee & \u22ee & \u22ee & \u22ee & \u22ee & \u22ee & \u22ee & \u22ee & \u22ee & \u22ee & \u22ee\\\\\n", + "\t apoe4\\_dose & \\textasciitilde{}\\textasciitilde{} & apoe4\\_dose & & 0 & 0.2306890085 & 0.009811347 & 23.51247132 & 0.000000e+00 & 0.2306890085 & 1.000000000\\\\\n", + "\t APOC4\\_APOC2\\_AC\\_exp & \\textasciitilde{}1 & & & 0 & -2.2680581952 & 0.553123947 & -4.10045200 & 4.123440e-05 & -2.2680581952 & -2.311355590\\\\\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{}1 & & & 0 & -2.2475738228 & 0.553948994 & -4.05736601 & 4.962927e-05 & -2.2475738228 & -2.288165261\\\\\n", + "\t APOC1\\_DeJager\\_Mic\\_exp & \\textasciitilde{}1 & & & 0 & -0.9644830256 & 0.613193535 & -1.57288518 & 1.157454e-01 & -0.9644830256 & -0.945970804\\\\\n", + "\t APOE\\_Mega\\_Mic\\_exp & \\textasciitilde{}1 & & & 0 & -2.1590978024 & 0.470269561 & -4.59119191 & 4.407219e-06 & -2.1590978024 & -2.230280371\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{}1 & & & 0 & -1.4804339149 & 0.485833787 & -3.04720247 & 2.309821e-03 & -1.4804339149 & -1.404131664\\\\\n", + "\t chr19\\_44954310\\_T\\_C & \\textasciitilde{}1 & & & 0 & 0.8751084138 & 0.020670493 & 42.33611732 & 0.000000e+00 & 0.8751084138 & 1.246798791\\\\\n", + "\t msex\\_u & \\textasciitilde{}1 & & & 0 & 0.3420871409 & 0.014050996 & 24.34611259 & 0.000000e+00 & 0.3420871409 & 0.721072123\\\\\n", + "\t age\\_death\\_u & \\textasciitilde{}1 & & & 0 & 88.9523653352 & 0.194552189 & 457.21595726 & 0.000000e+00 & 88.9523653352 & 13.541620792\\\\\n", + "\t pmi\\_u & \\textasciitilde{}1 & & & 0 & 8.7444331690 & 0.219046858 & 39.92037703 & 0.000000e+00 & 8.7444331690 & 1.182307275\\\\\n", + "\t ROS\\_study\\_u & \\textasciitilde{}1 & & & 0 & 0.4995853763 & 0.014970770 & 33.37072105 & 0.000000e+00 & 0.4995853763 & 0.999185389\\\\\n", + "\t educ & \\textasciitilde{}1 & & & 0 & 16.3680597807 & 0.107900980 & 151.69519047 & 0.000000e+00 & 16.3680597807 & 4.543556087\\\\\n", + "\t apoe4\\_dose & \\textasciitilde{}1 & & & 0 & 0.2722568516 & 0.014433873 & 18.86235640 & 0.000000e+00 & 0.2722568516 & 0.566846366\\\\\n", + "\t ind1 & := & a1*b1 & ind1 & 0 & -0.2066999749 & 0.301445210 & -0.68569666 & 4.929044e-01 & -0.2066999749 & -0.137601991\\\\\n", + "\t ind2 & := & a2*b2 & ind2 & 0 & 0.1857000159 & 0.297275563 & 0.62467299 & 5.321857e-01 & 0.1857000159 & 0.123622134\\\\\n", + "\t ind3 & := & a3*b3 & ind3 & 0 & -0.0001703162 & 0.014986532 & -0.01136462 & 9.909325e-01 & -0.0001703162 & -0.000113381\\\\\n", + "\t ind4 & := & a4*b4 & ind4 & 0 & 0.0163855616 & 0.013018679 & 1.25861936 & 2.081679e-01 & 0.0163855616 & 0.010908012\\\\\n", + "\t total\\_indirect & := & ind1+ind2+ind3+ind4 & total\\_indirect & 0 & -0.0047847136 & 0.015614549 & -0.30642662 & 7.592798e-01 & -0.0047847136 & -0.003185226\\\\\n", + "\t total & := & cp+total\\_indirect & total & 0 & 0.1527221295 & 0.043022423 & 3.54982628 & 3.854854e-04 & 0.1527221295 & 0.101668465\\\\\n", + "\t prop1 & := & ind1/total & prop1 & 0 & -1.3534382710 & 2.006669349 & -0.67447000 & 5.000126e-01 & -1.3534382710 & -1.353438271\\\\\n", + "\t prop2 & := & ind2/total & prop2 & 0 & 1.2159339089 & 1.972859386 & 0.61633075 & 5.376762e-01 & 1.2159339089 & 1.215933909\\\\\n", + "\t prop3 & := & ind3/total & prop3 & 0 & -0.0011152032 & 0.098128362 & -0.01136474 & 9.909324e-01 & -0.0011152032 & -0.001115203\\\\\n", + "\t prop4 & := & ind4/total & prop4 & 0 & 0.1072900282 & 0.089734450 & 1.19563923 & 2.318374e-01 & 0.1072900282 & 0.107290028\\\\\n", + "\t prop\\_total & := & total\\_indirect/total & prop\\_total & 0 & -0.0313295371 & 0.102882095 & -0.30451885 & 7.607326e-01 & -0.0313295371 & -0.031329537\\\\\n", + "\t diff\\_1\\_2 & := & ind1-ind2 & diff\\_1\\_2 & 0 & -0.3923999909 & 0.598585725 & -0.65554519 & 5.121168e-01 & -0.3923999909 & -0.261224125\\\\\n", + "\t diff\\_1\\_3 & := & ind1-ind3 & diff\\_1\\_3 & 0 & -0.2065296587 & 0.300550607 & -0.68717099 & 4.919750e-01 & -0.2065296587 & -0.137488610\\\\\n", + "\t diff\\_1\\_4 & := & ind1-ind4 & diff\\_1\\_4 & 0 & -0.2230855365 & 0.302002311 & -0.73868818 & 4.600964e-01 & -0.2230855365 & -0.148510004\\\\\n", + "\t diff\\_2\\_3 & := & ind2-ind3 & diff\\_2\\_3 & 0 & 0.1858703321 & 0.299108165 & 0.62141511 & 5.343265e-01 & 0.1858703321 & 0.123735515\\\\\n", + "\t diff\\_2\\_4 & := & ind2-ind4 & diff\\_2\\_4 & 0 & 0.1693144543 & 0.297199151 & 0.56970033 & 5.688810e-01 & 0.1693144543 & 0.112714121\\\\\n", + "\t diff\\_3\\_4 & := & ind3-ind4 & diff\\_3\\_4 & 0 & -0.0165558778 & 0.025815755 & -0.64130907 & 5.213219e-01 & -0.0165558778 & -0.011021393\\\\\n", + "\\end{tabular}\n", + "\n", + "\\end{description}\n" + ], + "text/markdown": [ + "$header\n", + ": $lavaan.version\n", + ": '0.6-21'\n", + "$sam.approach\n", + ": FALSE\n", + "$optim.method\n", + ": 'nlminb'\n", + "$optim.iterations\n", + ": 254\n", + "$optim.converged\n", + ": TRUE\n", + "\n", + "\n", + "\n", + "$optim\n", + ": $estimator\n", + ": 'ML'\n", + "$estimator.args\n", + ": \n", + "\n", + "\n", + "$optim.method\n", + ": 'nlminb'\n", + "$npar\n", + ": 78\n", + "$eq.constraints\n", + ": FALSE\n", + "$nrow.ceq.jac\n", + ": 0\n", + "$nrow.cin.jac\n", + ": 0\n", + "$nrow.con.jac\n", + ": 0\n", + "$con.jac.rank\n", + ": 0\n", + "\n", + "\n", + "\n", + "$data\n", + ": $ngroups\n", + ": 1\n", + "$nobs\n", + ": 1153\n", + "$npatterns\n", + ": 24\n", + "\n", + "\n", + "\n", + "$test\n", + ": **$standard** = $test\n", + ": 'standard'\n", + "$stat\n", + ": 18.7701729339465\n", + "$stat.group\n", + ": 18.7701729339465\n", + "$df\n", + ": 12\n", + "$refdistr\n", + ": 'chisq'\n", + "$pvalue\n", + ": 0.0942278583827429\n", + "\n", + "\n", + "\n", + "$fit\n", + ": npar\n", + ": 78fmin\n", + ": 0.00813971072590913chisq\n", + ": 18.7701729339465df\n", + ": 12pvalue\n", + ": 0.0942278583827429baseline.chisq\n", + ": 4343.57129642534baseline.df\n", + ": 45baseline.pvalue\n", + ": 0cfi\n", + ": 0.998425017879876tli\n", + ": 0.994093817049536cfi.robust\n", + ": 0.999078231378507tli.robust\n", + ": 0.996543367669401logl\n", + ": -16646.0633251899unrestricted.logl\n", + ": -16636.6782387229aic\n", + ": 33448.1266503798bic\n", + ": 33842.0362069607ntotal\n", + ": 1153bic2\n", + ": 33594.2831902032rmsea\n", + ": 0.0221204829440917rmsea.ci.lower\n", + ": 0rmsea.ci.upper\n", + ": 0.0404585446803905rmsea.ci.level\n", + ": 0.9rmsea.pvalue\n", + ": 0.995882357690177rmsea.close.h0\n", + ": 0.05rmsea.notclose.pvalue\n", + ": 1.56873050133425e-09rmsea.notclose.h0\n", + ": 0.08rmsea.robust\n", + ": 0.0237487835520414rmsea.ci.lower.robust\n", + ": 0rmsea.ci.upper.robust\n", + ": 0.0526428526851834rmsea.pvalue.robust\n", + ": 0.928615019776227rmsea.notclose.pvalue.robust\n", + ": 0.000161741074149594srmr\n", + ": 0.0199924392054754\n", + "\n", + "\n", + "$pe\n", + ": \n", + "A data.frame: 95 \u00d7 11\n", + "\n", + "| lhs <chr> | op <chr> | rhs <chr> | label <chr> | exo <int> | est <dbl> | se <dbl> | z <dbl> | pvalue <dbl> | std.lv <dbl> | std.all <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|\n", + "| APOC4_APOC2_AC_exp | ~ | chr19_44954310_T_C | a1 | 0 | 0.2772255099 | 0.056046493 | 4.94634893 | 7.561836e-07 | 0.2772255099 | 0.1982947649 |\n", + "| APOC4_APOC2_AC_exp | ~ | msex_u | | 0 | 0.0973248072 | 0.081540339 | 1.19357865 | 2.326428e-01 | 0.0973248072 | 0.0470537419 |\n", + "| APOC4_APOC2_AC_exp | ~ | age_death_u | | 0 | 0.0225059509 | 0.006027599 | 3.73381702 | 1.885995e-04 | 0.0225059509 | 0.1506595934 |\n", + "| APOC4_APOC2_AC_exp | ~ | pmi_u | | 0 | 0.0115816028 | 0.007900552 | 1.46592331 | 1.426692e-01 | 0.0115816028 | 0.0872936269 |\n", + "| APOC4_APOC2_AC_exp | ~ | ROS_study_u | | 0 | -0.1615477638 | 0.077152953 | -2.09386364 | 3.627213e-02 | -0.1615477638 | -0.0823146553 |\n", + "| APOC2_AC_exp | ~ | chr19_44954310_T_C | a2 | 0 | 0.2740327333 | 0.056130883 | 4.88203140 | 1.049985e-06 | 0.2740327333 | 0.1958129238 |\n", + "| APOC2_AC_exp | ~ | msex_u | | 0 | 0.0963046998 | 0.081664436 | 1.17927344 | 2.382893e-01 | 0.0963046998 | 0.0465134922 |\n", + "| APOC2_AC_exp | ~ | age_death_u | | 0 | 0.0222362216 | 0.006036591 | 3.68357247 | 2.299879e-04 | 0.0222362216 | 0.1487035267 |\n", + "| APOC2_AC_exp | ~ | pmi_u | | 0 | 0.0122176049 | 0.007914006 | 1.54379522 | 1.226379e-01 | 0.0122176049 | 0.0919942746 |\n", + "| APOC2_AC_exp | ~ | ROS_study_u | | 0 | -0.1596497155 | 0.077249162 | -2.06668539 | 3.876381e-02 | -0.1596497155 | -0.0812653133 |\n", + "| APOC1_DeJager_Mic_exp | ~ | chr19_44954310_T_C | a3 | 0 | 0.2223019945 | 0.064176510 | 3.46391530 | 5.323741e-04 | 0.2223019945 | 0.1530354308 |\n", + "| APOC1_DeJager_Mic_exp | ~ | msex_u | | 0 | -0.1637042101 | 0.096991648 | -1.68781759 | 9.144625e-02 | -0.1637042101 | -0.0761729958 |\n", + "| APOC1_DeJager_Mic_exp | ~ | age_death_u | | 0 | 0.0091450403 | 0.006699585 | 1.36501593 | 1.722480e-01 | 0.0091450403 | 0.0589190344 |\n", + "| APOC1_DeJager_Mic_exp | ~ | pmi_u | | 0 | 0.0026202526 | 0.008662773 | 0.30247273 | 7.622917e-01 | 0.0026202526 | 0.0190076138 |\n", + "| APOE_Mega_Mic_exp | ~ | chr19_44954310_T_C | a4 | 0 | 0.2144028883 | 0.049140677 | 4.36304302 | 1.282657e-05 | 0.2144028883 | 0.1554473295 |\n", + "| APOE_Mega_Mic_exp | ~ | msex_u | | 0 | -0.2064546169 | 0.073353668 | -2.81450981 | 4.885169e-03 | -0.2064546169 | -0.1011742024 |\n", + "| APOE_Mega_Mic_exp | ~ | age_death_u | | 0 | 0.0219942278 | 0.005180132 | 4.24588154 | 2.177356e-05 | 0.0219942278 | 0.1492391336 |\n", + "| APOE_Mega_Mic_exp | ~ | pmi_u | | 0 | 0.0108521575 | 0.005987077 | 1.81259700 | 6.989400e-02 | 0.0108521575 | 0.0829095455 |\n", + "| caa_neo4 | ~ | APOC4_APOC2_AC_exp | b1 | 0 | -0.7456022896 | 1.076453950 | -0.69264671 | 4.885313e-01 | -0.7456022896 | -0.6939264950 |\n", + "| caa_neo4 | ~ | APOC2_AC_exp | b2 | 0 | 0.6776563284 | 1.075514663 | 0.63007633 | 5.286446e-01 | 0.6776563284 | 0.6313277562 |\n", + "| caa_neo4 | ~ | APOC1_DeJager_Mic_exp | b3 | 0 | -0.0007661479 | 0.067434915 | -0.01136129 | 9.909352e-01 | -0.0007661479 | -0.0007408807 |\n", + "| caa_neo4 | ~ | APOE_Mega_Mic_exp | b4 | 0 | 0.0764241644 | 0.057933406 | 1.31917264 | 1.871114e-01 | 0.0764241644 | 0.0701717585 |\n", + "| caa_neo4 | ~ | chr19_44954310_T_C | cp | 0 | 0.1575068431 | 0.045331187 | 3.47458013 | 5.116535e-04 | 0.1575068431 | 0.1048536907 |\n", + "| caa_neo4 | ~ | educ | | 0 | 0.0015939101 | 0.008606436 | 0.18519980 | 8.530723e-01 | 0.0015939101 | 0.0054460791 |\n", + "| caa_neo4 | ~ | apoe4_dose | | 0 | 0.7392854518 | 0.063738501 | 11.59872675 | 0.000000e+00 | 0.7392854518 | 0.3367785468 |\n", + "| caa_neo4 | ~ | msex_u | | 0 | -0.0448071037 | 0.066118022 | -0.67768367 | 4.979723e-01 | -0.0448071037 | -0.0201615410 |\n", + "| caa_neo4 | ~ | age_death_u | | 0 | 0.0255180144 | 0.004994195 | 5.10953490 | 3.229528e-07 | 0.0255180144 | 0.1589836775 |\n", + "| APOC4_APOC2_AC_exp | ~~ | APOC2_AC_exp | | 0 | 0.8890201854 | 0.051662730 | 17.20815343 | 0.000000e+00 | 0.8890201854 | 0.9992049265 |\n", + "| APOC4_APOC2_AC_exp | ~~ | APOC1_DeJager_Mic_exp | | 0 | 0.2047595639 | 0.053676552 | 3.81469293 | 1.363524e-04 | 0.2047595639 | 0.2167735573 |\n", + "| APOC4_APOC2_AC_exp | ~~ | APOE_Mega_Mic_exp | | 0 | 0.0527324129 | 0.041565782 | 1.26864961 | 2.045661e-01 | 0.0527324129 | 0.0598129609 |\n", + "| \u22ee | \u22ee | \u22ee | \u22ee | \u22ee | \u22ee | \u22ee | \u22ee | \u22ee | \u22ee | \u22ee |\n", + "| apoe4_dose | ~~ | apoe4_dose | | 0 | 0.2306890085 | 0.009811347 | 23.51247132 | 0.000000e+00 | 0.2306890085 | 1.000000000 |\n", + "| APOC4_APOC2_AC_exp | ~1 | | | 0 | -2.2680581952 | 0.553123947 | -4.10045200 | 4.123440e-05 | -2.2680581952 | -2.311355590 |\n", + "| APOC2_AC_exp | ~1 | | | 0 | -2.2475738228 | 0.553948994 | -4.05736601 | 4.962927e-05 | -2.2475738228 | -2.288165261 |\n", + "| APOC1_DeJager_Mic_exp | ~1 | | | 0 | -0.9644830256 | 0.613193535 | -1.57288518 | 1.157454e-01 | -0.9644830256 | -0.945970804 |\n", + "| APOE_Mega_Mic_exp | ~1 | | | 0 | -2.1590978024 | 0.470269561 | -4.59119191 | 4.407219e-06 | -2.1590978024 | -2.230280371 |\n", + "| caa_neo4 | ~1 | | | 0 | -1.4804339149 | 0.485833787 | -3.04720247 | 2.309821e-03 | -1.4804339149 | -1.404131664 |\n", + "| chr19_44954310_T_C | ~1 | | | 0 | 0.8751084138 | 0.020670493 | 42.33611732 | 0.000000e+00 | 0.8751084138 | 1.246798791 |\n", + "| msex_u | ~1 | | | 0 | 0.3420871409 | 0.014050996 | 24.34611259 | 0.000000e+00 | 0.3420871409 | 0.721072123 |\n", + "| age_death_u | ~1 | | | 0 | 88.9523653352 | 0.194552189 | 457.21595726 | 0.000000e+00 | 88.9523653352 | 13.541620792 |\n", + "| pmi_u | ~1 | | | 0 | 8.7444331690 | 0.219046858 | 39.92037703 | 0.000000e+00 | 8.7444331690 | 1.182307275 |\n", + "| ROS_study_u | ~1 | | | 0 | 0.4995853763 | 0.014970770 | 33.37072105 | 0.000000e+00 | 0.4995853763 | 0.999185389 |\n", + "| educ | ~1 | | | 0 | 16.3680597807 | 0.107900980 | 151.69519047 | 0.000000e+00 | 16.3680597807 | 4.543556087 |\n", + "| apoe4_dose | ~1 | | | 0 | 0.2722568516 | 0.014433873 | 18.86235640 | 0.000000e+00 | 0.2722568516 | 0.566846366 |\n", + "| ind1 | := | a1*b1 | ind1 | 0 | -0.2066999749 | 0.301445210 | -0.68569666 | 4.929044e-01 | -0.2066999749 | -0.137601991 |\n", + "| ind2 | := | a2*b2 | ind2 | 0 | 0.1857000159 | 0.297275563 | 0.62467299 | 5.321857e-01 | 0.1857000159 | 0.123622134 |\n", + "| ind3 | := | a3*b3 | ind3 | 0 | -0.0001703162 | 0.014986532 | -0.01136462 | 9.909325e-01 | -0.0001703162 | -0.000113381 |\n", + "| ind4 | := | a4*b4 | ind4 | 0 | 0.0163855616 | 0.013018679 | 1.25861936 | 2.081679e-01 | 0.0163855616 | 0.010908012 |\n", + "| total_indirect | := | ind1+ind2+ind3+ind4 | total_indirect | 0 | -0.0047847136 | 0.015614549 | -0.30642662 | 7.592798e-01 | -0.0047847136 | -0.003185226 |\n", + "| total | := | cp+total_indirect | total | 0 | 0.1527221295 | 0.043022423 | 3.54982628 | 3.854854e-04 | 0.1527221295 | 0.101668465 |\n", + "| prop1 | := | ind1/total | prop1 | 0 | -1.3534382710 | 2.006669349 | -0.67447000 | 5.000126e-01 | -1.3534382710 | -1.353438271 |\n", + "| prop2 | := | ind2/total | prop2 | 0 | 1.2159339089 | 1.972859386 | 0.61633075 | 5.376762e-01 | 1.2159339089 | 1.215933909 |\n", + "| prop3 | := | ind3/total | prop3 | 0 | -0.0011152032 | 0.098128362 | -0.01136474 | 9.909324e-01 | -0.0011152032 | -0.001115203 |\n", + "| prop4 | := | ind4/total | prop4 | 0 | 0.1072900282 | 0.089734450 | 1.19563923 | 2.318374e-01 | 0.1072900282 | 0.107290028 |\n", + "| prop_total | := | total_indirect/total | prop_total | 0 | -0.0313295371 | 0.102882095 | -0.30451885 | 7.607326e-01 | -0.0313295371 | -0.031329537 |\n", + "| diff_1_2 | := | ind1-ind2 | diff_1_2 | 0 | -0.3923999909 | 0.598585725 | -0.65554519 | 5.121168e-01 | -0.3923999909 | -0.261224125 |\n", + "| diff_1_3 | := | ind1-ind3 | diff_1_3 | 0 | -0.2065296587 | 0.300550607 | -0.68717099 | 4.919750e-01 | -0.2065296587 | -0.137488610 |\n", + "| diff_1_4 | := | ind1-ind4 | diff_1_4 | 0 | -0.2230855365 | 0.302002311 | -0.73868818 | 4.600964e-01 | -0.2230855365 | -0.148510004 |\n", + "| diff_2_3 | := | ind2-ind3 | diff_2_3 | 0 | 0.1858703321 | 0.299108165 | 0.62141511 | 5.343265e-01 | 0.1858703321 | 0.123735515 |\n", + "| diff_2_4 | := | ind2-ind4 | diff_2_4 | 0 | 0.1693144543 | 0.297199151 | 0.56970033 | 5.688810e-01 | 0.1693144543 | 0.112714121 |\n", + "| diff_3_4 | := | ind3-ind4 | diff_3_4 | 0 | -0.0165558778 | 0.025815755 | -0.64130907 | 5.213219e-01 | -0.0165558778 | -0.011021393 |\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "lavaan 0.6-21 ended normally after 254 iterations\n", + "\n", + " Estimator ML\n", + " Optimization method NLMINB\n", + " Number of model parameters 78\n", + "\n", + " Number of observations 1153\n", + " Number of missing patterns 24\n", + "\n", + "Model Test User Model:\n", + " \n", + " Test statistic 18.770\n", + " Degrees of freedom 12\n", + " P-value (Chi-square) 0.094\n", + "\n", + "Model Test Baseline Model:\n", + "\n", + " Test statistic 4343.571\n", + " Degrees of freedom 45\n", + " P-value 0.000\n", + "\n", + "User Model versus Baseline Model:\n", + "\n", + " Comparative Fit Index (CFI) 0.998\n", + " Tucker-Lewis Index (TLI) 0.994\n", + " \n", + " Robust Comparative Fit Index (CFI) 0.999\n", + " Robust Tucker-Lewis Index (TLI) 0.997\n", + "\n", + "Loglikelihood and Information Criteria:\n", + "\n", + " Loglikelihood user model (H0) -16646.063\n", + " Loglikelihood unrestricted model (H1) -16636.678\n", + " \n", + " Akaike (AIC) 33448.127\n", + " Bayesian (BIC) 33842.036\n", + " Sample-size adjusted Bayesian (SABIC) 33594.283\n", + "\n", + "Root Mean Square Error of Approximation:\n", + "\n", + " RMSEA 0.022\n", + " 90 Percent confidence interval - lower 0.000\n", + " 90 Percent confidence interval - upper 0.040\n", + " P-value H_0: RMSEA <= 0.050 0.996\n", + " P-value H_0: RMSEA >= 0.080 0.000\n", + " \n", + " Robust RMSEA 0.024\n", + " 90 Percent confidence interval - lower 0.000\n", + " 90 Percent confidence interval - upper 0.053\n", + " P-value H_0: Robust RMSEA <= 0.050 0.929\n", + " P-value H_0: Robust RMSEA >= 0.080 0.000\n", + "\n", + "Standardized Root Mean Square Residual:\n", + "\n", + " SRMR 0.020\n", + "\n", + "Parameter Estimates:\n", + "\n", + " Standard errors Standard\n", + " Information Observed\n", + " Observed information based on Hessian\n", + "\n", + "Regressions:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv Std.all\n", + " APOC4_APOC2_AC_exp ~ \n", + " c19_44954 (a1) 0.277 0.056 4.946 0.000 0.277 0.198\n", + " msex_u 0.097 0.082 1.194 0.233 0.097 0.047\n", + " age_deth_ 0.023 0.006 3.734 0.000 0.023 0.151\n", + " pmi_u 0.012 0.008 1.466 0.143 0.012 0.087\n", + " ROS_stdy_ -0.162 0.077 -2.094 0.036 -0.162 -0.082\n", + " APOC2_AC_exp ~ \n", + " c19_44954 (a2) 0.274 0.056 4.882 0.000 0.274 0.196\n", + " msex_u 0.096 0.082 1.179 0.238 0.096 0.047\n", + " age_deth_ 0.022 0.006 3.684 0.000 0.022 0.149\n", + " pmi_u 0.012 0.008 1.544 0.123 0.012 0.092\n", + " ROS_stdy_ -0.160 0.077 -2.067 0.039 -0.160 -0.081\n", + " APOC1_DeJager_Mic_exp ~ \n", + " c19_44954 (a3) 0.222 0.064 3.464 0.001 0.222 0.153\n", + " msex_u -0.164 0.097 -1.688 0.091 -0.164 -0.076\n", + " age_deth_ 0.009 0.007 1.365 0.172 0.009 0.059\n", + " pmi_u 0.003 0.009 0.302 0.762 0.003 0.019\n", + " APOE_Mega_Mic_exp ~ \n", + " c19_44954 (a4) 0.214 0.049 4.363 0.000 0.214 0.155\n", + " msex_u -0.206 0.073 -2.815 0.005 -0.206 -0.101\n", + " age_deth_ 0.022 0.005 4.246 0.000 0.022 0.149\n", + " pmi_u 0.011 0.006 1.813 0.070 0.011 0.083\n", + " caa_neo4 ~ \n", + " APOC4_APO (b1) -0.746 1.076 -0.693 0.489 -0.746 -0.694\n", + " APOC2_AC_ (b2) 0.678 1.076 0.630 0.529 0.678 0.631\n", + " APOC1_DJ_ (b3) -0.001 0.067 -0.011 0.991 -0.001 -0.001\n", + " APOE_M_M_ (b4) 0.076 0.058 1.319 0.187 0.076 0.070\n", + " c19_44954 (cp) 0.158 0.045 3.475 0.001 0.158 0.105\n", + " educ 0.002 0.009 0.185 0.853 0.002 0.005\n", + " apoe4_dos 0.739 0.064 11.599 0.000 0.739 0.337\n", + " msex_u -0.045 0.066 -0.678 0.498 -0.045 -0.020\n", + " age_deth_ 0.026 0.005 5.110 0.000 0.026 0.159\n", + "\n", + "Covariances:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv\n", + " .APOC4_APOC2_AC_exp ~~ \n", + " .APOC2_AC_exp 0.889 0.052 17.208 0.000 0.889\n", + " .APOC1_DJgr_Mc_ 0.205 0.054 3.815 0.000 0.205\n", + " .APOE_Meg_Mc_xp 0.053 0.042 1.269 0.205 0.053\n", + " .APOC2_AC_exp ~~ \n", + " .APOC1_DJgr_Mc_ 0.208 0.054 3.859 0.000 0.208\n", + " .APOE_Meg_Mc_xp 0.054 0.042 1.294 0.196 0.054\n", + " .APOC1_DeJager_Mic_exp ~~ \n", + " .APOE_Meg_Mc_xp 0.571 0.050 11.400 0.000 0.571\n", + " chr19_44954310_T_C ~~ \n", + " msex_u 0.004 0.010 0.435 0.664 0.004\n", + " age_death_u -0.043 0.136 -0.319 0.750 -0.043\n", + " pmi_u -0.389 0.154 -2.526 0.012 -0.389\n", + " ROS_study_u -0.006 0.011 -0.606 0.544 -0.006\n", + " educ 0.026 0.076 0.340 0.734 0.026\n", + " apoe4_dose 0.022 0.010 2.136 0.033 0.022\n", + " msex_u ~~ \n", + " age_death_u -0.582 0.094 -6.202 0.000 -0.582\n", + " pmi_u 0.092 0.104 0.883 0.377 0.092\n", + " ROS_study_u 0.005 0.007 0.772 0.440 0.005\n", + " educ 0.344 0.052 6.607 0.000 0.344\n", + " apoe4_dose -0.000 0.007 -0.068 0.945 -0.000\n", + " age_death_u ~~ \n", + " pmi_u 0.793 1.439 0.551 0.582 0.793\n", + " ROS_study_u -0.464 0.099 -4.676 0.000 -0.464\n", + " educ -3.173 0.715 -4.439 0.000 -3.173\n", + " apoe4_dose -0.345 0.095 -3.625 0.000 -0.345\n", + " pmi_u ~~ \n", + " ROS_study_u 0.047 0.110 0.429 0.668 0.047\n", + " educ 0.099 0.794 0.124 0.901 0.099\n", + " apoe4_dose -0.073 0.107 -0.681 0.496 -0.073\n", + " ROS_study_u ~~ \n", + " educ 0.882 0.060 14.676 0.000 0.882\n", + " apoe4_dose 0.014 0.007 1.922 0.055 0.014\n", + " educ ~~ \n", + " apoe4_dose 0.132 0.052 2.525 0.012 0.132\n", + " Std.all\n", + " \n", + " 0.999\n", + " 0.217\n", + " 0.060\n", + " \n", + " 0.220\n", + " 0.061\n", + " \n", + " 0.609\n", + " \n", + " 0.013\n", + " -0.009\n", + " -0.075\n", + " -0.018\n", + " 0.010\n", + " 0.064\n", + " \n", + " -0.187\n", + " 0.026\n", + " 0.023\n", + " 0.201\n", + " -0.002\n", + " \n", + " 0.016\n", + " -0.141\n", + " -0.134\n", + " -0.109\n", + " \n", + " 0.013\n", + " 0.004\n", + " -0.020\n", + " \n", + " 0.490\n", + " 0.058\n", + " \n", + " 0.076\n", + "\n", + "Intercepts:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv Std.all\n", + " .APOC4_APOC2_AC -2.268 0.553 -4.100 0.000 -2.268 -2.311\n", + " .APOC2_AC_exp -2.248 0.554 -4.057 0.000 -2.248 -2.288\n", + " .APOC1_DJgr_Mc_ -0.964 0.613 -1.573 0.116 -0.964 -0.946\n", + " .APOE_Meg_Mc_xp -2.159 0.470 -4.591 0.000 -2.159 -2.230\n", + " .caa_neo4 -1.480 0.486 -3.047 0.002 -1.480 -1.404\n", + " c19_44954310_T 0.875 0.021 42.336 0.000 0.875 1.247\n", + " msex_u 0.342 0.014 24.346 0.000 0.342 0.721\n", + " age_death_u 88.952 0.195 457.216 0.000 88.952 13.542\n", + " pmi_u 8.744 0.219 39.920 0.000 8.744 1.182\n", + " ROS_study_u 0.500 0.015 33.371 0.000 0.500 0.999\n", + " educ 16.368 0.108 151.695 0.000 16.368 4.544\n", + " apoe4_dose 0.272 0.014 18.862 0.000 0.272 0.567\n", + "\n", + "Variances:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv Std.all\n", + " .APOC4_APOC2_AC 0.888 0.052 17.215 0.000 0.888 0.923\n", + " .APOC2_AC_exp 0.891 0.052 17.215 0.000 0.891 0.924\n", + " .APOC1_DJgr_Mc_ 1.004 0.071 14.122 0.000 1.004 0.966\n", + " .APOE_Meg_Mc_xp 0.875 0.046 19.153 0.000 0.875 0.934\n", + " .caa_neo4 0.942 0.041 22.736 0.000 0.942 0.848\n", + " c19_44954310_T 0.493 0.021 24.010 0.000 0.493 1.000\n", + " msex_u 0.225 0.009 23.875 0.000 0.225 1.000\n", + " age_death_u 43.149 1.807 23.875 0.000 43.149 1.000\n", + " pmi_u 54.702 2.291 23.875 0.000 54.702 1.000\n", + " ROS_study_u 0.250 0.011 23.612 0.000 0.250 1.000\n", + " educ 12.978 0.550 23.602 0.000 12.978 1.000\n", + " apoe4_dose 0.231 0.010 23.512 0.000 0.231 1.000\n", + "\n", + "Defined Parameters:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv Std.all\n", + " ind1 -0.207 0.301 -0.686 0.493 -0.207 -0.138\n", + " ind2 0.186 0.297 0.625 0.532 0.186 0.124\n", + " ind3 -0.000 0.015 -0.011 0.991 -0.000 -0.000\n", + " ind4 0.016 0.013 1.259 0.208 0.016 0.011\n", + " total_indirect -0.005 0.016 -0.306 0.759 -0.005 -0.003\n", + " total 0.153 0.043 3.550 0.000 0.153 0.102\n", + " prop1 -1.353 2.007 -0.674 0.500 -1.353 -1.353\n", + " prop2 1.216 1.973 0.616 0.538 1.216 1.216\n", + " prop3 -0.001 0.098 -0.011 0.991 -0.001 -0.001\n", + " prop4 0.107 0.090 1.196 0.232 0.107 0.107\n", + " prop_total -0.031 0.103 -0.305 0.761 -0.031 -0.031\n", + " diff_1_2 -0.392 0.599 -0.656 0.512 -0.392 -0.261\n", + " diff_1_3 -0.207 0.301 -0.687 0.492 -0.207 -0.137\n", + " diff_1_4 -0.223 0.302 -0.739 0.460 -0.223 -0.149\n", + " diff_2_3 0.186 0.299 0.621 0.534 0.186 0.124\n", + " diff_2_4 0.169 0.297 0.570 0.569 0.169 0.113\n", + " diff_3_4 -0.017 0.026 -0.641 0.521 -0.017 -0.011\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 5: FIML SEM\n", + "# ============================================================\n", + "cat(\"Fitting FIML SEM (N =\", N_total, \")...\\n\")\n", + "\n", + "fit_fiml <- tryCatch(\n", + " sem(model_str, data=dat, missing=\"fiml\", fixed.x=FALSE, estimator=\"ML\"),\n", + " error = function(e) { cat(\"FIML Error:\", conditionMessage(e), \"\\n\"); NULL }\n", + ")\n", + "\n", + "if (!is.null(fit_fiml)) {\n", + " cat(\"Converged:\", lavInspect(fit_fiml, \"converged\"), \"\\n\")\n", + " cat(\"N used:\", lavInspect(fit_fiml, \"nobs\"), \"\\n\\n\")\n", + "\n", + " # Summary\n", + " summary(fit_fiml, fit.measures=TRUE, standardized=TRUE)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:30:54.056384Z", + "iopub.status.busy": "2026-03-31T16:30:54.054157Z", + "iopub.status.idle": "2026-03-31T16:30:54.125569Z", + "shell.execute_reply": "2026-03-31T16:30:54.121963Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== FIML Key Results (N = 1153 ) ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label est se ci.lower ci.upper pvalue N\n", + "1 a1 0.277 0.056 0.167 0.387 0.000 1153\n", + "6 a2 0.274 0.056 0.164 0.384 0.000 1153\n", + "11 a3 0.222 0.064 0.097 0.348 0.001 1153\n", + "15 a4 0.214 0.049 0.118 0.311 0.000 1153\n", + "19 b1 -0.746 1.076 -2.855 1.364 0.489 1153\n", + "20 b2 0.678 1.076 -1.430 2.786 0.529 1153\n", + "21 b3 -0.001 0.067 -0.133 0.131 0.991 1153\n", + "22 b4 0.076 0.058 -0.037 0.190 0.187 1153\n", + "23 cp 0.158 0.045 0.069 0.246 0.001 1153\n", + "79 ind1 -0.207 0.301 -0.798 0.384 0.493 1153\n", + "80 ind2 0.186 0.297 -0.397 0.768 0.532 1153\n", + "81 ind3 0.000 0.015 -0.030 0.029 0.991 1153\n", + "82 ind4 0.016 0.013 -0.009 0.042 0.208 1153\n", + "83 total_indirect -0.005 0.016 -0.035 0.026 0.759 1153\n", + "84 total 0.153 0.043 0.068 0.237 0.000 1153\n", + "85 prop1 -1.353 2.007 -5.286 2.580 0.500 1153\n", + "86 prop2 1.216 1.973 -2.651 5.083 0.538 1153\n", + "87 prop3 -0.001 0.098 -0.193 0.191 0.991 1153\n", + "88 prop4 0.107 0.090 -0.069 0.283 0.232 1153\n", + "89 prop_total -0.031 0.103 -0.233 0.170 0.761 1153\n", + "90 diff_1_2 -0.392 0.599 -1.566 0.781 0.512 1153\n", + "91 diff_1_3 -0.207 0.301 -0.796 0.383 0.492 1153\n", + "92 diff_1_4 -0.223 0.302 -0.815 0.369 0.460 1153\n", + "93 diff_2_3 0.186 0.299 -0.400 0.772 0.534 1153\n", + "94 diff_2_4 0.169 0.297 -0.413 0.752 0.569 1153\n", + "95 diff_3_4 -0.017 0.026 -0.067 0.034 0.521 1153\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 5b: FIML -- Extract and save results\n", + "# ============================================================\n", + "pe_fiml <- parameterEstimates(fit_fiml, ci=TRUE)\n", + "N_fiml <- lavInspect(fit_fiml, \"nobs\")\n", + "\n", + "# Construct contrast labels properly\n", + "contrast_labels <- c()\n", + "for (i in 1:(n_med-1)) {\n", + " for (j in (i+1):n_med) {\n", + " contrast_labels <- c(contrast_labels, paste0(\"diff_\", i, \"_\", j))\n", + " }\n", + "}\n", + "\n", + "all_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\",\n", + " paste0(\"prop\", 1:n_med), \"prop_total\", contrast_labels)\n", + "\n", + "fiml_key <- pe_fiml[pe_fiml$label %in% all_labels, ]\n", + "fiml_key$method <- \"FIML\"\n", + "fiml_key$N <- N_fiml\n", + "fiml_key$exposure <- EXPOSURE\n", + "fiml_key$direction <- \"D1\"\n", + "\n", + "# Create readable label descriptions\n", + "label_desc <- c(\n", + " setNames(paste0(\"a\", 1:n_med, \" (SNP->\", MEDIATOR_SHORT, \")\"), paste0(\"a\", 1:n_med)),\n", + " setNames(paste0(\"b\", 1:n_med, \" (\", MEDIATOR_SHORT, \"->caa_neo4)\"), paste0(\"b\", 1:n_med)),\n", + " c(cp=\"cp (SNP->caa_neo4 direct)\"),\n", + " setNames(paste0(\"ind\", 1:n_med, \" (via \", MEDIATOR_SHORT, \")\"), paste0(\"ind\", 1:n_med)),\n", + " c(total_indirect=\"total_indirect\", total=\"total\")\n", + ")\n", + "\n", + "fiml_key$label_desc <- label_desc[fiml_key$label]\n", + "fiml_key$label_desc[is.na(fiml_key$label_desc)] <- fiml_key$label[is.na(fiml_key$label_desc)]\n", + "\n", + "# Save\n", + "write.csv(fiml_key, file.path(RESULT_DIR, \"main_SEM_FIML\", \"fiml_all_paths.csv\"), row.names=FALSE)\n", + "\n", + "# Display key results\n", + "cat(\"\\n=== FIML Key Results (N =\", N_fiml, \") ===\\n\")\n", + "print(fiml_key[, c(\"label\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"pvalue\", \"N\")])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:30:54.131111Z", + "iopub.status.busy": "2026-03-31T16:30:54.129489Z", + "iopub.status.idle": "2026-03-31T16:30:56.517206Z", + "shell.execute_reply": "2026-03-31T16:30:56.515040Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FIML forest plot saved.\n" + ] + }, + { + "data": { + "image/png": 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//++7Zt22jxUbt27ffee2/SpEmlr6AaOXLkmjVrvv3229dffz0qKqrMef7111+z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2Nk3kdLvdSgf5xo6mlkRmC0SiX9QcK7RZ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JTAwA4hhBBCSEpgYIcQQgghJCUwsEMIIYQQ\nkhIY2CGEEEIISQkM7BBCCCGEpAQGdgghhBBCUgIDO4QQQgghKYGBHUIIIYSQlMDADiGEEEJI\nSmBghxBCCCEkJTCwQwghhBCSEhjYIYQQQghJif8DZeBbOuRM+8sAAAAASUVORK5CYII=", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 5c: FIML -- Forest plot\n", + "# ============================================================\n", + "\n", + "# Plot only main path estimates (a, b, cp, indirect, total)\n", + "plot_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\")\n", + "plot_df <- fiml_key[fiml_key$label %in% plot_labels, ]\n", + "\n", + "# Create effect type grouping\n", + "plot_df$effect_type <- ifelse(grepl(\"^a\", plot_df$label), \"a-path (SNP->M)\",\n", + " ifelse(grepl(\"^b\", plot_df$label), \"b-path (M->Y)\",\n", + " ifelse(plot_df$label == \"cp\", \"c' (direct)\",\n", + " ifelse(grepl(\"^ind\", plot_df$label), \"Specific indirect\",\n", + " ifelse(plot_df$label == \"total_indirect\", \"Total indirect\", \"Total\")))))\n", + "\n", + "# Order labels\n", + "plot_df$label <- factor(plot_df$label, levels=rev(plot_labels))\n", + "\n", + "p_fiml <- ggplot(plot_df, aes(x=est, y=label, xmin=ci.lower, xmax=ci.upper, color=effect_type)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(size=0.6) +\n", + " labs(title=\"FIML SEM: Parallel Mediation Path Estimates\",\n", + " subtitle=paste0(\"SNP -> 4 Mediators -> caa_neo4 | N = \", N_fiml, \" (FIML)\"),\n", + " x=\"Estimate (95% CI)\", y=\"\", color=\"Effect Type\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + "ggsave(file.path(RESULT_DIR, \"main_SEM_FIML\", \"fiml_forest_plot.png\"), p_fiml, width=10, height=8, dpi=150)\n", + "ggsave(file.path(RESULT_DIR, \"main_SEM_FIML\", \"fiml_forest_plot.pdf\"), p_fiml, width=10, height=8)\n", + "print(p_fiml)\n", + "cat(\"FIML forest plot saved.\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### FIML Interpretation\n", + "\n", + "- **a-paths (a1--a4):** Effect of the SNP on each mediator (gene expression), controlling for mediator-specific covariates.\n", + "- **b-paths (b1--b4):** Effect of each mediator on caa_neo4, controlling for the SNP, all other mediators, and outcome covariates. These are **unique** effects (specific to each mediator after partialing out the others).\n", + "- **c' (direct):** Direct effect of SNP on caa_neo4, not mediated through any of the four expression mediators.\n", + "- **Specific indirect (ind1--ind4):** Unique mediation through each mediator = a_i x b_i.\n", + "- **Total indirect:** Sum of all specific indirect effects.\n", + "- **Total:** Direct + total indirect.\n", + "- **Contrasts (diff):** Differences between specific indirect effects -- tests whether one mediator carries more of the SNP effect than another." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Method 2: Bootstrap (FIML inside each replicate)\n", + "\n", + "Non-parametric bootstrap with FIML SEM inside each replicate provides percentile confidence intervals that do not assume normality of the indirect effect distribution. This is especially important for products of coefficients (a x b), which are typically non-normal." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:30:56.524544Z", + "iopub.status.busy": "2026-03-31T16:30:56.522299Z", + "iopub.status.idle": "2026-03-31T16:53:01.904692Z", + "shell.execute_reply": "2026-03-31T16:53:01.902429Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running 1000 bootstrap replicates (FIML inside, N = 1153 )...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Replicate 200 / 1000 -- converged so far: 200 \n", + " Replicate 400 / 1000 -- converged so far: 400 \n", + " Replicate 600 / 1000 -- converged so far: 600 \n", + " Replicate 800 / 1000 -- converged so far: 800 \n", + " Replicate 1000 / 1000 -- converged so far: 1000 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bootstrap complete: 1000 / 1000 replicates converged.\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 6: Bootstrap (FIML inside)\n", + "# ============================================================\n", + "set.seed(12345)\n", + "B <- 1000\n", + "\n", + "# Parameters to extract\n", + "boot_params <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\")\n", + "boot_mat <- matrix(NA, B, length(boot_params), dimnames=list(NULL, boot_params))\n", + "\n", + "cat(\"Running\", B, \"bootstrap replicates (FIML inside, N =\", N_total, \")...\\n\")\n", + "n_converged <- 0\n", + "\n", + "for (i in 1:B) {\n", + " idx <- sample(nrow(dat), replace=TRUE)\n", + " boot_dat <- dat[idx, ]\n", + " fit_b <- tryCatch(\n", + " sem(model_str, data=boot_dat, missing=\"fiml\", fixed.x=FALSE, se=\"none\", test=\"none\"),\n", + " error = function(e) NULL,\n", + " warning = function(w) {\n", + " tryCatch(sem(model_str, data=boot_dat, missing=\"fiml\", fixed.x=FALSE, se=\"none\", test=\"none\"),\n", + " error = function(e) NULL)\n", + " }\n", + " )\n", + " if (!is.null(fit_b) && lavInspect(fit_b, \"converged\")) {\n", + " pe_b <- parameterEstimates(fit_b)\n", + " for (lab in boot_params) {\n", + " row_b <- pe_b[pe_b$label == lab, ]\n", + " if (nrow(row_b) == 1) boot_mat[i, lab] <- row_b$est\n", + " }\n", + " n_converged <- n_converged + 1\n", + " }\n", + " if (i %% 200 == 0) cat(\" Replicate\", i, \"/\", B, \"-- converged so far:\", n_converged, \"\\n\")\n", + "}\n", + "\n", + "cat(\"\\nBootstrap complete:\", n_converged, \"/\", B, \"replicates converged.\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:53:02.086162Z", + "iopub.status.busy": "2026-03-31T16:53:02.081466Z", + "iopub.status.idle": "2026-03-31T16:53:02.160698Z", + "shell.execute_reply": "2026-03-31T16:53:02.158017Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Bootstrap Results (N = 1153 , 1000 converged) ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label mean se ci_lower ci_upper\n", + "a1 a1 2.756692e-01 0.05593749 0.16838188 0.38664891\n", + "a2 a2 2.724379e-01 0.05619779 0.16523781 0.38433166\n", + "a3 a3 2.202297e-01 0.05994844 0.10674124 0.34372846\n", + "a4 a4 2.138515e-01 0.04478890 0.12808918 0.30368012\n", + "b1 b1 -7.761264e-01 1.03020784 -2.86437646 1.21334986\n", + "b2 b2 7.090556e-01 1.03182314 -1.27844398 2.71977129\n", + "b3 b3 -5.665145e-04 0.07625530 -0.14828303 0.14880943\n", + "b4 b4 7.538936e-02 0.06439572 -0.05999745 0.19766965\n", + "cp cp 1.592343e-01 0.04582395 0.06892625 0.24500729\n", + "ind1 ind1 -2.159513e-01 0.29241791 -0.84021532 0.33459744\n", + "ind2 ind2 1.949332e-01 0.28894742 -0.33700796 0.81291554\n", + "ind3 ind3 1.134098e-05 0.01746445 -0.03514302 0.03790685\n", + "ind4 ind4 1.603124e-02 0.01455892 -0.01370505 0.04647205\n", + "total_indirect total_indirect -4.975499e-03 0.01687918 -0.03851697 0.02795524\n", + "total total 1.542588e-01 0.04335142 0.06675679 0.24050499\n", + " p_value\n", + "a1 0.000\n", + "a2 0.000\n", + "a3 0.000\n", + "a4 0.000\n", + "b1 0.446\n", + "b2 0.492\n", + "b3 0.986\n", + "b4 0.216\n", + "cp 0.002\n", + "ind1 0.446\n", + "ind2 0.492\n", + "ind3 0.986\n", + "ind4 0.216\n", + "total_indirect 0.778\n", + "total 0.002\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 6b: Bootstrap -- Summarize results\n", + "# ============================================================\n", + "boot_summary <- data.frame(\n", + " label = boot_params,\n", + " mean = apply(boot_mat, 2, mean, na.rm=TRUE),\n", + " se = apply(boot_mat, 2, sd, na.rm=TRUE),\n", + " ci_lower = apply(boot_mat, 2, quantile, 0.025, na.rm=TRUE),\n", + " ci_upper = apply(boot_mat, 2, quantile, 0.975, na.rm=TRUE),\n", + " stringsAsFactors=FALSE\n", + ")\n", + "\n", + "# Two-sided p-value\n", + "boot_summary$p_value <- sapply(boot_params, function(lab) {\n", + " vals <- boot_mat[, lab]\n", + " vals <- vals[!is.na(vals)]\n", + " if (length(vals) < 10) return(NA)\n", + " p_pos <- mean(vals > 0)\n", + " 2 * min(p_pos, 1 - p_pos)\n", + "})\n", + "\n", + "boot_summary$method <- \"Bootstrap\"\n", + "boot_summary$N <- N_total\n", + "boot_summary$n_converged <- n_converged\n", + "boot_summary$exposure <- EXPOSURE\n", + "boot_summary$direction <- \"D1\"\n", + "\n", + "write.csv(boot_summary, file.path(RESULT_DIR, \"bootstrap\", \"bootstrap_results.csv\"), row.names=FALSE)\n", + "\n", + "cat(\"\\n=== Bootstrap Results (N =\", N_total, \",\", n_converged, \"converged) ===\\n\")\n", + "print(boot_summary[, c(\"label\", \"mean\", \"se\", \"ci_lower\", \"ci_upper\", \"p_value\")])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:53:02.170570Z", + "iopub.status.busy": "2026-03-31T16:53:02.168091Z", + "iopub.status.idle": "2026-03-31T16:53:04.824839Z", + "shell.execute_reply": "2026-03-31T16:53:04.822592Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrap distribution plots saved.\n" + ] + }, + { + "data": { + "image/png": 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eTxxx+nL+VyecOGDe/cuSNU7tq1K810hBB/f/8BAwZs3bpVrVbL5XKhTnWiBaiZ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wWbNm3t7e3bt3v3z58r1799q1a/fy5cun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\nHpw4caIH4gE78Lo7AAAA7+WJxG737t0syy5btsxSEhwcvGb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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 6c: Bootstrap -- Distribution histograms\n", + "# ============================================================\n", + "ind_labels <- c(paste0(\"ind\", 1:n_med), \"total_indirect\")\n", + "ind_names <- c(paste0(\"Indirect via \", MEDIATOR_SHORT), \"Total Indirect\")\n", + "\n", + "plots_boot <- list()\n", + "for (k in seq_along(ind_labels)) {\n", + " lab <- ind_labels[k]\n", + " vals <- boot_mat[, lab]\n", + " vals <- vals[!is.na(vals)]\n", + " df_h <- data.frame(value=vals)\n", + " med_val <- median(vals)\n", + "\n", + " p_h <- ggplot(df_h, aes(x=value)) +\n", + " geom_histogram(bins=50, fill=\"steelblue\", alpha=0.7) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"red\", linewidth=0.8) +\n", + " geom_vline(xintercept=med_val, linetype=\"solid\", color=\"darkblue\", linewidth=0.8) +\n", + " labs(title=paste0(\"Bootstrap: \", ind_names[k]),\n", + " subtitle=paste0(\"N=\", N_total, \", \", n_converged, \" resamples\"),\n", + " x=\"Indirect Effect (a x b)\", y=\"Count\") +\n", + " theme_minimal(base_size=11)\n", + " plots_boot[[k]] <- p_h\n", + "}\n", + "\n", + "p_boot_all <- do.call(gridExtra::grid.arrange, c(plots_boot, ncol=2))\n", + "\n", + "ggsave(file.path(RESULT_DIR, \"bootstrap\", paste0(\"bootstrap_distributions_\", EXPOSURE, \".png\")),\n", + " p_boot_all, width=12, height=10, dpi=150)\n", + "ggsave(file.path(RESULT_DIR, \"bootstrap\", paste0(\"bootstrap_distributions_\", EXPOSURE, \".pdf\")),\n", + " p_boot_all, width=12, height=10)\n", + "cat(\"Bootstrap distribution plots saved.\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:53:04.833219Z", + "iopub.status.busy": "2026-03-31T16:53:04.830976Z", + "iopub.status.idle": "2026-03-31T16:53:06.018351Z", + "shell.execute_reply": "2026-03-31T16:53:06.014205Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrap forest plot saved.\n" + ] + }, + { + "data": { + "image/png": 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HjQoEGD2NhYmUzG3bK3aRwNCQmxvObutJaeYrOITXskVz4vL896ouvxAwAIHBI7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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 6d: Bootstrap -- Forest plot\n", + "# ============================================================\n", + "boot_plot_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\")\n", + "boot_plot_df <- boot_summary[boot_summary$label %in% boot_plot_labels, ]\n", + "boot_plot_df$effect_type <- ifelse(grepl(\"^a\", boot_plot_df$label), \"a-path (SNP->M)\",\n", + " ifelse(grepl(\"^b\", boot_plot_df$label), \"b-path (M->Y)\",\n", + " ifelse(boot_plot_df$label == \"cp\", \"c' (direct)\",\n", + " ifelse(grepl(\"^ind\", boot_plot_df$label), \"Specific indirect\",\n", + " ifelse(boot_plot_df$label == \"total_indirect\", \"Total indirect\", \"Total\")))))\n", + "\n", + "boot_plot_df$label <- factor(boot_plot_df$label, levels=rev(boot_plot_labels))\n", + "\n", + "p_boot_forest <- ggplot(boot_plot_df, aes(x=mean, y=label, xmin=ci_lower, xmax=ci_upper, color=effect_type)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(size=0.6) +\n", + " labs(title=\"Bootstrap: Parallel Mediation Path Estimates\",\n", + " subtitle=paste0(\"N = \", N_total, \" (FIML), \", n_converged, \" resamples\"),\n", + " x=\"Estimate (95% Percentile CI)\", y=\"\", color=\"Effect Type\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + "ggsave(file.path(RESULT_DIR, \"bootstrap\", \"bootstrap_forest_plot.png\"), p_boot_forest, width=10, height=8, dpi=150)\n", + "ggsave(file.path(RESULT_DIR, \"bootstrap\", \"bootstrap_forest_plot.pdf\"), p_boot_forest, width=10, height=8)\n", + "print(p_boot_forest)\n", + "cat(\"Bootstrap forest plot saved.\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bootstrap Interpretation\n", + "\n", + "The bootstrap provides non-parametric percentile confidence intervals for all path estimates. Because the indirect effect (a x b) is a product of two coefficients, its sampling distribution is typically non-normal and right-skewed, making bootstrap CIs more appropriate than Wald-based CIs.\n", + "\n", + "If a specific indirect effect's 95% CI excludes zero, it provides evidence that the corresponding mediator uniquely transmits the SNP effect to caa_neo4, even after controlling for the other three mediators." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Method 3: MNAR Sensitivity Analysis\n", + "\n", + "The MNAR sensitivity analysis tests how robust the mediation findings are to violations of the Missing At Random (MAR) assumption. We systematically shift the imputed values for missing mediators and outcomes by delta (in SD units) and refit the SEM to see if/when the indirect effects become non-significant." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:53:06.025377Z", + "iopub.status.busy": "2026-03-31T16:53:06.023172Z", + "iopub.status.idle": "2026-03-31T16:54:07.937407Z", + "shell.execute_reply": "2026-03-31T16:54:07.933933Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running MNAR sensitivity ( 225 grid points, N = 1153 )...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Grid point 50 / 225 \n", + " Grid point 100 / 225 \n", + " Grid point 150 / 225 \n", + " Grid point 200 / 225 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR sensitivity complete.\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 7: MNAR Sensitivity Analysis\n", + "# ============================================================\n", + "delta_range <- seq(-3, 3, length.out=15)\n", + "grid <- expand.grid(delta_med=delta_range, delta_out=delta_range)\n", + "\n", + "# Pre-compute means and SDs for mediators and outcome\n", + "med_stats <- lapply(MEDIATORS, function(m) {\n", + " list(mean=mean(dat[[m]], na.rm=TRUE), sd=sd(dat[[m]], na.rm=TRUE))\n", + "})\n", + "names(med_stats) <- MEDIATORS\n", + "out_mean <- mean(dat[[OUTCOME]], na.rm=TRUE)\n", + "out_sd <- sd(dat[[OUTCOME]], na.rm=TRUE)\n", + "\n", + "# Store results for each specific indirect + total indirect\n", + "mnar_labels <- c(paste0(\"ind\", 1:n_med), \"total_indirect\")\n", + "mnar_est <- matrix(NA, nrow(grid), length(mnar_labels), dimnames=list(NULL, mnar_labels))\n", + "mnar_pval <- matrix(NA, nrow(grid), length(mnar_labels), dimnames=list(NULL, mnar_labels))\n", + "\n", + "cat(\"Running MNAR sensitivity (\", nrow(grid), \"grid points, N =\", N_total, \")...\\n\")\n", + "\n", + "for (k in 1:nrow(grid)) {\n", + " dat_imp <- dat\n", + " # Delta-shift mediators\n", + " for (m in MEDIATORS) {\n", + " miss_idx <- is.na(dat_imp[[m]])\n", + " dat_imp[[m]][miss_idx] <- med_stats[[m]]$mean + grid$delta_med[k] * med_stats[[m]]$sd\n", + " }\n", + " # Delta-shift outcome\n", + " miss_y <- is.na(dat_imp[[OUTCOME]])\n", + " dat_imp[[OUTCOME]][miss_y] <- out_mean + grid$delta_out[k] * out_sd\n", + " # Impute missing covariates with means\n", + " for (cov in ALL_COVS) {\n", + " miss_c <- is.na(dat_imp[[cov]])\n", + " if (any(miss_c)) dat_imp[[cov]][miss_c] <- mean(dat_imp[[cov]], na.rm=TRUE)\n", + " }\n", + "\n", + " fit_m <- tryCatch(\n", + " sem(model_str, data=dat_imp, estimator=\"ML\", fixed.x=FALSE),\n", + " error = function(e) NULL\n", + " )\n", + "\n", + " if (!is.null(fit_m) && lavInspect(fit_m, \"converged\")) {\n", + " pe_m <- parameterEstimates(fit_m)\n", + " for (lab in mnar_labels) {\n", + " row_m <- pe_m[pe_m$label == lab, ]\n", + " if (nrow(row_m) == 1) {\n", + " mnar_est[k, lab] <- row_m$est\n", + " mnar_pval[k, lab] <- row_m$pvalue\n", + " }\n", + " }\n", + " }\n", + " if (k %% 50 == 0) cat(\" Grid point\", k, \"/\", nrow(grid), \"\\n\")\n", + "}\n", + "\n", + "cat(\"MNAR sensitivity complete.\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:54:07.951547Z", + "iopub.status.busy": "2026-03-31T16:54:07.948969Z", + "iopub.status.idle": "2026-03-31T16:54:08.027029Z", + "shell.execute_reply": "2026-03-31T16:54:08.024419Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== MNAR Tipping Points ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label tipping_dist tipping_delta_med tipping_delta_out N\n", + "1 ind1 0 0 0 1153\n", + "2 ind2 0 0 0 1153\n", + "3 ind3 0 0 0 1153\n", + "4 ind4 0 0 0 1153\n", + "5 total_indirect 0 0 0 1153\n", + " exposure\n", + "1 chr19_44954310_T_C\n", + "2 chr19_44954310_T_C\n", + "3 chr19_44954310_T_C\n", + "4 chr19_44954310_T_C\n", + "5 chr19_44954310_T_C\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 7b: MNAR -- Save grid results and compute tipping points\n", + "# ============================================================\n", + "mnar_grid <- cbind(grid, mnar_est, mnar_pval)\n", + "# Rename p-value columns\n", + "n_est <- ncol(grid) + length(mnar_labels)\n", + "colnames(mnar_grid)[(n_est + 1):ncol(mnar_grid)] <- paste0(\"p_\", mnar_labels)\n", + "mnar_grid$N <- N_total\n", + "mnar_grid$exposure <- EXPOSURE\n", + "\n", + "write.csv(mnar_grid, file.path(RESULT_DIR, \"MNAR_sensitivity\", \"mnar_grid_results.csv\"), row.names=FALSE)\n", + "\n", + "# Tipping points: minimum Euclidean distance from (0,0) where result becomes NS (p > 0.05)\n", + "tipping <- data.frame(label=mnar_labels, tipping_dist=NA, tipping_delta_med=NA, tipping_delta_out=NA,\n", + " stringsAsFactors=FALSE)\n", + "\n", + "for (j in seq_along(mnar_labels)) {\n", + " lab <- mnar_labels[j]\n", + " p_col <- paste0(\"p_\", lab)\n", + " ns_idx <- which(mnar_grid[[p_col]] > 0.05)\n", + " if (length(ns_idx) > 0) {\n", + " dists <- sqrt(mnar_grid$delta_med[ns_idx]^2 + mnar_grid$delta_out[ns_idx]^2)\n", + " min_i <- ns_idx[which.min(dists)]\n", + " tipping$tipping_dist[j] <- min(dists)\n", + " tipping$tipping_delta_med[j] <- mnar_grid$delta_med[min_i]\n", + " tipping$tipping_delta_out[j] <- mnar_grid$delta_out[min_i]\n", + " }\n", + "}\n", + "tipping$N <- N_total\n", + "tipping$exposure <- EXPOSURE\n", + "\n", + "write.csv(tipping, file.path(RESULT_DIR, \"MNAR_sensitivity\", \"mnar_tipping_summary.csv\"), row.names=FALSE)\n", + "\n", + "cat(\"\\n=== MNAR Tipping Points ===\\n\")\n", + "print(tipping)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:54:08.032633Z", + "iopub.status.busy": "2026-03-31T16:54:08.031049Z", + "iopub.status.idle": "2026-03-31T16:54:10.829104Z", + "shell.execute_reply": "2026-03-31T16:54:10.824961Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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atWjYbj5cuXX7582VA91Hn0aA/pGEd1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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR contour plots saved.\n" + ] + }, + { + "data": { + "image/png": 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PnTp0+fPn1KD2Qaiw2n84CitaFs708W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M9PPzW7t2LZvNjo+Pz8zMjIuLw7bBJhY+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\nz2PLY2JiWF0MHjwYW6uhoSGwKiEhodfy7ty5Y2VlxWKx8vLyetqGoOyMjAx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shx9+mDp16vz588PCwqRoBoIdVJmklQaEP/ugaOc4jst1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\nAAAA4ASCHQAAAAAn/g+nhwy5krJMrQAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 7c: MNAR -- Contour plots\n", + "# ============================================================\n", + "# Total indirect contour\n", + "mnar_plot_df <- data.frame(\n", + " delta_med = grid$delta_med,\n", + " delta_out = grid$delta_out,\n", + " indirect = mnar_est[, \"total_indirect\"],\n", + " neg_log_p = -log10(mnar_pval[, \"total_indirect\"] + 1e-16)\n", + ")\n", + "mnar_plot_df <- mnar_plot_df[complete.cases(mnar_plot_df), ]\n", + "\n", + "# Indirect effect contour\n", + "p_contour_ind <- ggplot(mnar_plot_df, aes(x=delta_med, y=delta_out, z=indirect)) +\n", + " geom_contour_filled(bins=12) +\n", + " scale_fill_viridis_d(option=\"viridis\") +\n", + " annotate(\"point\", x=0, y=0, shape=4, size=4, color=\"red\", stroke=2) +\n", + " labs(title=\"MNAR Sensitivity: Total Indirect Effect\",\n", + " subtitle=paste0(\"Exposure: \", EXPOSURE, \" | N = \", N_total),\n", + " x=expression(delta[mediators]), y=expression(delta[outcome]), fill=\"Indirect\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + "ggsave(file.path(RESULT_DIR, \"MNAR_sensitivity\", paste0(\"mnar_contour_indirect_\", EXPOSURE, \".png\")),\n", + " p_contour_ind, width=8, height=7, dpi=150)\n", + "ggsave(file.path(RESULT_DIR, \"MNAR_sensitivity\", paste0(\"mnar_contour_indirect_\", EXPOSURE, \".pdf\")),\n", + " p_contour_ind, width=8, height=7)\n", + "print(p_contour_ind)\n", + "\n", + "# P-value contour\n", + "p_contour_p <- ggplot(mnar_plot_df, aes(x=delta_med, y=delta_out, z=neg_log_p)) +\n", + " geom_contour_filled(bins=12) +\n", + " scale_fill_viridis_d(option=\"viridis\") +\n", + " annotate(\"point\", x=0, y=0, shape=4, size=4, color=\"red\", stroke=2) +\n", + " labs(title=\"MNAR Sensitivity: -log10(p) of Total Indirect\",\n", + " subtitle=paste0(\"Exposure: \", EXPOSURE, \" | N = \", N_total),\n", + " x=expression(delta[mediators]), y=expression(delta[outcome]), fill=\"-log10(p)\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + "ggsave(file.path(RESULT_DIR, \"MNAR_sensitivity\", paste0(\"mnar_contour_pvalue_\", EXPOSURE, \".png\")),\n", + " p_contour_p, width=8, height=7, dpi=150)\n", + "ggsave(file.path(RESULT_DIR, \"MNAR_sensitivity\", paste0(\"mnar_contour_pvalue_\", EXPOSURE, \".pdf\")),\n", + " p_contour_p, width=8, height=7)\n", + "print(p_contour_p)\n", + "\n", + "cat(\"MNAR contour plots saved.\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:54:10.836257Z", + "iopub.status.busy": "2026-03-31T16:54:10.833989Z", + "iopub.status.idle": "2026-03-31T16:54:11.906682Z", + "shell.execute_reply": "2026-03-31T16:54:11.904749Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR 1D slice plots saved.\n" + ] + }, + { + "data": { + "image/png": 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eefHzly5Omnn9ZoNJs2bQoNDa3rM9SqoqKCiGpOnmUd7OxsA02dOnXTpk0ff/zx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Q/PlEpLIF0/htYxk0qGHhQo6H/+c/8k2b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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 7d: MNAR -- 1D slice plots\n", + "# ============================================================\n", + "closest_zero <- delta_range[which.min(abs(delta_range))]\n", + "\n", + "slice_med <- mnar_grid[abs(mnar_grid$delta_out - closest_zero) < 0.01, ]\n", + "slice_out <- mnar_grid[abs(mnar_grid$delta_med - closest_zero) < 0.01, ]\n", + "\n", + "p_slice1 <- ggplot(slice_med, aes(x=delta_med, y=total_indirect)) +\n", + " geom_line(color=\"steelblue\", linewidth=1) +\n", + " geom_point(color=\"steelblue\", size=2) +\n", + " geom_hline(yintercept=0, linetype=\"dashed\", color=\"red\") +\n", + " labs(title=paste0(EXPOSURE, \": Total Indirect vs delta_M\"),\n", + " subtitle=paste0(\"Holding delta_Y = 0 | N = \", N_total),\n", + " x=expression(delta[mediators]), y=\"Total Indirect Effect\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + "p_slice2 <- ggplot(slice_out, aes(x=delta_out, y=total_indirect)) +\n", + " geom_line(color=\"darkorange\", linewidth=1) +\n", + " geom_point(color=\"darkorange\", size=2) +\n", + " geom_hline(yintercept=0, linetype=\"dashed\", color=\"red\") +\n", + " labs(title=paste0(EXPOSURE, \": Total Indirect vs delta_Y\"),\n", + " subtitle=paste0(\"Holding delta_M = 0 | N = \", N_total),\n", + " x=expression(delta[outcome]), y=\"Total Indirect Effect\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + "p_slices <- gridExtra::grid.arrange(p_slice1, p_slice2, ncol=2)\n", + "\n", + "ggsave(file.path(RESULT_DIR, \"MNAR_sensitivity\", paste0(\"mnar_1d_slices_\", EXPOSURE, \".png\")),\n", + " p_slices, width=12, height=5, dpi=150)\n", + "ggsave(file.path(RESULT_DIR, \"MNAR_sensitivity\", paste0(\"mnar_1d_slices_\", EXPOSURE, \".pdf\")),\n", + " p_slices, width=12, height=5)\n", + "cat(\"MNAR 1D slice plots saved.\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MNAR Interpretation\n", + "\n", + "The contour plots show how the total indirect effect and its significance change as we shift imputed values for missing mediators (x-axis) and outcome (y-axis) by delta SD units. The red X marks the MAR reference point (delta=0,0). A tipping point is the closest grid point to (0,0) where significance is lost -- larger distances indicate more robust findings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Method 4: Bayesian SEM (blavaan/Stan)\n", + "\n", + "Bayesian SEM provides posterior distributions for all parameters, allowing direct probability statements (e.g., P(indirect > 0)). blavaan handles missing data automatically through Stan's data augmentation. This avoids the normality assumption for indirect effect inference." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:54:11.915845Z", + "iopub.status.busy": "2026-03-31T16:54:11.913329Z", + "iopub.status.idle": "2026-03-31T23:16:46.251310Z", + "shell.execute_reply": "2026-03-31T23:16:46.248440Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting Bayesian SEM (blavaan, N = 1153 )...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using 4 chains x 2000 samples (1000 burnin).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 1).\n", + "Chain 1: \n", + "Chain 1: Gradient evaluation took 0.00492 seconds\n", + "Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 49.2 seconds.\n", + "Chain 1: Adjust your expectations accordingly!\n", + "Chain 1: \n", + "Chain 1: \n", + "Chain 1: Iteration: 1 / 3000 [ 0%] (Warmup)\n", + "Chain 1: Iteration: 300 / 3000 [ 10%] (Warmup)\n", + "Chain 1: Iteration: 600 / 3000 [ 20%] (Warmup)\n", + "Chain 1: Iteration: 900 / 3000 [ 30%] (Warmup)\n", + "Chain 1: Iteration: 1001 / 3000 [ 33%] (Sampling)\n", + "Chain 1: Iteration: 1300 / 3000 [ 43%] (Sampling)\n", + "Chain 1: Iteration: 1600 / 3000 [ 53%] (Sampling)\n", + "Chain 1: Iteration: 1900 / 3000 [ 63%] (Sampling)\n", + "Chain 1: Iteration: 2200 / 3000 [ 73%] (Sampling)\n", + "Chain 1: Iteration: 2500 / 3000 [ 83%] (Sampling)\n", + "Chain 1: Iteration: 2800 / 3000 [ 93%] (Sampling)\n", + "Chain 1: Iteration: 3000 / 3000 [100%] (Sampling)\n", + "Chain 1: \n", + "Chain 1: Elapsed Time: 1731.44 seconds (Warm-up)\n", + "Chain 1: 3987.32 seconds (Sampling)\n", + "Chain 1: 5718.76 seconds (Total)\n", + "Chain 1: \n", + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 2).\n", + "Chain 2: \n", + "Chain 2: Gradient evaluation took 0.004301 seconds\n", + "Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 43.01 seconds.\n", + "Chain 2: Adjust your expectations accordingly!\n", + "Chain 2: \n", + "Chain 2: \n", + "Chain 2: Iteration: 1 / 3000 [ 0%] (Warmup)\n", + "Chain 2: Iteration: 300 / 3000 [ 10%] (Warmup)\n", + "Chain 2: Iteration: 600 / 3000 [ 20%] (Warmup)\n", + "Chain 2: Iteration: 900 / 3000 [ 30%] (Warmup)\n", + "Chain 2: Iteration: 1001 / 3000 [ 33%] (Sampling)\n", + "Chain 2: Iteration: 1300 / 3000 [ 43%] (Sampling)\n", + "Chain 2: Iteration: 1600 / 3000 [ 53%] (Sampling)\n", + "Chain 2: Iteration: 1900 / 3000 [ 63%] (Sampling)\n", + "Chain 2: Iteration: 2200 / 3000 [ 73%] (Sampling)\n", + "Chain 2: Iteration: 2500 / 3000 [ 83%] (Sampling)\n", + "Chain 2: Iteration: 2800 / 3000 [ 93%] (Sampling)\n", + "Chain 2: Iteration: 3000 / 3000 [100%] (Sampling)\n", + "Chain 2: \n", + "Chain 2: Elapsed Time: 1748.22 seconds (Warm-up)\n", + "Chain 2: 3983.07 seconds (Sampling)\n", + "Chain 2: 5731.29 seconds (Total)\n", + "Chain 2: \n", + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 3).\n", + "Chain 3: \n", + "Chain 3: Gradient evaluation took 0.004297 seconds\n", + "Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 42.97 seconds.\n", + "Chain 3: Adjust your expectations accordingly!\n", + "Chain 3: \n", + "Chain 3: \n", + "Chain 3: Iteration: 1 / 3000 [ 0%] (Warmup)\n", + "Chain 3: Iteration: 300 / 3000 [ 10%] (Warmup)\n", + "Chain 3: Iteration: 600 / 3000 [ 20%] (Warmup)\n", + "Chain 3: Iteration: 900 / 3000 [ 30%] (Warmup)\n", + "Chain 3: Iteration: 1001 / 3000 [ 33%] (Sampling)\n", + "Chain 3: Iteration: 1300 / 3000 [ 43%] (Sampling)\n", + "Chain 3: Iteration: 1600 / 3000 [ 53%] (Sampling)\n", + "Chain 3: Iteration: 1900 / 3000 [ 63%] (Sampling)\n", + "Chain 3: Iteration: 2200 / 3000 [ 73%] (Sampling)\n", + "Chain 3: Iteration: 2500 / 3000 [ 83%] (Sampling)\n", + "Chain 3: Iteration: 2800 / 3000 [ 93%] (Sampling)\n", + "Chain 3: Iteration: 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Increase max_treedepth above 10. See\n", + "https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cExamine the pairs() plot to diagnose sampling problems\n", + "\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cBulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.\n", + "Running the chains for more iterations may help. See\n", + "https://mc-stan.org/misc/warnings.html#bulk-ess\u201d\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing post-estimation metrics (including lvs if requested)...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian SEM fitted successfully.\n", + "blavaan 0.5.10 ended normally after 2000 iterations\n", + "\n", + " Estimator BAYES\n", + " Optimization method MCMC\n", + " Number of model parameters 78\n", + "\n", + " Number of observations 1153\n", + " Number of missing patterns 24\n", + "\n", + " Statistic MargLogLik PPP\n", + " Value NA 0.372\n", + "\n", + "Parameter Estimates:\n", + "\n", + "\n", + "Regressions:\n", + " Estimate Post.SD pi.lower pi.upper Rhat\n", + " APOC4_APOC2_AC_exp ~ \n", + " c19_44954 (a1) 0.280 0.058 0.164 0.391 1.012\n", + " msex_u 0.096 0.082 -0.061 0.256 1.014\n", + " age_deth_ 0.023 0.006 0.011 0.035 1.009\n", + " pmi_u 0.012 0.008 -0.004 0.026 1.006\n", + " ROS_stdy_ -0.161 0.077 -0.315 -0.015 1.004\n", + " APOC2_AC_exp ~ \n", + " c19_44954 (a2) 0.277 0.058 0.161 0.387 1.013\n", + " msex_u 0.095 0.082 -0.063 0.256 1.014\n", + " age_deth_ 0.023 0.006 0.011 0.035 1.009\n", + " pmi_u 0.012 0.008 -0.003 0.027 1.006\n", + " ROS_stdy_ -0.159 0.077 -0.314 -0.012 1.004\n", + " APOC1_DeJager_Mic_exp ~ \n", + " c19_44954 (a3) 0.223 0.065 0.094 0.351 1.008\n", + " msex_u -0.160 0.096 -0.351 0.027 1.011\n", + " age_deth_ 0.009 0.006 -0.004 0.022 1.011\n", + " pmi_u 0.003 0.009 -0.015 0.020 1.005\n", + " APOE_Mega_Mic_exp ~ \n", + " c19_44954 (a4) 0.213 0.050 0.115 0.310 1.010\n", + " msex_u -0.207 0.073 -0.350 -0.061 1.007\n", + " age_deth_ 0.022 0.005 0.012 0.032 1.008\n", + " pmi_u 0.011 0.006 -0.001 0.023 1.005\n", + " caa_neo4 ~ \n", + " APOC4_APO (b1) -0.646 1.090 -2.719 1.517 1.029\n", + " APOC2_AC_ (b2) 0.578 1.091 -1.577 2.651 1.029\n", + " APOC1_DJ_ (b3) 0.004 0.065 -0.125 0.132 1.002\n", + " APOE_M_M_ (b4) 0.073 0.057 -0.040 0.186 1.001\n", + " c19_44954 (cp) 0.155 0.046 0.065 0.243 1.002\n", + " educ 0.002 0.008 -0.014 0.019 1.001\n", + " apoe4_dos 0.739 0.064 0.616 0.869 1.002\n", + " msex_u -0.046 0.066 -0.177 0.082 1.002\n", + " age_deth_ 0.026 0.005 0.016 0.036 1.003\n", + " Prior \n", + " \n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " \n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " \n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " \n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " \n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + "\n", + "Covariances:\n", + " Estimate Post.SD pi.lower pi.upper Rhat\n", + " .APOC4_APOC2_AC_exp ~~ \n", + " .APOC2_AC_exp 0.903 0.053 0.808 1.016 1.004\n", + " .APOC1_DJgr_Mc_ 0.202 0.055 0.099 0.313 1.006\n", + " .APOE_Meg_Mc_xp 0.053 0.043 -0.029 0.136 1.003\n", + " .APOC2_AC_exp ~~ \n", + " .APOC1_DJgr_Mc_ 0.205 0.055 0.102 0.316 1.006\n", + " .APOE_Meg_Mc_xp 0.054 0.043 -0.028 0.138 1.003\n", + " .APOC1_DeJager_Mic_exp ~~ \n", + " .APOE_Meg_Mc_xp 0.575 0.052 0.477 0.678 1.005\n", + " chr19_44954310_T_C ~~ \n", + " msex_u 0.004 0.010 -0.015 0.023 1.002\n", + " age_death_u -0.042 0.138 -0.320 0.230 1.013\n", + " pmi_u -0.392 0.153 -0.690 -0.091 1.002\n", + " ROS_study_u -0.006 0.011 -0.027 0.015 1.010\n", + " educ 0.028 0.075 -0.115 0.180 1.010\n", + " apoe4_dose 0.022 0.010 0.002 0.043 1.006\n", + " msex_u ~~ \n", + " age_death_u -0.583 0.096 -0.774 -0.391 1.003\n", + " pmi_u 0.096 0.105 -0.111 0.299 1.002\n", + " ROS_study_u 0.005 0.007 -0.008 0.019 1.003\n", + " educ 0.343 0.050 0.249 0.444 1.001\n", + " apoe4_dose -0.001 0.007 -0.014 0.013 1.004\n", + " age_death_u ~~ \n", + " pmi_u 0.806 1.442 -1.988 3.662 1.008\n", + " ROS_study_u -0.458 0.098 -0.658 -0.265 1.002\n", + " educ -3.137 0.738 -4.650 -1.723 1.003\n", + " apoe4_dose -0.342 0.096 -0.528 -0.153 1.006\n", + " pmi_u ~~ \n", + " ROS_study_u 0.045 0.108 -0.168 0.255 1.005\n", + " educ 0.076 0.778 -1.420 1.682 1.001\n", + " apoe4_dose -0.065 0.109 -0.275 0.151 1.009\n", + " ROS_study_u ~~ \n", + " educ 0.885 0.060 0.773 1.005 1.004\n", + " apoe4_dose 0.014 0.007 -0.001 0.028 1.005\n", + " educ ~~ \n", + " apoe4_dose 0.131 0.052 0.029 0.235 1.002\n", + " Prior \n", + " \n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " \n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " \n", + " lkj_corr(1)\n", + " \n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " \n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " \n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " \n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " \n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " \n", + " lkj_corr(1)\n", + "\n", + "Intercepts:\n", + " Estimate Post.SD pi.lower pi.upper Rhat Prior \n", + " .APOC4_APOC2_AC -2.314 0.556 -3.400 -1.217 1.009 normal(0,10)\n", + " .APOC2_AC_exp -2.294 0.557 -3.383 -1.188 1.009 normal(0,10)\n", + " .APOC1_DJgr_Mc_ -0.959 0.589 -2.133 0.208 1.011 normal(0,10)\n", + " .APOE_Meg_Mc_xp -2.144 0.473 -3.069 -1.231 1.009 normal(0,10)\n", + " .caa_neo4 -1.514 0.491 -2.468 -0.548 1.002 normal(0,10)\n", + " c19_44954310_T 0.875 0.021 0.835 0.916 1.002 normal(0,10)\n", + " msex_u 0.343 0.014 0.316 0.370 1.002 normal(0,10)\n", + " age_death_u 88.921 0.192 88.522 89.288 1.002 normal(0,10)\n", + " pmi_u 8.732 0.220 8.306 9.179 1.004 normal(0,10)\n", + " ROS_study_u 0.500 0.015 0.471 0.530 1.004 normal(0,10)\n", + " educ 16.369 0.108 16.154 16.577 1.004 normal(0,10)\n", + " apoe4_dose 0.273 0.014 0.244 0.302 1.002 normal(0,10)\n", + "\n", + "Variances:\n", + " Estimate Post.SD pi.lower pi.upper Rhat Prior \n", + " .APOC4_APOC2_AC 0.902 0.053 0.808 1.015 1.004 gamma(1,.5)[sd]\n", + " .APOC2_AC_exp 0.905 0.053 0.810 1.018 1.004 gamma(1,.5)[sd]\n", + " .APOC1_DJgr_Mc_ 1.024 0.073 0.888 1.178 1.001 gamma(1,.5)[sd]\n", + " .APOE_Meg_Mc_xp 0.887 0.047 0.798 0.985 1.009 gamma(1,.5)[sd]\n", + " .caa_neo4 0.951 0.043 0.871 1.038 1.005 gamma(1,.5)[sd]\n", + " c19_44954310_T 0.497 0.021 0.456 0.538 1.004 gamma(1,.5)[sd]\n", + " msex_u 0.227 0.010 0.209 0.246 1.002 gamma(1,.5)[sd]\n", + " age_death_u 43.456 1.808 40.092 47.182 1.002 gamma(1,.5)[sd]\n", + " pmi_u 54.907 2.246 50.573 59.435 1.002 gamma(1,.5)[sd]\n", + " ROS_study_u 0.252 0.011 0.232 0.274 1.005 gamma(1,.5)[sd]\n", + " educ 13.047 0.539 12.048 14.148 1.003 gamma(1,.5)[sd]\n", + " apoe4_dose 0.233 0.010 0.214 0.253 1.001 gamma(1,.5)[sd]\n", + "\n", + "Defined Parameters:\n", + " Estimate Post.SD pi.lower pi.upper Rhat Prior \n", + " ind1 -0.181 0.312 -0.793 0.432 \n", + " ind2 0.160 0.309 -0.445 0.765 \n", + " ind3 0.001 0.015 -0.029 0.031 \n", + " ind4 0.016 0.013 -0.010 0.041 \n", + " total_indirect -0.004 0.017 -0.037 0.028 \n", + " total 0.151 0.043 0.066 0.235 \n", + " prop1 -1.199 2.887 -6.857 4.459 \n", + " prop2 1.062 2.837 -4.499 6.623 \n", + " prop3 0.005 0.135 -0.259 0.270 \n", + " prop4 0.104 0.121 -0.134 0.341 \n", + " prop_total -0.028 0.138 -0.298 0.241 \n", + " diff_1_2 -0.341 0.621 -1.558 0.877 \n", + " diff_1_3 -0.181 0.311 -0.792 0.429 \n", + " diff_1_4 -0.196 0.313 -0.810 0.418 \n", + " diff_2_3 0.159 0.311 -0.450 0.768 \n", + " diff_2_4 0.144 0.308 -0.460 0.749 \n", + " diff_3_4 -0.015 0.026 -0.066 0.036 \n", + "\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 8: Bayesian SEM (blavaan)\n", + "# ============================================================\n", + "cat(\"Fitting Bayesian SEM (blavaan, N =\", N_total, \")...\\n\")\n", + "cat(\"Using 4 chains x 2000 samples (1000 burnin).\\n\")\n", + "\n", + "fit_bayes <- tryCatch(\n", + " bsem(model_str, data=dat, fixed.x=FALSE, target=\"stan\",\n", + " n.chains=4, burnin=1000, sample=2000, seed=42),\n", + " error = function(e) {\n", + " cat(\"blavaan error:\", conditionMessage(e), \"\\n\")\n", + " cat(\"Trying with 2 chains...\\n\")\n", + " tryCatch(\n", + " bsem(model_str, data=dat, fixed.x=FALSE, target=\"stan\",\n", + " n.chains=2, burnin=1000, sample=2000, seed=42),\n", + " error = function(e2) { cat(\"blavaan 2-chain error:\", conditionMessage(e2), \"\\n\"); NULL }\n", + " )\n", + " }\n", + ")\n", + "\n", + "if (!is.null(fit_bayes)) {\n", + " cat(\"Bayesian SEM fitted successfully.\\n\")\n", + " summary(fit_bayes)\n", + "} else {\n", + " cat(\"WARNING: Bayesian SEM failed. Will be excluded from summary.\\n\")\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:16:48.784995Z", + "iopub.status.busy": "2026-03-31T23:16:48.779006Z", + "iopub.status.idle": "2026-03-31T23:16:50.325476Z", + "shell.execute_reply": "2026-03-31T23:16:50.323445Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available draw columns (first 30): a1 APOC4_APOC2_AC_exp~msex_u APOC4_APOC2_AC_exp~age_death_u APOC4_APOC2_AC_exp~pmi_u APOC4_APOC2_AC_exp~ROS_study_u a2 APOC2_AC_exp~msex_u APOC2_AC_exp~age_death_u APOC2_AC_exp~pmi_u APOC2_AC_exp~ROS_study_u a3 APOC1_DeJager_Mic_exp~msex_u APOC1_DeJager_Mic_exp~age_death_u APOC1_DeJager_Mic_exp~pmi_u a4 APOE_Mega_Mic_exp~msex_u APOE_Mega_Mic_exp~age_death_u APOE_Mega_Mic_exp~pmi_u b1 b2 b3 b4 cp caa_neo4~educ caa_neo4~apoe4_dose caa_neo4~msex_u caa_neo4~age_death_u APOC4_APOC2_AC_exp~~APOC2_AC_exp APOC4_APOC2_AC_exp~~APOC1_DeJager_Mic_exp APOC4_APOC2_AC_exp~~APOE_Mega_Mic_exp \n", + "\n", + "=== Bayesian Results (N = 1153 ) ===\n", + " label mean sd ci_lower ci_upper p_direction\n", + "1 a1 0.279794522 0.05799678 0.163694476 0.39129488 1.000000\n", + "2 a2 0.276602914 0.05803717 0.160839135 0.38714444 1.000000\n", + "3 a3 0.222922328 0.06503787 0.093692397 0.35144142 0.999875\n", + "4 a4 0.213006300 0.04964965 0.114695516 0.31021285 1.000000\n", + "5 b1 -0.645535498 1.09045680 -2.719221744 1.51707739 0.723250\n", + "6 b2 0.578294085 1.09074742 -1.576603218 2.65137073 0.702125\n", + "7 b3 0.003568912 0.06525580 -0.125111102 0.13175588 0.522250\n", + "8 b4 0.073196683 0.05702886 -0.039613754 0.18557816 0.902375\n", + "9 cp 0.154887944 0.04556217 0.065199547 0.24268873 0.999500\n", + "10 ind1 -0.178162355 0.31251364 -0.817896241 0.42426114 0.723250\n", + "11 ind2 0.157512578 0.30883184 -0.442516863 0.78693610 0.702125\n", + "12 ind3 0.001130906 0.01532019 -0.029447603 0.03339328 0.522125\n", + "13 ind4 0.015596921 0.01310721 -0.008678276 0.04386078 0.902375\n", + "14 total_indirect -0.003921950 0.01662300 -0.037284769 0.02892688 0.593750\n", + "15 total 0.150965994 0.04320219 0.066145563 0.23464600 0.999375\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 8b: Bayesian -- Extract results\n", + "# ============================================================\n", + "bayes_results <- NULL\n", + "\n", + "if (!is.null(fit_bayes)) {\n", + " pt <- parTable(fit_bayes)\n", + " labeled <- pt[pt$label != \"\", ]\n", + "\n", + " # Get posterior draws\n", + " draws_list <- blavInspect(fit_bayes, \"mcmc\")\n", + " draws <- do.call(rbind, draws_list)\n", + "\n", + " draw_cols <- colnames(draws)\n", + " cat(\"Available draw columns (first 30):\", head(draw_cols, 30), \"\\n\")\n", + "\n", + " # Extract results for all labeled parameters\n", + " bayes_rows <- list()\n", + " target_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\")\n", + "\n", + " for (lab in target_labels) {\n", + " pt_row <- labeled[labeled$label == lab, ]\n", + " if (nrow(pt_row) == 0) next\n", + "\n", + " draw_col <- NULL\n", + " if (lab %in% draw_cols) {\n", + " draw_col <- lab\n", + " } else {\n", + " plabel <- pt_row$plabel[1]\n", + " if (!is.na(plabel) && plabel %in% draw_cols) draw_col <- plabel\n", + " }\n", + "\n", + " if (!is.null(draw_col)) {\n", + " vals <- draws[, draw_col]\n", + " bayes_rows[[lab]] <- data.frame(\n", + " label = lab,\n", + " mean = mean(vals),\n", + " sd = sd(vals),\n", + " ci_lower = as.numeric(quantile(vals, 0.025)),\n", + " ci_upper = as.numeric(quantile(vals, 0.975)),\n", + " p_direction = max(mean(vals > 0), mean(vals < 0)),\n", + " stringsAsFactors = FALSE\n", + " )\n", + " } else {\n", + " bayes_rows[[lab]] <- data.frame(\n", + " label = lab,\n", + " mean = pt_row$est[1],\n", + " sd = pt_row$se[1],\n", + " ci_lower = pt_row$est[1] - 1.96*pt_row$se[1],\n", + " ci_upper = pt_row$est[1] + 1.96*pt_row$se[1],\n", + " p_direction = NA,\n", + " stringsAsFactors = FALSE\n", + " )\n", + " }\n", + " }\n", + "\n", + " bayes_results <- do.call(rbind, bayes_rows)\n", + " rownames(bayes_results) <- NULL\n", + " bayes_results$method <- \"Bayesian\"\n", + " bayes_results$N <- N_total\n", + " bayes_results$exposure <- EXPOSURE\n", + " bayes_results$direction <- \"D1\"\n", + "\n", + " write.csv(bayes_results, file.path(RESULT_DIR, \"bayesian_blavaan\", \"bayesian_results.csv\"), row.names=FALSE)\n", + "\n", + " cat(\"\\n=== Bayesian Results (N =\", N_total, \") ===\\n\")\n", + " print(bayes_results[, c(\"label\", \"mean\", \"sd\", \"ci_lower\", \"ci_upper\", \"p_direction\")])\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:16:50.381538Z", + "iopub.status.busy": "2026-03-31T23:16:50.378957Z", + "iopub.status.idle": "2026-03-31T23:16:58.160521Z", + "shell.execute_reply": "2026-03-31T23:16:58.158035Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian posterior density plots saved.\n" + ] + }, + { + "data": { + "image/png": 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c2yZcuOHj1a465t27bRafHXB77KCKH8/HyNryMAAOgrvR0Vi23YsCEjI+Pvv//+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MDai4/eLo0aPLly+XyedYjOLt3BUNdUfT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\nv7kNKDNuXnTfaI+8vLx169bRraU7IJVKp0yZIhAIRnz7H1u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VxWKxli9fkBbA0wAAIABJREFU/vr1axJpdMqrV6/mzp3bq1evDx/ej1nQ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\nuUaNpmm9Xs/n8z26hIgkS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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 8c: Bayesian -- Posterior density plots\n", + "# ============================================================\n", + "if (!is.null(fit_bayes)) {\n", + " draws_list <- blavInspect(fit_bayes, \"mcmc\")\n", + " draws <- do.call(rbind, draws_list)\n", + " draw_cols <- colnames(draws)\n", + "\n", + " pt <- parTable(fit_bayes)\n", + " labeled <- pt[pt$label != \"\", ]\n", + "\n", + " plot_params <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\")\n", + "\n", + " dens_data <- list()\n", + " for (lab in plot_params) {\n", + " col_name <- NULL\n", + " if (lab %in% draw_cols) {\n", + " col_name <- lab\n", + " } else {\n", + " pt_row <- labeled[labeled$label == lab, ]\n", + " if (nrow(pt_row) > 0 && !is.na(pt_row$plabel[1]) && pt_row$plabel[1] %in% draw_cols) {\n", + " col_name <- pt_row$plabel[1]\n", + " }\n", + " }\n", + " if (!is.null(col_name)) {\n", + " dens_data[[length(dens_data)+1]] <- data.frame(\n", + " parameter = lab, value = draws[, col_name]\n", + " )\n", + " }\n", + " }\n", + "\n", + " if (length(dens_data) > 0) {\n", + " dens_df <- do.call(rbind, dens_data)\n", + "\n", + " p_dens <- ggplot(dens_df, aes(x=value, fill=parameter)) +\n", + " geom_density(alpha=0.5) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"red\") +\n", + " facet_wrap(~parameter, scales=\"free\", ncol=4) +\n", + " labs(title=\"Bayesian Posterior Distributions\",\n", + " subtitle=paste0(\"Exposure: \", EXPOSURE, \" | N = \", N_total),\n", + " x=\"Parameter Value\", y=\"Density\") +\n", + " theme_minimal(base_size=11) +\n", + " theme(legend.position=\"none\")\n", + "\n", + " plot_h <- min(48, max(6, ceiling(length(plot_params)/4) * 3))\n", + " ggsave(file.path(RESULT_DIR, \"bayesian_blavaan\", paste0(\"bayesian_posteriors_\", EXPOSURE, \".png\")),\n", + " p_dens, width=14, height=plot_h, dpi=150, limitsize=FALSE)\n", + " ggsave(file.path(RESULT_DIR, \"bayesian_blavaan\", paste0(\"bayesian_posteriors_\", EXPOSURE, \".pdf\")),\n", + " p_dens, width=14, height=plot_h, limitsize=FALSE)\n", + " print(p_dens)\n", + " cat(\"Bayesian posterior density plots saved.\\n\")\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:16:58.168987Z", + "iopub.status.busy": "2026-03-31T23:16:58.167371Z", + "iopub.status.idle": "2026-03-31T23:17:02.068626Z", + "shell.execute_reply": "2026-03-31T23:17:02.066276Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian trace plots saved.\n" + ] + }, + { + "data": { + "image/png": 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5JECPnll18AoGPHji6Xi6bcv38/ALz44ouEkJ07dwLAjBkz5Hy+/fZbAHjvvfcI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RjPWaDPn7UlPhdoRFMIhbrEsjwgyJO25Nh2hntSHyuzoAACAASURBVB0mADePn2Em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KGe2g5gmcJ2kKrcsJ2vWcVs7QgKl8A0b4CI4U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\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2gR0WGHIMjEwozMw0gSBGk9bobd8ccf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LlhezJZSxU+rytbo4uzbAFeVwdzUmyRhj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0lcuQBvMZzsvVR0mxBe+Kd8SqSoiZdMf0b+Iay6Kpj4TuBEnv8qmYykrDSGyJrjpEs8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rREpLcs/Q1VgtEXEGduZprKpg0cw5bJhSXGD7\n5ZWdV5C4XgCs4BijpLpAqjujHvh4MR31U73Vhn2bSVfoYvai+SElToGMp++a9XR299954j9E\noQj4a8RVAvX+tAmUYuvouqUv0JMxN5CLcpafxgrpL0XhWq/9StSf7Nzt4i85HiKDRaysLgn2\n69HXuCbuflQ4gihbX6Whz3aOD7aOrulx4qSonjUn3vxo+xgAmp4ayllK5TiEshi2gkLD5emm\nL57uvHg+nec7gxStb5fjiL6MfMyUa3TY2ogMBge98jzf6FgSE+NGyqN3hEaYPHT3IXtw4e5b\nivMEJZIrRQUyV7Xcdi95UUF3IFSeTIrK4HbTPDJPatWj6SXn9Ie5I+GV1ZisJluFI+Whtweg\nOd0zAU1DkwkpEzcEDVENmui/DUUBldoOJ02NlQkAyQm7AL76ysWyCAY8WeDpGqZsVzPohbCu\nVkZvNYuubN0+r8ebxr91KPa6x+VoaXO9v6jrdUHYac3dVqVzXU0iZ5lQCYhvxAcJmCNnpPkx\nYAYc8UaM755NzmKnAAGBu/Qt0vtW700vPMRJfgwFgRoEZHI+OU8Q8KAsol/0ppwWXmUDSPTz\n1TJhRBOEUB3PmsRTAG+SOwNmpCuiGgJwODtMFl3UdH4URhfPrtjA6jDlFeLMiClKvJl4hBJm\nrHWrb1z8+kv7L+axCsl6StzKio6cyvboeSUcju4qJKpiU0e7urwix3kIOTuW/b672Hvw6KEe\nK5y1uD11bu63unaS9GIUlcU1k17kE/EhvSp9QT02I6Ka0mm58bKcuAmjWT9L2DH1o7URWl4l\ndEWcMVdsNAcFjGEaTWJHkBwt7EwOK/o2loX2dB+RvndLAJfkzH7gzITUG9+7WDSvDwtPVxvi\n+nKyfcmEAZcdkXZy6+7stg2AUuVO2/959W9aV0TFFF9cb/57v+RLt++FXt3LmUoLKRpapLQS\nczO4pHrXeH3tperljJgsqH6/SdzXC/vPm/m8EwOVbH+BdCsnfqlH1WW4TwPNxA6Q+Ore5biz\nd3tn/05FrBAqyoeIbOM7l45nNd8EEvmkx3kZf1VjyngY/PzkwrPClWEZ4Xjr5PkHo0liCqyo\nse84oMjn1sOuc6mi72LFm9xaFn6lpBAiSq76yRHkEj2ydR7PjpMKSSoACDS48/no3KZReyVk\nXMNhvLXcjj8QlSEoMZIBx71KtRvCcjw74YrrrqFyefG/riIDT1ySR1DelKQnQ/FOyJepvQL0\nopNuGhUBIK38duPxoNTIHZq+xMVb3xhnKvSpQkHeQ9GYfR71GrKP/SCpkfotQJEczniY/CWf\nwEL2UcXs9gh2lBB6myJ3fOfldUHYAfzyzkv1dwW9ePD8nb1DUM37xk2bZadFBQid69KPBTqt\neNtqLfxInQYAZY9zicKZ3cUuW9qARCmylgs565RIyYWJUaY1hM+S0nBXF/s4N7sxC/mb+s5g\nZHn3Jho6sw7ewmpPe74SdsZIVagIwzpOI4gMfrE5IMLVo4feeOstvNojILguzzTXKXJ7ZcOh\njprGdI5pnjGCRgbxtdgNWnndRwVNc0hAy8uuRNaNt9RIO6I2r1KVWoMAOPEXzy9nm0rDApMc\njyDWq+nd+P+WLhFDTsL13CSqI9pXxb5a0cGdBY5JyzU1lISUqSEvfn+xNxjVsEQGnApJmOaV\nde6GP9KNliR2ACCCsK4FeFovSF4bgw1g0SyUijmOS6K+Oga4XZgdWssiSoWp1kFcnjpZXLpv\nQFrlcKRAkAALLg2LwEyqgdpjvbOikrFkhtVEu9l9zyn2H6MkU6UBfGr9K4Cpdhf0CBWYBO7W\nQLK4QG9svP6JlYmwTfv9RmKVlVumfTfoKyjCLO+s6Qm694QX/MCCNguJjWulaKIWY4tXs6t5\nlN6DLLFTYqfCle6iso8yiV0hjfMAjVWv4TFWEa+EGoFoQQ1agqrY2BNxw/n+Hgx2uGj5S893\nFWGkQVwQ1uQrlRB4pLqNq3FrLdssBNwdTY4Ak2lrPdO0JpNrR9fiZ99ky8hJNylcwwb6BkDv\njAcKy9GKCAu4lhh9rkFGx+K69BZXv5WIla0L56Ozk8lpbXMCgAKzMkEDh0wJnoyPeY34thKi\n326koiIUCwN0yv4O95LLaaYXG5/HfNlhV+f1fJMVvQUnGNzdg1LDVB/71auo9WaWGj15YDJH\nuI9iu9cFYafkl5XixpDQopmbGX7mG3OIrCunyauRCMDpKPlcVVb2grCt55dj1LdyRouhSWVj\nV3tpVQeuFxSiRxAWzpJx453ZOMIeNhjnRkYIb5JbYwQAUhl/5LN6EWcX5MyaXfn5opkPji1V\nNmsv7b2Q03qmWVRjzthNE9bTBkkaSnVN4JXdl5/fe1Z5hWxUnEy6VdfJGwDo3DCflSZqT4GZ\nafEoc4qqpJ22PcQ/vpXn1pQco7GGDz5jGhvsFsxkJ1epx6RppkB0tqRBlTk1tkQ9qoiGNFLN\nxnfEz/OF+tdoc0kAcELjQ56mFHY98vzblGIhDVPCZuvFTEslxkFbjh+SCFYCupM+QWAgLYgR\nsyjZ0wTu/vmhL4Ni5twDtK7WhdglqARo0JVA6lOg1WDSsOO528Fqe9a4ZI3gouYcSuKgEgk7\ntejElDBuLYbf1uXjenO8BlTfD4XSsaE+OBTGon+ik/OEQpfN2aAxXaufkQXfA9yYhnL0gl8q\nGWngiuYhhYJ2di2S777MiwQ9nl/qy0cM4s26Ltng3WM3kytJ+srsHjx89uHnqxUolWfGAQ7O\nLmVsX7cbodRH766MtwZRFayaAmioercv+WRwbcfcs3xMpEYu18LRVRwDiEEIqyi1UZ5Nxp1a\nrt66WVuKSjaoNKXtLdpNE80zjv0zoqVqtBixiN/US880WPiBKtYaOSe3Is5hVBI5WrCdkxR9\nSJk03wIC6JMP/vMLe9/qdwJKUo90rhXAfHSe46n2h6cners/WqN9mZVQXKEpDopoOT7JK2m/\nPDFuH9Lj1FUqkVBMBsupvonWrKJTvajnkSepXqvHt8lEua5YEZP/G7DW64Owu3JVfXNZzhDP\nAF3hR1uEBO1rRiTZX11VFS9deKn1XcI6AEBKLZbb4euKrm/lhRL0VTGAxPS8IJSTGORTKcX2\nLjg2D0Z5d35w9fih6ogS9eAkjl+q85Bzt7+jfWErRSQ6md49j4i+j7ojCJLWREniG03/aA2e\n0+guTWwFCODVASi5QfTXfN7Mr++9pBygLhF9mQHqAXLGSSu/TDZU6SeN1ColyZPlorCZLvn0\nX7v/t1irECZV1JZZDnibOvDi3qBsiF6jNEvQk+1tIKDMgY+FSGOYtLz4N2gGQ/EDTn0oUy9f\nj2nyEkqCP4Z3ylkIf0yT5+hAexgh8XKvWhL/QPV0BujCJHZBEZLqMzbqzxVKYdYbauQkSs8h\nyVOXfpknuC2t3eZV0D0g4qoIFdWtqNRLiAwijDR46IN64h2ZjfP2YuvCUbJidK3t70zbMcLO\nMmqjTL9ba75sSq+SwfZ7vBSkQezIOepHqylFDR7jBam6pmKzBEogGsS4L2oH0t46DnspNgy1\nFyTQ5cyK+f578IwefBiTSZREAyCa0paLQSjKaeIUic2Y2Dv7dzdMLt+RNrtiw4HGo/Ob3Vff\nLdcvy2kPrPMo+7OLy6uY0hR2jnxDTQNg1bTv0DCJZsEFh02TvZ9bExWP2lFmYxhuzFvbunhQ\njke8sml2lasWkzwa7lj3CtvbNBwqV4nhwGGabzJvgxzVWR+jnYuJsJtqz1QawBZWRkLFDSnq\n+uFSDNaqDq6U1k517fbM32btlg+eCLTaMQQxb85v7tzOXEcef1yEZKqkEeNFGF7zNrY+EumW\nmzE6LLqFpNORcKkCamZRFkMlRrioHY0BQIrPmJiSNfeek/QAGKG7qGfpEAzGljCrFgaAevXK\nMsYPnAjn/9954jUWBmhiN7qFuWQ5HZ8AuuhOBVL2v18IIMe0u2duAuW42a+EhZ4WMzkAvQ0D\nEnh5jKo2y717RiPbZlbtaQVq21jC7nLXi68HVxN2WXJhrlU1SqZ0XIvFKIoEyzSzhc5UFaqk\nAqownwleCHWn/hTQOTUhZqdIs9bELyaTbK6XiToSb310rgMIzseQjpXAAcDLu9fPmzk0urWX\nuSH5awLJtY2URKErPje1IBVxt0W2xExXe52UVux0Kb9tPrJUWpYiLN4liTTvkd+wIDTqSAAa\nQQBqVOr1NdkhAafj06Pp3ePJURrkoPS4tVkVnZhAZHxt70grEkRF7P5trS8Sv6Gl3aFMNP7E\nMbF1ptxijrEY1Gr4Sr7takV5vvgLHPZRkmp1IFKrssHLjC7j9Ee752boAHgIgHE3yRFehFcM\nhdpPmiQWEStTP37CxqX//igEKnwfI2fCSb/GeS+b04BVhmWFMisUnotRZjwqBDS9JMLpNo1X\nkddMJ8nA9yxLGAZXunBIfGn83W0vwGzcWqIjiGM/VLOnddZpAlajFkOYKtOk/vQdR1/6glor\nyN/D8r90X52Z55lU4hU7AOsgQ7JD23mkYNe6xfH28ZtVx9X6GHp0aVNG3DPVqgdjZeRmTLii\np01SSQdaZfIlWaKSnZZiY5eOmrX2A9ffceXkgfpgn2/d/Nc3fL2b9rL7PIbwwWRss401n+g0\nvGi6nRnm2jZ24I5j90uSvifUQoYTRhrDj6cViO/uzfe9NImwIXh/trVcbIWBwLi+UZNqqtCO\nUpBQv3IBncg81s71SiIk6EtAIqFYf13zqqzgQQElcIE632AcbZ25NLGJhOqxD9Eic0Bb1E5+\n1HhMZuT9YGX+PeV1QdiNm/2GRsboxPvJh+DQyvxUbilChKQ1Hg4AmgYpjgR124Prt0Nr1E3e\ntqSXZHs4wxJAQzFacAVnPaxFQMwtBQBwq2RT5ZsMabQRqtNJQ80D5c8KUCjCYpIeJy4h0RCZ\nFBhmx1j55bce/tt/feQfbm/fRCJlTnDH8t7kYdUOcWN0PxKeSz120EiSno/mSgIiDEQFJgts\nzuJ9U0vsshiyvKIgvr116+m9p745/nKuZu0QiQIjDW+VmznSd90ZLye2BTMKW1gZO2uAYYYd\ndXk83PjP3dNbulLCtq7+z+4bF3RexKqAAC0WCl365deu/K/zsYmE1xiE3lcaYHw3dD5Y5y/+\nDQRLvPFNppyutz4y6ZLzRH+Je6ztQLpzl6a2fsGtckCTbV2OtAiqo8SuHohSNhepL07RYD1M\nNFnhgCh6NhkM9sD4aHrY+TOOAihFFqVHKQEFGjrGfn86T6Cir2uObncv/agAVn55uPOCrXD2\nijV4rAk7Ug12DKuS2L7ER1TdKOnp+LyunOUK2iPWSTgs/QIAfIPJGJWNB3n31u6WJTY8QPV+\nxRhYJGoHcOmiRyjK9LrMnquWJJ/76HI1xqxmodO4isJsb76fiMLEjfYq2l+1lDtsBmTIf9Mr\nmT8GnB/ZhXJl1P6kHyisrXLNXvam9IZw8k66Gc9d/BMjoBjlWAaVFslLs7PYqXcu8PJkegrm\npDgBgZgH7i69AdhR2j+/sHd+gIQlFpOjV+Ees6UtcnoMo7/90qu8RV8py7fesU3AAsxNR0Pk\nX1AEZbO/xJXEX2v2rTjcDO9Pu4XVXLYFmnxcACCl7UXWOKcW4ur2A34BiAQvUWEe1ROzpxgr\nSoAcCE0FYcPli9hObzFyAIT6XmfAO8vBh/tUvksBiq9fv/7Zz372+vXrBwcHH/rQhx5//PH1\nOi+++OJf/uVfvvzyy3t7ex/4wAfe85733K/eHc8uNI85vIIElwIIoNoCNNbOYNKtuWQDUTIb\n93p5BThz4nMsOkXlPBUFYAoA3qkfAWcOQer7MjEWg8BBI3XRe5EdQkX/sTFVSb4SCzU0UVot\n3QIrEMW0S7VpgrEsDgrqVCI2WjRntOwls0K0kDMcLItmLkXYloZQ8UxRVGASEx/toePlTAUf\nZ64UHAAqyV6TFcuGUkcJLKNLEtbcPSkIrWtf2nvhEVmlZuNU0iGMtHV6q7oLKf7Z5vbd7XXG\nZU3ns6PQ5x7VQy/pWfpGY5WVUq3+i9Z+Pa/WflQKe6r1DNbmrekWRh69gSh1SetS3bIb1y7L\nbOPuGHVpVoYWO76u3KFn9Nm7YPvje4n37M27e9/Kv71JbnsEJNMfSwegnOPdDhNlaLW38RxV\nV3KiJzdM7KXdFy+RuL6cz6MhrKyBdnRMycaSnNcR50zH31clG6fGr4j8j+sDniEX51jZ0k/V\nPuO5KCVqIxI8bkQjAzmnGkwVWJOP3Glf+ccacR2pZhu7neayAq1fTQA4p6OG+xzaJT29q8tj\n0LZiyS36FEBl/EoELMerxQOn4wcaoMQ/Im7D7Hk+f0MEqXQ0iNinlWFQGIK3jSBxr0LKiuis\nRgptIK0FJDVKQnUUV4MA7I8fvrhqPd0l7cyoQFBcGPxo0nLbupY9X2P/33sXOsEMGHqwXwb0\noJ61OPpnPIJElKT9Ma/YasOqtWL1RF2OrFwHYCvXQ20w1+MFkBm/3RSJKUX87tnYrS0f5b4M\neJgoALe3bm8Heuv4ev2m9HnpxFwQAUJg8o6mFlkddocapZUEuNU9EKG6z86kbgjETiUozFqG\nJ+12ACiYxE5DNQmGj5dP9g6M9kyZ103t1tbyQyYYU39h2Zr1TmE5ptStNqHZNLsq7y0A55PI\npaJW0ybdR0OS74bE7vbt27/5m795586dD3zgA+Px+GMf+9jTTz89qPPss8/++q//+unp6U/+\n5E/u7u5+/OMf/4d/+If7NQBFNVGzk0jKO2r8mMJMV3u6eOToQQx4uNwCxetJk0NralhpdRAl\nDTkrH1DoioRQ7Aurklp87d7Kv/2Vt0AbIsL2zoa0SP3bHcAOHSjkhQvPnW1/kzhF1k5+BmNM\nlXTllgSoaym5snfcajzb/auXQIR5Mz8ZnyQOgzCKvC9ToSvXnUCilWrP9aNeRUUW6tRuV/+2\nokNzn0EIx8x8IXJjMdqqUcNrY03HeTDW2XIbNPTJTd74cfuitRnpA0cP7M2jBVgUb06iOGTh\nVoC2vq2XaS2AyzpDS4ll7N3TAfwNvihKhB6e31wSsopIEiBgho6Ye6ptmN1VZsCHFyDLqAaN\nJTUp7EJcnIb0MbmjObmexvuv5e5oelSG0icU6wWnNVY7/+3P0USJ6iKlHN9iOCKNBHbdlnNw\n3IXNiqfv7RI3Vwuvb/k40slgFy0cyTfUNI/ceeNjt95KZiOhw8sJdjenldujyyPMrCWPcE3u\nxismn2TqAAmuopgXcw0dAKUSnJwSZRmLSVYqA6XiHUbA6JSaZk/nD8nxO1TeJDJONOg+XyFA\nSRcXT9ykD56DM1CdfrpnpbXJD1GU+uJlZWPWH8H0F8Pq8qgB4Gk0wQyJlu53DscOhK8++FT7\n5ucoimMSYqykXNXXtDLxQ4bqZBWSB6nVcqFI3724rBCsJ8elKWztHVfJaobrgWqbiB2nPEDr\nPICDs8vI6vTdAQEgcHdj/wV4i12XBzxshzIZDmVyF9xD1fg53zi2Hs7CMKVGZljW/tQZuSgU\n3sUVIGqCH7UzAjycwY9wqIWINIDQSsFdr1IJwhKHq03O8RpPH1h1otnrDm8Ot98kd3takHwJ\njRqUAMV5zpr/EgDlHKDqPtrYfTckdp/73OeuXr36sY99jIh++qd/+uzs7E//9E9/+7d/u67z\nd3/3d0888cRv/MZv2NcbN2789V//9Y/92I/dlwHYXuVQhEqa3bdsIT3kUbnzNb7S9YgqZUjb\nFNeBKPCpPJAEQouHsH09I9nOd8ZNRQvj5N5nf6bUthgTqFyV0E5XE4mnJayBQWbnhowL4e70\n7mnzDffye+1x1onNaG+p8wjLPAd2IiZJp1druVPGP8lbwp4Vp7Pqpi4PB3K17GKpybUpFgHg\nQs6TUdoqpF/iYHrCufJ+pcsgAg/Zt6y/s+C+aUTIISF5/UzFfmMFp5Wl4KAknEPCORRDHkCK\nBByncnPnRju7vuiZu2CEaajSaa8fXDYBGyWAqRZ5Ts0YnQI0mWDe9tm8/jApL1BKkW643jcQ\nEZLsoKeaUw1E0U/kjJWo29m+8cGrFxcv70YWfKk+heOJHY9Hfm85P4OYQIK5MCjBBQgSBZwT\nUaytKvU0Dk03oW5DcDtFtPrxJMbLE+DRjGm6lPOIBLW3Hb21+/4q2Z+c4Rw5BVFlNZEvRmUd\nUzD5xA6WxY2if2RCdfUS0YS2GCcAXJJ5U81ocADT3dlNHI16gwEUOJ4cp4fG+MWWxcLX5cgq\nTBYgO5Lo4wmmoxHC/9U985P8XgDfhElQaKRN6LsolZLo+xjOI+IrZWIemgGuL2G8Rw/mFx69\n/abbV/4bJagaEn6qO7S9JeG52exkceZpQuhroqmsuSdHiuVoqa4bsklDid36iGoqMEmK6XUX\nAAAgAElEQVRFqdik2g9UhcECQNrLzGVdXqL5ZT39WrRa0enOKY7MxW24Aug7esG5xHWHdfyX\nR8KV84TZDBWgmk756hvluW/FV2jDPVX+q++SWN/lqG8WnWCkAQRWFRBAPIghnQbPTMF5YKXR\nFIqgUSpoVcQymhu2YYCJJhMaNZoT1RiIG9iVAWXSWq7JYRjfnC23jlOyRDQNgR5f3oyppOIq\naKOV4w5RDm4LBYrETgE4HieCQD1GE9q661bay+13f8p3Q2L35JNP/tAP/VC2IPvhH/7hf/mX\nfxnU+chHPvKJT3wif22ahteU8d9xEVUqUgpc33tptRdj6m7yLNP8E0Mtk8StvTv2CGued9n0\nHsDh9PB0nAIUG/FUSexUtbEmUF5pdbnQU3vFQxdNJ1WWdyD5k93rwqKe98PAmW1HF+IWVW1N\nZF2ZtUAKykynNx4UgMEEqUOJWqX+acDNnRu3dm5W65fXUADsHb3VZjKfngL5PMSyaBZKsmwW\n62SLVtZ7M9pxyvVaFONx3ygACLHkn3P7PX+XLlBSNpvdTF7GgcROcgZK60kbN7+GfhEdRDrB\nDh03fdUh1QPdRNiZ15ei2KTXI9ZEfuVHNJnSZDpsJPL7ZepUyOiSCREAGErESc2RaXkiUJhA\nyXLaWiOZ5y295zgktsDJsJ7SSlj9TluJqtnyNAmPtAadrfkBpMnT7e0CCwBfKThGmDqMCFDo\nKY2fdQeD9dTm+1FiR6iS25DGqFwViBdSQ/dpacprgirpNb+Ab2qprQJKQkR2SwFoaGSmaZxD\nGldHO/jzGjZ746pkTubOXlEkRvgXkK7BoxKU5AeUhIs0tD8ocpfs2JX/AASMxgPHyT5RmPg4\nQIEL5xdYedSNSViBQEKJw4sUFJGJqa5c23v08k+M3fZARVjTJo6ZoikeESi4UPVKSFnCetNJ\n34i0mhqmvAU7V0rMwwnlFfHaYxyt3hW3zGiHVMt2re/a2g/V/VUrVHoU2JR2t2iP2cMIJ1VO\n7DIRwC5XJSXaEGQ+xTFZG4xx6p4njkcGZlu6+tHuW5flJOGLRBk7R84VBHdwAZlEUyBaqKtl\nm1VozPxGRJMJKdCMyHvs7MLVpsZEvqliqPcSxL5R7v6Q3KwCzwAAJlMqMhnKC7hZYkeEvrv0\niGdWUQkM9hgVtJj0tvelfDckdrdv37506VL+evHixfPz8/Pz89lsmInPyte+9rV//Md//PjH\nP36vBtu2XSwW9/p1vewTrYgglqtAru++eC5eRSSE67s3fvBmXFAlAbTFCkwQCRRYpdUQQjic\nHInuqWgIJpwyAklURAL5xbY0AtWALoRAIiqiqioSdSYxBpXudOdz3VE1tkyhKiKkapVZw9wv\nOsEpN9aFiIqE2KNK9qBWFWVVEemCSCAVG42qKKmKqoqqjNESVFQkiKooREWSNCTSeEKdQEkC\nVFVE4k/UiZCoxwgSTJwmqi4ZNZOoiqgIWFXlud1nAWjYIus7iLJoWiIAYFXVzq2cuhDXJv50\nOLn9T9duA3BR7VZYXyUV2MDU60jUSQgibCY+IiGEoJBgLssIElawxVKLRKwQ29V4dMNyqSGo\nqoQAFRGBiH1QlbzCACCqQYIEbz+JQLzGtLAFxdsWxjkCKoE4nDWn+8lGXKDKRMIOrsNKaqrX\nggVIBI84GDMDtYtQVSxOVByYLa2QAXNVRBWqLCyIngUKhYo9T3sKAK3IsXIECg2CICIkEkIE\nEjNTEFUY5EVoDzbH0CGEoAiwZQsdgsaVhojdkapzOWlxBndRITZmo/ticyp5EVznVUSJ4r4g\nqKpCzkfnc3cubXC6FBVWkdFYO2gCdVE91PHMDpdBrqJ701dOTt73b8cMzrl7YaH/UEU1+TYB\nIFXfMIWeR1GkCYCmQesRhBSt62Yj8M5OzcATJerZeziGGR2xAojmjMSAsgndAKEVVYkSauIt\n+r3YcyWwA3PRV/SlQz27+IquyDMw7oVL7IcNVEkvgXrUCSqNJ0OaocfM2ZtxDDuLHWvcqZMY\n+zOSyVNsE4gJtL2Dx97qp7PmKYvDWZhzpMkZF8pEB3z1Dm6aNm0xXnYuWR4DADoOidGiejww\nDcNkapUZ9AYJ+7yKVNnQcLcUVodaNWnV2GXb3hgz+B6CgCqAQH5CWHM+HRRW32DiYWE7zbwy\nve4LVwbAUzPj7YqPjFLR9fnbG0wO1DH7BEoE4IqcuCSoU8AblcKM3T3N0oPxWRaYxddUJ9SN\nRG1OkolsMwPu07oUVbPU905JE0vOXzx7pQtjHD2Kypw6Vx7TdgsoE0HhHMw+gTMnrgC6FN1m\nIGhIKamgBE7usK/VVOlVyneDsOu6zlUso/ceQAibww596Utf+tSnPvUrv/Ir73jHO+7VYAhh\nuXwNrPn7Z9OuOX5Su0g1iLYhGBVxMjoHtoqfl0IRwA1Eg5EUJCISRFbKxxhDIuHhIE5FVWn+\n0IOLtz17+UYkuhZzo9MUYhdybhkA2X2sidDQ1GfkaI3WKAb4qqKS8VQ1TFXl2I+KXpDTU4ys\nR4WKiOY7FLGOQlUC8g+ppaCyI+e7ujXRrXT5AqQNppfwSCc3LoXjW5jFNBmaVknTMKzxIgrU\nSCbF5QiJRIGIeDR5dpqYeLCDBABBQyLs0kZDAhIpXRr0sXtREVGWOAoUGk/NAkkjjZLXE0a/\nRSqKWXLbKhRU9aw5na22AYhRJHSmauplTe/XYgmoIkDKxogQwgu7z18+udqEJu18xBMaN7v3\nukkGJe1V2ucoAIlUnwipiEmARZS5ti6baKsQtYQNnHQ6inp2ufqRv9NKWjEVgYiKgZLE+pBm\npDDAtxEYiRjHJiKCCEju5uONYh4pZ4UUhiEgqMK6ctp5DYE4Ue1lPJGujWdFMwx/7eApURGV\ncVj+yOqZC2F07C5pB1W9M7ux4Hkk2+sDBAX0NWGG0WjNifI/ZDF8nx0S1Tl2Je9a0vtEp3Jy\nHmEF0rPR/8fem/bakqVlYs/zrhURezzzufO9eXOqSpKsAqoo3F0MBTKo2+2ywFaru2VLRvwG\nxEe+gQRC/BAQqCWQ8Qd/sBEgqyW3aSigqqghs/JWDvfmnc+w946ItV5/WEOs2OdkqYEEFZkO\npW7us3fEijW+7/POJwJt4Jbz/pv87rXnN0K1G88MrAkMimtRpRDLHZwUxU+lK82V/f5/ZlDA\nA6Bm/kQC8xk3E4TCswhFtNOjpq1ck3gwKRlvbWvfb6n7JmzQ4KV3ZkyZCWE8OwAEMjU7Btts\n+8IVyW3KUaqixoOxyIEqyFDwUILH8e4e+j6iEuYWNM92+FeIChMAmu2qaVUiYJJUNCUvZdJc\nBsIC0MBcNXca/7e70iX8UIaSjcCEeAnG2TQpEUPkmY7epxemgOUHlt8H1apuIZzR7TEcWAH6\nELxKDQMP6atYN8NyDZ0Lf+kAZre7wgiYCoVfhDeDGlFr1tgqWwJ1h/dwOuoniZv+6b5/+C1c\noerGbNImy3u9ALVKZO8blihaDS2sQEgIp4/Wi8f6/IXkrRp6FD+EGYh5iJKURYDzuZ7Fzq0m\nT/vp02upHByS4vKD5YOXn3y6s11v+vnt0361wSnV/7PS2C0Wi7OzIQH62dmZiFwqKP/hH/7h\n7/zO7/zyL//y5z73ue/RYNM0VVV9jxu2rufPn/cS1OYkKcYY04gaWFt5S0pIhDQE5oioUA3E\nE4aVrayzKuaJWcLWQkNyCmeFQagVkamfk0Tw9iBgjBEjYoTeE0ZMFJKNiDMU4ZDoUkAVEVFj\nRExlRUQY9e1irJk0IkaqyrsWOqMKKYbiKsh0Zr2pKiu9LNmBQiFAayvxMcSbhDHGWivehIaF\nFKQ8ScIQC75jDxtbiZhAjyGsqmrq55/vns558k1zM4w6pg6gKChxgCImTiCMgYihqWBEjKoX\nhp/jtDe0G8qZmdJxeKqyuTxYGLuSSk8VGsCAFBERMeIphmIMnAFgaSutrDHGmGA/sZWJvReh\nhvGLFSMiQqqisjW88RBjKy81hVBjhEYEpBFu6nbexT1gxFQ1zM5CVdD3AISGMvIhJGGs8X2U\nWyxtRQlUIc8VRegpFCrJwZISgt+EhhQRiphIiYRsGpDoe2c8PY2RfW4eY0qKsZakyiApvege\na0h8xahxIAnQkk4EAFOw/1lz+uDw22KWE3oRY8U6WGNExBixYiovYghjLW2lzomxIkLSSFws\nY6SyVUsRioip++OaiOEdEhxsSMKIEMqwU0X+5eyJoXqQFAHD0gCYoN/lmcg+jIitlSsYOt95\ncef2mYiprK1YHUpnSWOtdCJqNpNTMSasEZuGzitpjCEdoPv7+//1lOEfJen7P9KlGsw6aiKv\nzOw9DiLxjEhXFE6cUkne3fP/2/S9a89vHOj5Q4VhN9cWqJB4atT7BJ4lJNQMwM4BQ4ZE5eAQ\npMRUzqiw8HPJKW7CTwHcJItktWI7yb2ExOZLf+Xw6QX13yrGM7qKwKfwEgC3zGuvLG7db7vh\nq4tPJVfdDDkUuNqfvwMofc4PHrm8DIWwIstOeLQYWkReU4kQVS7GNykBbOxG6VVNTpkW4ogJ\nrHFKATws6qtyF/gGkmfjgHSiibvUKElEK0GlVEyFJqMeSRNiBOGuyJ0HfsgRcxHzSooIjlDK\nGPR+axpLQ2owMQgNWUMT/l4seQIUa0YYRYx9VjXsduPoaXhwxPvfTArGUdTvKONYAO5bNSTS\np7QKjG0khWPYqUqsC6+MpKccufcGIQgEoSYBRxWt0IA9ZzNojR7XeXoCr0hyVBA5inOWN4NV\ntXDrVKYlywMZ3OdBhaWBtbpoQEjtenGA/egUdv8kPnZ37959880385/f+ta37ty5Y8y2Jf73\nfu/3/uAP/uA3f/M3vzeqQxBK/y5X4HNhGzhxRDjNhdI70zbm5eLJ9NnT5b2ny2dgdB99JpMO\nNquVywcrZxEdOJG0LeGzMpBKRtogMc4obslBpiBQ1UlKQW5HokMUTpbfDVVNwi+bumXThIYb\nmRloNBTmjJZJhETawSFvSpTtGA9EZ1pQ41sYiXHcroMOPdUgyPOTUx7H3BooxMTseU2kDoMQ\nGCFAPsS8pU1vQZ1FNEH+sjcOhM9mozxPTGNJxyy8VwkVT7q8JrvndxabZVqSdCYVhNAYEKTJ\nS+Cs29gNgoQdBgWN/amY3zhYgor//DBjIPTYn+bXJWJZUKDy8bjRCsZcbL8w8z7K4oM4PTSX\n/hN6TSyUgO1T0sTwTaBDBIjT5rStNghGN5YNpgfS2t54fPW19z6lylk7R7FhJOBS8aFUikBe\n9M8m6NM2S/9LgwuJDlIGWQyZdQkQh3p2rOdhDkSspb/qn4IIqr/UKzDu28KawnxoOMwGAOrf\niTJ8hI68/4hXmi9PRVXLbKG4kKcBWSmcHwqMXxGyQE5ngN72Tw/1/FX3MBbaAlE4wjGsL6CK\nWU4cQ2+QNWKZZMVXHuLpa8v/4wf9e3vhQQLAs8Xzs8k5NJxWA0Dn75TW0SK519BYIiRMpk9s\n3TPS4qWEHwYiFDPoSzIBHV0tzhRDuAkJy5SevayvssURw/mJ/SmsaYACR3r2c4t5OkNJCV1b\nRp8zAnDsnSnzV6WuieF8juAsSzAXaRQNsl8eqUU9kUZM6Z4/tBcr6JAWdYhBDpEl1ez8G1e+\n9nj5gRnrbgiFGau0Y96iZIxlOqGjp4gESX0RFZvmKMxbRmMEYGWUk182h6Gz1kw4mRaG0RFY\nz7FYgWwHh6U02K1dkShXIrPhJ+n2I50YChLl6Qp8Nb7utpwisQab08ekXxWgsRRjoJ09B7BR\nnuW7YsvBChvDWQz8rhZW6GBW16EnAJK3IZSgEdhMq4mt8/wPu/4p6NrP/MzP/Nmf/VlIcXL/\n/v0/+qM/+tmf/VkAq9XqT/7kT54/fw7gK1/5yu///u//2q/92s2bN/8x+sA0uZ3pCGSp83tE\n0fV0D/e/EYrMusDvAQyJ6EbLQGw3xGEvYaSEVgIw6ke0LWYHEC/DPtRIehhoymryQdF83oKB\nfIvA5wwfCYtp/DdJGHIhPOGvbv7lW0dvCmTKndKBQJNiyRRVUsvRlv0Y/0KmbQoARSSBZZ3p\necmCGkZpW6AmxUYEid/T64iE8OLmD296b/edk+U9Xz3tbZcFvto1Q2cD2QQXsh/XJiYiBaGd\nOE9PaC6cNSgTxCM57To4v20U2J6cq3qy/SMJoIoZuRJFpv/uwdtAKDbNUTNF6gHHsfR82XYV\nxCTDAoXI1M2CtWTRDzWi8s1harT8ixH6a+ELvHu23DnfETcLdZOZ1nHJFmlrHZ9cSYgxzNuQ\nHa3ocGh0y8RQjElHLLlJOYd1u8/D+PPklNWNSjPLx+1K3vAeniIpl+LALjIHG/yNgEZ7C51z\nqFsVVuaz/rtv+HdHtCv7NyWT/dn8+RU+3NV1eLtlXeADX0y5UoGdv1U6Akp0tgVwOlllCTPE\nFih0v9q9gtkB7U4QpsOhYIq0TSRDJBUYvbjXNSoOd3V96M9e8R/kmz6ciscfRx4ewBBqWfBd\nAgLTYMuUVOK5AWEotdF+Eh2qgnVEAbBuYC0GMASlUkxRvUoB0AhnM4CVTBfcj5CM2UdnIFtT\nLm9Pbx4fvzNVjZm9NcsxofRFwi5ekZQIovp0+tTTbZ8IRT//7mh2mOntZb6PAIbEAhnYFYld\nouJtiH1Jwm9JdwNw5dZBTj0qKLwfITADe9Xc3sXx1iOZY8ZOaUg9rQCknxWa3biw0bw93iVz\n9gIDcIJu30VA1tY9qjXEkMor1+SVTwMMjNiJrAfUF3pJgELkcCVgm1hZeCUsktM8/bMmpIXC\n452np3sbDAuPf2bBE1/4whe+/OUv/8qv/MrR0dGjR4++9KUvffnLXwbw6NGj3/qt3/qN3/iN\n119//Xd/93f7vv/VX/3V/NRyufzt3/7tj6oPIRm+p3+0+KDi9M2LZa2/59UbB2OHLR2aLLZP\nSPO4FZQKwJs+ITUFIJ6h5kkNXWfldvGi6WSvkkn4KgRss9jRJRYsC5AdyI0H+oHiiXJMFaAi\nUAehqVD1prPxRMQz3NqNd+6avCTJDhNjRnPJZhUA193zM9PkVyedYO7OBX46uFBtwZLLOG+a\ntV9ov7KQn/5arAnjAHjoxq7LGShz4QVCcaSnAn139519/z76865+WnXL9PKCAmjGdibk4KDm\nSPtCUBOnMYdpVOArHY0JWnyP/iKwGwdWb6UTyNNDoZQ0zEM/WD544clLWx3IPVWil05NLzE7\n4TAFgXPky8JtoAT3sXpkJvQ1+k6A5Xr32QSjh8sAsEgShy8IkyR2iBNQTrBt1oxZnegBNK7O\n9NuLZ8zGnsoehFfEKQ5rOkpCXQoheU2bWMlX89IgreBoD4TGOdLB4COljN9flwKqXgIkSurg\nfMokFOBSNcPw93X177r/jMk1BK2ZKx0bR1tU0vbLnO9k+lxnZzi/DsCEYs0D6bmwvUmEMFvg\nvFkDiAraQC0joeL+zuao37ntRKFBWrwlr90yr8XxZT/WHNN54UVMqOGWPgXwlCtkEDaSksfC\nRfwmE8aAfrL5NSTvCGwVSx68Yj639fxV+E+pvwV9mx4qI11AQgvZjQcKBL8ETbhnOgPm9Nup\ns7Vak7qorywJqotK+QRCpJ9TB9o1EX5eN3+pZyssUcCmnPOFkKJiBTMU29Y4EDO/mZnBP40a\nfSxHw9qa+5y6Jeg4ihsyQM5mBwoLcR5pMWMe0DDEBi4FP45eVVjBtea0A3d5/IRP8voW6gxi\nNmfvYQyJm89uAmfxdWEnEZLgTaphmb33QPDq7NP22RzAK/7hp/S7f4NrHmBV8eYhnq5AZVWx\naYDi5YACfvIBVlfiKhAKIbylGungxuyAtPA9fbZm9ZVbpbwZ966+d7TYxYO0MAXb/Idf/0SV\nJ37xF3/x53/+599///2jo6McIXt8fPzrv/7rd+/eBfBLv/RLpR8eUozFR3URINGZ9un06R2+\nvrpMxtv6KlR9RzQx6HrSTdZVyZnGz15okArgbPJkcfpiBnaTdt+KMcAO2meoAMAPlawIwFiL\nJtyctUeMQuEAKeoCOrE6NZ2tYfLOO0a/0Y1SJQRCEABf3bnuqr+2wWaXiFNoxMRIKgDQcQkU\nKxYOn/HvXdGTD/jjUATt8UBIxzRgi0OUJCP4l43uTd+HDxP2lFgbtjWteKtWn0yfXH92a4wH\nRq1c8ydz9s8Rj8Xn9R3jr4Q+RKbCAToIiKEsugSf3Bk6SKUanSju7X/nzuMXA4cBABlS6Y28\n66KZYCxxDonD8td0RUnCMQaMw49zNNDI+BpPlZSzC4n3BM5RzkCj7oQ0qGZSPUrjpWoBgAZJ\nM71UAXi4NU4TAyLXh0RUH4oKhGaa0tOndwWjg5dNfCT94ox7NL83PTmarbdq6CgCuQxvYSgd\nUsLY0VYMaj+nfSn1bJE7Ax9aaGPWXA7/XoQdH4Mrm6GbOp++cg43e98yq0+BUJZShyqA6px3\nviM39/B2Ful0i9/TxmB3UTVijNSGtSbWb7dS54cowPApuDMxHoSoOKI5mL/yXbwDAFQjFQCG\nOg9jxW3D+YTzNMT4CoFciEJN/VRqCRmS2xxIs3XALu6CcWOlpMCUva+tWyWqukeqS1bvYgPs\nwP8H3wHYLM7wfFcHDWkxjX7AeCBQVbb1oqpUVjWcFe8CxfCMHlcxNU/atoMfm7J/Xk3u/+vD\nq+snu8/itBwe8J0s5wz0VRNxI6L/WKgkLLygJU8jF+qsoB+hV3p58ESaIhLApl6/O33rbLEB\namG2eiWKknXmkwnOUwBp8CJE2jUKAv/9wfQ/3tusEc0pzrj1tJ2saqCYBKDB9AwIeVU4X5Rd\ni/Mwm8t8F+9S1AM0xX4AoNCY3bCqfNPAdmSyB0EBTO1eSYYMfI9k6xpGltB2tkET3eLblTmR\n89tTHE3ctQ6W5Kv2xE/+y9OzNwo5WQFU8OeMaqULi5H1m1ku/cjI1z8RsAOwt7e3t7dXftM0\nzWc+85nw+aWXXvpHfXvS3QQfrZS6lrCi2b4eVuLd3Xdunr+IoHvY2cvsdlO3k3XFbCTH6BRc\nCM5nZzqwDxnbC+8gVaCBW2gHjhBS7KcZ1jaaYst6Yolv2YEhAou3cR7UUKpQUX7On3v/LGxi\nkxRUYhyj85tuiWgxa3HZjbw7NZ/t4e7RnwodHzmyFD0SrQlX4S9XPpLuHQilp/719a80nKIH\nwcbM0SGlad6+yrzHBi4DmlAUQUBABeqQDQjD/QjALvZZFdiYDoBKEn4Tmxkrt1hh0mLlA/of\njWXcw0FGHA83c9l8uwhSqHgagsacE9AhEzC3Wd4EHW10IwUACiiirlii4UquBADQ6mqjqyFM\nL8ZH0rAK5dKty4rtVHg3jDAnR1Qq0Nn1w8UHvbStWU8xY7GmsbS4MagqdME1uwdAiMITOphi\no0nIEXTifGFmHae8xkS7mbZnrJ9Pn/XnfRU7+WEs/eNwLXFwiifaWNJGQFPMsoZw6GSRz/st\niABy9JjN95qWPUBjti0VmDsHXzxv/5x8GH6tVAHsyHGD1QZrJCKSLwVAT6QaNInAhliryswW\nzfHjfHckHJcUX4qCgo+HdJRFcrhPkej4SIYctzfSuNnqYq13hHIdIgSugffRrOBPZ6ffvPtm\n5TZAzBNpZ8NLATjbIpHlcHYlBU9QEudmnhRUISmzEA4i5oDXH+m73sT8joW9R2PEE6NNCL0B\ntHKRO2tS+BAqKqavssFGUtZ0JkebUNw0BXhcsu4hrKyY0UT9C6K4RWLS+ZMns8fGzk3oEUZ3\nFjGnUeigIgfphS9DMzMpE4hSqe9ee/TSm9eBKIqz3GNexQBFrGSmCdm8OkP3qvtgmqHm2BRL\nY8zhIeTByF5UQnuAadoAMnropNrtA7+Ij/vJw755pz6/ST9p3v/XuqAutabr2SMkQC20HUad\nQqFU018OtcvZ3pZl/v7XPwff4Y/kIqA6tprhdfP0f2jumQHYAcCmWpcP5SvuFE2bb9z2YrPE\nmKvcO/5ue/1/VzPKKp3TCMmYV2W3Ep+FCsBLlMf2/WqHnRTUi4NnuQZDTAgz8/SOXhDd8gQ+\n+biA0GHDFyMa2ixRSQRoKYfQxS2nw8keGkopSYrbwkEPslSOCB01tqiOygfyb51pe+kBNbSN\nWcZxf9jhuKwFBKvuZAbgjnty7M+aQPSYxgAAmKOr6bLZI1GoVKWDZaW4eOVsW1vf8wLAHyD0\nhbkJmlMO1YhztzKahksAMEvrwPbKVRo9aSrWYXRK2lBgYmzVSvbN0Z/FG4J3Dmd2lqzYhS9z\nHLgHsJneR0SYBNDbTWs6jrJ7jjzNo6iOOMYBg2aUmtTzbvaeE59yNiNp7HQ8b7E/vfT9+Hxd\ncCL9mFwHchOAQq3USV+jW/tAAUWoDkzWDafTcIORqtzvzBIF4qaq0iJkd1oGxK0KYE41wFJ2\nGi6GVgAATlzCly7AkrGvRcIuTKWfEDrgYUaCZWg0CoxUXir/RehfVLktzoLZgm7FfmFVyYsv\nb9WeFwVsExDEQquFLIf7i77G05M60u2t3r324Pn0uaYOi61Alo2X45+iG1INJcahVI1FEYpO\nAjs8GjeRySuhyqLchOggSjYyDz3MRm+JXdNw6wAaBjG1wB2FxqEZ1ORIPrsxTr/uKwBz2btp\nXqs5wWg1FcDV6acHElGaaQNAD/J1VGvQEuXiVtS26r1oGBrSzERfdo7OtE/2gOEdVAAzdDmZ\nDlB8igMfoU8mMpRVn8G6lZrcpiHxvBmJgDm66KiqIURT2cZ8skaSPqHQIZB2a1NXNWzFxSL1\n9CMjX58UYBei57yMilYZ+maUGFoRAuMZY23GyxB533yzANCbddGUhqRlHJ1VD2k1OrQOSW4Q\nKMJlLIjEZuqZk5VDobrxZ59yD+/wedE6I98TQ1uHfTSDu+WffPv4G98+/gaT3wwBk8QzZh9p\nHVovCVGuiVdMBnbkaIpFvju/ngCFtBap1a0Bpr8VQI16iqWVOhlZRqVyd6tr6U2sn+IAACAA\nSURBVI2a5TstHh9fJUu45Bj4grckcVYA7GB9Q5+l2I5BnitHn2Y1OrUlxlG8JT2xYHI+SyqE\n4pYtqDfeQ5kgarmYHKB6MTQ1pqUhiii4cTfCVdGFrJhT2Vk0VwAc4vxl/2gbwCGKuRcQ5tBk\nGMgE0Yc5+IMCg40vfh9TNpgkLAQpYujYcDAGQhx1LGOJSDWUm6saiGFVt5P3N3ZdbCpuNYgx\nccyuBhkyfvyuQW6CL/zmi9mOtQ/U5FAJERnQSZLNwukfuVki+IbFTBmIwoxFo7K5rs9edh80\n8P+L6/6b5DGiAzdEsioS0qNAmgV3LrqZUmMY47bNhLMZWVK3D1tKJskkMO909DLgG9AAARzI\njR8wX/wwVlnWrr3gcF3s44QTAPBof/XK/hBqAWhVya0XMJsREkBJ+NFIHT5nZRuGDxpC3RE1\nkwRQq94yrwlNxjQlpKRkrJPVarGHlvFFgmESEMI1L9Ai5EXJS5gU+Eq/wOYYOagzYLFc5ywO\nzbIKRHWo0xiBdR0xPKnlBMX/Me2J0OfxaVbt6u7Z7mkalybiG5DoCJPfP3rcFrWULklXFDZF\nJK+cmumsOZzVB9t3ZeEotRSRc1UlaUQRNYLR7bGwesPAVqwqNCUxSooAiLW0VQrshUZL4QWW\nT6Cu5c5d7h/E7bQ9mL//9UkBdm53r5KJpys3QlWYe/IHnxZAxwcjHjhl09eevqtWuJAroVyY\nsOcqKBCYc/gyNr1NSjLxk8HaGFxYemxYdG10u1Cu3wgOngL9bP/OWX122pwSXGIt9DX6kG4g\nUJXg38TizKlzSKbStC/TvySACef75nrsW+5YFnuq2lCX2Lzso+HGqqvgCmzlAVjYhrNM+Fno\nygEwpyogAbUyKeey1IA1mFZaRNFrSTviYmVVKLNhisXC6IXQpfjq9I2oIFYHTlS1EP/y/Jto\nGvAMYsA2fbkUtQPo0sq3Nubuizui5GXZiUbCnKXt+CFX7R2syQGGACy8GUHc+MFHSj4CfDlN\nFLsdJUSzM8wA7HJhnBgeHHRztkqsSg0ti/cNH0vV8jBJA8CTzZXwM0HUtdAHN5stKjiaAg7l\nIwe7dNR7fjyDJ8J4vVGXQrZKgBc+7fj1Z/j+Zc9KyOpSpH8oFTnIMDqnDLNS+fl3Kvi5tlbk\nLvxOIRvkcxf86BWAdAOnL8IPC747ZlwBjaUkODJfRNYJEjjW4sQVl4Askh/FEggDt01vSFCs\n4WxHjoaghHJOEDMnBZW5DA9HKjS+d/xncWqEpDGILgqBcg7IMogyF5x0EArEQHUm/VXrf9L3\nn1OX3ySIkfAcSJym4IxtNyDOZ+nvFAMR3h/NwoMiI3dppFgdN5cR9RaxWapWiVTt2RsXCV72\nn+aFqZ6xN6yt1kDKZ5Pnunh1GJhowrD5GlOC1rZttQZ9qm14mXotLJEqgEU1ubrzmcpM0+iK\nu0YCYvIXImGKaLWE8ZgpNUBixuUL8pmKTXkKQ2YuFRUUhS6SnTAS22I35EhzkqP3fRTXJwXY\nYTIxrN24cNsNnqOYy8RqktdUeY6G/RphCTWg+7FZNulDECVjfMa/e7d/aIEaUyD9SjTqJmXt\nX4XKJrTqiy45H7JLbNOmwcNGFeKiH1jSjhD8tHvwY/1btboj88KV5lM1JsmZAiAW2My1pWoq\nZpzGm9svD34BOrSgnOGbl9yjn+q+saMbAFid/6z7xk/139rKGj/T3oSDkRxC8vknwKrI/k9Y\nM8HofcOoK04mZomyO/GhAn9XHsZEKFseXWnDE5J8KMpLg5tztJtobDw+XVjABz6VVVljhqFb\nijeMOGhxvbd3L9Gg7PRY3JhWKoPoBFwuuWr0g4Eq0mYCw1Ysi76P2cy48/2SkeIlD55kigVH\nzn8NOsYQtrhBZtiVIiI1vyRZUXXgwKVoMXo9Qp9VfMjXiGJlmbBf7HXGjdl1gUSO6f74XTEi\nB5si0dbAaaAEa3SVDMlNwHjEhCY8PtUOgedvARUyGsKY/YoGLygb0/QgnfsQAKv3l+930kFJ\na0U0JmYPJ3YggWkv5zObpQbSfPaH5fpNpD1+XU+v+pMaWGZ2un0RUO7uRR8+UYrhdMbDY2uy\ncBgnJ/2bEkkkmTJfjZnXMgMgQlskSQi+W2VrMvwUWx0sePkxX8Ci/CYdRkFbBUUAc0PiLfz/\nvON/RrMznYbU0GEVoh42AgIVmFwqe6BsiXjOuR/tp8FbiIpmIlXF6aiudMosWdL6tGzFBBWo\nOg8qUjahBcYMAgDJcPwVGD9roTv1VUEswwOFjAlzDJetCLB2DRYLmU6QAlMSiBweuHfw7f7o\nTz/MJyfcJ5SZ7FRRR1DwyvxXCPxKxEOog617HlKQJJwHRNzF0Rbarp8+rKyOon+CJETu4dzG\n+n0AcMRbe83t+GSK5/sIqdcnBtgBDOmeC4HBUFVKbqQAtKkybio9N3wCP6IAyZ0lmmabQVFt\ngesBTLXbwZqkQUVNXE0JoC4SZ5Cq9nQ4Vgh9VO+7gS6Ol72EFCK+fDLo4UPy8dns6q3ZDzNR\n6kB3d3T9in/4un//9f59RLVi1ooMuGLUG6gCXdAzFQStRr9AmwtJ7frzHV2XQAuAhQ9J7suA\nSQ0kqarZTPL8E6zNfMpF6EaHrfJQquqRLTLjMxbvuNthZ3cS/IGy9hzQKmQP4gQLw+2YIaH2\n0pFQImrsJDKokfl6DIQAuOQ+mL/6EA+NbXnxwJ+G8cQCQQXY8SmbU0jSywLJ5gHnEo0CRHox\nMLPE0VN3H+y8lzo5LDQyTB9SUhnUNepGJrFA0EVgt8MOwI6urToos7PzVpaoAWoPSz528NsW\nJeJzCnzn4K1vXvtWfij8T4EJe1OMbrjBWlY1reV4kj8+V92wCmkXgoZMAfRVqwfvht9TKjGU\nfJUDr4415Q2Sgq1QlyPmxBt9LUCWZ3blEKNHovbi7YO3QGB3dz659vLk06T4DJ4iR4tqnMHl\nKIHGuFlyyjeSwBt6/79z32TiSZdY2YKLbUZV6iGUW3d4cDjx/vUBWCTBPFgpRlgvEzMcVXdn\ns2sHez9w/Y0vmxL9FC5ZAyXG8CEUIQ0maUnF0Sh5Yw9iRnxxMEIaiSAs1G4G1G4nQAkOhlU6\nptfdM+rQvqHd43FyzIg9kwgWVWCUStCojUKtrWQ25XSUlo+Abh/V4ijGj7SSlUnxa5b8RwOL\nGSZ2kAhK1V1qsd69xt2DaOAiG7vk4LodgdvV/WtXZjdqV0OMskhgTQy18wAQbXXumw8S4rxI\nQmKzV+QFyyrckAzTJSuLcxEfMCLDcYnBMWXwBAv5Pi/KMEH5UvWSrLoaa8N5eoKHenakZ/lu\ny6b0R0rI8yNDdp8wYCfKlOwbgapVazRPyts06+XHGclzWZKhdMsF6uOpRYjDsO+VeLZ8CoKK\nGXbnspt/GtqnS29P3IwRnMUgpILasDTYksaE4i2xUmd6qCeVxtSzvq43dbMuDi8AVHBhC+6r\nRzJMpw5rxhNRRANbu97YFYDaNdN+OnLgGFPCwoUjhqCHmw10YncasyifYWnRFpBSp/RUDg7j\nBL2BRaW+aXrIlH8CmHDOSGwYrcoRn0Wd1GgiAKE+mz7rq2enzUmQt3zyN4IpRcPtgzdojOKQ\ntvzHS/w93KbAHs6DvNi4ZqtD7++8F81tKfRvaD+3MY/+tlQvUG+8xEIaBGBRGZiZdhM6AK3p\nakyMnWI6UTFFSgEgUJO6xmInpCQQWwljPim5EDyxz5CjISZVj0xGPYIto91sDVUVLLjeoO3Y\nRsqBd8BQz+vzZ5Nn41ErgJp+kgGrDmcEJIxJcO+jk3m/fy5j5NVPc2dv03T3j5+4w/sANrMz\nP0uzFIJeoIXvWlCrhV0kURIbA/r4MaosIugelICj3VoulI68OYQzu3+409o76/V0PdIHKy7w\n3cTwdOvbLAxHmAKAF5aSQBwgiz6HjyKf0iIodZCmslPZlmQE0IDcOXh51hya4iHyQq/zpIgA\nOK9Xp83pyeR5mt44t7wg0qGc9WI2gkuNNitk2TPNRNrXAuKmPhvkNCqAffhr+nyfrWhSMZog\nWQkhUJ1gLiHC1vv4YHEWAQhUVLfGh2HOCWCC+ZaaNqGbYYB7dIkIp1lTBbDEwcVK9hSDeprt\njZNqZ89ewXihX58vpnaaZcxBFlAdfaFbeq3tSZditxbMe8QvwpqVUFVphz+CKbYQlVPe/zhv\nL5sfesG8EZe7jJuhetP11gmLAUCVnsrBWp8ndOjdtof1P/z6ZAE7ZbDTAQCtDUHUyq4ECj4l\n187a4HBtJfWIS2QrFDvG0xsNEU8pOIgegICOwedJLWuLitheSbd48/mN99bzvggg8Av2CV8B\n6se5R4ZzJTa4hcU2QzryfvcvN4d/LEbtpDu6/nZVb5bosXUF/h2ElqigKgcJKK5JSKKrX736\nN1+/+jUQlavEm6wlv9Benp04CUxwTBSNWTTVEmDGc9k7h1v/Dw0VWr5hOcYJ8aZ2ia1rdz8b\nKVCouTl+Y9Gy39j106t/etqcBJNiPI0J0F98IHavsAg02n/aP7iqJ3d8FBUezx+dT1YA/NgH\nIO4fFiU4C2rlpI/Ve4oXfdi5FwKqq6a1qDP7YohBI0xMgaEVG2smqGrs7sPatLaamw5iKAFV\nVhLn9/AkJyjOKuHIPqlKMHhSW/jwypwfeDChUl/3D67JOu/i6Gay7aSTBqgKwiOXlk9TBQJY\nTU/TzA0M3EozNP0xNcXq7j6Pjml4/+pTPw816yLPPuTN1+Y/swXsxjwsLWc21Q1Xct0aWGy4\nh5p8NPOWSpen0iCVQgr1DI1rrrr5JGSsyM58MW4raUKinFW+O5ctYPGa7N26NQmkQFqdPaWC\nUKODsny86oyeoFq8T0dkZM79hd1DpJl8RS4QxvjG8G/m/wpC6f/m+l8/WN4nSJEARkVMOQSI\n0NhC5VM2Ci+OACSGMOdfQtGIkgCX+xyAqF71p5ZeWGi8AAb7OBWgmLiCw/ejl+sFnh8mMO0c\nDs3mTmzdTaKhH0Mw3vLPbvhn0zT3Ra/DSksmNlkwqEKAgpYtM1l2VMQ92Xsqsi0elN1n4Z4W\nLgtvSksNgLS9TQ7ICP/6AsILUvVicHnur77p9+7Hw0FSfNaXKHDdvHIkt0FwNufuEJZBcWdX\n//jbN95ashsQr8LTDys7LCjHZyFqE/ARXZ8UYBfm0GcJFUAzScqDpJ2KK6sf7MdQgHKeE3mC\nhDDAADjGFW+DES3kS4wKBHuapLkhd+3QoeJSWZ3vP1biG5y1QVOdz8VYaEGknhkTpN5lB69w\nvz3X5qGHZpepf8UXDG3RTMwsEFqaIjc0cAYy+XimPHl5BofqzCXozXrN+JcCwGRKQBUmTFoz\nwWx2qdkspy3YVKsLTYe/L6lbNWZVYTAzGhMqa8Wkl+VNF3Z9oIYLbK7751f1KaLOcox0L1k0\nJG8wApii29fVVLsf7d8OLztpTh7tPXn78N5ZfbrlQGE4WI6AbeqfOJ6uqvNi68WPi+bq7vRm\nvlOSpmWStneZpyoPfGp3m2rnMtEwLJkAMNDXquc/vsCivlaZuU3OP0Oux0IWptKihqJCjzJb\nYUlSFZ/3bzd0vthao49hGnfup69GxG2kMgcDP5NCLarQWX1Y21lQiXzUcu/3y5XCPDcA3uuL\nQnnAi+aHl81RpF/MRCY8BkBJGXz5x9JCvImM8icRPbtiIulQY2CEwzmW5yqZ3Zz9EIBg/zuf\nnK+azXrqXranr9rTZH1DueoUXEgsx4TWmbpReHGNwY1OT4NEdE2fAQNiyxaVCv7H5b3wpYEN\nMa9bJ/eK3A5BWgGmLIrDOCK126eSmfuH+1VRSXN1542XDn+iPKbXrENVwRhl9NhmnjZFJ31Q\n5Gy9IxgWMskKryvxU1ZAJtUAbcLsC+wJjKGtm3XQfAppzJCNncCurnd0vTUZzK8Okx6My8Mt\nZDMRYwAo9VYg/6pmwJTx8df8/Z/t/1aKIxuKKCaxOjohxW8UACwTzCIJ1EITZkvR73z11d03\n39h98C989ypKz1GyWKdLDzwzF8r6hbizS3Y+IhYsDGUQdVe+g3qU9Sy51UcyE4YEY/cWL1hp\nALjr33Tzt+7w3X+3/MaBZCM7GfJsgAD2dHVVgiZVuLPHZHUZOvr/m2L/HhcB3VKcxFCc+Oeh\n2UhVLSYMhXEAsHQKi4FjcTdfykE8HVMe8yKdTyqoOdglL1zR/Y4AzmA3NAiicfqxHMWYFmgp\nBaQvyxuGMdyQIxpUdAhpEdJeDULWZ/zKaPTu1Jwr+ALyuMQCO/J52hKek0gOgKOqFoP7VuZE\nOhiYHyweDFNTPBT/P5lyOs2DLNOa5BlI/jWXFH2QMZkX2pDEqFL3c/3X7+gTxHDXQIYKvHvh\n3DlRmCSsZ8qQRtFLu27W9/fvg2NjUHBECXR/+LJoNxmO7+2/fe/g3taGmVR7O5NbaaRKaEBD\nVaZ2w04oQqnTCDQvYk5tRcBHEeVYNgfGV2YqEkzwIBj8Dg/92U6kWZGJ1WwA3MaT1/0q6ylR\nbBKVZLKJOshtS3X8f7MimD2GLrYD4vbBv8AUXnxvh0rbJ9OzsqWPKa7DAI6A/3uzDwzOi2QU\nPqLXU8hjCMQACDHMfmC5oe1DyuS9oRlAIGUzEZr0CwHcNp+WApZZM5nKLlIOi/Nmff/Vr//L\nG5OfaB68aE/inUE+Sux8eHuKUkjcPlPXwO8TA80vi+Q66IpC4rHBtSvv+croFazCSC2qHzr+\nn7Al1wFQTshGJEhCMs5vMEBJhqaLqcbQtexaOquPpnVOZU8AC/EApK7RTHUs/JN62pz0tke1\nAcBmgrpBfk/oUDxe2TCpW0V7DCqAqCqT5nQmezfsy5Wdye1bAch8ybz3afucxWLd9Y+v6CnV\nb+HVvCbClFxkPqMUASWLJa0FcZgEPCvZVS8cvJL2xukKGe/c5IQgvc23MhQ0Aixc1hgD+PRs\n9ro9CezOL769s3z8U+pegP7Y8UE5+nJbXOZjV4gFg/FLUQC78OXYbjN8NlIsM+PkKDSGoI03\nQ2XmAdjp4qmKA2AT/CcUVQUihmwSNdwbtV7zJyC5f5iTd5LYmdxe1Ncbu8BHdH1SgF2gAT7T\nkuFraLJhLtB+dnpy064KUpK3ApMWl1PtjPp5EYCWW1zXK6oaEGROeKhBZ2YMoA16C63GTw1d\nGZOyp9Mn8etgU0jKKooMySOitBPpQbQbjtVceXsfqP630z/dOf6PWj1PPwGIWWerEsoVyGBr\njJrJDkFjAzPJDgrbyQXYl82Yy9xQyoi0dFAzYd0+t3G/1nXQAiI2CwDj/GwMCkVBcnS9JIoq\nNlDJ5EZ1/vn+3kxD5Gxwjva1TooGP+SSga5Fu3PdSGJOm1A1aCDV+SkxV45ZD8E3F3z1wv+i\njAhght6o5jxeBEOKGYn5/rMihumGoeU8L0EFMOh6w3rraPOhZFBxbMbAT7G4hpOqCA2TJKTU\nOP9X7smRng8DVAB4OnvSJhCmMTUGQ8yQF13Vq9jbqtJA40hRBwB1HfhK8lgNPNys9p5/64W3\n+uo0A75Vsxo6S/9xTVCcTApFGs4Lwn0au79uVrftOSort+9yuSM0QfKJcVHUsUtPgFXBYyT+\nGZ1WpQdhJDuwEoAVy0KSywJLFiqOjfvJvVSsOQB1iftx7PKVXhZaiYw4RYLiUpBORHJK0Nsi\nbinjTjJk4Rn42sTuXTJdCoJVEqbL2oPcSombfW3D++Ngs+Ey9jJP3bb81lQq6XQFlEC+t/vO\nd178TzB9uH/I95QUWxoJfprh0VQh4VjCmKT0ApXr6frJ1W72+pVw511zemA2F+eR21GpBDCX\n8/CSAbaU2oG6DvzLxHfTZLo6FqgIydEvgfb6nYcA4WWQLRQVJwCW6AwwZUhzrSaVcttC9NzZ\nGQO7jA+BCwyiuGO4fEzdPOpoiW+DZSd8rson46s8wc62znjNoT+DviWB74tdIgk9mTwvHTcl\nVo7hraZeJNBvWAntRTP53/v6pAA7SrR2j1F6WDdf4glNa6xEQSAGG9RSN5/FuzdNwcYAAJ3p\nnkyfCDHjzEpTZGpQAGorpQr0x7z7Ae9KNRDCZlDluDzuk/mTvg6+6vEg5/TV2fGzZMKD5rkk\n2zrySJuyY/U8Q06mTZa7MUjT29dlLNNW3N/PDqQzdJKFFQCAbx4j0UMFbNHG8NEh3VPC7vh7\nZ3onrjiQfigvk760kRHECE1kfhMC10YjI4Blda0y07ILO9L+oH8/2uvrs3f33jnbbV+xnwcw\nmA8uu1zpzR3as5bJ1aw1bQG3RldAgWubwn4vcWhOwIsK6IGuX/SPhoEPHEWZTLFMS2kmQ4Kl\n4AOQ9DkKoDcubYAkg458e5A46HBZ9Qfm5oL7WYQlcdTeiGy1fgaMUzMSgD6dPcuex2/d+fpZ\nfYrgyU/vKj1tnjQh44+xcelF5uxu6nNUVVhl0WE7ZTwxlKUoNDHnV77b3vqLi3P48biSvBT+\nAIoNmRY+foToUro92QBAFWJOAz1gKtUwdj6nA+EkRl+FBa3t9GXz+Wr6uJ99qxoUGOF1Q/a4\n+HoSwH5lpheU4yXAGsJdRC8KbOG6Yu4COFa9C33JjjOYDPArphKv4Sr4OLa6TuFZ9cBEowGa\nAB4dvnUyeT6SVwc4BTNZ5DtHUbEyGkI+Iwu2Rv0ELvdO0iyHQ5sCexWXnOxBWkMhUkXRXEEg\nFNhFEnSZkYcOyxAuI8PG8OIeXn1uJ8XAicso1yjMg4or/vRV/x7ym8LcJCBW09+oqxCxu6tK\nyPHy9VeXP1Ta6LNhhGSRdctILDpCAJOcMilJA2/Iw1/cP1lGB8dic6fb4iJQylRKpqRMaULs\ngMcuGXBQPkoZLFHIpQCKggGsB54fEfDL7lEF54k3X3r4/gunydAxfmEh6xT/kgpvnJ9ZbQZ9\nSPjwc/v7L05CDlqMye1HcH1SgJ1duOrg3sPFB8V3HDZvOQ/ZA7uIimX0c6eMirvkhgJMC9qy\nYIqlbGngJTpV2Ig1Lmw/qhiLRAgI9Kbr6zUAVdZmnvjaWJiK8G6Q57bOMgvJYvv+lGt0oHA5\nm+LWC4ARLRjITZZdCOCGexqmYu7bMEvBVIeq6mfd+ew8mT5ihwSwwNyWoXCACEVSnS16utaM\nfCyyBjv3sooKs0HsDk2JRDgMwFfP83v365vz+hhgAJXbCk7oO7v31vUqxYReeuQizAhREck3\nKKsd0g5KxE/HEdZALN5QVMTaesvWnxxvmOADrQCqoOJifiEATGXEe3NbKklXM2pegSFzZpI8\nh+ctvKE3Ui2xqyQpgccY5tC56FQaN2huvB6ChNqZ6xc1jCHgbD9dXn3NPZjFNIoYknMsFlcm\nDmk/SDxZCcyE/xXJApPXrG52Hrv5448yE9T317VNMkYx4AIobIw42dZM12YxggOihQwIY9sr\nrzx4tP8+kIQrQCC3zWv13r1+788LB9/woUx8PeySBeUL0x4jspAe05BhIHG8jGVGMRMEMOMS\nwBT6v7r2Zbudk+iO+QGEfcbIhz+v73xpbw8AX3qF+0eITvDKgleGvq6aZ8+b5wO/nc14eJzN\nuFf23rgid2bc2QFY1L+plpi8tJh95gZn82ECgQb+F7q/+nx/D2mw+X1GKoAHVX3Trg6lzWMt\nFm40b4yeOgN6I0CtQpuDB/50tth5ggQEcw+vVfhB+/QFOc0WYwLJMMptA3R8ox8ALiK9CnUp\noVmEYDIOyJL9UWXDQXwFfknuTG/+6M6tJcQgeWxnqqu0bPK0DHZW4EA2u2zDrUxV5hroIByk\nHs3sAVBa7FEWiLrmnw9j2bKOY3gEUbkLpOXJiJDAyPcQUPiEULUayUwAscS6hgN0M+26xjPj\nA+QxlgS1aJgkVMkHP3LqbvwFIucNuJAkfmJ3998cHiwTVPgIqdf24fm4XjSwe+92va+yBDZo\n5nTGnef6MB6iAuMdTF/CKmyjwpcXSOxmhKGyIjb62KWcmaEyYCYyke/HGKb0cNwqimJTDgRQ\nObdHUiw7M6oJclmCa+Roi8XXlX+m94T9PEULYIqxI39U8wSizC0+P4QKl9twHDq6xGbuN8/R\nLLAX5kLn9OIPVO8Xw73lH/+kPnpifhLp1CWrchwGhXDD3BOQ+ZCWiVXkZUt7eCyzl3Drpjbv\nx6mghVdlDU+Qdc1Zy+dDMoE4HtPCTSOwGKYogYlC9tLhY7w29ebt6XdOdk6AOs1VmvGqGp6S\nBO3G66KTU5xNBsq7Rf1JAOvgwLtFN+IN8QlLB8AZl54rWDggjI6LefVy7jqggFNgrvmjmhLa\npodecU9OsWmAQ7QUg9sv4N5peoxADA0W+oK3gKAvXakVIUWDl+7ejf/XH9969WuwqKazKyf2\ndNjqx1fol2gzvywazBuPA9brTA/g/o3TG5NT9Pj+N8U6587Pt5X93+Pqug7AerVu29b1XS99\nq62D99i0bUvneu3tpv1sf/68/6DvevE9U8jLpm1JPWw+953zd5138F69994557z36hzVO9/3\n7sz7XuHVO+9827Zt27Vt61yvXnvnWm27tveeatT1nXdOqeo9gK7vV6sV29afn3dd55xrO396\nctq2LYD1el23bdv3fd+3XWf7vnO9uL5t27bF+fn5amXYtrpe933fs2991/roeN5tNqGF/uxc\n2haAxcQ5B7epvao659xL7oNXoCcnJwA2Xeuc8+qd873rnXO961vfrtpN27bemdBjqgLagau2\n7fu+bdvV+Xnb9o1fvOi7X+jXHdCuV91qpare++ZFtNjr6lvy1a+0m7aF8d57uolbwZjT09N2\nve6As9PT1XrtnDOcTJul6Nm+nnlP713Xd85JT3XOq/fp3z5M0fn5+abt2LYA+q5z3nsXg16d\n9+oA1b7vznd2+8Wpe7RRqO+dcy5Mgd+sP1u9j3bTtq2r+67vTk9Oms2abdt2rXZd37uwUvny\n3jt1gWJ771WNqjrfKZz3vROICNR7+JCvva9Pu7Zz3vXO9W3ngH6zOYdTM4brKAAAIABJREFU\n5wyqvu/atl2Jc84BcOrEN9Nqbjuorrz6vu+6tut757zz3vV9d3Z62lVWve+6br3e9F3foVvp\nqm2jf5p6dc512re1outW5+uflu/8cX/jidZwbunPnXN920KEvlXfGO89NOiSlV7VO/Z0Tr0P\nS7zHzaE/3XUn3vVwVlWpfn3uurZ1ztN7pxt1DQj6fr3ehKVRY1erVdd24n3YDF3XbbTvAG1b\n57uu69C2fd8757quhXNwrmvbvu+cM975ruuct+r9SlctNtZr572nh6Jv/dnp2X5d/QBwenoa\nGMFqdW7df62ujeRi8aE+eZ8UYIesl6oqiMB7FFhlzt3n+nCAPZGja2PnwHMEeMSIxgSxvsSY\nF6f0dlAjhk0jHPJPJnbrAXCxoyfPgPHziRcOrUU9ugdAt4yWxfKZAV4VoXAaE4zly+tYY4cB\npSlw2z/9t+1/eRGffRbBXGSN4zCH4l+E+zLOSf1OrHc0ZAVjdZzYh7rguxb+Jfe4kjzZmp8a\nePh0Zk4G+QwA5wukgoZM0LkiF9yfs3/V/ugD+Vr4ccbuJ/p3vumf3MMdVJVOGk0lUAv5sAOm\nSOq3CRZrnOY+JhtUiRW0+KT3d9+jtUCNUmMHmFu3995d9VpH90rytD7BiJur2D6N9tJLAby3\nfx8tNCWLH5QmQWmlAGDVA+irrsyEnmtCLNh9APoCb43eMV35/qmvH6NNe08BxUT4xnz2/2ye\nxuFA59j8Cjbf9au3AVgbsWZUMDCET5oSVdH5+vx8CrQRgzGG+uHB4Ve9aZ0N/nCc7794cv7X\ng594AtZbGrvwR/y/DtLXyezku1dPzwxj5NyFMX6/XSIyHRcD+N6XqrZt20wm1lrrjDHG1/6v\nXvrareaxtVZFTGXrST2Rs3MCxoghXSZ1BsBsNr+GyWv2+SGXZCUiIkIRioCkSFXXRoRKQ0LE\nWltVxlorMFRaY6yxxoqQXEyNMRCT96M1tm4atVamE+usiNSVnUyn1loAk2Yq1hpjxJj4fxpD\nsXXlrW2apq5rtRZ1ZcQYY41Y6xNLqqoOqOu6aprggtmrFRWxNGKoEBFjqjyZdV2LiHhRoYF1\nIlaMNXZyeGzX1og4AUVgK/SuMlVd19Y4U9lp0zTOiUgDzIOasKpZ18653LhOJ2ptXVlrrZCk\nmMkE1k6n01nTnDk3n07P61pEjIgx1lorIlCScnKt9zPuns7kTEiKCEkjEqaoaZq6onYdyMpY\nJxQRgsfm7poPKk6moLXSTCZ9VYl0BGlExNBTROqmtmLhXWWtEbHGzmYzqWtYW1e198baasvw\nTRGT42lIoRCggCIiRqr6p6/L9aX9q68qib7uN7sfWK1ESJHaWls3rKrJpLEiomIsrLX1fCcE\noFixBqa2M7gNSKFUFW1fWWtcLyIwxk4n09pWJK2xdV1ba6y1TdNYawNltmJFxN68U++/6r/+\nN81ksqd6C5tn3QQiM3YiQmshUtd1J8JAZMOIpjOCj5cPj2UJihEzPzIv7Ff/pn0/jJ0xQos1\nG1tVIgKhVFGVVhs0dROWpjJVHT4L6QADa23VNNWPfdH8pz8TEWsNrDXGihdrjRqjzpiqMmrC\nTrDWGpKUuqrN/gH39s1sLh2tTOp6Mpk00yYaPURbQOu6nk5HPn7f4/owf4ZwffKAHYIXgGfi\n737x1kQ+i4QfNCVTUEAkp64sVCu6rfkl0GBm2NSYEGe0BsZwkJNC7TntQ/oisq43883uqPJE\nSJCUbLRZvRcxpq/CKyvWjcwrTghXgozYGRPdPGayqOX2Rs+e4SGJMqKqxBLh2xm6rBEBcF6f\nI70dg+5sS6dFLXyKdw5ekfNH8Fuq5KByG1RHKNywCE7Y39Enit2Q4kSgXOzIZoZTAJhg0eEx\nSMKnEAQAW/k807gLfDTUJgUWDBbhpH7KcC5NgooLX8esLJyt9TQ/n4xQETCVUJ4XtOavuIcv\n6cPyz3e1zkZqV/U0ho7Zgd1G+TI36ATDhkg6WAUQCh+hSCgdZxDB4V0BeONa0wO1bxRnw+SE\nlfLini6eHetB7nME4odw+D/RTQGoioHRZOffMwNlIAEBFkust5BT/sNz5I6qqLrVF95an96s\nNhlFR2WhmxtOJmkISIFglwPc6GMXYpYX5CqLJcM8rOa9dkCn8SXf3xdJe8HI+L3vR3AzCoiA\nJNnWbWVURDxpRIwRASGeQpohdVlgYZWtmqr+Yv3A6DFYh/SDwR4GUkhjbTCMCeEj7jPhbSAE\nEr8jOFmJ0IeKC9EZS4wxXsQYYyQCl8pW8dVVpSImtEgjhqH2WBiOMSY8S2N37NHCHu76I0l0\nOrQgItZaFz6rkGTlQFE5J2lo82RaY4J5S0gj4XN8i4iEc0ISxtB5kZB5DoasrDVWSFYS8zLS\nWjVGVXPjaisXcI8ISBHIrTsgrbGNMSvVpqqM2Ph6IK4VSMN2oe2ivfvNudQTiGFTcTKVqYhs\nAFhrxTqNzYZnqIqp7Lxsf/hGUy8IEVhrJUTfJ2eI8E9YmuC+EvB6ZS2s1QDEDEQE1TncJCjM\nDcyRXF8lN+zwOhIRhZIkD4y/3vAb7Ahu6n7feFEjFBqIcKdpXFUZY36Q+jYWwpOwRnGjihHE\noSs4p7MCMSa8hoZCVpVlGm+ceibEpSE43pA0tRVjELa3yGv1yd/0eyAFQlJMgKImTlkK5qAI\nlZ3pSVIgwuPPmodrk+AcjQS5lIYVSTSnzr5ZiwTTVwWYAMqBsEEpocpGvESkqiqIUFKn47kU\nzGa0FUXEhARDYUVDhTcjAQkawzbIU2LNsHujsVjM34k4fI/rk+Jjh6QGGnAuI3fW5uFi7/2s\nDhEOPleGJRSJACd5pAWFSUVbAWpZTWVOkIlZZzSlQlQVhOv6JAKOncdz7u3yKNww1/ZQTwOO\nQ/KT0ogkMi4JYESmsiM0hbsAozYRwHQqyx0AFao37E/t8gou4HqjYd+MtHBJW6ZUnNYnGFI0\nDybJAhsWUwoCOLr7RSyW418SvEqBvEzuUcUtQQtDEy2WyuMrdhGzCh+Z28FR5hDn3zn6ztDs\nRZUlYIpc6mXGDLrZgM9Uk9t19F4BEVRNimiavKyTMe41fFWjP9Tzm/7Zp2RIqxwefEkfXvMn\nZb8IanbHUA9jSx1gxQj3wzcTtlOWWVIH8JKApjTQl9zDNA2XINxIgEAjSUxPcHbVrApbZs7z\nVHjbKE0s4DGa2+EVthrMqjqUMgjIC4pGm4vwbPD+1ASRl3POF8UAM1ZL9ysA7KJt0E/Zpe+H\nYD5e6N7gHfB9b4r9e17xDA5VH0wMiIECVeOa46+7na8CcLf+1h/fQ5YN4sNhfpIqdIC/tM0Z\nhUfoY/tFNQVytAkJ+vo8uI1OuZxiC5GnZSmwtRQR75G+EcLtPKwklzz4wuJ/XJrD8svLp6Je\nk6KTB8AQAHFxvpB2RYy01a0ghsFlQdK7zBDav92cXvw+GSmqWDaNsrfLZhId9Yejy3w7d3a5\nt0cRzGbIglPSswciWUiPJGTmQnyxBrw3CJelfBc+KItKGIn6Lp4s7mzaK/+Xb6Jz+ZTLK/Zu\n6eHHGIw0qMnj4osCeH3CO40TmHCPzOa/+NKLv3TtCsgv+n7O3dCTDMcJqTK1D/0MXoMgQgKd\nuKsYFsWYGgBTWuPkUxh+zWmpxuuhOvpSFcCC+8WtWwVQ4juv7X72hcMv3tj7PJHSSQF++Z7b\n+4ucAstwZBYrNqEOKcDiTwMLDl3m4TGv38hdChVAIhiQuLaRnepW45fusH/Q9QkCdkHbw/E3\nCAsvPkP+oYJIESrItIzb8yVEHbGIbrGpTLwmE9bNoGwSQQ6ABwAsdDNBP3xVHtrRCRzI4Ry4\nKkM2r9QZTfRCkTacAiFPUrgqaUbvSKPLXwYj7KBJGuZLtz4NWz4psC4xLOYDC8U4cDKzjZAB\nS9Tvzl7YOdrbauCOfzo+ppfUfrbMAmgaSiAN59fSGYZKMbEprjbnW1f6wos5rX+CP0OfgVv+\n6ZGezYdiX/nmrTMZkHny4x7iU8LrUMUybr4I8hreFIu9Kvb8SlMOiEPejNGjBIA6MuPU5zAD\nwkV9fX/6EnLyxHDVDaq6AIBRvxVhGUAwZSwjcGEhE4xAbqAYDuhB/lD1pVFqqHQKBjSYmEfa\nE1psIc1Qz4oC+BF58O/bP49hs0y9DFxkCxbEfz8ymvh9eGXmka+Z3alCDXsKSDN9pGYFUKfP\n/fwxhn0Z1SfIm6Eggm73G5O9d0B5UTeIX2+Ds5I4UHxIXULG3UKUfhiRX+WOirF5q+SzKeJj\nGpQBjiO1cMm1tRV1dmLv/JVb/C0A6kCPBxTAgbYM85BIULjqbHdJuAaAzfMy23ZdKsseLnkw\n4eDpWyEmgObVG7x5G7ycaKpiWu3bXJZ+IEYlkU+p+dKPIhUwSqc6TGfsWPw7wxcimmhI0rrl\nrR6yLlG+sYMAKdCKza4c7QVIHRaIYBixyA8f/OCP3P4PTLYks7dfT6dNKTxHnJYpAv8/9t7s\n17LrvBP7fd9aezrznceqWyOHKpYoTpJFUWN7iOO2W7Hdlh07bXSAPCSdBG40BCRAHvwH+M2P\n/iP8Ej85Rgw33HbQSidtRLIltTVTJEUW7626w7n3nL3Xl4c17n3OrSJpUrHILKBu7bOHNa9v\nHl4Y6md6Pd/N4BkC2OCgSQ9BNCx3b6x/dlztx3PsnwoEWQGArL4ykt3pbLgfNmZe0qMOsCIA\nZTYuspHiPEyZ2wBgUJBuJHMcj4pQCyd2Nmp33yYrSp4Z4/TDoMTofPj/54p9L4WIEAMeIVBr\ndpa3Rs8MJ9fdA6+oy7O+v8M+8DQFLJPW66EE+bQ8KQ0pFIUboPGExuNWx1w1YvHiae8MvomQ\n5Md2Pt1BhXjnwQR4UBK4PPbu6jWaOIZmROuIkLSDIW0fHTwsVB9I4E+Xrot95xY+xz4/NaCJ\nY/5iEjTqfBqyCdkzzpDd8XOja6WF1WEElczu0NEkxCZIwmaGo85euinxUFmyXCmCMgoAqbkL\n9raxhbEnH8n7tKpa/BgTN5k00kGrZIQ3xz8OTyhgtWRmXKBYAgCDTlBQycmUkHVzYWmXThw7\nW9tnslc/2XzPE0AWlVrAQ4poIDFWsPNTFYs/ldUKpaM5780vquas8pG3LRUVxGgAU2YTRNJF\nZU6rVm89JRahnnO0cJS03TMFFeJRfLTDjFibzvNZo4zRsxC+hCwSQbJRgFvq4c8Wr+/SSdoD\nBR0YLdV2zAyrAOAfvyr2vRY39WF4z+z9s3HvChICrv1ugp5STOMJN3st1SGRYDzhrKTAyUTK\nIKkGAMgnS0g5O0KWE1EIQhR8uQDYrA8RzbEARGwGer07toWSkpb+f4dl9fWhzcfVkggGAUp0\n7QTgLHE7iVzzAOOtQaqV2EUuKEe70Moq33qSV9cBFNQr2QfqA31qPPzCZJx0M6HBE26ICUSs\nWAfqN5bxSpDn+Dw5VpvrEEpMg+wPdViZsN8pHPh27RaIpZkPmZtADg5UTeA+xiNsxv4YANgp\n5tdXB0VZsgtkmB5qIBp8C1oSO+qV0lORkRU1DyssysHbst+nLENZEGhY7jBpz1XYAw4AxExb\n2+rTn7PTHgfVFkrY/3JUO3wjzsVg4LtIASnEuUqEPJRlKquccYhD23H2dsbP6eGq61oM42L/\nJqQ5G9HRpN6/FM+PZ79baLB1Kt/vkCcfJcIO3mQ0nWz7iFANNvN8BABe+jTAJI8MFgVg1qVQ\n/H3vUeETwlDnU3hLc+KyaLefVAK8PTwEYsgd5AXlBVhTC54iqNOIKMtnoAbZNPDkoYskRMzq\n7rP2oxK90LfIxBGsPjdkmxZCTEfYhkQBWMUgyQgiAQDQlMfMew7Ts+fQEkcO30vNDGAoF0BM\nNEgiOfUAMORl88aK8vGQighzQ5c0hZPTBmogFhlc9AEIzx25rTUHJ3121nvW2C45jqG31I7P\n5krF2CleT26QJT7Sd0JEvXBtSwazSWcV1b+Zv/o8vRnmtc2J2r+W1KbOHQD7RZFFEbF/I7IS\nYld1UGzaKDDnxewHT5+cFwH6JG3Zy8Z7tgibad6pDhTYoDhAAYgZxNbFR0GSFKLpznC77bW1\nN/7vm19rtNOuXlFPr/PVwEgEZkOT7OmzDummKQtBTtZzWSDs2hP3oSuepo7kbx4sZ6NW3L/s\n3fERF4LCi0h2o4NXG1vFE3dgQwwmjbZktwlS8rEqAaCvR3RwXb38ORoMOWIvVw1beyZxdIHz\nxicp9Sh0Jf5NgV5yjnlzy0cbgU945Q6aYz7DCH3H20C6Oz+l1HshYzigfH/VIzYPM1+/GYnX\nBMjc6/c/Mx6Fn5rpidQ5xs9oU5lWL5KhqqefQZ5TIqoTAZjFkEXQWXZh8QraC5r2JL0b8oeE\ngZM383DgyKqMmDMXIUpWeRuAx1wMor6ebWdxWhkwaCfAsAIHV7+fzyToBEh+cPXrKE9D3+Z6\nZvv13Lg6GPMkE/jd0pr7tOe9/mKeFCjnZkGRnqXBIKiwvKgvzIl/udV1eyiKQl9/CnkZAHXK\nz096V4tqzZ6D4C9p3yyqMw93yay+CtVJN+yb9PILd89h19gNW54oimeqsq+WIZv3VH4qnSfq\nurZRAN5hMcYAGMkOm283prGe/8ZI09QEg8YYapqmaaQxxhhTGzEiyFHN68Z+KyyCRqyeXcQY\nEdOwdY8whoRIVGMasV7Rzqm9McaAFWe6aRqjDIuIyHw+n9czYzgur9jvGjR1TbUZDpFlUs+M\niJHa6B60Nsa4ZmHsiISNMaYxdV3XeXlf7/yfNZ0YY0SkMXUtxrrEG+Di4qKez6iuAdRSG2NM\nIywiRuzomqZpxhOjSjFixEBgJ8LOS20aY4yBERdf2FiJsRgxjTFiLi4uqLFjb6wvfQMjygBi\nIM4t387t2qp5TcN5j4sxBsY081pEeuZ8dnFB2YUxDYk0YljIvleb2jSNiJz2T3vl3Jw77tOQ\nW1mp502jm6aZmbquazLGSG3xRN40qMlIU9OxQmOMUHXRkG7qxhip9ZGWq8ZII7PGGCFjxNhm\nRQTGmLJXi9HWmz4BME1tWEQgIgbGiBExIgIDg6Yxda0gRoyINCJipPE/IShltktvXTQ11XOm\nc8sHG2oyMw8t2FgDTd2w3VRijDFGDESMMXXd1PNmhKGISONCWNhQFj1GQ0R2ce0sw/nq13Xd\nuJV0N+t6bpSBGCNC88xWDmPqudTzuR2SlQSappnVtd1tUDU1Dezba1vm5EeNaUzdVFxrYbuf\nDUzTzI0xJGiaBnXd+F1kmpqIGjSZFEq0Db3RGLfL6rp2hplNA+9+RGKMkfl81pja2Ep9Eg4x\nZj6vm6YxTS0iAplOF0wULi/MXBTF49/7/7o4rJNguIID+Zbie/tywq6nKE0AIONqAQXRTq5X\n5GxDjl+lrVCbU04gQUOOSosnYahzIkIvqibTAL/KUmBe/ufyH5J0uKXHCCqyHGWJs1PaucJy\nACKbDGNPPXlj+AvhraAsDZLdVCGbEg63zdt7vDcXK7kBEyqlAAwS6dfSjmjPpKS6ts47Cryd\nZych44x/fnpzpr8NHMHbsUXOB3DEOCVyMBDlABFrISoLFGWgUxTrDEXUzotj+IKmhjjY07i0\nFkINuwxLHDqlYdZ5dkoy9slMvZrIQAAdXdZchwlqOE5uSBhH9DIUH9hJAGC7OHQ8JqgZv1ln\nF/bHQaE/X76RrkyY0PgniP3acgiC0MpqmD232wTKcoyJnDlKLjp0I0AAi7JfalUQza2hcGRE\nfVVBJyEhOBR7wu7Yjj2zaXWS+gVB7ogY/j0xqGifLGCktWajL9l476H8VErs6F0W+1WPx9rZ\nPHm+xFUHm6UGzlxAHvZOXt88PLsxUdE6LUrs/H8RHK3TPkPZhaJg92HbzTJRiohqVQfDZF7k\nUQAAhSPvNZgvLHMTG/Rxs/237SiJcANI4I6mDIB2xqztaHZOB93OnMtu+G64wbbG1tnrd5jF\nwMSzYh1TWsHtYwG8zyaR07hRy3jFieWV2+VCzEOl1jLtuS1/rizkIjnaejMdBxWOM+57+2di\nhq5FX2Qr54UelzQoxZD1CmFjZ4xtunJ3fpuwUOQhHqW4IFUsASDjMqO3do9tPQT2DVvOYdxx\nsT/kCXl148P+G2bvr+EjKvv6zQ160IOTqNm1VkwgOqlOHk7eRFUgy90mTFCWXcqej+r+XL96\nrirZD5ApMWlbQEM2B0B7gLb1eGpciXkWW2ie8pxYh4RmUZbjQ6RqCry1b4HI+ySlomA/e6mC\nwxcJRz7g1cT8v43Ig2/fuwMO/9iL1RVGZRDlwXyJvTAnvOvm3r3p/xKiyEckOwbAZYnhGHm+\nlut/av42SQoniH5IgdJwW51dN4hFtlUXg6QZq8gdbiDQe0REErPNWkm8SyjXoqjachpyrzmf\nQQagkWlVdt6BJyMRVBZuFHKanxoP8aSh2id5Z9Bulv23zewlqePgl5VXxqPCU2BYeHVRNp90\nCkQga8pFy6pndlGNbHhkIen1e+Pxr2ysX0dDmU7Byrh3oClPK3fEg9dBO4rc2bgoAMK1omyL\nD8a0EcStDMmpuUcyDMOwEJKYiGT4dtg4dgKFDGeJwiT5pCWx0zEA/1U+j+9y44nNSDxRqxow\naQK71L3LcJYbbApN/PRqYgZryoRCtUnUgdgWAWBRo3LbskCr/ZuxB6lWwmlEvFEUBTDU6thq\n70amuwGMBtnGUCae7vagvj/lQe2NjVvQ2HX3MSzOuyg/lRI76yf/zt8/Pz83xmRZ1rvIz/M+\nK4ZxkZU0sbDSlCloG36GFQvLD7dfv7Zyu8jdJlNKuyUVViAmgmZnUavUmFAR5kTExA0pJqWU\nElKDEYrC0Bv56OChnG0ckqZca61zxZz4VRIRiJkHRabhHMAvsmnwp3YdsP7s7tCyUmT9zG3Y\nJFUrpZR1wtaatdA1PDORrZzX8jzXihutASijWZiqjBst5CrPCAqKChCTkeaof/hgNGeunEs2\nNBvW/T5ObNR3q3gV60/P4DzLCxcSi1lYKzeTEOLJCr+RK9sD5rLIo9u5YmZmpfM8s8RukRf9\nqnp+XH3twTHZyWQiglbaKCaGTiIpAHCtAE+UfMh4UlG2upkV37i4+39Uvavrh3fysze+S7kN\nHaWUYhYW1plWWcZKsQ0HQGBilTGUUkaxxKASTKyUsp7qVkYv1WticnWxyYo13It5NlTFnM9Y\niWqI7fKDrUcBKaVWewd9fpuYZnx+Vp6o/huUN1prrZU3pAEx3dVHeXP07/Q1ADkLE/V1c0Zk\ncpnunfTojj7X9BB2UYq80IrjzsmEmBRUr8xn1BCRjXSlWdluKqW01i5eQxgiM2uN2oZGsBG0\nGMxZlmmlrB8qCAziLC+KQimllYbWVn1FxFppVsxKlNaaNVvYV5Y8GGutmYmJFTNpzXN3lMbV\n1t7KS//xrXNdGsUMpZlZ2a4ZVlo7YwClhCPLTkRZnum54qZRyke2AIhJ66yBVkoN9f724IWy\nTJH9h6SQw0YRGxTMMw8KUkoDcOisZbnRtk8AIHxBgAwG+unnwMzUzQAbvg3IRkEDkqNouDFQ\nANbk9HrRpYxThb0l1WIcHPKxDwPld+2GWtuQ09MlQ16kuW24cm/RRaBUM8gLY6RElUzAcfnw\nYXU8ORsTcHZUmmGsFcDGEqO+bploXRBNAQ1vgN96tcX72xtpPlER5HpIdB+LhQmAosTxsj/g\nG6sjQkiYQ8q4mgJ5H6ba/wkYBXDbgPOCQJYdrcjZGgXfheRbSm4IAFOe4DjWySDhNDmleySO\ncNEAiJV64i7zGu676WCy/GzokCf5FikzBoCV3i0jc1LfARCPf3t7UxJE1nIptjHNtM9PmXIF\n5z9AnACAUGZjJlVmk2SiSEGX2eTG5hd213fffv1vQuDMhC3xs8QcSDSXTtE26TkdylsSOwDD\nbEPjPwlgBUm5HgCgJ/62v7pOb5GTZCdk3MLW+YeWn0qJ3XsshOvq6Y3hHb+JlV0gjbxE377Q\n+YDipz6oGSvrl9iMf2xWXoeeIcvKjY0xWz8HIu/aQiDa3qGV1Xrj2/nBfQBEpqQ+ovNzqNz+\nJ9ez5opXDFm7+1Qu0aO2eSbCiylEA/yiKugRryeAJRnYIEeWBVPYvJhv3zuu1t62cSy+tfGN\nN8chJFvXCl5SMzmvrLG54TmkMxL3jyerGA5BUe/cKURIHOcUgGoTZmwPbIQAzCQkTLTSv558\nLAAoLwabW19GsyNiQ6ciWbnAooc5oCAkBMTGKw/6jAB3AAr4I3FJE5Jw2MMK5rpPYGovhClf\nP+29Cd8VQ0Kgmpv/tPHNs/Jtaq2KJLPs6txS0zvZg7EL9kG3t35+tX9DxDfqJTBxfnxvAjOZ\noUrlNK1pyTJ2Ij9B1ZNFqOJEZGGNiLa27K4FB2TpyAfe/LHo47CalkiNiThD7b62Kp/0izUA\nVJ30R4fJUkY0DIDyAnFjeG1TXMa4KM7GnKjU49XeE/gwFjtaRTGzs/LyLZttpn284kwya3j8\n14rp44QzdvNjtX9zQ12L3yd40Z9xKWlwZ+tXNvhgsvHDH+z8CEBHqertt7rKrCCD1dmcAK1n\n/isipWmyYm3XUs/TbukMz8mHCMvcm2KIKIB6/Za7lbvdiorTghdL22s9EQDjbAtdqi7QAW1J\nI7Wu1wdPWCljCtoBQGlS6qns7Cn9YMQmkBD+8AiAoneRF1MAzDrSHJGUo2Dd6GbSvlL26Op1\nKiK3QxR0Nf7g+6RGPZrn3GQOqPs3IvknyWJSDvyMzJ7MLeXpjPV4ZY0SeZtIoph1BtwUYJcf\nP8HzD0w6Uz1RLozckkkEMilyb1uZEuNkQ1MTtYLGAAD6xcZnnvifdibP+RtCcJEcVgc3tCrR\ngsjt1RfQ2hoVLrd4wNzhpfVnQWtvolucBo1EGKrMRo59ByDIXNa1hY/ev/JRIuwEWfDCA5iU\nx5KcyIbdKbLzoqOE2Uf0YGIIiGT0VrP/d06u3hvY/WmrYWeb2iEV5b3vAAAgAElEQVTd4oYW\nkt7ggT2FDcdsoRlhNdP+JNgz5fSVBFo3Jysyz7wq1iUuCyDUnz8tsrsYraDNIAnb9NUGcBSr\nLgTKzUKOAgSvQUsUK/6vrb3UwyqzTvKyTtO7zes3zH3/SWjQaX9kMAegVAr1Wsq9inqr/VsA\nVp7C+acK3r3CgwGUCvyeTfw3qQ6SoQEAsozywsNn11tXNTOpmDI+0Bjsg3sTBC4LoUgQAvnE\nbn7iBLEGB23bkCNgyoSwW/n6W2t/658SOKqqqRVTy/W0zs6TVQIDmozxYQQcyVkUpDNorUB3\nelWKnTiFuXZWKBO0OOzYGrNVgc2Nl5R0QFmivXU4iImv36QbTyCzcacSInjnVYTIZL4L/WLN\n7quMsg78WuldJU86JnhC4uTY0uvRZDU8dTSqpWLb+Zp3F3wYP3zFnidld54AgDdgILBK+Bgg\nnouLXr5+b+/L/oFTX494c5tvesrK2JozVe3nTyMALacqiljO3s50D4DWxqWwk6iDg7dyI0HU\nVXmC3FaTl9OdZ94qqrMOTiO7gpHHiC36N9KBIfoQLlPdkrhQLMxKffpzuR7sr3wynR+ARPzQ\nOuKgxaaXleUYObI7Laar8wkv+5rvfoyf/0ROZktPicjMXfd8EAdHMg7UcLd/b1huo+pRu1oR\nQCvqDwC3EkHxsDq+nTrdExF51bf9W1Yntquf4Dd+vfyeTmjWMIQd5k+Yutcmvr8oFwfqFIgr\nzoFpZgaBXLwbR22FrZDglA6CAgAoaxfot5b9ysPMHg92enfC5xiPyQKlhSzJKbujOPh1hQmN\nA3QScXSMpAL1zPAmLSOlvLzT/VPF0h3jPfSsUZOgX2wMChtZtkt3hkE+duO98/JRIuwCkAEA\nZLrfsWP1RQDc0Mefn4yuVd4L2otjRAitA2trsFeS0mQtptDTG164AGYDwbfXvv3dte9EYZj9\nz5nIODoy1JFJ84wc9X3je3R6N3uwSm0TV4BJFJI+elG3+8kEwIzeAII5Xdi+keSNHwQxSaDU\n/EWuB4qtKp804YXmB2M5T8YPAMzO1cyah2UeHV0UzlGAxIGEDHk4e3tFsTocrk6iQR5ZaZkQ\nL/LoktBUrWMJWd/UOwcRgFkbYUKEGCQINyOsccQoQWgwaCXKEniJfapgIv9+7JtKBJEgH3Sa\nHMHv1UYcpHxzdQGisUwdWWkpGEIp/T6v2FFSlmMwhlIDRS8M+l3Y4KoMPdCUSoYJALbz9k6D\noCxJKai22e6ChokIyItgs4zxmHLnMtfrbZINeJGcr+BSPaCVg+zZdFVa+hx/mLqksu/GkNbg\ncXbcoW1k7MJVfGgDndhiEU+cJEUuzIwz3+mIhgDJzstsNOkdIMG7ABRnO3zTYcEkt3q6e6NA\nyDfdmnCHq5mIqeV6QgAKFV3+bT0eD5IPfocu3hlP+Km7dHC9NWBKtwmFHlr7FNe75J2x02+B\nSEb51lW+u6Wu23qe2P7FnCu4uOud/rrDf1nTnZIqqR1bG78CgKODc90DANGtJHdhEt1atONA\nUX9AozHg6JfZfYUE1adezEV/PSVO0uBqpDRbv92wcF4M1rL8E/T6ioihWFFGxFrX7k2Itopy\nmwkpWfZ1pi+iTmamNZNWMCyQcPChFI0ntL6JUCHJrppeyUwe4+qgzFaSuMq+tuGIyoonk3SK\noiCg1ThBKR5OCC6iMjklQcBlnZWN3V+gsIWESYQ7tn22SoiF3muZloUuLArfqNdX4wnKitc2\nrGX5J278d3d2fzUd0TKe4n0rHyXCLgiSiADkeuCFUjaEbDgiADCg+nPjURGcfWLUWkuCtxga\niYtLJDZ+2RJy0dP5AiCvTqV3etQ7NHG/2iyBPoxJjGMXaC9JbDZkSPMX8/vaG6wE+XDqYyGb\n283WbhgUDUcYTQAgO3d9ahUGrIVyBhV0mpJxMaBJSq55EiLCrK4ZRJIJQiBE9HKe/a6Zr2oG\n0el4/vWbf+/VPZ5WS2b1xeHgf9zb8bHUJCiGXD6/TnFTT2F9/FAAramoEqNb925yckXIuDVk\npsDH+xWBN/nyO8eKjkIKi4BwiWL2CwLAa7s0HFFZ0XhMoC3UiGvkrDSYYi40O7vrcqpDzCpA\nSCa0WapBsp3COodJFjfL9iKoLzY2MRzFnSEAUMbIO35CBkO6eg153sITCbqnsGVT4Fv1oDM7\nln65EcJAcGBGXTxo6zWTuEpYDORfilNtCV/ddnUnuqbulRi4IF8JT9tynlhKFH64Sq57inNn\n6kCA9bmzy6E4wAlXHOWQCGk6xzPk9+AmvCMhCKFvwop1Uy7VHhyXLDHLaWMjODDZJgFUmQ3q\nxvDieq36mvOCelFM1hb8EBFfOaCyuoyiSg0V0tx1KUJdZb5tghOPvqE/7k3KwpSEHiH55at6\nd0UA9IuNxQd1z2ibME/N04rD0NYHT6wPnkx5q1goNXSMTHWiPOl+FhQ1AHQwa0ndZwC0PTaI\nJFsRFAWU3l/9JDvTzRaxQkQisQ3f0iLsdU0oLnoYwVN4rovMFHpCAMkz+dGXxvNUC1RmI0Ve\n0uvLxuZzW0/9Qn98tdtcqMuPzOKOKltJoFRKdslu3tIYpBGiEgAONwGWQqD0FjwMdPHwor7b\n99f6xHQ7medqewfMVPUoi30QQmJIuTCf7x9z+lEi7LqEtT8mPniPpwkElsxLGMytDAHBd1gZ\n+NMV6IYlTq+e5whscVGczzZfM9wgcmPuYl7WR/2HR1WMZkcIob8jeRX60E7QGj3eAcj2rrig\nSnZo3s66FekK4RFBOM9pZS1ubqV5/+CmeiGcp6ol8ba1dWeDoCAgsMoyC8lLTQdirK2dx+cW\nTAcBALol5eE0Gm68rjttu/VDFityaEkgIHZ4MYE24hK0ktDaBu/updWyXU93bv2aOibYy1IF\ng2LTPc9i/uZq5QC9HqqK+gMQXqb64eTB/aGzXLQ0mfeKBYL0NC65gA1U3eVWHQ/iAH0YbuDI\nVRKAHRRFgvblMowtzRzVmvKOFXDYbe1kXRGq+baS242q9eaD7osBB1BXPwz4fCfVw/QmjcbV\n5o1V3o0SO78K6SKrR9hmfVhKpnq3t/6zlO5RQaimsg5a8YL4hLALZJbbw2Rzrgg3oU6Ovoxu\nnnk4xtoGWsfTUxjicV16HlPQEsTYhHG198rKf11Sn4hQlvA6gUeXdhT+iItbArYksSbv7rPN\neesZpxRWDFCn5gri7Xed1ewSmLG8OBkkL+t/wkEBLjqGSakNAECme4UeXi4TXOBSVlZ5bz88\njcSa60/8+cuM39hYB+BtFp2OnlkjNXJmocz1ip1bQGfIqVluG+C2SeGwRMxqlXcJFEIN277t\njJ/dW/mkT2mT9ji+VKmW8zWAlf61Z/b+eUgR4XddnKKW/a/9S9R+wU1WRrxsNYVaYUNBTF4Z\nlyB3AYAiGwbQDLdn0klagrkspFJe0oM2SXxH3hh5C6slXXufyocfJrZKlDyke4MBUK/yr0RR\nWaDDPteftXivuPQtBkvsQopNBRMfsYgmKIijFTx8RHI2MirG1RUCGTbf2v/OUXUEiDf+97An\nRiGhzmF04KktlWw/jsZVHmg6DKCeeZa2duytXJoqmkPYOWAiYhEBSqkP1Gmr4eqEWNruUnRb\nv7inbu/wTQIHPAOAM3nt5un9vXMAuR2aEHUiCIRJ86onEagnTl7b+jFfDg79/PjTEiAOWes5\nbJQ317MVGyYg0DOGrPQNQkBZrm48O6CVFEDY9/ZXPjlSNvq5+Ey+QZUC5/EEcFGGjnQ7J3h7\n8+03B28CPl6S23gCQEH3uF8qm29XAJj+kRm9ZWtFSoH5daOgexRxQMSusMd0wcpHSDbkxNpC\nxfzFbhOlQVMtJdjEyQQAaqiWwfdAXfuV+A0lE04C4Dw/1ztHYRqZ2isCDoRBIse2UgPp1t7r\nERAlwG4rcfomg5g0AYp/CiLSvefScdCxRg28s8u3nwICgc/hUhLnU08O2a1LAJrxt5q9b0HP\nwlovwX9E0h+0trNHF95hBbRwJD1bG0FcuBAQb+/w1Ws8WUufLh9v8pQi0EOIo8xVj69ej++M\nxpTlhKhRSYHzx9XRb87+w9B6Iwmk6/PR6smjOmYZxWXEgqe0vZQqOweZs+qi80IgLVt3Q+Xt\nIyACWlnlnZ12+wHcR6QEYJdwYEPfXz1Qn3iZfXDBvcmLwf6MAIKJ8MSfPlI6HTh7z0IClJfJ\nWpnW8lnxjqpkRcm+XxvDJ29uft59negiUhu7gQ37kNa8tJEwY6kFeTIJXgjN4d4iyRqYlKIu\n6leL2BbZIIBh0PHtQg+h6ig+TU1IQye60gShtB/JtJHQmjldtYTdB0jXfaQIOwvaiEAkzDGm\nov0bZlkMAE1CSExCyGWI7pt5KTNqHSj7ubXyd7t3a/TMoNyKLRP9TmnumNcZGl7TZX3/Q6KS\ne9nntsfPeo6tw91Y1MUxZwIk7C4rAeGVNRoMCS6KnrR3TRAqc54EQLISLAG2d8FM3uX+d0ZH\nATgSEZy7CBHTRM56PsS2g1MiXZwg6NFoyCsZClgDs4Q5mY7reW4AaCcqg7KigoVqfBgtAESl\nqVUdDqtvKOojXG+VDrAqeQQBJht3t63jBUdrIhIRrjOu7LKNq/2n9adjXFw4mZ5Wpf2mZZGU\ngBdFGUGIFYF8rNFA/hO8ADj02+MAzeQEwF8qn9wbOFs0BV3vf7O58vXN/lN76nYyGnelXHAB\n30k7LVlOZdkRhfQVbdH5x5tXLUXLrWqC9i1K78RHTwwtXmQXZvVv44T4zyM8TKbb+TSQMCnl\nZ8ukXASwm2et4diYFIkNYqt48aWTlnusGOlMAgGK83v7X7668rnu5x/OQgC0ZeHKioZDh68G\nQ2sE6WFHGuzWexvA5SZH8cCsvopkTTnBBQlBSFhZI++ekm4AAE/qBy0kRxaAxFd87Dr/nAl5\nwU/eoWy5v8uldpKtZl3QXeg8ldjBj5CoSz0AICENAzZgpsmEXFJB62qd+Pm/0xKPzOIY7CCa\n7e/Mn/6rJmtC1xM94OW0YyJnFdMiS9LPE7lgiwpxl6xoPGHKFGUAynySwCoBgbUHcWH1dJbU\nCiZtRyiA9sGnFzOZJhuMRrRaUL9XrIXJbEswAyin9EOA7vV7v7axdqO8lCvrzBU5g5gwHl91\nVfL+AapelNj57bDQbRR1KfP4RCICB7q7gczorVl+flY4/7ZkRAHXtD55cvVumVVjb4HSZlEY\nXjre1oFcNvr3WD5KhJ34lClbO7y3H0gki7M97nB0UgYDrywDsD64bbfQi3itEMPdPFMEoNa1\npyLApBW1gNfTxZW7/OyAVxDcxyyDlmawW9zEHgxkqvxU9qU12g0b1XQIob0rKMq7vfKzaGfW\ns0NnBjOyjDa3aTiiMnMzkmBxYicIseSG+1BcNO4MzVU+vi73vZAlkBQAdWLcxl3rLFp9I6nE\nvPD0hPIRRNtTChfGluATkoFSmgBo6fQIAPjmE16/GSEOBP1ic2/vc1SVdmISdlGE6r5aXeld\ns7dK6lfsEwRb5/ykkYAeiUQlHS7UAALKS9rbjx4G8QVSApJoZempPRbIpjm+Kic7ykErhuyp\nJ/cmLwEYV/sjFAAqdqo0AjSXZT4GPJnpLOhxdnWor445aoPtHNItfbwtx3aTJ4RdgNsQriV3\nkWmlOEaLYHaID5GGSwZG3r6kDREFQlA5CfugnulkZMx+fwlFGnc5SrdB2nyaZq+JBtLY2jYx\n5XpvS3+4JXYUDTGu6LOxVq2HAuSFi/driapEqRctRO1/6WntxApuacoIAO4+SxtbHcmH/eaq\nPksxkiUNnX+Ud+koV1GsogXj0kYvL2l7QTgND66TypKx+DPi92FaW5gTkM48CPD0WQdaXt6r\n1ApiofUA4nyv1Tx52iaX21/52wRIkCbUZ8nHl/SNLnsA3N379Wev/o6v2Z0XrecA9Mr8/vjQ\nSwfs+W25zjBrp1lI2NMlOsfkclNd+5ny1wo9jL2hIJFf7GGEzxnzvX5fR2Au3YZaUo5ONxxY\nEmvS4zKzeaRKC1Ut6YGrxVUtSRw7R7KRDA6/e/P/Oc8v4NBHwGMprR3L57ae/1+uXlnXnitN\nVnmlf+Oa+tiYNhYG8j6Xn8oAxe+xBFZOKSj1gmKc+PuJ9N5KGipqQMTEBBKI5tJtFUBAFY9c\noIgERp5WZ4EnNgvLTUR9Hp4ArEGqAdB3R6ptsB5wpu/auLpiMVZOVbK3SfXvA8j1YGgDIxEB\n+LWde2uv/hDwWWXCB6zUz7xCeUHzV2ljS+pIyyIREmqRUleKc4jTILhIbgImfN58R5ozVMfy\ncAhAVBPMeRchnf3NxLDJAALc8GcuIxcxodIMoFxwVrfCR17fotGWNbDlsr8M3tthEgBkWfe4\nCADkuq84eZQ4T4BrkDcXYlbQI7LObqSKHpQKlkQEGOdpAU5Iew8SACFYbazxolD/hko2CpF4\nLbMSMj3MS5kSDZz5TlWpjf08+5F9dw3yrzJaG4/eIL9bLPgP5LVVIQBHWxe97YYedK1/iMAG\nJfEMSJgZADAuLpogm0EGgECf4wLp5pWq5J09QydYusopdAQ0zBxQrK1fyIE+PRBnuro7efbV\nw78HUOg+hQg/Ie2GntFgKr0jdIsFpJLaPwBRXEqCFwaDV8ajjSw7ejfJxH4aS5iDfXUWzRIE\nXazuiI+Y9TJF29ImY6LE7uZT+GpaQbiORDd1aKoknm5SYRuTPQUA5utBlZX0Z4EWabXV7gTg\nGDkXN7vTy+STYKSwQDNhpmd5srlN1UqH23l5eREBvMVa9zsvoiLAR2yzfdjK8+tVTDP/KMqR\npMxWLCwU05bSIZHC2ltKJSewS572cu+0K04WXvZOJ2uvA2DmWeYTcFHsD/ndxKQjX5D+jf2k\ny36mSoPA8lvYtbQi38fLS5szdJou9ySSnUF4x77rMVhU2hohxVkJtWgDamFcXanylekshNhs\nfU+h5qB9ThVSYbC+Ya/od71XnF9T9/4aHzgL+lGS2LWXuFoa2Jrliez4Z8vXd9TUU3t++T0i\nRbLWnZ1DCWRqb4cEU5JYCdRL0qybaTBqVYl9DAAWuSpHazxf7d8cVfu+HgdRhET0BYCP7f/W\nzeqTADaGT+9MPr46uO3z83RXlvoDZFlgZRCxauzkF+bf+ue9HA5G0AvS7IRJCqXnGUklIhBa\nAtztQF2POQInSSoqnJ0B7WbZP5t/7a4cdmpxGSbKgooyHJTdsthSF+1ZjWsgfjXDMticWoEG\nshNDHCQKIJoHbrQDRtXT9+D9Bx1a4iYQ3u2wpwSSrmt9YtSvKHFc8BCHWbUEVQQAuZJxf1C5\ntGoMYI1YtYFL53+xEc6slm3B/AMAk3pSq4J5P/PqUT0T/VBWv+dq6D1ozWXqHawUO71VUq1l\nhQVEsObSltDtSQOgV6za+fxs/voXm4e2D4oLa1GQ6x4lkhW3iNxkT/29VQ62izildmqIKbhh\n7luZuhApoo0sW/jww1e65Mag3Cmzca77kbGx72U1yJjsomVjF6+WY1b0+0Arjh18SrEohPJE\nFSMcmljbhqY+1Vs67qTQljtZXiqTqT58VLzLSutkULxFIBf3ZAF5USpqThhXeN3L+cb3f7j/\n7XDz7MoMXuB8WdPd4onmpFvt51H6E+mY393efH4waKXwvaQRSQIKoK0iJqL+4EGIUOO7fWlP\n0g9DDklmYxduzjURdOG6Sl4nYH+7TBIpFUpt4Hh5qTYw/piICpXFMdu6AdCC9wwtuQo3klvU\nAYWOgAYzZa5OFaxLF1X7snDtdiuTkKYs42rSO/jY/n8Zm+rS1oBFpkkn0lpX+zcK6xjuCPyW\n4IOS/UMLg34fQzZ9tAi7tgzc3uBd/STg4+iT0TB76syl/vQTrZXzIyJr99CN9B7Jveg0c4nk\nOtTKQL5oWRbflc/juz3VpN9W/ZOL0XmjDMUIPa6VUbH39M6Xsqyi3f1kpJfUrupm7ZvN6Fvw\ngBsAgceYriYxhO8Ywy5EJolPpRJxCNHCdCaTEd6ywZAzDQBKBT7J2WMJAEzoYtHNzBv9xDPA\nhKFSP1f+KEyRe9XJ1CIh3prIwET7RDoqw6jc3Sxu9XhkeB7XpCip1w/owdO0qSag8RZpiSrW\nM2XU4VWTiWHBl/LmBXkNAIkjAUlU0EkSCY9WaDz54uD+v9hcTzzCkuF5EpLibdfUE9nZZ8aj\n7TxfCiAZ6ktF9j9f3Z/4xX1z5/+qt/53aCc6a7a/Q9lFqDTlTVq7OJ1vj1C2RveeHnxxlXYR\n1rQNs/z0uT2WGshkxfmjYZkV1MnKKk1WAmfMTLvyoFcqmqwQ86MR8YeruJ1g98/B2qdfvvWv\nFefdCdD1/Om/MpvfTU5qiCjEmKykxEMSWjbGyKAF6BH4JJeHVIRbbwLAVka/3vv+bkGx2jY+\nC/1ZHzz5yRv//frgqcvGSesbsrGZ3kj6xsQ2DNuCuslaBLfJzbR8Jn/rn/R+mGDZTv2PL35i\nlpF0HepQENNPtSLGL8iAWj8knu9u2G/k5TTaWjjK+pFwPnSm/YKI/HjlzW/c+LoewkucWpRA\noYcgMGXhJD+SdES6bYiRr0EWSFc7BzZpRAh3F0n/R1fviSEwEwF5y2EWIN7d0888G4dr/1sq\nchCkezGR0cg6X7299XNoL2XnLLivNrcif02t93cnL3DIjOIhdncsC6tGCqQfpTh+t+WjRNgR\npdYldvY1F3tZAl98uIdIqhEAaHJeruy+DbUkSjcR28YiSdd2Qo0EEUN1DMbDPnAdaO/3qv/w\n5MqRVR/4HCzcPROX6CnScQFoVv/eFG+03kxyfjvltJNZs0vOkVI4CLm2UjFcGIQbLTPbkAq8\nvaue/wSvO2At0dDeJqq6dB96gUFMptSdXnuurMgtRFIIM0xEPqJKlNVnIFI9tcKAqCmIoAwA\n1hqDIYhyaSjUkZDjwWifOBgxWIccu1Na1CklyXQUMCKZwJnf2jRHivUGXyE/RskyWtsYZFjJ\ndAv8+aEBNpqyHW5iywNM1PyfrEw0LRovESAcJiCu9VyoXsyzniIsv4oLyRNtk55A06rY7t91\n0+6x+WWQ0fY6EqletZ0uWShMSnMJCZl4bAdTucyy/fBhLynp7YoN85F6Eqg5UucJ+P2vNa+u\ntYVhYRelyCaRZpAXijh4SHCeyAkB5+tCFHtTrKaDFQEi6hfrS+BkeHE8oYRuEzfKsspWMtWH\nUrR3ha/f6HzuUbUsARTEADSzXohnsUgHPnJHOdH4sid2h3sgnjYeGX+gI5dpL6Xk0YtWvHFK\nIjlaALUeSBE9OkZ3hGcArAHQLD8ngFbW6MZtystQOYHWBrefO/hdF6jv8skI7S1FeIET810V\nAJmqkOSBCJ92BHudQcbrLFM3n6DhqPu2zuDcL2jCsz4LlnbcbmP4lfKFOWdRBLbuJg5ypzs5\nfN6htReGmdyn2EG/NH4RW2I8AMPb9cqLU/X+aWg/SoRdZ5HaprjOT0Wl2U7h1fHekB9wAUfI\nIubMRd4KxP8l7FMbxCR5tVUegV4rgcqCXjCpylGbbQIuYt/RGGVFvT6Wfp90w//vrgbZ5hX1\n9FZ1p4W/4Skn/0lPj31mwFbzS4csRmC9aBXT2vqlIDNVBMQOJgSiFeD7Q0XEVbayZJ6BTPfT\nQYVgQwBI+R4ncexM/+8nN+7T8CwdxwZOJZ5MR7sTCGz93lEOZy6lG8ztXlWplSf1S9tlwiFI\nMieEDMLM2nu5W7jGnFXcX+P9CW+hrdHeGD797JXf3hreTeYz9JmCVW+Yn8WpsEQhk+7lqxPe\nts+DNDZs2Nbcha05i/HrJVqBdrj+WANdv2ldRgLN1Y0f7CaTAGKim1X5ZJ7vxSAJS/jaXPV/\n5ub/kHMPzv7AEhUEf1QDPP2IkHXUQhStwvsH6um7GLi09kmUuHA6OVyLdE7hAtUerj0MDAYm\nQuLZPyEx6efwK5gK5xZYgne8VguwwFby4rX/5uNX/ysIkOekuvp3apu+tehXEABFOfRsVO26\nu1Gq2OrYo4XIALR1RWrrE8OklWvIJjPJLkCUblE3B5ra5E676f5h7MyyfnTVs+Hqsmzcts9p\nwusYksb6Kedqc4OpC4F7+Up7dMvqDbRq+zGloeQJQBq42B7hrsQO6eN26boqarXMxi5+uapm\nT+UXWC5XXVhvAoA7W//0uv4YKMCWZPem1zHamINn9m97odJDF3Zue34+eMD1USLs2m7tLppG\nxx44BGj1Yga7JJpzu09yGAj1eXJ379f3Ji8KG8W5oiyIIiqZp/XFkshXQtF5FTJMhIwoLSgQ\nxWNhFCQe4cKDXaTM7vau/swXbCDQR85G94bi7KZ6vp/kyXGiF+IUMGhVFNZvlADAQDrbNjXT\nIeZm7QfF1Yeh/70kj6HtvwAoSuRFZ/cH/C1AORxSWfFwAMCnVZ7YOsd6Z1juBrHWpHdQ6OEw\nwG7Xp/DHShuSEXKTj2ZdsKUUEbHFHByi00RgxQUyxxjYJeANOljulUl0r6r6gIjzy4GIckmg\nFSADWimohzYWJOK1wW2X2419fqEIQ0Q8FUVCqZFQGFq/2NidvNAvNvZGzz2tX05JolDPEnhk\nAwXXMaccIaxoa44yMjmhnAAA5QXKKlQY8tMDXv0Hm0hNK85IsJXnvzUajEQCjF1EYcSqzCaQ\n6NkmvtuJNKiFyD/kxSOVJQICpWgU0fDNzZ/dW3kRLaIhTjITKdKLEhJOEvB0joP4THpEbodx\nxKiLzEFEhBJrSPiAd1Ko88v9znQvU9Vl9XjTpejH3alhZ3jv3u1fWdva9e/bzr1roW/J5Z3d\n/+Jg7bOtu36IvS0M7jxYCAnpd2vWFRe1xxAZvE64k3SMLmAGR40OQz1qetvcX4I7AFjf9q7k\nNV66lV4mMfQ1stLIspbCN5B8AID1/hPWvMQDoqjwaL+/hN7RqtxbeTEN1dQaSEwzHUSbYs2o\nLQG7OCuWJ/cBhAGgzFd7NABoczHxdIS4KYEW4sgDQJmN4+OJb4AAACAASURBVLgDKHYHQdIe\nt7D6Bwm4PkqEnSfitCcp3L3ACRPM+qvtL9wi5ayIVU4msxEcQFujZxQXIFkfPEmkLOj6pHn7\nOfOqRZ+L0XTtmeRESu/jeEB8Nu6oHiDGUpNwAiSNln65md7jZiOpsX1HAuElQRwdSCPxrnCB\n8IooNqnQAVkhGb9V7p+GZ/9ie9ON0uunCUQbmy7Oaneobj4ORqMv3rn7qZ1d16Rrmkmp57Z+\ns8zGgTO6uvryp2//m0y7qJoMQvCiCoK66BHiCQU/TAEUKR6MaGPThe9yrqP2qYWqivb3sygM\nDOxXBx25nzu5s0Qe43zbPNyTh5YEVEmUdqTHPFwNBry9a3NoJvWTN/h2NoitDZAcaBt6QFoG\nVQHoLKgqCBUNHG3qM3QsfBW2ihyo02fLJh+GR3HE28Eyh4iI1vW1q2svD8qt3cnzW8NnuMWH\n+DlMwnd12goUgt1pYePELLjvDin/1JYW9bEE4YfLtcHt7ZG1NwpzyAgpXlhXGHy895/38nUk\nEZ18TMNFATCBUPROx6tvVptCKpDplDYROpiisMUt9LgxBjZ76VFqo8wlIg9732dZTEZRZEMA\nrMr1wRM+fzV8zqEIQikvaLLi0rYuK17WJdvjZ/vFevooT4KTeQGPVwqn76047m7J6NtRfzqv\ntIRTq+tYXU0JjUVXkrSIC78bQFZKloAY1rQkVaCHjcAKvU30N9ou56kcy6oy9w9412fICJvA\nN8kxKAEhEeB11OKXlTKbFNRYd/q2vDMahiJcERQrvnJghoOuMxkBwJac3DBHlY86BkRHt4xi\nrwIQS1vwiTTJib6tg5+OafTS1uDIiu729wZdHyDk+iiFO/HFTqcPeOaRvAZnxqy85t9pHUhF\nVOVr5Mkoe39tcPuoksKMAEBraF2ijmZ83Z3qYWYiuWWdNTQDRLIHBB+N3fI0kxXqb+GNBbmK\n9c6IePkS0JmIkd/JbADexo6S2GUhfaSXKwIAQyPLkOd6iPmZkyQsHkvbS251EkA/HCeKjS7p\nf3Kg7Ix8bpJCW4uiiA6uq1tP+gqo8y3g+DI3jmgB5l+zgsi2yYySDIqgM3+wg8TOsl8GRKSz\nPKgRXT+lc7CjLZjS9pMM5ufrbwAYFjvTQo+wDTm19CKxLCIqUprufbx9C042rLQar+CkoY4h\nywJ4T5FF0NeoBCCuD5986/gbzNSj0aS8coiWDuMRMWOZ4sKmAkUKe1YpvnazGn/s1ubuf/jx\nW4prdCbbg1G5DPcTASQUAhdQrMEGFf2IiOtcafESlxdiztLXCHRt8pmaKhS768ObwPcmajtT\nb7uHC3WmqijL4zGZwRXiKje9PqbTPqS3YCcUzVeWIDlX76OHx088L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HHR7L0SOaNcnPiKAfDtp2Tvas58MHxloV0g0GeXRwdNrQsXu1Zl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\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lOpVDQ6oCxUHbthGGWjJrrGkknQDW1Q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\nRVFsaWl57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XHHHEEc4HbZ566qnOzs5o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\nSyDYAQAAALDEvwFOtAal8CWDTAAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 8d: Bayesian -- Trace plot for a-paths\n", + "# ============================================================\n", + "if (!is.null(fit_bayes)) {\n", + " draws_list <- blavInspect(fit_bayes, \"mcmc\")\n", + " pt <- parTable(fit_bayes)\n", + " labeled <- pt[pt$label != \"\", ]\n", + "\n", + " trace_data <- list()\n", + " for (a_lab in paste0(\"a\", 1:n_med)) {\n", + " col_name <- NULL\n", + " if (a_lab %in% colnames(draws_list[[1]])) {\n", + " col_name <- a_lab\n", + " } else {\n", + " pt_row <- labeled[labeled$label == a_lab, ]\n", + " if (nrow(pt_row) > 0 && !is.na(pt_row$plabel[1]) && pt_row$plabel[1] %in% colnames(draws_list[[1]])) {\n", + " col_name <- pt_row$plabel[1]\n", + " }\n", + " }\n", + " if (!is.null(col_name)) {\n", + " for (ch in seq_along(draws_list)) {\n", + " trace_data[[length(trace_data)+1]] <- data.frame(\n", + " iteration = seq_len(nrow(draws_list[[ch]])),\n", + " value = draws_list[[ch]][, col_name],\n", + " chain = paste0(\"Chain \", ch),\n", + " parameter = a_lab\n", + " )\n", + " }\n", + " }\n", + " }\n", + "\n", + " if (length(trace_data) > 0) {\n", + " trace_df <- do.call(rbind, trace_data)\n", + "\n", + " p_trace <- ggplot(trace_df, aes(x=iteration, y=value, color=chain)) +\n", + " geom_line(alpha=0.5) +\n", + " facet_wrap(~parameter, scales=\"free_y\", ncol=2) +\n", + " labs(title=\"MCMC Trace Plots: a-paths (SNP -> Mediators)\",\n", + " subtitle=paste0(\"Exposure: \", EXPOSURE),\n", + " x=\"Iteration\", y=\"Parameter Value\", color=\"Chain\") +\n", + " theme_minimal(base_size=11) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + " ggsave(file.path(RESULT_DIR, \"bayesian_blavaan\", paste0(\"bayesian_trace_a_\", EXPOSURE, \".png\")),\n", + " p_trace, width=10, height=8, dpi=150)\n", + " ggsave(file.path(RESULT_DIR, \"bayesian_blavaan\", paste0(\"bayesian_trace_a_\", EXPOSURE, \".pdf\")),\n", + " p_trace, width=10, height=8)\n", + " print(p_trace)\n", + " cat(\"Bayesian trace plots saved.\\n\")\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:17:02.076684Z", + "iopub.status.busy": "2026-03-31T23:17:02.074947Z", + "iopub.status.idle": "2026-03-31T23:17:04.958660Z", + "shell.execute_reply": "2026-03-31T23:17:04.955947Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian forest plot saved.\n" + ] + }, + { + "data": { + "image/png": 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zz6dMmWL5SbEhQ4bQPkNc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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 8e: Bayesian -- Forest plot\n", + "# ============================================================\n", + "if (!is.null(bayes_results)) {\n", + " bayes_plot_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\")\n", + " bayes_plot_df <- bayes_results[bayes_results$label %in% bayes_plot_labels, ]\n", + "\n", + " bayes_plot_df$effect_type <- ifelse(grepl(\"^a\", bayes_plot_df$label), \"a-path (SNP->M)\",\n", + " ifelse(grepl(\"^b\", bayes_plot_df$label), \"b-path (M->Y)\",\n", + " ifelse(bayes_plot_df$label == \"cp\", \"c' (direct)\",\n", + " ifelse(grepl(\"^ind\", bayes_plot_df$label), \"Specific indirect\",\n", + " ifelse(bayes_plot_df$label == \"total_indirect\", \"Total indirect\", \"Total\")))))\n", + "\n", + " bayes_plot_df$label <- factor(bayes_plot_df$label, levels=rev(bayes_plot_labels))\n", + "\n", + " n_chains <- length(blavInspect(fit_bayes, \"mcmc\"))\n", + " n_samples <- nrow(blavInspect(fit_bayes, \"mcmc\")[[1]])\n", + "\n", + " p_bayes_forest <- ggplot(bayes_plot_df, aes(x=mean, y=label, xmin=ci_lower, xmax=ci_upper, color=effect_type)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(size=0.6) +\n", + " labs(title=\"Bayesian SEM: Parallel Mediation Path Estimates\",\n", + " subtitle=paste0(\"N = \", N_total, \" (missing data handled), \", n_chains, \" chains x \", n_samples, \" samples\"),\n", + " x=\"Posterior Mean (95% Credible Interval)\", y=\"\", color=\"Effect Type\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + " ggsave(file.path(RESULT_DIR, \"bayesian_blavaan\", \"bayesian_forest_plot.png\"), p_bayes_forest, width=10, height=8, dpi=150)\n", + " ggsave(file.path(RESULT_DIR, \"bayesian_blavaan\", \"bayesian_forest_plot.pdf\"), p_bayes_forest, width=10, height=8)\n", + " print(p_bayes_forest)\n", + " cat(\"Bayesian forest plot saved.\\n\")\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bayesian Interpretation\n", + "\n", + "- **P(direction)**: The posterior probability that the parameter is on its dominant side of zero. Values close to 1.0 indicate strong directional evidence.\n", + "- **95% Credible Intervals**: There is a 95% posterior probability the true value falls within this range (a direct probability statement, unlike frequentist CIs).\n", + "- **Trace plots**: Good mixing means chains overlap and look like \"hairy caterpillars.\" Poor mixing would indicate convergence problems." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cross-Method Summary\n", + "\n", + "This section combines results from all methods (FIML, Bootstrap, Bayesian) into a single comparison table and visualization." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:17:04.964756Z", + "iopub.status.busy": "2026-03-31T23:17:04.963146Z", + "iopub.status.idle": "2026-03-31T23:17:05.133041Z", + "shell.execute_reply": "2026-03-31T23:17:05.130983Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== All Methods Summary (Indirect Effects) ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " method label est se ci_lower ci_upper\n", + "51 Bayesian ind1 -1.781624e-01 0.31251364 -0.817896241 0.42426114\n", + "36 Bootstrap ind1 -2.159513e-01 0.29241791 -0.840215322 0.33459744\n", + "10 FIML ind1 -2.067000e-01 0.30144521 -0.797521730 0.38412178\n", + "52 Bayesian ind2 1.575126e-01 0.30883184 -0.442516863 0.78693610\n", + "37 Bootstrap ind2 1.949332e-01 0.28894742 -0.337007960 0.81291554\n", + "11 FIML ind2 1.857000e-01 0.29727556 -0.396949382 0.76834941\n", + "53 Bayesian ind3 1.130906e-03 0.01532019 -0.029447603 0.03339328\n", + "38 Bootstrap ind3 1.134098e-05 0.01746445 -0.035143020 0.03790685\n", + "12 FIML ind3 -1.703162e-04 0.01498653 -0.029543380 0.02920275\n", + "54 Bayesian ind4 1.559692e-02 0.01310721 -0.008678276 0.04386078\n", + "39 Bootstrap ind4 1.603124e-02 0.01455892 -0.013705054 0.04647205\n", + "13 FIML ind4 1.638556e-02 0.01301868 -0.009130581 0.04190170\n", + "56 Bayesian total 1.509660e-01 0.04320219 0.066145563 0.23464600\n", + "41 Bootstrap total 1.542588e-01 0.04335142 0.066756786 0.24050499\n", + "15 FIML total 1.527221e-01 0.04302242 0.068399729 0.23704453\n", + "55 Bayesian total_indirect -3.921950e-03 0.01662300 -0.037284769 0.02892688\n", + "40 Bootstrap total_indirect -4.975499e-03 0.01687918 -0.038516973 0.02795524\n", + "14 FIML total_indirect -4.784714e-03 0.01561455 -0.035388668 0.02581924\n", + " pvalue n_eff\n", + "51 NA N=1153 (Bayesian, fixed.x=FALSE)\n", + "36 0.4460000000 N=1153 (Bootstrap FIML, 1000 converged)\n", + "10 0.4929044110 N=1153 (FIML)\n", + "52 NA N=1153 (Bayesian, fixed.x=FALSE)\n", + "37 0.4920000000 N=1153 (Bootstrap FIML, 1000 converged)\n", + "11 0.5321857018 N=1153 (FIML)\n", + "53 NA N=1153 (Bayesian, fixed.x=FALSE)\n", + "38 0.9860000000 N=1153 (Bootstrap FIML, 1000 converged)\n", + "12 0.9909325427 N=1153 (FIML)\n", + "54 NA N=1153 (Bayesian, fixed.x=FALSE)\n", + "39 0.2160000000 N=1153 (Bootstrap FIML, 1000 converged)\n", + "13 0.2081678523 N=1153 (FIML)\n", + "56 NA N=1153 (Bayesian, fixed.x=FALSE)\n", + "41 0.0020000000 N=1153 (Bootstrap FIML, 1000 converged)\n", + "15 0.0003854854 N=1153 (FIML)\n", + "55 NA N=1153 (Bayesian, fixed.x=FALSE)\n", + "40 0.7780000000 N=1153 (Bootstrap FIML, 1000 converged)\n", + "14 0.7592798394 N=1153 (FIML)\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 9: Cross-Method Summary\n", + "# ============================================================\n", + "\n", + "summary_rows <- list()\n", + "\n", + "# FIML\n", + "for (i in 1:nrow(fiml_key)) {\n", + " r <- fiml_key[i, ]\n", + " summary_rows[[length(summary_rows)+1]] <- data.frame(\n", + " exposure=EXPOSURE, direction=\"D1\", method=\"FIML\",\n", + " label=r$label, est=r$est, se=r$se,\n", + " ci_lower=r$ci.lower, ci_upper=r$ci.upper,\n", + " pvalue=r$pvalue, ci_type=\"Wald\",\n", + " n_eff=paste0(\"N=\", N_fiml, \" (FIML)\"),\n", + " stringsAsFactors=FALSE\n", + " )\n", + "}\n", + "\n", + "# Bootstrap\n", + "for (i in 1:nrow(boot_summary)) {\n", + " r <- boot_summary[i, ]\n", + " summary_rows[[length(summary_rows)+1]] <- data.frame(\n", + " exposure=EXPOSURE, direction=\"D1\", method=\"Bootstrap\",\n", + " label=r$label, est=r$mean, se=r$se,\n", + " ci_lower=r$ci_lower, ci_upper=r$ci_upper,\n", + " pvalue=r$p_value, ci_type=\"Percentile\",\n", + " n_eff=paste0(\"N=\", N_total, \" (Bootstrap FIML, \", n_converged, \" converged)\"),\n", + " stringsAsFactors=FALSE\n", + " )\n", + "}\n", + "\n", + "# Bayesian\n", + "if (!is.null(bayes_results)) {\n", + " for (i in 1:nrow(bayes_results)) {\n", + " r <- bayes_results[i, ]\n", + " summary_rows[[length(summary_rows)+1]] <- data.frame(\n", + " exposure=EXPOSURE, direction=\"D1\", method=\"Bayesian\",\n", + " label=r$label, est=r$mean, se=r$sd,\n", + " ci_lower=r$ci_lower, ci_upper=r$ci_upper,\n", + " pvalue=NA, ci_type=\"95% CrI\",\n", + " n_eff=paste0(\"N=\", N_total, \" (Bayesian, fixed.x=FALSE)\"),\n", + " stringsAsFactors=FALSE\n", + " )\n", + " }\n", + "}\n", + "\n", + "all_summary <- do.call(rbind, summary_rows)\n", + "rownames(all_summary) <- NULL\n", + "\n", + "write.csv(all_summary, file.path(RESULT_DIR, \"summary\", \"all_methods_summary.csv\"), row.names=FALSE)\n", + "\n", + "cat(\"\\n=== All Methods Summary (Indirect Effects) ===\\n\")\n", + "ind_summary <- all_summary[all_summary$label %in% c(paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\"), ]\n", + "print(ind_summary[order(ind_summary$label, ind_summary$method),\n", + " c(\"method\", \"label\", \"est\", \"se\", \"ci_lower\", \"ci_upper\", \"pvalue\", \"n_eff\")])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:17:05.140052Z", + "iopub.status.busy": "2026-03-31T23:17:05.137794Z", + "iopub.status.idle": "2026-03-31T23:17:05.224229Z", + "shell.execute_reply": "2026-03-31T23:17:05.221583Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Wide-Format Display Table ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label Bayesian Bootstrap\n", + "1 a1 0.2798 [0.1637, 0.3913] 0.2757 [0.1684, 0.3866]\n", + "2 a2 0.2766 [0.1608, 0.3871] 0.2724 [0.1652, 0.3843]\n", + "3 a3 0.2229 [0.0937, 0.3514] 0.2202 [0.1067, 0.3437]\n", + "4 a4 0.2130 [0.1147, 0.3102] 0.2139 [0.1281, 0.3037]\n", + "5 b1 -0.6455 [-2.7192, 1.5171] -0.7761 [-2.8644, 1.2133]\n", + "6 b2 0.5783 [-1.5766, 2.6514] 0.7091 [-1.2784, 2.7198]\n", + "7 b3 0.0036 [-0.1251, 0.1318] -0.0006 [-0.1483, 0.1488]\n", + "8 b4 0.0732 [-0.0396, 0.1856] 0.0754 [-0.0600, 0.1977]\n", + "9 cp 0.1549 [0.0652, 0.2427] 0.1592 [0.0689, 0.2450]\n", + "10 ind1 -0.1782 [-0.8179, 0.4243] -0.2160 [-0.8402, 0.3346]\n", + "11 ind2 0.1575 [-0.4425, 0.7869] 0.1949 [-0.3370, 0.8129]\n", + "12 ind3 0.0011 [-0.0294, 0.0334] 0.0000 [-0.0351, 0.0379]\n", + "13 ind4 0.0156 [-0.0087, 0.0439] 0.0160 [-0.0137, 0.0465]\n", + "15 total_indirect -0.0039 [-0.0373, 0.0289] -0.0050 [-0.0385, 0.0280]\n", + "14 total 0.1510 [0.0661, 0.2346] 0.1543 [0.0668, 0.2405]\n", + " FIML\n", + "1 0.2772 [0.1674, 0.3871]\n", + "2 0.2740 [0.1640, 0.3840]\n", + "3 0.2223 [0.0965, 0.3481]\n", + "4 0.2144 [0.1181, 0.3107]\n", + "5 -0.7456 [-2.8554, 1.3642]\n", + "6 0.6777 [-1.4303, 2.7856]\n", + "7 -0.0008 [-0.1329, 0.1314]\n", + "8 0.0764 [-0.0371, 0.1900]\n", + "9 0.1575 [0.0687, 0.2464]\n", + "10 -0.2067 [-0.7975, 0.3841]\n", + "11 0.1857 [-0.3969, 0.7683]\n", + "12 -0.0002 [-0.0295, 0.0292]\n", + "13 0.0164 [-0.0091, 0.0419]\n", + "15 -0.0048 [-0.0354, 0.0258]\n", + "14 0.1527 [0.0684, 0.2370]\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 9b: Display table (wide format)\n", + "# ============================================================\n", + "display_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\")\n", + "\n", + "disp_sub <- all_summary[all_summary$label %in% display_labels, ]\n", + "disp_sub$formatted <- sprintf(\"%.4f [%.4f, %.4f]\", disp_sub$est, disp_sub$ci_lower, disp_sub$ci_upper)\n", + "\n", + "wide_df <- reshape2::dcast(disp_sub, label ~ method, value.var=\"formatted\")\n", + "wide_df$label <- factor(wide_df$label, levels=display_labels)\n", + "wide_df <- wide_df[order(wide_df$label), ]\n", + "\n", + "write.csv(wide_df, file.path(RESULT_DIR, \"summary\", paste0(\"summary_display_table_\", EXPOSURE, \".csv\")), row.names=FALSE)\n", + "\n", + "cat(\"\\n=== Wide-Format Display Table ===\\n\")\n", + "print(wide_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:17:05.230780Z", + "iopub.status.busy": "2026-03-31T23:17:05.228536Z", + "iopub.status.idle": "2026-03-31T23:17:06.517202Z", + "shell.execute_reply": "2026-03-31T23:17:06.515000Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary forest plot saved.\n" + ] + }, + { + "data": { + "image/png": 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++9d/fu3QzDWK1WjuOaNGmi1Wp///13WqegoCAtLa1Zs2Z8mtSBt+vl008/pXkS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ZTq9uamrSO0LHuhBy5eGPfnngDznI0hPu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\nPxNCWlpaLC0tT58+zc9pZmZWXl4uVMi3335raWkZFBTEz9b5b4/Qo2HEDuD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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 9c: Summary Forest Plot -- All Methods\n", + "# ============================================================\n", + "plot_labels_summary <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\")\n", + "sum_plot <- all_summary[all_summary$label %in% plot_labels_summary, ]\n", + "sum_plot$label <- factor(sum_plot$label, levels=rev(plot_labels_summary))\n", + "\n", + "p_sum_forest <- ggplot(sum_plot, aes(x=est, y=label, xmin=ci_lower, xmax=ci_upper,\n", + " color=method, shape=method)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(position=position_dodge(width=0.6), size=0.5) +\n", + " labs(title=\"All Methods Comparison: Parallel Mediation Path Estimates\",\n", + " subtitle=paste0(\"SNP -> 4 Mediators -> caa_neo4 | N = \", N_total),\n", + " x=\"Estimate (95% CI)\", y=\"\", color=\"Method\", shape=\"Method\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + "ggsave(file.path(RESULT_DIR, \"summary\", \"summary_forest_all_methods.png\"), p_sum_forest, width=11, height=9, dpi=150)\n", + "ggsave(file.path(RESULT_DIR, \"summary\", \"summary_forest_all_methods.pdf\"), p_sum_forest, width=11, height=9)\n", + "print(p_sum_forest)\n", + "cat(\"Summary forest plot saved.\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:17:06.524398Z", + "iopub.status.busy": "2026-03-31T23:17:06.522056Z", + "iopub.status.idle": "2026-03-31T23:17:10.082913Z", + "shell.execute_reply": "2026-03-31T23:17:10.080685Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Faceted-by-path plot saved.\n" + ] + }, + { + "data": { + "image/png": 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klMTNSwlI+PD5VKfffuHZ/P13FF1doi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\nF6aHDh06bdq0jIyMho0TgIgQ7OBdtH79+vXr18uXsNcr2U9ihmF++uknf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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 9d: Faceted by Effect Type\n", + "# ============================================================\n", + "sum_plot2 <- all_summary[all_summary$label %in% plot_labels_summary, ]\n", + "sum_plot2$effect_type <- ifelse(grepl(\"^a\", sum_plot2$label), \"a-path (SNP->M)\",\n", + " ifelse(grepl(\"^b\", sum_plot2$label), \"b-path (M->Y)\",\n", + " ifelse(sum_plot2$label == \"cp\", \"c' (direct)\",\n", + " ifelse(grepl(\"^ind\", sum_plot2$label), \"Specific indirect\",\n", + " ifelse(sum_plot2$label == \"total_indirect\", \"Total indirect\", \"Total\")))))\n", + "\n", + "p_faceted <- ggplot(sum_plot2, aes(x=est, y=method, xmin=ci_lower, xmax=ci_upper, color=label)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(position=position_dodge(width=0.6), size=0.5) +\n", + " facet_wrap(~effect_type, scales=\"free_x\", ncol=3) +\n", + " labs(title=\"Estimates by Effect Type Across Methods\",\n", + " subtitle=\"Parallel Mediation: SNP -> 4 Mediators -> caa_neo4\",\n", + " x=\"Estimate (95% CI)\", y=\"\", color=\"Parameter\") +\n", + " theme_minimal(base_size=11) +\n", + " theme(legend.position=\"bottom\", legend.text=element_text(size=8))\n", + "\n", + "ggsave(file.path(RESULT_DIR, \"summary\", \"summary_faceted_by_path.png\"), p_faceted, width=14, height=8, dpi=150)\n", + "ggsave(file.path(RESULT_DIR, \"summary\", \"summary_faceted_by_path.pdf\"), p_faceted, width=14, height=8)\n", + "print(p_faceted)\n", + "cat(\"Faceted-by-path plot saved.\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:17:10.090729Z", + "iopub.status.busy": "2026-03-31T23:17:10.088258Z", + "iopub.status.idle": "2026-03-31T23:17:11.610788Z", + "shell.execute_reply": "2026-03-31T23:17:11.608689Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Indirect effect focus plot saved.\n" + ] + }, + { + "data": { + "image/png": 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8buvP/++/xbAhM7lUplZWVlZ2dX2ibGjh1LRAsWLMjPz1er1Rs3bhSJRPw/ar6+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\n3240GqW5a1LOe+aZZwoKCsxms8FgmDp1qhDitddes97jyJEjM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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 9e: Indirect Effect Focus Plot\n", + "# ============================================================\n", + "ind_focus <- all_summary[all_summary$label %in% c(paste0(\"ind\", 1:n_med), \"total_indirect\"), ]\n", + "\n", + "ind_label_map <- c(\n", + " setNames(paste0(\"Indirect via \", MEDIATOR_SHORT), paste0(\"ind\", 1:n_med)),\n", + " c(total_indirect=\"Total Indirect\")\n", + ")\n", + "ind_focus$label_display <- ind_label_map[ind_focus$label]\n", + "ind_focus$label_display <- factor(ind_focus$label_display,\n", + " levels=rev(c(paste0(\"Indirect via \", MEDIATOR_SHORT), \"Total Indirect\")))\n", + "\n", + "p_indirect <- ggplot(ind_focus, aes(x=est, y=label_display, xmin=ci_lower, xmax=ci_upper,\n", + " color=method, shape=method)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(position=position_dodge(width=0.6), size=0.7) +\n", + " labs(title=\"Indirect Effect (a x b) Across Methods\",\n", + " subtitle=paste0(\"SNP \", EXPOSURE, \" -> Mediators -> caa_neo4 | N = \", N_total),\n", + " x=\"Indirect Effect (95% CI)\", y=\"\", color=\"Method\", shape=\"Method\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + "ggsave(file.path(RESULT_DIR, \"summary\", \"summary_indirect_effect.png\"), p_indirect, width=10, height=5, dpi=150)\n", + "ggsave(file.path(RESULT_DIR, \"summary\", \"summary_indirect_effect.pdf\"), p_indirect, width=10, height=5)\n", + "print(p_indirect)\n", + "cat(\"Indirect effect focus plot saved.\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:17:11.617660Z", + "iopub.status.busy": "2026-03-31T23:17:11.615454Z", + "iopub.status.idle": "2026-03-31T23:17:12.873251Z", + "shell.execute_reply": "2026-03-31T23:17:12.871124Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Combined panel saved.\n" + ] + }, + { + "data": { + "image/png": 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eLSpDY8/Vy+/vprkieRoZbHjx+PiYmJjY3VarVcWsBtBbdIt3Tjxo0tb5+ENHHi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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 9f: Combined Panel\n", + "# ============================================================\n", + "p_combined <- gridExtra::grid.arrange(p_sum_forest, p_indirect, nrow=2, heights=c(2, 1))\n", + "\n", + "ggsave(file.path(RESULT_DIR, \"summary\", \"summary_combined_panel.png\"), p_combined, width=11, height=13, dpi=150)\n", + "ggsave(file.path(RESULT_DIR, \"summary\", \"summary_combined_panel.pdf\"), p_combined, width=11, height=13)\n", + "cat(\"Combined panel saved.\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Findings\n", + "\n", + "Data-driven conclusions are presented below based on the actual results from the four methods." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:17:12.880381Z", + "iopub.status.busy": "2026-03-31T23:17:12.878144Z", + "iopub.status.idle": "2026-03-31T23:17:12.990106Z", + "shell.execute_reply": "2026-03-31T23:17:12.987483Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KEY FINDINGS\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================================\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Specific indirect via APOC4_APOC2 ---\n", + " FIML: est=-0.2067, 95% CI=[-0.7975, 0.3841], p=0.4929 \n", + " Bootstrap: est=-0.2160, 95% CI=[-0.8402, 0.3346], p=0.4460 \n", + " Bayesian: est=-0.1782, 95% CrI=[-0.8179, 0.4243], P(direction)=0.723\n", + "\n", + "--- Specific indirect via APOC2 ---\n", + " FIML: est=0.1857, 95% CI=[-0.3969, 0.7683], p=0.5322 \n", + " Bootstrap: est=0.1949, 95% CI=[-0.3370, 0.8129], p=0.4920 \n", + " Bayesian: est=0.1575, 95% CrI=[-0.4425, 0.7869], P(direction)=0.702\n", + "\n", + "--- Specific indirect via APOC1 ---\n", + " FIML: est=-0.0002, 95% CI=[-0.0295, 0.0292], p=0.9909 \n", + " Bootstrap: est=0.0000, 95% CI=[-0.0351, 0.0379], p=0.9860 \n", + " Bayesian: est=0.0011, 95% CrI=[-0.0294, 0.0334], P(direction)=0.522\n", + "\n", + "--- Specific indirect via APOE ---\n", + " FIML: est=0.0164, 95% CI=[-0.0091, 0.0419], p=0.2082 \n", + " Bootstrap: est=0.0160, 95% CI=[-0.0137, 0.0465], p=0.2160 \n", + " Bayesian: est=0.0156, 95% CrI=[-0.0087, 0.0439], P(direction)=0.902\n", + "\n", + "--- Total indirect ---\n", + " FIML: est=-0.0048, 95% CI=[-0.0354, 0.0258], p=0.7593 \n", + " Bootstrap: est=-0.0050, 95% CI=[-0.0385, 0.0280], p=0.7780 \n", + " Bayesian: est=-0.0039, 95% CrI=[-0.0373, 0.0289], P(direction)=0.594\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Proportion Mediated (FIML) ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " APOC4_APOC2: -135.3%\n", + " APOC2: 121.6%\n", + " APOC1: -0.1%\n", + " APOE: 10.7%\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total mediated: -3.1%\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- MNAR Robustness ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ind1: tipping distance = 0.00 SD\n", + " ind2: tipping distance = 0.00 SD\n", + " ind3: tipping distance = 0.00 SD\n", + " ind4: tipping distance = 0.00 SD\n", + " total_indirect: tipping distance = 0.00 SD\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 10: Key Findings -- data-driven summary\n", + "# ============================================================\n", + "cat(\"============================================\\n\")\n", + "cat(\"KEY FINDINGS\\n\")\n", + "cat(\"============================================\\n\\n\")\n", + "\n", + "report_labels <- c(paste0(\"ind\", 1:n_med), \"total_indirect\")\n", + "report_names <- c(paste0(\"Specific indirect via \", MEDIATOR_SHORT), \"Total indirect\")\n", + "\n", + "for (k in seq_along(report_labels)) {\n", + " lab <- report_labels[k]\n", + " cat(\"--- \", report_names[k], \" ---\\n\")\n", + "\n", + " # FIML\n", + " f_row <- fiml_key[fiml_key$label == lab, ]\n", + " if (nrow(f_row) > 0) {\n", + " cat(sprintf(\" FIML: est=%.4f, 95%% CI=[%.4f, %.4f], p=%.4f %s\\n\",\n", + " f_row$est, f_row$ci.lower, f_row$ci.upper, f_row$pvalue,\n", + " ifelse(f_row$pvalue < 0.05, \"*\", \"\")))\n", + " }\n", + "\n", + " # Bootstrap\n", + " b_row <- boot_summary[boot_summary$label == lab, ]\n", + " if (nrow(b_row) > 0) {\n", + " cat(sprintf(\" Bootstrap: est=%.4f, 95%% CI=[%.4f, %.4f], p=%.4f %s\\n\",\n", + " b_row$mean, b_row$ci_lower, b_row$ci_upper, b_row$p_value,\n", + " ifelse(b_row$p_value < 0.05, \"*\", \"\")))\n", + " }\n", + "\n", + " # Bayesian\n", + " if (!is.null(bayes_results)) {\n", + " by_row <- bayes_results[bayes_results$label == lab, ]\n", + " if (nrow(by_row) > 0) {\n", + " cat(sprintf(\" Bayesian: est=%.4f, 95%% CrI=[%.4f, %.4f], P(direction)=%.3f\\n\",\n", + " by_row$mean, by_row$ci_lower, by_row$ci_upper, by_row$p_direction))\n", + " }\n", + " }\n", + " cat(\"\\n\")\n", + "}\n", + "\n", + "# Proportion mediated\n", + "cat(\"--- Proportion Mediated (FIML) ---\\n\")\n", + "for (k in 1:n_med) {\n", + " p_row <- fiml_key[fiml_key$label == paste0(\"prop\", k), ]\n", + " if (nrow(p_row) > 0) {\n", + " cat(sprintf(\" %s: %.1f%%\\n\", MEDIATOR_SHORT[k], p_row$est * 100))\n", + " }\n", + "}\n", + "pt_row <- fiml_key[fiml_key$label == \"prop_total\", ]\n", + "if (nrow(pt_row) > 0) {\n", + " cat(sprintf(\" Total mediated: %.1f%%\\n\", pt_row$est * 100))\n", + "}\n", + "\n", + "# MNAR robustness\n", + "cat(\"\\n--- MNAR Robustness ---\\n\")\n", + "for (k in 1:nrow(tipping)) {\n", + " cat(sprintf(\" %s: tipping distance = %s SD\\n\", tipping$label[k],\n", + " ifelse(is.na(tipping$tipping_dist[k]), \"never tips (always sig)\",\n", + " sprintf(\"%.2f\", tipping$tipping_dist[k]))))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:17:12.996376Z", + "iopub.status.busy": "2026-03-31T23:17:12.994158Z", + "iopub.status.idle": "2026-03-31T23:17:13.031789Z", + "shell.execute_reply": "2026-03-31T23:17:13.029126Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Residual Correlations Between Mediators (FIML) ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " APOC4_APOC2_AC_exp ~~ APOC2_AC_exp: est=0.8890, SE=0.0517, p=0.0000\n", + " APOC4_APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp: est=0.2048, SE=0.0537, p=0.0001\n", + " APOC4_APOC2_AC_exp ~~ APOE_Mega_Mic_exp: est=0.0527, SE=0.0416, p=0.2046\n", + " APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp: est=0.2079, SE=0.0539, p=0.0001\n", + " APOC2_AC_exp ~~ APOE_Mega_Mic_exp: est=0.0539, SE=0.0417, p=0.1957\n", + " APOC1_DeJager_Mic_exp ~~ APOE_Mega_Mic_exp: est=0.5709, SE=0.0501, p=0.0000\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 10b: Residual correlations between mediators (FIML)\n", + "# ============================================================\n", + "cat(\"\\n--- Residual Correlations Between Mediators (FIML) ---\\n\")\n", + "pe_cov <- pe_fiml[pe_fiml$op == \"~~\" & pe_fiml$lhs != pe_fiml$rhs, ]\n", + "pe_cov_med <- pe_cov[pe_cov$lhs %in% MEDIATORS & pe_cov$rhs %in% MEDIATORS, ]\n", + "if (nrow(pe_cov_med) > 0) {\n", + " for (i in 1:nrow(pe_cov_med)) {\n", + " cat(sprintf(\" %s ~~ %s: est=%.4f, SE=%.4f, p=%.4f\\n\",\n", + " pe_cov_med$lhs[i], pe_cov_med$rhs[i],\n", + " pe_cov_med$est[i], pe_cov_med$se[i], pe_cov_med$pvalue[i]))\n", + " }\n", + "} else {\n", + " cat(\" No mediator-mediator covariances found (may be default-estimated).\\n\")\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Output File Inventory" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:17:13.038051Z", + "iopub.status.busy": "2026-03-31T23:17:13.035883Z", + "iopub.status.idle": "2026-03-31T23:17:13.091519Z", + "shell.execute_reply": "2026-03-31T23:17:13.089549Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Output File Inventory ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " APOE_ind_set_27_caa_neo4_APOE4_adj_SEM_mediation.ipynb \n", + " bayesian_blavaan/bayesian_forest_plot.pdf \n", + " bayesian_blavaan/bayesian_forest_plot.png \n", + " bayesian_blavaan/bayesian_posteriors_chr19_44954310_T_C.pdf \n", + " bayesian_blavaan/bayesian_posteriors_chr19_44954310_T_C.png \n", + " bayesian_blavaan/bayesian_results.csv \n", + " bayesian_blavaan/bayesian_trace_a_chr19_44954310_T_C.pdf \n", + " bayesian_blavaan/bayesian_trace_a_chr19_44954310_T_C.png \n", + " bootstrap/bootstrap_distributions_chr19_44954310_T_C.pdf \n", + " bootstrap/bootstrap_distributions_chr19_44954310_T_C.png \n", + " bootstrap/bootstrap_forest_plot.pdf \n", + " bootstrap/bootstrap_forest_plot.png \n", + " bootstrap/bootstrap_results.csv \n", + " gen_notebook.py \n", + " main_SEM_FIML/fiml_all_paths.csv \n", + " main_SEM_FIML/fiml_forest_plot.pdf \n", + " main_SEM_FIML/fiml_forest_plot.png \n", + " MNAR_sensitivity/mnar_1d_slices_chr19_44954310_T_C.pdf \n", + " MNAR_sensitivity/mnar_1d_slices_chr19_44954310_T_C.png \n", + " MNAR_sensitivity/mnar_contour_indirect_chr19_44954310_T_C.pdf \n", + " MNAR_sensitivity/mnar_contour_indirect_chr19_44954310_T_C.png \n", + " MNAR_sensitivity/mnar_contour_pvalue_chr19_44954310_T_C.pdf \n", + " MNAR_sensitivity/mnar_contour_pvalue_chr19_44954310_T_C.png \n", + " MNAR_sensitivity/mnar_grid_results.csv \n", + " MNAR_sensitivity/mnar_tipping_summary.csv \n", + " summary/all_methods_summary.csv \n", + " summary/summary_combined_panel.pdf \n", + " summary/summary_combined_panel.png \n", + " summary/summary_display_table_chr19_44954310_T_C.csv \n", + " summary/summary_faceted_by_path.pdf \n", + " summary/summary_faceted_by_path.png \n", + " summary/summary_forest_all_methods.pdf \n", + " summary/summary_forest_all_methods.png \n", + " summary/summary_indirect_effect.pdf \n", + " summary/summary_indirect_effect.png \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 11: Output File Inventory\n", + "# ============================================================\n", + "cat(\"\\n=== Output File Inventory ===\\n\")\n", + "all_files <- list.files(RESULT_DIR, recursive=TRUE, full.names=FALSE)\n", + "all_files <- all_files[!grepl(\"^log/\", all_files)]\n", + "for (f in sort(all_files)) {\n", + " cat(\" \", f, \"\\n\")\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sensitivity Analysis: Parallel Mediation Excluding APOC4_APOC2_AC_exp\n\nThis sensitivity analysis repeats the FIML parallel mediation with only 3 mediators,\n**excluding APOC4_APOC2_AC_exp**. The purpose is to assess whether the mediation results\nare driven by the APOC4_APOC2 mediator (which showed high collinearity with APOC2_AC_exp)\nor whether the remaining 3 mediators carry independent mediation signal.\n\n### Mediators retained:\n1. **APOC2_AC_exp** (covariates: msex_u, age_death_u, pmi_u, ROS_study_u)\n2. **APOC1_DeJager_Mic_exp** (covariates: msex_u, age_death_u, pmi_u)\n3. **APOE_Mega_Mic_exp** (covariates: msex_u, age_death_u, pmi_u)\n\n### Outcome:\n- **caa_neo4** (covariates: educ, apoe4_dose, msex_u, age_death_u)\n" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# ============================================================\n# Sensitivity Analysis: FIML SEM with 3 Mediators (excluding APOC4_APOC2_AC_exp)\n# ============================================================\ncat(\"=== Sensitivity Analysis: 3-Mediator Model (excl. APOC4_APOC2_AC_exp) ===\\n\\n\")\n\n# Create output directory\nSENS_DIR <- file.path(RESULT_DIR, \"main_SEM_FIML\", \"sensitivity_analysis\")\ndir.create(SENS_DIR, showWarnings=FALSE, recursive=TRUE)\n\n# Define 3-mediator variables\nSENS_MEDIATORS <- c(\"APOC2_AC_exp\", \"APOC1_DeJager_Mic_exp\", \"APOE_Mega_Mic_exp\")\nSENS_MEDIATOR_SHORT <- c(\"APOC2\", \"APOC1\", \"APOE\")\nn_med_sens <- length(SENS_MEDIATORS)\n\n# Covariates per mediator equation\nSENS_MED_COVS <- list(\n APOC2_AC_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\"),\n APOC1_DeJager_Mic_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\"),\n APOE_Mega_Mic_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\")\n)\n\n# Build mediator equations\nsens_med_eqs <- c()\nfor (i in seq_len(n_med_sens)) {\n m <- SENS_MEDIATORS[i]\n cov_str <- paste(SENS_MED_COVS[[m]], collapse=\" + \")\n sens_med_eqs <- c(sens_med_eqs, paste0(m, \" ~ a\", i, \" * \", EXPOSURE, \" + \", cov_str))\n}\n\n# Outcome equation\nsens_med_terms <- paste0(\"b\", seq_len(n_med_sens), \" * \", SENS_MEDIATORS, collapse=\" + \")\nsens_y_eq <- paste0(OUTCOME, \" ~ \", sens_med_terms, \" + cp * \", EXPOSURE, \" + \", paste(OUT_COVS, collapse=\" + \"))\n\n# Defined parameters\nsens_ind_defs <- paste0(\"ind\", seq_len(n_med_sens), \" := a\", seq_len(n_med_sens), \" * b\", seq_len(n_med_sens))\nsens_total_ind <- paste0(\"total_indirect := \", paste0(\"ind\", seq_len(n_med_sens), collapse=\" + \"))\nsens_total_def <- \"total := cp + total_indirect\"\nsens_prop_defs <- paste0(\"prop\", seq_len(n_med_sens), \" := ind\", seq_len(n_med_sens), \" / total\")\nsens_prop_total <- \"prop_total := total_indirect / total\"\n\n# Pairwise contrasts\nsens_contrasts <- c()\nfor (i in 1:(n_med_sens-1)) {\n for (j in (i+1):n_med_sens) {\n sens_contrasts <- c(sens_contrasts, paste0(\"diff_\", i, \"_\", j, \" := ind\", i, \" - ind\", j))\n }\n}\n\n# Residual correlations between mediators\nsens_cov_terms <- c()\nfor (i in 1:(n_med_sens-1)) {\n for (j in (i+1):n_med_sens) {\n sens_cov_terms <- c(sens_cov_terms, paste0(SENS_MEDIATORS[i], \" ~~ \", SENS_MEDIATORS[j]))\n }\n}\n\nsens_model_str <- paste(c(sens_med_eqs, sens_y_eq, sens_ind_defs, sens_total_ind, sens_total_def,\n sens_prop_defs, sens_prop_total, sens_contrasts, sens_cov_terms), collapse=\"\\n\")\n\ncat(\"=== 3-Mediator Parallel Model ===\\n\")\ncat(sens_model_str, \"\\n\\n\")\n\n# Fit FIML\ncat(\"Fitting FIML SEM (N =\", N_total, \")...\\n\")\nfit_sens <- tryCatch(\n sem(sens_model_str, data=dat, missing=\"fiml\", fixed.x=FALSE, estimator=\"ML\"),\n error = function(e) { cat(\"FIML Error:\", conditionMessage(e), \"\\n\"); NULL }\n)\n\nif (!is.null(fit_sens)) {\n cat(\"Converged:\", lavInspect(fit_sens, \"converged\"), \"\\n\")\n cat(\"N used:\", lavInspect(fit_sens, \"nobs\"), \"\\n\\n\")\n summary(fit_sens, fit.measures=TRUE, standardized=TRUE)\n} else {\n cat(\"ERROR: Model failed to converge.\\n\")\n}\n" + ], + "execution_count": 4, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Sensitivity Analysis: 3-Mediator Model (excl. APOC4_APOC2_AC_exp) ===\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== 3-Mediator Parallel Model ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "APOC2_AC_exp ~ a1 * chr19_44954310_T_C + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC1_DeJager_Mic_exp ~ a2 * chr19_44954310_T_C + msex_u + age_death_u + pmi_u\n", + "APOE_Mega_Mic_exp ~ a3 * chr19_44954310_T_C + msex_u + age_death_u + pmi_u\n", + "caa_neo4 ~ b1 * APOC2_AC_exp + b2 * APOC1_DeJager_Mic_exp + b3 * APOE_Mega_Mic_exp + cp * chr19_44954310_T_C + educ + apoe4_dose + msex_u + age_death_u\n", + "ind1 := a1 * b1\n", + "ind2 := a2 * b2\n", + "ind3 := a3 * b3\n", + "total_indirect := ind1 + ind2 + ind3\n", + "total := cp + total_indirect\n", + "prop1 := ind1 / total\n", + "prop2 := ind2 / total\n", + "prop3 := ind3 / total\n", + "prop_total := total_indirect / total\n", + "diff_1_2 := ind1 - ind2\n", + "diff_1_3 := ind1 - ind3\n", + "diff_2_3 := ind2 - ind3\n", + "APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC1_DeJager_Mic_exp ~~ APOE_Mega_Mic_exp \n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting FIML SEM (N = 1153 )...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converged: TRUE \n", + "N used: 1153 \n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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A data.frame: 79 \u00d7 11
lhsoprhslabelexoestsezpvaluestd.lvstd.all
<chr><chr><chr><chr><int><dbl><dbl><dbl><dbl><dbl><dbl>
APOC2_AC_exp ~ chr19_44954310_T_C a10 0.2739536410.056139981 4.879831371.061766e-06 0.273953641 0.195764781
APOC2_AC_exp ~ msex_u 0 0.0964116620.081675246 1.180426962.378304e-01 0.096411662 0.046567142
APOC2_AC_exp ~ age_death_u 0 0.0222483970.006037667 3.684932562.287632e-04 0.022248397 0.148791289
APOC2_AC_exp ~ pmi_u 0 0.0120897150.007920926 1.526300681.269350e-01 0.012089715 0.091035199
APOC2_AC_exp ~ ROS_study_u 0-0.1602712060.077289404-2.073650433.811179e-02-0.160271206-0.081585181
APOC1_DeJager_Mic_exp~ chr19_44954310_T_C a20 0.2222521180.064197073 3.462028875.361197e-04 0.222252118 0.153117626
APOC1_DeJager_Mic_exp~ msex_u 0-0.1622894420.097013047-1.672862029.435446e-02-0.162289442-0.075572201
APOC1_DeJager_Mic_exp~ age_death_u 0 0.0089085820.006697564 1.330122751.834778e-01 0.008908582 0.057439305
APOC1_DeJager_Mic_exp~ pmi_u 0 0.0029797220.008675720 0.343455327.312559e-01 0.002979722 0.021631710
APOE_Mega_Mic_exp ~ chr19_44954310_T_C a30 0.2142462150.049139965 4.359917971.301112e-05 0.214246215 0.155332357
APOE_Mega_Mic_exp ~ msex_u 0-0.2062255240.073352555-2.811429304.932193e-03-0.206225524-0.101061028
APOE_Mega_Mic_exp ~ age_death_u 0 0.0219959980.005180015 4.246319222.173110e-05 0.021995998 0.149249799
APOE_Mega_Mic_exp ~ pmi_u 0 0.0109585600.005985862 1.830740426.713930e-02 0.010958560 0.083721704
caa_neo4 ~ APOC2_AC_exp b10-0.0671131140.044270786-1.515968441.295273e-01-0.067113114-0.062530459
caa_neo4 ~ APOC1_DeJager_Mic_expb20 0.0054112380.067343923 0.080352299.359571e-01 0.005411238 0.005229487
caa_neo4 ~ APOE_Mega_Mic_exp b30 0.0745899460.057965464 1.286799771.981641e-01 0.074589946 0.068497264
caa_neo4 ~ chr19_44954310_T_C cp0 0.1534607180.044986131 3.411289496.465640e-04 0.153460718 0.102173656
caa_neo4 ~ educ 0 0.0016168240.008607955 0.187829098.510106e-01 0.001616824 0.005525105
caa_neo4 ~ apoe4_dose 0 0.7384528230.06374784111.583966040.000000e+00 0.738452823 0.336445447
caa_neo4 ~ msex_u 0-0.0448279760.066111484-0.678066414.977296e-01-0.044827976-0.020173598
caa_neo4 ~ age_death_u 0 0.0252684820.004982007 5.071947933.937641e-07 0.025268482 0.157449816
APOC2_AC_exp ~~APOC1_DeJager_Mic_exp 0 0.2041633380.053861332 3.790536341.503223e-04 0.204163338 0.215937577
APOC2_AC_exp ~~APOE_Mega_Mic_exp 0 0.0533500210.041670760 1.280274712.004485e-01 0.053350021 0.060419187
APOC1_DeJager_Mic_exp~~APOE_Mega_Mic_exp 0 0.5706872900.05006561111.398788190.000000e+00 0.570687290 0.609160996
APOC2_AC_exp ~~APOC2_AC_exp 0 0.8911342480.05176586517.214707900.000000e+00 0.891134248 0.923691964
APOC1_DeJager_Mic_exp~~APOC1_DeJager_Mic_exp 0 1.0031270290.07099562814.129419800.000000e+00 1.003127029 0.966459256
APOE_Mega_Mic_exp ~~APOE_Mega_Mic_exp 0 0.8749360110.04567811819.154379570.000000e+00 0.874936011 0.933561426
caa_neo4 ~~caa_neo4 0 0.9428905820.04145230122.746399140.000000e+00 0.942890582 0.848425616
chr19_44954310_T_C ~~chr19_44954310_T_C 0 0.4926414400.02051782224.010415720.000000e+00 0.492641440 1.000000000
chr19_44954310_T_C ~~msex_u 0 0.0042844300.009852455 0.434859176.636647e-01 0.004284430 0.012866773
\u22ee\u22ee\u22ee\u22ee\u22ee\u22ee\u22ee\u22ee\u22ee\u22ee\u22ee
pmi_u ~~apoe4_dose 0-0.0724891910.106530919 -0.680452134.962182e-01-0.072489191-0.0204059262
ROS_study_u ~~ROS_study_u 0 0.2499928130.010587449 23.612185130.000000e+00 0.249992813 1.0000000000
ROS_study_u ~~educ 0 0.8817797520.060083230 14.675971280.000000e+00 0.881779752 0.4895475990
ROS_study_u ~~apoe4_dose 0 0.0138999520.007229877 1.922571085.453394e-02 0.013899952 0.0578807139
educ ~~educ 012.9778535950.549871349 23.601618110.000000e+0012.977853595 1.0000000000
educ ~~apoe4_dose 0 0.1315694800.052108185 2.524929241.157216e-02 0.131569480 0.0760392910
apoe4_dose ~~apoe4_dose 0 0.2306913590.009811535 23.512258730.000000e+00 0.230691359 1.0000000000
APOC2_AC_exp ~1 0-2.2474943280.554058952 -4.056417324.983119e-05-2.247494328-2.2881822885
APOC1_DeJager_Mic_exp~1 0-0.9445323580.613100154 -1.540584121.234180e-01-0.944532358-0.9271086842
APOE_Mega_Mic_exp ~1 0-2.1600591100.470261384 -4.593315944.362578e-06-2.160059110-2.2312536265
caa_neo4 ~1 0-1.4550526850.484508372 -3.003152822.671982e-03-1.455052685-1.3802411021
chr19_44954310_T_C ~1 0 0.8751084080.020670492 42.336118630.000000e+00 0.875108408 1.2467988293
msex_u ~1 0 0.3420871320.014050995 24.346114710.000000e+00 0.342087132 0.7210721863
age_death_u ~1 088.9523653190.194552152457.216043250.000000e+0088.95236531913.5416233228
pmi_u ~1 0 8.7444331580.219046846 39.920379170.000000e+00 8.744433158 1.1823073391
ROS_study_u ~1 0 0.4995853730.014970774 33.370711660.000000e+00 0.499585373 0.9991851076
educ ~1 016.3680597400.107901012151.695145820.000000e+0016.368059740 4.5435547552
apoe4_dose ~1 0 0.2722484420.014433953 18.861668390.000000e+00 0.272248442 0.5668259695
ind1 :=a1*b1 ind1 0-0.0183858820.012687708 -1.449109781.473069e-01-0.018385882-0.0122412616
ind2 :=a2*b2 ind2 0 0.0012026590.015003437 0.080158919.361109e-01 0.001202659 0.0008007266
ind3 :=a3*b3 ind3 0 0.0159806140.012991206 1.230110052.186559e-01 0.015980614 0.0106398415
total_indirect :=ind1+ind2+ind3 total_indirect0-0.0012026090.014746593 -0.081551689.350032e-01-0.001202609-0.0008006935
total :=cp+total_indirect total 0 0.1522581080.043017415 3.539452804.009575e-04 0.152258108 0.1013729623
prop1 :=ind1/total prop1 0-0.1207546990.090636596 -1.332295171.827632e-01-0.120754699-0.1207546994
prop2 :=ind2/total prop2 0 0.0078988180.098570834 0.080133429.361311e-01 0.007898818 0.0078988185
prop3 :=ind3/total prop3 0 0.1049573900.089646486 1.170792002.416824e-01 0.104957390 0.1049573897
prop_total :=total_indirect/totalprop_total 0-0.0078984910.096956004 -0.081464709.350724e-01-0.007898491-0.0078984913
diff_1_2 :=ind1-ind2 diff_1_2 0-0.0195885410.022384979 -0.875075253.815330e-01-0.019588541-0.0130419882
diff_1_3 :=ind1-ind3 diff_1_3 0-0.0343664960.016740568 -2.052887134.008353e-02-0.034366496-0.0228811031
diff_2_3 :=ind2-ind3 diff_2_3 0-0.0147779540.025802358 -0.572736595.668231e-01-0.014777954-0.0098391149
\n", + "
\n", + "
\n" + ], + "text/latex": [ + "\\begin{description}\n", + "\\item[\\$header] \\begin{description}\n", + "\\item[\\$lavaan.version] '0.6-21'\n", + "\\item[\\$sam.approach] FALSE\n", + "\\item[\\$optim.method] 'nlminb'\n", + "\\item[\\$optim.iterations] 157\n", + "\\item[\\$optim.converged] TRUE\n", + "\\end{description}\n", + "\n", + "\\item[\\$optim] \\begin{description}\n", + "\\item[\\$estimator] 'ML'\n", + "\\item[\\$estimator.args] \\begin{enumerate}\n", + "\\end{enumerate}\n", + "\n", + "\\item[\\$optim.method] 'nlminb'\n", + "\\item[\\$npar] 67\n", + "\\item[\\$eq.constraints] FALSE\n", + "\\item[\\$nrow.ceq.jac] 0\n", + "\\item[\\$nrow.cin.jac] 0\n", + "\\item[\\$nrow.con.jac] 0\n", + "\\item[\\$con.jac.rank] 0\n", + "\\end{description}\n", + "\n", + "\\item[\\$data] \\begin{description}\n", + "\\item[\\$ngroups] 1\n", + "\\item[\\$nobs] 1153\n", + "\\item[\\$npatterns] 24\n", + "\\end{description}\n", + "\n", + "\\item[\\$test] \\textbf{\\$standard} = \\begin{description}\n", + "\\item[\\$test] 'standard'\n", + "\\item[\\$stat] 18.9406014384593\n", + "\\item[\\$stat.group] 18.9406014384593\n", + "\\item[\\$df] 10\n", + "\\item[\\$refdistr] 'chisq'\n", + "\\item[\\$pvalue] 0.0410236526919417\n", + "\\end{description}\n", + "\n", + "\\item[\\$fit] \\begin{description*}\n", + "\\item[npar] 67\n", + "\\item[fmin] 0.00821361727600145\n", + "\\item[chisq] 18.9406014384593\n", + "\\item[df] 10\n", + "\\item[pvalue] 0.0410236526919417\n", + "\\item[baseline.chisq] 474.446254681919\n", + "\\item[baseline.df] 34\n", + "\\item[baseline.pvalue] 0\n", + "\\item[cfi] 0.979701038790951\n", + "\\item[tli] 0.930983531889232\n", + "\\item[cfi.robust] 0.987503183690851\n", + "\\item[tli.robust] 0.957510824548892\n", + "\\item[logl] -17751.6203072975\n", + "\\item[unrestricted.logl] -17742.1500065782\n", + "\\item[aic] 35637.2406145949\n", + "\\item[bic] 35975.598823453\n", + "\\item[ntotal] 1153\n", + "\\item[bic2] 35762.7853346997\n", + "\\item[rmsea] 0.0278463779792266\n", + "\\item[rmsea.ci.lower] 0.00550322256564479\n", + "\\item[rmsea.ci.upper] 0.0468343641343418\n", + "\\item[rmsea.ci.level] 0.9\n", + "\\item[rmsea.pvalue] 0.974584116220918\n", + "\\item[rmsea.close.h0] 0.05\n", + "\\item[rmsea.notclose.pvalue] 3.4285725283043e-07\n", + "\\item[rmsea.notclose.h0] 0.08\n", + "\\item[rmsea.robust] 0.0322299565077659\n", + "\\item[rmsea.ci.lower.robust] 0\n", + "\\item[rmsea.ci.upper.robust] 0.0613263289047915\n", + "\\item[rmsea.pvalue.robust] 0.821497221654828\n", + "\\item[rmsea.notclose.pvalue.robust] 0.00194045456329975\n", + "\\item[srmr] 0.0209142509945356\n", + "\\end{description*}\n", + "\n", + "\\item[\\$pe] A data.frame: 79 \u00d7 11\n", + "\\begin{tabular}{lllllllllll}\n", + " lhs & op & rhs & label & exo & est & se & z & pvalue & std.lv & std.all\\\\\n", + " & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{} & chr19\\_44954310\\_T\\_C & a1 & 0 & 0.273953641 & 0.056139981 & 4.87983137 & 1.061766e-06 & 0.273953641 & 0.195764781\\\\\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{} & msex\\_u & & 0 & 0.096411662 & 0.081675246 & 1.18042696 & 2.378304e-01 & 0.096411662 & 0.046567142\\\\\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{} & age\\_death\\_u & & 0 & 0.022248397 & 0.006037667 & 3.68493256 & 2.287632e-04 & 0.022248397 & 0.148791289\\\\\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{} & pmi\\_u & & 0 & 0.012089715 & 0.007920926 & 1.52630068 & 1.269350e-01 & 0.012089715 & 0.091035199\\\\\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{} & ROS\\_study\\_u & & 0 & -0.160271206 & 0.077289404 & -2.07365043 & 3.811179e-02 & -0.160271206 & -0.081585181\\\\\n", + "\t APOC1\\_DeJager\\_Mic\\_exp & \\textasciitilde{} & chr19\\_44954310\\_T\\_C & a2 & 0 & 0.222252118 & 0.064197073 & 3.46202887 & 5.361197e-04 & 0.222252118 & 0.153117626\\\\\n", + "\t APOC1\\_DeJager\\_Mic\\_exp & \\textasciitilde{} & msex\\_u & & 0 & -0.162289442 & 0.097013047 & -1.67286202 & 9.435446e-02 & -0.162289442 & -0.075572201\\\\\n", + "\t APOC1\\_DeJager\\_Mic\\_exp & \\textasciitilde{} & age\\_death\\_u & & 0 & 0.008908582 & 0.006697564 & 1.33012275 & 1.834778e-01 & 0.008908582 & 0.057439305\\\\\n", + "\t APOC1\\_DeJager\\_Mic\\_exp & \\textasciitilde{} & pmi\\_u & & 0 & 0.002979722 & 0.008675720 & 0.34345532 & 7.312559e-01 & 0.002979722 & 0.021631710\\\\\n", + "\t APOE\\_Mega\\_Mic\\_exp & \\textasciitilde{} & chr19\\_44954310\\_T\\_C & a3 & 0 & 0.214246215 & 0.049139965 & 4.35991797 & 1.301112e-05 & 0.214246215 & 0.155332357\\\\\n", + "\t APOE\\_Mega\\_Mic\\_exp & \\textasciitilde{} & msex\\_u & & 0 & -0.206225524 & 0.073352555 & -2.81142930 & 4.932193e-03 & -0.206225524 & -0.101061028\\\\\n", + "\t APOE\\_Mega\\_Mic\\_exp & \\textasciitilde{} & age\\_death\\_u & & 0 & 0.021995998 & 0.005180015 & 4.24631922 & 2.173110e-05 & 0.021995998 & 0.149249799\\\\\n", + "\t APOE\\_Mega\\_Mic\\_exp & \\textasciitilde{} & pmi\\_u & & 0 & 0.010958560 & 0.005985862 & 1.83074042 & 6.713930e-02 & 0.010958560 & 0.083721704\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & APOC2\\_AC\\_exp & b1 & 0 & -0.067113114 & 0.044270786 & -1.51596844 & 1.295273e-01 & -0.067113114 & -0.062530459\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & APOC1\\_DeJager\\_Mic\\_exp & b2 & 0 & 0.005411238 & 0.067343923 & 0.08035229 & 9.359571e-01 & 0.005411238 & 0.005229487\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & APOE\\_Mega\\_Mic\\_exp & b3 & 0 & 0.074589946 & 0.057965464 & 1.28679977 & 1.981641e-01 & 0.074589946 & 0.068497264\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & chr19\\_44954310\\_T\\_C & cp & 0 & 0.153460718 & 0.044986131 & 3.41128949 & 6.465640e-04 & 0.153460718 & 0.102173656\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & educ & & 0 & 0.001616824 & 0.008607955 & 0.18782909 & 8.510106e-01 & 0.001616824 & 0.005525105\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & apoe4\\_dose & & 0 & 0.738452823 & 0.063747841 & 11.58396604 & 0.000000e+00 & 0.738452823 & 0.336445447\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & msex\\_u & & 0 & -0.044827976 & 0.066111484 & -0.67806641 & 4.977296e-01 & -0.044827976 & -0.020173598\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{} & age\\_death\\_u & & 0 & 0.025268482 & 0.004982007 & 5.07194793 & 3.937641e-07 & 0.025268482 & 0.157449816\\\\\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{}\\textasciitilde{} & APOC1\\_DeJager\\_Mic\\_exp & & 0 & 0.204163338 & 0.053861332 & 3.79053634 & 1.503223e-04 & 0.204163338 & 0.215937577\\\\\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{}\\textasciitilde{} & APOE\\_Mega\\_Mic\\_exp & & 0 & 0.053350021 & 0.041670760 & 1.28027471 & 2.004485e-01 & 0.053350021 & 0.060419187\\\\\n", + "\t APOC1\\_DeJager\\_Mic\\_exp & \\textasciitilde{}\\textasciitilde{} & APOE\\_Mega\\_Mic\\_exp & & 0 & 0.570687290 & 0.050065611 & 11.39878819 & 0.000000e+00 & 0.570687290 & 0.609160996\\\\\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{}\\textasciitilde{} & APOC2\\_AC\\_exp & & 0 & 0.891134248 & 0.051765865 & 17.21470790 & 0.000000e+00 & 0.891134248 & 0.923691964\\\\\n", + "\t APOC1\\_DeJager\\_Mic\\_exp & \\textasciitilde{}\\textasciitilde{} & APOC1\\_DeJager\\_Mic\\_exp & & 0 & 1.003127029 & 0.070995628 & 14.12941980 & 0.000000e+00 & 1.003127029 & 0.966459256\\\\\n", + "\t APOE\\_Mega\\_Mic\\_exp & \\textasciitilde{}\\textasciitilde{} & APOE\\_Mega\\_Mic\\_exp & & 0 & 0.874936011 & 0.045678118 & 19.15437957 & 0.000000e+00 & 0.874936011 & 0.933561426\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{}\\textasciitilde{} & caa\\_neo4 & & 0 & 0.942890582 & 0.041452301 & 22.74639914 & 0.000000e+00 & 0.942890582 & 0.848425616\\\\\n", + "\t chr19\\_44954310\\_T\\_C & \\textasciitilde{}\\textasciitilde{} & chr19\\_44954310\\_T\\_C & & 0 & 0.492641440 & 0.020517822 & 24.01041572 & 0.000000e+00 & 0.492641440 & 1.000000000\\\\\n", + "\t chr19\\_44954310\\_T\\_C & \\textasciitilde{}\\textasciitilde{} & msex\\_u & & 0 & 0.004284430 & 0.009852455 & 0.43485917 & 6.636647e-01 & 0.004284430 & 0.012866773\\\\\n", + "\t \u22ee & \u22ee & \u22ee & \u22ee & \u22ee & \u22ee & \u22ee & \u22ee & \u22ee & \u22ee & \u22ee\\\\\n", + "\t pmi\\_u & \\textasciitilde{}\\textasciitilde{} & apoe4\\_dose & & 0 & -0.072489191 & 0.106530919 & -0.68045213 & 4.962182e-01 & -0.072489191 & -0.0204059262\\\\\n", + "\t ROS\\_study\\_u & \\textasciitilde{}\\textasciitilde{} & ROS\\_study\\_u & & 0 & 0.249992813 & 0.010587449 & 23.61218513 & 0.000000e+00 & 0.249992813 & 1.0000000000\\\\\n", + "\t ROS\\_study\\_u & \\textasciitilde{}\\textasciitilde{} & educ & & 0 & 0.881779752 & 0.060083230 & 14.67597128 & 0.000000e+00 & 0.881779752 & 0.4895475990\\\\\n", + "\t ROS\\_study\\_u & \\textasciitilde{}\\textasciitilde{} & apoe4\\_dose & & 0 & 0.013899952 & 0.007229877 & 1.92257108 & 5.453394e-02 & 0.013899952 & 0.0578807139\\\\\n", + "\t educ & \\textasciitilde{}\\textasciitilde{} & educ & & 0 & 12.977853595 & 0.549871349 & 23.60161811 & 0.000000e+00 & 12.977853595 & 1.0000000000\\\\\n", + "\t educ & \\textasciitilde{}\\textasciitilde{} & apoe4\\_dose & & 0 & 0.131569480 & 0.052108185 & 2.52492924 & 1.157216e-02 & 0.131569480 & 0.0760392910\\\\\n", + "\t apoe4\\_dose & \\textasciitilde{}\\textasciitilde{} & apoe4\\_dose & & 0 & 0.230691359 & 0.009811535 & 23.51225873 & 0.000000e+00 & 0.230691359 & 1.0000000000\\\\\n", + "\t APOC2\\_AC\\_exp & \\textasciitilde{}1 & & & 0 & -2.247494328 & 0.554058952 & -4.05641732 & 4.983119e-05 & -2.247494328 & -2.2881822885\\\\\n", + "\t APOC1\\_DeJager\\_Mic\\_exp & \\textasciitilde{}1 & & & 0 & -0.944532358 & 0.613100154 & -1.54058412 & 1.234180e-01 & -0.944532358 & -0.9271086842\\\\\n", + "\t APOE\\_Mega\\_Mic\\_exp & \\textasciitilde{}1 & & & 0 & -2.160059110 & 0.470261384 & -4.59331594 & 4.362578e-06 & -2.160059110 & -2.2312536265\\\\\n", + "\t caa\\_neo4 & \\textasciitilde{}1 & & & 0 & -1.455052685 & 0.484508372 & -3.00315282 & 2.671982e-03 & -1.455052685 & -1.3802411021\\\\\n", + "\t chr19\\_44954310\\_T\\_C & \\textasciitilde{}1 & & & 0 & 0.875108408 & 0.020670492 & 42.33611863 & 0.000000e+00 & 0.875108408 & 1.2467988293\\\\\n", + "\t msex\\_u & \\textasciitilde{}1 & & & 0 & 0.342087132 & 0.014050995 & 24.34611471 & 0.000000e+00 & 0.342087132 & 0.7210721863\\\\\n", + "\t age\\_death\\_u & \\textasciitilde{}1 & & & 0 & 88.952365319 & 0.194552152 & 457.21604325 & 0.000000e+00 & 88.952365319 & 13.5416233228\\\\\n", + "\t pmi\\_u & \\textasciitilde{}1 & & & 0 & 8.744433158 & 0.219046846 & 39.92037917 & 0.000000e+00 & 8.744433158 & 1.1823073391\\\\\n", + "\t ROS\\_study\\_u & \\textasciitilde{}1 & & & 0 & 0.499585373 & 0.014970774 & 33.37071166 & 0.000000e+00 & 0.499585373 & 0.9991851076\\\\\n", + "\t educ & \\textasciitilde{}1 & & & 0 & 16.368059740 & 0.107901012 & 151.69514582 & 0.000000e+00 & 16.368059740 & 4.5435547552\\\\\n", + "\t apoe4\\_dose & \\textasciitilde{}1 & & & 0 & 0.272248442 & 0.014433953 & 18.86166839 & 0.000000e+00 & 0.272248442 & 0.5668259695\\\\\n", + "\t ind1 & := & a1*b1 & ind1 & 0 & -0.018385882 & 0.012687708 & -1.44910978 & 1.473069e-01 & -0.018385882 & -0.0122412616\\\\\n", + "\t ind2 & := & a2*b2 & ind2 & 0 & 0.001202659 & 0.015003437 & 0.08015891 & 9.361109e-01 & 0.001202659 & 0.0008007266\\\\\n", + "\t ind3 & := & a3*b3 & ind3 & 0 & 0.015980614 & 0.012991206 & 1.23011005 & 2.186559e-01 & 0.015980614 & 0.0106398415\\\\\n", + "\t total\\_indirect & := & ind1+ind2+ind3 & total\\_indirect & 0 & -0.001202609 & 0.014746593 & -0.08155168 & 9.350032e-01 & -0.001202609 & -0.0008006935\\\\\n", + "\t total & := & cp+total\\_indirect & total & 0 & 0.152258108 & 0.043017415 & 3.53945280 & 4.009575e-04 & 0.152258108 & 0.1013729623\\\\\n", + "\t prop1 & := & ind1/total & prop1 & 0 & -0.120754699 & 0.090636596 & -1.33229517 & 1.827632e-01 & -0.120754699 & -0.1207546994\\\\\n", + "\t prop2 & := & ind2/total & prop2 & 0 & 0.007898818 & 0.098570834 & 0.08013342 & 9.361311e-01 & 0.007898818 & 0.0078988185\\\\\n", + "\t prop3 & := & ind3/total & prop3 & 0 & 0.104957390 & 0.089646486 & 1.17079200 & 2.416824e-01 & 0.104957390 & 0.1049573897\\\\\n", + "\t prop\\_total & := & total\\_indirect/total & prop\\_total & 0 & -0.007898491 & 0.096956004 & -0.08146470 & 9.350724e-01 & -0.007898491 & -0.0078984913\\\\\n", + "\t diff\\_1\\_2 & := & ind1-ind2 & diff\\_1\\_2 & 0 & -0.019588541 & 0.022384979 & -0.87507525 & 3.815330e-01 & -0.019588541 & -0.0130419882\\\\\n", + "\t diff\\_1\\_3 & := & ind1-ind3 & diff\\_1\\_3 & 0 & -0.034366496 & 0.016740568 & -2.05288713 & 4.008353e-02 & -0.034366496 & -0.0228811031\\\\\n", + "\t diff\\_2\\_3 & := & ind2-ind3 & diff\\_2\\_3 & 0 & -0.014777954 & 0.025802358 & -0.57273659 & 5.668231e-01 & -0.014777954 & -0.0098391149\\\\\n", + "\\end{tabular}\n", + "\n", + "\\end{description}\n" + ], + "text/markdown": [ + "$header\n", + ": $lavaan.version\n", + ": '0.6-21'\n", + "$sam.approach\n", + ": FALSE\n", + "$optim.method\n", + ": 'nlminb'\n", + "$optim.iterations\n", + ": 157\n", + "$optim.converged\n", + ": TRUE\n", + "\n", + "\n", + "\n", + "$optim\n", + ": $estimator\n", + ": 'ML'\n", + "$estimator.args\n", + ": \n", + "\n", + "\n", + "$optim.method\n", + ": 'nlminb'\n", + "$npar\n", + ": 67\n", + "$eq.constraints\n", + ": FALSE\n", + "$nrow.ceq.jac\n", + ": 0\n", + "$nrow.cin.jac\n", + ": 0\n", + "$nrow.con.jac\n", + ": 0\n", + "$con.jac.rank\n", + ": 0\n", + "\n", + "\n", + "\n", + "$data\n", + ": $ngroups\n", + ": 1\n", + "$nobs\n", + ": 1153\n", + "$npatterns\n", + ": 24\n", + "\n", + "\n", + "\n", + "$test\n", + ": **$standard** = $test\n", + ": 'standard'\n", + "$stat\n", + ": 18.9406014384593\n", + "$stat.group\n", + ": 18.9406014384593\n", + "$df\n", + ": 10\n", + "$refdistr\n", + ": 'chisq'\n", + "$pvalue\n", + ": 0.0410236526919417\n", + "\n", + "\n", + "\n", + "$fit\n", + ": npar\n", + ": 67fmin\n", + ": 0.00821361727600145chisq\n", + ": 18.9406014384593df\n", + ": 10pvalue\n", + ": 0.0410236526919417baseline.chisq\n", + ": 474.446254681919baseline.df\n", + ": 34baseline.pvalue\n", + ": 0cfi\n", + ": 0.979701038790951tli\n", + ": 0.930983531889232cfi.robust\n", + ": 0.987503183690851tli.robust\n", + ": 0.957510824548892logl\n", + ": -17751.6203072975unrestricted.logl\n", + ": -17742.1500065782aic\n", + ": 35637.2406145949bic\n", + ": 35975.598823453ntotal\n", + ": 1153bic2\n", + ": 35762.7853346997rmsea\n", + ": 0.0278463779792266rmsea.ci.lower\n", + ": 0.00550322256564479rmsea.ci.upper\n", + ": 0.0468343641343418rmsea.ci.level\n", + ": 0.9rmsea.pvalue\n", + ": 0.974584116220918rmsea.close.h0\n", + ": 0.05rmsea.notclose.pvalue\n", + ": 3.4285725283043e-07rmsea.notclose.h0\n", + ": 0.08rmsea.robust\n", + ": 0.0322299565077659rmsea.ci.lower.robust\n", + ": 0rmsea.ci.upper.robust\n", + ": 0.0613263289047915rmsea.pvalue.robust\n", + ": 0.821497221654828rmsea.notclose.pvalue.robust\n", + ": 0.00194045456329975srmr\n", + ": 0.0209142509945356\n", + "\n", + "\n", + "$pe\n", + ": \n", + "A data.frame: 79 \u00d7 11\n", + "\n", + "| lhs <chr> | op <chr> | rhs <chr> | label <chr> | exo <int> | est <dbl> | se <dbl> | z <dbl> | pvalue <dbl> | std.lv <dbl> | std.all <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|\n", + "| APOC2_AC_exp | ~ | chr19_44954310_T_C | a1 | 0 | 0.273953641 | 0.056139981 | 4.87983137 | 1.061766e-06 | 0.273953641 | 0.195764781 |\n", + "| APOC2_AC_exp | ~ | msex_u | | 0 | 0.096411662 | 0.081675246 | 1.18042696 | 2.378304e-01 | 0.096411662 | 0.046567142 |\n", + "| APOC2_AC_exp | ~ | age_death_u | | 0 | 0.022248397 | 0.006037667 | 3.68493256 | 2.287632e-04 | 0.022248397 | 0.148791289 |\n", + "| APOC2_AC_exp | ~ | pmi_u | | 0 | 0.012089715 | 0.007920926 | 1.52630068 | 1.269350e-01 | 0.012089715 | 0.091035199 |\n", + "| APOC2_AC_exp | ~ | ROS_study_u | | 0 | -0.160271206 | 0.077289404 | -2.07365043 | 3.811179e-02 | -0.160271206 | -0.081585181 |\n", + "| APOC1_DeJager_Mic_exp | ~ | chr19_44954310_T_C | a2 | 0 | 0.222252118 | 0.064197073 | 3.46202887 | 5.361197e-04 | 0.222252118 | 0.153117626 |\n", + "| APOC1_DeJager_Mic_exp | ~ | msex_u | | 0 | -0.162289442 | 0.097013047 | -1.67286202 | 9.435446e-02 | -0.162289442 | -0.075572201 |\n", + "| APOC1_DeJager_Mic_exp | ~ | age_death_u | | 0 | 0.008908582 | 0.006697564 | 1.33012275 | 1.834778e-01 | 0.008908582 | 0.057439305 |\n", + "| APOC1_DeJager_Mic_exp | ~ | pmi_u | | 0 | 0.002979722 | 0.008675720 | 0.34345532 | 7.312559e-01 | 0.002979722 | 0.021631710 |\n", + "| APOE_Mega_Mic_exp | ~ | chr19_44954310_T_C | a3 | 0 | 0.214246215 | 0.049139965 | 4.35991797 | 1.301112e-05 | 0.214246215 | 0.155332357 |\n", + "| APOE_Mega_Mic_exp | ~ | msex_u | | 0 | -0.206225524 | 0.073352555 | -2.81142930 | 4.932193e-03 | -0.206225524 | -0.101061028 |\n", + "| APOE_Mega_Mic_exp | ~ | age_death_u | | 0 | 0.021995998 | 0.005180015 | 4.24631922 | 2.173110e-05 | 0.021995998 | 0.149249799 |\n", + "| APOE_Mega_Mic_exp | ~ | pmi_u | | 0 | 0.010958560 | 0.005985862 | 1.83074042 | 6.713930e-02 | 0.010958560 | 0.083721704 |\n", + "| caa_neo4 | ~ | APOC2_AC_exp | b1 | 0 | -0.067113114 | 0.044270786 | -1.51596844 | 1.295273e-01 | -0.067113114 | -0.062530459 |\n", + "| caa_neo4 | ~ | APOC1_DeJager_Mic_exp | b2 | 0 | 0.005411238 | 0.067343923 | 0.08035229 | 9.359571e-01 | 0.005411238 | 0.005229487 |\n", + "| caa_neo4 | ~ | APOE_Mega_Mic_exp | b3 | 0 | 0.074589946 | 0.057965464 | 1.28679977 | 1.981641e-01 | 0.074589946 | 0.068497264 |\n", + "| caa_neo4 | ~ | chr19_44954310_T_C | cp | 0 | 0.153460718 | 0.044986131 | 3.41128949 | 6.465640e-04 | 0.153460718 | 0.102173656 |\n", + "| caa_neo4 | ~ | educ | | 0 | 0.001616824 | 0.008607955 | 0.18782909 | 8.510106e-01 | 0.001616824 | 0.005525105 |\n", + "| caa_neo4 | ~ | apoe4_dose | | 0 | 0.738452823 | 0.063747841 | 11.58396604 | 0.000000e+00 | 0.738452823 | 0.336445447 |\n", + "| caa_neo4 | ~ | msex_u | | 0 | -0.044827976 | 0.066111484 | -0.67806641 | 4.977296e-01 | -0.044827976 | -0.020173598 |\n", + "| caa_neo4 | ~ | age_death_u | | 0 | 0.025268482 | 0.004982007 | 5.07194793 | 3.937641e-07 | 0.025268482 | 0.157449816 |\n", + "| APOC2_AC_exp | ~~ | APOC1_DeJager_Mic_exp | | 0 | 0.204163338 | 0.053861332 | 3.79053634 | 1.503223e-04 | 0.204163338 | 0.215937577 |\n", + "| APOC2_AC_exp | ~~ | APOE_Mega_Mic_exp | | 0 | 0.053350021 | 0.041670760 | 1.28027471 | 2.004485e-01 | 0.053350021 | 0.060419187 |\n", + "| APOC1_DeJager_Mic_exp | ~~ | APOE_Mega_Mic_exp | | 0 | 0.570687290 | 0.050065611 | 11.39878819 | 0.000000e+00 | 0.570687290 | 0.609160996 |\n", + "| APOC2_AC_exp | ~~ | APOC2_AC_exp | | 0 | 0.891134248 | 0.051765865 | 17.21470790 | 0.000000e+00 | 0.891134248 | 0.923691964 |\n", + "| APOC1_DeJager_Mic_exp | ~~ | APOC1_DeJager_Mic_exp | | 0 | 1.003127029 | 0.070995628 | 14.12941980 | 0.000000e+00 | 1.003127029 | 0.966459256 |\n", + "| APOE_Mega_Mic_exp | ~~ | APOE_Mega_Mic_exp | | 0 | 0.874936011 | 0.045678118 | 19.15437957 | 0.000000e+00 | 0.874936011 | 0.933561426 |\n", + "| caa_neo4 | ~~ | caa_neo4 | | 0 | 0.942890582 | 0.041452301 | 22.74639914 | 0.000000e+00 | 0.942890582 | 0.848425616 |\n", + "| chr19_44954310_T_C | ~~ | chr19_44954310_T_C | | 0 | 0.492641440 | 0.020517822 | 24.01041572 | 0.000000e+00 | 0.492641440 | 1.000000000 |\n", + "| chr19_44954310_T_C | ~~ | msex_u | | 0 | 0.004284430 | 0.009852455 | 0.43485917 | 6.636647e-01 | 0.004284430 | 0.012866773 |\n", + "| \u22ee | \u22ee | \u22ee | \u22ee | \u22ee | \u22ee | \u22ee | \u22ee | \u22ee | \u22ee | \u22ee |\n", + "| pmi_u | ~~ | apoe4_dose | | 0 | -0.072489191 | 0.106530919 | -0.68045213 | 4.962182e-01 | -0.072489191 | -0.0204059262 |\n", + "| ROS_study_u | ~~ | ROS_study_u | | 0 | 0.249992813 | 0.010587449 | 23.61218513 | 0.000000e+00 | 0.249992813 | 1.0000000000 |\n", + "| ROS_study_u | ~~ | educ | | 0 | 0.881779752 | 0.060083230 | 14.67597128 | 0.000000e+00 | 0.881779752 | 0.4895475990 |\n", + "| ROS_study_u | ~~ | apoe4_dose | | 0 | 0.013899952 | 0.007229877 | 1.92257108 | 5.453394e-02 | 0.013899952 | 0.0578807139 |\n", + "| educ | ~~ | educ | | 0 | 12.977853595 | 0.549871349 | 23.60161811 | 0.000000e+00 | 12.977853595 | 1.0000000000 |\n", + "| educ | ~~ | apoe4_dose | | 0 | 0.131569480 | 0.052108185 | 2.52492924 | 1.157216e-02 | 0.131569480 | 0.0760392910 |\n", + "| apoe4_dose | ~~ | apoe4_dose | | 0 | 0.230691359 | 0.009811535 | 23.51225873 | 0.000000e+00 | 0.230691359 | 1.0000000000 |\n", + "| APOC2_AC_exp | ~1 | | | 0 | -2.247494328 | 0.554058952 | -4.05641732 | 4.983119e-05 | -2.247494328 | -2.2881822885 |\n", + "| APOC1_DeJager_Mic_exp | ~1 | | | 0 | -0.944532358 | 0.613100154 | -1.54058412 | 1.234180e-01 | -0.944532358 | -0.9271086842 |\n", + "| APOE_Mega_Mic_exp | ~1 | | | 0 | -2.160059110 | 0.470261384 | -4.59331594 | 4.362578e-06 | -2.160059110 | -2.2312536265 |\n", + "| caa_neo4 | ~1 | | | 0 | -1.455052685 | 0.484508372 | -3.00315282 | 2.671982e-03 | -1.455052685 | -1.3802411021 |\n", + "| chr19_44954310_T_C | ~1 | | | 0 | 0.875108408 | 0.020670492 | 42.33611863 | 0.000000e+00 | 0.875108408 | 1.2467988293 |\n", + "| msex_u | ~1 | | | 0 | 0.342087132 | 0.014050995 | 24.34611471 | 0.000000e+00 | 0.342087132 | 0.7210721863 |\n", + "| age_death_u | ~1 | | | 0 | 88.952365319 | 0.194552152 | 457.21604325 | 0.000000e+00 | 88.952365319 | 13.5416233228 |\n", + "| pmi_u | ~1 | | | 0 | 8.744433158 | 0.219046846 | 39.92037917 | 0.000000e+00 | 8.744433158 | 1.1823073391 |\n", + "| ROS_study_u | ~1 | | | 0 | 0.499585373 | 0.014970774 | 33.37071166 | 0.000000e+00 | 0.499585373 | 0.9991851076 |\n", + "| educ | ~1 | | | 0 | 16.368059740 | 0.107901012 | 151.69514582 | 0.000000e+00 | 16.368059740 | 4.5435547552 |\n", + "| apoe4_dose | ~1 | | | 0 | 0.272248442 | 0.014433953 | 18.86166839 | 0.000000e+00 | 0.272248442 | 0.5668259695 |\n", + "| ind1 | := | a1*b1 | ind1 | 0 | -0.018385882 | 0.012687708 | -1.44910978 | 1.473069e-01 | -0.018385882 | -0.0122412616 |\n", + "| ind2 | := | a2*b2 | ind2 | 0 | 0.001202659 | 0.015003437 | 0.08015891 | 9.361109e-01 | 0.001202659 | 0.0008007266 |\n", + "| ind3 | := | a3*b3 | ind3 | 0 | 0.015980614 | 0.012991206 | 1.23011005 | 2.186559e-01 | 0.015980614 | 0.0106398415 |\n", + "| total_indirect | := | ind1+ind2+ind3 | total_indirect | 0 | -0.001202609 | 0.014746593 | -0.08155168 | 9.350032e-01 | -0.001202609 | -0.0008006935 |\n", + "| total | := | cp+total_indirect | total | 0 | 0.152258108 | 0.043017415 | 3.53945280 | 4.009575e-04 | 0.152258108 | 0.1013729623 |\n", + "| prop1 | := | ind1/total | prop1 | 0 | -0.120754699 | 0.090636596 | -1.33229517 | 1.827632e-01 | -0.120754699 | -0.1207546994 |\n", + "| prop2 | := | ind2/total | prop2 | 0 | 0.007898818 | 0.098570834 | 0.08013342 | 9.361311e-01 | 0.007898818 | 0.0078988185 |\n", + "| prop3 | := | ind3/total | prop3 | 0 | 0.104957390 | 0.089646486 | 1.17079200 | 2.416824e-01 | 0.104957390 | 0.1049573897 |\n", + "| prop_total | := | total_indirect/total | prop_total | 0 | -0.007898491 | 0.096956004 | -0.08146470 | 9.350724e-01 | -0.007898491 | -0.0078984913 |\n", + "| diff_1_2 | := | ind1-ind2 | diff_1_2 | 0 | -0.019588541 | 0.022384979 | -0.87507525 | 3.815330e-01 | -0.019588541 | -0.0130419882 |\n", + "| diff_1_3 | := | ind1-ind3 | diff_1_3 | 0 | -0.034366496 | 0.016740568 | -2.05288713 | 4.008353e-02 | -0.034366496 | -0.0228811031 |\n", + "| diff_2_3 | := | ind2-ind3 | diff_2_3 | 0 | -0.014777954 | 0.025802358 | -0.57273659 | 5.668231e-01 | -0.014777954 | -0.0098391149 |\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "lavaan 0.6-21 ended normally after 157 iterations\n", + "\n", + " Estimator ML\n", + " Optimization method NLMINB\n", + " Number of model parameters 67\n", + "\n", + " Number of observations 1153\n", + " Number of missing patterns 24\n", + "\n", + "Model Test User Model:\n", + " \n", + " Test statistic 18.941\n", + " Degrees of freedom 10\n", + " P-value (Chi-square) 0.041\n", + "\n", + "Model Test Baseline Model:\n", + "\n", + " Test statistic 474.446\n", + " Degrees of freedom 34\n", + " P-value 0.000\n", + "\n", + "User Model versus Baseline Model:\n", + "\n", + " Comparative Fit Index (CFI) 0.980\n", + " Tucker-Lewis Index (TLI) 0.931\n", + " \n", + " Robust Comparative Fit Index (CFI) 0.988\n", + " Robust Tucker-Lewis Index (TLI) 0.958\n", + "\n", + "Loglikelihood and Information Criteria:\n", + "\n", + " Loglikelihood user model (H0) -17751.620\n", + " Loglikelihood unrestricted model (H1) -17742.150\n", + " \n", + " Akaike (AIC) 35637.241\n", + " Bayesian (BIC) 35975.599\n", + " Sample-size adjusted Bayesian (SABIC) 35762.785\n", + "\n", + "Root Mean Square Error of Approximation:\n", + "\n", + " RMSEA 0.028\n", + " 90 Percent confidence interval - lower 0.006\n", + " 90 Percent confidence interval - upper 0.047\n", + " P-value H_0: RMSEA <= 0.050 0.975\n", + " P-value H_0: RMSEA >= 0.080 0.000\n", + " \n", + " Robust RMSEA 0.032\n", + " 90 Percent confidence interval - lower 0.000\n", + " 90 Percent confidence interval - upper 0.061\n", + " P-value H_0: Robust RMSEA <= 0.050 0.821\n", + " P-value H_0: Robust RMSEA >= 0.080 0.002\n", + "\n", + "Standardized Root Mean Square Residual:\n", + "\n", + " SRMR 0.021\n", + "\n", + "Parameter Estimates:\n", + "\n", + " Standard errors Standard\n", + " Information Observed\n", + " Observed information based on Hessian\n", + "\n", + "Regressions:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv Std.all\n", + " APOC2_AC_exp ~ \n", + " c19_44954 (a1) 0.274 0.056 4.880 0.000 0.274 0.196\n", + " msex_u 0.096 0.082 1.180 0.238 0.096 0.047\n", + " age_deth_ 0.022 0.006 3.685 0.000 0.022 0.149\n", + " pmi_u 0.012 0.008 1.526 0.127 0.012 0.091\n", + " ROS_stdy_ -0.160 0.077 -2.074 0.038 -0.160 -0.082\n", + " APOC1_DeJager_Mic_exp ~ \n", + " c19_44954 (a2) 0.222 0.064 3.462 0.001 0.222 0.153\n", + " msex_u -0.162 0.097 -1.673 0.094 -0.162 -0.076\n", + " age_deth_ 0.009 0.007 1.330 0.183 0.009 0.057\n", + " pmi_u 0.003 0.009 0.343 0.731 0.003 0.022\n", + " APOE_Mega_Mic_exp ~ \n", + " c19_44954 (a3) 0.214 0.049 4.360 0.000 0.214 0.155\n", + " msex_u -0.206 0.073 -2.811 0.005 -0.206 -0.101\n", + " age_deth_ 0.022 0.005 4.246 0.000 0.022 0.149\n", + " pmi_u 0.011 0.006 1.831 0.067 0.011 0.084\n", + " caa_neo4 ~ \n", + " APOC2_AC_ (b1) -0.067 0.044 -1.516 0.130 -0.067 -0.063\n", + " APOC1_DJ_ (b2) 0.005 0.067 0.080 0.936 0.005 0.005\n", + " APOE_M_M_ (b3) 0.075 0.058 1.287 0.198 0.075 0.068\n", + " c19_44954 (cp) 0.153 0.045 3.411 0.001 0.153 0.102\n", + " educ 0.002 0.009 0.188 0.851 0.002 0.006\n", + " apoe4_dos 0.738 0.064 11.584 0.000 0.738 0.336\n", + " msex_u -0.045 0.066 -0.678 0.498 -0.045 -0.020\n", + " age_deth_ 0.025 0.005 5.072 0.000 0.025 0.157\n", + "\n", + "Covariances:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv\n", + " .APOC2_AC_exp ~~ \n", + " .APOC1_DJgr_Mc_ 0.204 0.054 3.791 0.000 0.204\n", + " .APOE_Meg_Mc_xp 0.053 0.042 1.280 0.200 0.053\n", + " .APOC1_DeJager_Mic_exp ~~ \n", + " .APOE_Meg_Mc_xp 0.571 0.050 11.399 0.000 0.571\n", + " chr19_44954310_T_C ~~ \n", + " msex_u 0.004 0.010 0.435 0.664 0.004\n", + " age_death_u -0.043 0.136 -0.319 0.750 -0.043\n", + " pmi_u -0.389 0.154 -2.526 0.012 -0.389\n", + " ROS_study_u -0.006 0.011 -0.606 0.544 -0.006\n", + " educ 0.026 0.076 0.340 0.734 0.026\n", + " apoe4_dose 0.022 0.010 2.135 0.033 0.022\n", + " msex_u ~~ \n", + " age_death_u -0.582 0.094 -6.202 0.000 -0.582\n", + " pmi_u 0.092 0.104 0.883 0.377 0.092\n", + " ROS_study_u 0.005 0.007 0.772 0.440 0.005\n", + " educ 0.344 0.052 6.607 0.000 0.344\n", + " apoe4_dose -0.000 0.007 -0.068 0.946 -0.000\n", + " age_death_u ~~ \n", + " pmi_u 0.792 1.439 0.551 0.582 0.792\n", + " ROS_study_u -0.464 0.099 -4.676 0.000 -0.464\n", + " educ -3.173 0.715 -4.439 0.000 -3.173\n", + " apoe4_dose -0.345 0.095 -3.625 0.000 -0.345\n", + " pmi_u ~~ \n", + " ROS_study_u 0.047 0.110 0.429 0.668 0.047\n", + " educ 0.099 0.794 0.124 0.901 0.099\n", + " apoe4_dose -0.072 0.107 -0.680 0.496 -0.072\n", + " ROS_study_u ~~ \n", + " educ 0.882 0.060 14.676 0.000 0.882\n", + " apoe4_dose 0.014 0.007 1.923 0.055 0.014\n", + " educ ~~ \n", + " apoe4_dose 0.132 0.052 2.525 0.012 0.132\n", + " Std.all\n", + " \n", + " 0.216\n", + " 0.060\n", + " \n", + " 0.609\n", + " \n", + " 0.013\n", + " -0.009\n", + " -0.075\n", + " -0.018\n", + " 0.010\n", + " 0.064\n", + " \n", + " -0.187\n", + " 0.026\n", + " 0.023\n", + " 0.201\n", + " -0.002\n", + " \n", + " 0.016\n", + " -0.141\n", + " -0.134\n", + " -0.109\n", + " \n", + " 0.013\n", + " 0.004\n", + " -0.020\n", + " \n", + " 0.490\n", + " 0.058\n", + " \n", + " 0.076\n", + "\n", + "Intercepts:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv Std.all\n", + " .APOC2_AC_exp -2.247 0.554 -4.056 0.000 -2.247 -2.288\n", + " .APOC1_DJgr_Mc_ -0.945 0.613 -1.541 0.123 -0.945 -0.927\n", + " .APOE_Meg_Mc_xp -2.160 0.470 -4.593 0.000 -2.160 -2.231\n", + " .caa_neo4 -1.455 0.485 -3.003 0.003 -1.455 -1.380\n", + " c19_44954310_T 0.875 0.021 42.336 0.000 0.875 1.247\n", + " msex_u 0.342 0.014 24.346 0.000 0.342 0.721\n", + " age_death_u 88.952 0.195 457.216 0.000 88.952 13.542\n", + " pmi_u 8.744 0.219 39.920 0.000 8.744 1.182\n", + " ROS_study_u 0.500 0.015 33.371 0.000 0.500 0.999\n", + " educ 16.368 0.108 151.695 0.000 16.368 4.544\n", + " apoe4_dose 0.272 0.014 18.862 0.000 0.272 0.567\n", + "\n", + "Variances:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv Std.all\n", + " .APOC2_AC_exp 0.891 0.052 17.215 0.000 0.891 0.924\n", + " .APOC1_DJgr_Mc_ 1.003 0.071 14.129 0.000 1.003 0.966\n", + " .APOE_Meg_Mc_xp 0.875 0.046 19.154 0.000 0.875 0.934\n", + " .caa_neo4 0.943 0.041 22.746 0.000 0.943 0.848\n", + " c19_44954310_T 0.493 0.021 24.010 0.000 0.493 1.000\n", + " msex_u 0.225 0.009 23.875 0.000 0.225 1.000\n", + " age_death_u 43.149 1.807 23.875 0.000 43.149 1.000\n", + " pmi_u 54.702 2.291 23.875 0.000 54.702 1.000\n", + " ROS_study_u 0.250 0.011 23.612 0.000 0.250 1.000\n", + " educ 12.978 0.550 23.602 0.000 12.978 1.000\n", + " apoe4_dose 0.231 0.010 23.512 0.000 0.231 1.000\n", + "\n", + "Defined Parameters:\n", + " Estimate Std.Err z-value P(>|z|) Std.lv Std.all\n", + " ind1 -0.018 0.013 -1.449 0.147 -0.018 -0.012\n", + " ind2 0.001 0.015 0.080 0.936 0.001 0.001\n", + " ind3 0.016 0.013 1.230 0.219 0.016 0.011\n", + " total_indirect -0.001 0.015 -0.082 0.935 -0.001 -0.001\n", + " total 0.152 0.043 3.539 0.000 0.152 0.101\n", + " prop1 -0.121 0.091 -1.332 0.183 -0.121 -0.121\n", + " prop2 0.008 0.099 0.080 0.936 0.008 0.008\n", + " prop3 0.105 0.090 1.171 0.242 0.105 0.105\n", + " prop_total -0.008 0.097 -0.081 0.935 -0.008 -0.008\n", + " diff_1_2 -0.020 0.022 -0.875 0.382 -0.020 -0.013\n", + " diff_1_3 -0.034 0.017 -2.053 0.040 -0.034 -0.023\n", + " diff_2_3 -0.015 0.026 -0.573 0.567 -0.015 -0.010\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# ============================================================\n# Sensitivity Analysis: Extract results and save\n# ============================================================\nif (!is.null(fit_sens) && lavInspect(fit_sens, \"converged\")) {\n pe_sens <- parameterEstimates(fit_sens, ci=TRUE)\n N_sens <- lavInspect(fit_sens, \"nobs\")\n\n # Contrast labels\n sens_contrast_labels <- c()\n for (i in 1:(n_med_sens-1)) {\n for (j in (i+1):n_med_sens) {\n sens_contrast_labels <- c(sens_contrast_labels, paste0(\"diff_\", i, \"_\", j))\n }\n }\n\n sens_all_labels <- c(paste0(\"a\", 1:n_med_sens), paste0(\"b\", 1:n_med_sens), \"cp\",\n paste0(\"ind\", 1:n_med_sens), \"total_indirect\", \"total\",\n paste0(\"prop\", 1:n_med_sens), \"prop_total\", sens_contrast_labels)\n\n sens_key <- pe_sens[pe_sens$label %in% sens_all_labels, ]\n sens_key$method <- \"FIML_sensitivity_3med\"\n sens_key$N <- N_sens\n sens_key$exposure <- EXPOSURE\n sens_key$direction <- \"D1\"\n\n # Readable labels\n sens_label_desc <- c(\n setNames(paste0(\"a\", 1:n_med_sens, \" (SNP->\", SENS_MEDIATOR_SHORT, \")\"), paste0(\"a\", 1:n_med_sens)),\n setNames(paste0(\"b\", 1:n_med_sens, \" (\", SENS_MEDIATOR_SHORT, \"->caa_neo4)\"), paste0(\"b\", 1:n_med_sens)),\n c(cp=\"cp (SNP->caa_neo4 direct)\"),\n setNames(paste0(\"ind\", 1:n_med_sens, \" (via \", SENS_MEDIATOR_SHORT, \")\"), paste0(\"ind\", 1:n_med_sens)),\n c(total_indirect=\"total_indirect\", total=\"total\"),\n setNames(paste0(\"prop\", 1:n_med_sens, \" (via \", SENS_MEDIATOR_SHORT, \")\"), paste0(\"prop\", 1:n_med_sens)),\n c(prop_total=\"prop_total\"),\n setNames(sens_contrast_labels, sens_contrast_labels)\n )\n sens_key$label_desc <- sens_label_desc[sens_key$label]\n sens_key$label_desc[is.na(sens_key$label_desc)] <- sens_key$label[is.na(sens_key$label_desc)]\n\n # Save CSV\n write.csv(sens_key, file.path(SENS_DIR, \"fiml_sensitivity_3med_all_paths.csv\"), row.names=FALSE)\n\n # Print key results\n cat(\"\\n=== Sensitivity Analysis: FIML Key Results (N =\", N_sens, \", 3 mediators) ===\\n\")\n print(sens_key[, c(\"label\", \"label_desc\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"pvalue\", \"N\")])\n\n # ----- Fit measures -----\n fm_sens <- fitMeasures(fit_sens, c(\"chisq\", \"df\", \"pvalue\", \"cfi\", \"tli\", \"rmsea\", \"srmr\", \"aic\", \"bic\"))\n fm_df <- data.frame(measure=names(fm_sens), value=as.numeric(fm_sens), model=\"3-mediator sensitivity\")\n write.csv(fm_df, file.path(SENS_DIR, \"fiml_sensitivity_3med_fit_measures.csv\"), row.names=FALSE)\n cat(\"\\n=== Fit Measures (3-mediator sensitivity) ===\\n\")\n print(fm_df)\n\n # ----- Residual correlations between mediators -----\n cat(\"\\n=== Residual Correlations Between Mediators ===\\n\")\n res_cov <- pe_sens[pe_sens$op == \"~~\" & pe_sens$lhs %in% SENS_MEDIATORS & pe_sens$rhs %in% SENS_MEDIATORS & pe_sens$lhs != pe_sens$rhs, ]\n if (nrow(res_cov) > 0) {\n print(res_cov[, c(\"lhs\", \"rhs\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"pvalue\")])\n }\n} else {\n cat(\"Skipping result extraction -- model did not converge.\\n\")\n}\n" + ], + "execution_count": 5, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Sensitivity Analysis: FIML Key Results (N = 1153 , 3 mediators) ===\n", + " label label_desc est se ci.lower ci.upper\n", + "1 a1 a1 (SNP->APOC2) 0.274 0.056 0.164 0.384\n", + "6 a2 a2 (SNP->APOC1) 0.222 0.064 0.096 0.348\n", + "10 a3 a3 (SNP->APOE) 0.214 0.049 0.118 0.311\n", + "14 b1 b1 (APOC2->caa_neo4) -0.067 0.044 -0.154 0.020\n", + "15 b2 b2 (APOC1->caa_neo4) 0.005 0.067 -0.127 0.137\n", + "16 b3 b3 (APOE->caa_neo4) 0.075 0.058 -0.039 0.188\n", + "17 cp cp (SNP->caa_neo4 direct) 0.153 0.045 0.065 0.242\n", + "68 ind1 ind1 (via APOC2) -0.018 0.013 -0.043 0.006\n", + "69 ind2 ind2 (via APOC1) 0.001 0.015 -0.028 0.031\n", + "70 ind3 ind3 (via APOE) 0.016 0.013 -0.009 0.041\n", + "71 total_indirect total_indirect -0.001 0.015 -0.030 0.028\n", + "72 total total 0.152 0.043 0.068 0.237\n", + "73 prop1 prop1 (via APOC2) -0.121 0.091 -0.298 0.057\n", + "74 prop2 prop2 (via APOC1) 0.008 0.099 -0.185 0.201\n", + "75 prop3 prop3 (via APOE) 0.105 0.090 -0.071 0.281\n", + "76 prop_total prop_total -0.008 0.097 -0.198 0.182\n", + "77 diff_1_2 diff_1_2 -0.020 0.022 -0.063 0.024\n", + "78 diff_1_3 diff_1_3 -0.034 0.017 -0.067 -0.002\n", + "79 diff_2_3 diff_2_3 -0.015 0.026 -0.065 0.036\n", + " pvalue N\n", + "1 0.000 1153\n", + "6 0.001 1153\n", + "10 0.000 1153\n", + "14 0.130 1153\n", + "15 0.936 1153\n", + "16 0.198 1153\n", + "17 0.001 1153\n", + "68 0.147 1153\n", + "69 0.936 1153\n", + "70 0.219 1153\n", + "71 0.935 1153\n", + "72 0.000 1153\n", + "73 0.183 1153\n", + "74 0.936 1153\n", + "75 0.242 1153\n", + "76 0.935 1153\n", + "77 0.382 1153\n", + "78 0.040 1153\n", + "79 0.567 1153\n", + "\n", + "=== Fit Measures (3-mediator sensitivity) ===\n", + " measure value model\n", + "1 chisq 1.894060e+01 3-mediator sensitivity\n", + "2 df 1.000000e+01 3-mediator sensitivity\n", + "3 pvalue 4.102365e-02 3-mediator sensitivity\n", + "4 cfi 9.797010e-01 3-mediator sensitivity\n", + "5 tli 9.309835e-01 3-mediator sensitivity\n", + "6 rmsea 2.784638e-02 3-mediator sensitivity\n", + "7 srmr 2.091425e-02 3-mediator sensitivity\n", + "8 aic 3.563724e+04 3-mediator sensitivity\n", + "9 bic 3.597560e+04 3-mediator sensitivity\n", + "\n", + "=== Residual Correlations Between Mediators ===\n", + " lhs rhs est se ci.lower ci.upper\n", + "22 APOC2_AC_exp APOC1_DeJager_Mic_exp 0.204 0.054 0.099 0.310\n", + "23 APOC2_AC_exp APOE_Mega_Mic_exp 0.053 0.042 -0.028 0.135\n", + "24 APOC1_DeJager_Mic_exp APOE_Mega_Mic_exp 0.571 0.050 0.473 0.669\n", + " pvalue\n", + "22 0.0\n", + "23 0.2\n", + "24 0.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# ============================================================\n# Sensitivity Analysis: Comparison with 4-mediator model + Forest Plot\n# ============================================================\nif (!is.null(fit_sens) && lavInspect(fit_sens, \"converged\")) {\n\n # --- Comparison table ---\n # Map 3-med labels to comparable 4-med labels\n # In 4-med model: a1=APOC4_APOC2, a2=APOC2, a3=APOC1, a4=APOE\n # In 3-med model: a1=APOC2, a2=APOC1, a3=APOE\n # We compare matching mediators\n cat(\"=== Comparison: 4-Mediator vs 3-Mediator Model ===\\n\\n\")\n\n fiml_4med <- read.csv(file.path(RESULT_DIR, \"main_SEM_FIML\", \"fiml_all_paths.csv\"), stringsAsFactors=FALSE)\n\n # Build comparison for matching paths\n comparison_rows <- list()\n\n # a-paths: map 3-med indices to 4-med mediator names\n med_map <- data.frame(\n mediator = SENS_MEDIATOR_SHORT,\n label_3med = paste0(\"a\", 1:n_med_sens),\n label_4med = c(\"a2\", \"a3\", \"a4\"), # APOC2=a2, APOC1=a3, APOE=a4 in 4-med\n stringsAsFactors = FALSE\n )\n\n for (r in seq_len(nrow(med_map))) {\n row_3 <- sens_key[sens_key$label == med_map$label_3med[r], ]\n row_4 <- fiml_4med[fiml_4med$label == med_map$label_4med[r], ]\n if (nrow(row_3) == 1 && nrow(row_4) == 1) {\n comparison_rows[[length(comparison_rows)+1]] <- data.frame(\n mediator = med_map$mediator[r],\n path = paste0(\"a (SNP->\", med_map$mediator[r], \")\"),\n est_4med = row_4$est, se_4med = row_4$se, p_4med = row_4$pvalue,\n est_3med = row_3$est, se_3med = row_3$se, p_3med = row_3$pvalue,\n stringsAsFactors = FALSE\n )\n }\n }\n\n # b-paths\n b_map <- data.frame(\n mediator = SENS_MEDIATOR_SHORT,\n label_3med = paste0(\"b\", 1:n_med_sens),\n label_4med = c(\"b2\", \"b3\", \"b4\"),\n stringsAsFactors = FALSE\n )\n for (r in seq_len(nrow(b_map))) {\n row_3 <- sens_key[sens_key$label == b_map$label_3med[r], ]\n row_4 <- fiml_4med[fiml_4med$label == b_map$label_4med[r], ]\n if (nrow(row_3) == 1 && nrow(row_4) == 1) {\n comparison_rows[[length(comparison_rows)+1]] <- data.frame(\n mediator = b_map$mediator[r],\n path = paste0(\"b (\", b_map$mediator[r], \"->caa_neo4)\"),\n est_4med = row_4$est, se_4med = row_4$se, p_4med = row_4$pvalue,\n est_3med = row_3$est, se_3med = row_3$se, p_3med = row_3$pvalue,\n stringsAsFactors = FALSE\n )\n }\n }\n\n # Specific indirect effects\n ind_map <- data.frame(\n mediator = SENS_MEDIATOR_SHORT,\n label_3med = paste0(\"ind\", 1:n_med_sens),\n label_4med = c(\"ind2\", \"ind3\", \"ind4\"),\n stringsAsFactors = FALSE\n )\n for (r in seq_len(nrow(ind_map))) {\n row_3 <- sens_key[sens_key$label == ind_map$label_3med[r], ]\n row_4 <- fiml_4med[fiml_4med$label == ind_map$label_4med[r], ]\n if (nrow(row_3) == 1 && nrow(row_4) == 1) {\n comparison_rows[[length(comparison_rows)+1]] <- data.frame(\n mediator = ind_map$mediator[r],\n path = paste0(\"indirect (via \", ind_map$mediator[r], \")\"),\n est_4med = row_4$est, se_4med = row_4$se, p_4med = row_4$pvalue,\n est_3med = row_3$est, se_3med = row_3$se, p_3med = row_3$pvalue,\n stringsAsFactors = FALSE\n )\n }\n }\n\n # cp, total_indirect, total\n for (lab in c(\"cp\", \"total_indirect\", \"total\")) {\n row_3 <- sens_key[sens_key$label == lab, ]\n row_4 <- fiml_4med[fiml_4med$label == lab, ]\n if (nrow(row_3) == 1 && nrow(row_4) == 1) {\n comparison_rows[[length(comparison_rows)+1]] <- data.frame(\n mediator = \"global\",\n path = lab,\n est_4med = row_4$est, se_4med = row_4$se, p_4med = row_4$pvalue,\n est_3med = row_3$est, se_3med = row_3$se, p_3med = row_3$pvalue,\n stringsAsFactors = FALSE\n )\n }\n }\n\n comp_df <- do.call(rbind, comparison_rows)\n comp_df$delta_est <- comp_df$est_3med - comp_df$est_4med\n write.csv(comp_df, file.path(SENS_DIR, \"fiml_sensitivity_comparison_4v3med.csv\"), row.names=FALSE)\n\n cat(\"Comparison table (4-mediator vs 3-mediator):\\n\")\n print(comp_df)\n\n # --- Forest Plot ---\n # Plot the 3-mediator results (a, b, cp, indirect, total)\n plot_labels <- c(paste0(\"a\", 1:n_med_sens), paste0(\"b\", 1:n_med_sens), \"cp\",\n paste0(\"ind\", 1:n_med_sens), \"total_indirect\", \"total\")\n plot_df <- sens_key[sens_key$label %in% plot_labels, ]\n # Exclude proportion rows from forest\n plot_df <- plot_df[!grepl(\"^prop\", plot_df$label), ]\n\n # Assign effect type for color grouping\n plot_df$effect_type <- ifelse(grepl(\"^a\", plot_df$label), \"a-path (SNP->M)\",\n ifelse(grepl(\"^b\", plot_df$label), \"b-path (M->Y)\",\n ifelse(plot_df$label == \"cp\", \"c' (direct)\",\n ifelse(grepl(\"^ind\", plot_df$label), \"indirect\",\n ifelse(plot_df$label == \"total_indirect\", \"total indirect\",\n \"total\")))))\n\n # Order\n plot_df$label_desc <- factor(plot_df$label_desc, levels=rev(plot_df$label_desc))\n\n p_sens <- ggplot(plot_df, aes(x=est, y=label_desc, xmin=ci.lower, xmax=ci.upper, color=effect_type)) +\n geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n geom_pointrange(size=0.6) +\n labs(title=\"Sensitivity Analysis: 3-Mediator FIML SEM\",\n subtitle=paste0(\"Excluding APOC4_APOC2_AC_exp | N = \", N_sens, \" (FIML)\"),\n x=\"Estimate (95% CI)\", y=\"\", color=\"Effect Type\") +\n theme_minimal(base_size=12) +\n theme(legend.position=\"bottom\")\n\n ggsave(file.path(SENS_DIR, \"fiml_sensitivity_3med_forest_plot.png\"), p_sens, width=10, height=7, dpi=150)\n ggsave(file.path(SENS_DIR, \"fiml_sensitivity_3med_forest_plot.pdf\"), p_sens, width=10, height=7)\n cat(\"\\nForest plot saved.\\n\")\n print(p_sens)\n\n # --- Side-by-side forest: 4-med vs 3-med for matched paths ---\n # Build a combined df for the overlay\n matched_3 <- sens_key[sens_key$label %in% plot_labels, c(\"label\", \"est\", \"ci.lower\", \"ci.upper\")]\n matched_3$model <- \"3-mediator\"\n # Remap labels to mediator names for matching\n matched_3$mediator_label <- matched_3$label\n matched_3$mediator_label <- gsub(\"a1\", \"a_APOC2\", matched_3$mediator_label)\n matched_3$mediator_label <- gsub(\"a2\", \"a_APOC1\", matched_3$mediator_label)\n matched_3$mediator_label <- gsub(\"a3\", \"a_APOE\", matched_3$mediator_label)\n matched_3$mediator_label <- gsub(\"b1\", \"b_APOC2\", matched_3$mediator_label)\n matched_3$mediator_label <- gsub(\"b2\", \"b_APOC1\", matched_3$mediator_label)\n matched_3$mediator_label <- gsub(\"b3\", \"b_APOE\", matched_3$mediator_label)\n matched_3$mediator_label <- gsub(\"ind1\", \"ind_APOC2\", matched_3$mediator_label)\n matched_3$mediator_label <- gsub(\"ind2\", \"ind_APOC1\", matched_3$mediator_label)\n matched_3$mediator_label <- gsub(\"ind3\", \"ind_APOE\", matched_3$mediator_label)\n\n # 4-mediator matched paths (excluding APOC4_APOC2 specific paths)\n matched_4_labels <- c(\"a2\", \"a3\", \"a4\", \"b2\", \"b3\", \"b4\", \"cp\", \"ind2\", \"ind3\", \"ind4\", \"total_indirect\", \"total\")\n matched_4 <- fiml_4med[fiml_4med$label %in% matched_4_labels, c(\"label\", \"est\", \"ci.lower\", \"ci.upper\")]\n matched_4$model <- \"4-mediator\"\n matched_4$mediator_label <- matched_4$label\n matched_4$mediator_label <- gsub(\"a2\", \"a_APOC2\", matched_4$mediator_label)\n matched_4$mediator_label <- gsub(\"a3\", \"a_APOC1\", matched_4$mediator_label)\n matched_4$mediator_label <- gsub(\"a4\", \"a_APOE\", matched_4$mediator_label)\n matched_4$mediator_label <- gsub(\"b2\", \"b_APOC2\", matched_4$mediator_label)\n matched_4$mediator_label <- gsub(\"b3\", \"b_APOC1\", matched_4$mediator_label)\n matched_4$mediator_label <- gsub(\"b4\", \"b_APOE\", matched_4$mediator_label)\n matched_4$mediator_label <- gsub(\"ind2\", \"ind_APOC2\", matched_4$mediator_label)\n matched_4$mediator_label <- gsub(\"ind3\", \"ind_APOC1\", matched_4$mediator_label)\n matched_4$mediator_label <- gsub(\"ind4\", \"ind_APOE\", matched_4$mediator_label)\n\n overlay_df <- rbind(matched_3[, c(\"mediator_label\", \"est\", \"ci.lower\", \"ci.upper\", \"model\")],\n matched_4[, c(\"mediator_label\", \"est\", \"ci.lower\", \"ci.upper\", \"model\")])\n overlay_df$mediator_label <- factor(overlay_df$mediator_label,\n levels=rev(c(\"a_APOC2\", \"a_APOC1\", \"a_APOE\", \"b_APOC2\", \"b_APOC1\", \"b_APOE\",\n \"cp\", \"ind_APOC2\", \"ind_APOC1\", \"ind_APOE\", \"total_indirect\", \"total\")))\n\n p_compare <- ggplot(overlay_df, aes(x=est, y=mediator_label, xmin=ci.lower, xmax=ci.upper, color=model)) +\n geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n geom_pointrange(position=position_dodge(width=0.5), size=0.5) +\n scale_color_manual(values=c(\"4-mediator\"=\"steelblue\", \"3-mediator\"=\"firebrick\")) +\n labs(title=\"Sensitivity: 4-Mediator vs 3-Mediator Model Comparison\",\n subtitle=paste0(\"Matched paths for APOC2, APOC1, APOE | N = \", N_sens),\n x=\"Estimate (95% CI)\", y=\"\", color=\"Model\") +\n theme_minimal(base_size=12) +\n theme(legend.position=\"bottom\")\n\n ggsave(file.path(SENS_DIR, \"fiml_sensitivity_4v3_comparison_forest.png\"), p_compare, width=10, height=7, dpi=150)\n ggsave(file.path(SENS_DIR, \"fiml_sensitivity_4v3_comparison_forest.pdf\"), p_compare, width=10, height=7)\n cat(\"\\nComparison forest plot saved.\\n\")\n print(p_compare)\n} else {\n cat(\"Skipping plots -- model did not converge.\\n\")\n}\n" + ], + "execution_count": 6, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Comparison: 4-Mediator vs 3-Mediator Model ===\n", + "\n", + "Comparison table (4-mediator vs 3-mediator):\n", + " mediator path est_4med se_4med p_4med\n", + "1 APOC2 a (SNP->APOC2) 0.2740327333 0.05613088 1.049985e-06\n", + "2 APOC1 a (SNP->APOC1) 0.2223019945 0.06417651 5.323741e-04\n", + "3 APOE a (SNP->APOE) 0.2144028883 0.04914068 1.282657e-05\n", + "4 APOC2 b (APOC2->caa_neo4) 0.6776563284 1.07551466 5.286446e-01\n", + "5 APOC1 b (APOC1->caa_neo4) -0.0007661479 0.06743492 9.909352e-01\n", + "6 APOE b (APOE->caa_neo4) 0.0764241644 0.05793341 1.871114e-01\n", + "7 APOC2 indirect (via APOC2) 0.1857000159 0.29727556 5.321857e-01\n", + "8 APOC1 indirect (via APOC1) -0.0001703162 0.01498653 9.909325e-01\n", + "9 APOE indirect (via APOE) 0.0163855616 0.01301868 2.081679e-01\n", + "10 global cp 0.1575068431 0.04533119 5.116535e-04\n", + "11 global total_indirect -0.0047847136 0.01561455 7.592798e-01\n", + "12 global total 0.1527221295 0.04302242 3.854854e-04\n", + " est_3med se_3med p_3med delta_est\n", + "1 0.273953641 0.05613998 1.061766e-06 -7.909182e-05\n", + "2 0.222252118 0.06419707 5.361197e-04 -4.987609e-05\n", + "3 0.214246215 0.04913996 1.301112e-05 -1.566734e-04\n", + "4 -0.067113114 0.04427079 1.295273e-01 -7.447694e-01\n", + "5 0.005411238 0.06734392 9.359571e-01 6.177386e-03\n", + "6 0.074589946 0.05796546 1.981641e-01 -1.834218e-03\n", + "7 -0.018385882 0.01268771 1.473069e-01 -2.040859e-01\n", + "8 0.001202659 0.01500344 9.361109e-01 1.372975e-03\n", + "9 0.015980614 0.01299121 2.186559e-01 -4.049480e-04\n", + "10 0.153460718 0.04498613 6.465640e-04 -4.046126e-03\n", + "11 -0.001202609 0.01474659 9.350032e-01 3.582104e-03\n", + "12 0.152258108 0.04301741 4.009575e-04 -4.640213e-04\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Forest plot saved.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Comparison forest plot saved.\n" + ] + }, + { + "data": { + "image/png": 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Imi0+m4meTWrVsIoYqKirz/ktWSh5+m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\nBjtCyOTJk5OTkydOnNhiPZMmTaIoKjMzUyQSzZs3LzQ0VCaT5eXlyWQyY7C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XL7927dqFCxdsXUunqq6u3rp169tvv92jRw9b1wIAANA1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\ni4tr/prNn0p2dra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F27Vm0psViMowdc5bS0NAMDAz6fr1YMgiDkcjk+F4aGhionvn//3sjIiMVi3b59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\ntVrtwoULnQ3Ky8tXrFjBP66vry8vLx8/fvz06dMJISzLEkKad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1xYWJicnKx5zYWFhePHj1efmUfr27dv375909PTHR0dHR0djYyMNK8n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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sensitivity Analysis Interpretation\n\nThis sensitivity analysis compares the 4-mediator parallel model (original) with a reduced 3-mediator\nmodel that excludes APOC4_APOC2_AC_exp. Key points to assess:\n\n1. **a-paths (SNP->M):** These should be virtually identical between models since mediator equations\n are independent of each other (each mediator regressed on SNP + its own covariates).\n2. **b-paths (M->Y):** These may change because removing a correlated mediator alters the conditional\n associations. If APOC4_APOC2 and APOC2 were highly correlated, the b-path for APOC2 may become\n more stable (less multicollinearity).\n3. **Indirect effects:** Changes here reflect both any b-path changes and the removal of the\n APOC4_APOC2-specific indirect pathway.\n4. **Direct effect (cp):** If APOC4_APOC2 was absorbing some of the direct effect, cp may change.\n5. **Total effect:** Should remain similar (the total causal effect of SNP on outcome is model-invariant\n if the model is correctly specified).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:17:13.099395Z", + "iopub.status.busy": "2026-03-31T23:17:13.097157Z", + "iopub.status.idle": "2026-03-31T23:17:13.125934Z", + "shell.execute_reply": "2026-03-31T23:17:13.123318Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "session_notes.md saved.\n" + ] + } + ], + "source": [ + "# ============================================================\n# Section 12: Save session_notes.md\n# ============================================================\nnotes <- paste0(\n \"# Session Notes: SEM Parallel Mediation Analysis\\n\",\n \"\\n\",\n \"## Analysis Summary\\n\",\n \"- **Design:** Parallel mediation (Design 2) with 4 mediators\\n\",\n \"- **Direction:** Unidirectional (SNP -> M -> Y)\\n\",\n \"- **Exposure:** chr19_44954310_T_C\\n\",\n \"- **Mediators:** APOC4_APOC2_AC_exp, APOC2_AC_exp, APOC1_DeJager_Mic_exp, APOE_Mega_Mic_exp\\n\",\n \"- **Outcome:** caa_neo4\\n\",\n \"- **Sample size:** N = \", N_total, \" (FIML, all subjects with exposure observed)\\n\",\n \"- **Covariate strategy:** Covariates-in-model (Strategy A)\\n\",\n \"\\n\",\n \"## Methods\\n\",\n \"1. FIML SEM (lavaan) -- primary analysis\\n\",\n \"2. Bootstrap (1000 replicates, FIML inside) -- non-parametric CIs\\n\",\n \"3. MNAR Sensitivity (15x15 delta grid) -- robustness to missing data assumptions\\n\",\n \"4. Bayesian SEM (blavaan/Stan) -- posterior inference\\n\",\n \"\\n\",\n \"## Key Findings\\n\",\n \"See all_methods_summary.csv and summary plots for detailed results.\\n\",\n \"\\n\",\n \"## Notes\\n\",\n \"- All four mediators tested simultaneously in one model (parallel/joint mediation)\\n\",\n \"- Specific indirect effects represent the UNIQUE contribution of each mediator\\n\",\n \"- Residual correlations between mediators freely estimated\\n\",\n \"- Substantial missingness in mediators (37-64%) handled by FIML\\n\",\n \"\\n\",\n \"## Sensitivity Analysis (added)\\n\",\n \"- 3-mediator model excluding APOC4_APOC2_AC_exp\\n\",\n \"- Compared matched paths between 4-med and 3-med models\\n\",\n \"- Results in main_SEM_FIML/sensitivity_analysis/\\n\",\n \"\\n\",\n \"## Recommended Follow-up\\n\",\n \"- Compare with separate (marginal) mediation for each mediator to see if results differ\\n\",\n \"- Test interaction terms (SNP x mediator) if suppression effects detected\\n\",\n \"- Sensitivity analysis with different APOE adjustment strategies\\n\"\n)\n\nwriteLines(notes, file.path(RESULT_DIR, \"session_notes.md\"))\ncat(\"session_notes.md saved.\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Post-Analysis Sanity Check" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:17:13.132141Z", + "iopub.status.busy": "2026-03-31T23:17:13.130428Z", + "iopub.status.idle": "2026-03-31T23:17:13.483200Z", + "shell.execute_reply": "2026-03-31T23:17:13.481014Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "POST-ANALYSIS SANITY CHECK\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================================\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Check 1: Sign Consistency ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ANOMALY: Sign disagreement for: b3, ind3 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 2: Magnitude Discrepancies ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " WARNING: Magnitude discrepancy: b3 (ratio=6.3); ind3 (ratio=99.7) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 3: Significance Disagreements ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PASSED\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 4: Proportion Mediated Range ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " WARNING: prop1 (APOC4_APOC2) = -1.35 -- suppression effect \n", + " WARNING: prop2 (APOC2) = 1.22 -- direct/indirect opposite signs \n", + " WARNING: prop3 (APOC1) = -0.00 -- suppression effect \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 5: Large Standard Errors ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " WARNING: ind3 (FIML): SE=0.0150, |est|=0.0002 \n", + " WARNING: ind3 (Bootstrap): SE=0.0175, |est|=0.0000 \n", + " WARNING: ind3 (Bayesian): SE=0.0153, |est|=0.0011 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 7: Bootstrap Convergence ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PASSED: 1000/1000 (100.0%)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 8: MNAR Tipping Points ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " WARNING: ind1 tipping distance = 0.00 SD (fragile) \n", + " WARNING: ind2 tipping distance = 0.00 SD (fragile) \n", + " WARNING: ind3 tipping distance = 0.00 SD (fragile) \n", + " WARNING: ind4 tipping distance = 0.00 SD (fragile) \n", + " WARNING: total_indirect tipping distance = 0.00 SD (fragile) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 10: Suppression Effects ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " WARNING: Indirect (-0.0048) and direct (0.1575) have opposite signs -- suppression pattern \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 11: Component Path Significance ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PASSED\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SANITY CHECK SUMMARY\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Passed:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [PASS] Significance agreement: All methods agree on indirect effect significance \n", + " [PASS] Bootstrap convergence: 1000/1000 (100.0%) \n", + " [PASS] Component paths: No significant indirect with non-significant component \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Warnings:\n", + " [WARN] Magnitude discrepancy: b3 (ratio=6.3); ind3 (ratio=99.7) \n", + " [WARN] prop1 (APOC4_APOC2) = -1.35 -- suppression effect \n", + " [WARN] prop2 (APOC2) = 1.22 -- direct/indirect opposite signs \n", + " [WARN] prop3 (APOC1) = -0.00 -- suppression effect \n", + " [WARN] ind3 (FIML): SE=0.0150, |est|=0.0002 \n", + " [WARN] ind3 (Bootstrap): SE=0.0175, |est|=0.0000 \n", + " [WARN] ind3 (Bayesian): SE=0.0153, |est|=0.0011 \n", + " [WARN] ind1 tipping distance = 0.00 SD (fragile) \n", + " [WARN] ind2 tipping distance = 0.00 SD (fragile) \n", + " [WARN] ind3 tipping distance = 0.00 SD (fragile) \n", + " [WARN] ind4 tipping distance = 0.00 SD (fragile) \n", + " [WARN] total_indirect tipping distance = 0.00 SD (fragile) \n", + " [WARN] Indirect (-0.0048) and direct (0.1575) have opposite signs -- suppression pattern \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Anomalies:\n", + " [ANOM] Sign disagreement for: b3, ind3 \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 13: Sanity Check\n", + "# ============================================================\n", + "cat(\"============================================\\n\")\n", + "cat(\"POST-ANALYSIS SANITY CHECK\\n\")\n", + "cat(\"============================================\\n\\n\")\n", + "\n", + "passed <- c()\n", + "warnings_list <- c()\n", + "anomalies <- c()\n", + "\n", + "# 1. Sign consistency across methods\n", + "cat(\"--- Check 1: Sign Consistency ---\\n\")\n", + "check_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\")\n", + "sign_issues <- c()\n", + "for (lab in check_labels) {\n", + " ests <- all_summary$est[all_summary$label == lab]\n", + " if (length(ests) >= 2 && !all(is.na(ests))) {\n", + " signs <- sign(ests[!is.na(ests)])\n", + " if (length(unique(signs[signs != 0])) > 1) {\n", + " sign_issues <- c(sign_issues, lab)\n", + " }\n", + " }\n", + "}\n", + "if (length(sign_issues) == 0) {\n", + " passed <- c(passed, \"Sign consistency: All methods agree on direction for all paths\")\n", + " cat(\" PASSED\\n\")\n", + "} else {\n", + " msg <- paste0(\"Sign disagreement for: \", paste(sign_issues, collapse=\", \"))\n", + " anomalies <- c(anomalies, msg)\n", + " cat(\" ANOMALY:\", msg, \"\\n\")\n", + "}\n", + "\n", + "# 2. Magnitude discrepancies\n", + "cat(\"\\n--- Check 2: Magnitude Discrepancies ---\\n\")\n", + "mag_issues <- c()\n", + "for (lab in check_labels) {\n", + " ests <- abs(all_summary$est[all_summary$label == lab])\n", + " ests <- ests[!is.na(ests) & ests > 0]\n", + " if (length(ests) >= 2) {\n", + " if (max(ests) / min(ests) > 3) {\n", + " mag_issues <- c(mag_issues, sprintf(\"%s (ratio=%.1f)\", lab, max(ests)/min(ests)))\n", + " }\n", + " }\n", + "}\n", + "if (length(mag_issues) == 0) {\n", + " passed <- c(passed, \"Magnitude consistency: No method differs by >3x from others\")\n", + " cat(\" PASSED\\n\")\n", + "} else {\n", + " msg <- paste0(\"Magnitude discrepancy: \", paste(mag_issues, collapse=\"; \"))\n", + " warnings_list <- c(warnings_list, msg)\n", + " cat(\" WARNING:\", msg, \"\\n\")\n", + "}\n", + "\n", + "# 3. Significance disagreements for indirect effects\n", + "cat(\"\\n--- Check 3: Significance Disagreements ---\\n\")\n", + "sig_disagree_found <- FALSE\n", + "for (lab in c(paste0(\"ind\", 1:n_med), \"total_indirect\")) {\n", + " sub <- all_summary[all_summary$label == lab, ]\n", + " sig_fiml <- any(sub$pvalue[sub$method == \"FIML\"] < 0.05, na.rm=TRUE)\n", + " boot_row <- sub[sub$method == \"Bootstrap\", ]\n", + " sig_boot <- nrow(boot_row) > 0 && !is.na(boot_row$ci_lower) && !is.na(boot_row$ci_upper) && (boot_row$ci_lower * boot_row$ci_upper > 0)\n", + " bayes_row <- sub[sub$method == \"Bayesian\", ]\n", + " sig_bayes <- nrow(bayes_row) > 0 && !is.na(bayes_row$ci_lower) && !is.na(bayes_row$ci_upper) && (bayes_row$ci_lower * bayes_row$ci_upper > 0)\n", + "\n", + " sigs <- c(FIML=sig_fiml, Bootstrap=sig_boot, Bayesian=sig_bayes)\n", + " sigs <- sigs[!is.na(sigs)]\n", + "\n", + " if (length(unique(sigs)) > 1) {\n", + " msg <- sprintf(\"%s: %s\", lab, paste(names(sigs), ifelse(sigs, \"sig\", \"NS\"), sep=\"=\", collapse=\", \"))\n", + " warnings_list <- c(warnings_list, msg)\n", + " cat(\" WARNING:\", msg, \"\\n\")\n", + " sig_disagree_found <- TRUE\n", + " }\n", + "}\n", + "if (!sig_disagree_found) {\n", + " passed <- c(passed, \"Significance agreement: All methods agree on indirect effect significance\")\n", + " cat(\" PASSED\\n\")\n", + "}\n", + "\n", + "# 4. Proportion mediated out of range\n", + "cat(\"\\n--- Check 4: Proportion Mediated Range ---\\n\")\n", + "prop_ok <- TRUE\n", + "for (k in 1:n_med) {\n", + " p_row <- fiml_key[fiml_key$label == paste0(\"prop\", k), ]\n", + " if (nrow(p_row) > 0 && !is.na(p_row$est)) {\n", + " if (p_row$est < 0 || p_row$est > 1) {\n", + " msg <- sprintf(\"prop%d (%s) = %.2f -- %s\", k, MEDIATOR_SHORT[k], p_row$est,\n", + " ifelse(p_row$est < 0, \"suppression effect\", \"direct/indirect opposite signs\"))\n", + " warnings_list <- c(warnings_list, msg)\n", + " cat(\" WARNING:\", msg, \"\\n\")\n", + " prop_ok <- FALSE\n", + " }\n", + " }\n", + "}\n", + "if (prop_ok) {\n", + " passed <- c(passed, \"Proportion mediated: All within [0, 1] range\")\n", + " cat(\" PASSED\\n\")\n", + "}\n", + "\n", + "# 5. Large SEs\n", + "cat(\"\\n--- Check 5: Large Standard Errors ---\\n\")\n", + "se_issues <- c()\n", + "for (lab in c(paste0(\"ind\", 1:n_med), \"total_indirect\")) {\n", + " sub <- all_summary[all_summary$label == lab, ]\n", + " for (i in 1:nrow(sub)) {\n", + " if (!is.na(sub$se[i]) && !is.na(sub$est[i]) && abs(sub$est[i]) > 0) {\n", + " if (sub$se[i] > 5 * abs(sub$est[i])) {\n", + " se_issues <- c(se_issues, sprintf(\"%s (%s): SE=%.4f, |est|=%.4f\",\n", + " lab, sub$method[i], sub$se[i], abs(sub$est[i])))\n", + " }\n", + " }\n", + " }\n", + "}\n", + "if (length(se_issues) == 0) {\n", + " passed <- c(passed, \"Standard errors: No excessively large SEs detected\")\n", + " cat(\" PASSED\\n\")\n", + "} else {\n", + " for (s in se_issues) { warnings_list <- c(warnings_list, s); cat(\" WARNING:\", s, \"\\n\") }\n", + "}\n", + "\n", + "# 7. Bootstrap convergence rate\n", + "cat(\"\\n--- Check 7: Bootstrap Convergence ---\\n\")\n", + "conv_rate <- n_converged / B\n", + "if (conv_rate >= 0.80) {\n", + " passed <- c(passed, sprintf(\"Bootstrap convergence: %d/%d (%.1f%%)\", n_converged, B, 100*conv_rate))\n", + " cat(sprintf(\" PASSED: %d/%d (%.1f%%)\\n\", n_converged, B, 100*conv_rate))\n", + "} else {\n", + " msg <- sprintf(\"Bootstrap convergence LOW: %d/%d (%.1f%%)\", n_converged, B, 100*conv_rate)\n", + " anomalies <- c(anomalies, msg)\n", + " cat(\" ANOMALY:\", msg, \"\\n\")\n", + "}\n", + "\n", + "# 8. MNAR tipping points\n", + "cat(\"\\n--- Check 8: MNAR Tipping Points ---\\n\")\n", + "for (k in 1:nrow(tipping)) {\n", + " if (!is.na(tipping$tipping_dist[k]) && tipping$tipping_dist[k] < 0.5) {\n", + " msg <- sprintf(\"%s tipping distance = %.2f SD (fragile)\", tipping$label[k], tipping$tipping_dist[k])\n", + " warnings_list <- c(warnings_list, msg)\n", + " cat(\" WARNING:\", msg, \"\\n\")\n", + " } else if (is.na(tipping$tipping_dist[k])) {\n", + " passed <- c(passed, sprintf(\"%s: never tips (always significant) -- robust\", tipping$label[k]))\n", + " cat(sprintf(\" PASSED: %s never tips\\n\", tipping$label[k]))\n", + " } else {\n", + " passed <- c(passed, sprintf(\"%s: tipping at %.2f SD\", tipping$label[k], tipping$tipping_dist[k]))\n", + " cat(sprintf(\" OK: %s tipping at %.2f SD\\n\", tipping$label[k], tipping$tipping_dist[k]))\n", + " }\n", + "}\n", + "\n", + "# 10. Suppression check\n", + "cat(\"\\n--- Check 10: Suppression Effects ---\\n\")\n", + "total_est <- fiml_key$est[fiml_key$label == \"total\"]\n", + "ti_est <- fiml_key$est[fiml_key$label == \"total_indirect\"]\n", + "cp_est <- fiml_key$est[fiml_key$label == \"cp\"]\n", + "if (length(total_est) > 0 && length(ti_est) > 0 && length(cp_est) > 0) {\n", + " if (sign(ti_est) != sign(cp_est) && abs(ti_est) > 0.001 && abs(cp_est) > 0.001) {\n", + " msg <- sprintf(\"Indirect (%.4f) and direct (%.4f) have opposite signs -- suppression pattern\",\n", + " ti_est, cp_est)\n", + " warnings_list <- c(warnings_list, msg)\n", + " cat(\" WARNING:\", msg, \"\\n\")\n", + " } else {\n", + " passed <- c(passed, \"No suppression: indirect and direct effects have consistent signs\")\n", + " cat(\" PASSED\\n\")\n", + " }\n", + "}\n", + "\n", + "# 11. Near-zero component with significant indirect\n", + "cat(\"\\n--- Check 11: Component Path Significance ---\\n\")\n", + "comp_ok <- TRUE\n", + "for (k in 1:n_med) {\n", + " a_p <- fiml_key$pvalue[fiml_key$label == paste0(\"a\", k)]\n", + " b_p <- fiml_key$pvalue[fiml_key$label == paste0(\"b\", k)]\n", + " ind_p <- fiml_key$pvalue[fiml_key$label == paste0(\"ind\", k)]\n", + " if (length(a_p) > 0 && length(b_p) > 0 && length(ind_p) > 0) {\n", + " if (!is.na(ind_p) && ind_p < 0.05 && ((!is.na(a_p) && a_p > 0.1) || (!is.na(b_p) && b_p > 0.1))) {\n", + " msg <- sprintf(\"ind%d significant (p=%.4f) but a%d p=%.4f, b%d p=%.4f -- check\",\n", + " k, ind_p, k, a_p, k, b_p)\n", + " anomalies <- c(anomalies, msg)\n", + " cat(\" ANOMALY:\", msg, \"\\n\")\n", + " comp_ok <- FALSE\n", + " }\n", + " }\n", + "}\n", + "if (comp_ok) {\n", + " passed <- c(passed, \"Component paths: No significant indirect with non-significant component\")\n", + " cat(\" PASSED\\n\")\n", + "}\n", + "\n", + "# Summary\n", + "cat(\"\\n============================================\\n\")\n", + "cat(\"SANITY CHECK SUMMARY\\n\")\n", + "cat(\"============================================\\n\")\n", + "cat(\"\\nPassed:\\n\")\n", + "for (p in passed) cat(\" [PASS]\", p, \"\\n\")\n", + "if (length(warnings_list) > 0) {\n", + " cat(\"\\nWarnings:\\n\")\n", + " for (w in warnings_list) cat(\" [WARN]\", w, \"\\n\")\n", + "}\n", + "if (length(anomalies) > 0) {\n", + " cat(\"\\nAnomalies:\\n\")\n", + " for (a in anomalies) cat(\" [ANOM]\", a, \"\\n\")\n", + "}\n", + "if (length(warnings_list) == 0 && length(anomalies) == 0) {\n", + " cat(\"\\nAll checks passed -- no anomalies detected.\\n\")\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T23:17:13.490217Z", + "iopub.status.busy": "2026-03-31T23:17:13.488003Z", + "iopub.status.idle": "2026-03-31T23:17:13.952745Z", + "shell.execute_reply": "2026-03-31T23:17:13.950627Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Session Info ===\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in system(\"timedatectl\", intern = TRUE):\n", + "\u201crunning command 'timedatectl' had status 1\u201d\n" + ] + }, + { + "data": { + "text/plain": [ + "R version 4.4.3 (2025-02-28)\n", + "Platform: x86_64-conda-linux-gnu\n", + "Running under: Ubuntu 24.04.3 LTS\n", + "\n", + "Matrix products: default\n", + "BLAS/LAPACK: /mnt/lustre/home/yl4437/.pixi/envs/python/lib/libopenblasp-r0.3.30.so; LAPACK version 3.12.0\n", + "\n", + "locale:\n", + " [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 \n", + " [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 \n", + " [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C \n", + "[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C \n", + "\n", + "time zone: Etc/UTC\n", + "tzcode source: system (glibc)\n", + "\n", + "attached base packages:\n", + "[1] stats graphics grDevices utils datasets methods base \n", + "\n", + "other attached packages:\n", + "[1] reshape2_1.4.5 gridExtra_2.3 ggplot2_4.0.2 blavaan_0.5-10 Rcpp_1.1.1 \n", + "[6] lavaan_0.6-21 \n", + "\n", + "loaded via a namespace (and not attached):\n", + " [1] gtable_0.3.6 QuickJSR_1.9.0 inline_0.3.21 \n", + " [4] CompQuadForm_1.4.4 lattice_0.22-9 quadprog_1.5-8 \n", + " [7] vctrs_0.7.1 tools_4.4.3 generics_0.1.4 \n", + "[10] stats4_4.4.3 parallel_4.4.3 sandwich_3.1-1 \n", + "[13] tibble_3.3.1 pkgconfig_2.0.3 Matrix_1.7-4 \n", + "[16] RColorBrewer_1.1-3 S7_0.2.1 RcppParallel_5.1.11-2\n", + "[19] uuid_1.2-2 lifecycle_1.0.5 tmvnsim_1.0-2 \n", + "[22] stringr_1.6.0 compiler_4.4.3 farver_2.1.2 \n", + "[25] textshaping_1.0.4 mnormt_2.1.2 repr_1.1.7 \n", + "[28] codetools_0.2-20 htmltools_0.5.9 bayesplot_1.15.0 \n", + "[31] pillar_1.11.1 crayon_1.5.3 MASS_7.3-65 \n", + "[34] StanHeaders_2.32.10 parallelly_1.46.1 rstan_2.32.7 \n", + "[37] tidyselect_1.2.1 digest_0.6.39 stringi_1.8.7 \n", + "[40] mvtnorm_1.3-3 future_1.69.0 nonnest2_0.5-8 \n", + "[43] dplyr_1.2.0 listenv_0.10.0 labeling_0.4.3 \n", + "[46] fastmap_1.2.0 grid_4.4.3 cli_3.6.5 \n", + "[49] magrittr_2.0.4 loo_2.9.0 base64enc_0.1-6 \n", + "[52] pkgbuild_1.4.8 IRdisplay_1.1 future.apply_1.20.1 \n", + "[55] pbivnorm_0.6.0 withr_3.0.2 scales_1.4.0 \n", + "[58] IRkernel_1.3.2 matrixStats_1.5.0 globals_0.19.0 \n", + "[61] igraph_2.2.2 pbdZMQ_0.3-14 ragg_1.5.0 \n", + "[64] zoo_1.8-15 coda_0.19-4.1 evaluate_1.0.5 \n", + "[67] viridisLite_0.4.3 rstantools_2.6.0 rlang_1.1.7 \n", + "[70] isoband_0.3.0 glue_1.8.0 jsonlite_2.0.0 \n", + "[73] plyr_1.8.9 R6_2.6.1 systemfonts_1.3.1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 14: Cleanup and Session Info\n", + "# ============================================================\n", + "# Clean up Stan/lavaan temp files\n", + "temp_patterns <- c(\"lavExport*\", \"*.stan\", \"tmp_*\", \"*.bak\")\n", + "for (pat in temp_patterns) {\n", + " files <- Sys.glob(file.path(RESULT_DIR, pat))\n", + " if (length(files) > 0) file.remove(files)\n", + "}\n", + "for (pat in temp_patterns) {\n", + " files <- Sys.glob(pat)\n", + " if (length(files) > 0) file.remove(files)\n", + "}\n", + "\n", + "cat(\"\\n=== Session Info ===\\n\")\n", + "sessionInfo()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_tdp_st4_APOE4_2_adj_SEM_mediation.ipynb b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_tdp_st4_APOE4_2_adj_SEM_mediation.ipynb new file mode 100644 index 0000000..b23bcbf --- /dev/null +++ b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_tdp_st4_APOE4_2_adj_SEM_mediation.ipynb @@ -0,0 +1,3014 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parallel Mediation Analysis: APOE Region Gene Expression Mediating SNP Effect on TDP-43 Pathology\n", + "\n", + "## Aim\n", + "\n", + "This analysis tests whether four APOE-region gene expression traits **jointly mediate** the effect of the APOE4/2 tagging SNP `chr19_45001091_C_G` on TDP-43 stage 4 pathology (`tdp_st4`), using a parallel (Design 2) mediation model.\n", + "\n", + "The four mediators represent expression of genes in the APOE locus measured across different brain cell types / datasets:\n", + "- **APOC4_APOC2_AC_exp** -- APOC4/APOC2 expression (Anterior Cingulate)\n", + "- **APOC2_AC_exp** -- APOC2 expression (Anterior Cingulate)\n", + "- **APOC1_DeJager_Mic_exp** -- APOC1 expression (DeJager Microglia)\n", + "- **APOE_Mega_Mic_exp** -- APOE expression (Mega Microglia)\n", + "\n", + "All four mediators enter the model simultaneously, providing **specific indirect effects** (unique contribution of each mediator controlling for the others), a **total indirect effect**, and pairwise **contrasts** between mediators.\n", + "\n", + "Four complementary methods are used: FIML SEM, Bootstrap (FIML inside), MNAR Sensitivity Analysis, and Bayesian SEM (blavaan/Stan)." + ], + "id": "a134316c" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Directed Acyclic Graph (DAG)\n", + "\n", + "```\n", + " a1 b1\n", + " +---------> [APOC4_APOC2_AC_exp] ----------+\n", + " | a2 b2 |\n", + " +---------> [APOC2_AC_exp] ----------------+\n", + " [chr19_45001091 | a3 b3 +---> [tdp_st4]\n", + " _C_G (SNP)] +---------> [APOC1_DeJager_Mic_exp] ------+\n", + " | a4 b4 |\n", + " +---------> [APOE_Mega_Mic_exp] ----------+\n", + " | ^\n", + " +------------------ c' (direct) --------------------+\n", + "```\n", + "\n", + "**Specific indirect effects:**\n", + "- ind1 = a1 x b1 (through APOC4_APOC2_AC_exp)\n", + "- ind2 = a2 x b2 (through APOC2_AC_exp)\n", + "- ind3 = a3 x b3 (through APOC1_DeJager_Mic_exp)\n", + "- ind4 = a4 x b4 (through APOE_Mega_Mic_exp)\n", + "\n", + "**Total indirect = ind1 + ind2 + ind3 + ind4**\n", + "\n", + "**Total effect = c' + total_indirect**\n", + "\n", + "Covariates:\n", + "- APOC4_APOC2_AC_exp & APOC2_AC_exp: msex_u, age_death_u, pmi_u, ROS_study_u\n", + "- APOC1_DeJager_Mic_exp & APOE_Mega_Mic_exp: msex_u, age_death_u, pmi_u\n", + "- tdp_st4: educ, apoe4_dose, apoe2_dose, msex_u, age_death_u\n", + "\n", + "Direction: **Unidirectional** (SNP -> M -> Y only)" + ], + "id": "bde3012e" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Findings & Conclusion\n\n### Overall Result: **No significant mediation \u2014 the SNP effect on TDP-43 pathology is primarily direct.**\n\n### Summary of Effects (N = 1,153, FIML)\n\n| Effect | Estimate | 95% CI | p-value | Interpretation |\n|--------|----------|--------|---------|----------------|\n| **Total effect** (c) | 0.162 | [0.056, 0.268] | 0.003 | SNP significantly affects TDP-43 pathology |\n| **Direct effect** (c') | 0.164 | [0.052, 0.276] | 0.004 | Effect persists after accounting for all mediators |\n| **Total indirect** | -0.002 | [-0.040, 0.037] | 0.932 | No significant mediation through any pathway |\n| Proportion mediated | -1.0% | [-24.9%, 22.8%] | 0.932 | Essentially zero mediation |\n\n### Specific Indirect Effects (all non-significant)\n\n| Mediator | Indirect (a*b) | 95% CI | p-value |\n|----------|---------------|--------|---------|\n| APOC4_APOC2_AC_exp (ind1) | -0.481 | [-1.298, 0.336] | 0.249 |\n| APOC2_AC_exp (ind2) | 0.468 | [-0.343, 1.278] | 0.258 |\n| APOC1_DeJager_Mic_exp (ind3) | -0.002 | [-0.044, 0.040] | 0.924 |\n| APOE_Mega_Mic_exp (ind4) | 0.014 | [-0.012, 0.040] | 0.302 |\n\nNote: ind1 and ind2 have large but opposing estimates that nearly cancel out (suppression effect), and neither is individually significant.\n\n### a-paths (SNP -> Mediator): All significant\n- The SNP significantly predicts all four mediators (all p < 0.001), confirming the SNP influences APOE-region gene expression.\n\n### b-paths (Mediator -> Outcome): None significant\n- No mediator significantly predicts TDP-43 pathology after controlling for the SNP and other mediators (all p > 0.2).\n\n### Cross-Method Consistency\nResults are highly consistent across all four methods:\n- **FIML SEM**: Direct effect p = 0.004, total indirect p = 0.932\n- **Bootstrap** (1,000 replicates): Direct effect p = 0.002, total indirect p = 0.908\n- **Bayesian SEM** (blavaan/Stan): Direct effect 95% CrI [0.049, 0.278], total indirect CrI includes 0\n- **MNAR Sensitivity**: Tipping point at 1.55 SD \u2014 the null mediation result is robust to moderate departures from MAR assumptions\n\n### Interpretation\nThe APOE-region SNP chr19_45001091_C_G has a significant effect on TDP-43 pathology stage (tdp_st4), but this effect is **not mediated** by the expression of APOC4-APOC2, APOC2, APOC1, or APOE in the tested tissues. The SNP effect operates through a **direct pathway** (or through unmeasured mediators not included in this model). The large but opposing indirect effects through APOC4_APOC2 and APOC2 suggest potential collinearity between these two mediators, but neither pathway is individually significant.\n\n### Sensitivity Analysis: Excluding APOC4_APOC2_AC_exp (3-Mediator Model)\n\nTo assess whether collinearity between APOC4_APOC2_AC_exp and APOC2_AC_exp drove the observed suppression effect, a sensitivity analysis was run excluding APOC4_APOC2_AC_exp.\n\n| Effect | 4-Mediator Model | 3-Mediator Model | Change |\n|--------|-----------------|-----------------|--------|\n| b-path (APOC2_AC->tdp) | 1.533 (p=0.247) | -0.030 (p=0.573) | Estimate collapsed from inflated to near-zero |\n| Indirect (via APOC2_AC) | 0.468 (p=0.258) | -0.009 (p=0.574) | No longer inflated; still non-significant |\n| Total Indirect | -0.002 (p=0.932) | 0.005 (p=0.804) | Essentially unchanged |\n| Direct (c') | 0.164 (p=0.004) | 0.157 (p=0.006) | Stable |\n| Total Effect | 0.162 (p=0.003) | 0.162 (p=0.003) | Identical |\n\n**Conclusion:** Removing APOC4_APOC2_AC_exp does **not** make APOC2_AC_exp a significant mediator. The large opposing indirect effects in the 4-mediator model (ind1=-0.481, ind2=0.468) were artifacts of collinearity between the two overlapping transcripts, not evidence of masked mediation. The core finding is unchanged: the SNP effect on TDP-43 pathology operates through a direct pathway, not through APOE-region gene expression." + ], + "id": "66ee8769" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Input Specification\n", + "\n", + "| Parameter | Value |\n", + "|-----------|-------|\n", + "| Data file | `/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/tdp_st4/APOE_ind_set_27_tdp_st4_mediation_all_input.txt` |\n", + "| Exposure | `chr19_45001091_C_G` |\n", + "| Mediators | `APOC4_APOC2_AC_exp`, `APOC2_AC_exp`, `APOC1_DeJager_Mic_exp`, `APOE_Mega_Mic_exp` |\n", + "| Outcome | `tdp_st4` |\n", + "| Design | Parallel mediation (Design 2) -- all 4 mediators simultaneously |\n", + "| Direction | Unidirectional: SNP -> M -> Y |" + ], + "id": "ad063ece" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Methods Table\n", + "\n", + "| Method | Purpose | Missing Data Handling |\n", + "|--------|---------|----------------------|\n", + "| FIML SEM (lavaan) | Primary analysis; ML estimation | FIML uses all available data (N=full) |\n", + "| Bootstrap (1000 replicates) | Non-parametric CIs; no distributional assumptions for indirect | FIML inside each replicate |\n", + "| MNAR Sensitivity | Robustness to non-ignorable missingness | Delta-shift imputation + ML |\n", + "| Bayesian SEM (blavaan/Stan) | Posterior distributions; probability of direction | Stan data augmentation for missing data |" + ], + "id": "df23f941" + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3906cd31", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T04:33:36.215055Z", + "iopub.status.busy": "2026-04-01T04:33:36.210027Z", + "iopub.status.idle": "2026-04-01T04:33:46.196457Z", + "shell.execute_reply": "2026-04-01T04:33:46.193684Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setup complete.\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# SETUP: Libraries, paths, output directories\n", + "# ============================================================\n", + "suppressPackageStartupMessages({\n", + " library(lavaan)\n", + " library(blavaan)\n", + " library(ggplot2)\n", + " library(gridExtra)\n", + " library(reshape2)\n", + "})\n", + "\n", + "# Paths\n", + "DATA_FILE <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/tdp_st4/APOE_ind_set_27_tdp_st4_mediation_all_input.txt\"\n", + "RESULT_DIR <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/tdp_st4/APOE4_2_adj\"\n", + "\n", + "dir_fiml <- file.path(RESULT_DIR, \"main_SEM_FIML\")\n", + "dir_boot <- file.path(RESULT_DIR, \"bootstrap\")\n", + "dir_mnar <- file.path(RESULT_DIR, \"MNAR_sensitivity\")\n", + "dir_bayes <- file.path(RESULT_DIR, \"bayesian_blavaan\")\n", + "dir_summ <- file.path(RESULT_DIR, \"summary\")\n", + "\n", + "for (d in c(dir_fiml, dir_boot, dir_mnar, dir_bayes, dir_summ)) {\n", + " dir.create(d, showWarnings=FALSE, recursive=TRUE)\n", + "}\n", + "\n", + "# Variable definitions\n", + "exposure <- \"chr19_45001091_C_G\"\n", + "med_cols <- c(\"APOC4_APOC2_AC_exp\", \"APOC2_AC_exp\", \"APOC1_DeJager_Mic_exp\", \"APOE_Mega_Mic_exp\")\n", + "outcome <- \"tdp_st4\"\n", + "n_med <- length(med_cols)\n", + "\n", + "# Covariates per equation\n", + "med_covs <- list(\n", + " APOC4_APOC2_AC_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\"),\n", + " APOC2_AC_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\"),\n", + " APOC1_DeJager_Mic_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\"),\n", + " APOE_Mega_Mic_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\")\n", + ")\n", + "out_covs <- c(\"educ\", \"apoe4_dose\", \"apoe2_dose\", \"msex_u\", \"age_death_u\")\n", + "\n", + "# Label definitions\n", + "key_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\")\n", + "\n", + "nice_labels <- c(\n", + " a1=\"a1 (SNP->APOC4_APOC2)\", a2=\"a2 (SNP->APOC2)\",\n", + " a3=\"a3 (SNP->APOC1_DeJager)\", a4=\"a4 (SNP->APOE_Mega)\",\n", + " b1=\"b1 (APOC4_APOC2->tdp)\", b2=\"b2 (APOC2->tdp)\",\n", + " b3=\"b3 (APOC1_DeJager->tdp)\", b4=\"b4 (APOE_Mega->tdp)\",\n", + " cp=\"c' (direct SNP->tdp)\",\n", + " ind1=\"ind1 (via APOC4_APOC2)\", ind2=\"ind2 (via APOC2)\",\n", + " ind3=\"ind3 (via APOC1_DeJager)\", ind4=\"ind4 (via APOE_Mega)\",\n", + " total_indirect=\"Total Indirect\", total=\"Total Effect\", prop_med=\"Proportion Mediated\"\n", + ")\n", + "\n", + "cat(\"Setup complete.\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8ad28de1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T04:33:46.240918Z", + "iopub.status.busy": "2026-04-01T04:33:46.202531Z", + "iopub.status.idle": "2026-04-01T04:33:46.328043Z", + "shell.execute_reply": "2026-04-01T04:33:46.325406Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N with exposure observed: 1153 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chr19_45001091_C_G : 1153 obs, 0 NA\n", + " APOC4_APOC2_AC_exp : 593 obs, 560 NA\n", + " APOC2_AC_exp : 593 obs, 560 NA\n", + " APOC1_DeJager_Mic_exp : 419 obs, 734 NA\n", + " APOE_Mega_Mic_exp : 733 obs, 420 NA\n", + " tdp_st4 : 992 obs, 161 NA\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# DATA LOADING\n", + "# ============================================================\n", + "dat_raw <- read.delim(DATA_FILE, header=TRUE, sep=\"\\t\", stringsAsFactors=FALSE)\n", + "\n", + "all_covs <- unique(c(unlist(med_covs), out_covs))\n", + "keep_cols <- c(exposure, med_cols, outcome, all_covs)\n", + "dat <- dat_raw[, keep_cols]\n", + "dat <- dat[!is.na(dat[[exposure]]), ]\n", + "N_total <- nrow(dat)\n", + "cat(\"N with exposure observed:\", N_total, \"\\n\")\n", + "\n", + "# Missingness summary\n", + "for (v in c(exposure, med_cols, outcome)) {\n", + " n_obs <- sum(!is.na(dat[[v]]))\n", + " cat(sprintf(\" %-30s: %d obs, %d NA\\n\", v, n_obs, N_total - n_obs))\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Sample Overlap ===\n", + "\n", + "APOC4_APOC2_AC_exp vs tdp_st4:\n", + " Both observed: 548\n", + " Mediator only: 45\n", + " Outcome only: 444\n", + " Neither (SNP only): 116\n", + "\n", + "APOC2_AC_exp vs tdp_st4:\n", + " Both observed: 548\n", + " Mediator only: 45\n", + " Outcome only: 444\n", + " Neither (SNP only): 116\n", + "\n", + "APOC1_DeJager_Mic_exp vs tdp_st4:\n", + " Both observed: 373\n", + " Mediator only: 46\n", + " Outcome only: 619\n", + " Neither (SNP only): 115\n", + "\n", + "APOE_Mega_Mic_exp vs tdp_st4:\n", + " Both observed: 654\n", + " Mediator only: 79\n", + " Outcome only: 338\n", + " Neither (SNP only): 82\n", + "\n", + "Overall: 813 have at least one mediator, 992 have outcome, 722 have both\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# SAMPLE OVERLAP STRUCTURE\n", + "# ============================================================\n", + "cat(\"=== Sample Overlap ===\\n\")\n", + "# For each mediator: how many have mediator only, outcome only, both, neither\n", + "for (m in med_cols) {\n", + " has_m <- !is.na(dat[[m]])\n", + " has_y <- !is.na(dat[[outcome]])\n", + " cat(sprintf(\"\\n%s vs %s:\\n\", m, outcome))\n", + " cat(sprintf(\" Both observed: %d\\n\", sum(has_m & has_y)))\n", + " cat(sprintf(\" Mediator only: %d\\n\", sum(has_m & !has_y)))\n", + " cat(sprintf(\" Outcome only: %d\\n\", sum(!has_m & has_y)))\n", + " cat(sprintf(\" Neither (SNP only): %d\\n\", sum(!has_m & !has_y)))\n", + "}\n", + "\n", + "# Overall: how many have at least one mediator or outcome observed\n", + "has_any_med <- rowSums(!is.na(dat[, med_cols])) > 0\n", + "has_out <- !is.na(dat[[outcome]])\n", + "cat(sprintf(\"\\nOverall: %d have at least one mediator, %d have outcome, %d have both\\n\",\n", + " sum(has_any_med), sum(has_out), sum(has_any_med & has_out)))" + ], + "id": "0db2f61e" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Covariate Specification\n", + "\n", + "**Strategy A: Covariates-in-model.** Each equation includes its own covariates directly. FIML handles different covariate missingness patterns automatically.\n", + "\n", + "| Equation | Covariates |\n", + "|----------|------------|\n", + "| APOC4_APOC2_AC_exp ~ SNP | msex_u, age_death_u, pmi_u, ROS_study_u |\n", + "| APOC2_AC_exp ~ SNP | msex_u, age_death_u, pmi_u, ROS_study_u |\n", + "| APOC1_DeJager_Mic_exp ~ SNP | msex_u, age_death_u, pmi_u |\n", + "| APOE_Mega_Mic_exp ~ SNP | msex_u, age_death_u, pmi_u |\n", + "| tdp_st4 ~ M1 + M2 + M3 + M4 + SNP | educ, apoe4_dose, apoe2_dose, msex_u, age_death_u |" + ], + "id": "1f2df82b" + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e3be75ab", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T04:33:46.392966Z", + "iopub.status.busy": "2026-04-01T04:33:46.391294Z", + "iopub.status.idle": "2026-04-01T04:33:46.487877Z", + "shell.execute_reply": "2026-04-01T04:33:46.485216Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Parallel Mediation Model ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "APOC4_APOC2_AC_exp ~ a1 * chr19_45001091_C_G + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC2_AC_exp ~ a2 * chr19_45001091_C_G + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC1_DeJager_Mic_exp ~ a3 * chr19_45001091_C_G + msex_u + age_death_u + pmi_u\n", + "APOE_Mega_Mic_exp ~ a4 * chr19_45001091_C_G + msex_u + age_death_u + pmi_u\n", + "tdp_st4 ~ b1 * APOC4_APOC2_AC_exp + b2 * APOC2_AC_exp + b3 * APOC1_DeJager_Mic_exp + b4 * APOE_Mega_Mic_exp + cp * chr19_45001091_C_G + educ + apoe4_dose + apoe2_dose + msex_u + age_death_u\n", + "ind1 := a1 * b1\n", + "ind2 := a2 * b2\n", + "ind3 := a3 * b3\n", + "ind4 := a4 * b4\n", + "total_indirect := ind1 + ind2 + ind3 + ind4\n", + "total := cp + total_indirect\n", + "prop_med := total_indirect / total\n", + "diff_1_2 := ind1 - ind2\n", + "diff_1_3 := ind1 - ind3\n", + "diff_1_4 := ind1 - ind4\n", + "diff_2_3 := ind2 - ind3\n", + "diff_2_4 := ind2 - ind4\n", + "diff_3_4 := ind3 - ind4\n", + "APOC4_APOC2_AC_exp ~~ APOC2_AC_exp\n", + "APOC4_APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC4_APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC1_DeJager_Mic_exp ~~ APOE_Mega_Mic_exp \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# BUILD PARALLEL MEDIATION MODEL (Design 2)\n", + "# ============================================================\n", + "\n", + "# Mediator equations\n", + "med_eqs <- c()\n", + "for (i in seq_len(n_med)) {\n", + " cov_str <- paste(med_covs[[med_cols[i]]], collapse=\" + \")\n", + " eq <- paste0(med_cols[i], \" ~ a\", i, \" * \", exposure, \" + \", cov_str)\n", + " med_eqs <- c(med_eqs, eq)\n", + "}\n", + "\n", + "# Outcome equation\n", + "med_terms <- paste0(\"b\", seq_len(n_med), \" * \", med_cols, collapse=\" + \")\n", + "out_cov_str <- paste(out_covs, collapse=\" + \")\n", + "y_eq <- paste0(outcome, \" ~ \", med_terms, \" + cp * \", exposure, \" + \", out_cov_str)\n", + "\n", + "# Defined parameters\n", + "ind_defs <- paste0(\"ind\", seq_len(n_med), \" := a\", seq_len(n_med), \" * b\", seq_len(n_med))\n", + "total_ind <- paste0(\"total_indirect := \", paste0(\"ind\", seq_len(n_med), collapse=\" + \"))\n", + "total_def <- \"total := cp + total_indirect\"\n", + "prop_med_def <- \"prop_med := total_indirect / total\"\n", + "\n", + "# Pairwise contrasts\n", + "contrasts <- c()\n", + "for (i in 1:(n_med-1)) {\n", + " for (j in (i+1):n_med) {\n", + " contrasts <- c(contrasts, paste0(\"diff_\", i, \"_\", j, \" := ind\", i, \" - ind\", j))\n", + " }\n", + "}\n", + "\n", + "# Residual correlations between mediators\n", + "cov_terms <- c()\n", + "for (i in 1:(n_med-1)) {\n", + " for (j in (i+1):n_med) {\n", + " cov_terms <- c(cov_terms, paste0(med_cols[i], \" ~~ \", med_cols[j]))\n", + " }\n", + "}\n", + "\n", + "model_str <- paste(c(med_eqs, y_eq, ind_defs, total_ind, total_def, prop_med_def, contrasts, cov_terms),\n", + " collapse=\"\\n\")\n", + "\n", + "cat(\"=== Parallel Mediation Model ===\\n\")\n", + "cat(model_str, \"\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Loading Pre-Computed Results\n", + "\n", + "The FIML, Bootstrap, and MNAR analyses were computed in a prior SLURM job. ", + "Results are loaded from CSV files on disk." + ], + "id": "e838a179" + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4fc74f4b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T04:33:46.334322Z", + "iopub.status.busy": "2026-04-01T04:33:46.332121Z", + "iopub.status.idle": "2026-04-01T04:33:46.387086Z", + "shell.execute_reply": "2026-04-01T04:33:46.384439Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FIML results loaded: 22 rows\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrap results loaded: 16 rows, convergence: 1000 / 1000 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR tipping distance: 1.55 SD\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# LOAD EXISTING RESULTS (FIML, Bootstrap, MNAR)\n", + "# ============================================================\n", + "fiml_res <- read.csv(file.path(dir_fiml, \"fiml_all_paths.csv\"), stringsAsFactors=FALSE)\n", + "boot_summary <- read.csv(file.path(dir_boot, \"bootstrap_results.csv\"), stringsAsFactors=FALSE)\n", + "mnar_tip <- read.csv(file.path(dir_mnar, \"mnar_tipping_summary.csv\"), stringsAsFactors=FALSE)\n", + "tip_dist <- mnar_tip$tipping_dist_SD[1]\n", + "\n", + "# Extract bootstrap convergence info\n", + "B <- 1000\n", + "n_converged <- boot_summary$n_converged[1]\n", + "\n", + "cat(\"FIML results loaded:\", nrow(fiml_res), \"rows\\n\")\n", + "cat(\"Bootstrap results loaded:\", nrow(boot_summary), \"rows, convergence:\", n_converged, \"/\", B, \"\\n\")\n", + "cat(\"MNAR tipping distance:\", sprintf(\"%.2f\", tip_dist), \"SD\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Method 1: FIML SEM (Primary Analysis)\n", + "\n", + "Full Information Maximum Likelihood estimation uses all available data (N = all subjects with SNP observed). No listwise deletion. `fixed.x=FALSE` ensures the SNP is modeled as a random variable so SNP-only subjects contribute to estimation." + ], + "id": "5b41e587" + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loading FIML results from CSV (already computed)...\n", + "=== FIML Results (loaded) ===\n", + " label est se ci_lower ci_upper pvalue\n", + "1 a1 0.307473539 0.05754170 0.19469388 0.42025320 9.117355e-08\n", + "2 a2 0.305148195 0.05762211 0.19221094 0.41808545 1.185748e-07\n", + "3 a3 0.248888901 0.06559217 0.12033061 0.37744720 1.479467e-04\n", + "4 a4 0.172634729 0.05046691 0.07372140 0.27154806 6.244853e-04\n", + "5 b1 -1.564594995 1.32450160 -4.16057042 1.03138043 2.374952e-01\n", + "6 b2 1.532544456 1.32401253 -1.06247242 4.12756133 2.470682e-01\n", + "7 b3 -0.008244204 0.08686289 -0.17849234 0.16200393 9.243859e-01\n", + "8 b4 0.079853899 0.07384064 -0.06487109 0.22457889 2.795034e-01\n", + "9 cp 0.163769486 0.05713159 0.05179362 0.27574535 4.149974e-03\n", + "10 ind1 -0.481071561 0.41705680 -1.29848786 0.33634474 2.487086e-01\n", + "11 ind2 0.467653175 0.41361835 -0.34302390 1.27833025 2.582069e-01\n", + "12 ind3 -0.002051891 0.02163714 -0.04445992 0.04035613 9.244484e-01\n", + "13 ind4 0.013785556 0.01335619 -0.01239210 0.03996321 3.020032e-01\n", + "14 total_indirect -0.001684721 0.01970591 -0.04030760 0.03693816 9.318693e-01\n", + "15 total 0.162084765 0.05405733 0.05613435 0.26803518 2.714131e-03\n", + "16 prop_med -0.010394071 0.12170285 -0.24892728 0.22813913 9.319392e-01\n", + "\n", + "N = 1153 (FIML)\n" + ] + } + ], + "source": [ + "# ============================================================\n# FIML SEM: Load previously computed results\n# ============================================================\ncat(\"Loading FIML results from CSV (already computed)...\\n\")\n\nfiml_res <- read.csv(file.path(dir_fiml, \"fiml_all_paths.csv\"), stringsAsFactors=FALSE)\nN_fiml <- fiml_res$N[1]\nN_total <- N_fiml # Set for downstream use\n\n# Reconstruct key_labels and nice_labels\nkey_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\", \"prop_med\")\n\nnice_labels <- c(\n a1=\"a1 (SNP->APOC4_APOC2)\", a2=\"a2 (SNP->APOC2)\", \n a3=\"a3 (SNP->APOC1_DeJager)\", a4=\"a4 (SNP->APOE_Mega)\",\n b1=\"b1 (APOC4_APOC2->tdp)\", b2=\"b2 (APOC2->tdp)\",\n b3=\"b3 (APOC1_DeJager->tdp)\", b4=\"b4 (APOE_Mega->tdp)\",\n cp=\"c' (direct SNP->tdp)\",\n ind1=\"ind1 (via APOC4_APOC2)\", ind2=\"ind2 (via APOC2)\",\n ind3=\"ind3 (via APOC1_DeJager)\", ind4=\"ind4 (via APOE_Mega)\",\n total_indirect=\"Total Indirect\", total=\"Total Effect\", prop_med=\"Proportion Mediated\"\n)\n\ncat(\"=== FIML Results (loaded) ===\\n\")\nprint(fiml_res[fiml_res$label %in% key_labels, c(\"label\", \"est\", \"se\", \"ci_lower\", \"ci_upper\", \"pvalue\")])\ncat(sprintf(\"\\nN = %d (FIML)\\n\", N_fiml))\n" + ], + "id": "12c9e93a" + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "FIML forest plot displayed (files already saved).\n" + ] + } + ], + "source": [ + "# ============================================================\n# FIML: Forest plot (from loaded results)\n# ============================================================\nplot_df <- fiml_res[fiml_res$label %in% key_labels, ]\nplot_df$label_f <- factor(plot_df$label, levels=rev(key_labels))\nplot_df$display_label <- nice_labels[as.character(plot_df$label)]\nplot_df$display_label <- factor(plot_df$display_label, levels=rev(nice_labels[key_labels]))\nplot_df$effect_type <- ifelse(grepl(\"^a\", plot_df$label), \"a-path\",\n ifelse(grepl(\"^b\", plot_df$label), \"b-path\",\n ifelse(plot_df$label == \"cp\", \"direct (c')\",\n ifelse(grepl(\"^ind[0-9]\", plot_df$label), \"specific indirect\",\n ifelse(plot_df$label == \"total_indirect\", \"total indirect\",\n ifelse(plot_df$label == \"total\", \"total effect\", \"proportion\"))))))\n\np_fiml <- ggplot(plot_df, aes(x=est, y=display_label, color=effect_type)) +\n geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n geom_pointrange(aes(xmin=ci_lower, xmax=ci_upper), size=0.5) +\n labs(title=\"FIML SEM: Parallel Mediation Path Estimates\",\n subtitle=paste0(\"SNP -> {APOC4_APOC2, APOC2, APOC1, APOE} -> tdp_st4 | N = \", N_fiml, \" (FIML)\"),\n x=\"Estimate (95% CI)\", y=\"\", color=\"Effect Type\") +\n theme_minimal(base_size=12) +\n theme(legend.position=\"bottom\")\nprint(p_fiml)\ncat(\"FIML forest plot displayed (files already saved).\\n\")\n" + ], + "id": "fca705e5" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### FIML Interpretation\n", + "\n", + "The FIML results show the path coefficients from the parallel mediation model. Key quantities:\n", + "- **a-paths** (a1-a4): Effect of SNP on each mediator (gene expression)\n", + "- **b-paths** (b1-b4): Effect of each mediator on tdp_st4 (controlling for SNP and other mediators)\n", + "- **c' (direct)**: Direct SNP effect on tdp_st4 (controlling for all mediators)\n", + "- **Specific indirect effects** (ind1-ind4): Mediation through each individual pathway\n", + "- **Total indirect**: Sum of all specific indirect effects\n", + "- **Proportion mediated**: Fraction of total effect operating through the mediators" + ], + "id": "9be1d97a" + }, + { + "cell_type": "markdown", + "id": "sens_3med_header", + "metadata": {}, + "source": [ + "### Sensitivity Analysis: 3-Mediator Model (Excluding APOC4_APOC2_AC_exp)\n", + "\n", + "The 4-mediator model revealed large but opposing indirect effects through APOC4_APOC2_AC_exp (ind1 = -0.481) and APOC2_AC_exp (ind2 = 0.468), suggesting collinearity-driven suppression. These two mediators measure overlapping gene expression (APOC4-APOC2 fusion transcript vs APOC2 alone).\n", + "\n", + "**Key question:** Does removing APOC4_APOC2_AC_exp allow APOC2_AC_exp to emerge as a significant mediator?\n", + "\n", + "This sensitivity analysis re-fits the parallel mediation model with only 3 mediators:\n", + "- APOC2_AC_exp (covariates: msex_u, age_death_u, pmi_u, ROS_study_u)\n", + "- APOC1_DeJager_Mic_exp (covariates: msex_u, age_death_u, pmi_u)\n", + "- APOE_Mega_Mic_exp (covariates: msex_u, age_death_u, pmi_u)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "sens_3med_code", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Data loaded: 1153 rows, 58 columns\n", + "N with exposure observed: 1153 \n", + "\n", + "=== Sensitivity Model (3 Mediators, excluding APOC4_APOC2_AC_exp) ===\n", + "APOC2_AC_exp ~ a1 * chr19_45001091_C_G + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC1_DeJager_Mic_exp ~ a2 * chr19_45001091_C_G + msex_u + age_death_u + pmi_u\n", + "APOE_Mega_Mic_exp ~ a3 * chr19_45001091_C_G + msex_u + age_death_u + pmi_u\n", + "tdp_st4 ~ b1 * APOC2_AC_exp + b2 * APOC1_DeJager_Mic_exp + b3 * APOE_Mega_Mic_exp + cp * chr19_45001091_C_G + educ + apoe4_dose + apoe2_dose + msex_u + age_death_u\n", + "ind1 := a1 * b1\n", + "ind2 := a2 * b2\n", + "ind3 := a3 * b3\n", + "total_indirect := ind1 + ind2 + ind3\n", + "total := cp + total_indirect\n", + "prop_med := total_indirect / total\n", + "diff_1_2 := ind1 - ind2\n", + "diff_1_3 := ind1 - ind3\n", + "diff_2_3 := ind2 - ind3\n", + "APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC1_DeJager_Mic_exp ~~ APOE_Mega_Mic_exp \n", + "\n", + "FIML converged. N = 1153 \n", + "\n", + "=== Sensitivity Analysis Results (3 mediators, no APOC4_APOC2_AC_exp) ===\n", + " label nice_label est se ci_lower\n", + "1 a1 a1 (SNP->APOC2_AC) 0.3052232730 0.05763116 0.19226828\n", + "2 a2 a2 (SNP->APOC1_DeJager) 0.2455942671 0.06558160 0.11705670\n", + "3 a3 a3 (SNP->APOE_Mega) 0.1723975327 0.05046504 0.07348787\n", + "4 b1 b1 (APOC2_AC->tdp) -0.0304630036 0.05401391 -0.13632833\n", + "5 b2 b2 (APOC1_DeJager->tdp) 0.0039506671 0.08565565 -0.16393131\n", + "6 b3 b3 (APOE_Mega->tdp) 0.0751371881 0.07341487 -0.06875331\n", + "7 cp c' (direct SNP->tdp) 0.1571826295 0.05686920 0.04572104\n", + "8 ind1 ind1 (via APOC2_AC) -0.0092980177 0.01655714 -0.04174942\n", + "9 ind2 ind2 (via APOC1_DeJager) 0.0009702612 0.02103402 -0.04025566\n", + "10 ind3 ind3 (via APOE_Mega) 0.0129534659 0.01320139 -0.01292079\n", + "11 total_indirect Total Indirect 0.0046257094 0.01860572 -0.03184082\n", + "12 total Total Effect 0.1618083389 0.05406102 0.05585068\n", + "13 prop_med Proportion Mediated 0.0285875834 0.11521802 -0.19723559\n", + " ci_upper pvalue N\n", + "1 0.41817826 1.182694e-07 1153\n", + "2 0.37413183 1.804903e-04 1153\n", + "3 0.27130719 6.350684e-04 1153\n", + "4 0.07540232 5.727648e-01 1153\n", + "5 0.17183265 9.632125e-01 1153\n", + "6 0.21902768 3.060903e-01 1153\n", + "7 0.26864422 5.710938e-03 1153\n", + "8 0.02315339 5.744081e-01 1153\n", + "9 0.04219619 9.632081e-01 1153\n", + "10 0.03882772 3.264845e-01 1153\n", + "11 0.04109224 8.036566e-01 1153\n", + "12 0.26776599 2.761878e-03 1153\n", + "13 0.25441076 8.040437e-01 1153\n", + "\n", + "CSV saved: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/tdp_st4/APOE4_2_adj/main_SEM_FIML/sensitivity_analysis/fiml_sensitivity_3med.csv \n", + "\n", + "=== Comparison: 4-Mediator vs 3-Mediator Model ===\n", + " Effect 4med_est 4med_p 3med_est 3med_p\n", + "1 b-path (APOC2_AC->tdp) 1.532544 0.247068 -0.030463 0.572765\n", + "2 Indirect (via APOC2_AC) 0.467653 0.258207 -0.009298 0.574408\n", + "3 Total Indirect -0.001685 0.931869 0.004626 0.803657\n", + "4 Direct (c') 0.163769 0.004150 0.157183 0.005711\n", + "5 Total Effect 0.162085 0.002714 0.161808 0.002762\n", + "\n", + "Comparison CSV saved: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/tdp_st4/APOE4_2_adj/main_SEM_FIML/sensitivity_analysis/comparison_4med_vs_3med.csv \n", + "\n", + "Forest plots saved.\n", + "\n", + "=== KEY FINDING ===\n", + "APOC2_AC_exp b-path: est=-0.0305, p=0.5728 (not significant)\n", + "APOC2_AC_exp indirect: est=-0.0093, p=0.5744 (not significant)\n", + "Total indirect: est=0.0046, p=0.8037 (not significant)\n", + "\n", + "Conclusion: Removing APOC4_APOC2_AC_exp does NOT make APOC2_AC_exp significant.\n", + "The suppression effect was artifactual collinearity, not masking a real mediation signal.\n", + "\n", + "Sensitivity analysis complete.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ }, + "metadata": {} + } + ], + "source": [ + "# ============================================================\n", + "# SENSITIVITY ANALYSIS: Parallel Mediation WITHOUT APOC4_APOC2_AC_exp\n", + "# ============================================================\n", + "\n", + "sens_dir <- file.path(RESULT_DIR, \"main_SEM_FIML\", \"sensitivity_analysis\")\n", + "dir.create(sens_dir, showWarnings=FALSE, recursive=TRUE)\n", + "\n", + "# 3-mediator setup\n", + "sens_med_cols <- c(\"APOC2_AC_exp\", \"APOC1_DeJager_Mic_exp\", \"APOE_Mega_Mic_exp\")\n", + "sens_n_med <- length(sens_med_cols)\n", + "\n", + "sens_med_covs <- list(\n", + " APOC2_AC_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\"),\n", + " APOC1_DeJager_Mic_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\"),\n", + " APOE_Mega_Mic_exp = c(\"msex_u\", \"age_death_u\", \"pmi_u\")\n", + ")\n", + "\n", + "# Build model\n", + "sens_med_eqs <- c()\n", + "for (i in seq_len(sens_n_med)) {\n", + " cov_str <- paste(sens_med_covs[[sens_med_cols[i]]], collapse=\" + \")\n", + " eq <- paste0(sens_med_cols[i], \" ~ a\", i, \" * \", exposure, \" + \", cov_str)\n", + " sens_med_eqs <- c(sens_med_eqs, eq)\n", + "}\n", + "\n", + "sens_med_terms <- paste0(\"b\", seq_len(sens_n_med), \" * \", sens_med_cols, collapse=\" + \")\n", + "sens_y_eq <- paste0(outcome, \" ~ \", sens_med_terms, \" + cp * \", exposure, \" + \", paste(out_covs, collapse=\" + \"))\n", + "\n", + "sens_ind_defs <- paste0(\"ind\", seq_len(sens_n_med), \" := a\", seq_len(sens_n_med), \" * b\", seq_len(sens_n_med))\n", + "sens_total_ind <- paste0(\"total_indirect := \", paste0(\"ind\", seq_len(sens_n_med), collapse=\" + \"))\n", + "sens_total_def <- \"total := cp + total_indirect\"\n", + "sens_prop_def <- \"prop_med := total_indirect / total\"\n", + "\n", + "sens_contrasts <- c()\n", + "for (i in 1:(sens_n_med-1)) {\n", + " for (j in (i+1):sens_n_med) {\n", + " sens_contrasts <- c(sens_contrasts, paste0(\"diff_\", i, \"_\", j, \" := ind\", i, \" - ind\", j))\n", + " }\n", + "}\n", + "\n", + "sens_cov_terms <- c()\n", + "for (i in 1:(sens_n_med-1)) {\n", + " for (j in (i+1):sens_n_med) {\n", + " sens_cov_terms <- c(sens_cov_terms, paste0(sens_med_cols[i], \" ~~ \", sens_med_cols[j]))\n", + " }\n", + "}\n", + "\n", + "sens_model_str <- paste(c(sens_med_eqs, sens_y_eq, sens_ind_defs, sens_total_ind,\n", + " sens_total_def, sens_prop_def, sens_contrasts, sens_cov_terms),\n", + " collapse=\"\\n\")\n", + "\n", + "cat(\"=== Sensitivity Model (3 Mediators, excluding APOC4_APOC2_AC_exp) ===\\n\")\n", + "cat(sens_model_str, \"\\n\\n\")\n", + "\n", + "# Fit FIML\n", + "sens_fit <- tryCatch(\n", + " sem(sens_model_str, data=dat, missing=\"fiml\", fixed.x=FALSE, estimator=\"ML\"),\n", + " error = function(e) { cat(\"SEM ERROR:\", e$message, \"\\n\"); NULL }\n", + ")\n", + "\n", + "if (!is.null(sens_fit) && lavInspect(sens_fit, \"converged\")) {\n", + " N_sens <- lavInspect(sens_fit, \"nobs\")\n", + " cat(\"FIML converged. N =\", N_sens, \"\\n\\n\")\n", + " \n", + " pe_sens <- parameterEstimates(sens_fit, ci=TRUE)\n", + " \n", + " sens_key_labels <- c(paste0(\"a\", 1:sens_n_med), paste0(\"b\", 1:sens_n_med), \"cp\",\n", + " paste0(\"ind\", 1:sens_n_med), \"total_indirect\", \"total\", \"prop_med\")\n", + " \n", + " sens_nice_labels <- c(\n", + " a1=\"a1 (SNP->APOC2_AC)\", a2=\"a2 (SNP->APOC1_DeJager)\", a3=\"a3 (SNP->APOE_Mega)\",\n", + " b1=\"b1 (APOC2_AC->tdp)\", b2=\"b2 (APOC1_DeJager->tdp)\", b3=\"b3 (APOE_Mega->tdp)\",\n", + " cp=\"c' (direct SNP->tdp)\",\n", + " ind1=\"ind1 (via APOC2_AC)\", ind2=\"ind2 (via APOC1_DeJager)\", ind3=\"ind3 (via APOE_Mega)\",\n", + " total_indirect=\"Total Indirect\", total=\"Total Effect\", prop_med=\"Proportion Mediated\"\n", + " )\n", + " \n", + " sens_results <- list()\n", + " for (lab in sens_key_labels) {\n", + " row <- pe_sens[pe_sens$label == lab, ]\n", + " if (nrow(row) == 1) {\n", + " sens_results[[lab]] <- data.frame(\n", + " exposure=exposure, direction=\"D1\", label=lab,\n", + " nice_label=sens_nice_labels[lab],\n", + " est=row$est, se=row$se, ci_lower=row$ci.lower, ci_upper=row$ci.upper,\n", + " pvalue=row$pvalue, N=N_sens, method=\"FIML_sensitivity_3med\",\n", + " stringsAsFactors=FALSE\n", + " )\n", + " }\n", + " }\n", + " sens_df <- do.call(rbind, sens_results)\n", + " rownames(sens_df) <- NULL\n", + " \n", + " cat(\"=== Sensitivity Analysis Results ===\\n\")\n", + " print(sens_df[, c(\"label\", \"nice_label\", \"est\", \"se\", \"ci_lower\", \"ci_upper\", \"pvalue\", \"N\")], digits=4)\n", + " \n", + " # Save CSV\n", + " write.csv(sens_df, file.path(sens_dir, \"fiml_sensitivity_3med.csv\"), row.names=FALSE)\n", + " cat(\"\\nCSV saved:\", file.path(sens_dir, \"fiml_sensitivity_3med.csv\"), \"\\n\")\n", + " \n", + " # Comparison with 4-mediator model\n", + " orig_path <- file.path(dir_fiml, \"fiml_all_paths.csv\")\n", + " if (file.exists(orig_path)) {\n", + " orig <- read.csv(orig_path, stringsAsFactors=FALSE)\n", + " compare_df <- data.frame(\n", + " Effect = c(\"b-path (APOC2_AC->tdp)\", \"Indirect (via APOC2_AC)\",\n", + " \"Total Indirect\", \"Direct (c')\", \"Total Effect\"),\n", + " `4med_est` = c(orig$est[orig$label==\"b2\"], orig$est[orig$label==\"ind2\"],\n", + " orig$est[orig$label==\"total_indirect\"], orig$est[orig$label==\"cp\"],\n", + " orig$est[orig$label==\"total\"]),\n", + " `4med_p` = c(orig$pvalue[orig$label==\"b2\"], orig$pvalue[orig$label==\"ind2\"],\n", + " orig$pvalue[orig$label==\"total_indirect\"], orig$pvalue[orig$label==\"cp\"],\n", + " orig$pvalue[orig$label==\"total\"]),\n", + " `3med_est` = c(sens_df$est[sens_df$label==\"b1\"], sens_df$est[sens_df$label==\"ind1\"],\n", + " sens_df$est[sens_df$label==\"total_indirect\"], sens_df$est[sens_df$label==\"cp\"],\n", + " sens_df$est[sens_df$label==\"total\"]),\n", + " `3med_p` = c(sens_df$pvalue[sens_df$label==\"b1\"], sens_df$pvalue[sens_df$label==\"ind1\"],\n", + " sens_df$pvalue[sens_df$label==\"total_indirect\"], sens_df$pvalue[sens_df$label==\"cp\"],\n", + " sens_df$pvalue[sens_df$label==\"total\"]),\n", + " stringsAsFactors=FALSE, check.names=FALSE\n", + " )\n", + " cat(\"\\n=== Comparison: 4-Mediator vs 3-Mediator Model ===\\n\")\n", + " print(compare_df, digits=4)\n", + " write.csv(compare_df, file.path(sens_dir, \"comparison_4med_vs_3med.csv\"), row.names=FALSE)\n", + " }\n", + " \n", + " # Forest plot\n", + " plot_df <- sens_df[sens_df$label %in% sens_key_labels, ]\n", + " plot_df$nice_label <- factor(plot_df$nice_label, levels=rev(sens_nice_labels[sens_key_labels]))\n", + " \n", + " p_sens <- ggplot(plot_df, aes(x=est, y=nice_label, xmin=ci_lower, xmax=ci_upper)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(color=\"steelblue\", size=0.5) +\n", + " labs(\n", + " title=\"Sensitivity Analysis: FIML SEM (3 Mediators, excl. APOC4_APOC2_AC_exp)\",\n", + " subtitle=paste0(\"N = \", N_sens, \" (FIML) | Exposure: \", exposure, \" | Outcome: \", outcome),\n", + " x=\"Estimate (95% CI)\", y=NULL\n", + " ) +\n", + " theme_minimal(base_size=12) +\n", + " theme(plot.title=element_text(size=11, face=\"bold\"), plot.subtitle=element_text(size=9))\n", + " \n", + " plot_h <- max(5, nrow(plot_df) * 0.35 + 1.5)\n", + " ggsave(file.path(sens_dir, \"fiml_sensitivity_3med_forest.png\"), p_sens,\n", + " width=10, height=plot_h, dpi=150, limitsize=FALSE)\n", + " ggsave(file.path(sens_dir, \"fiml_sensitivity_3med_forest.pdf\"), p_sens,\n", + " width=10, height=plot_h, limitsize=FALSE)\n", + " print(p_sens)\n", + " cat(\"\\nForest plots saved.\\n\")\n", + " \n", + " # Key finding\n", + " ind1_p <- sens_df$pvalue[sens_df$label == \"ind1\"]\n", + " ind1_est <- sens_df$est[sens_df$label == \"ind1\"]\n", + " b1_p <- sens_df$pvalue[sens_df$label == \"b1\"]\n", + " b1_est <- sens_df$est[sens_df$label == \"b1\"]\n", + " ti_p <- sens_df$pvalue[sens_df$label == \"total_indirect\"]\n", + " ti_est <- sens_df$est[sens_df$label == \"total_indirect\"]\n", + " \n", + " cat(\"\\n=== KEY FINDING ===\\n\")\n", + " cat(sprintf(\"APOC2_AC_exp b-path: est=%.4f, p=%.4f (%s)\\n\", b1_est, b1_p,\n", + " ifelse(b1_p < 0.05, \"SIGNIFICANT\", \"not significant\")))\n", + " cat(sprintf(\"APOC2_AC_exp indirect: est=%.4f, p=%.4f (%s)\\n\", ind1_est, ind1_p,\n", + " ifelse(ind1_p < 0.05, \"SIGNIFICANT\", \"not significant\")))\n", + " cat(sprintf(\"Total indirect: est=%.4f, p=%.4f (%s)\\n\", ti_est, ti_p,\n", + " ifelse(ti_p < 0.05, \"SIGNIFICANT\", \"not significant\")))\n", + " \n", + " if (ind1_p >= 0.05) {\n", + " cat(\"\\nConclusion: Removing APOC4_APOC2_AC_exp does NOT make APOC2_AC_exp significant.\\n\")\n", + " cat(\"The suppression effect was artifactual collinearity, not masking a real mediation signal.\\n\")\n", + " } else {\n", + " cat(\"\\nConclusion: Removing APOC4_APOC2_AC_exp DOES make APOC2_AC_exp significant!\\n\")\n", + " cat(\"The suppression effect was masking a genuine mediation pathway through APOC2_AC_exp.\\n\")\n", + " }\n", + "} else {\n", + " cat(\"ERROR: Sensitivity FIML did not converge.\\n\")\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Method 2: Bootstrap (FIML Inside Each Replicate)\n", + "\n", + "Non-parametric bootstrap with FIML SEM inside each replicate. This provides percentile confidence intervals that do not assume normality of the indirect effect distribution. 1000 resamples of the full dataset." + ], + "id": "f342c834" + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loading Bootstrap results from CSV (already computed)...\n", + "=== Bootstrap Results (loaded) ===\n", + " label est se ci_lower ci_upper pvalue\n", + "1 a1 0.309801518 0.05697373 0.19958362 0.42008011 0.000\n", + "2 a2 0.307610239 0.05727292 0.19686237 0.41759542 0.000\n", + "3 a3 0.249416015 0.06522680 0.12844105 0.38332076 0.000\n", + "4 a4 0.174839894 0.04916388 0.08016570 0.26691441 0.000\n", + "5 b1 -1.563669048 1.35058642 -4.17932564 1.05352036 0.244\n", + "6 b2 1.530685288 1.34932553 -1.10542100 4.14489659 0.260\n", + "7 b3 -0.010305205 0.09089422 -0.18525135 0.17405443 0.890\n", + "8 b4 0.081997760 0.07674822 -0.06890696 0.22841881 0.280\n", + "9 cp 0.163636786 0.05880451 0.05657163 0.28336728 0.002\n", + "10 ind1 -0.483344705 0.44065229 -1.40619721 0.33166531 0.244\n", + "11 ind2 0.469770712 0.43711513 -0.32477765 1.38819774 0.260\n", + "12 ind3 -0.003027796 0.02364969 -0.05266264 0.04362675 0.890\n", + "13 ind4 0.014489895 0.01465326 -0.01122902 0.04614738 0.280\n", + "14 total_indirect -0.002111895 0.02138359 -0.04387664 0.03868571 0.908\n", + "15 total 0.161524891 0.05521717 0.05710852 0.27840068 0.000\n", + "16 prop_med -0.015979188 0.17173699 -0.36028180 0.31261630 0.908\n", + "\n", + "Bootstrap: 1000/1000 converged\n" + ] + } + ], + "source": [ + "# ============================================================\n# BOOTSTRAP: Load previously computed results\n# ============================================================\ncat(\"Loading Bootstrap results from CSV (already computed)...\\n\")\n\nboot_summary <- read.csv(file.path(dir_boot, \"bootstrap_results.csv\"), stringsAsFactors=FALSE)\nn_converged <- boot_summary$n_converged[1]\nB <- 1000 # original number of replicates\n\ncat(\"=== Bootstrap Results (loaded) ===\\n\")\nprint(boot_summary[boot_summary$label %in% key_labels, c(\"label\", \"est\", \"se\", \"ci_lower\", \"ci_upper\", \"pvalue\")])\ncat(sprintf(\"\\nBootstrap: %d/%d converged\\n\", n_converged, B))\n" + ], + "id": "4930fa81" + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Bootstrap forest plot displayed (files already saved).\n" + ] + } + ], + "source": [ + "# ============================================================\n# BOOTSTRAP: Forest plot (from loaded results)\n# ============================================================\nboot_plot <- boot_summary[boot_summary$label %in% key_labels, ]\nboot_plot$display_label <- nice_labels[boot_plot$label]\nboot_plot$display_label <- factor(boot_plot$display_label, levels=rev(nice_labels[key_labels]))\nboot_plot$effect_type <- ifelse(grepl(\"^a\", boot_plot$label), \"a-path\",\n ifelse(grepl(\"^b\", boot_plot$label), \"b-path\",\n ifelse(boot_plot$label == \"cp\", \"direct (c')\",\n ifelse(grepl(\"^ind[0-9]\", boot_plot$label), \"specific indirect\",\n ifelse(boot_plot$label == \"total_indirect\", \"total indirect\",\n ifelse(boot_plot$label == \"total\", \"total effect\", \"proportion\"))))))\n\np_boot_forest <- ggplot(boot_plot, aes(x=est, y=display_label, color=effect_type)) +\n geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n geom_pointrange(aes(xmin=ci_lower, xmax=ci_upper), size=0.5) +\n labs(title=\"Bootstrap: Parallel Mediation Path Estimates\",\n subtitle=paste0(\"N = \", N_total, \" (FIML), \", n_converged, \" resamples\"),\n x=\"Estimate (95% Percentile CI)\", y=\"\", color=\"Effect Type\") +\n theme_minimal(base_size=12) +\n theme(legend.position=\"bottom\")\nprint(p_boot_forest)\ncat(\"Bootstrap forest plot displayed (files already saved).\\n\")\n" + ], + "id": "89ee51fe" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bootstrap Interpretation\n", + "\n", + "The bootstrap results provide non-parametric percentile confidence intervals. The key comparison with FIML:\n", + "- If bootstrap CIs are similar to FIML CIs, the normality assumption for the indirect effect is reasonable\n", + "- If bootstrap CIs are wider or shifted, the indirect effect distribution may be skewed\n", + "- The convergence rate indicates model stability across resampled datasets" + ], + "id": "d9f885d9" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Method 3: MNAR Sensitivity Analysis\n", + "\n", + "Assesses robustness of mediation results to departures from the Missing At Random (MAR) assumption. For each point on a 2D grid (delta_mediators x delta_outcome), missing values are imputed at `mean + delta * SD`, then the model is re-fit with ML. The tipping point is the minimum delta distance where the indirect effect becomes non-significant." + ], + "id": "d5f7f2d0" + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loading MNAR results from CSV (already computed)...\n", + "MNAR grid: 225 points\n", + "Tipping point distance: 1.55 SD\n", + "Origin estimate: 0.00030, p = 0.9749\n" + ] + } + ], + "source": [ + "# ============================================================\n# MNAR SENSITIVITY: Load previously computed results\n# ============================================================\ncat(\"Loading MNAR results from CSV (already computed)...\\n\")\n\nmnar_grid <- read.csv(file.path(dir_mnar, \"mnar_grid_results.csv\"), stringsAsFactors=FALSE)\ntip_df <- read.csv(file.path(dir_mnar, \"mnar_tipping_summary.csv\"), stringsAsFactors=FALSE)\ntip_dist <- tip_df$tipping_dist_SD[1]\n\ncat(sprintf(\"MNAR grid: %d points\\n\", nrow(mnar_grid)))\ncat(sprintf(\"Tipping point distance: %.2f SD\\n\", tip_dist))\ncat(sprintf(\"Origin estimate: %.5f, p = %.4f\\n\", tip_df$origin_est[1], tip_df$origin_p[1]))\n" + ], + "id": "6a0d8369" + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MNAR contour plots and 1D slices already saved to disk.\n", + "Files:\n", + " mnar_contour_indirect_chr19_45001091_C_G.png\n", + " mnar_contour_pvalue_chr19_45001091_C_G.png\n", + " mnar_1d_slices_chr19_45001091_C_G.png\n" + ] + } + ], + "source": [ + "# ============================================================\n# MNAR: Display contour plots (already saved)\n# ============================================================\ncat(\"MNAR contour plots and 1D slices already saved to disk.\\n\")\ncat(\"Files:\\n\")\ncat(\" mnar_contour_indirect_chr19_45001091_C_G.png\\n\")\ncat(\" mnar_contour_pvalue_chr19_45001091_C_G.png\\n\")\ncat(\" mnar_1d_slices_chr19_45001091_C_G.png\\n\")\n" + ], + "id": "a1c44202" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MNAR Interpretation\n", + "\n", + "The MNAR sensitivity analysis examines how robust the mediation result is to departures from the Missing At Random assumption. The tipping point distance (in SD units) tells us how far the missing data mechanism would need to deviate from MAR before the indirect effect becomes non-significant. A tipping point > 1.0 SD is generally considered robust; < 0.5 SD is fragile." + ], + "id": "9fc86a48" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Method 4: Bayesian SEM (blavaan / Stan)\n", + "\n", + "Bayesian estimation via Stan provides posterior distributions for all parameters, enabling direct probability statements (e.g., P(indirect > 0)). Missing data is handled via Stan's data augmentation. Using 4 chains with 1000 burnin + 2000 sampling iterations each." + ], + "id": "e2456149" + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c485b99b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T04:33:46.494440Z", + "iopub.status.busy": "2026-04-01T04:33:46.492180Z", + "iopub.status.idle": "2026-04-01T11:41:21.803505Z", + "shell.execute_reply": "2026-04-01T11:41:21.800650Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting Bayesian SEM via blavaan/Stan...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This may take 30-60 minutes for a parallel mediation model with 4 mediators.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start time: 2026-04-01 00:33:46 \n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 1).\n", + "Chain 1: \n", + "Chain 1: Gradient evaluation took 0.006033 seconds\n", + "Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 60.33 seconds.\n", + "Chain 1: Adjust your expectations accordingly!\n", + "Chain 1: \n", + "Chain 1: \n", + "Chain 1: Iteration: 1 / 3000 [ 0%] (Warmup)\n", + "Chain 1: Iteration: 300 / 3000 [ 10%] (Warmup)\n", + "Chain 1: Iteration: 600 / 3000 [ 20%] (Warmup)\n", + "Chain 1: Iteration: 900 / 3000 [ 30%] (Warmup)\n", + "Chain 1: Iteration: 1001 / 3000 [ 33%] (Sampling)\n", + "Chain 1: Iteration: 1300 / 3000 [ 43%] (Sampling)\n", + "Chain 1: Iteration: 1600 / 3000 [ 53%] (Sampling)\n", + "Chain 1: Iteration: 1900 / 3000 [ 63%] (Sampling)\n", + "Chain 1: Iteration: 2200 / 3000 [ 73%] (Sampling)\n", + "Chain 1: Iteration: 2500 / 3000 [ 83%] (Sampling)\n", + "Chain 1: Iteration: 2800 / 3000 [ 93%] (Sampling)\n", + "Chain 1: Iteration: 3000 / 3000 [100%] (Sampling)\n", + "Chain 1: \n", + "Chain 1: Elapsed Time: 1919.15 seconds (Warm-up)\n", + "Chain 1: 4454.56 seconds (Sampling)\n", + "Chain 1: 6373.71 seconds (Total)\n", + "Chain 1: \n", + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 2).\n", + "Chain 2: \n", + "Chain 2: Gradient evaluation took 0.002469 seconds\n", + "Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 24.69 seconds.\n", + "Chain 2: Adjust your expectations accordingly!\n", + "Chain 2: \n", + "Chain 2: \n", + "Chain 2: Iteration: 1 / 3000 [ 0%] (Warmup)\n", + "Chain 2: Iteration: 300 / 3000 [ 10%] (Warmup)\n", + "Chain 2: Iteration: 600 / 3000 [ 20%] (Warmup)\n", + "Chain 2: Iteration: 900 / 3000 [ 30%] (Warmup)\n", + "Chain 2: Iteration: 1001 / 3000 [ 33%] (Sampling)\n", + "Chain 2: Iteration: 1300 / 3000 [ 43%] (Sampling)\n", + "Chain 2: Iteration: 1600 / 3000 [ 53%] (Sampling)\n", + "Chain 2: Iteration: 1900 / 3000 [ 63%] (Sampling)\n", + "Chain 2: Iteration: 2200 / 3000 [ 73%] (Sampling)\n", + "Chain 2: Iteration: 2500 / 3000 [ 83%] (Sampling)\n", + "Chain 2: Iteration: 2800 / 3000 [ 93%] 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Increase max_treedepth above 10. See\n", + "https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cExamine the pairs() plot to diagnose sampling problems\n", + "\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cBulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.\n", + "Running the chains for more iterations may help. See\n", + "https://mc-stan.org/misc/warnings.html#bulk-ess\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cTail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.\n", + "Running the chains for more iterations may help. See\n", + "https://mc-stan.org/misc/warnings.html#tail-ess\u201d\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing post-estimation metrics (including lvs if requested)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201clavaan->lav_model_vcov_se(): \n", + " The following boostrapped defined parameters have a high (>5) ratio of \n", + " standard deviation to median absolute deviation: prop_med. P-values and \n", + " confidence intervals may not match.\u201d\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian SEM fit complete at: 2026-04-01 07:41:21 \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# BAYESIAN SEM: Fit model (blavaan)\n", + "# ============================================================\n", + "cat(\"Fitting Bayesian SEM via blavaan/Stan...\\n\")\n", + "cat(\"This may take 30-60 minutes for a parallel mediation model with 4 mediators.\\n\")\n", + "cat(\"Start time:\", format(Sys.time()), \"\\n\\n\")\n", + "\n", + "fit_bayes <- tryCatch(\n", + " bsem(model_str, data=dat, fixed.x=FALSE, target=\"stan\",\n", + " n.chains=4, burnin=1000, sample=2000, seed=42),\n", + " error = function(e) {\n", + " cat(\"blavaan error (4 chains):\", e$message, \"\\n\")\n", + " cat(\"Retrying with 2 chains and more iterations...\\n\")\n", + " tryCatch(\n", + " bsem(model_str, data=dat, fixed.x=FALSE, target=\"stan\",\n", + " n.chains=2, burnin=1500, sample=3000, seed=42),\n", + " error = function(e2) {\n", + " cat(\"blavaan retry error:\", e2$message, \"\\n\")\n", + " NULL\n", + " }\n", + " )\n", + " }\n", + ")\n", + "\n", + "if (!is.null(fit_bayes)) {\n", + " cat(\"Bayesian SEM fit complete at:\", format(Sys.time()), \"\\n\")\n", + "} else {\n", + " cat(\"WARNING: Bayesian SEM failed completely.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b9ddf0fe", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T11:41:24.605472Z", + "iopub.status.busy": "2026-04-01T11:41:24.603665Z", + "iopub.status.idle": "2026-04-01T11:41:26.156265Z", + "shell.execute_reply": "2026-04-01T11:41:26.153577Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== parTable: labeled rows ===\n", + " id lhs op rhs label est\n", + "1 1 APOC4_APOC2_AC_exp ~ chr19_45001091_C_G a1 0.308\n", + "6 6 APOC2_AC_exp ~ chr19_45001091_C_G a2 0.306\n", + "11 11 APOC1_DeJager_Mic_exp ~ chr19_45001091_C_G a3 0.251\n", + "15 15 APOE_Mega_Mic_exp ~ chr19_45001091_C_G a4 0.176\n", + "19 19 tdp_st4 ~ APOC4_APOC2_AC_exp b1 -1.642\n", + "20 20 tdp_st4 ~ APOC2_AC_exp b2 1.613\n", + "21 21 tdp_st4 ~ APOC1_DeJager_Mic_exp b3 -0.013\n", + "22 22 tdp_st4 ~ APOE_Mega_Mic_exp b4 0.083\n", + "23 23 tdp_st4 ~ chr19_45001091_C_G cp 0.164\n", + "89 89 ind1 := a1*b1 ind1 -0.507\n", + "90 90 ind2 := a2*b2 ind2 0.494\n", + "91 91 ind3 := a3*b3 ind3 -0.003\n", + "92 92 ind4 := a4*b4 ind4 0.015\n", + "93 93 total_indirect := ind1+ind2+ind3+ind4 total_indirect -0.002\n", + "94 94 total := cp+total_indirect total 0.162\n", + "95 95 prop_med := total_indirect/total prop_med -0.010\n", + "96 96 diff_1_2 := ind1-ind2 diff_1_2 -1.000\n", + "97 97 diff_1_3 := ind1-ind3 diff_1_3 -0.503\n", + "98 98 diff_1_4 := ind1-ind4 diff_1_4 -0.521\n", + "99 99 diff_2_3 := ind2-ind3 diff_2_3 0.497\n", + "100 100 diff_2_4 := ind2-ind4 diff_2_4 0.479\n", + "101 101 diff_3_4 := ind3-ind4 diff_3_4 -0.018\n", + " se\n", + "1 0.057\n", + "6 0.057\n", + "11 0.065\n", + "15 0.052\n", + "19 1.283\n", + "20 1.284\n", + "21 0.086\n", + "22 0.074\n", + "23 0.058\n", + "89 0.410\n", + "90 0.407\n", + "91 0.022\n", + "92 0.014\n", + "93 0.021\n", + "94 0.055\n", + "95 3.979\n", + "96 0.816\n", + "97 0.407\n", + "98 0.411\n", + "99 0.411\n", + "100 0.406\n", + "101 0.034\n", + "\n", + "Posterior draw matrix: 8000 draws x 101 columns\n", + "Column names:\n", + " [1] \"a1\" \n", + " [2] \"APOC4_APOC2_AC_exp~msex_u\" \n", + " [3] \"APOC4_APOC2_AC_exp~age_death_u\" \n", + " [4] \"APOC4_APOC2_AC_exp~pmi_u\" \n", + " [5] \"APOC4_APOC2_AC_exp~ROS_study_u\" \n", + " [6] \"a2\" \n", + " [7] \"APOC2_AC_exp~msex_u\" \n", + " [8] \"APOC2_AC_exp~age_death_u\" \n", + " [9] \"APOC2_AC_exp~pmi_u\" \n", + " [10] \"APOC2_AC_exp~ROS_study_u\" \n", + " [11] \"a3\" \n", + " [12] \"APOC1_DeJager_Mic_exp~msex_u\" \n", + " [13] \"APOC1_DeJager_Mic_exp~age_death_u\" \n", + " [14] \"APOC1_DeJager_Mic_exp~pmi_u\" \n", + " [15] \"a4\" \n", + " [16] \"APOE_Mega_Mic_exp~msex_u\" \n", + " [17] \"APOE_Mega_Mic_exp~age_death_u\" \n", + " [18] \"APOE_Mega_Mic_exp~pmi_u\" \n", + " [19] \"b1\" \n", + " [20] \"b2\" \n", + " [21] \"b3\" \n", + " [22] \"b4\" \n", + " [23] \"cp\" \n", + " [24] \"tdp_st4~educ\" \n", + " [25] \"tdp_st4~apoe4_dose\" \n", + " [26] \"tdp_st4~apoe2_dose\" \n", + " [27] \"tdp_st4~msex_u\" \n", + " [28] \"tdp_st4~age_death_u\" \n", + " [29] \"APOC4_APOC2_AC_exp~~APOC2_AC_exp\" \n", + " [30] \"APOC4_APOC2_AC_exp~~APOC1_DeJager_Mic_exp\" \n", + " [31] \"APOC4_APOC2_AC_exp~~APOE_Mega_Mic_exp\" \n", + " [32] \"APOC2_AC_exp~~APOC1_DeJager_Mic_exp\" \n", + " [33] \"APOC2_AC_exp~~APOE_Mega_Mic_exp\" \n", + " [34] \"APOC1_DeJager_Mic_exp~~APOE_Mega_Mic_exp\" \n", + " [35] \"APOC4_APOC2_AC_exp~~APOC4_APOC2_AC_exp\" \n", + " [36] \"APOC2_AC_exp~~APOC2_AC_exp\" \n", + " [37] \"APOC1_DeJager_Mic_exp~~APOC1_DeJager_Mic_exp\"\n", + " [38] \"APOE_Mega_Mic_exp~~APOE_Mega_Mic_exp\" \n", + " [39] \"tdp_st4~~tdp_st4\" \n", + " [40] \"chr19_45001091_C_G~~chr19_45001091_C_G\" \n", + " [41] \"chr19_45001091_C_G~~msex_u\" \n", + " [42] \"chr19_45001091_C_G~~age_death_u\" \n", + " [43] \"chr19_45001091_C_G~~pmi_u\" \n", + " [44] \"chr19_45001091_C_G~~ROS_study_u\" \n", + " [45] \"chr19_45001091_C_G~~educ\" \n", + " [46] \"chr19_45001091_C_G~~apoe4_dose\" \n", + " [47] \"chr19_45001091_C_G~~apoe2_dose\" \n", + " [48] \"msex_u~~msex_u\" \n", + " [49] \"msex_u~~age_death_u\" \n", + " [50] \"msex_u~~pmi_u\" \n", + " [51] \"msex_u~~ROS_study_u\" \n", + " [52] \"msex_u~~educ\" \n", + " [53] \"msex_u~~apoe4_dose\" \n", + " [54] \"msex_u~~apoe2_dose\" \n", + " [55] \"age_death_u~~age_death_u\" \n", + " [56] \"age_death_u~~pmi_u\" \n", + " [57] \"age_death_u~~ROS_study_u\" \n", + " [58] \"age_death_u~~educ\" \n", + " [59] \"age_death_u~~apoe4_dose\" \n", + " [60] \"age_death_u~~apoe2_dose\" \n", + " [61] \"pmi_u~~pmi_u\" \n", + " [62] \"pmi_u~~ROS_study_u\" \n", + " [63] \"pmi_u~~educ\" \n", + " [64] \"pmi_u~~apoe4_dose\" \n", + " [65] \"pmi_u~~apoe2_dose\" \n", + " [66] \"ROS_study_u~~ROS_study_u\" \n", + " [67] \"ROS_study_u~~educ\" \n", + " [68] \"ROS_study_u~~apoe4_dose\" \n", + " [69] \"ROS_study_u~~apoe2_dose\" \n", + " [70] \"educ~~educ\" \n", + " [71] \"educ~~apoe4_dose\" \n", + " [72] \"educ~~apoe2_dose\" \n", + " [73] \"apoe4_dose~~apoe4_dose\" \n", + " [74] \"apoe4_dose~~apoe2_dose\" \n", + " [75] \"apoe2_dose~~apoe2_dose\" \n", + " [76] \"APOC4_APOC2_AC_exp~1\" \n", + " [77] \"APOC2_AC_exp~1\" \n", + " [78] \"APOC1_DeJager_Mic_exp~1\" \n", + " [79] \"APOE_Mega_Mic_exp~1\" \n", + " [80] \"tdp_st4~1\" \n", + " [81] \"chr19_45001091_C_G~1\" \n", + " [82] \"msex_u~1\" \n", + " [83] \"age_death_u~1\" \n", + " [84] \"pmi_u~1\" \n", + " [85] \"ROS_study_u~1\" \n", + " [86] \"educ~1\" \n", + " [87] \"apoe4_dose~1\" \n", + " [88] \"apoe2_dose~1\" \n", + " [89] \"ind1\" \n", + " [90] \"ind2\" \n", + " [91] \"ind3\" \n", + " [92] \"ind4\" \n", + " [93] \"total_indirect\" \n", + " [94] \"total\" \n", + " [95] \"prop_med\" \n", + " [96] \"diff_1_2\" \n", + " [97] \"diff_1_3\" \n", + " [98] \"diff_1_4\" \n", + " [99] \"diff_2_3\" \n", + "[100] \"diff_2_4\" \n", + "[101] \"diff_3_4\" \n", + "\n", + "=== Label -> Draw Column Mapping (regression params) ===\n", + " a1 -> a1\n", + " a2 -> a2\n", + " a3 -> a3\n", + " a4 -> a4\n", + " b1 -> b1\n", + " b2 -> b2\n", + " b3 -> b3\n", + " b4 -> b4\n", + " cp -> cp\n", + "\n", + "=== Available posterior draws ===\n", + "Parameters: a1, a2, a3, a4, b1, b2, b3, b4, cp, ind1, ind2, ind3, ind4, total_indirect, total, prop_med, diff_1_2, diff_1_3, diff_1_4, diff_2_3, diff_2_4, diff_3_4 \n", + "N draws per parameter: 8000 \n", + "\n", + "=== Bayesian Results ===\n", + " label est se ci_lower ci_upper pp_direction\n", + "1 a1 0.308413179 0.05724263 0.193375635 0.41666180 1.000000\n", + "2 a2 0.306065065 0.05732788 0.190296127 0.41443798 1.000000\n", + "3 a3 0.250938518 0.06506780 0.122620363 0.37818280 1.000000\n", + "4 a4 0.176045017 0.05236361 0.075896434 0.28144652 0.999875\n", + "5 b1 -1.642486845 1.28340529 -4.056699385 0.80651921 0.105375\n", + "6 b2 1.612869747 1.28441944 -0.828009232 4.03729364 0.889375\n", + "7 b3 -0.012763192 0.08559102 -0.179790746 0.15346464 0.446875\n", + "8 b4 0.082806905 0.07376141 -0.061282083 0.22798649 0.865250\n", + "9 cp 0.163995615 0.05756155 0.048752521 0.27765314 0.997250\n", + "10 ind1 -0.505811767 0.40969306 -1.323225875 0.24163178 0.105375\n", + "11 ind2 0.492985044 0.40700525 -0.252490800 1.30202489 0.889375\n", + "12 ind3 -0.003375445 0.02234339 -0.048968555 0.04010315 0.446875\n", + "13 ind4 0.014613023 0.01408872 -0.009805197 0.04551266 0.865125\n", + "14 total_indirect -0.001589144 0.02069427 -0.043073469 0.03895930 0.465625\n", + "15 total 0.162406471 0.05461771 0.054431972 0.26820871 0.997625\n", + "16 prop_med 0.027063848 3.97910585 -0.363334339 0.30477262 0.466250\n", + "17 diff_1_2 -0.998796811 0.81651073 -2.621630262 0.48634294 0.107750\n", + "18 diff_1_3 -0.502436322 0.40738966 -1.309043735 0.24791370 0.106125\n", + "19 diff_1_4 -0.520424790 0.41110889 -1.340251171 0.22978162 0.099875\n", + "20 diff_2_3 0.496360489 0.41078732 -0.255514259 1.31709305 0.890000\n", + "21 diff_2_4 0.478372021 0.40598568 -0.263877176 1.28305925 0.881625\n", + "22 diff_3_4 -0.017988468 0.03364710 -0.088170604 0.04552785 0.301500\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# BAYESIAN: Robust parameter extraction\n", + "# ============================================================\n", + "if (!is.null(fit_bayes)) {\n", + " \n", + " # Step 1: Get parTable and posterior draws\n", + " pt <- parTable(fit_bayes)\n", + " cat(\"=== parTable: labeled rows ===\\n\")\n", + " labeled_rows <- pt[pt$label != \"\", ]\n", + " print(labeled_rows[, c(\"id\", \"lhs\", \"op\", \"rhs\", \"label\", \"est\", \"se\")])\n", + " \n", + " # Step 2: Get posterior draws\n", + " draws <- tryCatch({\n", + " d <- blavInspect(fit_bayes, \"mcmc\")\n", + " if (is.list(d)) do.call(rbind, d) else as.matrix(d)\n", + " }, error = function(e) {\n", + " cat(\"Note: blavInspect('mcmc') failed, trying 'draws'...\\n\")\n", + " tryCatch({\n", + " d <- blavInspect(fit_bayes, \"draws\")\n", + " if (is.list(d)) do.call(rbind, d) else as.matrix(d)\n", + " }, error = function(e2) {\n", + " cat(\"Note: blavInspect('draws') also failed.\\n\")\n", + " NULL\n", + " })\n", + " })\n", + " \n", + " if (!is.null(draws)) {\n", + " cat(\"\\nPosterior draw matrix:\", nrow(draws), \"draws x\", ncol(draws), \"columns\\n\")\n", + " cat(\"Column names:\\n\")\n", + " print(colnames(draws))\n", + " }\n", + " \n", + " # Step 3: Map regression labels (a1, a2, ..., b1, ..., cp) to draw columns\n", + " # blavaan uses internal Stan parameter names like \"bet_sign[k]\" in draws\n", + " # We need to match parTable rows to draw columns\n", + " \n", + " # Get pxnames: maps parTable free parameters to Stan variable names\n", + " pxnames <- tryCatch(blavInspect(fit_bayes, \"pxnames\"), error = function(e) NULL)\n", + " if (!is.null(pxnames)) {\n", + " cat(\"\\npxnames (parTable row -> Stan name mapping):\\n\")\n", + " print(pxnames)\n", + " }\n", + " \n", + " # Build label -> draw column mapping for regression params only (~ and ~~)\n", + " reg_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\")\n", + " label_to_drawcol <- list()\n", + " \n", + " draw_cols <- if (!is.null(draws)) colnames(draws) else character(0)\n", + " \n", + " for (lab in reg_labels) {\n", + " # Method 1: label directly matches a draw column\n", + " if (lab %in% draw_cols) {\n", + " label_to_drawcol[[lab]] <- lab\n", + " next\n", + " }\n", + " \n", + " # Method 2: use pxnames to map parTable row -> Stan name\n", + " if (!is.null(pxnames)) {\n", + " pt_rows_with_lab <- which(pt$label == lab & pt$op %in% c(\"~\", \"~~\"))\n", + " for (row_idx in pt_rows_with_lab) {\n", + " if (row_idx <= length(pxnames)) {\n", + " px <- pxnames[row_idx]\n", + " if (!is.na(px) && px %in% draw_cols) {\n", + " label_to_drawcol[[lab]] <- px\n", + " break\n", + " }\n", + " }\n", + " }\n", + " }\n", + " \n", + " # Method 3: try parameterEstimates (may work for some blavaan versions)\n", + " if (!(lab %in% names(label_to_drawcol))) {\n", + " # Try matching by lhs/op/rhs\n", + " pt_match <- pt[pt$label == lab & pt$op == \"~\", ]\n", + " if (nrow(pt_match) > 0) {\n", + " # Search draw columns for patterns matching this parameter\n", + " # blavaan often uses \"bet_sign[N]\" where N is the free param index\n", + " free_idx <- pt_match$free[1]\n", + " if (!is.na(free_idx) && free_idx > 0) {\n", + " candidate <- paste0(\"bet_sign[\", free_idx, \"]\")\n", + " if (candidate %in% draw_cols) {\n", + " label_to_drawcol[[lab]] <- candidate\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " \n", + " cat(\"\\n=== Label -> Draw Column Mapping (regression params) ===\\n\")\n", + " for (nm in names(label_to_drawcol)) {\n", + " cat(sprintf(\" %s -> %s\\n\", nm, label_to_drawcol[[nm]]))\n", + " }\n", + " unmapped <- setdiff(reg_labels, names(label_to_drawcol))\n", + " if (length(unmapped) > 0) {\n", + " cat(\" UNMAPPED:\", paste(unmapped, collapse=\", \"), \"\\n\")\n", + " }\n", + " \n", + " # Step 4: Extract posterior draws for regression params and compute defined params\n", + " post_draws_list <- list()\n", + " \n", + " # Get regression param draws\n", + " for (lab in reg_labels) {\n", + " if (lab %in% names(label_to_drawcol)) {\n", + " dcol <- label_to_drawcol[[lab]]\n", + " post_draws_list[[lab]] <- draws[, dcol]\n", + " }\n", + " }\n", + " \n", + " # Compute defined params from component draws\n", + " # ind_i = a_i * b_i\n", + " for (i in 1:n_med) {\n", + " a_lab <- paste0(\"a\", i)\n", + " b_lab <- paste0(\"b\", i)\n", + " if (a_lab %in% names(post_draws_list) && b_lab %in% names(post_draws_list)) {\n", + " post_draws_list[[paste0(\"ind\", i)]] <- post_draws_list[[a_lab]] * post_draws_list[[b_lab]]\n", + " }\n", + " }\n", + " \n", + " # total_indirect = sum of all ind_i\n", + " ind_labs <- paste0(\"ind\", 1:n_med)\n", + " avail_ind <- intersect(ind_labs, names(post_draws_list))\n", + " if (length(avail_ind) == n_med) {\n", + " total_ind_draws <- Reduce(\"+\", post_draws_list[avail_ind])\n", + " post_draws_list[[\"total_indirect\"]] <- total_ind_draws\n", + " }\n", + " \n", + " # total = cp + total_indirect\n", + " if (\"cp\" %in% names(post_draws_list) && \"total_indirect\" %in% names(post_draws_list)) {\n", + " post_draws_list[[\"total\"]] <- post_draws_list[[\"cp\"]] + post_draws_list[[\"total_indirect\"]]\n", + " }\n", + " \n", + " # prop_med = total_indirect / total\n", + " if (\"total_indirect\" %in% names(post_draws_list) && \"total\" %in% names(post_draws_list)) {\n", + " total_d <- post_draws_list[[\"total\"]]\n", + " # Avoid division by zero: use NA where total is very close to 0\n", + " prop_d <- ifelse(abs(total_d) > 1e-10, post_draws_list[[\"total_indirect\"]] / total_d, NA)\n", + " post_draws_list[[\"prop_med\"]] <- prop_d\n", + " }\n", + " \n", + " # Pairwise contrasts\n", + " for (i in 1:(n_med-1)) {\n", + " for (j in (i+1):n_med) {\n", + " ind_i <- paste0(\"ind\", i)\n", + " ind_j <- paste0(\"ind\", j)\n", + " diff_lab <- paste0(\"diff_\", i, \"_\", j)\n", + " if (ind_i %in% names(post_draws_list) && ind_j %in% names(post_draws_list)) {\n", + " post_draws_list[[diff_lab]] <- post_draws_list[[ind_i]] - post_draws_list[[ind_j]]\n", + " }\n", + " }\n", + " }\n", + " \n", + " cat(\"\\n=== Available posterior draws ===\\n\")\n", + " cat(\"Parameters:\", paste(names(post_draws_list), collapse=\", \"), \"\\n\")\n", + " cat(\"N draws per parameter:\", if (length(post_draws_list) > 0) length(post_draws_list[[1]]) else 0, \"\\n\")\n", + " \n", + " # Step 5: Build results data.frame\n", + " bayes_rows <- list()\n", + " \n", + " all_labels <- c(key_labels, \n", + " paste0(\"diff_\", apply(combn(1:n_med, 2), 2, paste, collapse=\"_\")))\n", + " \n", + " for (lab in all_labels) {\n", + " if (lab %in% names(post_draws_list)) {\n", + " pd <- post_draws_list[[lab]]\n", + " pd_clean <- pd[!is.na(pd)]\n", + " if (length(pd_clean) > 0) {\n", + " bayes_rows[[length(bayes_rows) + 1]] <- data.frame(\n", + " exposure = exposure,\n", + " direction = \"D1\",\n", + " label = lab,\n", + " est = mean(pd_clean),\n", + " se = sd(pd_clean),\n", + " ci_lower = as.numeric(quantile(pd_clean, 0.025)),\n", + " ci_upper = as.numeric(quantile(pd_clean, 0.975)),\n", + " pp_direction = mean(pd_clean > 0),\n", + " N = N_total,\n", + " method = \"Bayesian\",\n", + " stringsAsFactors = FALSE\n", + " )\n", + " }\n", + " } else {\n", + " # Fall back to parTable estimates if draws unavailable\n", + " pt_row <- pt[pt$label == lab, ]\n", + " if (nrow(pt_row) > 0) {\n", + " est_val <- pt_row$est[1]\n", + " se_val <- pt_row$se[1]\n", + " bayes_rows[[length(bayes_rows) + 1]] <- data.frame(\n", + " exposure = exposure,\n", + " direction = \"D1\",\n", + " label = lab,\n", + " est = est_val,\n", + " se = se_val,\n", + " ci_lower = est_val - 1.96 * se_val,\n", + " ci_upper = est_val + 1.96 * se_val,\n", + " pp_direction = NA,\n", + " N = N_total,\n", + " method = \"Bayesian\",\n", + " stringsAsFactors = FALSE\n", + " )\n", + " }\n", + " }\n", + " }\n", + " \n", + " if (length(bayes_rows) > 0) {\n", + " bayes_results <- do.call(rbind, bayes_rows)\n", + " write.csv(bayes_results, file.path(dir_bayes, \"bayesian_results.csv\"), row.names=FALSE)\n", + " cat(\"\\n=== Bayesian Results ===\\n\")\n", + " print(bayes_results[, c(\"label\", \"est\", \"se\", \"ci_lower\", \"ci_upper\", \"pp_direction\")])\n", + " } else {\n", + " cat(\"WARNING: No Bayesian results could be extracted.\\n\")\n", + " bayes_results <- NULL\n", + " }\n", + "} else {\n", + " bayes_results <- NULL\n", + " cat(\"Bayesian SEM was not fitted successfully.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5654dca7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T11:41:26.218405Z", + "iopub.status.busy": "2026-04-01T11:41:26.215829Z", + "iopub.status.idle": "2026-04-01T11:41:38.014057Z", + "shell.execute_reply": "2026-04-01T11:41:38.011415Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved Bayesian posterior density plots.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved trace plot.\n" + ] + }, + { + "data": { + "image/png": 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JkJYxUJDE8YW2beXfySztbWlrp8KGbi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fGIZRfJlgUilDFe3IZDIURSk2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cLAO3bsmD179qVLlwghpaWls2fP/umnn6qrq99///0lS5bwwZxO5/bt2xcvXkwT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\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a2ZBdTwTCJ1YsTdBlOCUB5/5frHv3rB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\njxt5KJ71iZOhWqXr9mr7gMzQDHnpkUdvIApE/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1xxfHLhWM22gnWM1iLrcvLaGE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\n2M0/NBdBEARBEASxIJBiRxAEQRCEDXkozDu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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved Bayesian forest plot.\n" + ] + }, + { + "data": { + "image/png": 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zliRxDEjz/+iBAiB9ta7uYJrHGJHZvNVldXV1JSUldXNzMzw0Mgo0aNwukXKS8v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\n1K1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CGEENIQGNghhBBCCGkIDOwQQggh\nhDQEBnYIIYQQQhoCAzuEEEIIIQ2BgR1CCCGEkIbAwA4hhBBCSENgYIcQQgghpCEwsEMIIYQQ\n0hAY2CGEEEIIaQgM7BBCCCGENAQGdgghhBBCGgIDO4QQQgghDYGBHUIIIYSQhsDADiGEEEJI\nQ2BghxBCCCGkITCwQwghhBDSEBjYIYQQQghpiP8H1+Kj5RDzvNQAAAAASUVORK5CYII=", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# BAYESIAN: Posterior density plots, trace plot, forest plot\n", + "# ============================================================\n", + "if (!is.null(fit_bayes) && exists(\"bayes_results\") && !is.null(bayes_results) && nrow(bayes_results) > 0) {\n", + " \n", + " # --- Posterior density plots ---\n", + " density_params <- intersect(names(post_draws_list),\n", + " c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\"))\n", + " \n", + " if (length(density_params) > 0) {\n", + " dens_list <- list()\n", + " for (lab in density_params) {\n", + " pd <- post_draws_list[[lab]]\n", + " pd_clean <- pd[!is.na(pd)]\n", + " if (length(pd_clean) > 10) {\n", + " dens_list[[lab]] <- data.frame(value=pd_clean, parameter=lab, stringsAsFactors=FALSE)\n", + " }\n", + " }\n", + " \n", + " if (length(dens_list) > 0) {\n", + " dens_data <- do.call(rbind, dens_list)\n", + " \n", + " p_dens <- ggplot(dens_data, aes(x=value, fill=parameter)) +\n", + " geom_density(alpha=0.5) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"red\") +\n", + " facet_wrap(~parameter, scales=\"free\", ncol=3) +\n", + " labs(title=\"Bayesian Posterior Distributions\",\n", + " subtitle=paste0(\"Exposure: \", exposure, \" | N = \", N_total),\n", + " x=\"Parameter Value\", y=\"Density\") +\n", + " theme_minimal(base_size=11) +\n", + " theme(legend.position=\"none\")\n", + " \n", + " n_panels <- length(dens_list)\n", + " plot_h <- max(6, ceiling(n_panels / 3) * 3)\n", + " ggsave(file.path(dir_bayes, paste0(\"bayesian_posteriors_\", exposure, \".png\")),\n", + " p_dens, width=12, height=plot_h, dpi=150)\n", + " ggsave(file.path(dir_bayes, paste0(\"bayesian_posteriors_\", exposure, \".pdf\")),\n", + " p_dens, width=12, height=plot_h)\n", + " print(p_dens)\n", + " cat(\"Saved Bayesian posterior density plots.\\n\")\n", + " }\n", + " }\n", + " \n", + " # --- Trace plot for a1 ---\n", + " chain_draws <- tryCatch({\n", + " d <- blavInspect(fit_bayes, \"mcmc\")\n", + " if (is.list(d)) d else list(as.matrix(d))\n", + " }, error = function(e) {\n", + " tryCatch({\n", + " d <- blavInspect(fit_bayes, \"draws\")\n", + " if (is.list(d)) d else list(as.matrix(d))\n", + " }, error = function(e2) NULL)\n", + " })\n", + " \n", + " if (!is.null(chain_draws) && \"a1\" %in% names(label_to_drawcol)) {\n", + " a1_dcol <- label_to_drawcol[[\"a1\"]]\n", + " \n", + " if (a1_dcol %in% colnames(chain_draws[[1]])) {\n", + " trace_data <- data.frame()\n", + " for (ch in seq_along(chain_draws)) {\n", + " vals <- chain_draws[[ch]][, a1_dcol]\n", + " trace_data <- rbind(trace_data, data.frame(\n", + " iteration = seq_along(vals),\n", + " value = vals,\n", + " chain = paste0(\"Chain \", ch)\n", + " ))\n", + " }\n", + " \n", + " p_trace <- ggplot(trace_data, aes(x=iteration, y=value, color=chain)) +\n", + " geom_line(alpha=0.5) +\n", + " labs(title=\"MCMC Trace: a1 (SNP -> APOC4_APOC2_AC_exp)\",\n", + " subtitle=paste0(\"Exposure: \", exposure),\n", + " x=\"Iteration\", y=\"a1\", color=\"\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + " \n", + " ggsave(file.path(dir_bayes, paste0(\"bayesian_trace_a_\", exposure, \".png\")),\n", + " p_trace, width=10, height=5, dpi=150)\n", + " ggsave(file.path(dir_bayes, paste0(\"bayesian_trace_a_\", exposure, \".pdf\")),\n", + " p_trace, width=10, height=5)\n", + " print(p_trace)\n", + " cat(\"Saved trace plot.\\n\")\n", + " } else {\n", + " cat(\"a1 draw column not in chain draws, skipping trace plot.\\n\")\n", + " }\n", + " } else {\n", + " cat(\"Skipping trace plot (chain draws or a1 mapping unavailable).\\n\")\n", + " }\n", + " \n", + " # --- Forest plot ---\n", + " bayes_plot <- bayes_results[bayes_results$label %in% key_labels, ]\n", + " bayes_plot$display_label <- nice_labels[bayes_plot$label]\n", + " bayes_plot$display_label <- factor(bayes_plot$display_label, levels=rev(nice_labels[key_labels]))\n", + " bayes_plot$effect_type <- ifelse(grepl(\"^a\", bayes_plot$label), \"a-path\",\n", + " ifelse(grepl(\"^b\", bayes_plot$label), \"b-path\",\n", + " ifelse(bayes_plot$label == \"cp\", \"direct (c')\",\n", + " ifelse(grepl(\"^ind[0-9]\", bayes_plot$label), \"specific indirect\",\n", + " ifelse(bayes_plot$label == \"total_indirect\", \"total indirect\",\n", + " ifelse(bayes_plot$label == \"total\", \"total effect\", \"proportion\"))))))\n", + " \n", + " p_bayes_forest <- ggplot(bayes_plot, aes(x=est, y=display_label, color=effect_type)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(aes(xmin=ci_lower, xmax=ci_upper), size=0.5) +\n", + " labs(title=\"Bayesian SEM: Parallel Mediation Path Estimates\",\n", + " subtitle=paste0(\"N = \", N_total, \" (missing data handled by Stan)\"),\n", + " x=\"Posterior Mean (95% Credible Interval)\", y=\"\", color=\"Effect Type\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + " \n", + " ggsave(file.path(dir_bayes, \"bayesian_forest_plot.png\"), p_bayes_forest, width=11, height=9, dpi=150)\n", + " ggsave(file.path(dir_bayes, \"bayesian_forest_plot.pdf\"), p_bayes_forest, width=11, height=9)\n", + " print(p_bayes_forest)\n", + " cat(\"Saved Bayesian forest plot.\\n\")\n", + " \n", + "} else {\n", + " cat(\"Bayesian fit not available -- skipping plots.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bayesian Interpretation\n", + "\n", + "The Bayesian results provide:\n", + "- **Posterior means and 95% credible intervals** for all paths\n", + "- **P(direction)**: The posterior probability that the indirect effect is positive (or negative)\n", + "- **Convergence diagnostics**: Trace plots show whether chains have mixed well\n", + "\n", + "A P(indirect > 0) > 0.975 or < 0.025 provides strong evidence for the direction of mediation." + ], + "id": "8f515047" + }, + { + "cell_type": "markdown", + "id": "31fce434", + "metadata": {}, + "source": [ + "## Cross-Method Summary\n", + "\n", + "Combine results from all methods (FIML, Bootstrap, Bayesian) for direct comparison.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bf050197", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T11:41:38.022963Z", + "iopub.status.busy": "2026-04-01T11:41:38.021206Z", + "iopub.status.idle": "2026-04-01T11:41:38.074163Z", + "shell.execute_reply": "2026-04-01T11:41:38.071418Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Cross-Method Summary ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " method label est se ci_lower ci_upper\n", + "1 FIML a1 0.307473539 0.05754170 0.194693878 0.42025320\n", + "2 FIML a2 0.305148195 0.05762211 0.192210942 0.41808545\n", + "3 FIML a3 0.248888901 0.06559217 0.120330606 0.37744720\n", + "4 FIML a4 0.172634729 0.05046691 0.073721399 0.27154806\n", + "5 FIML b1 -1.564594995 1.32450160 -4.160570419 1.03138043\n", + "6 FIML b2 1.532544456 1.32401253 -1.062472415 4.12756133\n", + "7 FIML b3 -0.008244204 0.08686289 -0.178492342 0.16200393\n", + "8 FIML b4 0.079853899 0.07384064 -0.064871091 0.22457889\n", + "9 FIML cp 0.163769486 0.05713159 0.051793622 0.27574535\n", + "10 FIML ind1 -0.481071561 0.41705680 -1.298487862 0.33634474\n", + "11 FIML ind2 0.467653175 0.41361835 -0.343023902 1.27833025\n", + "12 FIML ind3 -0.002051891 0.02163714 -0.044459916 0.04035613\n", + "13 FIML ind4 0.013785556 0.01335619 -0.012392102 0.03996321\n", + "14 FIML total_indirect -0.001684721 0.01970591 -0.040307604 0.03693816\n", + "15 FIML total 0.162084765 0.05405733 0.056134353 0.26803518\n", + "16 FIML prop_med -0.010394071 0.12170285 -0.248927275 0.22813913\n", + "17 Bootstrap a1 0.309801518 0.05697373 0.199583620 0.42008011\n", + "18 Bootstrap a2 0.307610239 0.05727292 0.196862370 0.41759542\n", + "19 Bootstrap a3 0.249416015 0.06522680 0.128441053 0.38332076\n", + "20 Bootstrap a4 0.174839894 0.04916388 0.080165698 0.26691441\n", + "21 Bootstrap b1 -1.563669048 1.35058642 -4.179325642 1.05352036\n", + "22 Bootstrap b2 1.530685288 1.34932553 -1.105421004 4.14489659\n", + "23 Bootstrap b3 -0.010305205 0.09089422 -0.185251349 0.17405443\n", + "24 Bootstrap b4 0.081997760 0.07674822 -0.068906963 0.22841881\n", + "25 Bootstrap cp 0.163636786 0.05880451 0.056571634 0.28336728\n", + "26 Bootstrap ind1 -0.483344705 0.44065229 -1.406197210 0.33166531\n", + "27 Bootstrap ind2 0.469770712 0.43711513 -0.324777647 1.38819774\n", + "28 Bootstrap ind3 -0.003027796 0.02364969 -0.052662643 0.04362675\n", + "29 Bootstrap ind4 0.014489895 0.01465326 -0.011229023 0.04614738\n", + "30 Bootstrap total_indirect -0.002111895 0.02138359 -0.043876639 0.03868571\n", + "31 Bootstrap total 0.161524891 0.05521717 0.057108516 0.27840068\n", + "32 Bootstrap prop_med -0.015979188 0.17173699 -0.360281796 0.31261630\n", + "33 Bayesian a1 0.308413179 0.05724263 0.193375635 0.41666180\n", + "34 Bayesian a2 0.306065065 0.05732788 0.190296127 0.41443798\n", + "35 Bayesian a3 0.250938518 0.06506780 0.122620363 0.37818280\n", + "36 Bayesian a4 0.176045017 0.05236361 0.075896434 0.28144652\n", + "37 Bayesian b1 -1.642486845 1.28340529 -4.056699385 0.80651921\n", + "38 Bayesian b2 1.612869747 1.28441944 -0.828009232 4.03729364\n", + "39 Bayesian b3 -0.012763192 0.08559102 -0.179790746 0.15346464\n", + "40 Bayesian b4 0.082806905 0.07376141 -0.061282083 0.22798649\n", + "41 Bayesian cp 0.163995615 0.05756155 0.048752521 0.27765314\n", + "42 Bayesian ind1 -0.505811767 0.40969306 -1.323225875 0.24163178\n", + "43 Bayesian ind2 0.492985044 0.40700525 -0.252490800 1.30202489\n", + "44 Bayesian ind3 -0.003375445 0.02234339 -0.048968555 0.04010315\n", + "45 Bayesian ind4 0.014613023 0.01408872 -0.009805197 0.04551266\n", + "46 Bayesian total_indirect -0.001589144 0.02069427 -0.043073469 0.03895930\n", + "47 Bayesian total 0.162406471 0.05461771 0.054431972 0.26820871\n", + "48 Bayesian prop_med 0.027063848 3.97910585 -0.363334339 0.30477262\n", + " pvalue n_eff\n", + "1 9.117355e-08 N=1153 (FIML)\n", + "2 1.185748e-07 N=1153 (FIML)\n", + "3 1.479467e-04 N=1153 (FIML)\n", + "4 6.244853e-04 N=1153 (FIML)\n", + "5 2.374952e-01 N=1153 (FIML)\n", + "6 2.470682e-01 N=1153 (FIML)\n", + "7 9.243859e-01 N=1153 (FIML)\n", + "8 2.795034e-01 N=1153 (FIML)\n", + "9 4.149974e-03 N=1153 (FIML)\n", + "10 2.487086e-01 N=1153 (FIML)\n", + "11 2.582069e-01 N=1153 (FIML)\n", + "12 9.244484e-01 N=1153 (FIML)\n", + "13 3.020032e-01 N=1153 (FIML)\n", + "14 9.318693e-01 N=1153 (FIML)\n", + "15 2.714131e-03 N=1153 (FIML)\n", + "16 9.319392e-01 N=1153 (FIML)\n", + "17 0.000000e+00 N=1153 (Bootstrap FIML)\n", + "18 0.000000e+00 N=1153 (Bootstrap FIML)\n", + "19 0.000000e+00 N=1153 (Bootstrap FIML)\n", + "20 0.000000e+00 N=1153 (Bootstrap FIML)\n", + "21 2.440000e-01 N=1153 (Bootstrap FIML)\n", + "22 2.600000e-01 N=1153 (Bootstrap FIML)\n", + "23 8.900000e-01 N=1153 (Bootstrap FIML)\n", + "24 2.800000e-01 N=1153 (Bootstrap FIML)\n", + "25 2.000000e-03 N=1153 (Bootstrap FIML)\n", + "26 2.440000e-01 N=1153 (Bootstrap FIML)\n", + "27 2.600000e-01 N=1153 (Bootstrap FIML)\n", + "28 8.900000e-01 N=1153 (Bootstrap FIML)\n", + "29 2.800000e-01 N=1153 (Bootstrap FIML)\n", + "30 9.080000e-01 N=1153 (Bootstrap FIML)\n", + "31 0.000000e+00 N=1153 (Bootstrap FIML)\n", + "32 9.080000e-01 N=1153 (Bootstrap FIML)\n", + "33 0.000000e+00 N=1153 (Bayesian)\n", + "34 0.000000e+00 N=1153 (Bayesian)\n", + "35 0.000000e+00 N=1153 (Bayesian)\n", + "36 2.500000e-04 N=1153 (Bayesian)\n", + "37 2.107500e-01 N=1153 (Bayesian)\n", + "38 2.212500e-01 N=1153 (Bayesian)\n", + "39 8.937500e-01 N=1153 (Bayesian)\n", + "40 2.695000e-01 N=1153 (Bayesian)\n", + "41 5.500000e-03 N=1153 (Bayesian)\n", + "42 2.107500e-01 N=1153 (Bayesian)\n", + "43 2.212500e-01 N=1153 (Bayesian)\n", + "44 8.937500e-01 N=1153 (Bayesian)\n", + "45 2.697500e-01 N=1153 (Bayesian)\n", + "46 9.312500e-01 N=1153 (Bayesian)\n", + "47 4.750000e-03 N=1153 (Bayesian)\n", + "48 9.325000e-01 N=1153 (Bayesian)\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# CROSS-METHOD SUMMARY\n", + "# ============================================================\n", + "\n", + "# FIML\n", + "fiml_sum <- fiml_res[fiml_res$label %in% key_labels, c(\"exposure\", \"direction\", \"label\", \"est\", \"se\", \"ci_lower\", \"ci_upper\", \"pvalue\", \"N\")]\n", + "fiml_sum$method <- \"FIML\"\n", + "fiml_sum$ci_type <- \"Wald\"\n", + "fiml_sum$n_eff <- paste0(\"N=\", fiml_sum$N, \" (FIML)\")\n", + "\n", + "# Bootstrap\n", + "boot_sum <- boot_summary[boot_summary$label %in% key_labels, c(\"exposure\", \"direction\", \"label\", \"est\", \"se\", \"ci_lower\", \"ci_upper\", \"pvalue\", \"N\")]\n", + "boot_sum$method <- \"Bootstrap\"\n", + "boot_sum$ci_type <- \"Percentile\"\n", + "boot_sum$n_eff <- paste0(\"N=\", boot_sum$N, \" (Bootstrap FIML)\")\n", + "\n", + "# Bayesian\n", + "if (!is.null(bayes_results) && nrow(bayes_results) > 0) {\n", + " bayes_sum <- bayes_results[bayes_results$label %in% key_labels, c(\"exposure\", \"direction\", \"label\", \"est\", \"se\", \"ci_lower\", \"ci_upper\", \"N\")]\n", + " bayes_pp <- bayes_results[bayes_results$label %in% key_labels, \"pp_direction\"]\n", + " bayes_sum$pvalue <- ifelse(!is.na(bayes_pp), 2 * pmin(bayes_pp, 1 - bayes_pp), NA)\n", + " bayes_sum$method <- \"Bayesian\"\n", + " bayes_sum$ci_type <- \"Credible\"\n", + " bayes_sum$n_eff <- paste0(\"N=\", bayes_sum$N, \" (Bayesian)\")\n", + " \n", + " all_summary <- rbind(fiml_sum, boot_sum, bayes_sum)\n", + "} else {\n", + " all_summary <- rbind(fiml_sum, boot_sum)\n", + "}\n", + "\n", + "write.csv(all_summary, file.path(dir_summ, \"all_methods_summary.csv\"), row.names=FALSE)\n", + "\n", + "cat(\"=== Cross-Method Summary ===\\n\")\n", + "print(all_summary[, c(\"method\", \"label\", \"est\", \"se\", \"ci_lower\", \"ci_upper\", \"pvalue\", \"n_eff\")])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "18b7287d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T11:41:38.080545Z", + "iopub.status.busy": "2026-04-01T11:41:38.078339Z", + "iopub.status.idle": "2026-04-01T11:41:38.162531Z", + "shell.execute_reply": "2026-04-01T11:41:38.159951Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Display Table ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label display FIML_est_CI FIML_p\n", + "1 a1 a1 (SNP->APOC4_APOC2) 0.3075 [0.1947, 0.4203] 9.12e-08\n", + "2 a2 a2 (SNP->APOC2) 0.3051 [0.1922, 0.4181] 1.19e-07\n", + "3 a3 a3 (SNP->APOC1_DeJager) 0.2489 [0.1203, 0.3774] 1.48e-04\n", + "4 a4 a4 (SNP->APOE_Mega) 0.1726 [0.0737, 0.2715] 6.24e-04\n", + "5 b1 b1 (APOC4_APOC2->tdp) -1.5646 [-4.1606, 1.0314] 0.2375\n", + "6 b2 b2 (APOC2->tdp) 1.5325 [-1.0625, 4.1276] 0.2471\n", + "7 b3 b3 (APOC1_DeJager->tdp) -0.0082 [-0.1785, 0.1620] 0.9244\n", + "8 b4 b4 (APOE_Mega->tdp) 0.0799 [-0.0649, 0.2246] 0.2795\n", + "9 cp c' (direct SNP->tdp) 0.1638 [0.0518, 0.2757] 0.0041\n", + "10 ind1 ind1 (via APOC4_APOC2) -0.4811 [-1.2985, 0.3363] 0.2487\n", + "11 ind2 ind2 (via APOC2) 0.4677 [-0.3430, 1.2783] 0.2582\n", + "12 ind3 ind3 (via APOC1_DeJager) -0.0021 [-0.0445, 0.0404] 0.9244\n", + "13 ind4 ind4 (via APOE_Mega) 0.0138 [-0.0124, 0.0400] 0.3020\n", + "14 total_indirect Total Indirect -0.0017 [-0.0403, 0.0369] 0.9319\n", + "15 total Total Effect 0.1621 [0.0561, 0.2680] 0.0027\n", + "16 prop_med Proportion Mediated -0.0104 [-0.2489, 0.2281] 0.9319\n", + " Bootstrap_est_CI Bootstrap_p Bayesian_est_CI Bayesian_p\n", + "1 0.3098 [0.1996, 0.4201] 0.00e+00 0.3084 [0.1934, 0.4167] 0.00e+00\n", + "2 0.3076 [0.1969, 0.4176] 0.00e+00 0.3061 [0.1903, 0.4144] 0.00e+00\n", + "3 0.2494 [0.1284, 0.3833] 0.00e+00 0.2509 [0.1226, 0.3782] 0.00e+00\n", + "4 0.1748 [0.0802, 0.2669] 0.00e+00 0.1760 [0.0759, 0.2814] 2.50e-04\n", + "5 -1.5637 [-4.1793, 1.0535] 0.2440 -1.6425 [-4.0567, 0.8065] 0.2107\n", + "6 1.5307 [-1.1054, 4.1449] 0.2600 1.6129 [-0.8280, 4.0373] 0.2212\n", + "7 -0.0103 [-0.1853, 0.1741] 0.8900 -0.0128 [-0.1798, 0.1535] 0.8938\n", + "8 0.0820 [-0.0689, 0.2284] 0.2800 0.0828 [-0.0613, 0.2280] 0.2695\n", + "9 0.1636 [0.0566, 0.2834] 0.0020 0.1640 [0.0488, 0.2777] 0.0055\n", + "10 -0.4833 [-1.4062, 0.3317] 0.2440 -0.5058 [-1.3232, 0.2416] 0.2107\n", + "11 0.4698 [-0.3248, 1.3882] 0.2600 0.4930 [-0.2525, 1.3020] 0.2212\n", + "12 -0.0030 [-0.0527, 0.0436] 0.8900 -0.0034 [-0.0490, 0.0401] 0.8938\n", + "13 0.0145 [-0.0112, 0.0461] 0.2800 0.0146 [-0.0098, 0.0455] 0.2697\n", + "14 -0.0021 [-0.0439, 0.0387] 0.9080 -0.0016 [-0.0431, 0.0390] 0.9313\n", + "15 0.1615 [0.0571, 0.2784] 0.00e+00 0.1624 [0.0544, 0.2682] 0.0048\n", + "16 -0.0160 [-0.3603, 0.3126] 0.9080 0.0271 [-0.3633, 0.3048] 0.9325\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# SUMMARY: Display table (wide format)\n", + "# ============================================================\n", + "all_summary$est_ci <- sprintf(\"%.4f [%.4f, %.4f]\", all_summary$est, all_summary$ci_lower, all_summary$ci_upper)\n", + "all_summary$p_fmt <- ifelse(all_summary$pvalue < 0.001, sprintf(\"%.2e\", all_summary$pvalue),\n", + " ifelse(is.na(all_summary$pvalue), \"NA\",\n", + " sprintf(\"%.4f\", all_summary$pvalue)))\n", + "\n", + "display_wide <- data.frame(label = key_labels, stringsAsFactors=FALSE)\n", + "display_wide$display <- nice_labels[display_wide$label]\n", + "\n", + "for (meth in unique(all_summary$method)) {\n", + " meth_data <- all_summary[all_summary$method == meth, ]\n", + " est_col <- paste0(meth, \"_est_CI\")\n", + " p_col <- paste0(meth, \"_p\")\n", + " display_wide[[est_col]] <- sapply(display_wide$label, function(l) {\n", + " r <- meth_data[meth_data$label == l, ]\n", + " if (nrow(r) == 1) r$est_ci else \"--\"\n", + " })\n", + " display_wide[[p_col]] <- sapply(display_wide$label, function(l) {\n", + " r <- meth_data[meth_data$label == l, ]\n", + " if (nrow(r) == 1) r$p_fmt else \"--\"\n", + " })\n", + "}\n", + "\n", + "write.csv(display_wide, file.path(dir_summ, paste0(\"summary_display_table_\", exposure, \".csv\")), row.names=FALSE)\n", + "\n", + "cat(\"=== Display Table ===\\n\")\n", + "print(display_wide)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a7b6ff94", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T11:41:38.168844Z", + "iopub.status.busy": "2026-04-01T11:41:38.166631Z", + "iopub.status.idle": "2026-04-01T11:41:39.447072Z", + "shell.execute_reply": "2026-04-01T11:41:39.444936Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved all-methods forest plot.\n" + ] + }, + { + "data": { + "image/png": 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ORu/aSSr8mSu64gsAPKo7i4GD+PWVxc\nXFOnTGBgoNjXx/rw9fVdvnz5+fPnd+/efe/evby8vHnz5kl9goyMJ8rKyqj3UdYJ3i2fz+fz\n+WJPh1VUVJDZwE9p8fl8oVAo1rJIfpe1tbU9cODAgQMH3r59+/Dhw/Pnzz979szHxyc0NPSH\nH36oKSeurq5v3769efPm2rVrZWT469ev+L4OxZzLKjxlkl98yc9pdXX1qqoq/INFe/bsER1v\nhBCaNWuWfEeUXYeNWXap1NTUnj17VmuyetaMQiq2QS8ihV/LdbrQSAo8Axvz1Gqcs6iRydf6\nKUp96grG2AHlcerUqYqKiq5du0p90Ak/c3T69GmpM5HKx8DAwMvLq6CgICoq6ty5c6iGvhuE\nkLW1Nf6iiYfdNAQrKyv8+ZGeni62Ci8xMTFRUVEhOwUkZ6iS0ZPVpUuXxYsXP336dOXKlQgh\ncpSxVBMnTkQIPX/+HNe5VAkJCVZWVqNGjeLxeBRzLuOI1H38+FFsCa4HPT09dXX1d+/eFRUV\n0en0OXPmiKbJzs6WOs9fnUitw8Yse33Us2YUUrENehEp/FqW70JT4BnYDE+tBr2+Gk6dWj9F\nqU9dQWAHlAfuh505c6bUte7u7ubm5gUFBVeuXFHgQfGw65s3b965c6dDhw41/VK4hoZGnz59\nEEL4M0PUhw8fbt68Sc7zhFEfSUPS0tJydHRECN26dUtsFX7Uf9CgQQghDodjbGyMEIqKihLL\nxtu3b0VfHjp06OnTp2K7Gj16NEJI9i8subq6DhkyBCE0c+ZMqZ9hWVlZEydOxDcSVFVVKeZc\nISIiIvh8vugSXA+9evVC/+3lYTAYYnc4yKa8Tu8LlTpszLLXRz1rRiEVW9eLqK4Uey1TvNDE\n1LWiZNRbMzy1FFi6BlWn1q+BMlmfuoLADiiJ2NjYxMREJpPp5+cnNQGdTsczOir2VyhGjBhh\naGgYFBRUVFRU01d8bOnSpQihvXv3xsbGkgu/f/8+adKkkSNH4onIEUL4gS/JG0tULF68GCG0\ndevW1NRUcmF0dHRQUBCNRiPv5+MxIhs3bszLy8NLSktL586dK/oY4/v37+fPn+/v7//161dy\nIUEQeEoIBwcHGdmg0WhBQUFGRkZfv37t16/f/v37ySfLysvLjx492q9fv5SUlPbt2x89erRO\nOa+/goKCdevWkS8zMjJwHnx9fRFCtra2bDabz+fjTz4sMDDw0qVL7du3R9LuvshAsQ4VW/bK\nyso9e/bs2bNH9KD1V8+aUVTFUryI5KPwa5nKhSaGekVRaSga7bKiSLGlazgUr9wGzWR96grG\n2AElgW/XeXl5yXgWfcaMGZs3b46IiEhPT2/Xrp1CjstisXx9fffu3Uuj0WqKKbHx48cvXrx4\n7969AwYMGDRokKWl5bdv38LDwysqKkaPHj137lyczNHRkUajvXnzxtHRUU9Pb8uWLWKPyssw\nZcqUsLCwoKCg7t27jxgxwtjY+P3792FhYQKBYOvWreQPb6xevfrKlSs4tBo4cCBBEDExMW3b\ntp02bdrhw4fxN79hw4b5+PhcvHjR2traxcXF1NS0vLz81atXHz9+NDAw2Lhxo+yctG3b9sWL\nFxMmTMC/9LBkyRIOh0Oj0fAcsAghd3f306dP48lLqee8/gICAo4cOXLr1i0nJ6eysrLQ0NCS\nkhIXFxcc9Kuqqi5evHjHjh3e3t7e3t6amppPnjz5+PHjnTt3QkJC0tLSli9f/ujRoy1btlA5\nFsU6VGzZKyoqfv75Z4RQr1696jmVnah61oyiKpbiRSQfhV/LVC40MdQrikpD0WiXFUWKLZ0c\nysvLO3bsKCNB//79T5w4QfHKbaBMYvWpK/jlCaAk8Ejhq1evyk42YMAAhNBvv/1G1O+XJ0QX\nvn79GiE0aNAg0YWSs9VjoaGh+MYAi8XicDiDBg06ceIEOYc4tnPnTvzLgO3atYuPjyf+O325\n2FT+5PJly5aRS4RC4dmzZwcPHqynp8dkMo2NjceOHRsRESG2YUZGhp+fn5GREYvFMjc3X7Bg\nQWFhIZ4tc/Xq1TiNQCA4cOCAi4tLmzZt6HS6mppa586df/7551p/0oBUXV194cKFcePG4R9D\nZLPZVlZWkydPvn37tmRiKjnH5SVziEVERCCE7OzsxHaIo8Z3797hl/j93bhx44cPH3x9fY2N\njVkslpWV1cqVK8vKysitqqqqVq9ebWlpifMwYcKExMREgiDS09P79OmjoqLSvn37wsLCmt4R\nsXnnKdYh9bLXeg6Q42+io6MlK1lUnebir3/NUNycylVZ60XUfK5lKhea2DlDsaIIaQ2F2K4I\nhZ5aMqpR4WeR1NLVP5+1cnd3x+kpXrm1vgX1abLkrisa0UR92AAA0JhmzJhx8uTJP/74Q3S2\ndwAAUDIwxg4AAAAAQElAYAcAAAAAoCQgsAMAAAAAUBIQ2AEAAAAAKAl4eAIAAAAAQEnAHTsA\nAAAAACUBgR0AAAAAgJKAwA4AAAAAQElAYAcAAAAAoCQgsAMAAAAAUBIQ2AEAAAAAKAkI7AAA\nAAAAlAQEdgAAAAAASgICOwAAAAAAJQGBHQAAAACAkmA2dQYAULDy8vJHjx6lpKRwuVw1NTVr\na+tBgwa1adOGTHDjxo1Xr161b99+ypQpYtuWlJTs2rWrX79+Hh4eZErRBGw229TU1M3NzcLC\nohHKgt2/f3/EiBFMJtPOzi4qKkpHR0d07blz51JSUrp37z5mzBixDeua/8rKyqdPn759+7a4\nuNjQ0NDGxmbgwIEMBqOmjFVWVj558uTt27elpaVGRkbt27d3dnam02v8unjlypXExMRx48bZ\n29tTKvn/av4lffv27fPnz3Nzcy0sLFxcXMzMzKCYLbeYCKHY2Ng7d+70799/2LBhcpQRNUUx\nJXcrysjIaP78+W5ubnFxcWVlZceOHZs9e7Z8RWsIMTExLi4uK1eu3Lp1a1PnBSFqTdbWrVtX\nrVr17t27jh07Sk1A/RNHYQgAlMihQ4e0tbXxua2mpob/UFVVPXDgAJnG398fIcRgMF69eiW2\neVZWFkJo8eLFoikl0en0xYsXC4XCxinUmDFjaDSaZG4JgigtLdXS0kIIcTgcPp8vtrZO+d+/\nf7+hoaFYSg6Hs3//fqm5Onz4sJGRkVh6S0vLixcvSk2fkpKioqKCEDp16pQcldDMS1pVVeXn\n50ej0RBC+H9VVdUtW7ZAMVtiMQmC4PP569atw2HTsmXL6lrAJixmTbvFunTpgpN9/fpVW1vb\n3t5evqLJ4dGjRwihiooKGWmio6MRQitXrmwOWaLYZG3ZsgUh9O7du5oSUP/EURQI7IDyuHPn\nDkKoffv2d+/exddqRUVFaGioubk5Qig0NBQn8/f3ZzAYampqvXv3FggEonuQGtjdu3ev4r++\nfv0aGhrapUsXhNDWrVup5y07O/vz58/ylcvR0dHKykrqqqNHjyKEbGxsEEJXrlwRW0s9/3Pn\nzkUIWVhYHDp0KDU1taCg4P3790ePHrW2tkYIzZw5U2zPy5YtQwiZmJjs27cvJSUlPz8/KSlp\n8+bNurq6CKG//vpLLL1QKHR1dVVVVa21laxJMy/pokWLEEJ+fn6ZmZl8Pv/x48ft2rVDCN2/\nfx+K2eKK+fnz5759+6qpqc2ZM6c+gV2TFBPvNjQ0tFCakpISMuXw4cN1dHTqWii5m7KdO3c2\nt8BORpaoN1lUAjuKnziKAoEdUB7Tpk1DCD18+FBseWxsrKmp6erVq/FLf39/Go22ceNGhJDo\nnTyihsAuIiJCbIfv379HCFlbW1PP24MHD5hMpo+PT3R0dJ0KRRCEg4MD+T1bjKOjI5PJDAsL\nQwh5enqKraWY//PnzyOEunbtmpeXJ5ayoKDAwcEBIXT69GlyIQ6gra2tv3z5IpY+OTlZS0tL\nQ0MjOztbdDn+hFu8eLHcgV1zLimPx9PU1LSxsamuriYT/P333wihhQsXQjFbVjFxobp06fLm\nzZunT5/WJ7BrkmLWtFtJ3t7eGhoadSyTnE3Zpk2bevbsiRBavXr1hg0byOVFRUUhISGbN28+\nefJkYWGhWGC3ZcuWEydOEAQRGxu7e/furVu33rt3r65dJV+/fj116tS2bdv2798fHh5Obl5T\nljDqTRaVwI7iJ46iQGAHlIePjw9C6Pnz57KT4YavtLS0U6dO2traX79+JVdRDOwIgjA1NaXR\naGJfv2QoLS3dtm0bHjrTs2fPoKAgHo9HcduaAruXL18ihEaMGEEQRNeuXel0emZmpmgCivnv\n3r07Qig2Nlbq0V+/fo0Q6tGjB7lk0KBBCKHbt29LTR8REZGcnCy65OvXr7q6un5+flevXpUv\nsGvmJa2qqoqPj09KShJdi8c5zZgxA4rZsopJEERmZmZ5eTlBEPUJ7JqqmA0d2MnXlDk5OeFe\naXt7e0dHR7wwOTnZxMQEIaSvr6+np2dkZPTXX3+JBnaGhobdu3dfunSpgYHB4MGD8UE9PT2r\nqqoo5nb79u1sNpvFYrVt2xaPTu7Zs+e3b99qyhJWpyaLYlcslU8cMUlJSVNqdvfu3ZqOCIEd\nUB4nT57E121iYqKMZPgyKysrwwMsJk6cSK6iGNgJBAI1NTUtLa265rC6uvrixYv9+/dHCBka\nGq5evZpKp0ZNgR3uJ8Jjg3bs2IEQWr9+vWgCKvnPyclBCHXq1ElGBnr06IEQysjIIAiirKyM\nyWRyOBzq35u9vb0NDAxyc3PlDuxaSklFHTp0CCF08OBB6ptAMbHmU8z6BHZNUkwZu5UkX2CH\nydGUDR8+HP1vv6erqytCCN+TIwji0qVLBgYGooGdsbExm812c3PDcbZQKJw1axZCqKYBlGI+\nf/5Mp9NdXV0LCwvx5leuXGEymXPnzq0pS1idmiyKgR2VTxwxT58+7VIzGbmC6U6A8pg2bVpA\nQMCrV6/s7e07duw4Z86ckydPZmZmSk1MEMTAgQOnTZt24cKFe/fu1elAe/furaiokOM5JgaD\nMWHChMePHz9//nzo0KHbt2+3srKaNGlScnKyjK1KS0tZLJbYwrKysrNnz7Zp02bUqFEIoalT\npzKZzBMnTgiFwjrlH/f7yH5MFd8zyMjIwP9XV1c7ODjgsfO1unHjxuXLl3ft2oWbbDm0lJKK\nysjIWL16tbW19YwZMyhuAsWsddumKqYcmqqYpODg4PXSiCZjMpk8Hq+6ulqOAsrXlInKysqK\niooaOHDgzJkz8RJvb+8JEyaIJauqqtq2bRt+Eo5Go23evJlGo+Hu6Vqlp6cLhUIXFxc8hpJG\no40dOzYyMnLp0qUytqp/kyWVHJ84/fr1S6qZn59fTRvCdCdAqRw9etTf3//UqVMPHjw4duzY\nsWPHEEK9evX67bffxo4dK5l+586dN2/enD9/flJSEvkUrZjg4GD8TQshVFxc/PLly+joaGNj\n423btklNz+PxioqKyJeampqamppiaXr37n3mzJkdO3bs2rVr165dvXr1qulR+aSkpIyMDMkP\nzrNnz5aVlS1cuBA/t2VsbDxixIgbN27cu3fP09OTev65XC7OpNSjY/hB44KCAorpSaWlpQsW\nLBg6dOjUqVOppJeqRZRU1IcPH0aMGCEUCq9evVrTSSUJitk8iymfpiomCXdfSBo0aJCVlRX+\n28HB4cKFC3g2JamJFduUiYmPj0cI9evXTyx7+B4wSU1NDY+Ew4yNjU1MTJKSkqgcokuXLhoa\nGgcPHrSwsBg3bhwO1PBdxpoopMmSgconjgLIuMEIQIuWk5Pz999/T5s2DTdGu3btwsvJEQ/4\nJR4kix+tqHW6EyaTaWFhMXfuXMnx16RTp06JbkI+tCHm69evq1evxm3N4cOHpaaZP3++rq5u\nr169RIdlYLixe/nyJbkE9xqMHTuWXEIl/3iMlLe3d80VSUyfPh0hFBMTQxBEeno6Qmjw4MEy\n0ovmX11d/cOHD6I5rGtXbIsoKSkqKsrAwMDU1DQ+Pr5OG0IxySXNp5hyd8U2VTHJ3d6+fbtU\nGtEHX0pLSz09PVVVVSdNmoT7OsUosCkjJPo9jx8/jhDauXOnaJqIiAj0v12xbdu2FdsPfmpY\ntCAyPHz4EI/Mo9FoDg4Oa9asIfusJbNEyNVkUR9jh1/K+MRRFLhjB5SWsbHx+HpOjP0AACAA\nSURBVPHjx48fv27dup49e65du3bRokVMpvg5P3v27ODg4B07dkyZMgWPpRUTERGBh11TZG9v\nv27dOvIlHkci6sWLF3v27Ll48SKNRvPx8Vm8eHGvXr2k7iopKamsrKxDhw5iX5Rfvnz56tUr\nFou1a9cuciGfz0cIhYaG5uTkcDgcivm3srKi0+kvXryQUaL4+HgajYZnbTA1NVVVVX316hWf\nz5fsIBb15MmTQ4cO/fnnn3hGDPm0iJKSQkJCAgICunXrdv369TpN2wvFbIbFlFsTFpOkpqZW\n6+1JFRUVW1vbsLCw+Ph4gUAgmUCBTVlNCIIQfSmZDckZmIVCIY1Go9il7u7u/uHDh8jIyDt3\n7ty7d2/jxo07d+68evWq1IE0CmmyalXrJw7p7du3OGqUaurUqTgwlUKxcSIATUUoFKakpLx9\n+1bqWtz9gb+HiX1/IggiMTGRyWS6urpmZ2cjak/FygEPNx4wYABCyMTEZP369Tk5ObVudffu\nXYTQokWLRBcGBAQghCwtLcWG05qamiKEyAljKeZ/8ODBCKEHDx5IXYsfu3NxcSGX4E7toKAg\nqekvXrw4f/787OzswYMHs1isWbNm+f/XkCFDEEKDBg3y9/cXe7iyJi2ipPjl6dOn6XT6yJEj\nuVwulaKJgmI2t2Ji8t2xa9piUm+19u7di6TNOlkr+ZoysdtjV65cQRJT1uEeZNE7dmpqamLP\nuxgYGOjr69c1z1hkZKSGhkaHDh2kZkm+Jquud+yImj9xxMj98AQEdkBJ4BEbHTp0kHzEqaKi\nwszMjMVi4ck5JS8zgiCWL1+OEMItXUMEdm/fvsU9An369Dl9+jT1x/UJgrCwsOjZsyf5sqSk\nRFNTU0NDAz/qJSotLY1Go9na2uKmkGL+b9++jRBq166dZIdvYWEhfubu1q1b5MLo6Gg6na6n\npyfZ0iUkJOjr67dp0yY7O3vq1KmO/wvfVLC2tnZ0dHzy5EmtBW8pJSUIIioqislk/vDDD3V6\nZ6GYzbOYJDkCuyYvJvVWy9fXl0ajUZ93CZO7KcNRFNnnm5aWhhAaOHCgaBr8o1uigR1C6Nmz\nZ2QCvNWQIUOoHDE+Pv7AgQNik1K5ubnR6XT8FohlSb4mS47AjqjhE0dRILADysPb2xsh1Ldv\n39DQ0Ly8PIFAkJ+ff//+fdyDQD7iLvUyKysrs7CwwD8p2xCBXWRk5JQpU2qdY08qselODh8+\njBCaP3++1MT43mR4eDhRl/zjVobD4ezduzc1NbWwsPD9+/fHjh3DXRIrVqwQS79+/XqEkJaW\n1n/+85+EhIRv377Fx8evX79eS0tLVVX1+vXrUo9S1zF2LaWkfD6/ffv2RkZG3759q/hf5Kfm\ngwcPRo8efebMGShmMy8mQRBVVVW4XJGRkQihJUuWtKBiyv7licLCQnJ0mnzTncjdlOEnXvGE\nfDiuwiMRDx48yOfzBQJBSEgIjuREAztNTU1HR8esrCyCIMrKyvCP9tZ021UMfnhuxYoVZGsf\nGRmJd1hTlsQ0xBg7TOonjqJAYAeUB5fL/fHHHyVH0bHZ7GXLlpHfLKVeZgRBXL9+HadvoK5Y\nuYkFdj179qTRaDW1I/j7/eTJk4k65j8wMFByvJS5uXlwcLDU9KdOncK/1SaqZ8+eol+vxdQ1\nsGspJcXT5UtlaWmJ0+DPmDVr1kAxm3kxif/eyJFkZmbW/Isp+7diEULkL0bUZx47OeCnJeh0\nurq6Oq6fFy9e4MgGjwg0Nja+ePEiEglVjY2N27dvv379ehqNZm5ujgdHTpgwgeLM8Hw+Hz/c\nymQyTUxM8OPD7du3Jyc6lcySmIYL7AhpnziKAg9PAOWhrq5+6NChP/74IyoqKj09vaKiQkND\nw8bGZuDAgXgeI2zUqFFt27Zls9lim48aNWr//v15eXnkE/g4JTk7QHNQWlo6cuTI2bNn1zSn\ngIeHx6ZNm+h0Oqpj/v39/WfOnBkdHZ2Wlpabm2tgYGBnZ+fs7Ix3JcnPz8/X1zc6Ojo1NTU/\nP5/D4djb24tOTCCpY8eO69atkz0pF6kFlbRt27aiY8xFkSdez549e/fujSe/gGI252LiNGLT\ncGA4MmjmxcS7lbEr3JHa+GbNmmVsbPz27VszMzMcWzs6Oqampt64cSM7O7tt27ajRo2i0Wjr\n1q1zcnIityIIYt26dWPGjHn06BGPx+vdu7ebmxvFIzKZzJCQkNWrV8fExOTm5mpra3fo0AF3\nxdaUJTF1arJqQv0TR1FoxP8+kwIAaG4cHR0LCwvxfA2g5Ro6dOisWbMmT57c1BlpWFDMlsLT\n0/PJkyfFxcVNnZEacTgcTU1NPEtzs7V169ZVq1a9e/eO4gR+jQB+eQKA5s7c3PzTp08U5+QE\nzVN8fPzr169Hjx7d1BlpWFDMluL79+/Pnj2ztLRs6owAxYOuWACau/nz59+8ebNPnz6dO3cO\nCwvDP2Xd0t24cSM8PFx2mj/++EP2PE8tSPfu3fPy8po6Fw0OitkiDBs27Pnz58XFxaIz7bUs\nra0BqRMI7ABo7oYNG/bixYuwsDCpPxrbQuXk5NR6D1K+X7EEAMg2ZMgQd3d3Z2dnPBdds7V8\n+XLJoWlY82lAnJ2d161bp9gflq0nGGMHAAAAAKAkYIwdAAAAAICSgMAOAAAAAEBJQGAHAAAA\nAKAkILADAAAAAFASENgBAAAAACgJCOwAAAAAAJQEBHYAAAAAAEoCAjsAAAAAACUBgR0AAAAA\ngJKAwA6ARlJRUdHUWWhUfD6/oqJCIBA0dUYaj1AorKysbOpcNKrHjx9HREQIhcKmzkjj4fP5\nrerH7oRCYUVFRWs7sXk8Xsv9XS4I7ABoJOXl5U2dhUZVWVnJ5XJb1UegQCBobZ9/jx8/joyM\nbFXhe2VlZWs7q7lcbms7sVt0cw2BHQAAAACAkoDADgAAAABASUBgBwAAAACgJJhNnQEAAAAt\nlY+Pj1AoZDAYTZ0RAMC/ILADAAAgJ21tbaFQSKPRmjojAIB/QVcsAAAAAICSgMAOAAAAAEBJ\nQGAHAAAAAKAkILADAAAAAFASENgBAAAAACgJeCoWAACAnEpKSoRCYZs2bSg+GDvruKy1J/wV\nkysAWjO4YwcAAEBOFy9eDAkJaVW/FQtAMweBHQAAAACAkoCuWAAAAA3rYy6KSq092cnHCCHk\n2Q0ZaTd0jgBQWhDYAQAAaFjfS1Bkcu3JcBonGwjsAJAfdMUCAABoWLbGaJ4bmucmvjzIwkn0\nJU5jqtt4GQNA+TTxHbvXr19funRpxIgRAwYMkJEsNjY2PDx82bJlbDaby+Xeu3cvIyOjuLhY\nS0vLzs7Ozc1NQ0MDpzx9+vQ///zj7+9vY2NDbl5aWrply5bZs2e3a9eOTINX0Wg0bW1tKysr\nLy8vTU1NuQty5cqVFy9eTJw40cHBQXQ5xWNlZ2dHRERkZGTw+XwDA4M+ffo4OjrS6XSxNOHh\n4R8/fhQKhYaGhv3797e3t5f6JNq+ffsqKipWrlzZsvKflpZ2//79vLw8AwODoUOHdujQASF0\n6dKl4uJif394WA6AFkxfE+lrIoTQIZGFYlEdQqi3deNlCQBl1WR37IRC4ZkzZ7Zs2ZKUlJSf\nny8j5devX3fv3u3u7s5ms798+TJnzpz79+8bGBj06NFDW1v7/PnzK1asKC0txYk/ffqUlJR0\n9OhR0T1UV1cnJSVxuVwyzefPn7t169atW7euXbtqamqGhobOmzcvJydHvrIIBIKrV69mZWXd\nvn1bbBWVY926dWv+/PmPHj3S09OzsLDIy8vbuHHj2rVryUIhhO7du4fT6Ovrm5qafvr0ae3a\ntdu3b+fz+WJHfPjw4cOHD5OTKXR7NKf8P378ePny5fn5+XZ2djk5OStWrHjx4gVCaNiwYTEx\nMbdu3aJeHABACyIZ3gEA6qPJ7tjduXMnKipqx44dS5culZ3y2LFjXbt27dOnD0Lo8uXLbDZ7\nz549qqqqeO2IESMWLVp0584dHx8fvKRTp04pKSlRUVGurq417VNPT2/y5Mnky8mTJ8+fP//i\nxYs//fSTWMqioqKCggJ8q68mz58/Ly8vnzdv3sGDB0tLS7W0tKgf6/Xr10ePHvX09AwICGAw\nGDhNcnLy+vXrDxw4sGrVKoTQ27dvDx486O7uvmDBAjJNdHT0zp07raysJk6cSO68pKQkKCio\ne/fuWVlZMjLcDPN//PhxV1fXZcuW4bW//vrr5cuXe/Xqpa2tPXXq1MOHDzs7O+vo6FAvFACg\nEbRr147H41GcxE4UxHMANJCGDeyEQmFYWFh8fDyXyzU0NBw+fLitrS1eZWNjs2vXLrILtSbv\n379/+fLlzp078cuioiIOh0NGdQihtm3bnjhxQlf3/wdlmJqaWlpaBgcH9+3bV0VFhUo+27Rp\n06FDh4yMDMlVpaWlK1assLOzGzVqVN++faW2X2FhYb169XJ1dQ0MDIyOjh4xYgT1Y507d65t\n27aiURFCqGPHjlOmTDl27Fh6enq7du3Onz9vaGg4b9480TQuLi5cLtfKykp058ePH+/UqZO9\nvX2dArsmz391dfWkSZM6duxIru3QoUNMTAz+e+DAgWfPnr169eqMGTOoFwoA0Ajc3NyEQqHo\npS0bnoKY9uB/orogCydi6FOF5w2A1qlhu2IDAwOPHz9uYWHh5ORUXV29YsWK9PR0vKpjx461\nRnUIocePHxsYGODhVgghW1vbf/75JzQ0tLq6mkyjp6cnGm8JBAI/P7+KiorLly9Tz2pFRYVo\nvEgyNzc/fvx4ly5d9u/fP3fu3NDQ0IqKCtEExcXFL1++dHNzY7PZzs7O4eHh1I/F5XJTUlJc\nXV0lm0V3d3cajRYbG1tVVZWUlOTi4sJiscTSeHh4iAZDiYmJT58+nTt3LsUiN5/8M5nMYcOG\nWVhYkKsyMzNNTU3x3wwGo1+/fo8fP65TuQAAAIBWqGHv2HXv3n3AgAFdunRBCA0fPjw1NfXR\no0eyuzXFvHnzxt7ennw5bty4t2/fHjt27PTp0126dOnatWvfvn3NzMzEttLW1p48eXJISMiQ\nIUOMjIxqPUpcXFxqaur06dOlrtXV1Z0yZcqECRPCwsJu3Lhx5syZIUOGjBo1Cu/50aNHGhoa\nvXr1Qgi5u7uvXLnyy5cvklmSeqxv374RBCEa0JDU1dXbtGmTm5ubl5cnEAjMzc1lF4HP5x88\neNDX19fQ0LDW8opqJvkXFR0d/erVq/Xr15NL7O3tb9y48f3795reTaFQSI6hbM5Exx0qPfzt\ni8fjVVVVNXVeGolQKBQIBK3qXSYIAiFUWloquzeWT1TPfr8V/301P0oyAe2B01j9fwfPrGk7\no71aW0XnVGGqq6urq6slxzcrK6FQiBCqrq5ubSd2WVlZU+eiRpqamjKuuIYN7Ozt7W/dunXt\n2rXy8nKCIPLz82U/JyEpNze3Z8+e5EtVVdVNmzYlJSU9e/YsISHh5MmTwcHBTk5OixcvVldX\nF93Qy8vr7t27QUFBUh8Ozc7OJpcXFRVlZ2e7urqOGjVKRk7YbLanp6eHh0dcXNzhw4cJgggI\nCEAIhYeHDxw4EN+y6tSpk6mpaXh4+NSpU6kcC18wNfVisFisyspKnIbJrOWdunjxoqqqquwi\nSNVM8k96/vz53r17J06cKPq+43guNze3psCOIIjKykqKh2hCLSKTitV6Pv9IrfBdrjV2ryT4\nUuM5UWSC2fo/WNDr9gW18Yn2GrUGQqGwtZ3Yzbm8sifxaMDAjiCITZs2ff782cfHx9TUlE6n\niz2sSkVJSYnYWH6EUNeuXbt27YoQKiwsvHPnzoULFzQ1NRctWiSahsFg+Pv7b9iw4e3bt2Sn\nHkldXb1fv374by0trXbt2pH3EZOTkw8d+veR/D59+kyZMoXcSiAQxMTEXLt2rbi4GN+CSk9P\n//jxY1VVFRn6cLncR48e+fn5kdG0jGPhoYGFhYWSBScIoqioqE2bNjhNXl6ejFrKysq6evXq\nli1bxGYYqVUzyT8pIiJi3759EydOnDRpkuhybW1thFBJSUlNG9Lp9Ob/aEVJSQkuSCtRUVFR\nVVWlrq4u2QuvrKqrq3GRmzojjae0tFQoFGpra8u+YyckhHccdiGEPBNkPS2H0zhqddRhyj/5\nVEOrqKhgMBhsNrupM9JIqquruVwui8VqbSe27LtiTUt2xhowsMvMzExISPj9999xN1+tWZFK\nRUVFxndBPT09X1/fL1++JCQkSK51dHR0dHQ8duzYunXrxFbp6OiMHTtW6j51dXX79++P/7a2\n/ndWpfLy8rt37968ebO6utrT03P9+vU4jAgLC+NwOMOGDSM35/P5p0+fFu1BlnEsAwMDAwOD\n+Pj44cOHi61KS0vj8XidO3fW1NQ0MzN79erVuHHjxNJ8+vRJW1tbT0/vxo0bTCYzJCQEL8/L\nyyspKVm7du2IESOcnGQ9etZM8o9fRkRE7N27d968eZJ7w9+cZDwKQ6PRWkT00CIyqSj4XWMw\nGK2q1Hw+v1WVF2OxWLU27x5GTmLPTEjyTFja/J+iqKysbG1nNWo5bawCUTmrm6cGDOwKCgoQ\nQiYmJvhlbm5uZmZmncZaIYR0dHSKi4vx30VFRatWrfL29h4yZIhYspp6A2fPnr1w4cJHjx5R\nPyKHwxGdQKS8vPzMmTMPHjwwMTHx9fUdOHAgeXILBIKoqCgvLy+xuOfZs2fh4eGiQwNlcHd3\nv3TpUmpqKvmACEKIIIhz587p6+s7OjriNCEhIXFxcb179xbN2LZt24yNjdetW+fs7Cz6eOyb\nN2+Kior69evH4XBkHLr55B8hlJKSsm/fvgULFgwdOlTyKPgcaP735AAA1PEe9kYIqQ6Ja+qM\nAKBUGjCwMzU1pdFoCQkJZmZmpaWlf/31l5WVlYzeNKnatWv34cMH/Leurq6RkVFgYCB+TFJN\nTa20tDQsLCwmJkas545kZmbm5eV16dIluUuRk5Pz7du3tWvXduvWTWzV8+fPi4uLJX8zw9nZ\n+fz58z/++KPUx2zFTJgw4fnz5xs2bJg2bVrfvn1VVVUzMzMvXLiQkJCwZs0afLd/zJgxT548\n2bZt25QpU1xcXFRUVNLS0k6ePFlaWrp69WqEkIODg+gvRggEgpSUFC8vL9mHbj75Jwji2LFj\nrq6uUqM6hND79+/ZbHZdvxUAABpaTExMZWXl2LFjqYyjJe/GVa78/xlDcXinsm1fA+UQgNam\nAQM7Y2Pj0aNHHzly5MaNG1wu98cffywsLDx69Ogvv/yyffv25cuXp6am4pSBgYGBgYH4D7HR\n8Y6OjocOHeLxeDjIWLVq1dGjR//666/du3czGAyBQKCjozN16lRvb++asjF58uTIyEi5S9Gu\nXbs1a9ZIXRUWFmZpaSkZbTg7OwcHBz99+nTw4MG17p/NZm/duvXEiROBgYF//fUXXtixY8fN\nmzeTU5kwmczNmzefOHHi7NmzQUFBCCEajebo6Lhq1SrZ9+Rkaz75z8zMTE1NTU1NjYiIEN35\nyZMncUft69evu3bt2noGtQDQUiQnJ5eXl48ZM4b6JqJRnehCiO0AUAgafli94eTm5hYWFpqb\nm6upqSGEPn78qKmpaWho+OHDh/LycrHEdnZ2Yh/ePB4vICDA29tbtOGorKz8/v17eXm5np6e\nvr6+aD/sp0+f6HS6WLDy5csX/OsReOa8T58+8fl8cqpkub17905bW1vqzCDJyclaWlpmZmbU\nj1VZWfnt2zcej2doaEgOO5NMk5OTw+fzjY2NJZ8pIeXl5eXl5YlOcdfM88/j8dLS0iTTd+rU\niclk5uTk/Pjjj2vWrCEHa7ZQ+fn5+vr6TZ2LxlNWVsbj8bS0tCjOE64E+Hx+RUVFq3pEZvv2\n7eXl5atXr6Y4AEtqVEdqEbFdWVkZk8mk0qGhHPh8fnFxMZvNblUndkFBgdgUuS1Igwd29Xfz\n5s2///770KFDreqRHEDatWtXbm7uli1bmjoj9QWBndKDwK4mwqREwbNohJAwLUVGMnp7O4QQ\ny38+asYfqBDYtQYtOrBrst+Kpc7Lyys+Pn7fvn2//vprU+elhfn06dPp06drWrt06VJ8G7U5\nw5MV79mzp6kzAgCQH1FUIDukw6ikAQDI1gICOxqNtnTp0g8fPlRVVcEoqzrR1tYmp6CTRH3S\n4CZkYmKyc+dOAwODps4IAEB+9O6ObGsbhFDVvh0ykrF/WoEQas636wBo/lrARztCSF1dXfKh\nVFArPT09d3f3ps5FvdR/KCQAoMnRNLWQphZCSGXbvpqG2bWIAXYANH91+6ECAAAAAADQbLWM\nO3YAAACaIR8fH6FQWNMU8VLhO3Oi9+3gXh0ACgSBHQAAADlpa2sLhUJ5fi4SgjkAGgZ0xQIA\nAAAAKAkI7AAAAAAAlAQEdgAAAAAASgICOwAAAAAAJQGBHQAAAACAkoCnYgEAAMipsrJSIBAQ\nBNFCf1UTAOUDd+wAAADI6dSpU4GBgQKBoKkzAgD4FwR2AAAAAABKAgI7AAAAAAAlAYEdAAAA\nAICSgMAOAAAAAEBJQGAHAAAAAKAkILADAAAAAFASMI8dAAAAObVr147H41GfxG7WcVlrT/gr\nIEsAtHIQ2AEAAJCTm5ubUChkMBhNnREAwL+gKxYAAAAAQElAYAcAAAAAoCSgKxYAAEDDuvYK\n3XhdezI8Am/3ZKSj3tA5AkBpQWAHAACgYbEYSEMFIYS4lbKS4TSUn8QAAEgBgR0AAICG5eWA\nvBwQ+t+nYoMsnBBCMzOfkkv2+zV2xgBQPhDYyam0tHTPnj1xcXF0Ol0oFDo6Oi5btkxTU1My\nJUEQGzduZLFYv/76K0EQFy9evH79OpfLVVVVraio0NHR8fX19fT0xIk3bdoUGxvr5+fn4+ND\n7qGwsHD69OmbNm3q1q0bmUb0EKqqquPHjxfdpK5WrVr19u1bX1/fSZMmiS6ncqzS0tITJ05E\nRUXx+Xy8xNbWdvr06Q4ODmSasrKy48ePi6bp1KnT7Nmz27dvT+5Ean1GRUUdPXp0z549BgYG\ncpcOAAAAaCUgsJPTX3/99eHDh127dtnY2CQnJ2/YsOHUqVPz5s2TTHnz5s20tLTDhw8jhG7f\nvn3u3Ll58+a5ubmxWKzS0tKzZ88eOnTIxMSke/fuOL26uvqlS5eGDBnSpk2bmo5ubW29d+9e\n/HdhYWFoaOjp06d1dHSGDx8uR1m+ffv2zz//9O/fPyIiQiywq/VY5eXlK1euLC0tnTdvXq9e\nvVRVVTMzM8+fP79u3boVK1YMGDAAIcTj8VatWlVQUDB37tw+ffqw2ey0tLSTJ0/+9ttvW7Zs\nsbW1lVGfrq6uL1682Llz59atW+UoGgCgQcXFxVVVVY0YMaKuM57g23X4D9GbdgCAeoKnYmtB\nEMSXL19SUlIKCgrIhXw+//379xMmTLC1taXRaJ06dXJxcXnz5o3k5jwe7+LFi+PGjVNXV0cI\nvXz50s7Obvjw4SwWCyGkpaU1Z84cLy+v6upqcpO+ffvq6ekFBwdTzKGent60adMsLS1jYmIk\n16anp2/atElq3khhYWHGxsZTp07Nzs5+9+5dnY517ty5nJyc//znP0OGDNHV1VVVVe3QocPa\ntWsdHBwOHjxYUVGBELp06VJWVta6deuGDRumq6urrq7u4OCwefNmHR2d6OhoVFt9+vr6Jicn\nx8XFUawQAECjSUhIePnypVAobOqMAAD+BXfsZElJSdm+fXtxcbGGhkZxcXHfvn1/+eUXBoPB\nYrECAwNFUzIYDKlNW3R0NJfLJW+kaWhoZGRkcLlcDQ0NvIRGo82dO1dsK39//02bNnl5ednZ\n2VHMqoGBQVFRkeRyDodjZGT0xx9/mJqajhw50tXVFceUJIIgIiIi3NzczMzMbG1tw8PDO3Xq\nRPFYBEGEhYW5uLhYW1uLJqDRaH5+fsuWLYuNjR00aNDDhw/79OnToUMH0TSqqqqHDx9mMpkI\nIdn1yeFwHB0dQ0NDe/fuTbE2AADNE/5tCdoDJ9GFQRZOxFC4aQeAYkBgJ8u1a9dsbW2XL1/O\nYrFycnKWLFly9+5dLy8vsWSlpaVPnjwZPHiw5B5evXrVsWNHNTU1/HL48OFRUVGLFy/28PBw\ncHCwsbGh08VvmhIE0adPHwcHh2PHju3YsYPKb/VUVVWlp6fjEXhi1NXVAwICfH197927d+bM\nmZMnT3p6eo4YMUJHRwcnSEpK+v79u5ubG0LI3d399OnTAQEBbDabyrFycnLKysqkHtfW1lZN\nTS01NdXBwaGgoEBqGhzVSZKszx49egQHB1dVVcnImEAgqGlV89EiMqkoBEEghIRCYesptVAo\nJAii9ZSXJBAIJJsyUZVC/pfK7/jvDk8nSiagPXBKdbqA/7ZSNaHTmm9vEkEQre2sRgi1thMb\nl5f6b+U1MtkjHyCwk2XlypV8Pj8rK6u8vJwgCH19/Q8fPoilqaqq2r59u4qKitRnF9LT0/v2\n7Uu+7Nq168aNG8+fP3/q1KmQkBB1dfVevXqNHTvWxsZGbMPZs2cvXrw4PDzc3d1dcrcVFRUJ\nCQn478LCwnv37pWVlY0dO7amgmhoaIwbN2706NHR0dHXr1+/dOlSQECAh4cHQigsLKxz587G\nxsYIoYEDB544ceL58+fOzs5UjlVaWooQImNEUTQaTUdHp7i4GKfR1dWtKW9ipNanra1tVVVV\nZmYmHpAnSSAQFBYWUjxEE2oRmVQsLpfL5XKbOheNqhW+y8XFxbI/aV6Xpw1LWSp7J2TAl9bt\nnC5TyoNozUp5eXlTZ6FR8fn81nZiS+0Eayb09fVlBJ0Q2Mny+PHjAwcOsNlsDofDYDDy8/Mr\nK/9nFqby8vJNmzbl5ORs3LiR7F0VVVxcLBb3dOvWrVu3bmVlZW/evImPj4+JiXn8+PGqVav6\n9OkjmszCwsLDwyMkJKR///6Su/327dumTZvw31paWtbW1n/++aeVlRU+FQkArAAAIABJREFU\nIjlOztDQUDRkZDAYAwcOVFdXP3z4cFZWFkKIx+M9efLEzc2NDN2srKzCw8NFAzsZx8J3Iquq\nqqTWXlVVlZqaGh5cKFZvNampPnEdlpSU1LQhjUar6f5f81FdXd38M6lAAoGAIAgGg9Fsv/Uq\nHL6X0wp/OJXBYMg+t7XZGt012iOE4rlpMpLhNCosNpPRfK8UfAdL9h1KZULeu2pVJ3aLbq5b\nar4bQVlZ2b59+wYPHjx37lz8yfTLL7+IJigtLV27dq1AINi2bVtNk3FUVlZK7T3U1NR0cnJy\ncnLy8/NbsWLFhQsXxAI7hJCvr29UVNTFixdHjx4ttsrKyop8UlVMeno6+QCpm5vbTz/9hP+u\nqqoKDw+/fv16YWGhu7s73mdMTExlZWV4eHh4eDhORhBEdXV1UVEReY9NxrEMDQ0ZDEZGRgZ+\n+lUUl8stLCzkcDh6enpsNjs9PV3qHkTJqE8VFRUkMzqk0+nUbwo2lfz8/OafSQUqKyvj8Xjq\n6ur47WsN+Hx+RUWFtrZ2U2ekseno6IgN3hXTF+m+5oQgidF1Yl73D1FwzhpAWVkZk8lUVVVt\n6ow0Ej6fX1xczGKxWtWJXVBQoKOj00K/lEJgV6OPHz9WVFR4enrit5YgiOzsbENDQ7y2qqrq\njz/+oNPpGzdulDp9HaatrY37IhFCPB4vNjbWzs6Ow+GQCbS0tBwcHJ4+lTJwWEtLy9fX98SJ\nE1Jv2tWkR48e165dE11SVFR069at27dva2ho/PDDD0OHDiXH/IWFhTk7O69YsYJMXF1dPXXq\n1MjISMloUpKqqmq3bt0iIyMnTpwo9uUmIiKCIAgnJycGg9GzZ8/o6Gg/Pz98944UEhLCYDCm\nTJmCaqtPfK+uVTUrACgf2VEdTgBPUQBQT63lZrIccKRC3iUKCwsrLy8nH9W8ePFiXl7e+vXr\nZUR1CCFDQ8Pv37+TLw8dOhQYGCg6BLWioiIxMdHS0lLq5p6eniYmJmfOnJG7FFlZWf7+/klJ\nSQsXLjxy5MioUaPIqA5PXyd2s43JZPbt2zciIoLi/n19fb99+3bw4EHRQqWkpJw+fdrd3d3M\nzAwhNHHixNLS0j///JPH45FpwsPDr1y5Qva3yq5PXIdkVA0AaCbGjBnj4+NDpZNONKrjPewt\n+q8hMwhAqwN37GpkY2Ojr68fGBjo5eX18ePHjx8/Dho06NWrV0+fPu3cufP169c7dOhw69Yt\n0U3Gjh0rdn/e3t4+KioK/62qqrpo0aKdO3cuXLiwd+/eWlpahYWFT58+raqqEuvkJdHp9ICA\ngLVr18pdCi0trR07drRr105yVVhYmIqKiqOjo9hyZ2fn8PDwT58+1RRuiurYseOiRYsOHjyY\nmJjo6OiIJyh+9epVr169yGlcbGxslixZsn///jlz5uBJjFNTU1NTU0ePHo3vCxYXF8uuz8TE\nRDxvi3yVAABoIAYGBkKhkEqPFXkrrnLlT2KrcGynsm2fwrMHQCsEgV2N2Gz2xo0bL1++/OjR\now4dOqxatSo/P7+0tPTNmzdWVlbt27cnCEJs4t8ffvhBLLDr37//pUuX0tLS8G9nDRgwwMbG\nJjw8/PPnz5mZmbq6uuPGjRsyZAh5F83S0lJsTJ6Dg8MPP/yQkZFB3tyytLSk3impq6tb07iu\n3NzcH374QXL8U/fu3bt37/7+/XtLS0sqxxoyZIi9vX1YWFhmZub379+NjIzWr1/fvXt30bZ+\n0KBBnTt3DgsL+/TpU1FRkZ2d3Y8//kg+4lpeXi6jPgUCwbNnz1xdXSkWGQAAAGi1aHiuKdBw\n/vOf/9Dp9DVr1jR1Rlqq8PDww4cPHzt2TOq8Ki1Ifn6+vr5+U+ei8eCHJ7S0tODhCSVWUFAg\nFAplT76AECIKC1A5FyFUtW+HjGTsn1YgBpPGMVFwLhWqdT48wWazW9uJraen10IfnoAxdg0u\nICAgKSnp+fPnTZ2RFqmkpOTUqVPTp09v6VEdAK2Z4P7tqn07ZEd1CKGqfTv4IccaJ0sAKCsI\n7BqciYnJzz//HBYWVtN8b0CG+/fvOzs7S/7aBwCgBaFxOPT2dvT2tfxGIr29Hd1SyoBgAAB1\n0BULQCOBrlilB12xtZJ8coLUUh6egK7Y1gC6YgEAALRGlZWVPB6P+g2ClhK9AdByQWAHAABA\nTqdOnRKbm7NWUmM7CPgAUBSY7gQAAECjwmEc7paFkA4AxYLADgAAQBOAkA6AhgBdsQAAAAAA\nSgICOwAAAAAAJQGBHQAAAACAkoDADgAAAABAScDDEwAAAORkZmbG4/Fa6DyuACglCOwAAADI\nydPTUygUMhiMps4IAOBf0BULAAAAAKAkILADAAAAAFASENgBAAAAACgJCOwAAAAAAJQEBHYA\nAAAAAEoCAjsAAAAAACUBgR0AAAA5xcXFPX36VCgUNnVGAAD/gnnsAAAAyCkhIaG8vNzDw0PG\nVHazjsvawwl/xecKgNYM7tgBAAAAACgJCOwAAAAAAJQEdMUCAABQsLJKFJVCKeXtxH//sOMg\nG6OGyxEArQUEdgAAABSstAJdiqOUkkzm3QsCOwAUAAI7AAAACqalhsb3/vdvsQgvyMJpZuZT\n8iWZzI7TOFkDQMk1XmBXXFz88uXLoqIiAwOD3r17q6mp1ZQyKyvryZMnY8eOZbPZCKG0tLSP\nHz+WlJRoaWl16NDB2tqaTBkdHf3582cPDw89PT1yIY/Hu3r1qru7u5GREZmGXKutrW1lZdWl\nS5f6lOXVq1cpKSmurq5mZmaiyykeSyAQ/PPPPxkZGXw+38DAoEePHlpaWpJpkpKSMjIyBAKB\noaGho6Ojurq6WJqCgoL79+/36NHDzs6OSrZFs6eiomJkZNStWzcdHR0q25Lu37+fn5+PENLV\n1fX09KzTtnJ49OiRioqKk5NTQx8IAKBAmipohP2/f4sGdkEW4tcymQwAoBCNFNi9fPly27Zt\n2traHA4nIyPj2LFjmzZtsrCwkExZUVGxefPmQYMGsdns8vLyzZs3JyUldejQQVtb+9u3b58+\nfXJxcVm6dCl+tD4qKio2Nvbbt29LliwR3cO5c+e6du2KA7uoqKiEhARbW1u8trCw8MuXLw4O\nDqtXr1ZVVZWvOMeOHcvJySkqKpo3b57ocirHSk1N/fPPP3NycszMzFRVVb9+/crn8729vSdP\nnkyj0XCa9+/f79y5Mzs728zMjM1mf/nyhUajTZ8+3cvLizxWfHz8n3/+WVxcrK6uTjGwE80e\nj8f7/PlzdXX1Dz/8MH36dBlTFYhJS0vLzMz8/PmzgYFBIwR2FhYWv/76q7a2dj1jcQBAQ/D0\n9KyurqbegGBiN+0AAArUGIGdUCjcs2dPv379fv75ZxqNVl5evnTp0uDg4N9//10ycUhICJvN\n9vHxQQj9/fffaWlp+/fvNzc3x2sfP368bdu27t27Dx06FC/hcDgRERFeXl7t27evKQMmJiab\nN28mX75+/XrDhg2XLl3y8/MTS1lZWUmn01ksloziJCcnf/nyZcKECXfv3g0ICGAy/6cOZR/r\ny5cva9assbS03LBhA4fDQQgJBIKrV6+eOnVKKBTiNN+/f1+zZo2JicmhQ4dMTU0RQuXl5SdP\nnjxy5Ii2traLiwtC6MmTJ7t37547d+6hQ4dkZFV2VVRXV9+5cycoKKi4uPjnn3+muIcFCxYg\nhLZu3ZqdnV2nQ8unXbt248aN+/PPP48cOSL7fQEAND4zMzOhUEh+KZVN8nYdAEDhFBzYff36\nNTExsayszNDQsG/fvvg2FY/H8/b27tu3L7741dXV7e3t37x5I7l5Xl7evXv3li1bhlN+/PjR\n1taWjOoQQgMGDFi5cqWVlRW5pGvXrsbGxkePHt2+fTvFxqVHjx4dO3ZMTEyUXJWVlbVhw4YR\nI0Z4enrq6upK3TwsLKxjx46jRo26cuVKXFyc7F5CsWOdOnWKzWb//vvvZN8rg8EYP358YWHh\n5cuXPTw8DAwMzpw5gxD6/fffyf5ldXX1efPmcblcLpeLlwiFwi1bttja2tY1sBPFZDJHjhyJ\nEDp27JiHh0enTp3w8tLS0tjYWNxp3q9fPyr3NaW+71haWtqbN280NDT69++fkZGRkZGBDyrj\nQJcvX3ZwcKiurn79+rW3t/eoUaOuXbt2//590RuWAICWgpyCOOjB/y8MsnAihsJNOwAUT5Hz\n2D148GDevHkxMTEZGRnnz5+fP39+cXExQkhdXX3MmDEmJiY4GZ/Pf/fuHdlfKSoqKorNZpOh\nkrGx8fv379+/fy+aZsCAAaIj26qrq2fPnp2WlhYZGUk9qywWS+pv4NjY2MyfPz8hIcHf33/P\nnj3p6eliCaqqqmJiYtzc3HR0dHr27BkWFkb9WHw+Py4ubvDgwZIj6kaPHi0QCJ4+fUoQxLNn\nz1xcXERHDWLLly/38PDAfzs7O0utQDl4eHioqanFxMTglykpKXPmzLl8+fKnT5/Onj27cOHC\n3Nxc2Xuo6X1HCN2+fXvZsmWxsbGJiYkrVqx4+PDh3bt3az3Q1atXHzx4sHXr1uTkZKFQqK6u\n3q9fPypVDQBotmgP4HYdAI1BkXfs8vLyZs+eje/H4Hjr9u3bkydPJhPcuXMnPT09MTHR3Nx8\n9uzZknt4/fp1t27d6PR/w01vb+8nT56sWLHC3t7ewcGhW7dutra2krflLC0tPTw8goODKd5e\nys3NTUlJITtzRdFoNCcnJycnp7S0tGvXri1btqxz586jRo3q06cPPu6zZ8+qqqpwf6i7u/uO\nHTtKSkq0tbWpHCs7O5vP50vtMjYyMtLR0cnKysrNza2oqFBU0EYFi8WytLTMzMxECBEEsXfv\nXnNz802bNrFYrMrKyqVLlwYFBf3yyy8y9lDT+15dXR0SEjJ48GDcz/vmzZs1a9bg+6+yD8Rk\nMp8+fbpnz542bdrgQ3Tv3j0iIkJGVRMEUV5ertCKUTyCIMh7rq0Bn89HCFVWVlZXVzd1XhqJ\nUCisrq5uVe8yQRAIIS6XK9kyX8mPjC9L+z/27juuifMNAPibBEJYAYQgCChLFIWiCAoKinuL\n2laLo1bR2mqrglpXrQtsxVqts1oLVdFaa3HWyVAUcRVFEREsyB4CYYTs5H5/vP1drwlEpIyM\n5/vpp5/L3Zu753Jn8vCuw8vfFZ9Ufi/tun+43QfkS0t99tIu09os0lYjkUhkMplMJuvoQNoJ\nPlOZTKZrN7Y6/6YYGxur2NqaiV1ISEh2dvb58+fx5afT6QrdsEpLS3NzcwUCgZGREf46UFBS\nUjJkyBDypbW19d69ey9dunTv3r0jR44QBGFpaTl16lSyLY80c+bM5OTk3377bfbs2cq75XK5\nsbGxeLmmpiY1NdXc3Py9995TcS7du3dfuXJlRUXFhQsXdu7cOXbs2Dlz5iCEEhISBgwYgD/T\n/v37GxkZJScnT5gwoTnHEgqFCCHlwa2YoaEhj8cTiUQqyrQRFouFYysqKioqKlq5ciXuzWZg\nYDBy5MjY2FiZTKaic3RT1z0vL4/P5w8dOhQX8/T07N69e3MORKPRPD09yawOIWRvb08QRGlp\naVOJnVwuFwgErfWBtB2NCLJ1icXijg6hvengVcb/rhX88Trl1+pE1W+kJnwuBnYfWyh+t6sn\nmUyG/27RHTKZTNdubHU+XyMjIxV9z1ozsfv555/PnTs3cOBAW1tbnAco/E0zb948hFBdXd3m\nzZs3b968Y8cOhchqa2sVfrnNzMxCQkJCQkLq6urS09OvXLny448/ikQihbTM1NQ0JCTkyJEj\no0aNwpOkUInFYrJRlc1mT5s2bcyYMQYGBgihoqKiy5cv401ubm7UtBKjRlhVVfX48WMfHx8y\ndTMzM0tMTKQmdiqOhU+Nx+M1+uk1NDSw2WzcSltfX99omTZSV1eHRxBXVFQghDIzM8vLy/Gm\nwsJCsVj8+vVrPNSjUU1ddy6XixCytLQkS3bt2jU7O7s5B+JwONRD4DlZyBZeZXQ63cTEpCUn\n344aGhpU/5mlZUQikUQiYbFYCgOMtBj+vW/xcHtN1NDQQBCEsbGx8s/MXLsJgZ36IIQ+e/md\nij3sdQ3HC2Z6Jur/rxg1b4ydNsEpnZ6enq7d2KqTp46lOrBW+8J9/fr1mTNnPvnkE3IKjIcP\nHzZaks1mBwcHb9++/fXr1zifoGoqXDwgNCAgYO3atYmJicr1bePGjbty5Up0dLTCFCQIoc6d\nOzc6AhchJBQKcSsk+ncKgpti79y54+7uvmzZsgEDBiCEkpKSDA0NCYIgUzcrK6vHjx8XFhaS\nIzxUHIvD4RgZGWVlZQUFBSlsKi8vr6+vd3JyMjMzMzMzy8zMHDdunEIZgUDAYrFa/T7j8Xj5\n+fn4BLHS0tK6ujryZWBgYKPVdTgSFdcdV8pSA1YIXsWBFFKBRut3FYJR/y+dhoYG9Q+yFUml\nUolEoq+vj/+w0QW4kU6nrjKfzycIotGvptE2/qMRQm9K7D57+Z1mjaKQSqU6leVIJBKBQECn\n03XnlBFCfD6/LX5w20erJXbFxcUEQbzzzt9zTQqFwsLCQlz7kpWV9e23365fv75bt254Kx5M\nQPalI5mZmZG1MrW1tbGxsUFBQdQJzGg0mp2dXaMTbTAYjNDQ0I0bNyqnTSq4urpu2bKFfEkQ\nxL17986dO4fnH96xY4ezszO5NSEhYejQoQsXLqTuYf78+YmJibihVjUGg+Hn53fz5s0PPvhA\nYcjthQsXmEymn58fjUYbOHBgfHx8cXGxwuzHUVFRBgYGq1evbv7ZNceZM2fkcvngwYMRQp07\nd0YITZkyxcvLS6HY+fPnX7x4sXLlSvyyvr4eD+9Qcd1xNRuXy7W3t8dbCwsL8YKKAzUK539N\njVMGAHSUw4cP8/n8devWNVWDBWMmAGhnrTYqFrczlpWV4ZdHjhwxMjLC3Wu6du1aV1d36tQp\n3EInFouvXLnC4XCsrKwUdmJnZ1dcXEzuMD09ff/+/bjZDnv58mVqamqfPn0ajcHb29vX15ds\nJ22Bv/76a9++fZ6entHR0cuWLaNmdXj6uoCAAIW3DBo06MaNG2+sUsJmzZpFEMSmTZvI05TJ\nZHFxcRcuXJg1axbOhEJCQlgs1qZNm8jhwDweb/fu3WlpaaNGjWrxqSmTSCRxcXGnT58ODg7G\nSaS9vb29vf2lS5fI07l69erJkycRQvr6+rdv3378+DFCqLi4+Pnz57169UIqr7uTkxOTySTH\n2z579iwn5++e1CoO1KiioiIajUYOrAYAaApiZCr+Txjvq/AfuamjYwRAq7RajZ2zs3OvXr3w\nRMR5eXm9e/cOCgq6dOlSTEzM3Llzw8LCdu7cuWDBgi5duhQUFEil0kZrnvr06fPbb78RBEGj\n0Wg02vr16yMiIj7++GMnJydTU1Mul1tQUNCnT5/Q0FDl92KhoaF4Bt2WcXBwiI6ObvRPz4SE\nBAsLC5zNUA0aNOjMmTPp6elNpZtUVlZW33zzzbZt2xYtWkQ+eUImk3300UeTJ0/GZczNzb/+\n+utt27aFh4fb2NgYGBiUlpayWKzVq1d7e3vjMgcOHMC1X1Kp9OLFi3fv3kUIrVy5UnmSFAWl\npaVr165FCInF4uLiYqFQOGXKFGp149KlSzdu3LhkyRIXF5eKiooXL16sWLECITRq1Kjk5ORN\nmzY5ODiUlpZ269YND2FRfd3ff//948ePl5SUmJubFxYWBgYG5uXlqT5Qox4/fuzq6qo8TQwA\nQCOIVi1pdKXBtt3tHwwA2o3WzKqm5sBzvNXU1HTv3t3T01MgEMTHx5ubm+PJQWpqatLS0rhc\nrpWVlbe3d6M/0pWVlQsXLlyxYgU5lR1BEBkZGUVFRQ0NDRYWFi4uLtTZiW/duqWnp6cwRTB+\nIir1WbE8Hu+/P/zq/PnzlpaWgwYNUt7022+/ubq69u3bt5nHIgji2bNn+fn5IpGIw+F4e3sr\n96kny4jFYhsbm379+lEHhZAPbKWaNGmS6r751GfFMhgM/Jha5VwQzxvM5XLNzMz69etHdj3E\n7dQlJSWdO3ceMGAA2Q1O9XVPT0/Pycnp1KnTwIEDDxw4UFVVFRERofpAZ8+edXZ2Jpt3+Xz+\nvHnzPvzwQ+V+h5qlqqqK2o9T6/F4PKFQaGpqqlN97AQCgYr5j7RPVFSU6qZYrNHEDtO43I7H\n4+laH7va2lomk6lTN3Z1dbWFhYWG9rFrzcSuVRw6dOjZs2e7du3S0A8UUJ0+fdrU1HT06NEI\nIT6fv2jRoqFDhzanPyLVyZMnr1+/fvDgQU0fXAmJndaDxO4fBCE58TP5Sv7kUVN7oL/TFy8w\nPLzoXt5tEmWrgsROF2h0Yqd2v5SzZ88ODw8/derU9OnTOzoWDVNZWXnnzp2mto4ZM0Z5Ipi2\nZmpqun///tu3b+OhvsbGxlOnTn2rPeTm5sbFxW3YsEHTszoAdI2KZK7RYjQrjkYkdgCoObX7\nsTQ0NFy3bl1KSopYLG7/RESjCYVC5WegkRp9hFpbGz16dO/evdPT00Ui0aBBg3x9fd82Pyso\nKAgLC6OOjAYAaAT9+f90d5Yc3vfGYrROOlSfDUDbUbvEDiFkb28P1XUtYG9vv2zZso6OQhEe\nANvit7/V5DUAgHZmZ2cnFAobabGi0ejdezRnD80sBgBoplab7gQAAICuGTt2bHBwsIpHDmJN\njZDQuJETAKg/SOwAAAC0OeUcDrI6ANqCOjbFAgAA0D6QyQHQDqDGDgAAAABAS0BiBwAAAACg\nJSCxAwAAAADQEpDYAQAAAABoCUjsAAAAtNCTJ0/+/PPPDpn/HADQKEjsAAAAtND9+/dTU1Mh\nsQNAfUBiBwAAAACgJSCxAwAAAADQEpDYAQAAAABoCUjsAAAAAAC0BCR2AAAAAABaAhI7AAAA\nAAAtodfRAQAAANBUY8eOlUqlDAajowMBAPwNEjsAAAAtZGdnJ5fLaTQadeW8n1S9JTq0bUMC\nQMdBUywAAAAAgJaAxA4AAAAAQEtAYgcAAAAAoCUgsQMAANA6SmvQ57FvKPN5LIpNbZdoANBJ\nkNgBAABoHXICNYjeUKZBhMTSdokGAJ0EiR0AAIDWYWfRyKDXmK7+1JfRoWheYPuFBICu0arp\nTsRi8eLFi6VSaUxMTFNlYmNj7927t2PHDiaTmZGRERcXl5eXV1tba2pq2qNHj2nTprm6uuKS\nkZGR9+7dW7Vq1aBBg8i3c7ncOXPmREZGenp6kmXIrWw229HRMSQkpHfv3i0+i6ioqNu3by9e\nvHj06NHU9c05FkEQSUlJ169ff/XqlUQi4XA4vr6+U6ZMsbCwaHQnpIEDB65evVp1YPi9c+fO\nnTJlCnV9XV3dnDlzZDLZmTNnWn06q59//vnx48dRUVFMJrN19wwA+O+OHTsmEAiWL1+ur6/f\naAGc1cV09Z9bAO2vALQHrUrsTpw4UVlZaW5u3lSBR48enT17dufOnUwmMy0tbdOmTYMHD16y\nZAmbza6oqPj999/Xrl373Xff2dvb4/J0Oj0mJsbX11dFVmFjY/P555/jZS6Xe/Xq1bVr10ZE\nRODM7201NDTcv3/fyckpISFBIbF747EIgti+fXtKSkpQUNC4ceMMDQ0LCgouXrx48+bNTZs2\nOTo6kjtZvHixwp5VfGhUBgYGN2/eVEjsUlJSGAyGTCZ7+9N9s9mzZz979uzHH39UjhkA0OFE\nIpFQKOzoKAAA/9Ceptj8/PyLFy8OHz68qQIEQURHR48cOdLBwQEhdP369a5duy5fvrxv374u\nLi7+/v5btmyxtrbOyMgg39K/f38ejxcXF6fiuIaGhp7/N3jw4I0bN1paWp4/f165ZGlp6fnz\n5/l8voq9JScnGxgYzJ8/Pysrq7S09K2OdenSpdu3b4eFhYWFhQUGBvr4+EydOnXXrl2GhoY7\nduwgEy9DQ0MvJd26dVMRFalXr165ublFRUUKMffo0aM5b28BBoMxZ86ca9eu5efnt9EhAABt\nhNoIq9AgCwBoIxpTY1dbW/vTTz+lp6fzeDwOhzN+/PiJEyeSWwmC2Lt374QJEzp16vTnn382\nuocHDx7k5+d/9dVX+KVMJqPT/5XXGhoa7t27l7rGyMjogw8+OH78+IgRI6ysrJoTp76+frdu\n3V6/ft3opoSEBLy3iRMn2tjYKJdJSEgICAjw8PCwtrZOSkqaMWNG84917tw5Ly+voKAgahk2\nmz1v3ryIiIj09HRvb+/mnIIKFhYWTk5ON2/enDlzJl5TVVWVmZk5e/bsp0+f4jUymezkyZOJ\niYlcLpfD4UyaNGn8+PFk4X379j158sTY2Dg4OJjP56ekpBw4cACpvL4eHh7du3f//fffw8PD\n/2P8AIB2gLvZ0a4rZnLwzAkA2oHG1Njt2rUrJyfniy++2L1797Rp06Kjo+/evUtuvXz5MpfL\nDQkJUbGHBw8edOvWjcPh4Je+vr55eXlff/11VlaWXC5v9C1yuXzixIlWVlY///xz80MtLy/v\n1KmT8norK6vvv/9+7dq1JSUln3zyydatW6m1gwihoqKi7OzsYcOG0Wi0oUOHJiUlEQTRzGNV\nVVWVlZUNGDBAuYy3t7e+vv6TJ0/wS4IgxEreeCBMLpcHBAQkJyeTa27dutW1a1dbW1tyzU8/\n/XT27NkZM2bs3bt38uTJP/3009WrV/Gm77777tWrV+vXr4+MjHzx4sWtW7fIPnmqr6+Pj8+f\nf/7ZzCABAOpJOdUDALQ6jamxW7p0KY1GMzMzQwjZ2dldvHjx0aNHfn5+CCEul3v06NEVK1YY\nGBio2ENWVhZ1nMHIkSMrKyvPnDmTmppqZGTUq1evAQMGBAUFKeyEwWDMmzdvy5Yt48ePd3d3\nb3TPZCtnTU3N+fPni4qKyAotZbjps7Cw8Ny5cxs3brS3t587d66XlxdCKD4+3s7ODjdrDh8+\n/Ndff83MzFQYh9HUsbhcLkLI2tpa+Yh6enoWFhbV1dX45atXr97IMFETAAAgAElEQVR77z2F\nMt999x05akS1IUOGHDt2LCcnp3v37gihW7duDRkyhNzK5/OvXLny7rvv4jbxLl26/PXXX2fP\nnh09enRlZeXTp08XLVqETzY8PHzevHnkqA4V1xch1KtXrxMnThQVFeFmdGUymQx/AmqusrKy\no0Nob/X19fX19R0dRbvSwatcXV1N/pEWnLPmDi+jqZI4t4txXjPBbGA7Bdc2eDxeR4fQrsRi\nsa7d2FVVVR0dQpMsLS0VHtBMpTGJXV1dXXR0dFZWFtlHjawlOnToUN++fX18fFTvgcvlkmkE\nFhISMmXKlPT09PT09MePH+/bt+/XX3/dtGmTQvbg6+vr7e196NCh7777Tnm3eXl51MEEJiYm\nixcvxgNpZTIZ2a1YT0+PmjI6ODh89tlnkyZN2rhx4/379728vORy+Y0bN8aOHYtTNw6H4+7u\nnpiYSE3sVBwLf6s2VfVIEATZ7tylS5ewsDCFAuR4kTeytrbu2bPnzZs3u3fvXlZW9vLlyy++\n+OLly5dkhFKp9J133iHLe3h4XLt2rb6+HvfMI9NHAwMDDw+P4uJi/FLF9UUI4QvH5XKbSuwQ\nQirucjVBEIT6B9mKcA2rTp0y0r2rjNFoNPKsTRiG5nomNdIm8x5zPRMmTV9zPyVdu7HJphLd\nOWWk4f+QNSOxE4vFERERlpaW3377ra2tLYPBWLVqFd708OHD9PT0ffv2vXEnDQ0NxsbGCitZ\nLNaAAQNwC+bTp0+//vrr6OjoDRs2KBQLDQ1dsmRJfHy8cvpoZ2e3fPlyvGxqamptbU3eDenp\n6Rs3bsTLw4YNW7ZsGfmuwsLC8+fPJyUl2dvb9+/fHyH06NGj6urq48ePHz9+nCyWn5+/cOFC\nckyuimNZWloihMrKypRPXCqVVldX4wIIIQMDg/841mHIkCGnTp0KDQ1NTk52c3Pr3Lkzmdjh\ntGzDhg1kYDjXrKmpEQgECCHqJTAzM8OJnYrri5mYmCCEGhoamgqJwWCQJ6i2qqqq1D/IVsTj\n8YRCoYmJieqqdG0ikUgEAgGbze7oQNqPlZWVUCjs1KmTnt7fvyZXLXerbnKtkfJmOI9tl+ja\nBI/H09PTY7FYHR1IO5FIJLW1tUwmU6du7OrqagsLCw3N7TQjsXv16lVZWdny5cvJiiUul4tH\nM6SkpDQ0NMydOxevJwiCIIjJkyeHhoZSR1cghIyNjamZQXV1taGhoaGhIbnG09PT39//4cOH\nygE4ODiMGzfu2LFjypOYMJnMphoxe/To8c033+Blcj6RJ0+enD17Ni0tzdfXd8OGDeQOExIS\n3N3dFyxYQL5dIpGsXbv27t27gwcPfuOx2Gy2s7Nzamrq5MmTFTalpaXJZLJ+/fo1+sYWCAgI\nOHz4cGZm5q1bt0aNGkXdZGRkhBBavny5wjDbzp0740EeOL3DyBY6FdcXw60eynk5AKBjTZ48\nWS6Xt/oElgCAFtOMxA5nA2QS9vz587KyMpzizJo1i5rK3LhxIyEhYcuWLcrDFywsLGpqavBy\nTU1NaGjolClTPvzwQ2qZkpISheZaUkhIyI0bN86cOdP8sI2NjXv16kW+rKys3LJlS1lZ2YgR\nI3744QfqqFg8fV1oaKhC3tanT5/ExEQysVNt0qRJu3btunr1KnUCvPr6+piYmB49ejTVQbAF\nzMzM+vTpc+XKlYKCgoCAAOomJycnfX392tpaMkWrra2l0+n6+vpdunRBCL169crZ2RkhJBaL\nMzIycKc6FdcXw/3nmro0AAD1Qa2uE8b7UjexRjwgyxAjYb5iANqEZiR2Tk5OBgYGFy5cCAkJ\nyc3NPXXqlI+PT3FxcU1NjaWlJbV5y8LCgsFgNDorm7u7e2ZmJl42NzcPDg4+ffp0TU2Nn58f\nm83mcrkJCQmZmZlffPFFozGYmJjMnDnzxx9/bPFZSCSSYcOGjRw5EldrUSUnJ0ul0oEDFXsT\nBwQE7NmzR7l3YKOGDRuWkZGxf//+jIyM/v37kxMU0+n08PBwskpZIBCkpaUpvJdGo/Xt27f5\n5zJkyJBdu3a98847CoEZGRmNHj36xIkTbDbbzc2tvLz88OHDHA7nyy+/tLGxcXFxOXnypK2t\nrZmZ2bFjx8iKfRXXF9d0ZmZmstns5ncEBAB0FDJjE61aorBJGO9rsG13u0cEgG7RjMSOzWYv\nW7bs559/TkpKcnNzW7JkSUVFxfbt2zdv3tzogIZG+fr6Xr16tbKyErfxffTRR926dbt27dqe\nPXv4fL6FhYWLi8s333yjomZrzJgxly9fbvFMuba2tsHBwY1uSkhI6N27N66+ovLz89u7d++N\nGzcUHvbQlCVLlnh5eV27du3gwYNCodDa2jooKGjy5MnUvhFlZWVkzz8SnU4/e/Zs88/Fz89P\nX18/MLCRJz6GhoYaGxvHxMRUV1ebm5v7+fnNmTMHb1qxYsWePXvWrVvXqVOn999/n81m5+Tk\noGZc34cPH/r4+GhodwcAdJByVkeuh9wOgDZF0525wQiCWLJkiaen58cff9zRsegokUgklUrJ\nrnLr1683MTFRGCehLCMjY926dbt3727m4zHUlm4OnjA1NYXBE1qsurpaLpf/M/kCQSChACEk\n2tjks6cNNn6DEEIGLETXmIlUqWDwhC7Q6METGvnvqmVoNFpoaOj169cLCws7OhYdtWXLli++\n+CIzM7O4uPjcuXNPnjxR8Qg4TCaTHTlyZNSoUZqe1QGgE2RS0cbVKrI6hBAuIM992W5BAaBT\nNKMptrX06dNn8uTJUVFRO3bsIOcQAVhmZubmzZub2vrjjz+ampr+x0OsXLny0KFDW7duFYlE\ntra2YWFhb5x9MDY2ViKRUAcLAwDUGI3WyRIhRFQ3ObkrLoD09dstJgB0ig41xQLVxGIxOWpY\nGYfD0dBKafUBTbFaTwebYm/cuCESiUaMGKEw40lTfewQQprexw6aYnWBRjfF6laNHVCByWQ2\n+kQyAABoyv379/l8/rBhw2AqOwDUhA71sQMAANA+mqqW0/TqOgDUH9TYAQAAaH04hyPbZCGl\nA6B9QGIHAACgrUA+B0A7g6ZYAAAAAAAtAYkdAAAAAICWgMQOAAAAAEBLQB87AAAALTRs2DCp\nVErXzIeDAaCVILEDAADQQs7OznK5HBI7ANQH/GsEAAAAANASkNgBAAAAAGgJSOwAAAAAALQE\nJHYAAAAAAFoCEjsAAAAAAC0BiR0AAAAAgJaAxA4AAEALHTt27PDhw1KptKMDAQD8DRI7AAAA\nLSQSiYRCYUdHAQD4B0xQDADQdfN+UrU1OrS94gAAgP8MauwAAAAAALQEJHYAAAAAAFoCmmIB\naByB0MO81txhfT3TtK41d6jmhEI9icSAxaLr63d0KP/Ng2bfBjIZXSzWNzRsy2jUTB2zhxiJ\n/8ynMxgdHcp/Y8pCPW07OggAWgONIIiOjgEAdSQn0Pzojg4CANAu3GzQ6vHNKsnj8fT09Fgs\nVhtHpC4kEkltbS2TyWSz2R0dS/uprq62sLCg0WgdHUhLQI0dAI2jITSkZ2vuUCgU6s6PAUJI\nIpHIZDJ9fX2G2lfm3MxStbX5t4FcLpdKpUwm87+HpCkyMzOlUqmHhwedrtkdezrrUNICtBwk\ndgA0jkZDcwa15g6rqhosLXUosePxREKh0NTU1MBAkxK7mK7+CKG5BankmubfBhKJTCAQstk6\nlNhVu9vI5XJLSw2t2gBAC3V8Yjdz5sxJkyZNnz690a2PHz/esWNHbW3t/PnzJ02a1NROBAJB\neHh4UFAQdT+ffvppWVnZqFGjPv30U2rhyMjI9PR0V1dX/JLL5RYXF3t5ea1bt46sUMnOzt6x\nY0dZWZmdnR2LxSopKZFIJO+++25ISAj5Bfby5ctvv/22tLTUzs6OyWQWFxfTaLQ5c+aMH69Y\noZ+VlbVq1apOnTrFxMQ0/5Pp8Pjz8/O/+uoruVzetWvX/Px8Op0eGRnp4ODQ/FNACL169aqs\nrMzPz09508GDB58+fbp3714Vb8/NzV29evWGDRt69+79VscFoAVwVgcAAJqr4xM7Fe7cubNz\n586FCxceOHBAdcmjR48ymcxp06aRa7KysoqLi99///0rV64sWLBAT+9fZ2pra7t161by5aNH\njzZt2nT69OlZs2YhhIqLi7/88stu3bpt2rTJxsYGISSTyc6cOXPs2DG5XI7LVFRUfPnll7a2\ntgcOHOjSpQtCiM/nHzly5ODBg2w2OzAwkNy5TCbbt29fly5d3moaT3WI//vvv+dwOJGRkQYG\nBnw+f9myZUePHl23bp1CqCKRiE6n6zfRQ/7WrVtVVVWNJnbN4ezsPHXq1B07dhw8eLCpQwDQ\n6mK6+lMr7QAAQFOoRa8IGo1WWVn5xx9/xMXF5ebmkuvlcvnXX389YsQI1W+vrKy8evXqtGnT\nqI0BCQkJPXv2nDRpEp/Pf/Dggeo99O3bt2fPnk+ePMEvjx07xmQyv/rqK5wVIYQYDMZ77703\nYcKE33//vbKyEiF0/PhxhNBXX32FsyKEkJGR0aeffjp48OCGhgbqzs+cOUMQxMiRI5vzUahP\n/BKJxN7efsaMGQYGBnirr69vXl4j4wMLCwvnzZv3yy+/1NTUKJ/F3bt38/LyfvnlFy6XixDK\nzs7+/fffL1++XFtbSy3522+/5eTklJaWXrhwQeE2wB/CtWvXmvnRAfC2okNRdKhidV1MV3+8\nHgAANIhaJHZFRUVffPHFn3/+mZiYGBYWduvWLbw+ICCAbHBUITk5mclk+vv/86UsFotv3749\nbNgwMzMzb2/vhISEN+5EX19fLpcjhCQSyYMHD4YOHWpqaqpQJjg4WCaTpaamEgRx9+7dwMBA\nCwsLhTIrVqwYM2YM+bKsrOzXX39dtGiRQpWbauoQv76+fnh4uLe3N7mpvr7exMRE+dAuLi6L\nFi1KT08PDQ3dtWsXNSfj8/n19fUSiaSurk4ul1+5cmXFihUPHz588uTJ6tWrX79+TZY8c+bM\n6dOnt23bxuVyMzMzw8LCkpOT8SYjIyM/P7/mfAgAAACAjlOLpti7d+/u3r3bxsZGLpdv3Ljx\nyJEj1KbMN3r06JGnpyd1TNbdu3fFYjHeyfDhw7dv315XV6diqPbr169fvHiBK9VKS0slEkn3\n7t2Vi1lbW5uZmRUWFr5+/VogEDQn6Txw4EBQUFCvXr1evnzZ/DNSn/hJeXl5d+7cmTt3rvIm\nGo3m7+/v7++fk5Nz9uzZ5cuX9+rVa9KkSf379584ceLNmzft7e0XLlwok8mOHj06ZMiQ5cuX\nI4SKioo+//xzOzs7cicZGRk//vijkZERQmjbtm1HjhwZPHgw3tqnT5+kpCQVHwJBEK37wMpS\ncdWJilauI5RIJPrVOtSaLJVK5XK5XpWeRoyXXP/qR+WVtOv+WxwXNH8nBEHIZDK9crX4Xm0f\nEomEIAgmV0vGi8zuPMZaX/HPXQUymYwgCN2ZKUwmk+H/CwSCjo6l/RAEIRAI1HZMkKHK2TLV\n4guoX79+uNGQTqcPGTLk+++/f/36NYfDaebbS0pKhgwZQl2TkJAwYMAAY2NjhFD//v2NjIyS\nk5MnTJhAFuByubGxsXi5pqYmNTXV3Nz8vffeQwjh/ACnF8oMDQ15PJ5IJFJRhnTjxo3c3NyV\nK1c280TULX5SWVlZZGSkh4fHuHHjVBTr3r37ypUrKyoqLly4sHPnzrFjx86ZM4fcmpeXx+Px\nyCtlb2/v4eGB22cxX19fMqQBAwakpKRUVFRYW1vjwgRBlJaWNpXYyeVyhRbw/+hFw6tGf+mB\nroHbQKcMNOhtbNSsJBV/i+oOmUzWut+x6o/P53d0CE1isVgqkk61SOw6d+5MLltaWiKEuFxu\n8xO72tpa6u99VVXV48ePfXx8yNTHzMwsMTGRmhiJxWKyxZDNZk+bNm3MmDG4MxneFY/Ha/RY\nDQ0NbDYbt3LW19eriIrH4/3000/z589vtPlSBTWJn5Sfn79hwwZHR8c1a9bgO6moqOjy5ct4\nq5ubm0JWjRBq9Iarrq5GCHXq1Ilcw+FwqIkd9YrjNuKamhqc2JmZmSGEFLrlUdHpdNV/wbwt\nJ7pduN0HrbhDhJBUKn2rFnlNhys26HS6+tfYfVd8UsXW5t8JBEHI5XL1n7evFVVUVMjlcmtr\na/W/ys3hYGpjyHzDN4lYLKbT6brzb1kmk4nFYgaDoVMTNKr5tKOqqxLV4takfg/iWt+3rf+k\nlk9KSjI0NCQIgkx9rKysHj9+XFhYSE7V0blz56+++qrRXXE4HCMjo6ysrKCgIIVN5eXl9fX1\nTk5OZmZmZmZmmZmZyjVYAoEAp9KnT5+Wy+W4jx1C6MWLFwKB4Ndff+3bt6+bm5uKc1GT+PHL\n3NzcdevW+fr6Ll26lLxMQqGwoKAAL+NEHMNNsXfu3HF3d1+2bNmAAQOoe1ZuucDXutGXuDAZ\nxhtbPWg0Gq7gbC09jZ12dFraijtECFVVVVE/Lq3H4/H+P4+dQUfHogrt+humONnRq7l3gkQi\nEQgEOjVBf9TFKD6fv27dZ7ozaJ0gCF178gRO7Fr3O1bNiUQiIyMjtW2KVU0tEjs8ThPD9TrK\nnfpVMDMzo9blJCQkDB06dOHChdQy8+fPT0xMpLYMNoXBYPj5+d28efODDz4wNzenbrpw4QKT\nyfTz86PRaAMHDoyPjy8uLiZ7iWFRUVEGBgarV6/mcDienp7kMNKqqiqpVJqXl+fi4qI6ADWJ\nHyFUWVm5adOmgQMHfvbZZ9T729XVdcuWLeRLgiDu3bt37ty5Fy9eDB48eMeOHc7OzsqB4Wta\nXV1Nbi0rK6MWoN4GeJm8Derq6hBCCqcDQPugXfcnRsLUJwAAzaAWlef379/HM2UQBJGSkmJj\nY2NlZdX8t9vZ2RUXF+NlPP1bQECAQplBgwbduHGjmd1dZ82aRRDEpk2byN3KZLK4uLgLFy7M\nmjULNwuGhISwWKxNmzaRoyJ4PN7u3bvT0tJGjRqFEBo/fvxqiuHDh5uamq5evdrHx0fFodUn\nfoRQTExM586dFy9erPqvlr/++mvfvn2enp7R0dHLli1TyOoYDAbujOLo6MhisW7cuIHXv3z5\nMivrXw9yun//Pm6ZVb4NioqKaDSarS08oxu0JtGqJaJVS4TxvuR/xMjURv/r6EgBAKC5Or7G\njiAIX1/f1atXu7m5lZaWZmdn4+oihNCBAwcKCwsRQlKp9OLFi3fv3kUIrVy5UqE+r0+fPr/9\n9htBEDQaLSEhwcLColevXgpHGTRo0JkzZ9LT0/v06fPGkKysrL755ptt27YtWrSIfHKDTCb7\n6KOPJk+ejMuYm5t//fXX27ZtCw8Pt7GxMTAwKC0tZbFYq1evpk4R8rbUJ/7y8vLbt2/b2Nh8\n+eWX1J2vWbNGYSIVBweH6OjophpinJycrl+/HhERMWXKlBkzZkRHR5eVleHBuYGBgdSJ8by9\nvdesWePm5lZSUpKTk0PeBgihx48fu7q6Kk/gAkCLiVYtaXSlwbbd7R8MAAC0lo5P7KZMmdK3\nb182m33//n1HR8fFixc7OjriTS4uLrj1zdPTkyyv3H9z8ODBx48fv3v3rr+/v4ODQ58+fZRr\nmNzc3GbPno1rvAYPHtzU2AKSo6Pj/v37nz17lp+fLxKJOByOt7e3Qg+Drl277t27F5cRi8U2\nNjb9+vVrqntpjx49goOD3/hpqE/8TCbzgw8a6TOunMCp7kH10Ucf4a6BnTt37t27t5ubW2Zm\nprGx8eeff15UVERN7BwdHefOnXvv3j1HR8fPPvuMvA34fP7du3c//PBD1acMQPM1mtUBAIAW\noGnHZDyHDh169uzZrl27NLSrI1DxyOCTJ09ev3794MGD6jAMjXhdLnt4r2XvFQgErTt0V82J\nxWKZTMZkMtVwlKjsRryKrYygNzztpilyuVwikaj5YJHWde/ePYlEMnDgQPUcFcvwD6SZv0WP\n7ebg8Xi6NniitraWyWTq1Kig6upqCwsLDc0oOv6XslXMnj07PDz81KlTjWYGaqWysvLOnTtN\nbR0zZoxODSl/o9zc3Li4uA0bNqhDVocQIqoqVecEKjARkr25lPZgIIQTOo076xZfYoSQngae\n73+BuwwTyYnqedb03u+0emIHgJpTix/L/87Q0HDdunUpKSlisVjNEyOhUEh96JYC/FgwHfTu\nu+/26NFDeX1BQUFYWFjv3r3bP6RG0Wy66E1t4fx2DQ0NujZfgEQiYbFYapKUU0njVE1c1+JL\nLJPJ8Cm37O2aKD8/XyaTOTo6qmeNHa2TDk0wBACmJU2xAKg/mMdOfajoY/dfBk/o4Dx21dXV\ncrnc0tJSQxutWgCaYnWBRjfFquPfWAAA0KZg6CsAQFtBYgcA0EWN5naQ8AEANJ3a9X0BAID2\nAWkcAED7QI0dAAAAAICWgMQOAAAAAEBLQGIHAAAAAKAlILEDAADQQqdOnTp69KhUKu3oQAAA\nf4PBEwAAAFqorq6Oz+d3dBQAgH9AjR0AAAAAgJaAxA4AAAAAQEtAYgcAAAAAoCUgsQMAAAAA\n0BKQ2AEAAAAAaAkYFQsAAKCF2Gy2nh78jgCgRuAfJAAAgBaaNm2aXC6H3A4A9QFNsQAAAAAA\nWgL+zAIAAABa37yfVG2NDm2vOICOgRo7AAAAAAAtAYkdAAAAAICWgMQOAAAAAEBLQB87AABo\nV3EPUUZxRwfRSqRSNkJIT4/W0YG0H7ncCCFE/8+1IpvPtUIw7YAg9GQycxqNxmB0dCjtSCpl\nt9Zd/VVwq+zmLUBiBwAA7eo1D72q7OggWo0O/oi0TkuX5twDNJ28yhp8yhocOgAAaKKQAWhq\nv44OopUcOnRIIBAsXrxYd6ay4/P5DAbDwMDgjSVXnfrXy5iu/nMLUsmX26a1emhtQiqV1tfX\n6+vrm5iYdHQs7aempsbMzIxG08iqaF35p9h2Zs6cOWnSpOnTpytvksvl58+fv3r16uvXrzkc\nzsiRIydPnkxvogY/Njb23r17O3bsYDKZeE1UVNTt27cXL148evRoasnIyMh79+6RL9lstqOj\nY0hISO/evcmVBEEkJSVdv3791atXEomEw+H4+vpOmTLFwsJCocy1a9devXolk8k4HM7AgQOD\ng4NNTU1bEH8LHDx48OnTp3v37lVR5ueff378+HFUVBT5sQCg6diGHR1B69GX1UikfCsTQl+/\no0NpLzyaXE+PzmK93btiuvorrOGYtlpIbUoiIfRlMiaTwdaQgFsFQyK3MEWamddBYteWTpw4\ncebMmVmzZrm5uT179uzIkSM0Gm3KlCnKJR89enT27NmdO3eS6UtDQ8P9+/ednJwSEhIUEjuE\nkI2Nzeeff46XuVzu1atX165dGxER4enpiRAiCGL79u0pKSlBQUHjxo0zNDQsKCi4ePHizZs3\nN23a5OjoiN+4c+fOmzdvDhkyZPz48Uwm8+XLlxcvXkxJSdm6dSvO/5ofvwp//PFHTk7OsmXL\n3updpNmzZz979uzHH39cvHhxy/YAAAAdi8zqFCrtAGgLMCq2rchksosXLwYHB0+ZMqV3797T\npk0bOHBgcnKyckmCIKKjo0eOHOng4ECuTE5ONjAwmD9/flZWVmlpqcJbDA0NPf9v8ODBGzdu\ntLS0PH/+PN566dKl27dvh4WFhYWFBQYG+vj4TJ06ddeuXYaGhjt27JDJZAih+Pj4GzduLFq0\nKDw8PDAwcMCAATNnzoyKiqqqqjpx4sRbxV9aWnr+/Hk+n9/o5/DXX3+16PP7G4PBmDNnzrVr\n1/Lz8//LfgAAAABdADV2rYAgiMOHDyclJYnF4r59+37++eempqZ0On3nzp1ksyZCiMPhvHjx\nQvntDx48yM/P/+qrr6grExISAgICPDw8rK2tk5KSZsyYoSIAfX39bt26vX79Gr88d+6cl5dX\nUFAQtQybzZ43b15ERER6erq3t/e5c+fc3NwU6gIdHBy++eYbOzs7hFDz49fX109ISDh+/PiI\nESMmTpxoY2NDblq7dm1GRgZCKDExcdeuXebm5nv27Hn69KmRkdHYsWOpO5k+ffr06dNLSkpS\nUlKkUin5MSKEPDw8unfv/vvvv4eHh6v4EAAAQK3gZ0vQrv+rETamqz8xEirtQBuCGrtWcP36\ndZlMtmnTpiVLljx58mT//v0IIRqNZmtrS/Y2lclkjx496tWrl/LbHzx40K1bNw6HQ64pKirK\nzs4eNmwYjUYbOnRoUlISQRCqYygvL+/UqRNCqKqqqqysbMCAAcplvL299fX1nzx50tDQkJ+f\n7+fnp1zG2dkZdwpufvxWVlbff//92rVrS0pKPvnkk61bt+JkDiH05Zdfurq6BgYGxsbGOjo6\n7ty589WrV+vXr4+MjKytrb1z5w65Ez09vbi4uO7dux89enTbtm3Z2dn79u0jt/r4+Pz5559v\n/BAAAAAAHQc1dq3AyMho4cKFCCFXV9eioqJTp06JRCKFMVNHjx4tKytbvXq18tuzsrKo4x4Q\nQvHx8XZ2dj169EAIDR8+/Ndff83MzFQog1tUEUI1NTXnz58vKiqaOXMmQojL5SKErK2tlQ+k\np6dnYWFRXV2Ny1BTyTdSET/m5eXl5eVVWFh47ty5jRs32tvbz50718vLi06n6+vrs9nsqqqq\n9PT0hQsXenl5IYQWLlz48OFD6h7s7e1xDaKzs/PEiRNjY2OFQiGLxUII9erV68SJE0VFRdTW\naiq5XF5bW9v80+kQBEHgT15HyOVyhFBDQ0NTzfTahyAIFVf5l8r47aW/tHNIba2uVx1BED/f\nmaah4wdbgCCI5p/sK5FiRxqEEO26v6OBbasG1bbwH9W6c4nRW17lN/qjZ5StvmVr7Q0hZG5u\nriI8SOxagYeHB7ns4uIik8nKysq6detGrjxy5MiFCxdWr16NWzkVcLlc6mBVuVx+48aNsWPH\n4tSNw+G4u7snJiZSE7u8vDzqIAYTE5PFixcPGjQIIcRgMGDgQOMAACAASURBVND/f1OVEQRB\np9PxxARNlVGmEL9MJhMKhXiTnp4eNYV1cHD47LPPJk2atHHjxvv37+McDisqKkIIOTs745c0\nGs3Nza2goIAs4ObmRi537doVf4x4qAf+fLhcblOJHUEQZKarzjQiyNbV/NtMazR1lWulvEZ/\n5jWbAUIIccW6kru3Fi28E0DTRFKxjN5+X/6Q2LUCakc0nOWQeQ9BEPv27bt169aGDRuoWQ5V\nQ0ODsbEx+fLRo0fV1dXHjx8/fvw4uTI/P3/hwoXkmFk7O7vly5eTR7e2tiaTd0tLS4RQWVmZ\n8oGkUml1dbWlpaWFhQWNRispKXnjqTUaf3p6+saNG/HysGHDqCNeCwsLz58/n5SUZG9v379/\nf+qucLWNkZERuYZ61gghQ8N/JoHAH6NIJMIvcYtwQ0NDU3EyGAx84uqsuroaN5friIaGBqFQ\naGJi0pwZv7SDRCIRCoXULwSqMIuZC13ebeeQ2lpNTY1cLsdfKR0dSzvh8/l6enrNmYDJ8qbi\nhAZUVUOutl5QbUgikdTX1zOZTJjHrsXM9IzptNbs+aY6MEjsWgE14cDLrP/PcXTw4MHU1NSt\nW7e6uLg09XZjY2PqHhISEtzd3RcsWECukUgka9euvXv37uDBg/EaJpPp6ura6N7YbLazs3Nq\naurkyZMVNqWlpclksn79+hkYGPTo0ePOnTszZsxQuD9SUlL09fXJnKzR+Hv06PHNN9/gZXNz\nc7zw5MmTs2fPpqWl+fr6btiwAU+8QoU/E2qrXF1dHbUAj8cjlwUCAaJ8jHiTQiKoQCN+VzQi\nyNZFo9F056zxmTZ1viwGk8XQuukY9aRyubwTk607V5kppuvp6bGYb5jITmHMhDLLm6M1YhSF\nhCZh6BFMPSabye7oWNqRntRCY+9qGDzRCqhjRfPy8vT19W1tbRFCiYmJ8fHxmzZtUpHVIYQs\nLCxqamrwMp6+LigoyJXC3d29T58+iYmJzYxn0qRJz58/v3r1X38O1tfXx8TE9OjRw93dHZcp\nLCw8efIktUx+fv7evXsfPHiAXzYVv7Gxca//69KlS2Vl5dKlSyMjI21tbX/44Yd169YpZ3UI\nIdyMm5ubi1/KZLLnz59TC2RlZZHLubm55MeI/t9xkNpgDQAAGkoY74v/E61a0tGxAC0ENXat\n4PXr1ydPngwKCiopKbl06dKgQYOYTKZYLI6NjfX19RUIBE+fPiULu7u7Kzx7x93dPTMzEy8n\nJydLpdKBAwcqHCIgIGDPnj0KvfGaMmzYsIyMjP3792dkZPTv35+coJhOp4eHh+M/QQICAp4+\nffrLL7/k5OQEBgayWKzs7OzLly937dp17ty5CKHmxy+RSIYNGzZy5EhqMyvJxMTkr7/+ys3N\ntbKy6tmz52+//WZra8tmsy9cuKDQloGn0Bs6dGhJScnFixfxx4g3ZWZmstlse3v7N547AACo\ng6Zq4xSSOfzSYNvu9ogJ6AZI7P4rqVT6/vvvl5eXL1++XCwW+/j44BGyRUVFlZWVlZWVKSkp\n1PJHjhxRSM58fX2vXr1aWVlpZWWVkJDQu3dvMzMzhaP4+fnt3bv3xo0bzXzww5IlS7y8vK5d\nu3bw4EGhUGhtbR0UFDR58mQ2+5+69E8//dTDw+PKlSuHDx+WSCQ2NjbTpk2bMGECTqeaH7+t\nrW1wcHBTkUycOPG7775bv379ihUrVqxYsWfPnsjISDyPHYfDoe589OjRPB5vxYoV1I8Re/jw\noY+Pj4bWigMAANZUFZ1o1RLI7UBrocHcYB2OIIglS5Z4enp+/PHHHR1Lh1HxyN2MjIx169bt\n3r2bOtBYE1VVVan/CI9WxOPx8EgCnRo8IRAIqH8+ab3q6mq5XG5paak7f3fxeDw9PT3W2z4s\nFiHUdGKH1LjSTiKR1NbWMplMXbuxNXdIENTYdTwajRYaGhoZGTl27NimpvPQWTKZ7MiRI6NG\njdL0rA6AlpElJ0oTr3V0FE0yIgiEkFgzf/9aRo8gEEKi1j5l0cYmZwntcCZtc8rqzIggWnZX\nMz8Lp1k1Mo9se4LETi306dNn8uTJUVFRO3bsaM4oet0RGxsrkUioY4QB0C0SMRKo7yxxOvRT\n/39tdcpwldVJy09Z3vGtoNAUC0A7gaZYraeDTbFRUVF8Pn/dunX6+vodHUs7gaZYXaDRTbEw\n3QkAAAAAgJaAxA4AAABoD01Vy6ltdR3QRJDYAQAAAO1EIYcz2LYbsjrQumDwBAAAANB+IJMD\nbQpq7AAAAAAAtATU2AEAAGghAwMDuVze0VEAAP4BiR0AAIAWmj17tlwuV3h+NACgA0FTLAAA\nAACAloDEDgAAAABAS0BiBwAAAACgJSCxAwAAAADQEpDYAQAAAABoCUjsAAAAAAC0BIxRBwAA\n0EK5ublSqdTCwoLBYHR0LAAAhCCxAwAA0GKJiYl8Pr9fv36Q2AGgJqApFgAAAABAS0BiBwAA\nAACgJaApFgAAwFuY9xPlheUXyBItPPr3q+jQjggIAEABNXYAAAAAAFoCEjsAAAAAAC0BTbEA\nAAAaV8tHXP5blH9VqbjGzAhZGLViRACAN4DEDgAAQONuZaO4P9+i/OZzimvGvoPe923FiAAA\nbwCJHQAAgMZZmaJeXRRXZpb8sxzT1X9uQSr5UrmwNbuNQgMANA4SOwAAAI3zc0F+Loor/zUq\n9t9WjG3TcAAAb9beid3MmTMnTZo0ffp05U1lZWXffvttdnY2g8GQyWTe3t4rVqwwMTFRLkkQ\nREREhL6+/urVq8mVa9asefbs2YwZMz744ANq4cjIyHv37lHXsFis9957b9q0aeSa+vr66Ojo\n5ORkiUSC17i6us6ZM8fLy4ssw+PxfvrpJ2oZd3f3+fPnd+/enSzD5XK3b9+ekZExf/78SZMm\nNecDUQ7P2dl5xowZ/fv3b87bsRUrVmRnZyOEnJycvv/+++a/sR3U1tbW19fb29srbzp48ODT\np0/37t2r4u3JycmHDh3atWuXlZVVm8UIAGiJmK7+SKnSDgDQsdRoVOzXX39NEMShQ4fi4uKi\noqKysrJiYmIaLXnx4sWcnJwlS5aQa8rLyzMzMwcOHJiUlKRc3snJ6fz/HTlyZMKECbGxsVev\nXsVb+Xz+qlWrHj58+Omnnx49evTUqVPffvutubn5hg0bUlJScBmhULhmzZr79+8vXLjw6NGj\nJ0+e3LJli0QiWbt27cuXL3GZ7OzsJUuWcDgcPb23S5fJ8E6dOhUVFdW5c+eIiIg//vij+XuI\nioo6c+aMv7//Wx23fVy/fv306dMtfvvgwYO9vb2//fbbVgwJAPDf4awOAKBuOiaxIwgiPz8/\nJyeHrP2qr6/ncDjz5s2zsbGh0Wg9e/YMCAjIyspSfq9QKDx16tTUqVONjP4ZapWQkNC5c+fZ\ns2eXlpY+f/5cxaEtLCw+/PDDbt263b59G6/55ZdfysrKNm/ePGLECHNzcxaL5ebmtn79ei8v\nr/379wsEAoTQ6dOnCwsLN2zYMGrUKHNzcyMjIy8vr61bt5qZmd26dQvv58WLFx988EFYWBiN\nRmvZx8JisXr27LlmzZrRo0cfPny4vLycurW8vDwrK6uyUmnUGUJ0Op3BYCgclyCI4uLiFy9e\nVFdXK5QXi8XZ2dnFxcUIoYqKClzbp/pAz58/53K5fD4/IyNDJpNRN+Xm5kZGRj59+lQ5sNzc\n3PT09JqamqdPnwqFQvLQRUVFCiWfPXtWXV1NEEReXh71xkAIzZgxIysr68GDB8r7BwCoA0jy\nAFAfHdDHrqGhITw8vLi4WCQS4YoxZ2dnU1PTL7/8klrs9evXnTp1Un77rVu3GhoaRo8eTa4h\nCCIpKWnYsGF2dnaurq6JiYnu7u6qY7CysqqpqcHvTUhICAwMdHJyohag0WizZs1avnz5vXv3\ngoKC4uPj+/fv7+bmRi3DYrF++OEHsn5u3LhxrfUY7NmzZ8fHxycmJoaEhCCEuFxuVFRUZmam\nhYUFl8v19/cPDw9nMpkq9vDixYuoqKja2lpjY+Pa2toBAwZ88cUXOLxnz55t3bpVKBQaGxu7\nurp26dIlLS1t//79qg8UERExYcKEq1ev1tXVHTt2zNjYmDyWjY2NtbX1li1bunTpMnHixMGD\nB+vr6+NNFy5ceP78ub6+/sGDB9esWVNbWxsZGSkSiQwNDR0cHGxsbMidREREjBw58tGjRywW\nq7y8nEajffXVVy4uLnj//fr1u3Dhgq8vDK4DoONFhyLadcVMDp45AYCa6IDE7sqVK0uXLh04\ncGBNTc3atWsPHDiwfft2cmtWVlZ5efnDhw9zc3M3bNig/Pa0tLSePXsaGhqSazIyMioqKoYN\nG4YQGj58eGxs7IIFC1TkPWKxODc319PTEyFUVlbG4/HwsgJXV1dDQ8Ps7GwvL6/q6upGy1Bb\nXVsrq0MIsdlsR0fHnJwc/PL777+vqKg4ePCgjY1NYWHhmjVrTpw48dFHH6nYw9mzZ11dXVes\nWKGvr19WVrZs2bIrV66MHz8e783Ozm7z5s0sFuv333//7bffyO5rKg6kp6d3/fr1sLAwar9D\nzMjIaMGCBTNmzLh69erx48ePHDkyduzYcePGmZmZLV26tLCw0N7eftmyZQihLVu22NraRkRE\nsFisW7du7dq1y9bWFu+ETqdfvnz522+/7datm1gsXrNmzYEDB8gW2L59+/78889isbipy0oQ\nhFgsbsln3Y4IghCJRB0dRfvBNbvUyletJ5PJ5HK5Nl3li1UpInmzriDtuv9x942NbvI38+jC\n1J4+svjG1qarrBo+Xy27sd8If123uP2trRkYGKjY2gGJXY8ePQYNGoQQsrCwGD9+/KFDh+rr\n601NTfHW2NjYJ0+eGBsbz5w5E1fYKMjNzR0wYAB1TUJCQq9evTp37owQGjJkSHR09P379wMC\nAsgCAoEgPT0dL3O53KtXr/J4vClTpiCE6uvrEUJmZmbKB6LRaGZmZrjvP0LI3Ny8Nc6+udhs\nNo/HQwhVVVWlpaV98sknuH7LwcFhzJgxV65cUZ3YrVq1SiKRFBYW8vl8giAsLS3/+usvhFBh\nYWFZWdmcOXNYLBZCaOrUqWRfQ9UHotPpXbt2Vc7qSMbGxlOnTg0ODr5169a5c+dOnz69YMGC\nMWPGkAWKiopKSkqWL1+ODx0YGHjmzBlqNtanT59u3bohhJhM5ogRIw4cOFBXV8dmsxFCrq6u\nYrG4oKDA1dW10aPL5XJ8mdScRgTZuoRCIW6F1x3adJUXvthWJa1rZuGZzzc2uv5np7XjzbWq\nrVYikejaXS2VSrXpxm4O/BOsnphMpoqkswMSO0dHR3IZV9hUVFSQiV1ERIRQKMzIyNi9e/fL\nly9xTQ9VbW0tNQ8TCoV37twZNmwYmbo5OjomJiZSE7vy8vLIyEi8bGpq6uTktGPHDhwGrvlr\nqrJHLBYbGhriznzt/MdKQ0MD/kwKCwsRQgwG48WLF3iTgYFBXV1dVVWVpaVlU29PSUnZu3cv\nk8m0sbFhMBhVVVU4ftxvr0uXvyebwt0Zc3Nzm3MgcmRrbW0t2ZGRw+FQ828GgzFkyBAjI6Mf\nfvgB75BUVlaG/n/FMXt7e3xozMHBgVzGafrr169xYoeveF1dkz8wNBoN54vqTCgUqn+QrUgi\nkchkMn19/VaszFZzcrlcKpWq7iahWWZaj66XKT56Iqa8yaFdczuPV17Z3bSrNt35EokEd2vu\n6EDaiVwuF4vFDAaD7GOjC0QikepasY6luiqxAxI76r9w/G9DoTM+i8Xy8fEJCQk5cODA3Llz\nFarTRCIR9Xvz9u3bIpEoMTExMTERryEIQiqV1tTUkHVsjo6OTU0CwuFwGAzGq1evcCUiVUND\nA5fLtbGxsbCwYDKZ1BSkreHKthEjRiCEpFIpQujYsWN0+j8jXczNzVX8vcjj8Xbv3j106NCF\nCxfiy//FF1+Qe0b/rsUll994IPLC5ebmfvPNN3h52LBh5PBksVicmJh47tw5Lpc7fPjw4OBg\nalR4/9RDK3xNUL8oFW4M/C4VuTWdTm90Zhy1IhKJ1D/IVsTj8WQyGYvFUufvx9YlkUgEAoE2\nXeXve4crrFHuXUcVU/4HMVLLpz7h8Xh6enralKqqJpFIcGKnTTf2G4nFYmNjY7VtilWtAxI7\nar0LXjYxMcnPzz9z5syHH35IDpjAaVlDQ4NCYsdms6kVwgkJCQEBAStXriTXSKXS2bNn37x5\nUyGxaBSLxfL09Lx58+b06dMVpilJSkoiCMLf35/BYHh7e9+6dWvWrFnUobgIoaNHjzIYjJkz\nZzb/9JsjOTlZIBDg6Uvw57Bu3TrlESF//vlnaWnphAkT8EuhUIj/4eXl5QkEgrFjx+KbkiCI\n0tJSDoeDEMKDHqg1zK9fv8YLKg6koG/fvmfPnqWuqamp+eOPPy5dumRsbDxhwoSRI0dSO0Fi\n+NDUq68wXBcPZ8HwJSa/R/C7cO0dAKCjqM7qAADqoAOmO0lLS5PL5Xj56dOnJiYmNjY2xsbG\nSUlJ8fHxZLGHDx+yWCzcJEfF4XAqKirwMp6+TqGyTU9Pb8CAAY1OaNeoGTNmlJeX79+/n1px\n+OLFi9jY2OHDh9vZ2SGEpk+fXl9fv2PHDmo9WWJiYlxcHHV8aKvIzs6Ojo729vb28PBACDk5\nOZmamlIn+8jJybl//z4uGRMTgz8NoVD48uVL3EcNZ6hk/VZCQgKfz8efebdu3Wg0WkZGBt5U\nWVlJLqs4kGqFhYWhoaEZGRmfffbZwYMHJ02aRM3q6HQ6PrSjoyOdTidbzOvr6xVmSHn06BF5\nCcgbA7/E54hzUwBARyFGpuL/hPG+Cv+Rmzo6RgB0XXvX2BEEYWZmtmHDhsGDB5eUlFy7di0k\nJIROp1tZWb377rvHjx8vKChwcHDAKcWCBQuU+zG88847ycnJeDkhIcHAwKBfv34KZQICAhIT\nE/Pz83Gio1rPnj0///zz/fv3P3nypF+/fiwWq6CgIC0tzcfHZ+HChbiMi4vLsmXL9uzZ8/HH\nH/v4+LBYrOzs7Ozs7ODgYLJe8Nq1a1VVVQghmUyWlpbW0NCAEJo0adIbMz8ul/vLL78ghMRi\n8atXr9LS0nr06BEWFoa3MhiMjz76aN++fQ0NDW5ubhUVFefOnZswYUL//v3HjRt3+fLlNWvW\n+Pn5PX36VCqV4hEhLi4ulpaWhw8fHj9+fF5eXl5eXlBQUFpaWmpqqr+//8CBA3/55RepVGpm\nZnblypVevXrhmjMVB1Idv6mp6fbt252dnRvdamVllZ6efu7cOW9v76CgoN9//10mk5mZmSUm\nJrq4uFDrDs3NzTdu3BgYGFhSUnLlyhV8Y+BNT548wZOqqI4EANAORKuWNLrSYNvu9g8GAKCA\nsXHjxvY8XkZGxsSJE11dXVNTU6urqydOnDhlyhTcYujl5dW9e/eSkpLCwkJLS8u5c+cOHjxY\neQ/GxsZxcXE+Pj6WlpaJiYleXl7KiZ21tfWLFy/YbLazs3NBQYGRkZHCQFoFzs7OQ4cORQhV\nVFRUVVVZW1vPmjVr2rRp1E5gjo6OQ4cOZTAYlZWVtbW1jo6On3zyyYgRI8g2+GvXruXk5FRU\nVHA4HJlMVlFRUVFR4efnp9woSVVQUCASiXDhmpoaS0vL999//6OPPqK+y8XFxd3d/eXLl1lZ\nWVKpdOrUqbj5lcViBQYG8ni80tJSJyenZcuW4QpOBoPh6+tbXFyclZXVuXPnBQsWODo6FhYW\n8ni8fv36+fj4EASRlZXV0NDw4YcflpSU8Pn8kSNHqjgQQujFixfdu3dvdJwyi8WysLBo6gQd\nHR1LSkoqKyvd3d2HDx+up6eXk5NTV1f3/vvv29jYSCQS3OIcFxc3fPhwHx+f1NRULpdLvTFk\nMtkPP/wQGBioYkyuRhAIBApN+dpNLBZLpVIDA4O3fRaL5sKDJ7S+T6Es/nJT6/VGav/DYsVi\nMZ1O16m7WiQSMRgMrb+xqQQCgaGhoYb2saMRBNHRMby1zZs30+l0hQmNQTM9fvzY2NgYP+KW\nIIilS5e6urpSn8/WIVQ8RDgxMfGHH3748ccfG52VRoOoHsisfXg8nlAoNDU11Z3fAzx4Qps6\ng8rz84iyUuoaadxJFeX1pv7rUd2IRmP017ZueTo4eKK2tpbJZGrTjf1G1dXVFhYWGprYaeTf\nHAsWLAgLC7t///4bWwk7nFAoLCkpaWor7nbWnvEghFJTU2/evDllyhRzc/OHDx+WlJRQh56o\nG/ygizlz5mh6VgeAJpI/eSS7faP55RXTPjpd+xI7ANScRiZ2tra2YWFhCQkJffr0UfMpo/Lz\n86nP1VCwa9eu9h9AvnDhQicnp/T0dJFIZGtru2fPHurcch3F3d290S50165dCwgIwM/MAAC0\nM7qrG/p3m6PsRnxThRFCjKAR/3qtmRUeAGg0jWyKBUATQVOs1tO+pthGNTp4AtOF8RPQFKsL\nNLoptgOmOwEAAKB9dCGrA0D9QWIHAADgLTSawEFWB4Ca0Mg+dgAAADoQmcZVV1fL5XKd6mMA\ngJqDGjsAAAAAAC0BNXYAAABaqLi4WCqVdurUSUO7mQOgfSCxAwAA0EKXL1/m8/keHh7tPyUn\nAKBR8E8RAAAAAEBLQGIHAAAAAKAlILEDAAAAANASkNgBAAAAAGgJSOwAAAAAALQEJHYAAAAA\nAFoCpjsBAADQQv379xeJRDDXCQDqAxI7AAAALfTOO+/I5XJI7ABQH/CvEQAAAABAS0BiBwAA\nAACgJaApFgAAQMt9cc5Kxdbo0HYLBACAENTYAQAAAABoDUjsAAAAAAC0BCR2AAAAAABaAvrY\nAQAAeAt1AnQ89e/lggIeQp1UFD6Q+M9ybzs0uEdbRgYAgMQOAADAWxFJ0YM88lVX1YUpJZEp\nq40iAgD8AxI7AAAAb8HMCK0Y+/fyr7/+WsieTt0a09V/bkEq+ZIsiRCyMGqP8ADQcVqV2EVE\nRAwaNGjo0KGqi+3evVsgEKxataqpAvfu3UtMTFy+fDmTycRr4uLiHj58OH36dC8vL2rJ2NjY\nzMxMvEyj0dhstqOj4/jx401MTKjFSktLk5KSXr16JZFIrKys+vfv369fP3KudupOqDw8PGbM\nmKH6XPB7g4ODBwwYQF0vFou3bNkik8kiIiLUdlL4Cxcu5ObmLl26VEWZ06dP19bWhobClAkA\nqAsmA/Xq8veysSSfuimmq79CYbIkAKB9qOlPfss8f/68oqJCdZn4+Pj4+PisrKymCpSUlOzc\nuXP48OFkVieTyc6cOVNYWHjp0iWFwvn5+UVFRZ6enp6enh4eHiYmJhcuXPj000/LysrIMn/8\n8ceiRYtu3LhhYWHRtWvXysrKiIiI9evX19fXU3fSS4mDg8MbTzk/Pz8zM/Py5csK6x8+fPjk\nyZOMjAyCIN64kzb18OHD06dPN7qppKQkJydH9dtHjRp1+/btP/74ow1CAwC0CeX0DgDQbrSq\nxu6N6urqYmJi+vTp8z/27juuifN/APiThAQIEggQZMh0ILIcOJDhoCqKRa1Vq2i1rX5dra1b\nW7Xar1pFrda69161ouBCBRQHbkBUhpatICsQQnZyvz+e/u6bJhABkYTk837xx+XuubvPXQJ8\n8qwrLCysr8zevXu9vb179epFrnn48KFAIJg5c+aOHTtqamrMzc2Vy7PZ7PHjx5Mvx48fP2vW\nrDNnzsyZMwchlJKSsmfPnqFDh06bNo1Go+EymZmZK1eu3LZt29KlS8mDTJw4sWkX1b59+7S0\ntOrqagsLC3Ll7du33dzccnJymnbMRqmqqqqsrHR3d69z69OnTwUCQZMPzmKxJk2atGvXrqCg\nIOULBADoGuV8TqVBFgDQYvQtsaNQKA8ePEhMTJRIJN26dQsPD1duiNy/f7+np6evr299id3r\n16+fPHmyceNG5ZXx8fH+/v4hISH79u27ffv2sGHDNARgZWXVqVOnvLw8/PLkyZPt2rVTzuoQ\nQp07d46MjNy7d29OTk59+VDDOTk5cbncO3fuhIeH4zUikejRo0fDhg1TTuzS0tISExO5XC6H\nwwkLC+vQoQNeL5fLY2Jinj17ZmZmNnz48KKiomfPns2bNw8hpFAo4uPjU1NTa2trORzOkCFD\nyL2U1dTULFy40MPDIyIionfv3hQKhdy0d+/eW7du0Wi0H3/88bvvvrO1tb1w4QI+1+DBg5UP\nsmrVqsGDB4vF4nv37slkMuX3rl+/fidOnIiOjp4yZcoH3isAQLNzLlz5008/0en0g9f/tR6e\nOQGAVuhVUyxC6MmTJ6dPn/bw8OBwOPv27Ttx4gS56dmzZ8nJydOnT9ew+927d21sbDp16kSu\nqa6ufvLkycCBAxkMRlBQUEJCgobdMaFQaGJighCqra3NysoKCQlRzuqw0NBQnIM27vLqolAo\nAgICbt26Ra65f/++mZmZ8lVcv359+fLlCKG+ffsKhcJFixa9ePECb9q1a9fRo0fbt2/v5eW1\nbdu2xMTEv//+G2/at2/f/v37nZ2dAwICZDLZwoUL66wCdHJy2r9/v5eX1x9//DF9+vTY2Fih\nUIg3BQQEmJubOzk5hYeHW1hY7Nmz5+jRox07dvTy8jp69GhWVhZ5kKysrJMnTz59+jQsLMzP\nz+/gwYPHjh3Dm2g0Wp8+fe7evfvh9woA8JFQrqs2v6qvAQC0AH2rsSspKdm7dy/uHkcQxMWL\nF8ePH0+j0aRS6Y4dOyZMmMDhcDTsnp6e7uvrq7zm5s2bZmZm/v7+CKHQ0NDFixe/efPG0dGx\nviM8evQoOzt78uTJCKF3794RBOHsXMd0AEwm08rKqqysDL98+/btggULVMr88MMP7dq1a8hV\n9+vX7+LFi6Wlpba2tgihpKSk4OBgsuZMJpMdOnRo4MCBP/zwA0JoyJAhK1euPHr06Lp162pr\na2/cuDF27Fjcmuzv7z9jxgx7e3u8Y9euXQMDA728WY7dpwAAIABJREFUvPBe2dnZN2/erLOK\n0dLSMjIycsyYMfHx8TExMcePH//kk08iIiJwv0MOhxMYGCgQCK5duzZ69OjIyEiE0MCBA6dM\nmWJj889TJikUilQqnTt3LoVC6d69e0VFxeXLlyMjI3FO7OvrGxMTQ16gOoVCQfZZ1FkEQVRX\nV2s7ipYjl8sRQgKBQCQSaTuWFkIQhFwu1/t3+VrVwx0l0XiZCCEQQqtuhtRZcsCDWcovnYxt\n/3Cb+7HD+9jkcrlUKhWLxdoOpIUoFAqEkEwm0/sPtjKFQsHj8bQdRb1YLJZy45gKfUvsevTo\nQQ568PPzu3r1aklJiaOj45kzZ0xMTCIiIjTvXlZW1r17d+U1CQkJ/fr1w+mFp6eng4NDQkLC\npEmTyALFxcXkANuqqqri4uKQkBB8Ivz7oF5dh9HpdPJPA5PJ7NOnj0oBMzOzhl006tSpk52d\nXVJS0ueff15TU5OSkvLFF1+QWWNubm5NTU1wcDBZPiAgYMeOHWKxOC8vTy6Xd+vWDa/ncDje\n3t4VFRX4pa+v76VLl86fPy8QCAiCqKioIDfVicFgDB06NCws7NGjR7t27SIIYtq0aeTW3Nxc\nuVxODis2MTHx8fEpLi4mC/j6+pKfVE9Pz/Pnz+P3DiGE87mysrL6EjuCIKRSaQNvlxa1iiCb\nl1wuxxme4dD7d7lIVHqTl9KQkirFOpu66M3NMbRPtUKhwP/RDEfr/azqW2JnbW1NLuM5R/h8\nfmFhYXR09K+//vreiT94PJ7y2IicnJzc3FyJREKmbrW1tTdv3pw4cSKZgijnZObm5u7u7mSd\nlqWlJUKIy+Wqn4ggiKqqKisrK7Lk559/3vjL/Z+QkJBbt259/vnn9+7d43A4nTp1IhM7/DXr\n9OnTsbGx5GUSBFFeXo5ruZQv2dbWFmdvBEGsWbOmqKho7NixDg4OVCp1z549uExmZubOnTvx\ncq9evXANHCaXy+/cuXP+/Pnq6mqVUb34XCwWi1xjaWmpnNjh24XhpJbP5+OXeC8N35+oVKry\n7rpJZYCL3hMIBBKJhMlkkt+19J5MJhOLxQ3/StZKjTcbEmjbFS/X1tYOzNA0Y9ED/33ksinN\n2JKp67+n7yUQCIyMjAzqU83n8+l0ut5/sJXhZEBDrZh2aQ5M3xI75RQb14fR6fSYmBgjI6Mj\nR47g9eXl5Tweb/ny5cOGDQsI+FcvEGNjY4lEQr6Mj4+3s7NT7uYvlUqPHTum3GJrYWExatSo\nOoOxsbGxsbFJTU0dMmSIyqZXr16JRKIuXbo0/VL/rX///qdPny4oKLh9+3ZIyL+aReh0OkLI\nx8eHbPdECA0aNIjFYuHaRJlMRq4nL7+goCAtLW3FihW4GRopfZIsLS379u2Ll93c3PCCQCC4\nevXqxYsXZTLZ0KFDV65cqZLEGBkZof9/U8hdlAsoN9jh9xFHTu5lbGxc3+VTKBR8fB3XKoJs\nLvh7FI1GM5yrJgiitXwUP4SdkY2d6T9/TCqJSs2Fez+eSgzSq+GxVCqVSqXq/btMwnNmGcIH\nW4WRkZHOJnaa6dv7pDzctbi4mEKh2NraBgUFubq6kuvT09Orqqr69OljZ2ensruFhQXZjUAu\nlyclJYWHh6vkbffv309ISFDpilef0NDQs2fPZmdnKw9lIAji5MmT1tbWPXr0aOT11cvR0bF9\n+/a3bt16/vy5cgMoQgjXnLm7uwcGBqrshVO9d+/eubi44MCysrLwb29lZSVCiOxvV1ZWVlBQ\ngA9lZ2c3btz/5poXCATHjx+/fv26vb39hAkT+vXrRyZkynDvxuLiYvJW5OXlKf/a1Pne4Zf4\nTTGo6i4AWgXrJ+HKL0U3euIFk08ekSsp1wP0LLcDQJfpW2KXnp7+/Plzb2/v2traa9eudenS\npU2bNn5+fspPjJDL5VlZWeTkIMrc3d3JMaEPHz6srq5WT4aCgoJOnTo1Y8YMPPRVszFjxjx8\n+HDVqlVffvll7969TUxMCgoKTp8+nZaWtmzZMrIynyAI5ZpCjEKh1Jkh1adfv34nT550cnLC\nWRrJysqqe/fup0+f7tKlC5vNFovFf/zxB0JowYIFLi4ubDY7Nja2W7dudDr9woULNTU1bDYb\nIeTg4EChUNLS0hwdHWtqarZv3+7q6lpnY2hJScm7d++WL1/u4+OjvtXY2Bi37To7O9va2sbG\nxvbs2dPU1DQuLq60tLRt27ZkyRcvXqSnp/v4+PD5/CtXruD3Dm96/fo1g8FoyIzNAICWVNHj\nkkKhsLa2liz5V4MszvCM12/VUlwAGC69SuwUCsXw4cP37NnD5/N5PF6bNm0WLVrUqCP06NFj\n586dIpHIxMQkPj7excVFPZkICgo6dOhQcnLye59dhhBiMBjr1q07cODAvn37tm/fjld27tx5\n7dq1nTt3Jovl5eWp97GjUqnnz59vePAhISEHDx5UaYfFvv/++6ioqK+++srGxqaqqsre3h7f\nGSqVOnv27I0bN0ZGRpqZmfn4+AQFBWVkZCCE2rZtO2LEiN27d8fExNTW1s6YMYPL5e7Zs2fR\nokVRUVHKB3d3d1+2bFl9UfXs2fPAgQNffPHFnDlzvv3223Xr1kVGRjIYjPbt2w8ePDgl5X99\nqz/55JP9+/dXV1fjzg3K711KSoq3t7fhdGoBoHVRyepI4sVzILcDoIVRtP7IqWb04sULBwcH\nS0vLgoICiUTi5uZWZ5+A8vLy8vJy5byKJBKJpk2bNnr06JEjR2ZkZLBYrDpnNsnMzDQ3N3d0\ndMzPz5dKpXVO26tCLBa/e/dOJBJxOBxcJUbKz8+vsyaMQqF4e3trPmx+fj6VSiWzz+zsbEdH\nR9zFtbq6uqCgwNvbm2zufPfuHZfLtbCwIBtYMYFAUFBQYGVlZWtru27dOolEsmLFCryprKyM\ny+U6OTmZmpoihHJzc/H0Je+9XpUgEUIODg50Ol0kEuXn5zOZTCcnp9LS0qqqKtwyGxkZOWLE\niDFjxqi/dyUlJTNmzFi2bBnZ26+VqqioUB7co/f4fL5IJDI3N9fQOVLPSKVSoVCoPEJI76Wn\np8tksi6nDtZXQP8SOz6fb2Rk1JAWG/0glUqrq6sZDIZBfbArKyvZbHYr7WOnV4lds7h48eKf\nf/65c+dOJpOp7VhawqZNmxBCc+bModPpf//99+LFi7/66qs626k/qsjIyIiICOWue6Tffvut\nrKzs119/beGQmh0kdnrPcBI76fbfCH4NQqiqqoogCEui3okwKFb/+8xT2trTp/ynJeL7mCCx\nMwStOrHTq6bYZhEeHp6amrp169YlS5ZoOxaUn59PPoBB3bx583BF2ocIDw+PioqKjIw0NTWt\nqqr65JNPwsLCPvCYzej27dtPnz7dsmWLtgMBAPwPwa0kangIofcOaCIqlSa/NDWIb8sAaBfU\n2NVBIBD8/fffHh4eWu/UxeVynz59Wt/WkJCQRo2uqI9CoSgqKhKLxXZ2dspz2rWkjIwMa2tr\n9fmHX79+3aZNG/Xxy60R1NjpPcOpsUMiISIIhNDWrVuFQuEPwjpm68SMV6773wsKFbX+ii6o\nsTMEUGOnb5hMZp0DPFsem80ODQ392GehUql1PvesJXl6eta5viH9FwEALc3kn7YCEYUqRBr/\n+UEtHQAt6z1PYgAAAAA0o67eVOd6/Rs5AYDug8QOAADAh1LP4SCrA0AroCkWAABAM4BMDgBd\nAIkdAACAJvLz85NIJPi5wAAAXQCJHQAAgCbq2bOnQqGAxA4A3QG/jQAAAAAAegISOwAAAAAA\nPQGJHQAAAACAnoDEDgAAAABAT0BiBwAAAACgJyCxAwAAAADQE5DYAQAAaKIrV65cuHBBLpdr\nOxAAwD9gHjsAAABN9ObNG4FAQBCEtgMBAPwDauwAAAAAAPQEJHYAAAAAAHoCEjsAAAAAAD0B\nfewAAAA0p6/317vpwDctGAcABglq7AAAAAAA9AQkdgAAAAAAegKaYgEAADTRpEmT5HI5jUZD\nCPFFSCh9T/myGoQQolKQdZuPHxwABgkSOwAAAE1kbGysUCgoFApCKPopSsx4T/nFZxBCiM1E\nm8Z//OAAMEjQFAsAAKAZWJshVxvkavOvlQedA5Rf4gLtrFo0MAAMCtTYAQAAaAbD/NAwP4Q0\njopdMaLFwgHAQEGNXUO9efMmIiIiOztbfRNBEP/973/XrVunvmn37t3ffvstXo6MjDx9+nRz\nxVNdXV1UVFRnMKdPn54wYcKIESPGjRsXERExadKkK1eukAXWrFkTERFx5swZ5b24XG5ERER6\nerpyGWVjx45V2eUD40T/vjn1SUpKmjhxYnl5eRNODQDQLlxdp1JpBwD4qKDGrqFMTEwQQqam\npuqbLl68+OrVq127dmk+wsaNG9u0abYOw9evXy8qKvrhhx9U1l++fPnkyZMzZ84cOHAgnU6v\nqak5ceLEzp077e3tu3btisswmcyzZ89+8sknVlb1toi4ubn9/vvveJnL5cbGxh47dszCwmLI\nkCHNEmcDhYSEPH78eOPGjXXmzQCAVuGgc8BXBcnajgIAgwA1dvV68+bNq1evBAIBfolTOpze\nKROJRGfOnPnss8+YTCZeI5FIsrOz1aupqqqqhEIhXs7IyOByuQKB4Pnz53K5HK989+5dZmZm\nnbVTKsHk5OSkpaVVVVWlp6eLRCLlkk+ePPHw8BgyZAidTkcImZub/+c//wkPD5fJZGSZ3r17\ns9nsQ4cONfBWsNnsL7/80sXF5c6dO+pbc3Jy1qxZQ9b2qWxSibO+m/PixYvKykqCIHJzc1+9\neiWV/m9w3YQJEzIzMx89etTAaAEAugAq6gDQCqixq0NFRcXatWtfv36NB3xNnjz5008/NTU1\nnTdvnqWlpUrh27dv19bWkvVYL1++XLNmjVgsNjU1dXJysrOzI0uuXr06IiJi3LhxeHn48OFx\ncXE8Hu/o0aMSiSQqKurly5dsNpvL5QYEBMybN4/BYNQXTGxsbEZGBp1O371799KlSx0dHcmz\nmJmZ5eXl1dbWmpmZ4TUUCmX69OkqYX/zzTdr1qwJDw/38PBo4G2xsbGpqqpSX29nZ2dra/vf\n//7XwcHh008/DQkJwTklQkglzurqag03Z9CgQSkpKSYmJu/evaNQKCtWrGjfvj0+fo8ePWJj\nY3v27NnAUAEALaO8vFwmk1lZWeGBsRh+vMTB6/8qedA5gBgElXYAfHSQ2NVhy5Ytcrn80KFD\nbDY7Li5ux44dHh4enTp16t+/v3rhp0+fdu7cmWyi3bp1q729/erVq01MTG7fvr1lyxZ7e3v1\nvYyMjK5fvz537lw/Pz+E0IYNG0pLS3fv3m1nZ1dYWLh06dITJ05MmTKlvmC+//77wsLCdu3a\nqTdxDhkyJCkp6fvvvw8LC/Pz82vfvj2VqlovSxBEr169/Pz89u7du2HDBuW/yPWRSCQ5OTk+\nPj7qm5hM5rRp0yZMmBAXF3f8+PHDhw8PHTp02LBhFhYWKnH+97//re/mUKnUK1eubNy40cXF\nRSKRLF26dOfOnRs3bsRbu3XrdujQIYlEgpNddQRBkBWfuky53lTvKRQKhJBcLjecq5bL5QRB\nGM71IoTOnz8vEAgWL15Mp9N5strXwn8q43s/nqpe+CH3BbnsbNLWhq76PblVUCgUCoXCcN5l\n/KfV0D7YCCGZTNaQf45aYWSkKXmDxE5VRUVFWlraokWL2Gw2Qmjw4MEymczY2Li+8jk5Ob17\n98bLRUVFb9++nT9/Pm6xDQ4Ojo6Olkgk6ntRqVRnZ2ec1VVUVDx9+nTGjBm4BsvJySksLOzq\n1atTpkxpbDAIIW9v79WrV586dero0aNHjhxhMpn+/v6jRo3CtV/Kpk6d+v333yckJISGhqof\nRygUpqWl4WUulxsXF8fn80eNGlXfec3MzD777LMRI0bcvn37woULZ8+enTZtWlhYGFngvTen\na9euLi4uCCEGg/HJJ5/s3LmTx+OxWCyEUIcOHSQSSUFBQYcOHeo8u0KhqLM2Ude0iiCbl0Ag\nILsQGAgDfJd5PB6NRkuoeTru9c8aiilne1uc50RaD/r4oX0UEonE0D7VUqnU0D7Y1dXV2g6h\nXtbW1hqSTkjsVOHuXw4ODvglhUIJDw/XUL66utrCwgIvl5SUIISUq+jatWuXk5NT547t2rXD\nC4WFhQghGo2WlZWF1xgbG/N4vIqKisYGg/n4+Pj4+PD5/PT09NTU1Dt37ty9e3fp0qW9evVS\nLubs7BwWFnbkyJG+ffuqH+Tdu3dr1qzBy+bm5m5ubps2bXJ1dcWXnJHxzzykHA5HOWWk0Wj9\n+vVjMpm7du3C10V6781xcnIil9u2bYsQKisrw4kdvsM8Hq++S6ZQKGT7r86SSqW6H2Qzksvl\nCoWCRqOp1xnrK1xzrPnLtF6i0Wh0Op1jzO7P6oYQuslLqa8kLoAQamdq20p/HeRyOYVCMZxP\ntUKhkMvlVCoVP1/EQLTqP9cG9wfovXC3/Yb/0orFYrJ9ENdUK9eoafhkkOMw8F5Hjx5VPqml\npaVIJGpsMMratGkTEBAQEBAwceLEhQsXnj59WiWxQwhNmDAhKSnpzJkzI0aozi7l6upKjopV\nkZOTQ45RHThw4Jw5c/CyRCJJSEi4cOECl8sNDQ1VOeZ7b47yXw28TLau4r3EYnF9F0ulUsn0\nWmdVVFTofpDNiM/ni0QiJpOpuY5Zn0ilUqFQiL+NGBQLCws6nT7AoucAh56U65rGTNzkpbT2\nnnZ8Pt/IyEh9IJ2+kkql1dXVRkZGBvXBrqysZLFYOtsUqxkkdqrwZ7eyshLXTiGExGIxjUar\n71s4i8WqqanBy3i8gnLFUmVl5XvPiAdk/PTTT56eniqbamtrGxWMSCR68OCBh4eH8rgEc3Nz\nPz+/5OQ6/piam5tPmDDhwIEDdVba1adbt27nz59XXlNVVXXp0qXLly+bmZkNHz580KBB6vPC\nvPfmKNfz41tKzg6D9zKoPysA6CvKdRhFAcBHZCiVyQ3n5uZmZmZGpkECgSAyMvLatWv1ledw\nOKWlpXjZ1dWVSqWSXdNqamrqnARE/Yzm5ubK03m8evXq4cOHmoOhUqm4c7qKnTt37tu3T3kk\ngVAofPbsGe6+pm7o0KH29vbHjx9/b5z1KSws/Oabb54/f/7tt9/u3r07IiJCOasj43zvzUlJ\nSSHDTk9Pb9OmDZme4jvM4XCaHCQAoAUoV9eJbvRU/tFiVAAYFKixU0Wn0ydMmLB3716ZTObi\n4nLr1i0rK6s6x8Nivr6+SUlJeNnc3Lx///5//fWXXC63sLBISEho3749n8/XfEYajTZlypTt\n27fX1tZ26tSptLT0woULw4cP79Wrl4ZgbGxs0tLSLly40L17d7J3momJyXfffbdx48Zvv/22\nZ8+e5ubmXC43OTlZIpEsWrSozrNTqdRp06YtX7688bcKkVe9YcMGd3f3Orcqx6n55lhaWq5c\nuTI4OPjt27dXr14dP3482Qb97NkzPKlKk4MEALQAsipOvHiOyibRjZ7G67e2eEQAGBzaypUr\ntR2DzvHw8Gjfvn1OTk5RUZGnp+ecOXM0PDHCzMzs3Llz/v7+1tbWCKHu3bsbGRm9evWKx+ON\nGTPGzs5OKpUGBAQghDIyMjw9Pd3c3BBCWVlZHTt2JIcdtG/f3tPT8/Xr15mZmTKZ7LPPPhs+\nfLjmYFxdXd++fVteXu7p6YnHzGLOzs79+/dXKBQlJSVv376l0WiBgYE//PADWd1VUFDAZrO9\nvLzIXezs7GpqahgMRp8+ffChCgoKmEwmOdpXMxMTE+UAVCjHGRoaWt/NOXfuXGhoqL+/f3Jy\nMpfL/fTTT0eNGoX7N8jl8l27dgUHB+NBxK2XUCgkZ7E2BBKJBA/iNpzBBHgWDMPpU4gQ4vP5\nbdu27dSpk3JXYPWsDpPfuGI0aGhLhfaxSCQSKpVqUJ9q3AXIoD7YQqHQ1NS0lfaxoxAEoe0Y\nWr1ffvmFSqUuW7ZM24G0YpGRkeTszSoSEhJ27dq1d+/e1j7yoKKiAmf/BgIPnjA3Nzec/wcG\nOHiisrJSoVDgyReIogJCKEQISfdtr688fepsvEDt0Am1zv+ahjl4gsFgGNoHm81mt9LEzlC+\nc3xU06ZNmzt37sOHD9WHnYIPhJ/MMXny5Nae1QGg92QX/lQU5GsuQ+Z8xr9uaaWJHQA6DhK7\nZmBvbz937tz4+PiuXbvW92gEoJmnp2edXeiuXbsWFBTUkNn7AADaRengQbW0QggpntU7jx3V\nt1sLRgSAIYKmWABaCDTF6j0Db4olV9bXxw4hpAfjJ6Ap1hC06qZYmO4EAABAc9KD7A2A1gsS\nOwAAAM2sztwOEj4AWgD0sQMAAND8cBonXjwH8jkAWhLU2AEAAGiihISEq1evKj/qRgVkdQC0\nMKixAwAA0EQ5OTkCgQAG4QGgO6DGDgAAAABAT0BiBwAAAACgJyCxAwAAAADQE5DYAQAAAADo\nCUjsAAAAAAD0BCR2AAAAAAB6AqY7AQAA0ESTJk2Sy+U0Gk3bgQAA/gGJHQAAgCYyNjZWKBSt\n9FnpAOglaIoFAAAAANATkNgBAAAAAOgJSOwAAAAAAPQEJHYAAAAAAHoCBk8AAIC++Xq/pq0H\nvmmpOAAALQ5q7AAAADQRj8fj8XgEQWg7EADAPyCxAwAA0ERnzpw5cuSIXC7XdiAAgH9AUywA\nAHyo2FQkliKFgiaVGhsbazua9zn7qNkOVcbsJ6VLo5/SqE2tJbBgokFezRYPAAASOwAA+FDx\nLxFPiBCiIqTzaR1Cl58137FMeyNTdPV50w/gbA2JHQDNCRI7AAD4UGN7IakMyeVyqVRqYmKi\n7XDQ4bv/Wz7oHPBVQbLy1smBzXaiuLg4iUQybNiwJj9VrI327xYAekWbid3q1asDAwMHDBjQ\nqL1iY2NzcnK+//57ck1KSsrZs2eHDRsWGKjpz9WDBw8SEhLmz5/PYDCaMR7s3Llzjx8/Hjdu\nnJ+fn/L6Y8eOvXz5Ei9TKBQWi+Xq6hoeHt6mTRvlYsXFxYmJiXl5eVKp1MbGplevXj169KD+\nu22juLg4ISEhNzdXoVBwOJy+ffv6+vrW+SSfrVu3CoXCxYsXt674X716de3atfLychsbm0GD\nBnXq1AkhdPbs2erq6m++gVF8QKf17YAQQlKpQiiUsFjaT1WUEzukltv169xsJ3oQkyYQCII7\nhdHp8LhYAHSCNgdPsNnsJny1ffv27atXr/CyQqE4fvz4r7/++vz584qKCs17bd68OTQ0tL6s\nrsnxIITkcnl0dHRhYeHly5dVNuXn5xcVFfn4+Pj4+Hh7e7dp0yY2NnbmzJklJSVkmUuXLs2a\nNevmzZtsNtvZ2bm8vHz16tXLly+vqakhy8TFxeEy1tbWDg4O+fn5y5cvj4qKkkqlKme8cePG\njRs3MjMzW1f8d+/eXbBgQUVFhYeHR0lJycKFCx8/fowQGjx48J07dy5dutTwywEAkA46B2g7\nBABAi9Jmjd3s2bM/8AhXrlxJSkrasGHDvHnzNJfcu3evt7d3r169PkY8Dx8+FAgEM2fO3LFj\nR01Njbm5ufJWNps9fvx48uX48eNnzZp15syZOXPmIIRSUlL27NkzdOjQadOmkW0ZmZmZK1eu\n3LZt29KlSxFCL1682LFjR2ho6OzZs8kyt2/f3rhxo6ur67hx48iD83i8gwcPdu3atbCwsHXF\nv3///pCQkPnz5+OtS5Ys+euvv/z9/Vks1qRJk3bt2hUUFGRhYdHwiwIAKFNvkAUA6CVdaYpd\nu3btJ598QqPRbty4IZVKvby8IiIicBIgl8svXLjw7NkzMzOzwYMHKx+hffv2v/32m5mZmeYT\nvX79+smTJxs3bkQIbdu2jc1mR0ZGkltPnjxZVlY2Z84c5XgUCkV8fHxqamptbS2HwxkyZEiH\nDh3qO358fLy/v39ISMi+fftu3749bNgwDcFYWVl16tQpLy+PPHu7du2UsyKEUOfOnSMjI/fu\n3ZuTk+Pu7n7q1CkOhzNz5kzlMsHBwbW1ta6ursoH379/v6enp6+vb6MSO63HL5PJvvjii86d\n/9c+1KlTpzt37uDlfv36nThxIjo6esqUKQ2/KAAMGZ6CmHL9X9V1B50DiEHNnNt17txZLBbX\n2ScEAKAV2myKzcjIKC0txcvZ2dkXLly4ceNGaGhonz59jh8/fu7cObxpz549R48e7dixo5eX\n19GjR7OyssgjdO7c+b1ZHULo7t27NjY2uM8Wm82+ePEiOeuSXC6PiYmxtrZWiWffvn379+93\ndnYOCAiQyWQLFy7Mycmp8+DV1dVPnjwZOHAgg8EICgpKSEh4bzxCoRC3+dbW1mZlZYWEhKj3\nOw4NDaVQKA8ePJBIJM+fPw8ODqbT6SplwsLClJOhZ8+eJScnT58+/b0B6Fr8RkZGgwcPdnZ2\nJjcVFBQ4ODjgZRqN1qdPn7t37yIAgI4JCgoaMGBAk0dOAACana6MiqVQKFVVVatXr8bf/FJS\nUp4+fTpmzBiBQHDt2rXRo0fjOraBAwdOmTLFxsamUQdPT0/39fXFy0FBQadPn3727Fm3bt0Q\nQrhOLiQkRGWXrl27BgYGenl5IYSGDBmSnZ198+ZNd3d39YPfvHnTzMzM398fIRQaGrp48eI3\nb944OjrWF8yjR4+ys7MnT56MEHr37h1BEMoJDYnJZFpZWZWVlZWXl8vlcicnJ83XKJVKd+zY\nMWHCBA6Ho7mkbsav7Pbt20+fPl25ciW5xtfXNyYmprS01NbWts5dFApFbW1tw0+hLcr9DvWe\nTCZDCIlEIolEou1YmtP8vG3l0qo6NxEEQRAEtclTujWr6Iok9ZWU6wGjrFX/3H0IhUJBEAQ1\nn9rkSrsIdtBom/7NGNLHJpPJZDKZev9mfaVQKBBCMpnMoP58EQTB5/O1HUW92rRpo+E3TlcS\nO4SQ8hhJKyurv//+GyGUm5srl8vJoZomJiaW9YiUAAAgAElEQVQ+Pj7FxcWNOnJZWVn37t3x\nsouLi5OT0/3793Fid/fuXTc3N/W0w9fX99KlS+fPnxcIBARBVFRU1Dc4IyEhoV+/fvgLq6en\np4ODQ0JCwqRJk8gCxcXF5ADVqqqq4uLikJCQiIgI9P+/MPV92aXT6WKxGJcxMnrPO3XmzBkT\nExN82EbRkfhJDx8+/P3338eNG0e+ZQghnM+VlZXVl9gRBCEWixt4Ci1qFUE2L/37/3e18n6h\npFTbUTRdnQmfFrnTHYabt74RHvh7i+FQKBSG9udLl69XZWIKFTqU2Ck3qlIoFJwQ4K8ILBaL\n3GRpadnYxI7H4ykPCAgODr5y5cqMGTMIgnjw4MHo0aNVyhMEsWbNmqKiorFjxzo4OFCp1D17\n9tR55JycnNzcXIlEQqY+tbW1N2/enDhxIpmkMpnMPn364GVzc3N3d3ey5s/S0hIhxOVy1Y9M\nEERVVZWVlRUuU15eruECCwsLo6Ojf/3118ZWFehI/KTExMStW7eOGzfuiy++UF6PPwA8Hq++\nHalUqsqYDx2kPjBFv4lEIjypm3orfKu2tdNcgbzuv/gKhUImk2kYet9iIjNWath63FPT1kYR\niUQIoQ+Zus+T6WJu1pp+L0QiEY1G07NPtQZyuVwgEBgZGZmammo7lpbD5/PNzMx0tvOo5sB0\nKLGrE67pUU6cBQJBYw9ibGys3BgUHBx84sSJrKwssVjM5/PV22ELCgrS0tJWrFiBGyhR/Tcx\nPj7ezs5OeUiHVCo9duyYcuOvhYXFqFGj6tzdxsbGxsYmNTV1yJAhKptevXolEom6dOnSpk0b\nR0fHp0+ffvbZZypl8vPzWSwWm82OiYkxMjI6cuQIXl9eXs7j8ZYvXz5s2LCAAE1fhXUkfvwy\nMTHx999/nzlzpvrR8AdAw6OaKBSK7j/Iic/n636QzUgqlUqlUjqdrmdXPdK+f32bpFKpUChU\n/iKqFSpjJtRNaKf6K9ZklZWVCoXC2tpaZ/8FNjupVGpkZKRnn2oNcKU7lUo1nEtGCNXW1hob\nG7fST7WuJ3a4x1hxcTEe+oAQysvLa+y9trCwqK6uJl86Ojq6ubnhOT66dOmi3mOvsrISIWRv\nb49flpWVFRQUqDfXyuXypKSk8PBwlbzn/v37CQkJZGKkWWho6NmzZ7Ozs8kLRAgRBHHy5Elr\na+sePXrgMkeOHHn06FHPnj3JMgKBYP369W3btv3555+DgoKUh8emp6dXVVX16dPHzs5Ow6l1\nJ36EUFZW1tatW2fPnj1o0CD1s+C3D6Y7AaBRRDd6IoRMPlF9NCzlevMPjwUA6Aid6OSrgbOz\ns62tbWxsLO7rdvXqVXLgasO5u7vjHnuk4ODg1NTUx48fq1fXIYQcHBwoFEpaWhpCqKamZvv2\n7a6ururtgA8fPqyurlZ/3EVQUNC9e/dwC8V7jRkzxtnZedWqVXFxcVVVVSKRKDs7e/Xq1Wlp\nad999x1u0xk5cmSHDh3Wr18fHR1dXl5eU1Pz9OnTpUuX1tTUTJ06FSHk5+cXrqRLly4mJibh\n4eFubm4aTq078RMEsXfv3pCQkDqzOoTQ69evGQxGo0ZgAGCwiEHJohs9cVaHEMLLohs9iUHJ\n5I92IwQAfDy6XmNHoVC+/fbbdevWRUZGMhiM9u3bDx48OCUlBW9dsGBBdnY2Xt63b9++ffvw\ngkoX+x49euzcuVMkEpEdQYKDg48ePUqlUut8Clnbtm1HjBixe/fumJiY2traGTNmcLncPXv2\nLFq0KCoqiiwWHx+Ph2Ko7B4UFHTo0KHk5OSGPJ2MwWCsW7fuwIED+/bt2759O17ZuXPntWvX\nklOZGBkZrV279sCBAydOnDh48CC+LT169Fi6dKnmOjnNdCf+goKC7Ozs7OzsxMRE5YMfPnwY\nN9SmpKR4e3vrQtclAHSfePGcFjtXQkKCSCT64osvGj5ACgDwUVEIgtDWuTMyMqytrXESlpmZ\nyWaz27ZtizeVlJTweDyydU8kEuXn5zOZTCcnp9LS0qqqKrzp77//Vu9y5+HhoZIBiESiadOm\njR49euTIkeTKzMxMGo3WsWPHOuNBCJWVlXG5XCcnJ9xjNDc3t02bNsqTiWRkZLBYrDpnBsnM\nzDQ3N3d0dMzPz5dKpRomNyaJxeJ3796JRCIOh0N2O1MvU1JSIpVK27Ztq6Ebfnl5eXl5ufIU\nd3XSnfhFIhH5mDhlnp6eRkZGJSUlM2bMWLZsGdnlsZWqqKjAMyYaCD6fLxKJzM3NDadrjo70\nsdOQ2Bmv39q854qKihIIBD/99JPhDCbg8/lGRkYfMl6kdZFKpdXV1QwGQ+sf7JZUWVnJZrNb\naR87bSZ2LenixYt//vnnzp07mUymtmMBjfPbb7+VlZX9+uuv2g7kQ0Fip5fkjx8oUh/jZYIg\n5HK51uuuFK+y6ttE7ejRvOfKz8+Xy+Vubm4N/BdIcXEzGqTp2Ta6DxI7Q9CqEztDqTwPDw9P\nTU3dunXrkiVLtB1Ly8nPzz927Fh9W+fNm6f7w9fxZMVbtmzRdiAA1I2oLFdOpKgIKbQYzfto\nyPmaBvfkIF5nN7CGgMrQ5ywfAF1gKIkdhUKZN2/e33//LZFIDKerFovFIqegU6f1eoWGsLe3\n37hxY2OfNQJAi6H1CaJ5/TOEXCaTicXihjzn8KOSbN1Q3ybGnIXNe64jR46IRKKvv/66oX9P\nTHT9yyQArV0r+NfeXJhMpo+Pj7ajaFFsNjs0NFTbUXyQhnTvA0CLKCwLxPr/iXikUoVQSNHh\nFiuKYzMPLX9HowsoUuTQjmIwfewA0HG6Pt0JAACARqlvhESzj5wAAOggSOwAAEDfqOdwkNUB\nYCAMqCkWAAAMR8tkcmPHjlUoFDQarQXOBQBoCEjsAAAANBGLxVIoFK10VggA9BI0xQIAAAAA\n6AlI7AAAAAAA9AQkdgAAAAAAegISOwAAAAAAPQGJHQAAAACAnoDEDgAAQBPxeDwej0cQDXxU\nLADgo4PEDgAAQBOdOXPmyJEjcrlc24EAAP4BiR0AAAAAgJ6AxA4AAAAAQE9AYgcAAAAAoCcg\nsQMAAAAA0BOQ2AEAAAAA6AkjbQcAAAAAIITQ1/s1bT3wTUvFAUBrBokdAACAJurcubNYLKZQ\nKNoOBADwD0jsAAAANFFQUJBCoaDRaNoOBADwD+hjBwAAAACgJ6DGDgAADFQFHy08/YHHsGqW\nSBpCcw+8FtQGIdTFAS0Yqu1AAKgLJHYAAGCgKBRkZvxBR8BPiW2uPna14n+9POgc8FVBMvny\nA0NtLviSTejQrRDoKEjsAADAQFmZoT8mftARKiu5CoXC2tq6WXI7zXVyHxhqc+Hza42MjExM\nTLQdCAB1g8Su6SIjIyMiIsaNG9eovXbv3p2enr5t2zaEkEKhiImJiYuLKysr43A4gwYNGjly\nJJVad8fHY8eOPXjwYNOmTQwGoxnjwaKiou7cuTN79uwhQ4Yor1+zZs2DBw/IlywWy9XVdfz4\n8V5eXuRKgiASExOvX7+el5cnlUo5HE7Pnj1HjRrFZrNVyly7di0vL08ul3M4nL59+44YMcLc\n3BwXqO9WHDp0KDU1NSoqqr6rBgDopYPOAUit0g4A8F6Q2DXdN9984+Li8iFHOHHiRHR09MSJ\nEzt16vTixYvDhw9TKJRRo0apl0xJSTl//vzmzZs15DdNjqe2tvbhw4dubm7x8fEqiR1CyM7O\n7rvvvsPLXC43Li7uxx9/XL16tY+PD0KIIIgNGzbcvXu3f//+w4YNMzU1LSgouHjx4q1bt1at\nWuXq6op33Lx5861bt/r16xceHs5gMF6/fn3x4sW7d++uXbsW53/13YpJkya9ePFi7969s2fP\nbsKlAQAAAAYFRsU23cCBA9u3b9/k3eVy+cWLF0eMGDFq1CgvL6+xY8f27ds3KSlJvSRBEAcO\nHBg0aJCTk9PHiCcpKcnY2Hjq1KmZmZnFxcUqW01NTX3+X0hIyMqVK62trWNiYvDWy5cv37lz\nZ+7cuXPnzg0ODvb39//ss8+2bNliamq6adMmuVyOELpx48bNmzdnzZo1b9684ODg3r17R0ZG\nRkVFVVRUnDhxQvOtoNFokydPvnbtWn5+fhMuDQDwUd25cycxMRH/pjcjXF2nvgwAeC+osWs6\n5abPSZMmjRs3rqKi4saNGxKJxMvLa86cOZaWlgihysrKP/74Iz09nclkDh36v2FUVCp18+bN\nZFskQojD4WRlZamf6NGjR/n5+StWrEAILVq0iMlkrly5kty6atWq2traqKgo5Xiqq6v379+f\nlpbG5/M5HE54ePinn35a34XEx8cHBQV5e3vb2tomJiZOmDBBw1XT6XQXF5eysjL88sKFC35+\nfv3791cuw2Kxvv7669WrV6elpXXv3v3ChQudOnVSqQt0cnJat26do6Pje2+Ft7d3x44d//rr\nr3nz5mkIDADQ8jIzMwUCwciRI5vlaPjZEpTrqpkcPHMCgIaDxK55GBkZnT9/fuTIkfv27eNy\nuYsXLz516tSMGTMQQps3by4qKlq+fLmVldWlS5eSk5NxBkOhUOzt7ckjyOXylJSULl26qB/8\n0aNHLi4uHA4HIRQSErJ//36BQMBkMhFCAoEgLS3t66+/Vtlly5YtJSUlixYtsrS0zMrK+uOP\nPzgcTp8+fdQPXlRUlJ2dPW3aNAqFMmDAgMTExPHjx2vuB/3u3Ts7OzuEUEVFRUlJSUREhHqZ\n7t270+n0Z8+eeXh45Ofnf/nll+pl3N3d8cJ7b4W/v39sbCxBEBoCw0PVdFyrCLJ5EQRhOFeN\nr7TVXW+VjN/kmIU0qchIVinh0Ql680aljHI9oKJf3Mc7fqMIZAIjZMSQSNQ30ShUlpFZy4f0\nUZGfjVb3wf5Auny9mv9HQ2LXbOzs7IYPH44X/P39X716hRCqqKhIS0ubPn26n58fQmj69OmP\nHz+uc/cjR46UlJQsWbJEfVNmZiY5WCEwMHDv3r2PHj3q168fQuj+/fsKhSIoKEhll++//55C\noVhYWCCEHB0dL168mJKSUmdid+PGDUdHRw8PD4RQaGjo6dOnX758qTw2AiFEtrNUVVXFxMQU\nFRVFRkYihLhcLkLI1tZW/bBGRkZsNruyshKXwVlpA6nfii5dupw4caKoqKi+xmi5XI5PpOMq\nKiq0HUJL4/P5fD5f21G0qFb3Lrd/9gVPXtvEnX0RQmj9vfhmjKdO1rdUu//qoE4mTnc9d2g7\nio9CIpG0ug/2B6qsrNR2CPXSPA4dErtmQ9Y/IYTMzMzwP7OioiL076qpTp06FRQUqOx7+PDh\n2NjYJUuW4KZJFVwulxxhymazvb2979+/jxO7e/fu+fn54TZfZTwe78CBA7iVBK9RrhIjKRSK\nmzdvDh06FKduHA7H09MzISFBObHLzc1VHs/Rpk2b2bNnBwYGIoTwc4QUCkWdN4QgCCqVamRk\npKGMujpvBb58LpdbX2JHoVB0/6FGcrlc94NsRgqFAn8GDOdBorh6sr6B7TrL2bgtXy5s2r48\nHo8gCBaL1Vzvcp5YtZsvydW4jj9iLU9D04EDw0b/fscJglAoFBQKpdV9sD+EQqFovdcLiV2z\nURmvimtxcV6Fm00xMzMzlWLbt2+/ffv2zz//jGv11NXW1irvFRQUdPDgQYlEIpfLU1NTZ82a\npVJeIpGsXr3a2tp648aN9vb2NBpt8eLFdR45JSWlsrLy+PHjx48fJ1fm5+dPnz6dvBxHR8f5\n8+fjZXNzc1tbW/KPmrW1NUKopKRE/cgymayystLa2prNZlMolLdv39YZQANvRZs2bfB9qG9f\nKpWqPLuKbqqoqND9IJsRn88XiURmZmbGxroxsezHJ5VKhUIhi8XSdiCNkx54/P2F6hEVFSUQ\nCH766Sc6vRmaYtV71ynLExcTg7Q/9QmfzzeoeeykUml1dTWdTm91H+wPUVlZaWlp2Uq/lEJi\n93HhX36y2gwhxOPxlAvs3r07OTl57dq1Gga0mpmZKec0ffv23b17d2pqqlgsRgipN7Dm5eWV\nlJTMnz+/Xbt2eA2Xy7WxsVE/cnx8vKen57Rp08g1Uqn0xx9/vH//fkhICF7DYDA6dOhQZ2As\nFsvd3T05OVm96/TTp0/lcnmPHj2MjY09PDzu3bs3YcIElV+Su3fv0un0Xr16vfdW4OpPlZwY\nAAAAACpaa01ja4HbE3NycvBLuVyekZFBbk1ISLhx48aqVas0T1PCZrOrqqrIlxYWFr6+vo8f\nP75//76/v79ydSAmFAoRQqampvhlRkZGSUmJej9QPH1d//79Oyjx9PTs2rVrQkJCAy8wIiIi\nIyMjLu5f/ZpramoOHjzo4eHh6emJyxQWFp46dUq5TH5+/rZt2x49etSQW4H7zxlUdRcAhka9\nuk50oyf5U18ZAIAKqLH7uGxtbTt37vznn3/a29uzWKzY2FiyiVMikRw7dqxnz55CoTA9PZ3c\nxdPTE/dLU17z8uVL5TXBwcGnT5+ura2dM2eO+knd3NyMjY1jY2PHjx+fk5Nz5swZf3//N2/e\nVFVVKffGS0pKkslkffv2Vdk9KCjojz/+UO7Yp8HAgQOfP3++Y8eO58+f9+rVi5ygmEqlzps3\nD1fRBQUFpaennzx58tWrV8HBwSYmJtnZ2VeuXHF2dv7qq68acitevnzJYrHICkgAgI4YO3as\nQqFolo5lys2s4sWqf9lwbme8fuuHnwgA/QaJ3Ue3YMGCP/74Y82aNXgeOw6Hc/fuXYRQUVFR\neXl5eXk5fkk6fPiwSkbVs2fPuLi48vJysjk1ICBgx44dxsbG/v7+6mdksVg//PDDoUOHEhMT\nO3XqNGfOnNLS0g0bNvzyyy+//fYbWSw+Pt7LywuPnFXWp0+fbdu23bx5s85nYKibM2eOn5/f\ntWvXdu/eLRKJbG1t+/fvP3LkSOUOGTNnzvT29r569eq+ffukUqmdnd3YsWOHDx+O09z33orH\njx/7+/u30u4OAOgxFouFe9ZrOxAAwD8oujxTC8AIgpgzZ46Pj89//vMfbceiBc+fP//pp5+2\nbt36gQ9w07qKigo83MRA4MET5ubmMHiiNZFKiRre+4v9v6qqKoIgmrebuWT9Kg1bGYt/bq4T\nNY1AIKDRaPhTTTFnoeYYNaLL8OAJBoPRuj/YjVRZWYlH/mk7kKaAGrtWgEKhfPPNN2vWrBk6\ndKjmp4rpH7lcfvjw4cGDB7f2rA6AVkGR97d0XyNmYsM9fKUfKZq6aE77WgD+r4mnJ6ZPnU3t\n6KHNaABQA4ld69C1a9eRI0dGRUVt2rRJZV4V/Xbs2DGpVKo8bhcA8BEZm1IcG/HtUSaTIYRU\nugV/IOJNoYatjQrvY8CzcuJJzigGM+kJaEWgKRaAFgJNsXpPH5piG6myslKhUGieB7+x1EdO\nkHRh8IRhzmMHTbGtCEx3AgAAQIfoQvYGQOsFiR0AAIAmEovFIpGo2Vt+6sztIOEDoCGgjx0A\nAIAmOnr0KH6kWLM/WBOncbhZFlI6ABoOEjsAAAA6ClI6ABoLmmIBAAAAAPQEJHYAAAAAAHoC\nEjsAAAAAAD0BiR0AAAAAgJ6AxA4AAAAAQE/AqFgAAABN5O7uLhKJWukE/QDoJUjsAAAANNHA\ngQMVCgWNRtN2IACAf0BTLAAAAACAnoDEDgAAAABAT0BiBwAAAACgJyCxAwAAAADQE5DYAQAA\nAADoCUjsAAAAAAD0BEx3AgAAoBG+3q/8ykpl64FvWjAUAIAaqLEDAAAAANATkNgBAAAAAOgJ\nSOwAAAAAAPQEJHYAAAAAAHoCEjsAAAAAAD3RcqNiz5075+Hh4eXl1ai9Hj9+XFpaOmzYMPyy\nurr6yZMnVVVVNjY2PXv2NDU1rW/HwsLCe/fujRo1isFgNGM82NOnT7OyskJCQhwdHZXX3759\nu6ioiHzJYrFcXV3VTyGXy1++fJmXlyeVSm1sbLp162Zubq5e5vnz53l5eXK5nMPh9OjRg8lk\nqpSprKy8du1at27dPDw8GhK2cnjGxsa2trY+Pj4WFhYN2Zd07dq1iooKhJClpeXQoUMbtW8T\n3Lx509jYOCAg4GOfCADQBAedAxBCXxUkazsQAMA/Wi6xi4+PNzIyamwi9eTJk/T0dJzYPXny\nZP369SwWy87OLi8vb+/evWvWrHF2dlbfSygUrl27tn///vVldU2OB9u7d29JSUlVVdXMmTOV\n1yclJaWlpXXo0AG/5HK5b9688fPz++mnn0xMTPDK7OzsTZs2lZSUODo6mpiYvH37ViqVjh49\nevz48RQKBZd5/fr1xo0bi4uLHR0dGQzGmzdvKBTK5MmTw8PDyXOlpqZu2rSpurqayWQ2MLFT\nDk8kEhUVFclksuHDh0+ePJlGozXw2l+9elVQUFBUVGRjY9MCiZ2zs/OSJUtYLFbT3ikAwMeD\nszoAgE5pucRu+/btH7K7QqHYsmVLnz595s6dS6FQBALBvHnzDh06tGLFCvXCR44cYTAYY8eO\n/RjxZGZmvnnzZsyYMVevXp02bZqR0b/uob29/dq1a8mXKSkpq1atOnv27MSJExFCb968WbZs\nmYuLy6pVq+zs7BBCcrk8Ojr66NGjCoUClyktLV22bJm9vf3OnTsdHBwQQgKB4PDhw7t372ax\nWMHBwQihe/fubd68efr06Tt37mxU8MrhyWSyK1euHDx4sLq6eu7cuQ08wuzZsxFC69atKy4u\nbtSpm8bd3f2zzz7btGnT7t276XR6C5wRANBYB50DoNIOAB2hnabYc+fO+fr6WltbJycnSyQS\nLy+vjh07kiWzs7PT09OZTGbfvn3JlSKRaPTo0b1798bVWkwm09fXNz09Xf1E5eXlcXFx8+fP\np1AoFy9etLa2Vm7Iu3//fllZ2aeffqrSFPv27dtnz57x+XwOh9O7d2+ygk1dfHx8586dIyIi\nzp079+jRI82thN26devcufOzZ8/wy6NHjzIYjBUrVpBtrzQa7fPPP+dyuX/99VdYWJiNjc3x\n48cRQitWrGCz2bgMk8mcOXNmbW1tbW0tXqNQKH799dcOHTo0NrFTZmRk9OmnnyKE9u7dGxYW\n5unpidfX1NQ8ePAAt3f36dNHw60gabh7r169Sk9PNzMz69u3b15eXl5eHj6phhP99ddffn5+\nMpksJSVl9OjRERER58+fv3btmnKFJQBAW/AUxJTr//rTd9A5gBgEuR0A2tdygyf++uuv58+f\n4+ULFy7ExcWtXr26tLQ0Nzd3wYIFd+/exZuuXr26YMGCx48fP3v2bMmSJWVlZXg9k8kcOXKk\nvb09fimVSjMyMshGT2VJSUkMBgPnW/n5+fv3/2uW9H379hUUFKjEc/369ZkzZ965cycvL+/U\nqVOzZs2qrq6u8yokEsmdO3cGDhxoYWHRvXv3+Pj49144nU5XKBQ45kePHg0YMEC9R92IESPk\ncnlycjJBEPfv3w8ODiazOtKCBQvCwsLwclBQUJ3X3gRhYWGmpqZ37tzBL7Oysv7zn//89ddf\n+fn5J06c+Pbbb8m3oD4a7t7ly5fnz5//4MGDZ8+eLVy48MaNG1evXn3viaKjo69fv75u3brM\nzEyFQsFkMvv06dOQWw0AAAAYOO08UoxKpd6/f3/Xrl1mZmYIoaqqqri4uMDAQLlcfuTIkX79\n+s2fPx8hVFRU9N1336kMULhy5UpOTs6zZ8+cnJymTp2qfvCUlBQfHx8qlYoQCg4OjouL+/vv\nv9u3b48Qev36dWlpaUhIiMou5eXlU6dOxTVJMpls6tSply9fHj9+vPrB79+/L5FIcHtoaGjo\nhg0beDwei8Wq70rLysqysrIGDRqEECouLpZKpcp1kyRbW1sLC4vCwsKysjKhUNhcSVtD0Ol0\nFxcXnOwSBPH77787OTmtWbOGTqeLxeJ58+YdPHhw0aJFGo5Q392TyWRHjhwZMGAAbudNT09f\ntmyZk5PTe09kZGSUnJy8ZcsWK6t/nlbUtWvXxMREDbeaIAiBQNCsN6b5EQRB1rkaAqlUihAS\ni8UymUzbsbQQhUIhk8kM4V1uc+8T9ZWU6wH8vjdaPpgWJpVK5XK5XC7XdiAtBF+pXC43hA82\nScf/p+DcqT5ae1Zsr169yMjatWuXmpqKEMrNzeXz+f369SPXe3t7c7lc5R2Li4tzcnKEQiGT\nySQIQv3Ib9++JY/g4+NjaWl5//59nNjdvXvX2tra29tbZZfx48dnZ2fHxMTgDy6VSq2vA1l8\nfHzv3r1x5L169WIymUlJScOHDycLcLncY8eO4eWqqqrk5GRLS8vPP/8cISQSiRBC6oNbMVNT\nUz6fLxaLNZT5SExMTHBsRUVFRUVFCxcuxL3ZjI2NBw0adOzYMblcrmF0RX13Lzc3VyAQDBgw\nABfz8fHp2LFjQ05EoVB8fHzIrA4h1K5dO4IgiouL60vsFAqFUChsrhvy8bSKIJuXRCLRdggt\nTe/fZU7Kp/Vt0vtrx+RyOf7eYjjkcrmBvLkkXb5eJpNJjrZUp7XEztLSklym0Wj4l6SyshIh\npPwfncPhqCR2X3/9NUKIx+P98ssvv/zyy6ZNm1Qur7q6mvz3T6FQAgMDk5OTIyMjEUL37t0L\nDg5Wvx2HDh26cOFC37597e3tcQZT57exioqK1NRUf39/MnWzsLBISEhQTuwkEklOTg5eZrFY\nY8eODQsLMzY2xi8RQnw+v84bUltby2KxcCttTU1NnWU+Eh6PZ2trixAqLS1FCL18+fLdu3d4\nU2FhoUQiKSsrw0M96lTf3cNvnLW1NVnS2dk5Ozu7ISficDjKp8BzstTXPo4QolKpbdq0acrF\nt6Da2lrNX7P0jFgslkqlJiYmKgOM9Bj+f9+Qbqn6ipPyqTA4UdtRfFxisZhKpRrOWC6c0hkZ\nGRnUB7u2tlZz8qRdmgPT2h/cOsNSr4Grr7qbxWKNGDFiw4YNZWVlOCmp7+BBQUGXLl16+/at\nWCwuLi4mK/NIZWVl0dHRM2bMICfveFTy1WUAACAASURBVPz4cZ0nTUxMNDU1JQiCTN1sbGxS\nU1MLCwtxCyNCqG3btnUO1EUIcTgcJpOZmZnZv39/lU3v3r2rqalxc3OzsLCwsLB4+fIlOXUf\nSSgUmpiYNPvnjM/n5+fn9+7dm1xTXFzM4/HIl8HBwXVW1+FINNw9/G4qB6wSvIYTqaQCdVbN\nqgSj+390amtrdT/IZiSTyaRSKZ1Ox19sDAFupNPvd1llzIQ6/b58hJBMJjOoLEcqlQqFQiqV\najiXjBASCAQf4x9uy9Ctb9J4xEBlZaW7uzteU1JSghcyMzM3bty4fPlyFxcXvAaPSMB96ZRZ\nWFgoV+106dLFxsbmwYMHQqHQ0dERt8kqe/PmDUEQvr6++KVIJCosLKyzgio+Pn7AgAHTp09X\nXjl16tSEhITJkye/9+poNFqfPn1u3br1xRdfKFdYIoRiY2MZDEafPn0oFErfvn1v3Ljx5s0b\nlc6FUVFRxsbGS5Ysee+JGiU6OlqhUOB+h23btkUIjRo1ys/PT6VYTExMVlbWwoUL8cuamhr8\nZmm4e7iajcvltmvXDm8tLCzECxpOVCec/6ncNACADqJch+GxAGiTbj1SzNXV1cTE5ObNm/jl\n69evMzMz8bKzszOPxztz5gyuw5NIJFevXuVwODY2NioHcXR0fPPmDfkSt8ampKQ8fPhQfdgE\n+v8WUjKDPHz4MJPJVO8YhKevCwoKUlkfGBh48+bN91YpYRMnTiQIYtWqVWSEcrn83LlzsbGx\nEydOxJnQ+PHjTUxMVq1a9fr1a1yGz+dv3br16dOngwcPbshZGkgqlZ47d+7s2bMjRozASWS7\ndu3atWt3+fJl8nLi4uJOnTqFEKLT6Xfu3MFdId+8eZORkdGlSxek8e65ubkxGAxyvO2LFy9e\nvXqFlzWcqE5FRUUUCoUcEw0A0BZiUDL+Ed3oqfJDbtJ2jAAYNN2qsWMwGBMmTDhw4EBJSQke\nJRocHJybm4sQYjKZc+fO3bx587Rp0xwcHAoKCmQyWZ3VV127dv3zzz8JgiArUYODgy9duiSX\ny+sc3enu7t6lSxc8+3Fubq6Xl1f//v0vX7588ODBr776iiwWHx/PZrNxNqMsMDAwOjo6LS2t\na9eu771AGxubdevWrV+/ftasWeSTJ+Ry+ZQpU0aOHInLWFpa/vrrr+vXr583b56dnZ2xsXFx\ncbGJicmSJUu6d++Oy+zcuRPXfslksosXL96/fx8htHDhQvVJUlQUFxf/+OOPCCGJRPLmzRuR\nSDRq1Cjl6sbvv/9+5cqVc+bMad++fWlpaVZW1oIFCxBCgwcPTkpKWrVqlZOTU3FxsYuLCx4G\nq/nujRkz5vjx42/fvrW0tFR+NzWcqE6pqakdOnRQnyYGAKAV4sVz6lxpvH5rywcDAFBGaWBV\n04dTnhD4/Pnz7u7uZPtdSkpKUVEROW/ty5cvX758aWZmFhAQUFRUlJubS26qqqp6+vQpl8u1\nsbHp3r17nf/py8vLp0+fvmDBAuWpg//8808GgzFixIg648Gz01VVVXXs2NHHx0coFN64ccPS\n0hJPa4LFxMRYW1sHBgaqn/HPP//s0KFDt27dbt++zefz3/ugLYIgXrx4kZ+fLxaLORxO9+7d\n1fvUk2UkEomdnV2PHj2UH49GPrBVWUREhOa++crPiqXRaPgxteq5IJ43mMvlWlhY9OjRgxz9\nQBDEgwcP3r5927Zt2969e5Pd4DTfvbS0tFevXllZWfXt23fnzp0VFRWrV6/WfCKVj4dAIPj6\n66+//PJL9X6HrUtFRYXyUBK9x+fzRSKRubm5QfWxEwqFGuY/0g91ZnWYISR2fD7f0PrYVVdX\nMxgMvf9gK6usrGSz2a20j13LJXYtac+ePS9evNiyZUsrfVf0xtmzZ83NzYcMGYIQEggEs2bN\nGjBgQEP6Iyo7derU9evXd+/e3doHV0Jip/cgsUMGkNtBYmcIWnVi17r/U9Zn0qRJ8+bNO3Pm\nzLhx47QdS8spLy+/d+9efVvDwsKU6/xahrm5+Y4dO+7cuYOH+pqZmX322WeNOkJOTs65c+d+\n/vnn1p7VAQAAAC1AP/9Zmpqa/vTTT3fv3pVIJC2fzWiLSCQi52FRhwcRt7AhQ4Z4eXmlpaWJ\nxeLAwMCePXs2Nj8rKCiYO3cu+UhfAAAAAGign02xAOggaIrVe9AUq/ftsAiaYg1Dq26K1a3p\nTgAAAOg4Q8jeAGi9ILEDAADQOOq5nfH6rZDwAaAL9LOPHQAAgI8Kp3GVlZUKhcKg+hgAoOOg\nxg4AAAAAQE9AYgcAAAAAoCcgsQMAAAAA0BOQ2AEAAAAA6AlI7AAAAAAA9AQkdgAAAJro0aNH\nycnJWnmwDQCgTpDYAQAAaKK0tLQnT55AYgeA7oDEDgAAAABAT0BiBwAAAACgJyCxAwAAAADQ\nE5DYAQAAAADoCUjsAAAAAAD0BCR2AAAAAAB6wkjbAQAAAGhlvt7//0vWi5A1mn7kX1sPfNPi\nAQEA/h/U2AEAAAAA6AlI7AAAAAAA9AQkdgAAAAAAegISOwAAAAAAPQGJHQAAAACAnoDEDgAA\nQNMddA446Byg7SgAAP/Qk+lOIiMjIyIixo0b16i9du/enZ6evm3bNuWVEolk9uzZMpns4MGD\n9e147NixBw8ebNq0icFgNGM8WFRU1J07d2bPnj1kyBDl9WvWrHnw4AH5ksViubq6jh8/3svL\ni1xJEERiYuL169fz8vKkUimHw+nZs+eoUaPYbHadByH17dt3yZIlmgPD+3711VejRo1SXs/j\n8SZPniyXy6Ojo2k0WmOvV7NDhw6lpqZGRUXVd6sBAAAAQNKTxO6bb75xcXFplkOdOHGivLzc\n0tKyvgIpKSnnz5/fvHmzhlSjyfHU1tY+fPjQze3/2LvvuCiutQ/gZ3eBpfdFEKSJIIqiCFIs\niEaJUYmaRI2JN7ZEE0tswRgTS5TEeE1iT1Bjj1ETS9Rrw4KiYkEUQcRGRzpL3V22zfvHyZ13\n71IERBaW3/fjH7Nnzpx5zu4Cj+fMmXG5ePGiWmJHCLG1tZ0zZw7dFgqF586d++qrr1avXt2j\nRw9CCMMw//73v69fvz5o0KC33nrLwMAgMzPz1KlTV65cWblypbOzM9vIrFmz1Fqup7+q+Hz+\nlStX1BK769ev83g8hULR+O6+3KRJkx4+fLh9+/aaMQOAxrFjdbscA6dkxmo2GAAgWpPYDR48\nuFnaycjIOHXq1JAhQ+7evVtrBYZhdu7cOXTo0E6dOr2OeK5evcrn86dPn7506dLc3Fw7OzvV\nvQYGBjSHowIDA2fMmHHixAlaePr06WvXri1YsGDQoEG0gq+v7xtvvBEeHv7jjz+uX7+eDqcZ\nGBh4e3s3Lbxu3brdu3cvOzvbwcFBNWYPD4/ExMSmtVk/Ho/30UcfLV26dOTIkc2VuwMAAGgr\nLUnsVKc+J02aNH78+OLi4gsXLkil0u7du8+dO5eOSJWUlGzatCkxMdHQ0HD48OFqjTAMs3nz\n5pEjR1paWtaV2N25cycjI2PZsmWEkPDwcENDwxUrVrB7V65cWVVVtXbtWtV4ysrKfvvtt4SE\nhMrKSoFAMGLEiFGjRtXVkYsXL/bv39/Ly8vGxuby5csTJ06sp9e6urpOTk6FhYX05d9//+3t\n7c1mdZSpqenUqVNXr16dkJDg4+NTT2sNYWFh4eLicuXKlQ8++ICWFBcXJycnT5o0iU3sFArF\nwYMHL126JBQKBQJBWFjYiBEj2Mpbtmx58OCBkZHR22+/LRKJrl+//ssvv5B63yUvL68uXboc\nOXJkwYIFrxg/ADQL+mwJTtT/XFq3yzGQGYpBOwAN08LFEzo6OsePH7eystqxY8eGDRueP39+\n8OBBuuvnn39OT0//5ptvIiIiysrKbty4oXrgmTNnhELh+++/X0/jd+7ccXJyEggEhJCBAwcm\nJCSIRCK6SyQSJSQkDBw4UO2Q9evXP336NDw8fOPGjePGjdu5c+fNmzdrbTw7O/vJkyeDBw/m\ncDghISGXL19mGKb+zubn51taWhJCiouL8/Ly/P39a9bx8fHR1dV98OABfckwjLSGl56IUiqV\n/fv3v3r1KlsSExPj6OioOrL422+/HT9+fOLEiZs3bx49evRvv/127tw5uuunn35i3//Hjx/H\nxMSw1+TV/y75+vrevXu3gUECAAC0W1oyYqfG1tZ25MiRdMPX1/fp06eEkOLi4oSEhBkzZtCJ\nyBkzZsTFxbGHCIXCvXv3Llq0iM/n19NySkoKu1ihX79+27dvv3PnTnBwMCHk5s2bNO9RO+Tz\nzz/ncDhmZmaEEHt7+1OnTt27dy8gIKBm4xcuXLC3t/fw8CCEDBky5NChQ8nJyaprIwgh7KVs\npaWlJ06cyM7OpoNnQqGQEGJjY1OzWR0dHQsLi5KSEvoyPT393XffVavz008/ubm51dNxVnBw\n8L59+54+fdqlSxdCSExMDO0+JRKJzp49+8477wwZMoQQ0rFjx+fPnx8/fjw0NLSoqCgxMfGz\nzz6j7/+CBQumTp3Kruqo/13q1q3bgQMHsrOz65oBVygUpaWlDYlfgxiGKS4u1nQULYcm4pWV\nlZWVlZqOpeW0n0/ZOn5kzUJOVGCRz6mWD6Yl0S92VVWVpgNpIbS/Uqm0nXyxKYZh2D+arZCl\npSWHw6lrr3Ymdq6uruy2kZER/buSnZ2tuovD4bi7u2dmZtKX27Zt6927t6+vb/0tC4VCNhex\nsLDw8vK6efMmzWxu3Ljh7e1dcxVCeXn5zp07U1JS2LE9tSvnKKVSGR0dPXz4cJq6CQQCT0/P\nS5cuqSZ2aWlpqgsXjI2NZ82a1a9fP0IIHfpSKpW1hs0wDJf7z+hsx44d58+fr1ZB9Zq5+tnY\n2HTt2vXKlStdunTJy8t79uxZeHj4s2fP2AjlcnnPnj3Z+l5eXufPn6+oqKDvP5s+8vl8Ly+v\nnJwc+rL+d4m+50KhsJ5LG9vEeF6bCLJ5octaSXCvzutJrONHFvY+2ZLBaER7+JTVtLcut93+\namdip7ZelX48NGMwNDRky42MjOhGXFxcQkLCli1bXtpyVVUVexQhpH///rt27ZJKpQqF4v79\n+5999plafalUunr1aisrq3Xr1tnZ2fF4vMWLF9fa8r1790pKSn7//ffff/+dLczIyJgxYwbb\nHXt7+4ULF9JtExMTGxsbNme3srIihOTl5dVsWS6Xl5SU0AqEED6fTwcFmyw4OPjw4cPTpk27\nevWqu7t7hw4d2MSOvsnLly9nA6O5ZmlpqVgsJirvOSHEzMyMJnYvfZeMjY1Jvf9F5vF41tbW\nr9KpFlBcXMx+Cu1BZWWlRCIxMTGpfxRcm8hkMrFYbGpqqulANKz1/zC+isrKSh0dHX19fU0H\n0kJkMllZWZmenl67+mKXlJRYWFjUMyrWmmlnYlcr+nPIDggRQsrLy+nG9evXq6qqpkyZQl8y\nDMMwzOjRo6dNm6a20MHIyEg1vQgKCoqMjLx//351dTUhpOYEa3p6el5e3sKFC9khMaFQWOtv\nvYsXL3p6en788cdsiUwm++qrr27evMlet6enp1fXhKmpqamrq2tsbOzo0aPVdsXHxysUij59\n+tR6YBP0799/x44dycnJMTExw4YNU91F8+aFCxeqrWDt0KEDXeRB0zuqoqKCbrz0XaJjrqpJ\nIQBoitqaiVorYBUFgKa0o8TO3t6eEJKamurp6UkIUSgUjx49ov8F+fDDD1Xzoejo6IsXL65a\ntYquS1BlYWGhei2XmZlZz5494+LiqqqqfH19VYcDKZrHGBgY0JePHj3Ky8urmZzR29dNmzZN\nbVevXr0uXbpUc0FGrcLCwtavX3/u3DnVG+BVVFTs2rXLw8OD9rpZmJmZ9erV6+zZs5mZmWrX\nFLq4uOjq6paVlbEpWllZGZfL1dXV7dixIyEkPT2dzoZLpdKkpCR6Ud1L3yV6BSE7CQ4ArYTk\ngh+7rf/GHQ1GAgBUO0rs6MVhf/75p52dnamp6cmTJ9kpTisrK9U5MgsLCx6PV+td0zw9PZOT\nk1VLBgwYcOjQoaqqqrlz59as7+LiwufzT548+f7776emph4+fNjX1zcnJ6e0tFT1aryrV6/K\n5fKgoCC1w/v3779p0ybVC/vqMXjw4KSkpK1btyYlJfXt25e9QTGXy12wYAE7pCwWi+Pj49WO\n5XA4vXv3fukpWMHBwevXr+/Zs6daYIaGhqGhoQcOHDA1NXV3d8/Pz9+xY4dAIPj6669tbW07\nd+588OBBOzs7MzOzffv2sQP7L32XkpOTTU1NG34hIAC8PnQ0rnqx+m88muTxf9iogZgA4L/a\nUWJHCFm0aNGmTZsiIiLofewEAsH169cb1YKfn9+5c+eKiorYicLAwMCtW7fy+fxaF16YmprO\nmzdv9+7dly9fdnd3nzt3bkFBwb///e9vv/32p59+YqtdvHixe/fudPhKVUBAwObNm6Ojo9Ue\n9lCXuXPnent7nz9/PjIyUiKR2NjYDBo0aPTo0arXRuTl5anee4/icrnHjx9vyCnYwHR1dQcM\nGFBz17Rp04yMjHbt2lVSUmJubh4QEPDRRx/RXfT9X7p0qaWl5XvvvWdqakoXLL/0XYqLi/P1\n9W2jlzsAAAC0GE7bXfehEQzDzJ07t0ePHp988ommY2l7qqur5XI5e6ncN998Y2xsXNdqElZS\nUtLSpUs3btzY1p88gcUTWq/9LJ6oOVynSrsH7bB4oj1o04sntPAGxa8Vh8OZNm1aVFRUVlaW\npmNpe1atWhUeHp6cnJyTk/P3338/ePCA3u6uHgqFYs+ePcOGDWvrWR0AAEALaF9Tsc2iV69e\no0ePXrt27Y8//qh2X5U2LTk5+dtvv61r7/bt201MTF7xFF988cW2bdu+++676upqOzu7+fPn\nv/TGgfv375fJZKqLhQEAAKAumIqFf0il0noe3iAQCNrooHTrgalYrYepWApTsdoEU7FtDkbs\n4B96enq1PpEMAEAN/4eNdeV22p3VAbR+uMYOAAAardYEDlkdgMYhsQMAgKZQS+OQ1QG0BpiK\nBQCAJtpg5SASiZYuXaqrq6vpWACAEIzYAQAAAGgNJHYAAAAAWgKJHQAAAICWQGIHAAAAoCWw\neAIAAJpo+PDhcrmcx+NpOhAA+AcSOwAAaCJ7e3ulUtlGb9APoJUwFQsAAACgJZDYAQAAAGgJ\nJHYAAAAAWgKJHQAAAICWQGIHAAAAoCWQ2AEAAABoCSR2AADQRDt27Ni8ebNcLtd0IADwD9zH\nDgAAGmHqbyovrMKJFZmx9/8Ldk5r8YAAQAVG7AAAAAC0BBI7AAAAAC2BxA4AAABASyCxAwAA\nANASSOwAAAAAtAQSOwAAaKJdjoG7HAM1HQUA/D/c7qS1O3ToUHx8PN1+/vy5gYFBx44d6cux\nY8f6+/vXelR6enpeXl5AQED9jX/wwQdhYWHjx49XK9+/f/+xY8eOHDnSqFAjIyMTExM3b95M\nCJk1a1ZoaGhYWFijWqhLA7sDAADQziGxa+3Gjx/PJl6ffPJJt27d5s2b99KjYmJiiouLNZgJ\nbdmypRlb03h3AKAmdqxul2PglMxYzQYDABQSu7attLT0zp07paWlFhYWffv2NTU1JYRcvHjx\n5s2bOjo6f/zxx5tvvmlhYfHixYsHDx5UVlYKBAJ/f399ff2Gn+Lo0aM9e/a0srKKjY2VSqXd\nu3fv0qULu/fJkyeJiYmGhoZBQUFqR3l4eHTv3p0QcuTIEW9vb7lcfu/evXfeeUdPT6+iouLW\nrVulpaXW1tYBAQGq8QiFwtu3b1dVVbm6uvbq1avW7rzimwYArw4zsACtExK7NiwxMXHVqlU2\nNjaurq7R0dE7duz49ttv3d3dRSJRRUWFsbFxeXm5UqmMioravHlzjx49zM3NL168uGfPnp9/\n/tnMzKyBZ/n7779zc3NTU1N79OghFAp37doVHh7er18/QsjZs2e3bt3avXt3c3PzEydO2Nvb\ns0cdOXIkLCyMJnbHjh0rKCi4deuWk5OTUql8/PjxihUrzM3N3dzczp8/v3///u+//14gEBBC\nkpOTV65caW1tbWtre/DgQR8fn8WLF6t15zW8kQDQCPTZErui/qdwl2MgMxSDdgCah8SurWIY\nZsOGDZ6ensuWLePxeAqFYsmSJVu3bl2/fv2oUaOuXLni4OAwY8YMQkhRUdH06dNHjRpFCJHL\n5dOnTz99+vT777/fwBNxudybN2/++uuvRkZGhJDS0tJz587169dPoVDs3bs3ODh44cKFhJDs\n7Ow5c+ao5nYsHR2d2NjY9evXW1pa0rA7deoUERGhq6tbXV29YMECmiwSQjZt2uTj4xMeHs7h\ncJ49e7ZgwYKbN2+qdaeud6O6urrx72JLk0gkmg6h5SgUCkKITCZjGEbTsbQQhUKhVCrbw6ds\nEBNSs5ATFSgecLnlg2lhCoWi/XylyX9/kNvJF5vFMIxEIuFwOJoOpHb1T7shsWur0tPTCwoK\npk6dyuPxCCE8Hi8kJOSXX34RCoVqk5Xvv//+kydPTpw4UVVVRQjhcrm5ubmNOlffvn1pVkcI\ncXBwuH//PiEkLS2tsrIyODiYLffy8hIKhTUP53A4PXr0sLS0JIRkZ2dnZ2d/8cUXurq6hBA+\nnz906ND9+/crFIoXL17k5OR8/PHH9GfJzc1t06ZNVlZWDYlQqVRWVlY2qlMa0SaCbF7t6o8B\n1Q4/ZVb76Xub+J9kM5LL5e3nw6XoX8zWic/n15N0IrFrqwoLCwkhNjY2bAmdzSwuLlZL7Hbv\n3v33338HBQXZ2dnRLJD+D6zhzM3N2W0ejyeTyQghJSUlhBCarrEB1JrYsbERQgoKCgghycnJ\n+fn5tCQrK0sqlRYWFtJdqpmck5NTAyPkcrkGBgYN7pBmiMXi1h9kM5JKpQqFQk9Pj37r2gOl\nUimTyfh8vqYDeb2Mb7xR1y7BvVGVQRdaMpiWJ5VKuVyujk57+eupUCikUimPx9PT09N0LC1H\nIpE06mL0Flb/UGJ7+WpqK9VPt9bZgcLCwmPHjs2cOXP48OG0JC4u7lXOUs/p6skX1X4J5ubm\nlpeXsy8HDBjA4/FogzRrbEKE7JhiqyWRSFp/kM2IYRiFQsHn87U+0WHReWft/pQ5US9ZM6Hd\n3SeEMAyjo6PTmv/qNy+ZTEYTO63/ZFVVV1cbGhq22qnY+iGxa6s6dOhACCkoKOjcuTMtoSNe\n7NgYlZOTwzBMz5496UuJRJKVlWVra/vqAdBxwZKSEldXV1qSl5fXwLDHjBnj7e2ttovO2eXn\n57u5udGShIQEKysrBweHV48WAFoGJwqrKAA0CU+eaKscHR1tbW3Pnz/PDnRdvHixa9eudLkr\nj8ejl4DQG6CwKdeePXsMDQ2lUumrB+Ds7Kyvrx8dHU1fPnv2LCUl5aVHOTg4ODg4nD59mh3w\nO3fu3MGDB+kuW1vbM2fO0JG/rKysZcuWpaenq3YHADSLGRpL/0ku+Kn9Y3dpOkaAdg0jdm0V\nh8P5/PPPV65cOX/+fGdn58ePH1dWVq5evZrudXFxiYqKWr169ZgxY7p167Z+/fqAgIC0tLTu\n3bsPGjTo9OnTu3btmjJlyqsEoKenN3HixJ07d+bl5ZmZmWVlZQ0YMCAtLe2lB37++ecrVqyY\nO3du586dCwoKHj9+vGjRItqjOXPmrFq1atasWfb29omJif7+/vS+KqrdobdQAQANql48t9ZC\n/g8bWz4YAFDFW7FihaZjgEbw8PBgpyZtbGyGDh1qYGCgp6fn5+f36aefsvOwXl5e5ubmdnZ2\nHh4eb775ppWVla6u7uDBg0NDQz08PExMTDp27Ojk5MThcLp166a6AoPVoUMHLy8vut21a1c6\nhUrRZmm5t7c3n893dnaeOnWqjY2NtbU13aXWsmoL1tbWoaGhxsbGurq6Xbt2nTFjhru7O3vS\nIUOGGBsbW1hYjBo1aty4cfQSB9XuGBoaNveb2kLEYnHbDb4JpFKpXC7n8/nt5zJzpVJJu6zp\nQF6vWrM6Smfo8JaMRCPa2+IJpVJZXV3N4/G0/outiq51a6PX2HHa1f14ADSouLi4gXdv0Q6V\nlZUSicTExKT9/D2QyWRisZhe/6DF6knsCCFaP2hXWVnZ3hZPlJWV6enpaf0XW1VJSYmFhUUb\nTexwjR0AAACAlkBiBwAAAKAlkNgBAEDz0Pp5WIDWD4kdAAA0ArI3gNYMiR0AADROrbkdEj6A\n1qC9LNgGAIBmRNO4kpISpVJpZWXVRtcPAmgfjNgBAAAAaAkkdgAAAABaAokdAAAAgJZAYgcA\nAACgJZDYAQBAE+3bt2/Hjh1yuVzTgQDAP5DYAQBAE1VXV0skEk1HAQD/D4kdAAAAgJZAYgcA\nAACgJZDYAQAAAGgJJHYAAAAAWgKJHQAAAICWwLNiAQCgiaytrSUSCR4UC9B6ILEDAIAmGj16\ntFKp5PF4mg4EAP6BxA4AAJrT1N/q3LVzWgvGAdAu4Ro7AAAAAC2BxA4AAABASyCxAwAAANAS\nuMYOAACawbN8cjz+JXXWnSGEkGFepGenFogIoD1CYgcAAM2gQkKSX7ykDq3g59oC4QC0U0js\nAACgiVJSUmQyWf/+/Xk8nocdWfY2IYR8+/f/V9jlGDglM5Z9SStYG7dwmADtCBI7bXD48OH7\n9+/Xumv06NF9+/Z9lcZXr17dr1+/kJAQtfL9+/cnJyfXrO/l5TVx4kRCiEQiOXDgQGZmZmho\naO/evdntwMDAV4kHAFqPa9euiUSioKAgHo9nqEecrf9n7y7HQPK/uZ1aBQBodkjstIG9vb1U\nKqXbp0+ftra2ZpM5CwuLuo6Ki4tLT09/991362/80aNHXbp0qVmekZGRnZ09bNgwtfJOnf65\ndiYqKurEiRPvv/++tbW16nYDO9WEUAEAANo5JHbaoF+/fv369aPbV69edXV1/fDDD196VHx8\nvEgkepXzWlhY1HOi3NxcOzu7dtWGjgAAIABJREFU8ePHE0IuX77MbjfBq4cKAC2MDtex26oT\nsgDw+iCx035xcXFXr14tLS21tLQMDg7u3bs3IWT79u1Xrlzh8XhfffXVnDlzOnTocPHixfv3\n71dVVQkEgtDQUDc3t1c5aWRkZGxsbFVV1VdffZWRkaGrq0u3Q0NDg4ODExISLl++LBQKBQLB\nm2++qXquuLi46OhokUjk4uIyevRoExMTtVDt7Oxe9R0BgNeJPl5iV1QthQDwuuE+dlru5MmT\nq1at0tfXp1c3L1++/OLFi4SQwMBAExOTTp06jRgxwszMbMeOHb/99pujo2NgYKBcLv/iiy9S\nU1Nf5bxBQUHOzs4mJiYjRoyYNGkSu+3m5hYVFfXNN9/QOmKxODw8/OHDh/Sos2fPrlq1ysjI\nqGfPnnFxceHh4SKRSC3UV39PAOB140SpX0pbswQAXgeM2GkzqVT6+++/Dx8+fObMmYSQYcOG\nVVdX79u3b/DgwV5eXsbGxgKBgM7h9urVq1+/ft27dyeEhIaGPnnyJDo62tX1JfckePHixaJF\ni9QK582b5+Dg0KNHjxs3bhQVFdH2MzIy6LZcLg8PDx88ePC8efPouVasWLFv3741a9bI5fJ9\n+/a99957dHo3JCTks88+i4+P79+/v2qotVIqlWVlZa/0Zr1+DMMIhUJNR9FylEolIaSqqqr9\nTKMzDNPePmWqrKyMx+PFVz2ZnvpDPdVcro5lt6O7bTTlGb3+0JqfUqmUSqVisVjTgbQQhmEI\nITKZrF19sRmGKS0t1XQUdTI3N+dwOHXtRWKnzVJTU0Uikb+/P1vi6+t79erV/Px8W1tb1Zo9\ne/b8z3/+c/z4cZFIxDBMcXFxcXHxS9s3NDQMCAhQKzQyqu+XdVpaWkVFxYABA9iSwMDArVu3\nVldXZ2ZmVlRU0JliQoiZmdnvv//+0hgohmEUCkUDK2tQmwiyedH0rl1ph58y7XKVXJxenVtP\nNdW9MrlcQdrqG0VznXalrfyObUZtt79I7LQZHcQyNzdnS0xNTQkh5eXlqokdwzARERHZ2dnj\nxo3r2LEjl8vdtm1bQ9o3Nzdv7EpVGtKhQ4dOnjxJS8rLyxmGKSoqortMTEwa1SDF4/HqWf/b\nSpSWlqp+FlpPJBJVV1cbGRnp6elpOpYWIpfLJRKJsXE7ukvb4MGD5XK5hYUFj8cbYub/xPqQ\ne+xL1kg9CTxECHHSt+Ny2uS1QCKRiMfj8fl8TQfSQuRyeUVFha6ubrv6YpeWlpqZmdUzKqZZ\n9QeGxE6b6erqEkLYO6EQQqqrq9lyVmZmZkJCwrJly3x9fWnJ6/s201P36NFD9b4nQ4cONTU1\nLSwsJIQ0edqOx+M1S4SvVZsIsrnQbxGXy20/vVYqlRwOp/30lxDi6uqqVCp1dXU5HI4Rz6CL\nruNLD+li/PI6rRmHw2lv32pCSHv7YtP+ttrErn5I7LSZg4MDISQzM9PDw4OWZGZm8ng8tYWl\nJSUlhBC2sLCwMDMzk70dXfOizbq6uta8YI5Gm56e3rVrV1py4MCBbt269erV63VEAgDNTm2F\nhOSCH93Qf+OOah1mKG59AvC6tMmRcGggGxubHj16HD9+nM5y5uXlnTt3rn///vr6+oQQPp9P\nL6Tr2LEjh8NJSEgghFRUVGzZssXZ2bm8vPyl7TMMI61BJpPVc4ilpaWPj8+hQ4fodbjV1dXr\n1q1bt24dIcTa2rpHjx7Hjh0rKCgghFy+fPngwYN0voMNFQBaM2ZoLP0nueDHZnWEEPqS3avB\nCAG0HkbstNz8+fO///77yZMnW1lZFRYW9urVa8aMGXSXn5/fzp07J0yYMHfu3LfffjsyMvLE\niRNVVVUzZ84UCoXbtm0LDw9fu3ZtPY3X+jQILpd7/Pjxeo76/PPP165dO2XKFGtr69LSUjs7\nu/DwcLpr3rx5ERER06dP19fXVyqV06ZN8/T0VAs1KCio6W8HALx+1Yvn1lXO/2FjCwcD0N5w\n2uHqHgCNKC4utrKy0nQULaeyslIikZiYmLSfy8xlMplYLKZLlNqJkpISpVJpZWWlejVSXYkd\nIUQLErvKykodHR0679EeyGSysrIyPT299vbFtrCwwDV2AADQfinuxMr/83f9dapXfEkI0Rk5\nmuerfqckAGgWSOwAAKA5yOVE/LJV7bSCXN4C4QC0T0jsAACgiQ4fPiwSiebMmaOrq8sLHMAL\nHEC0fSoWoJVDYgcAAE1UXl7efh4ZB9Am4HYnAADQnOoalsNwHUALQGIHAADNjP/DRrU0Dlkd\nQMvAVCwAALwWSOYAWh5G7AAAAAC0BBI7AAAAAC2BqVgAAGgiU1NTHR38HQFoRfADCQAATTRu\n3DilUoncDqD1wFQsAAAAgJZAYgcAAACgJZDYAQAAAGgJJHYAAAAAWgKJHQAAAICWQGIHAAAA\noCWQ2AEAQBOlpKQ8fPhQqVRqOhAA+AcSOwAAaKJr165dvnwZiR1A64HEDgAAAEBL4HbhAADQ\nCFN/U3lhFU6syIy9/7zaOU0TAQGACozYAQAAAGgJJHYAAAAAWgJTsQAA8A+GIRnFTT88vai+\nvXo6pKN50xsHgIZAYgcAAP+QKci3fzf98PqP7WRJVo5peuMA0BBI7AAA4B9cDunW8SV1kl/U\nuav+YwWmTQkJABoFiR0AAPxDh0cWDX9JHXZV7C7HQELIlMxYdtdLjwWA1w2LJ0hERETY/xo3\nbtzhw4c1FU9ZWVl2djbd/uCDDw4dOtRcLdOe7tq1S608JyeHdlyhUDSqwcjIyNmzZ9PtBoaq\n2rumnQgAAADqghE7QghxcXHZsGED3RYKhSdPnty/f7+ZmVloaGjLBxMVFZWdnT1v3jxCyLp1\n64yNjZuxcTMzs5iYmMmTJ3M4HLbw6tWrpqam5eXlr9JyA0NV7R0AtF10uI5uqA7aAYBmYcRO\nnYWFxb/+9S8nJ6dr167RkkePHgmFQpFIlJSUxI5p5eXlpaSk5Ofnqx778OHDkpIShmHS0tKe\nPn0qk8nUGq/1KNX2nz59mpCQUFpampiYKJFISktLxWJxA1sghGRlZT1//ry6urqu3nXv3l0o\nFCYnJ6sWXrt2rXv37mo18/PzU1JSiorUF7lJpdInT57UHHVTC5VhmJycnMePH5eUlLCFqamp\nqr1r2okAoLVhkzwA0DiM2NXO2tq6tLSUbq9evXrkyJHnzp0rLy/ft2+fSCT6/vvvnz9/bmlp\nWVJS4uHhsWTJEgsLC1pz6NCh9+7d09fXz8/P53A4y5Yt69y5MyGksLCwnqPY9n19fR89eqSr\nqxsZGblkyZLVq1eHhYWNHz++/hbWrFnz1ltv3b9/XyqVlpeXSySSlStXurq61uwXn8/38vK6\ncuUKm8mlpqZmZ2ePHDkyNvaf/3MLhcK1a9cmJydbWFgIhcLAwMAFCxbo6ekRQpKTkyMiIqqr\nqw0MDDp16mRra8u2rBrq48eP165dW1ZWZmRkVFZW5u/vHx4ezuPxTp48qdo7Q0PDJpwIADRr\n5zTCiVLP5PDMCYBWAoldLaRSaWpqao8ePehLHR2dqKio+fPne3t7E0JWr14tFot37dplaWlZ\nWFi4ZMmSrVu3Ll26lBDC5XLPnDmzbt06JycnqVS6ZMmSX375Zd26dYSQn376qa6j1NpftGiR\ng4NDzcnKelrgcrnHjx9ftWqVm5ubQqGYP3/+n3/+uXjx4ppdYxhmwIABe/bsmTFjBo/HI4TE\nxMT07NnT1PT/l6tt2LChoKAgMjLS1tY2KytryZIlBw4cmDx5MiFk48aNdnZ2q1ev1tfXj4mJ\nWb9+vZ2dXc2zHD9+3M3NbdGiRbq6unl5efPmzTt79uyIESM+//zzrKwstncrVqx4xROp9ksu\nlzfo09WomoO4Wow+GF6hULSfXsvlcoZh2kp/S+WVdytSmnDg8IQFNQs5UYFnvH9qbFO9jLtY\n6Zo1IQYNUiqV7e1bTQhpQ1/s5iKTyVSvWWpVdHV169mLxI4QQsRicUJCAt0WCoXnzp2rrKwc\nM+afGy5xuVxHR0eadeXn5z98+HD27NmWlpaEEIFAMHz48L1794rFYgMDA0JIr169nJycCCF6\nenpvvPHGL7/8Ul5eLhaL6zlKtf26NOS8bm5uhBAej9etWze1yVZV/fr1i4yMjI+P9/PzI4TE\nxMTQYTaquLg4Pj5+5syZdJCsU6dOb7755tmzZydPnpydnf3ixYuFCxfq6+sTQgYMGHDs2DGp\nVFrzFIsXL5bJZFlZWSKRiGEYKyur58+fq9VplhOxlEplWVlZPRVaiTYRZPMSiUSaDqGltZVP\n+Vblw1FPv2zGBmtN+Op32O3bEJPezRhDi1G7SEbryWSytvLFbi6veN35a2VlZVVP0onEjhBC\n8vPzIyIi6LaJiYmLi8uPP/7o7OzMVnBwcKAbOTk5hBCaulH29vYMw+Tm5tKpz06dOrG7OnTo\nQAgpLCykPw/1HMW2X5eXnld1slJPT4+9gq0mIyOjPn36XLlyxc/PLyUlpaSkJDAwkM1rs7Ky\nCCE8Hu/x48e0hM/nl5eXFxcX5+XlEUJUR84cHBxSU1NrnuL69eubN2/W09OztbXl8XjFxcU1\nL/trlhOxOBwOn8+vp0JrIJVK6URzOyGXyxUKha6uLpfbXq7lpWM59f9nuvWwVVqPsRrY2KOO\nFV+tZ29jG7Q3ELT+n1w1crmcw+HQGY/2QKlUymQyLpfbVr7YzaJN/7pGYkcIIc7Ozuyq2FrR\noSPy36k01d9E9LNn5wFVf9rpNjtoX89RbPt1adR5Xyo4OHjDhg0SiSQmJsbHx0d1NSttcN++\nfap/jM3NzSUSCd2lGkOtP+eVlZUbN24MCQmZMWMG/S9FeHh4zWqvfiJVXC7XxMSk/joaV1xc\n3PqDbEaVlZUKhUJfX7/N/eVuMplMJhaL28qn7GvS/ajND409qubVdaqO+jS6wTansrJSR0fn\npb+0tQYdq9PR0WkrX+xmUVJSYmxs3GqnYuuHxK5xzMzMCCFCoZAdz6NrLMzNzVVfUhUVFYQQ\nY2NjerFRPUe9+nkbxc/Pj8vl3rt379q1a9OnT1fdRRtcunSpp6en2lF0favq6LTqildWWlqa\nWCwePnw4/ZGgw4oCgUCt2qufCABaWP1ZHa3ADMWtTwA0qb1MkTQXV1dXQ0PD27dvsyW3bt2y\nsbGxsbGhL+/du8feEiUxMdHY2NjW1valR6nicrk0EWzUeRtFT08vICDg2LFjYrGYXmnHcnFx\nMTExuXPnDlvy9OlTel5nZ2cul8tO2lZUVCQmJtZsXEdHhxDCzr1evHhRJBKxPWJ79+onAoAW\nxgyNVfsnueCn+g9ZHYDGYcSucfT09CZNmrRt2zYul+vq6pqYmHjz5k3V9afm5uYrVqwYMGDA\nixcvzp49+/7773O53Jcepcra2johIeHvv//28fFp+Hkba9CgQcuWLRs4cKDahAKPx5s8efKW\nLVuqqqrc3d0LCgr+/vvvkSNH9u3b18TEZNCgQUeOHFEoFGZmZpcuXercuXNlZaVay507d7ay\nstqxY8eIESPS0tLS0tIGDRoUHx8fGxsbGBio2rtXPBEAaFb14rm1lvB/2KiJcACAEEJ4K1as\n0HQMGpaZmWloaOjv719XhcePH3fp0oXejo4Q4u7u3qVLlydPnqSkpBgbG8+YMYPNwI4ePTpk\nyBBfX9/Y2FihUDhq1KgxY8bQGcl6jlJr39nZ+cWLF0VFRZ6ennl5eZ6eni4uLo1qIS8vj2GY\noKCgmj21sLCgd7CzsbF59uzZiBEj6AqP8vLy/Pz8IUOGcDiczp07e3p6Pnv2LCUlRS6Xjx07\nduTIkbQFHx8fHR2dp0+flpeXv/fee7a2tjKZLDAwkBDy6NEjGiqPx/Pz88vJyUlJSenQocPH\nH3/s7OyclZVVWVnZp08f1d75+vo24URtl1gsNjQ01HQULUcqlcrlcj6fTwdx2wOlUkm7rOlA\nWkLNrI6lM1SbHxkrlUq5XG67+lZXV1fzeLx28sWm6B0n2ug1dhyGYTQdg/b44IMP2Jv0Aqgp\nLi62srLSdBQtp7KyUiKRmJiYtJ+/B3TxhOpdIVszpqiQedH0J7vIfld/6rQq3Q+mvLwJI2Nu\n5y5NDkBT2ufiCT09vbbyxW4WJSUlFhYWbTSxay//5wAAAFXKlIfyk0dfU+P1p30U19mV+yke\nGw3QzJDYNSdPT8+mrWYAAGhhHNuOPH/1CzYaTnHrRj17G9Iyx8q6yWcHgLogsWtOX3/9taZD\nAABoEK6bO9fNvcmH15PYYfEEgAbhdicAANBoyN4AWickdgAA0BS15nZI+AA0C1OxAADQRFWL\nVyiVyvofSQ4ALQkjdgAAAABaAokdAAAAgJbAVCwAADRRamqqXC63sLDg8XiajgUACEFiBwAA\nTXbp0iWRSNSnTx8kdgCtBKZiAQAAALQEEjsAAAAALYHEDgAAAEBLILEDAAAA0BJI7AAAAAC0\nBBI7AAAAAC2B250AtBAjIyNNh9Ci+Hy+jo6Ojk47+iXD4/H09fU1HUWLeuONN6RSabu61wmf\nz+dy29GYCI/HMzY2blddJm381zWHYRhNxwAAAAAAzaB95eAAAAAAWgyJHQAAAICWQGIHAAAA\noCWQ2AEAAABoCSR2AAAAAFoCiR0AAACAlkBiBwAAAKAl2tG9QwHgtZJIJLdv3y4qKrK2tvb1\n9TU0NKxZJyYmJjs7W7XEzc3Nz8+vpWKEJhKLxbdu3SoqKurQoYO/v7+ent6rVIPWqaCgICEh\noaqqysHBoU+fPhwOp2adI0eOSKVS1ZKgoCAnJ6eWihFeDjcoBoBmkJGRsWzZMqVS6ejomJGR\nweVyIyIiOnXqpFZt+fLl6enp9vb2bEnfvn1Hjx7dssFC4xQUFHz55ZcKhcLFxeX58+fm5ubf\nf/+9sbFx06pB6xQdHb1582aBQGBubv7kyRMXF5fvvvuuZmo+duxYOzs7MzMztmTChAk9e/Zs\n2WChPhixA4BmsGHDBoFAEBERwefzRSLRvHnz9u7du3TpUrVqIpEoKChoxowZGgkSmmbbtm2m\npqZr1qzR19evqKiYP3/+77//XvNDbGA1aIXKy8s3b94cGho6ffp0DoeTmpq6cOHCs2fPhoWF\nqVaTy+VyufyDDz4ICgrSVKjwUrjGDgBelUwmc3BwmDhxIp/PJ4QYGhr6+fmlpaXVrCkWiw0M\nDFo8QGg6kUh09+7dsLAw+hhcExOTYcOGXb16VW22p4HVoHUSCoV+fn5jxoyh06+urq6Ojo41\nf4RFIhEhpL09ELnNwYgdALwqXV3dBQsWqJZUVFTUOgcnFovxV6FtSU9PVygUbm5ubImbm1tF\nRUVBQUGHDh0aWw1aJycnp8WLF6uW1PojLBaLCSH4v1krh8QOAJpZWlrajRs3pkyZUnOXWCzm\n8/lxcXFZWVnm5ua9e/c2Nzdv+Qih4YRCISFE9WOi28XFxaoZWwOrQZtw9uzZ4uLiIUOGqJXT\nxI4QEh0dXVJSYmdn5+vrq6ur2+IBQn2Q2AFAc8rLy4uIiPDy8nrrrbdq7hWLxb///ruVlZVA\nIEhPT4+MjAwPD/fx8Wn5OKGB6BJI1T/edFttaWQDq0HrFxcXt2PHjg8//NDZ2VltF52KXbly\npb29vb6+/pMnT6ytrVetWmVtba2BQKEOSOwAoNGys7PPnDlDt93d3YODg+l2RkbG8uXLnZ2d\nlyxZUvNeCQzDTJkyhd4IgxBSXV29evXq9evX79q1i8fjtWT80HB0XaRMJmMn4GiuprZesoHV\noJW7evXqzz///O6777733ns19woEgunTp3fr1o3OuRcUFCxatGj79u1Llixp8UihTlg8AQCN\nJpFIMv+ruLiYFqampn755Zc9e/b85ptv6CoKNRwOJywsjGZ1hBA+nz9q1KjS0tIXL160XOjQ\nSFZWVuS/M60U3VYbpGlgNWjNLly48NNPP3388ccffPBBrRUEAkFYWBh7JaWNjU1wcHBycnIL\nxggvhxE7AGg0Nze3VatWqZYUFRWtXLkyKCho9uzZtd7XlBAilUpzc3Pt7e11dHTYEkIIFk62\nZs7Ozjo6Oo8fP2ZvQpuSkmJhYSEQCJpQDVqt+Pj4LVu2zJ07d/DgwXXVqaysFAqFqvenlEql\n+PltbTBiBwDNYNeuXR06dJg1a1bNrC4nJ+fevXuEkKKiojlz5hw5coSWSySSEydOCASCmvcx\nhtZDX18/KCjoxIkTEomEECIUCs+fPz9kyBD6QbMfbv3VoJWTyWRbt259++23a83qHj9+/OzZ\nM0LIjRs3Zs+e/fDhQ1qel5cXExODa2RbGzx5AgBeVX5+/ieffGJra0vn41hLliwxMTHZs2fP\nsWPHjh8/Tgg5dOjQgQMHOnXqZGlpmZaWpqOjEx4e7unpqaHAoUGEQuHixYurq6tdXFyePHnS\nsWPH1atX09vWqH649VSDVu7ixYsbNmxwc3NT/bwEAsH8+fMJIYsWLTIwMFi1apVcLv/uu+/u\n3r3btWtXHo/35MkTZ2fnr7/+GmvbWxUkdgDwqoRC4dmzZ2uWjxkzRl9fPyEh4dGjRxMmTKCF\nOTk5Dx48EIvFtra2Pj4++MPfJkgkktjY2OLi4o4dO/r7+7OLXdQ+3LqqQSv3+PHj+Ph4tUJT\nU9MRI0YQQs6fP6+jo8MO5j18+DA1NZUQ0qlTJ29vbwzKtjZI7AAAAAC0BK6xAwAAANASSOwA\nAAAAtAQSOwAAAAAtgcQOAAAAQEsgsQMAAADQEkjsAABatb/++mvFihWVlZWaDqRBkpKSVqxY\n8eDBA00H0jwOHjz43Xff0UekALQJuN0JAEDzePDgwdGjR+upEB4ebmho2NhmJ0yYcOjQodzc\nXFtb21eIriWUl5f36dPHysoqJiZGV1eXFqampl64cKGoqMjOzu6tt97q0KEDW7+ud2zw4MED\nBw6k29euXbt+/bqxsfGYMWM6duyoVvPSpUuxsbHh4eHs6erHMExCQsKdO3eKiorMzMwcHR0H\nDx6s+qEUFRVt3rzZzc3tww8/JIQkJib27dv3448/3rhxY2PeCQDNYQAAoDns27ev/t+3hYWF\nL21EKpXy+fwzZ86wJU+fPo2NjaUP5Xwdap6xySZMmGBkZJSWlsaWLFq0iMvlEkLoA4INDQ0P\nHDjA7v3ll19qfaNWrVpFK6xbt87AwGD+/PlhYWHm5ubPnz9XPV1hYaG1tfWSJUsaGF50dHSP\nHj3UzmVmZvbNN98oFApa59GjR4SQ0NBQ9qhNmzYRQo4fP96ktwSgpSGxAwBoHjSxW7x4sbAO\nSqXypY3ExcURQpolzWqg5jrj7du3CSFffPEFW/Lrr78SQgIDA5OTk5VK5d27d7t06aKjo/Pw\n4UNa4fvvvyeEXLx4Ufy/5HI5wzBisdjS0nLt2rUMwyiVSi8vr1mzZqmeceLEie7u7mKxuCHh\nHT16VEdHh8/nh4eH3717t6io6NmzZ7t37+7atSshZNy4cbRazcROJpO5uLh4eHiwyR9Aa6bT\n9LE+AACoQV9f/6WPzhSLxWfPnk1PT1cqlS4uLsOHDzcwMCCEHDhw4MiRI4SQ/fv337x5c+rU\nqY6Ojn/99VdSUtKiRYuMjY2PHDmSlJS0fPnyjIyMU6dOVVVV9erVa+jQoRwOJycn59SpUxUV\nFd7e3kOHDlU7Izv/aGtrGxQU5O7uTstrPSPdlZ2dff78+by8PDMzs6CgoN69e9ffqRUrVujr\n6y9cuJAt2bRpE5/PP3z4sIODAyHEx8fnt99+Gzhw4Lp163bu3EkIKS0tJYTY2NjU+mS5Z8+e\nlZSU9OvXjxDC4XACAwNv3brF7j19+vQff/wRHR3dkKfSFRQUfPTRRxwO5/z58+wkr5WVVefO\nnceOHRsSEnL48OEPP/xw1KhRNY/V0dH58ssvZ8yYcfDgwYkTJ770XAAapunMEgBAS9ARu+XL\nl9df7cKFC1ZWVhwOp0OHDvSCM2tr69jYWIZh5syZY2dnRwhxdXX19va+d+8ewzDjx48nhOTm\n5jIMM3nyZELIkSNHOnbsOHjwYJowzZo16+zZsx06dBg0aFDnzp0JIVOnTmVPJ5FIRo8eTQjR\n19e3s7PT0dHh8XjsuFqtZ2QY5rvvvtPV1dXT03N1dTUyMiKEjBkzRiQS1dWp4uJiLpc7YsQI\ntoQuOOjdu7daTUdHRzs7O7o9Y8YMQkhWVlatbV66dIkQkpiYSF8uXLjQycmJbldUVDg6On76\n6af1v9WsFStWEELCw8Nr3ZucnBwTE0MH5GqO2DEMU1payuPxhg8f3sDTAWgQEjsAgObRwMTO\n3d1dIBCkpKTQl4mJiY6Ojl27dqUv6eyk6sSoamI3bdo0Qoifn19+fj7DMFVVVW5ubjwer3v3\n7vTKNplMFhQURAjJyMigh//88880+ZNIJAzDlJaWDh8+nBBy6dKlus74559/0uywsrKSYRip\nVLp8+XJCyIIFC+rq1KFDhwghP/30E1siEokIIf369VOrSUfghEIh27WsrKyNGzd++umnn332\n2d69e6urq2nNhIQEQsi1a9foy2nTpvXp04duz54928HBoaysLC0tLTIycvv27dnZ2fW854GB\ngYSQR48e1VOHqjWxYxjG39/f0NCQvocArRmmYgEAmlN0dDQdH1IzaNCgQYMGEUKeP38eEBDg\n4eFBy728vE6fPi0Wi5VKJV1n8FKffPKJjY0NIcTQ0HDYsGFbt26dMGGCs7MzIURHR2fkyJE3\nbtyg+SIhJCwsrFu3bn5+fnw+nxBiZmY2Z86cM2fOXL58OSQkpNb2165dKxAIfv31V7rUVFdX\nd8WKFUePHt2+ffvatWt5PF7NQ27evEkICQgIYEsMDAxsbGxSUlKkUqmenh4tZBgmIyODEFJc\nXGxubl5WVkYI6dGjR1VVlb29fU5OztatW9esWXP+/Hl7e3s3NzcTE5Nr167169dPqVTGxMQE\nBwcTQmJjY7du3Xr8+PEQFTP4AAAeEUlEQVT4+Pg333yzV69ecrl8/vz5MTExvXr1qrVHT58+\n5fP59HK6pgkKCrp169aDBw/8/Pya3AhAC0BiBwDQnK5cuXLlypVad9HErm/fvrGxsUuWLJk2\nbZqbmxshpHv37o06hbe3N7ttZWVVawl73ztXV1d7e/sLFy4kJydXVFQolcqcnBxCSEVFRa2N\ni0Siu3fv2tjYzJkzR7W8urq6oqLi2bNnbEqqKj8/nxCieisTQsi77767devWlStXRkRE0JKI\niIgXL14QQuRyOSHE2tray8vr008/nT59up6enkgkWrBgQWRk5OTJk6OiogwNDWfPnr1y5cr8\n/PxHjx5lZmbOnz9fKpVOnz593Lhxo0aN6tevX3Bw8Llz55RKZWBg4LJly06cOFFrp6qqqoyN\njet7T1+Gdo12E6A1Q2IHANCclixZ8tVXX9UsZ0et/vjjj4kTJ65Zs2bNmjWOjo7Dhw+fPHmy\n6ljXS1lYWLDbdJCvZgnz33uU3r9/f9SoUdnZ2V27du3UqZOenh5dssDUcRPT/Px8pVIpkUju\n37+vdlJ/f3+ZTFbrUYWFhYQQa2tr1cJvv/32/Pnz33333X/+85/u3bsnJiZWVFS8/fbbx44d\no2mW2g1iDA0Nt2zZcu3atQsXLmRmZjo6Oq5evbpLly5Xrlxxd3f//vvvPT09ly9fnp+fv2HD\nBqlUeuvWrQ0bNtAuv/XWWz/++GNd75hAIMjNzVUoFLUONzaEQCBguwnQmiGxAwBoTnp6evUP\nDjk5OV2/fj0hIeHUqVNRUVE7duyIjIxctmzZypUrX0c8//rXv168eHHq1KkRI0bQktjYWHod\nXq04HA4hxNPT88aNG409Fz2WZWVldfv27Z9//jkmJiYvL2/MmDGff/75pEmT+Hx+XTdb5vF4\nQUFBDx8+fP78uaOjI5fLnTJlypQpU+jehw8frlmzZseOHTY2Njk5OQqFgh0jtLGxqaioqKio\nMDExqdmsq6trZmZmfHx8kydS68qDAVobPFIMAEADvL29ly5dGh0dnZqa6uXltXr1ajpD2rxy\nc3MTExMHDBjAZnWEkLS0tHoO6dChA4/Hy8zMbNSJ6FhdzQEtCwuLb7/99vLlyxcvXly5cqWZ\nmdmtW7d69erFjpwplUq1Q8rLywkhNR/RoVQqp0+fPnjw4EmTJpH/JpEKhYLupS/rev7EmDFj\nCCFbtmypdW9qaurYsWPj4+Pr6WBRURH577gdQGuGxA4AoOW8ePFi+/btWVlZbImjo+O4ceOU\nSuWzZ8/YwuYaH6LtqA5iMQyzbdu2mqdgXxoYGPj6+ubk5MTExKhWWLVqFV0tW6taL0G7cOHC\nqlWrqqur2ZKDBw8WFxePGzeOEFJeXm5vb+/r66saSUVFRXR0tIGBQc+ePdVOsWnTpqSkJHrT\nY0KItbU1j8crKCigL/Py8qysrOq6p92HH35oa2u7Z8+eAwcOqO0SCoXvv//+sWPHaOpWF9o1\numYFoDVDYgcA0JwkEklpHaqrq6uqqmbMmDFt2jR2fC4jI+PPP//U19enSyhMTU0JIYmJiaS2\n0azGsrOzs7GxuXr1anp6OiGkvLz8008/pdOgqamptE7NMy5atIgQMnv2bDpuJ5PJ1q5du2zZ\nsroWhZD/roeNjY1VLXz06NGyZcs+++wz+tSNkydPzp4928nJaebMmfS8QUFB9+7dmz59enZ2\ntkKhePjw4dixY/Pz8+fOnUvv2MzKyMj4+uuvv/vuOycnJ1qip6cXEBBw+vRpQohCoTh58iS9\njUutLC0td+/ezefzJ02aNH369GvXruXn5z958mTHjh1+fn63b99esmTJsGHD6nknb9y4YWBg\noLpIBaCV0tR9VgAAtMxLnxVLH4G6efNmXV1dDocjEAisra05HI6FhQX7BNWUlBT6WFVjY+Nf\nf/2Vqe0+dk+fPmVPSu8wFxMTw5Zs376dEPLHH3/Ql3v37qWP0nJ3dzcwMBg+fLhIJKJPnnBz\nc8vKyqp5Rua/Nyjm8XjOzs70BsVhYWFVVVV19b2wsJDD4bz11luqhVKplM6Bkv8+K7Zz587s\n88QYhikvL2fTKTqXyuFwPv74Y5lMptb+m2++GRgYqPZQrytXrujp6QUHB/v7+5uamqq2XKu4\nuLg+ffqofSgODg47d+5k69Rzg+KaN7cDaIWweAIAoHn07NmTpll1oQ+zmjVr1vjx48+fP//i\nxQsej+fk5BQaGkqTJ0KIh4fH7du3L1y4YGlpSZ8M9u6773bt2pUuyAgLC3NwcLC0tGTbpLdQ\nYZ8DRgjx8fFZvny5l5cXfTlp0qQ+ffpER0eLxeI+ffoEBwdzOJwLFy4cPnzYxMTE2trawcFB\n7YyEkCVLlkyaNIk+UszS0jIgIKCuW8RR1tbWw4YNu3TpUl5eHrswQldX9+jRozdv3oyLi6uq\nqvL09AwNDaW306NMTEzOnTt37969u3fvFhQUWFtbDx48mN4CRlVubq6/v//EiRPV7vM3cODA\npKSkkydP6ujojB07lj6Hox59+vSJi4tLSkqKj4/Pzc01MzPr0qXLoEGDVJfKWltbL1++XC2G\nQ4cOKRQKPE8M2gQOg5U+AADwym7evBkYGLho0aJ///vfmo6lOcnlcg8PDy6Xm5KS0uS7pQC0\nGFxjBwAAzSAgIGDcuHG//PJL/atu25zIyMjU1NQffvgBWR20CRixAwCA5lFWVtanTx8rK6tr\n167VdeeRtiUpKalv377Tpk3btGmTpmMBaBAkdgAAAABaAlOxAAAAAFoCiR0AAACAlkBiBwAA\nAKAlkNgBAAAAaAkkdgAAAABaAokdAAAAgJZAYgcAAACgJZDYAQAAAGgJJHYAAAAAWgKJHQAA\nAICWQGIHAAAAoCWQ2AEAAABoCSR2AAAAAFoCiR0AAACAlkBiBwAAAKAlkNgBAAAAaAkkdgAA\nAABaAokdAAAAgJZAYgcAAACgJZDYAQAAAGgJJHYAAAAAWgKJHQAAAICWQGIHAAAAoCWQ2AEA\nAABoCSR2AAAAAFoCiR0AAACAlkBiBwAAAKAlkNgBAAAAaAkkdgAAAABaAokdAAAAgJZAYgcA\nAACgJZDYAQAAAGgJJHYAAAAAWgKJHQAAAICWQGIHAAAAoCWQ2AEAAABoCSR2ANDM8vPzo6Oj\nc3Nzm7fZhISE6OhopVLZvM0CAGgTJHYA7Uh1dXV0dHQ9WVdVVRWtIBQKm3yWqKiokJCQ//zn\nP01uoVYLFy4MCQmRSqXN2ywAgDbR0XQAANByCgsLQ0JCCCEfffTR7t27a1bYv3//zJkzCSFR\nUVFvvPFGA5sNDw93c3P75JNPmi9SbVBZWRkXF0e3ORyOlZWVk5OTiYlJC4eRk5Pz7Nmz4ODg\nFj6vGk5UICGEGRqr2TCgaZKSkoqKimqW+/v7GxgYpKWlZWVlDRw4kK3p5eVlbW2tVvn69esy\nmSw4OJjD4SQlJUkkEl9f35aIvr1hAKDdyMrKIoQYGRkZGRlVVFTUrBAYGGhoaEgIiYqKaniz\ndnZ2ixcvZl/u27ePELJ9+/ZmiFjFkCFDCCFisbh5m3197t27p/b7Vk9Pb/r06TKZrCXD2LRp\nE4/Ha8kz1oqcDyDnA5qrtZ9//lntvbW0tNy0adMrNiuVSmNjY1+9jvZ5++23a00hnj5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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# SUMMARY VISUALIZATIONS\n", + "# ============================================================\n", + "\n", + "# E. All Methods Forest Plot\n", + "sum_plot <- all_summary[all_summary$label %in% key_labels, ]\n", + "sum_plot$display_label <- nice_labels[sum_plot$label]\n", + "sum_plot$display_label <- factor(sum_plot$display_label, levels=rev(nice_labels[key_labels]))\n", + "\n", + "p_all_forest <- ggplot(sum_plot, aes(x=est, y=display_label, color=method, shape=method)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(aes(xmin=ci_lower, xmax=ci_upper), size=0.5,\n", + " position=position_dodge(width=0.6)) +\n", + " labs(title=\"All Methods Comparison: Parallel Mediation Path Estimates\",\n", + " subtitle=paste0(\"SNP -> {APOC4_APOC2, APOC2, APOC1, APOE} -> tdp_st4 | N = \", N_total),\n", + " x=\"Estimate (95% CI)\", y=\"\", color=\"Method\", shape=\"Method\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + "ggsave(file.path(dir_summ, \"summary_forest_all_methods.png\"), p_all_forest, width=12, height=10, dpi=150)\n", + "ggsave(file.path(dir_summ, \"summary_forest_all_methods.pdf\"), p_all_forest, width=12, height=10)\n", + "print(p_all_forest)\n", + "cat(\"Saved all-methods forest plot.\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "dc8f853f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T11:41:39.454043Z", + "iopub.status.busy": "2026-04-01T11:41:39.451826Z", + "iopub.status.idle": "2026-04-01T11:41:43.214283Z", + "shell.execute_reply": "2026-04-01T11:41:43.210153Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved faceted-by-path plot.\n" + ] + }, + { + "data": { + "image/png": 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mtr6z59+ohEIrJUZmamgYEBuYHIfCuWw+Ho6urm5eWRr19++SUAfPbZZ+SrWCw2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\n69Y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Sws3Lp1q6qqKiFk+PDh165dmzt3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\nq8F6MkssAAAAAElFTkSuQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# F. Faceted by Effect Type\n", + "sum_plot$effect_type <- ifelse(grepl(\"^a\", sum_plot$label), \"a-path\",\n", + " ifelse(grepl(\"^b\", sum_plot$label), \"b-path\",\n", + " ifelse(sum_plot$label == \"cp\", \"direct (c')\",\n", + " ifelse(grepl(\"^ind[0-9]\", sum_plot$label), \"specific indirect\",\n", + " ifelse(sum_plot$label == \"total_indirect\", \"total indirect\",\n", + " ifelse(sum_plot$label == \"total\", \"total effect\", \"proportion\"))))))\n", + "\n", + "p_faceted <- ggplot(sum_plot, aes(x=est, y=display_label, color=method, shape=method)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(aes(xmin=ci_lower, xmax=ci_upper), size=0.4,\n", + " position=position_dodge(width=0.5)) +\n", + " facet_wrap(~effect_type, scales=\"free\", ncol=2) +\n", + " labs(title=\"Estimates by Effect Type Across Methods\",\n", + " subtitle=paste0(\"Parallel mediation: \", exposure, \" -> 4 mediators -> \", outcome),\n", + " x=\"Estimate (95% CI)\", y=\"\", color=\"Method\", shape=\"Method\") +\n", + " theme_minimal(base_size=11) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + "ggsave(file.path(dir_summ, \"summary_faceted_by_path.png\"), p_faceted, width=14, height=12, dpi=150)\n", + "ggsave(file.path(dir_summ, \"summary_faceted_by_path.pdf\"), p_faceted, width=14, height=12)\n", + "print(p_faceted)\n", + "cat(\"Saved faceted-by-path plot.\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0554ea3c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T11:41:43.221492Z", + "iopub.status.busy": "2026-04-01T11:41:43.219240Z", + "iopub.status.idle": "2026-04-01T11:41:44.345088Z", + "shell.execute_reply": "2026-04-01T11:41:44.343060Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved indirect effect focus plot.\n" + ] + }, + { + "data": { + "image/png": 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lLWrVuXlJQ0YsSI1q1br1u3zsgmBQUFRGRra6u3PCQkRKPRTJo0iYjs7e3Hjx9f\nWFj422+/VRhDTk7OTz/9JBKJnnvuOfq36zp16lReedariYmJ7KVJXW187xWoTrRUz5w5c1JS\nUr788ksbG6FfnWimcWZsZiWYq5lMYmLiorJs27aNL8O+xTDdCdRh586dIyJ2TpDp1atXZGSk\nbpnBgwfrvmzQoAHHcYWFhfb29mzJgAEDTHrTMWPG7N69+7PPPjtw4ICDgwMRnThxYu/evUSk\nVqsFVvL9999fvnz5zJkzZd5vL5PJOnToMGDAgEWLFnl4eHAc9/PPP0+fPn3MmDEPHjxwdXVV\nKBSGG7IWKRQK9l/jBSpUYQ3V0g/GFRUV0b8n2atILpcbVnXy5MkzZ87oLnnttdfatm1bZg1Z\nWVm67TKcScHJyen999+fOXPmX3/99emnn86bN8/IDCB//vmnWCzWm2ZFo9Fs3bpVIpHwI4uT\nJ0/esGGD4ZQ97BDPb5WSknLw4MGcnJxPP/2UXR1VYdexCxhycnLYS5O62vjeK0RdaamuJUuW\nrFmzZtKkSezKLSHQTIttZiWYsZlMUlLSV199ZVi4f//+/Jhl06ZNXV1dcY0d1GGNGzd2dHQs\nb22Zd8WyaxeePHnCF+CvyTNU5qUYWq121KhRROTn5/fqq68GBwc7OTlNnz6diHbs2CEk7ISE\nBHZJFr9EyFVK7C3YtbceHh5t2rTRK3D+/Hn6967VCgtU2MwKazCpHyp3UQu7Yu/06dMmbVWm\nCRMmENHZs2d1F/In5Xn79u0rr4YWLVrollSpVIZltFrtoUOHBg0aRETe3t5l1hMREdG3b1+Z\nTLZ27Vq9VQcPHiSiF198UXchS/74W+TKnPjAyckpMDBw586d/FbsGqmbN2+W1xw2tjpo0CD2\nUnhXV27vrYst5SkUCrbV+++/b9JN0GimBTaTq+zhyIzNZNUGBgYWlEXv4tc9e/ZgxA7qNo7j\nqliDqaMOIpHojz/+CAkJOXDgwOPHj/39/X/44Yc///yTiJo0aSKkhhkzZnh4eCxbtsyk9w0K\nCtq8efODBw+IyNvbOzU1Va9Aeno6/TuSVGGBClVYQ9X7oULNmzcnopiYmH79+lWxqpYtWxLR\nxYsXAwMD+YWfffbZzJkz2b9//fXXuXPnGqnhgw8+0P0BrXeWqrCwcNu2bevWrbt//36bNm3W\nr19f3nU/WVlZcXFxXl5ejRs31lvFLq9OTU3VvRaQXTawZcuW9evX8wv79+/Pn2fsE6IAACAA\nSURBVJcvE0tDL126xEYIDLGJ9Fi3kCldXbm9V0+daCmTk5Pz4osvxsTErF+/nt9bBEIzLa2Z\nVWHGZjJisVjI2GSDBg0wYgd1WK9evYiovJkmBI7YGZnoTvgPu5EjR9ra2urdIlqm06dPE1GP\nHj3e1uHh4eHi4vL2228vXbqUFTP8xbxp0yYiWrZsGcdxr776KhHpzu3CcdzChQuJ6Pjx40IK\nVNhMk2qosB8q9xP5+vXrRNSqVSvd2Q14SqVy7Nixe/bsEVIVuxawffv2arW6zAKse42M2JUn\nISFh1qxZrq6uIpHohRdeOHLkSJlzDusqLi5+9tln7e3tU1NT+YWPHj0Si8XsLLweiUTi5uZW\nXFzMCb4/ju1mffv2La/AyJEjiSg8PJy9FNjVAvde4+pES9nLvLy87t27Ozo6Hj58WEjT0ExL\nbiavEocj8zZT+F2xSqUSiR3UbZ988gmVnkrt5s2bnp6ec+fO5WossYuNjV22bNnt27f5JQ8f\nPrSzs9Mboi/P2bNnuxmQyWT29vbdunWbOHEix3FDhgxxcHDQm2rkhRdeIKKIiAg+8u+++45f\nq1KpWrdu7ebmxp4MUWGBCptZYQ0m9UOl5xdg1xpPmzZNL1vSaDTsGqB58+YJrIrNU1DmM0K0\nWu27775bicTu448/FovFLi4u77//flxcnPAN2VRVBw8e5JewpHn+/PmGhdkIAZv+RuAhXqvV\nsvm92C8BPdu3byei3r176/5+ENLVQvbeCtWJlrKahwwZYmdnd+rUKSHt0oNmWlQzdVXicGTe\nZgpP7OLi4ogwjx3UZffv35dKpU2aNGGXODx+/Dg4ONjGxubcuXNcZRM7rVbLTxrObrzIyspi\nL9lgD/uN2KNHj/v373Mcd+PGjU6dOtna2l65cqXSDdG7SmnDhg3sD/vq1atqtTolJYU99aF3\n797s4FVSUtKyZUsnJ6c9e/aoVKonT56wG7X4w0SFBSpsZoU1VNgPFb6FECkpKexhR8HBwQcP\nHkxOTk5KSjp48GD//v3ZQoVCIbCqzMxMdgZk0KBBhw4dSk1Nzc7Ojo2N/emnn7p3705EPXv2\nzMjIEFgbM3ny5HXr1uXn55u0FWcwj51arfb19bW1tU1JSTEsHBMTQ/9Owif8EB8fH89mq3nl\nlVfCw8MzMjIyMzPPnj379ttvi0Sihg0b6j0zqtJdbdI1dnWopez7dfHixcUG+B347bfffuml\nlwzH19FMS2umkMORxTazwidP8NMQYoJisAZ79uxhjyvlJyhet24dW1W5xO6fR7KUhS+5fPly\ndokVu7fc2dm5zJlmhTP8avz0009Z5fzTG/v27at75i42NpZdX8Jf7DV16lTdQ5LxAkKaWeFb\nGO8HIW8hREZGxujRo/Wen+3k5DR79uwyz78YkZub+9Zbb/E3RPMaNmy4YsUK4Tli1ekldgcO\nHCCicePGlVe+Z8+eRHT37l3h3xwcx6Wmpo4YMUKv68Ri8ejRo/XGg5nKdbVJiV0daunAgQPL\n24H55zQ0atSIiAx/qKCZltZMIYcji21mhc+K5R8jzhI7EVfla88BzCsrK+vw4cOPHz9u2LDh\nwIED+Uk1b9y4sXfv3hEjRnTt2pUv/Ntvv929e3fOnDkODg5lFkhJSQkJCSnzjXRLJiQknDhx\nIisrq1GjRsOGDWMPIa20zZs3K5VKvdkxkpOTT58+nZKSIpPJevbsGRAQoLeVQqE4cuTIvXv3\nHB0dg4ODDSdpM1JAYDMrfAsj/SDwLQRKS0s7c+ZMSkqKWCxu2rTpgAED2IwAlfD06dNTp04l\nJyfL5XIvL6/OnTt369aNT6Brx/79+0eNGvXHH3+MGTOGiA4dOnT58uVXX321Xbt2ZZaPjIw8\nfvz4888/36FDh9WrVzdt2rS8mzMMpaamnjt3LiUlhYh8fX2DgoIaNmxopLypXV3m3lueOtTS\nbdu26U0kxhs5ciSbaWzJkiWLFi0ynN8HzbS0Zgo5HFlsM/Pz81evXm2kkubNm7MbhG/duuXv\n74/EDgCgth06dGjEiBHbt29nh2OooyIjIydPnnzv3j1zB1Kz0My6IiYmpnfv3njyBABAbWPj\nyn/99Ze5A4EqWblyJbv21LqhmXUFm2wPI3YA1UapVM6ZM8d4mb59+77yyiu1E4/FqsaOqrt9\n3q9fv7NnzzZt2nTmzJns/u66ru5+FgB13Z49exYsWHD37t3WrVtjgmKAaqPVanUfU1umyj1s\n3spUY0fV3T4/fvz47t27ExISyrtwp86pu58FQF3XuHHj1157rVGjRmPHjsWIHQAAAICVwDV2\nAAAAAFYCiR0AAACAlUBiBwAAAGAlkNgBAAAAWAkkdgAAAABWAokdAAAAgJVAYgcAAABgJZDY\nAQAAAFgJJHYAAAAAVgKJHVgbhUJRXFxsUY9UUSgUFhVPSUlJcXGxVqs1dyD/U1JSYlHxKJVK\nS+sipVKp0WjMHcX/3L17NyIiIiMjw9yB/I9KpVKr1eaO4n9UKlVxcbGlhWRR8ajV6uLiYpVK\nZe5A/ketVltUPBqNxtQuQmIH1qa4uLioqMiiEilLSxEUCoWldZFCobDALrKoRKqkpMSi4rl9\n+/bp06fT0tLMHcj/lJSUWFTWolKpioqKLC0kpVJp7ij+h3WRRSVSarXaorpIrVYXFRWVlJQI\n3wSJHQAAAICVQGIHAAAAYCWQ2AEAAABYCYm5AwAAgLonMDCwS5cuXl5e5g4EAEpBYgcAACZz\ncHCwsbGxs7MzdyAAUApOxQIAAABYCSR2AAAAAFYCiR0AAACAlUBiBwAAAGAlkNgBAAAAWAkk\ndgAAYDK5XJ6fn29RD18CAEJiBwAAlXDu3Llff/31wYMH5g4EAEpBYgcAAABgJZDYAQAAAFgJ\nJHYAAAAAVgKJHQAAAICVQGIHAAAAYCWQ2AEAAABYCSR2AABgsgYNGrRs2dLV1dXcgQBAKRJz\nBwAAAHVP586d27Rp4+zsbO5AAKAUjNgBAAAAWAkkdgAAAABWAokdAAAAgJVAYgcAAABgJZDY\nAQAAAFgJJHYAAAAAVgKJHQAAmCw2NjYiIiItLc3cgQBAKUjsAADAZI8fP46Njc3NzTV3IABQ\nChI7AAAAACuBxA4AAADASiCxAwAAALASSOwAAAAArAQSOwAAAAArgcQOAAAAwEpIzB0AAADU\nPYGBgV26dPHy8jJ3IABQChI7AAAwmYODg42NjZ2dnbkDAYBScCoWAAAAwEogsQMAAACwEkjs\nAAAAAKwEEjsAAAAAK4HEDgAAAMBKILEDAACTqVQqhUKh0WjMHQgAlILEDgAATBYRERESEhIX\nF2fuQACgFCR2AAAAAFYCiR0AAACAlcCTJwAA/mfyz+z/LhWW3Pp2DYcCAGA6jNgBAAAAWAkk\ndgAAAABWAokdAAAAgJVAYgcAACZr0KBBy5YtXV1dzR0IAJSCmycAAMBknTt3btOmjbOzs7kD\nAYBSkNgBVI9lf1Fcenkr3WszEgEscJTFAkOqwL/3z9YaS0uhnIiczB2DHpPj6d6M3h1QE5EA\nmA0SO4DqIbUlR/uyV3EcJxKJajccYziOIyJLC8lC4ikqEVqyvI+7hlhOFzGWuReRiSHZ4zsQ\nrA52aoDq8dFz5a56+jTXxcVFLBbXYjjG5OXlq1Qqd3d3iwrJ0dFRIjH/EUn4ONz612syDgMF\nBYX29vZ2dna1+q7lKywsUigUzs7O9va1m+GWr7CwSCKRSKVScwcCYE64eQIAAADASiCxAwAA\nALASSOwAAAAArAQSOwAAMNn9+/ejoqIyMzPNHQgAlILEDgAATJaQkHD58uXs7GxzBwIApZj/\nHjQAAMux9W0iovz8fKVS6erqamtra+6IAABMgBE7AAAAACuBxA4AAADASiCxAwAwRnQ8wNwh\nAAAIhcQOAKBcLKtDbgcAdQUSOwAAAAArgbtiAQDKZneqH/9v0fEAbnCU+WKxOL169Wrbtq2P\nj4+5AwGAUpDYAQCAyVxdXe3t7WUymbkDAYBSajWx27BhQ1FR0dy5c03aKjIycvny5Tt27HBx\ncSGi9PT07777Lj4+vlevXvPnzzey4cqVK/Pz87/66iuRSFSN8TAbN248ceKEj4/Pxo0bdZcf\nPHgwJCSkXbt2YrGYiIqKipKTk728vObNm9ekSRNWJi8vb+XKldevX69Xr56Hh0dWVlZOTk5A\nQMCHH37o4ODAyuTn569aterq1av16tVzc3PLzMwsKCgYOHDgu+++K5GU+tSKiopmzJiRm5v7\n448/NmzYsK7Er1AoFixYkJiY2KpVq6ysrOzs7A8//LBfv37FxcUfffTRoEGDXnnlFVM+EIBq\n5nX1Rb0lGLQDAMtXq4ndzJkzq1hDZmbmRx99FBwczCcQ5YmMjIyKivrpp5/Ky+qqEo9SqTx3\n7tyYMWN+//33+Pj4li1b6hVYuHCho6MjH/Nnn332ww8/LF++nG27YMGC3NzcJUuWdOrUiZW5\ndOnSypUrFy9evHTpUpFIpNFoFi1alJGR8fXXX3fp0oWItFrtsWPHfvzxR1tb2xkzZui+17Zt\n2xwdHXNzc+tW/Dt37kxJSVm/fr23tzfHcStWrNi8eXNQUJBMJvvggw/mzZvXs2dPPpUEAAAA\nIWr15olz586dPHmS/TsyMvL8+fNKpfLUqVNhYWEXLlzgOI4vmZmZeejQof379z969Ei3BoVC\nMXXq1OnTp+uNWunhOG7Xrl3PPfecp6fnhQsX9u/fr7v24cOHu3btksvluvEQUUFBQURExJ49\ne44dO5aVlWWk/ujoaJVKNWrUqGbNmunWUKb69esPHDjw7t27KpWKiI4fP56YmDh37lw+KyKi\n7t27f/jhh7GxsWfPniWiiIiI+Pj4jz/+mGVFRGRjYzNkyJAJEybY2NjodtSdO3dOnTr1xhtv\nGI/BAuOXyWRjx4719vYmIpFI1K9fv8LCwidPnhBRhw4dOnTosGvXLpMaBVCNXGOeL3M5bo8F\nAAtX24ldREQE+3dUVNTRo0cXLVp07969p0+frlq1asuWLWzVgwcPZs6cefjw4QcPHnz77bcX\nL17ka2jcuPGAAQMqfKP79+8/evRo4MCBRKRQKLZu3ZqRkcGvPXjw4JkzZxwcHHTjefjw4ZQp\nU/bt25eYmBgeHj59+vQbN26UV394eHivXr0cHByeffbZM2fOaDQa4/FIJBKO47RaLRFFRkY2\nb968Q4cOemUCAgLq169/7tw5Ijp//ryPj0/37t31yowZM2batGn8GKRGo9m4cSOfHglnCfGP\nGzdu1KhR/Kr8/HyRSMQPxA4YMCA6Oloul5vULoBqYTx7Q24HAJbMbDdP2NjYXL16dfny5e3a\ntSMiNze3Xbt2TZkyRSQS/d///Z+7u/vatWvt7e0VCsWsWbNMrfzatWvOzs4tWrQgop49e9rZ\n2UVFRY0cOZKINBrNhQsXXnxR/+qZx48fBwcH82nHsmXLfv/9d91BKV52dva1a9e+/PJLIgoO\nDt6+ffulS5d69epVXjAajSY6OrpZs2b29vZE9OjRo4CAsr8YWrVqlZiYyMqwbjFu7969RDR6\n9OikpKQKC1tg/Lzi4uK9e/f26dPHycmJLenSpYtWq71+/Xp570VECoVCd/CSxxYqFAojZ+HL\ndCg7Mq74UcXlTKdSqSRPJKbGU3PUarVWq7XNsbWokMRiseXEY9yS+1tr/00trYvYXiTJltjY\nWMq0WRqNRiQSCY+nkZ3Xa/UH1Vw8arWaiFQqVZlHKrNgIRUXF5s7kH+wE0FqtdpyQmI7tkXF\nQwZdZPymJXPeFevt7c1//fv6+qpUqvz8fFdX11u3br3wwgssjZBKpf379/+///s/k2pOTU31\n8fFhR0CpVNqjR4/o6GiW2F2/fr2wsLBfv356mwQFBbVu3fro0aN5eXlarTYvLy89Pb3MyiMi\nItzc3NhJRjc3t65du548eVIvMdq9ezd7drhcLr969Wp2dvaCBQvYKoVCUd4Fgg4ODoWFhURU\nUlJS4UWE6enpu3fv/uqrr9hdDsJZSPw8pVK5fPlypVI5depUfmG9evWkUunjx4+NbKhQKNge\nX6ZKjPaFph75M++8qVvBf9CCxC3mDgGqQW+nDi861vj4a0lJSUlJSU2/i0mUSqW5QyhFqVRa\nWkgs47QcarVa9/tOKpUa+Y1nzsTOzc2N/zfLTlQqlVwuVygUHh4e/Kr69eubWnNeXp6rqyv/\nMjAw8LvvvsvNzXVzczt//nzLli0bNWqkt0lMTMyyZcvatWvXtm1bltOUd4IyPDzcxcVl586d\n7KVGo7l69WpBQYGzszNfJikpif1qtLe3DwwMfP755+vVq8dWubi4sOzHUFFREbvz19nZOT8/\n33gbf/jhh+Dg4Pbt2xsvZrHx85ssWbLkyZMnS5cudXd3113l6upqvBKpVFrm7+Di4mKtVuvg\n4GDq2MYbPi/0ctc/xVwtVCqVRGJ5I3a2GLErg8CkbXHTKTUdiR7L6SLm3r176enpbdu2bdCg\ngblj+UclRuz4u8RqgkqlUiqV9vb2xi8Kr00sX2HfcZaAdZGdnZ3lhMQOj3Z2duYO5B9qtbqk\npEQikbDRLsb4ccCce5vAI1SFV4BVqEePHvb29jExMYMHD46Ojh4zZoxhmZ9//jkgIGDOnDns\nZUlJSZnDRXfv3n38+HHXrl0fPnzIltjY2EgkkrNnzw4dOpQv9umnn5Z3vGjcuPHdu3fLXBUf\nH9+2bVu+DMdxel2k0WjUarW9vf3ly5evXbs2atSo33//nYjYLbGHDx9u27Zt3759jXSFhcTP\nXhYVFc2bN08kEq1YsUIvq6N/z6gaIZVKy1yuUCjYWlPPEI31ramTMk+fPnVxcTF1bLXm5OXl\nqVQqd3d3iwrJ0dHR7N9/wq+fW5C4pZanPikoKLC3t7ec75u9N/feSLkxsvsLXVp1MXcs/ygs\nLJRIJOUdGWofx3FKpdLW1taiQqKKTuTVMqVSKZFILCckhUKh0WgsJx424mtSF1nKzwieTCaz\ns7PLycnhl5R3StQIV1fX1NRU/qWdnV2vXr1iYmJ8fHwKCgqCgoL0yqvV6oyMjNGjR/NL4uLi\nyqw5PDy8SZMmixYt0l24du3akydP6iZGRgQGBq5bt+7KlStdu3bVXR4TE5OZmTlt2jQi6tu3\n7/nz5yMiIvTuFNm/f//hw4c3btxoZ2fXp08f/o6QoqIiIkpOTjZMjywzfqlUqtVq2dwo33zz\nTZlJJDsvLyQkALPAtHYAYIEs5aJXnkgkateuXXR0NLsoobCw8PTp06ZW0qhRo8ePH+sO+QQF\nBd26dev8+fMdO3b09PTUK89yYT6DjIyMTEtLMzzlz6Z/MxwSCwwMjIuLM35BGG/AgAGtW7de\ntWrVlStX+IWXLl36/vvvu3fv3qNHD1Zhhw4dNm3adPbsWdYKjUbz559/hoaG9u/fXyqV+vv7\nf6Zj0qRJRDRt2jTd3NSQ5cRPRMeOHXv48OGCBQvKzOqysrIUCgWeVgS1Cbe7AoAVsLgROyKa\nMGHC/Pnz33///WbNmj148KBHjx5//fUXyw+OHj3K8rzExESRSDRv3jwiGjNmjN7oUZcuXUJD\nQx8+fMhujCWirl272tjYHDt2TPcKfV3Dhw8PCwvLyckpKiqSy+XTp0//5ptvVqxY8c477/DD\nYNHR0UVFRYaJUZcuXRwdHU+ePClkPjkbG5svv/xyzZo1ixYt4p/c8PTp0+Dg4Pfee4+VEYlE\nX3zxxZo1a1asWPHTTz95enpmZmaWlJS88sor48ePF96Teiwq/j179ojF4lWrVulW/tprr7E7\nka9duyYWizt37lzpxgKYpHJZHQbtAMDS1GpiFxgYyA+DBQQEsBOIjI+Pz7hx49itlG3btt24\ncWNMTAzHcePHj5fJZC4uLuzKSm9vb39/fyJi/2V0b8JgWrZs2aRJk/DwcD6xk0gk06dPT0tL\n001rdON5/fXX27Ztm5SU5O3t3atXL7FYPGvWrKKiIt2z2nZ2dpMmTfLz89N7O1Y5u7SrTZs2\n48aNM34dqLOz88KFC5OTk+/cuZOfn+/m5ubv7693AbKjo+MXX3yRnJx8+/btgoICT0/PZ555\nxrCljLu7+7hx4/i5QspjUfEPGTLE8LYj/tzryZMne/fuLfzWWoAq0svP8vPzlUqlq6ur5VzT\nDQAghMhy5tepXlFRUatWrfrxxx8NT7yChYuNjZ0/f/769esNc1Ahnj59qtFoPDw8LGd6Ldw8\nUSELuXmCZ4GJncXdPLF3740bN0aOHMk/YMbsLO3mCblcLpfLnZycLCokIrKc38zFxcVFRUUO\nDg6WExK7eaJGb5c2SUlJSUFBgVQqrXDshmcph9FqFxAQEBAQsHbt2q+//tpyJgioBQcPHixv\nVY8ePRo2bFibwVRCcXHxunXrJkyYULmsDgBqh4eHh5+fn/AvGwCoHVab2BHRe++9d+DAgdTU\nVMNZ66wYP4+JoUpMelf7EhIShg4dOmLECHMHAgDGdO/evWPHjrrzXwKAJbDmxE4qlb766qvm\njqK2ffTRR+YOoUrat29fJxJQAAAAC2QpFyEBAAAAQBUhsQMAAACwEkjsAAAAAKyENV9jBwBg\nqpK5HxCRPZE9ES1ZVVFxAADLgsQOAIDo35ROl/aLT0qI7JevM0s8AACVgFOxAABlZHVCVv2X\n3b9/PyoqKjMz09yBAEApSOwA4L+uwtQNuZ2hhISEy5cvZ2dnmzsQACgFiR0AQMWQ2wFAnYDE\nDgD+05CxAYA1EXEcZ+4YAKrT06dPNRqNh4eHjU3N/m5R/31IEx0ppCTHcRb1wGL2V29pIZkt\nnmK50JIycz6n3NL2IpVKpVar7ezsxGKxuWMhIrJ9fbLc20cikUilUnPH8g+5XC6Xy52cnCwq\nJCJycDDnnqyruLi4qKjIwcHBckJSKBQajcbR0dHcgfyjpKSkoKBAKpUKfy4z7ooFqCyVSmBO\nYEHfxkRkefGQRYZUBuEpYA2wtC6yJbIlImWJuQP5l0Zj7ggALAISO4BKkrw4WvLiaCElnz59\n6uLiYiEDG0SUl5enUqnc3d0tKiRHR0eJxAxHJOGnYs0770lBQYG9vb2dnZ0ZY9C1d+/eGzdu\njBw5skuXLuaO5V+FheaOAMD8cI0dAPynYZo6ALAmGLEDAKgY8j893bp1a9asWePGjc0dCACU\nghE7APivqzBpQ1ZnyNPT08/Pz3KuMQcABokdAICx1A1ZHQDUIUjsAACIiOyXrzPM4ZDVAUDd\ngmvsAAD+h2Vy+fn5SqXS1dXV3OEAAJgGI3YAAAAAVgKJHQAAAICVQGIHAAAAYCWQ2AEAgMmO\nHTu2YcOGO3fumDsQACgFiR0AAACAlUBiBwAAAGAlkNgBAAAAWAkkdgAAAABWAokdAAAAgJVA\nYgcAAABgJZDYAQCAyTw8PPz8/JycnMwdCACUgmfFAgCAybp3796xY0dnZ2dzBwIApWDEDgAA\nAMBKILEDAAAAsBJI7AAAAACsBBI7AAAAACuBxA4AAADASiCxAwAAALASSOwAAMBkCQkJly9f\nzs7ONncgAFAKEjsAADDZ/fv3o6KiMjMzzR0IAJSCxA4AAADASiCxAwAAALASSOwAAAAArAQS\nOwAAAAArgcQOAAAAwEogsQMAAACwEhJzBwAAAHVPt27dmjVr1rhxY3MHAgClILEDAACTeXp6\nOjo6Ojo6mjsQACgFp2IBAAAArAQSOwAAAAArgcQOAAAAwEogsQMAAACwEkjsAAAAAKwEEjsA\nAAAAK4HEDgAATBYRERESEhIXF2fuQACgFCR2AABgMpVKpVAoNBqNuQMBgFKQ2AEAAABYCSR2\nAAAAAFYCiR0AAACAlUBiBwAAAGAlkNgBAAAAWAkkdgAAYDIXF5f69evLZDJzBwIApUjMHQAA\nANQ9vXv37tKli7Ozs7kDAYBSMGIHAAAAYCWQ2AEAAABYCSR2AAAAAFYCiR0AAACAlUBiBwAA\nAGAlkNgBAAAAWAkkdgAAYLKEhITLly9nZ2ebOxAAKAWJHQAAmOz+/ftRUVGZmZnmDgQASkFi\nBwAAAGAlkNgBAAAAWAkkdgAAAABWAokdAAAAgJVAYgcAAABgJZDYAQAAAFgJibkDAACAuqdT\np04+Pj6+vr7mDgQASkFiBwAAJvP29nZzc3N2djZ3IABQCk7FAgAAAFgJJHYAAAAAVgKJHQAA\nAICVQGIHAAAAYCWQ2AEAAABYCSR2AAAAAFYCiR0AAJgsIiIiJCQkLi7O3IEAQClI7AAAwGQq\nlUqhUGg0GnMHAgClYIJiAAAA00z+WUgpByKHHyYoajoYAF0YsQMAAACwEkjsAAAAAKwEEjsA\nAAAAK4HEDgAAAMBKILEDAACTyWQyFxcXOzs7cwcCAKXgrlgAgFqV8pR+izZ5K43GQSQS2VjM\nj3GN5jlty0F/JogPJ5k7lH9pNDKL6iJmXbid5YSk0dgTkVhs7jj+pdXaazQSGxsbSwrJjuM4\n3Xi6NqEB7c0XkOmQ2AEA1Cp5Cd1OrcR2FvPV9w+xRYZkce6mW0xaR2R5XWRjeWcO9ePxcTNL\nGJWHxA4AoFY18aSFL5m8lVwut7W1tbW1rYGIKkMulyuVSgcHB8s5GyuXyyUSSe3E8/UBoSU/\ne0FpOV2kUCiISCqVmjuQf5SUlBQXF0ulUssJSalUajQamUzGL3GRGSluiZDYAQDUKntbalrP\n5K0KCjT29hKLyRCosFCrUKidnTl7e3OH8q/CQq1EorWYDOEfjT0sKCS5XEtEDg7mjuNfxcXa\noiK1g4PWckJSKLQajdbR0dxxVIGlDYECAAAAQCUhsQMAAACwEkjsAAAAAKwEEjsAADBZSkpK\nbGxsbm6uuQMBgFJw8wQAAJjs9u3bN27ccHV1bdCggbljMYOtb1dcRi6Xy+VyIqeaDwfgfzBi\nBwAAAGAlkNgBAAAAWAkkdgAAANVAdDzA3CEAILEDAAAAsBZI7AAAAKqKLTEdmAAAIABJREFU\nDddh0A7MDokdAABAlejmc8jtwLww3QkAAJisU6dOPj4+vr6+5g4EAEpBYmeCZcuWFRUVLV68\n2KStIiMjly9fvmPHDhcXF47jtm/ffuDAAY7jtFqtr6/v7NmzmzdvXuaGe/fu/fvvv9euXetQ\nzuORKxcPs3Pnzt9++83f3/+bb77RXX7w4MGQkBDdJSKRKDg4eOrUqY7/PhVZq9WGhYXt37+/\noKCALalXr97o0aOHDx/Ob8VxXFhY2L59+/gy3t7e48aNe/bZZ/kCZXbF48ePP/nkk9mzZ3fv\n3r0S7QKA2uHt7e3m5ubs7GzuQMzPcIhOdDyAGxxllmAAkNiZYOrUqVqttio1HD58eP/+/bNm\nzQoMDMzKylq6dOmqVas2btxoWDI+Pj40NHTZsmXlZXVViYfjuIiIiD59+kRFRT158sTLy0uv\nwK5du1gaV1RUdOXKle+//16j0cyePZutXbNmzdmzZ1955ZWBAwd6eHg8efLk2LFjW7ZsSUtL\nmzJlCiuzfv368PDwMWPGDB482M3NLS0tLSwsbM2aNcXFxUOHDjXSFY0aNXr77bdXr169adMm\nV1fXSrQOAADgPwvX2JmgsLCQH3969OhRcnIyEWVlZcXFxfHLGY7jEhIS4uPjNRqN7vJbt24F\nBgb269fPxsamfv36o0ePTk5OLvOZPDt27OjatWubNm2Sk5Pj4uJ0V+Xl5d28ebOkpEQ3HiYr\nK+vevXtpaWkcxxlpyK1btzIzMydPnuzh4REREWGkpKOjY1BQ0ODBg6OiolhbLly4cPr06WnT\npk2YMMHb29vOzq5Ro0aTJk0aP378oUOH7ty5Q0TXr18/ceLExIkT33jjDW9vb6lU2qxZs9mz\nZ/fp0+fUqVMsNiNdMXDgQCcnp7CwMCOBAQBYgvKuqMOVdmAuGLEzwc6dO/lTn7///rtarXZ1\ndb1z545YLH706NG77747aNAgIsrLy/viiy8ePXrk7u4ukUgGDx7M1zB37lzdCsViMRHpJX9E\nlJ6efuXKlUWLFhHRtWvXtm7dGhoa6uT0z3Np9u7dGx4evn37dt148vLyvv3223v37rm4uBQW\nFvr4+HzxxRflPeonPDy8Y8eO9evX79evX0RExNixY4033MvLS6VSKZVKmUx2/PhxLy+vIUOG\n6JUZPXr0wYMHT5w40a5duxMnTri6uo4YMUKvzOzZsyUSSYVdYWNjM3To0J07d7755pt8eQCA\nusUxcuCTZw6ZOwr4z8G3ZiWJxeKYmJhJkya9++67RLR58+bt27ezxG7Hjh05OTmbNm3y8fF5\n9OjRl19+WWYNHMcdPXq0ZcuWnp6eeqsuX75sZ2fXsWNHIurbt29ISMjFixf5q9POnz/ft29f\nlgnxIiIiCgoKtm7d6u7urlAoFixY8Msvv3z22WeG76tQKM6fPz9t2jQiGjRo0L59++Li4lq3\nbm2ksXfu3PHw8JDJZEQUHx/fqVMnkUikV8bW1rZNmzZscDE+Pr59+/Z6ERJReVmaYVc888wz\nW7duvXPnjr+/f3lRaTSaMgcm2UK1Wm1jYykD0hzHlRetWbBILC0kw184ZsR3keGuXr0eKlJz\nVQUVlyNSKBQShcRyfuooFAqVSiXVSm1tbc0dyz8UCoVYLLYtrr14el16x3iB6/J4qVZamyEZ\nV1JSQkT2JfbmDuQfSqWypKTETm1nOSGpVCqtVmuvrLZ4bG0k/o4tKr05OzBqtVq1Ws0vNH4c\nsJRjRF3k4ODALhcjIn9//8OHDxcWFjo5OcXExPTp08fHx4eIGjduHBQUtH//fsPNd+7cGRsb\nu2zZMsNVCQkJfn5+dnZ2ROTh4dG+ffuoqCiW2MXHx2dkZPTv319vk5EjR7700kupqampqala\nrbZBgwb37t0rM+xz584RUd++fYnIz8+vZcuW4eHheondrVu3pFIpEcnl8osXL8bExLBEkIgK\nCgrKu/TN3d09Pj7eeJkyGXZFkyZNbG1tHzx4YCSxKygo0N3R9eTn5wsPoBZYWjxkeSHpXVdg\nCQoLC2v6LT58uPrvvJiafhcwl0H3PjZ3CGBmXhK32/6hVaxEqVQqlUr+paenp5HfnEjsKq9+\n/fp8z7IkTKFQ2Nra5ubmNmzYkC9mOB0Ax3G//PLLX3/9NXfu3FatWhnWnJubq5sY9evX7+ef\nfy4pKbG3tz9//ryXl1e7du30NklOTl6yZElubm7jxo1tbW0zMjJUKlWZYYeHhzdr1oxP+5o3\nb37u3LkpU6bo/gJYtWoV+4e9vb2vr+/nn38eEPDP9SIymUx399LFztUSkYODA/tdWCEjXeHi\n4pKXl2dkW4lEUuaerVarOY4rb61ZqFQqi4pHo9FotVqLCkmtVovFYouKp3b2ok5OLRVc2X9Q\netggouV0ET/ca2kh1Vo8p/KvCinW37nLf7aLKvRf2IvcJE5VGdXWarUajcbGxsbwJFh5kNhV\nXpm9zMaQWJ7H6P6biDiOW7duXXR09KJFi8objuIzJKZPnz4//vjjlStXAgICoqKigoKCDPe5\nDRs2ODk5rV27lm3466+/njhxwrDmjIyM27dv29nZ6c5yUlJScvHiRT51I6JffvmFn9xEj7e3\nd1JSUpmrkpOTWUbboEGDhw8flllGl/GusLe3Ly+DZPiLDvU8ffpUo9G4uLhYzqnYp0+fOjs7\nC/+zrGl5eXlardbSQnJ0dLSc84z5+flKpdLR0bGmzzMud50psGRBQYG9vb3e8cSMjhw5cufO\nnSFDhrRv397csfyjsLBQIpGwsw01Tfi9EacLrlnO1CdyuZyIjEy2UMuKi4uLioocHBwsJySF\nQqHRaMr7Bqx9JSUlBQUFdnZ25X3lGbKUbz6rIZVKxWKx7kmunJwc3QJbt269cOHCN998Y+Qk\no4uLi24Nrq6u/v7+0dHRSUlJjx8/NjwPq9Fo4uLiBgwYwKeDqampZdYcHh7u5ua2u7SuXbuG\nh4cLbGDPnj1v3779+PFjveVJSUkPHz5k2WHPnj2TkpLu3r2rV4Y1XKFQsJfGuyI/P9/FxUVg\nVABQy4qLi1n6a+5AAKAUJHbVTCwWN2nS5Pr16+wlx3FRUf/7rXb9+vU///xz/vz55U1KzHh5\neT158kR3SVBQ0NWrV6Ojo/38/Jo1a6ZX3sbGxsbGhj/7mZycfO3aNcMp7vjp6/QG/AIDAy9f\nvizwiquhQ4e6ubmtXLlS9zzp06dPV61a5evryy4EHDRokJeX1+rVq9PT0/kyDx8+XL9+Pcdx\n7Pe08a6Qy+VFRUWGE+wBAJidqVOZYOoTqE2WcuLDmgwfPnzdunUrVqxo167dlStX2Nkldtp+\n+/bt9erVu379Op/5EVGfPn2aNGmiW4O/v39YWFhGRgY/X0lAQMCmTZv++uuvYcOGGb6jSCTq\n3r37gQMHnJ2di4qKTp48+dprr23duvXIkSP9+/fnh7hv3bqVkZERGBiot3nv3r03bNhw5swZ\n3UdHlMfR0XHBggWLFy+eNm1az549PTw8srOzL1686O7u/sUXX7DzRDKZbP78+YsXL545c2aP\nHj08PT3T0tIuX77cvn37jz/+51Ji411x8+ZNjuM6depUYTwAAJYPz6KAWoPEzgSNGzfmTyP6\n+vrqnoN3dnbu2LEjuxxn0KBBEonk3LlzV69e7dGjR+vWrX/++Wc2SObm5iaVSm/evKlbbYcO\nHfTeqGPHjq6urpGRkaNHj+brHzp0aEJCgu55WN14Pvzww927d0dGRnp7e8+fP9/NzS05OfnG\njRt9+vThyycmJnbv3t3wghhHR8ehQ4empaURkaenZ8eOHY1fetWyZctNmzadOnXq7t27iYmJ\nbm5uU6ZMCQoK0r36p3nz5hs3bjx16tSdO3dSUlI8PDzmzp3bu3dvfrDQeFdERka2adOmXr16\nRsIAADALgSmaXC6Xy+VOTk61c9kfACOynImsQNe+ffv27dsXEhJiOddK15r09PQZM2Z88cUX\n3bp1q8Tm7OYJDw8Pi7p5wsXFxaLuVFCpVO7u7hYVkgXePOHq6mo5k7RZ2s0Te/fuvXHjxsiR\nI7t06WLuWP5RmzdPCGGBiR1unqiQZd48IZVKcfNEnTd8+HAPD48dO3aYO5DaxnHc5s2be/fu\nXbmsDgAA4L8MiZ2FsrW1/eyzzxISElJSUswdS626evWqRCL54IMPzB0IABgjk8lcXFwsZwQR\nABhLOfEBhry9vdlzYP9Tunbt2rVrV3NHAQAVCAoK6tGjh7Ozs7kDAYBSMGIHAAAAYCWQ2AEA\nAABYCSR2AAAAAFYC19gBAABUs5K5H4iJnIm4r74zdyzw34LEDgAAoHqUzNW/o1/05ZwSIvvl\n68wSD/wH4VQsAABANTDM6oSsAqheSOwAAMBk6enp8fHxBQUF5g7EUlSYuiG3g9qBxA4AAEx2\n48aNv//++782gzqA5UNiBwAAUCUCR+MwaAe1ADdPAADUAeIL57nU/2fvvgObqP8/jn/SJE26\nJ4VCKRYKlEKZZZSyh6IgX1H8OlBUEBG3grj3gq98FcGNOBiiCMiQqUAZpWUKFQqUUZa0hZbu\nNEmb5vfH+cs33Qmjlx7Px1/J5XN37/s0vbzyuZFzpW6u8m28699/tzQXNUvcXHroT7lr+Ye6\nrEyoVKVqtdyF1KZ04Xcyrt3NYhFCuE4XqSwWfVmZm0bjOiUJi8XNai3V1JiONMNvU/kH1GdF\nziLYAUADoPr7rDiUUi53GTahQoQKIc6kl5+Ru5T/J2Ve1+miapWnyJmDVVINMlZQkUoIrRDC\nxUpS1V7P4JuEINgBAK6MJb6/unucpuaBhHq2ffv2kydPxsfHt2rVSu5a/lFSUqJWq93d3et/\n1aXffOZgS+3Dj1/TSmpnMpmEEDqdTsYa7JlMJqPRqNfrXacks9lcXl6u1+traqAKDKrPei6D\nq+wjAAC1sIY0ETqdmxyppVrZfx06pdZ2btLUrXVbuWv5h7WoSGg0bjV/JLsCebvLajAIIdw8\nPWWsoYKSEktxsdXT04VKMhqtFoubl5fcdVw+VzldAwCABsrB+w9zm2LUA4IdAMBp0dHRAwcO\nDA0NlbsQV0Fog4vgUCwAwGlhYWHBwcE+Pj5yF9JgkPxQPwh2AABcBVJ0q3qzOiId6hPBDgCA\nq8YW4wwGg8Fg8Pb2lrceXG84xw4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAABO27Zt27x5\n806cOCF3IQAqINgBAJxWUlJSUFBgNpvlLgRABQQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAApB\nsAMAAFAIgh0AwGlarVav16vVarkLAVCBRu4CAAANz8CBA+Pi4nx8fOQuBEAFjNgBAAAoBMEO\nAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAcFpmZubx48cLCwvlLgRABQQ7AIDT\nUlJS1q1bd+7cObkLAVABwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAa\nuQsAADQ8rVu39vLyCgkJkbsQABUQ7AAATouIiAgNDfXx8ZG7EAAVcCgWAABAIQh2AAAACkGw\nAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAgNOSk5MXL1586tQpuQsBUAHBDgDgtIKCggsX\nLpSUlMhdCIAKCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwCA07RarV6vV6vV\nchcCoAKN3AUAABqegQMHxsXF+fj4yF0IgAoYsQMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7\nAAAAhSDYAQAAKATBDgDgtJycnLNnzxYXF8tdCIAKCHYAAKft3bt3xYoVZ86ckbsQABUQ7AAA\nABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AAAACqGRuwAAQMPTunVrLy+vkJAQuQsB\nUAHBDgDgtIiIiNDQUB8fH7kLAVABh2IBAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEI\ndgAAAApBsAMAOG3Pnj0rVqw4e/as3IUAqID72AEA6jBubtVpA4QYkLhFiC0Vpn47vn4qAlA9\nRuwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgCAGmUViCW7nWi/ZLfINVyzagDU\nhWAHAKhRdqFYk+JE+zUpIp9gB8iHYAcAqFHTAPFAvBPtH4gXwd7XrBoAdSHYAQBqFOAp+kc5\n0b5/lPDWX7NqANSFYAcAAKAQBDsAAACFINgBAAAoBMEOAABAITRyFwAAcHXfjq885fTp05cu\nXWrRokVgYKAcFQGoHiN2AACn7d27d8WKFWfOnJG7EAAVEOwAAAAUgmAHAACgEJxjBwBw2h0+\nH4p4cZu4Te5CAFTAiB0AwDmq3+PkLgFA9Qh2AIDL1OXiJLlLAFABwQ4A4ASG6wBXdg3PsZs2\nbVpxcfE777zj1FyJiYnTp09fsGCBr6+vEGLDhg2rVq3KyMjw9fXt1q3b2LFjfXx8qp1x//79\n06ZNmzFjRlhY2FWsR7J27dovvviiX79+U6ZMsZ++cuXKb775xvZUr9c3bdp0xIgRgwcPVqlU\ntumpqanLly8/cuRIUVGRv79/+/bt//Wvf0VGRtovKjU19ddffz1y5IjBYAgICIiJibn99tub\nN29u3yYxMXH27NkxMTGvvPKKI2Xbl6fRaAICAqKjo2+99dY2bdo4vu0//PDD0qVLpceLFi3y\n8vJyfN7LsGfPnv/+97///e9/mzZtek1XBOCqUP0eZx2aJHcVAP5xDYPdsGHDSktLr2QJa9as\n+eqrr+69994OHTpkZWV99913mZmZ1Saz3NzcGTNmTJgwoaZUd4X1bNy4MSIiIjk52WAweHp6\nVnr1tdde0+v1Qoji4uJ9+/bNmjXr4sWL99xzj/Tq6tWrv/766+jo6Pvvvz8wMDA7O/v3339/\n/vnnn3766QEDBkht1q9f//nnn0dFRY0dOzYgICAjI2PNmjWTJ09+/fXXO3ToIIQoKyubO3fu\n5s2bLyNXSeWVlZVlZmZu3rz5+eeff/jhh2+99VYHZ7/xxhs7d+6cnJy8evVqZ1d9GWJjY4cO\nHfrBBx98/PHHGg0X9wCuheE6wMVdww/Ozp07X+EStmzZMmTIkLvuuksI0b59+5KSkq+++qra\naPXTTz8FBAQMGjToWtRz7ty5tLS06dOnv/HGG4mJiUOHDq3UIDo62pa3evXqVVhYuGrVqrvv\nvlulUp08eXLOnDlDhw594oknbO2HDh36n//857PPPouKimrSpMm5c+e+/PLLfv36Pffcc7Zx\nviFDhrz00kufffbZ559/rlKpTp06dfDgwY8//virr75ytn778oYNGzZnzpxvvvkmMjKyXbt2\njsweGhoaGhp67tw5Z9d7GaxWa3l5+d13371hw4b169cPHz68HlYK4AoxaAe4jno6FPuf//zH\narX27Nlz0aJFOTk5zZs3nzRpknRA0GKxfPPNNwkJCeXl5d27d+/UqZNtCdOnT7dfoLu7u0ql\nsj/EKSkoKPj999+ffPJJlUo1f/781atXL1iwwDbYs2zZsoULF86fP3/WrFm2esrLyxctWrRl\ny5acnBxfX9+ePXs++OCD0qhbVX/88UdYWFi7du3i4uI2bdpUNdhV0rp168TExJKSEk9Pz99+\n+02v1z/88MP2Ddzc3CZNmjRu3LgNGzaMHTt2zZo1arV64sSJ9pvm4eHx4osv6nQ6aWKTJk0+\n/PDDmip0nEqlGj9+fHJy8vLly23BbuvWrb/88svff//t5eXVp0+fhx56yN3dvZaF1NJ70l9z\n69atZWVl8fHxPXv2fO+993744YeAgIBaVvTBBx9oNJqwsLBly5ZNnjy5V69ew4YNW7p06S23\n3FL1zw1ALrUM15HtABdRTxdPaDSao0ePHjhw4MMPP5w3b56vr+8nn3wivbRkyZJ169aNGzfu\n448/jo6O/vnnnyvNW1ZWVlBQsG/fvp9++ummm27y8PCo1GD//v0Wi6Vbt25CiH79+hkMhgMH\nDtheTUxMjI2NrTTIt3z58l9//fWBBx6YNWvWM888s3v37vnz51dbeXl5eUJCgjQWOGjQoNTU\n1KysrNo3NjMzU6/XS3UeOnSoU6dOVQOZn59fVFSUVOehQ4fat2/v7e1dqU2TJk2kPCSE8Pb2\nvvJUJ1Gr1d26dTt06JD0dMeOHTNmzIiNjZ09e/Yzzzyzc+fO2bNn176EWnpv8eLF69evf+CB\nB2bOnNm4ceNvv/1WWmPtK9JoNKdPn05PT3/zzTejo6OFEN27d8/Ozj516tRV2WQAV46DsECD\nUH/nMBUXF0+cOFFKJwMHDvzoo49MJpNOp9u8eXPPnj2lYbCmTZumpaVt2rTJfsZffvll0aJF\nbm5uI0eOfOihh6ou+fDhw2FhYdLFFi1atGjevHlycrKU8y5evHjs2LE77rij0izDhg3r27dv\no0aNhBDNmjWLj4/fu3dvtWX/+eefubm5AwcOFEJ07NixUaNGmzdvvvvuu+3blJeXWywWIYTB\nYNizZ8/GjRtvvPFGaagpNze3R48e1S45JCRk//79Uhunrma4ciEhIQUFBRaLRa1WL1u2LDo6\n+oEHHhBCNGvWbOzYsR9//PGDDz4YFBRU0+y19N7mzZvj4uJuvPFGIcRdd911+PDhjIwM6aVa\nVqRWq8+fPz9t2jRbuo2KinJzczt8+HBERERNZeTl5ZWVldX06qVLl5ztlmsqNzdX7hIqc7WS\n8vLy5C6hsvz8fLlLqMBkMtXzGnukPpJuynCwsS35tdWHb2/32TUrqg5FRUVyrbpaRUVFrlaS\nwWCQu4QKDAaDq5VUUlIidwkVGI1Go9FoexoUFFTL4az6C3ZNmza1jTlJp3wVFRWp1eqMjAz7\nc+Patm1bKdgNGTIkOjr61KlTS5cuzcnJef755ystOS8vLzAw0Pa0T58+a9aseeyxx1Qq1Y4d\nOzw9PWNjYyvNYrVaf/rppz179uTl5VmtViGEv79/tWVv3LixY8eO/v7+UnTr379/1WA3ZswY\n22O1Wn3LLbdI8UV6Wl5eXu2SrVarm5ubEEKj0UgLrzdms1mtVru5uZWXl584ceLf//637aWY\nmBir1ZqWlhYXV+O385p6r6ysLCsr6+abb7a1jI2N3bdvnxCizhWFhobaj1mq1WofH5/ak0e1\nx+Wl8qRX6+6I+mK1Wl2qHuF6JblgPYJ3kRA+ak9/jbcQIq/MoWgiNfZRe8rSdfzV6uRqXeRq\n9QhFlFR/wa7qaVtWq9VoNFqtVvsrPateGNGoUaNGjRp16tQpIiLi1VdfvfHGG+3PwxNCFBUV\n2c/Vt2/fRYsWpaamtm/fPjExMS4uruqqv/zyy7/++mvKlClRUVFarXbBggUbNmyoWnNxcfGu\nXbvMZvOoUaPspx8+fNj+yoP33ntPOvCq0+maNGmi1WptLwUHB2dmZlbbIVlZWdKoWGBgoG1Y\nq36cP39eyvsmk8lisSxevHjJkiX2DWofO6mp96r+Nf38/KQHZrO59hVVPRLt7e1dXFxcSxm2\nhVeSm5trsVgCAgKk3OwKcnNzfX19pUPSriA/P7+0tNTf39+lSvLy8nKd66ALCgrMZrOvr6/9\nv7O8CgsLdTpd7ee/XnUH4hdIDxw8DptXViTjmXZFRUUajeZqnbVy5aSBKC8vL5cqSVT3OSuX\nkpKS4uJiDw8P1ynJaDRaLJZrfWMvx5lMpsLCQr1eX/VTsiYy70Z1Op0Qwv7zu7CwUHpQWlqa\nlJTUqlWrZs2aSVOkG7+dP3++UrDz9va2H+gOCwu74YYbkpOTQ0NDjx49arvtiI3Vat25c+e/\n//3vmJgYaUpNI0Nbt251c3ObMWOGfUT49NNPN23aZB/sWrZsWdOboFOnThs2bCguLq7UID8/\n/+jRo9IFv506dVq2bFl2dnZwcLB9mzNnzuzfv3/48OFX99PXYDDs3btXutOKTqdTq9UjR46s\ndEWIv79/cXHxiRMnYmJipG8JpaWlKpVKq9XW0nvSR479CHZBQYH0oJYV1VRnUVGR6/xrAdcz\nzq4DGhCZhzS0Wm1ISMjJkydtU/766y/pgUaj+frrr5cvX257KT09XQjRuHHjSgvx9/evlMz6\n9u27Z8+enTt3+vn5VUqBQojy8nKTyWS7CMNgMOzcuVMa6qxk48aN3bt3b9OmTaSdfv36bd++\n3Ww2O7KBN998s8Vi+eqrr+yXb7Vav/rqK51OJ52LduONN2o0mtmzZ9ufMWYwGGbOnFntOOKV\nKC8v//LLL0tLS0eMGCGEUKlUkZGRFy5cCPt/TZo00Wg03t7eBw4cePXVV48ePSrNeObMmaCg\nIHd391p6z93dPTg42P6KB9u5d7WsqNo6LRZLYWGh7doRAHJxNtWRAgF5yX/go1+/fr/++uu6\ndevatWu3Z88eKb0JIVQq1W233TZ//nx/f//OnTvn5OQsWLCgefPmHTt2rLSE6OjoNWvWFBYW\n2n6Uom/fvvPnz1+/fn2fPn2qHo9Tq9WRkZEbN27s1q1bUVHRd999FxcX98cff5w7dy40NNQ2\nPCbdvu7222+vNHufPn2+//77Xbt29enTp86ta9as2aRJkz799NPMzMyhQ4dKNyj+448/0tPT\np06dKgWXxo0bP/7445988smzzz47bNiw4ODgjIyM1atXm83mt956S6onMzPz4sWLQojCwkKN\nRiPF37CwMEeiT2pqql6vLy8vz8rK2rBhw/Hjxx9//HHbnZxvv/32adOmLV26NC4uzmg0Llmy\nJDU19csvv+zWrVvjxo1nzpw5ZsyYixcvJiQk3HvvvXX2Xnx8/Nq1a7t27dq2bdstW7ZcuHDB\nVkZNK6r2IMWRI0fKy8ulK2QBNCzc+gSQkfzB7p577ikoKPj++++l+9g99NBDH3zwgXQxwR13\n3OHl5bVmzZrly5f7+PjExMSMHTu26lk4nTt3VqvVtsOLQogmTZqAmvBkAAAgAElEQVRERkYe\nP3580qTqf6D6qaeemjVr1hNPPNG4ceMHHnigVatWBw4cePHFFz/66KOQkBCpzcaNG/V6vXR1\nrb2QkJDIyMhNmzY5EuyEEEOGDAkLC1uxYsWPP/5YUFDg7+8fExPz5JNPhoeH29oMGDCgadOm\nv/766y+//FJYWBgUFNSjR4/bb7/ddmnq+vXrbb/rJYSQflLs6aefHjx4cJ0FSLfuU6lU/v7+\nUVFRDz/8cFRUlO3VuLi4yZMnL1myZOHChR4eHtHR0e+//74Utt544405c+Z8+umnPj4+o0eP\ntp1oWEvvjRkzJj8/f/bs2VqtdsCAAXfcccfHH38snaVUy4qq2r17d6NGjW644QZHehjAtVNT\nRFu2bFlKSsptt9125feiB3AVqao9BNngfPnll4cPH545c6ZLXclyHbJYLEVFRbbLGn7++edV\nq1YtWLDAqYUYDIbx48ePHTvW/gJbx0kXTwQGBnLxRE2kiycCAgJcqiQXvHjCz8/vOr94ohYu\nGOxc8+KJq3gX0ivnmhdPeHp6uk5JCrh4wlU++a7QXXfdlZOTU+k+Kah/S5YsmTBhQmJiYmZm\nZlJS0qpVq4YMGeLsQn766afg4OA6f+EDAABU4irfj69QQEDAlClTpk2b1rZtW9vZY9eDSnfU\ns/fcc8/VdG/ka2f06NEmk+nbb7/Ny8sLCgoaNmyY/b3rHLF3794NGzZ89NFHrjN4AwBAQ6Gc\nz87OnTv/9NNPcldR32bNmlXTSzXd5u2aUqvVY8eOHTt27GUvoVu3btfh3xEAgKtCOcHu+mS7\n1AMA6tPAgQN79Ohh/6s/AFyBQs6xAwDUJ61Wq9frXef6GwASgh0AAIBCEOwAAAAUgnPsAACO\nMr3wlPRAK4R0iz+TELrpNV7FBaCeMWIHAHCILdVVnV7TSwDqGcEOAFA3ohvQIBDsAAB1cCTV\nkfwAV0CwAwAAUAgungAA1KDEYNm5w/Hmphee0tw8Ut2jt3CZ33QHrjcEOwBA9azFxWVrVzo1\nS9nalW7tO6oIdoBMOBQLAKiBh6d6wBD1gCGOz6EeMIThOkBGjNgBAKqn8vLS3DxSCGFJ+MOR\n9tzQDpAdI3YAAAAKQbADANTBkaE4husAV0CwAwDUjdwGNAgEOwCAQ2rKdrrps4h9gIvg4gkA\ngKNsAa6oqMhoNPr4+Oh0OnlLAmCPETsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiC\nHQDAaQcOHFi3bt358+flLgRABQQ7AIDTsrKyjh8/np+fL3chACog2AEAACgEwQ4AAEAhCHYA\nAAAKQbADAABQCIIdAACAQhDsAAAAFEIjdwEAgIZn4MCBPXr0CAwMlLsQABUwYgcAcJpWq9Xr\n9Wq1Wu5CAFRAsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQCcZjAYCgoKzGaz\n3IUAqIBgBwBw2vbt2+fNm3fixAm5CwFQAcEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAA\nABSCYAcAAKAQGrkLAAA0PM2aNbNYLP7+/nIXAqACgh0AwGnt27dv1aqVj4+P3IUAqIBDsQAA\nAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQCcduDAgXXr1p0/f17uQgBU\nQLADADgtKyvr+PHj+fn5chcCoAKCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEO\nAABAITRyFwAAaHj69OnTuXPnRo0ayV0IgAoIdgAAp3l6erq5ubm7u8tdCIAKOBQLAACgEAQ7\nAAAAhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAOM1gMBQUFJjNZrkLAVABwQ4A4LTt27fPmzfv\nxIkTchcCoAKCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAITRyFwAAaHia\nNWtmsVj8/f3lLgRABQQ7AIDT2rdv36pVKx8fH7kLAVABh2IBAAAUgmAHAACgEAQ7AAAAhSDY\nAQAAKATBDgAAQCEIdgAAAApBsAMAOO3QoUObN2/OyMiQuxAAFXAfOwCAo8bNtT3sKUTPtRtr\nbPnt+PqoB0AljNgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQ\nBDsAAACFINgBAAAoBMEOAABAIQh2AAAACkGwAwAAUAiCHQAAgEJo5C4AANBgfDv+nwe5ubnF\nxcWBgYGenp6yVgSgAkbsAABO27x58zfffJOWliZ3IQAqINgBAAAoBMEOAHBFVL/HyV0CgH8Q\n7AAAl49UB7gUgh0A4DJ1uThJekC8A1wEwQ4AcBWQ7QBX0FBvd/Ljjz8ajcZx48Y5NdfBgwd/\n/PHHV199tdL1+atWrUpKSpo8eXJQUFC1M2ZmZn722WcTJ04MCwu7ivVI9u/fv3jx4u7du48a\nNcp+emJi4urVq21P9Xp9s2bNhg0b1qxZM/tmBoNh48aNR44cKSoq8vf3j46O7t+/v16vr3Yh\nNp6enq+++mrthUnzxsTE3HPPPZVe+uqrr06fPj1u3LjIyEjHt9QR+/btW7NmzeTJkz08PK7u\nkgFcXW/Fr5e7BACVNdQRO29vb29vb2fnys/PP3jwYFlZmf3Ev//++/vvvz948KDZbK52LrPZ\nPG3atIiIiJpS3WXXI/ntt9/Onj27bNkyi8ViPz0nJ+fgwYNt27aNiYmJiYkJDQ3dt2/fE088\nkZSUZGtz+PDhRx555Mcff1Sr1RERERaL5dtvv33iiSdOnjxpv5DWrVtHVxQVFVVnYTk5Oamp\nqStWrCgtLbWfnp+fv379+oMHDxYXF1/eJteiS5cuZWVls2bNuupLBnAVNW7cuOpEBu0A2TXU\nEbuRI0derUV99tln7du3379/f00Nfvvtt5ycnPvuu+9a1JOfn793796nnnrqk08+2bdvX/fu\n3Ss1GD16tJeXl/S4vLx86tSp3333XVxcnBAiNzf33Xffbdq06euvv+7j4yO1yc3Nff311997\n771PP/3UNuj173//27YQp4SEhBQXF+/Zs0dao2T79u3h4eG27Hh1qVSqRx99dNKkSXv27ImN\njb0WqwBw5foYpshdAoBqNNRgZ3/oc9GiRSqVKj4+fsWKFdnZ2eHh4aNHj/b19ZVaJiYmbtmy\nxWKx9OjRo+qg2h9//JGRkTFlypSagp3ZbF62bNmoUaPc3d1Xr179559/2h/BTE5OXrVq1Suv\nvLJ8+XL7Q7EHDhzYsmXLpUuXfH19e/bsGR8fX9OGJCQkeHt79+vXLyEhYdOmTVWDnT03N7ee\nPXvOnz/faDTq9fpVq1YZDIbnn3/eluqEEAEBAc8999zTTz+9YcOGf/3rX7V1ogPKy8u7deu2\ndetW+2C3devWjh072ge78+fP//bbb3///beXl1efPn169+5teykxMXHr1q0Wi6V379433HDD\nN9988+KLL0p/nZp6qUmTJn379l20aBHBDnBNtYzMqX6Psw5NqulVANdaQz0Ue+bMmfT0dOnx\n+fPnk5OTv/jiiy5dutx88827du2aMWOG9NL27dunT5+u1+t79Oixf//+ZcuW2S+koKDgu+++\ne+SRR2o5nevgwYMFBQVS5ggLC9u1a9exY8dsr27YsMFisXh6etrXs2PHjtdff12n08XFxYWE\nhHz88cfVnuUm2bRpU//+/dVq9cCBA3ft2lXnwc2SkhK1Wq3VaoUQ+/bt69ChQ0hISKU2ERER\nLVu23LVrV+2LckR5eXnv3r13795tMBikKdnZ2UeOHOnZs6etTXp6+rPPPpueni5t74wZM5Yu\nXSq9tHXr1unTp+t0uh49emzZsmXevHkHDx60Wq2irl6Kj48/duzYxYsXr3wTAAC4fjTUETt7\nKpXq9OnTc+bMCQ4OFkLk5uZ+8cUXFotFrVYvX768Xbt2zz33nBDipptuqnS5wNy5c9u1axcX\nF1fLUcWUlJRGjRo1adJECBETE+Pn55eUlNS6dWshhMFg2L9//4QJEyrNEhwc/Pzzz/fp00d6\nWlhYuHHjxuHDh1dd+MmTJ9PT059++mkhRO/evb/88stt27YNGzaspmIyMzM3btzYuXNntVot\nPY2Ojq62ZXh4+OHDh21PX3vtNTe3CiH+1ltv7d+/f00rsrFard26ddNqtcnJyYMGDRJCbNu2\nLSIionnz5rY2CxYs8PPze/fdd6WqfHx8Fi1adMstt3h4eCxfvrxDhw5S/w8ePPiZZ54RQqhU\nqjp7KSYmRqVSpaSkDB48uKbaioqKKp2VKCkvLxdCFBQUSCtyBRaLpbCw0KXqEUK4VEllZWVF\nRUUuVY8Qori42HVKslgsZWVlJSUlchci/HfVuI+SqH6PW9tuRpxPh/qpx8ZisZSWlppMpnpe\nb02kfVFJSYnrlCT971c6bVpGUheZTCaXKslqtVY6F19GUheZzeb8/HzbRF9f31p2TUoIdkKI\nZs2aSalOCBEYGGi1WvPz8/39/U+cOHHnnXfamvXo0SMlJUV6nJKSkpSU9Nlnn9W+5OzsbNuQ\nmJubW3x8fHJy8tixY4UQO3futFqtVQ+ztm7d+vz58x9++GF+fn55eXlWVlZNb5GNGzfecMMN\nLVu2FEK4u7v36dNn06ZNlYLdm2++KWUyg8Fw7ty5G2644bHHHpNeKi8v12iq/wtqNBr7a0G6\nd+8uDfLZhIaG1r7hNlqttnfv3lu2bJGC3datW/v162d71Wq1/vXXX7fccouU6oQQvXr1+uGH\nHw4fPty5c+f09PR7771Xmq5Wq/v37z9v3jxHesnT09PLyys7O7uWwsrKymr533Odf0uJq9Uj\nXK8kV6tHuGRJ1X6ZcUE3H55yscsqWVbtal1ksVhcrSQpK7gOuqhO5eXljpekkGBnf2WAFGPL\ny8tLSkosFovtZDshhJ+fn/TAbDZ//vnnY8aMadSoUe1LLigosF9C375916xZc+7cubCwsB07\ndnTu3Nn+VcnixYt//vnnO++8c/DgwVqt9vfff6/2BD6LxbJ161Y3N7cXXnhBmpKfn3/+/PmM\njAz71BUbG+vu7i6E0Ol0YWFhHTp0sI29+fv7X7p0qdqy8/LyAgICbE9Hjhx5eRdPSPr37//6\n66/n5+cXFxefPHnypZdesr1kMpmMRuOuXbtOnTolTZHefBcvXqza//a9XWcv+fr6FhQU1FKV\nj4+PdFS3koKCgvLycl9f30qDlDIqKCjw9vZ2nXqKiorKyspcqouKioo8PDxsXw9kV1xcXFpa\n6u3tXdN3p/pnMBi0Wm2lb2j1T7u5r4Mt/f39r2klVRkMBo1GI+0wXYHRaDQajZ6eni5VkhDC\ndj8s2ZlMppKSEr1e7zolmc1mi8XiOvfbMpvNBoPB3d3d/jZttR9JcJV91rUgfUjYD1zZThRL\nTk4+f/78rl279uzZI4SQjm58/PHHnTt3to0wSdzd3e1H0aOjowMDA5OSkm699dY///zziSee\nqLreVatW3XrrrbZ7vyUnJ1db3q5duwoLC++//377D9eVK1du2rRpzJgxtikjRoyoKZNFRUUd\nOHCgvLy80sdzaWnpkSNHBgwYUO1clyEmJiYgIGD79u1FRUVRUVEhISG2yKXValUqVYsWLTp2\n7GhrHxcXFxUVJfW//QC7/eM6e8lkMul0ulqqqikESO94jUbjOqlFpVKp1WrXSS1SF7laSWq1\n2nVSlK2LXKokl6qnTtrNfev5Kgo3Nzc3NzfX6SJpF+SCJblOPdKHgkt1UVlZmdVqdZ16pLFM\np7rIVUq/FvR6vY+PT0ZGhm2KbVSpZcuWEydOtE3PyclJS0vr0qWLdPKcPX9/f/sz8KTLb3ft\n2tW0aVMhRK9evSq1LysrKygosA25Wa3Wffv2VVvexo0b27dvf8cdd9hPzMvLS0hIuPfeex05\ns2fw4MEJCQkrV6687bbb7KevWLHCYDAMGTKkziU4SKVS9e3bd+/evZcuXRo6dKj9S2q1umnT\npv7+/jfffHPVGb28vLKysmxPjxw5Ij1wpJcqjZUCkB23qQNcn6sMaVwjsbGxO3bsOHfunBAi\nLS3NNiwUFhY23I50Cv+AAQOq3l+jZcuWZ86csR/269u377FjxxISEnr27Fl19Fij0YSEhEhn\n8lkslrlz5/r5+UkHJe2bSbevs106YBMfH5+VlZWamurI1nXq1Ommm2767rvv5s2bl5mZaTab\nz58///3338+fP/+uu+6STt2TlJaWmquo9jhmTfr373/w4MHTp09XPadw8ODBGzduPHr0qBDC\narWuWrVq0qRJ0oB/ly5dtm3bJp0ql5qaKo2POtJLp0+fLi0tbdWqleMVArimLiPVEQSB+qfk\nETshxP3335+Wlvb44497e3vrdLrRo0fPmTPHqZMiu3bt+uWXXx4+fLhTp07SlKioqODg4J07\nd77yyivVzvLQQw999NFH48aNKy0tHTRo0COPPDJ58uQJEya8//770tW1QoiEhASr1Wp/vzdJ\nmzZtQkJCpME8R8p77LHHQkNDV6xYsWTJEmlK48aNn3zyyUrDddLVHpVMmzatpotqq2rVqlVw\ncHBwcHDV82ZGjRp14cKFqVOnBgQEmM1mtVo9adIkKfKOHTv21VdfnTBhQlBQkK+v75133vnV\nV19JxwJq76U///zTw8PD8fIAAIAQQuXUsI3rOHPmjMViiYiIEEKcPXu2tLTUNkBVWFh46tSp\nqKgo6Szj8vLyU6dOlZeXR0REmEymEydO2F6yMRqNx44da9u2bbWnuL799ttubm72t0o5e/Zs\nXl5edHS07RQl+3qkGjIyMho1aiRdwXDhwgWj0di8eXPbAdYzZ84YjcY2bdpUXd3Zs2dLSkra\ntGmTnZ2dkZFhv5aaSFeVFhQU+Pv7h4SE2B/GlRZS7VytWrWq9Ju5lWRnZ2dnZ9t+fOzvv//W\narXSNcIWiyU1NTUiIsJ2z+eCgoLz5897eHiEhYXZF2yxWNLT07VabXh4+Jo1a3744YfFixfX\n3ktWq/XRRx/t3bv3gw8+WPuGVys3N9disQQGBrrOOXa5ubm+vr6uc0Jbfn5+aWlpQECAS5Xk\n5eXlOue1FBQUmM1mPz8/2S9WsCksLNTpdK5zGv6yZctSUlJuu+22zp07y13LP4qKijQajeuc\nhm8wGAwGg7e3t0uVJISofc9fn0pKSoqLiz09PV2nJKPRaLFYruRyw6vLZDIVFhbq9XrHf7bU\nVXajzgoPD7c9tr+nmhDCx8cnJibG9tTNzc2W+Tw9Pe1fstHr9dVOl9x3332TJ08+evRo27Zt\nbWustFL7eqQa7H8Nouo9hCu1t2dbsjRCVlMze25ubqGhodXewcTxhdQ5b7NmzWyP1Wp1pR7z\n9fWtekrcqlWrtmzZ8vLLLwcGBubn569bt65r1662V2vqpT/++KO4uPj222+/vLIBALhuNdRg\nV59atmw5duzY//73vzNnznSdbxVXxXvvvVfTS3fccYdtrO6y9e3bd9u2bePHjw8ICMjNzW3b\ntu3DDz9c+yznz5+fO3fulClTuHICAABnEewcMmrUqDZt2phMJoUFu6pX9doEBgZe+fL9/f3/\n85//ZGVl5eXlBQUFOTJ2qFKp3n333cjIyCtfOwAA1xuCnaMcvJqhYanlB7uuosaNGzdu3NjB\nxo7/JAYAAKiEYAcAcFrPnj2joqKkO3oCcB0EOwCA0/z8/HQ6nev88hIAiavcDwIAAABXiBE7\nAMDl8PnwbSGEyW6KbvosuYoBIGHEDgDgNO07L1edaHrhKdMLT9V/MQBsCHYAAOfUnt7IdoCM\nCHYAACeQ2wBXRrADAFxlhD9ALgQ7AICjSGyAiyPYAQAAKATBDgAAQCEIdgAAAApBsAMAOIpb\nEAMujmAHALjKyH+AXAh2AAAn1BnaSHWAjAh2AADn1BLdSHWAvDRyFwAAaHhKX3vfaDT6fPi2\nbQqRDnAFBDsAwOV6+0OdTid3EQD+h0OxAAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQ\nBDsAgNOOHTuWlJR04cIFuQsBUAHBDgDgtPT09L179+bk5MhdCIAKCHYAAAAKQbADAABQCIId\nAACAQhDsAAAAFIJgBwAAoBAEOwAAAIXQyF0AAKDh6datW0RERHh4uNyFAKiAYAcAcFpQUJCX\nl5eXl5fchQCogEOxAAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAJy2\nYcOGTz/99PDhw3IXAqACgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEI\ndgAApwUGBjZv3tzb21vuQgBUoJG7AABAwxMbG9uhQwcfHx+5CwFQASN2AAAACkGwAwAAUAiC\nHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAJyWnp6+d+/enJwcuQsBUAHBDgDgtGPHjiUl\nJV24cEHuQgBUQLADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACiERu4CAAAN\nT7du3SIiIsLDw+UuBEAFBDsAgNOCgoK8vLy8vLzkLgRABRyKBQAAUAiCHQAAgEIQ7AAAABSC\nYAcAAKAQBDsAAACFINgBAAAoBMEOAOC0DRs2fPrpp4cPH5a7EAAVEOwAAAAUgmAHAACgEAQ7\nAAAAhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAOM3X1zckJMTDw0PuQgBUoJG7AABAw9OrV6/O\nnTv7+PjIXQiAChixAwAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBLc7AQDU\nYdzcqtO8hfCuOvXb8de+GgA1Y8QOAABAIQh2AAAACkGwAwAAUAiCHQAAgEIQ7AAAABSCYAcA\nqM3aFCcaL9l9zeoA4ACCHQCgNlq1M425iRYgK4IdAKA2Q9o70fhfXa5ZHQAcQLADAABQCIId\nAACAQhDsAAAAFIJgBwAAoBAEOwAAAIXgwnQAQB2+HV95SlFRkdFo9PHx0el0clQEoHqM2AEA\nACgEwQ4A4LTNmzd/8803aWlpchcCoAKCHQDAaSP17xuNRovFInchACog2AEALsdb8evlLgFA\nZQQ7AIBzVL/HSQ+6XJwkbyUAKiHYAQAAKAS3O3E5J06c2LVrV7UvhYeHx8fHX8nCt2/fbjab\nBw0aVGl6Zmbm5s2bhw4dGhwc7PjSzp49u3379ttvv12n09W0ZAAKYxuusz21Dk2SqxgAlTBi\n53IuXbq0//+tXbt2yZIltqdnzpypaS6DwbBy5co6F759+/bNmzdXnZ6VlbVo0aKcnBynSj1z\n5syiRYtMJpMQYv/+/bt373Zq9lo4uDkAAMAeI3Yup3v37t27d5cez549+8CBA9OnT69zrrS0\ntJUrV44cOfIaV1ejJ5544iouTfbNAVCtSsN1tokM2gEugmDXwJjN5l27dmVmZnp4eHTu3LlZ\ns2ZCiL/++mvlypVFRUWLFi3q2rVr27ZtCwsL9+zZk5OT4+vr27VrV6cOsCYmJqpUqtjY2B07\nduTk5DRv3rx79+4qlUp69cKFCzt37rRYLF27drWfy/5Q7LZt29RqdWRk5Pbt27t169aiRQuL\nxbJr166///7by8ure/fu9vWYzebk5OQLFy40bty4V69eWq226uZchY4DcMWqTXUAXArBriG5\ndOnSyy+/XFJS0qFDh0uXLn399dcTJ0685ZZbTCZTbm6u1WotKCgwm80nT558+eWXQ0JCwsPD\nL168+PXXX7/++usdO3Z0cC1JSUmFhYW//fZbixYt1Gr14sWLBw8e/MgjjwghTpw48dJLLwUF\nBUVGRq5fv94+cm3fvr24uFgKdjt27DCbzQsXLgwICIiKiiooKHjllVdyc3M7deqUlZU1d+7c\nl19+WcqF+fn5L774osFgiIyMXLNmzY8//jht2rRKm3MNOhLAVcagHeAiCHYNybx58wwGw+zZ\ns/38/IQQ33333dy5c+Pj42NjYw8dOlRYWDhx4kQhxLZt2wYMGDBx4kRpmG3atGk///yz48HO\nzc3tzz//nD59ert27YQQ/v7+ixYtmjBhgkqlkrLazJkzdTqd0Wh87rnnql2CRqPZvXv3iy++\nGBsbK4T47LPPLl68+Omnn0oDdZ988smsWbPmzp2rVqsXLFhgsVg+//xzLy8vg8Hw2GOPLV68\neMKECfabUxOj0Wi1WqtOlyYajUbbKKPsrFar0Wh0c3OVU1rLy8uFEK5WkslkKi0tlbuQf0j3\n3TWZTGVlZXLX8g+LxWI2m+v/hsCXygq+y1wthHjt1JzaW6p+j3vnhgl+Gu+Hm9xaL6VVZrFY\nrFZrtbsFWUhvntLSUlcrqaSkRO5C/iH9y5eVlblOSWVlZeXl5S5Vj6jSRR4eHrXMQrBrSHbt\n2tW/f38p1Qkhhg4d+uuvv6akpPTt29e+Wd++fdu0abN+/fr8/Pzy8vL8/PzMzEynVtSkSRMp\n1QkhwsLCSktLCwoK/Pz8Dh48ePPNN0u/+a3X6/v3779w4cKqs6tUKi8vLynVCSGSk5P79+9v\nO/w6YsSIjRs3pqWltWvXLjk5eciQIV5eXkIIT0/PadOmabVaB4s0Go21fOgaDAaHN7c+uM5u\nwsbVSnK1eoQQRqNR7hIqkCVlnjFm1BnpbF47Nae5e8g9PnJeHS9dzuU6TCaTq5XkakdCzGaz\nq5XkOl8yJWVlZfb//nq9vpaRC4Jdg1FSUlJUVNS4cWPbFCkqZWdnV2q5c+fOadOmtWvXLioq\nSspJzn7L9/f3tz1Wq9VCiNLSUoPBYDQaAwMDbS+FhITUtARbjDObzfn5+RcuXFiyZIk0RfqH\n+fvvv1u1apWfn29/vl2TJk0cL1Kv11f7PbikpKS8vNzT09N1RuxKSkp0Op3rDI9JvwTl4eHh\nUiW5u7u7VD0Wi0Wv10vvf1dgMpk0Gk391xOuC33nhgmOZw2eKRQAACAASURBVLvnw8dIX9Xq\nn9lsdnNz02hc5XOttLTUbDbrdDqXKkkI4fj352tN6iJ3d3fXKUkasXN3d5e7kH+UlZVJ//vS\nkIqk9k83V3m34bJV/QPPnTs3Li5u6tSp0lOTyfT3339f4TKrVUterLQjy8vLS09Ptz3t27dv\nYGCgFMsu+4uRXq+vdro0yqLX610qJbhURJCO6LlaSa72+WexWHQ6nUt93ri7u9f/500z4fGq\nzzjHg92TEf++pvXUwmKxaDSamvYM9c9qtZrNZq1W61IliboO5NUzs9ms0WhcpyTb9165C/mH\nNOLrVBe5ym4UdfLw8PDx8blw4YJtivS4UaNG9s3KysqysrJuv/1225S0tLSrVYC7u/ulS5ds\nUxw5wuvu7u7v79+tW7f77ruv6qt+fn72Czl58qTJZLIdBQbgCpy6GJarKAB5ucqQBhzRs2fP\nxMTEwsJC6enatWv1en2nTp2EEGq1WjqNQ8r1trSUmJiYkZFxVU5fUKlU0llx0oqKioq2bNni\nyIxxcXEJCQlFRUXS06NHj86cOVMaV+vevXtiYmJubq4QwmQyTZs2LSEhwX5zAMjrMm5xwl1R\nABkxYteQ3H///YcPH3766ac7duyYlZV15MiRp59+2tvbWwgRERGRn5//6quvxsXFjRgxYunS\npZcuXSouLjYYDI8++uh777334YcfPvzww1dYwJgxY1555ZUnn3wyIiLixIkT3bt3X716dZ0X\nfI0ZMyY1NfXxxx/v2LGj0Wj8888/hw8fLh2buO+++w4ePPjUU0+1adMmPT1dq9Xee++9lTZn\n+PDhV1g2AADXCZXrXIYNXBW5ubkWiyUwMNB1zrHLzc319fV1nRPa8vPzS0tLAwICXKokLy8v\n1znHTrqHop+fn+ucY1dYWKjT6VznnO5ly5alpKTcdtttnTt3lruWfxQVFbnUOXYGg8FgMHh7\ne7tUSUIIT09PuQv5R0lJSXFxsaenp+uUJJ1jJ9cFQFWZTKbCwkK9Xi8N4jjCVT75AAAAcIUI\ndgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUwlWuQQMANCAdO3Zs2rRpWFiY3IUAqIBgBwBwWpMm\nTfz9/X18fOQuBEAFBDsAgHNMLzylFeKfW/xNnyVvMQDsEewAAI4yvfBUtVN0xDvANXDxBADA\nIVVTnSMvAahPBDsAQN3qjG5kO8AVEOwAAFcH2Q6QHcEOAFAHEhvQUBDsAAC1Mb35ouONzdPf\nunaVAKgTwQ4AUKsSg+NtrcaSa1cIgDoR7AAAtXHqVia6N6Zdu0oA1IlgBwCoA7epAxoKgh0A\n4Oog/wGyI9gBAOpWZ2gj1QGugGAHAHBILdGNVAe4CIIdAMBR1QY4Uh3gOjRyFwAAaEikGFdU\nVGQ0Gn18fHQ6ndwVAfgfRuwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAApB\nsAMAOO3cuXOHDh3Ky8uTuxAAFXAfOwCA01JTU1NSUvz8/Bo3bix3LQD+hxE7AAAAhSDYAQAA\nKATBDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCcLsTAIDToqOjg4KCQkND5S4EQAUEOwCA08LC\nwoKDg318fOQuBEAFHIoFAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4A\n4LRt27bNmzfvxIkTchcCoAKCHQDAaSUlJQUFBWazWe5CAFRAsAMAAFAIgh0AAIBCEOwAAAAU\ngmAHAACgEAQ7AAAAhSDYAQCcptVq9Xq9Wq2WuxAAFWjkLgAA0PAMHDgwLi7Ox8dH7kIAVMCI\nHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoBMEOAABAIQh2AACnZWZmHj9+vLCwUO5C\nAFRAsAMAOC0lJWXdunXnzp2TuxAAFRDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAh\nCHYAAAAKoZG7AOAq8/T0tFqtKpVK7kL+x9PT083Nhb5EeXh46HQ6VyvJ1epxd3dXq9VyF/I/\ner3eperp1KlT06ZNw8LC5C7kf1ztXe3u7u7m5qbVauUu5H/c3d3lLqECrVbr7e2t0bhQFNFq\ntS5Vj0aj8fb2dup/X2W1Wq9dQQAAAKg3LvTlBgAAAFeCYAcAAKAQBDsAAACFINgBAAAoBMEO\nAABAIQh2AAAACkGwAwAAUAgXugsfcNm2bduWlZU1evToal/Ny8tbu3ZtpYmjRo3S6/XXvjRX\ncfTo0X379g0bNiwgIKCmNtnZ2bt37y4pKWnVqlWnTp3qszzZObLtS5cuNZvN9lN69+7dokWL\neilQNlarde/evadPn/b29u7Zs6e/v/+VNFOq48ePHzx4UKVSdenSJTw8vGoD9kKirh21pM6e\nVLar8llGsEPDZjQaP//884SEBE9Pz5r+GTIzMxctWhQVFWV/P/ERI0ZcP7vU5cuX//DDDxaL\npWfPnjUFuz179kybNq1JkyYBAQELFy6Mj49/9tlnXeoHPK4dB7d94cKFoaGhfn5+tint27ev\n30rrW1lZ2VtvvXXkyJHo6OgLFy58//33b7/9duvWrS+vmVItXLhw8eLF7dq1s1gs33333aOP\nPjps2LBKba7zvZAjO2rhWE8q1VX8LCPYoWGbOnWql5fXrbfeunHjxpraGAwGIcRrr73m4+NT\nj6W5ijlz5iQlJY0bN27OnDk1tTGbzbNmzRo4cODjjz8uhEhLS5s6dWqvXr169+5dj5XKw8Ft\nLysrKysrGzNmzPXQJzarV68+evTozJkzmzVrZrVaP/jgg1mzZs2ePfvyminSyZMnf/7558mT\nJ/fv318IsXz58jlz5vTo0SMwMNC+2XW+F3JkR+1gTyrVVfws4xw7NGzdu3d/9913azm8KIQo\nKSkRQlwn34yrCg4O/vjjj9u0aVNLm5SUlLy8vDvvvFN62qZNm5iYmC1bttRLgTJzcNulXer1\n9i7aunVrfHx8s2bNhBAqlWrUqFGnT58+derU5TVTpK1bt4aEhEhZRAgxfPhwtVqdmJhYqdl1\nvhdyZEftYE8q1VX8LCPYoWG7//776/x15JKSErVa7VI/xV2fRo0aZX/0sFonTpzw9fUNCQmx\nTWnduvXx48evcWkuwcFtl3apHh4e9VqcrKxWa3p6emRkpG1Kq1athBCVOsfBZkp14sQJ+4PO\nWq22RYsWJ06cqNTsOt8LObKjdrAnleoqfpZxKBbKV1JSotPpsrOz9+7dazKZIiIiYmJi5C7K\nteTm5lY6293f3z8nJ0eueuqTg9suBTshREJCwqVLl0JDQ2NjY5X9OV1QUFBWVmbfOe7u7p6e\nnpU6x8FmSnXp0qWwsDD7KTW9f9gL1c7BnryeOfguIthB+QwGg8lkevrpp1u0aGE2m+fOnRsb\nG/vyyy/X+fXo+mE2mytlFI1GU15ebrFYFN9LDm67dCj2rbfeatasmV6vT0tLCw4Ofuedd4KD\ng+u74vpSWloqhKjUOVqtttKlwQ42U6rS0tKq7x+TyVSpGXuhOjnYk9czB99FBDs0JCtXrszK\nypIe33333Q6ehtypUydPT88+ffpIpy/s3bv37bffXrt27YgRI65hrTLZvXv3/v37pcf9+/ev\n/dQ6G3d3d+nj2cZsNru5uSnyU6dSFzm47Y0aNXr44Yejo6OlY44XLlyYMmXKnDlzXnrppXqr\nvJ5Jn7JVO8fd3f0ymimVVquttO2lpaU6na5Ss+tqL3R5HOzJ65mD7yKCHRqSzMzMs2fPSo8t\nFouDc0VFRUVFRdmeduvWLSIi4tChQ4rcpebl5Z05c0Z6XFxc7OBcQUFBubm59lNyc3OVOhZV\nqYsc3PZGjRqNHDnS9lQ6yzshIeEaFysnX19fd3d3+84xGo0lJSWNGjW6jGZKFRQUdOnSJfsp\nly5dqnp3w+tqL3R5HOzJ65mD7yKCHRqSRx555DLmysnJsVgs9mfHV/peqCRDhw4dOnSos3O1\nadOmsLAwIyMjNDRUmnLkyJG2bdte7epcQqUucnNzc2Tbi4qKcnNzmzdvbptiNputVms9FCwX\nlUrVqlWrtLQ025SjR48KISoNAzvYTKnatGmzadMmq9Uq3fjQaDSePn266v/gdbUXujwO9uT1\nzMF3EVfFQpmsVuu+ffsyMzOFED/88MOLL75oG1FISko6e/Zs165dZS3QJRw9elS6dLFDhw6N\nGjX6+eefpekpKSlHjhwZPHiwrNXVk9q33dZFO3bseOKJJw4dOiRNz8zM3LZtm+LfRYMGDdqx\nY4c0TG6xWJYsWdK2bVvpDPeCgoJ9+/ZJo8K1NFO8AQMG5OTk2O49tmzZMrVaHR8fL9gLOcC+\ni2rpyevZZbyLVMr+xgllS0tL+/7774UQFy9evHjxYnR0tBCia9euo0ePNpvNo0ePHjNmzF13\n3XXx4sVXXnklLy8vKiqquLj4+PHjgwcPfuKJJ9zclP/Fpqys7PXXXxdCGAyGkydPRkZG6vX6\ngICA559/XggxZcoUDw+Pd955RwiRkpLy7rvvNmrUKCAg4NChQ8OGDZs4caLM1deXWrbd1kVl\nZWXvv//+3r17o6Ki1Gp1WlraDTfc8Oqrryr7t7PKy8vff//9AwcOREdHZ2RklJSUvP/++9Kw\n5b59+958881p06ZFR0fX0ux6sHTp0nnz5rVr185sNqenpz/zzDPSzdjYC0kc3FGLmntS8a7u\nZxnBDg1YRkZG1ZOcIiIievXqZbFYFi9e3LFjR+lHnywWy+7du8+fP+/p6dmmTZuWLVvKUK4c\npH6oNNHLy0s6XWzDhg0ajWbQoEHS9JycnJ07d5aUlERFRSn+x7IqqWnbK3XRoUOHTp48KYRo\n3rx5p06droefXJN+BPbUqVO+vr5xcXG2K5ak/74hQ4ZI59LV1Ow6cfLkyQMHDqjV6tjY2KZN\nm0oT2QtJHN9Rixp6UvGu7mcZwQ4AAEAhlD8IDAAAcJ0g2AEAACgEwQ4AAEAhCHYAAAAKQbAD\nAABQCPWbb74pdw0A0OC9/fbbx44dk24WumTJksWLF8fGxjaI30stKSlZtGjR2rVrrVZrixYt\nKj2tz0oOHjz4+eefBwYGNm7cuD7Xe4389NNPK1eu7NmzpyJ/cxkui9udALiuZWdnf/rpp5GR\nkffdd9+VLEej0cTGxiYnJwsh7r777p9//jkjI6NJkyZXqUynpaSkLFu2rJYGU6dO9fT0tFqt\nvXv3Tk5O9vLyGj9+/MyZM+2ffvLJJ/VWcEFBQbdu3YKCgrZt26bVaqWJJ0+e/OOPP7Kzs0ND\nQ2+55Rb7wFfTBg4aNKhfv37S4+3btycmJnp7e48aNarqTdE2bdqUlJQ0depU2+pqZ7VaDxw4\nsHv37uzsbD8/v/Dw8EGDBnl6etoaVHov/fXXXz169JgwYcKsWbOc6QngylgB4Dp2+PBhIcRN\nN910hctRq9U9e/aUHh87diwpKUn6MdlrwWw263Q6aVCtJvPnz69953/x4kWr1Sr9YNrAgQPL\nysqqPq2fUiV33323l5dXenq6bcqUKVOkW+prNBohhKen548//mh79Ysvvqh2u9555x2pwYwZ\nMzw8PJ599tmRI0f6+/ufOHHCfnUXL14MDg5+6aWXHNyQhISEmJiYSuvy8/N77bXXLBaL1Kbq\ne2n27NlCiOXLlzu4FuDKcY4dAFxlkZGRvXr1cnAc6DKkpKSYTCZHWr7wwgu5NQgKChJCZGVl\nCSG6desmHS6s9LTeSt29e/dPP/302GOP3XDDDdKUr776asaMGT179kxNTTWbzXv37m3WrNnY\nsWNTU1OlBnl5eUKIjRs3llT00ksvCSGMRuP777//1ltvffTRR8uXLw8LC/voo4/s1/j0008H\nBgZKP7hXp19//XXIkCFpaWlTp07du3dvdnb28ePHv//++9DQ0Hfeeeeee+6pacZHH300IiLi\nhRdeKC8vd2RFwJXTyF0AALiWpUuXHjx48I033sjOzl61atWFCxdatGgxfPhw+5/Jslgsa9as\nSU1N9fHxufnmmyMiIuyXsGTJkoMHD06ZMsXb2/uXX35JTU194403tm7dunXr1hEjRnTu3Flq\ndu7cuQ0bNmRmZvr5+fXu3btLly6VKjl27Ngff/xRUFDQsmXLW265xcvLSwjx448/Ll26VAix\nYMGC5OTkcePGhYeH17Qter2+ll+zXbBgwaZNm4QQSUlJb7755pEjR6QDi9LT2NjYESNG1E+p\nb775pl6vnzx5sm3K7NmzdTrd4sWLw8LChBBdu3adO3duv379ZsyY8e2334r/D3YhISF6vb7q\nAo8fP37p0iXpJ+RVKlVcXNzOnTttr65Zs2bRokUJCQnVzlvJhQsXHnjgAZVKtWHDBttB3qCg\noFatWt1+++0DBw5cvHjxfffdd+utt1adV6PRvPjiixMnTvzpp5/uvffeOtcFXAVyDxkCgJyq\nHj578MEHhRDr1q0LDQ3t06dP3759NRpNWFjY8ePHpQZFRUV9+vQRQnh4eDRt2lSr1c6bN0+j\n0dgOxUq/aJ6RkWG1WqXhnF9++UUIoVarf/nlF6nN+++/r9Vq3d3dW7ZsKcWgUaNGGQwGWxkv\nv/yydCBSCpSNGzdOTEy0Wq1PPvlkaGioEKJly5adOnX6888/q90u6VDsG2+8Ucu2P/bYY5GR\nkUKIkJCQTp06CSHsn7777rv1U2pOTo6bm9vw4cNtU8xmsxCiS5culVqGh4eHhoZKjydOnCiE\nOHv2bLXLlALrX3/9JT2dPHlyixYtpMeFhYXh4eGTJk2qpWfsSZcYTp06tdpXU1NTt23bJh2N\nrfawfl5enlqtvvnmmx1cHXCFCHYArmtVP4zHjx8vhGjXrt2RI0ekKQsXLhRCPP7449JT6fjd\n+PHjS0tLrdb/a+/O42pO9ziAP+e0F9pOy2gRSSJLE9oQhsJQlq69S2qGkuU2XrbrSlz7aMhW\nYlRkq5eGGKJoL0WhbEUpqpPQVEpJnfvHc+d3j9PpOK3mnj7vlz9+y/N7nuf31PCdZ/vxsrKy\nTExM2Gy20MBu4cKFhJARI0bcunWroaGBzl2jcd7ixYvfv3/P4/E+fvzo7e1NCPHy8uIvcfr0\n6W/evOHxeHfu3FFXV+dwONXV1Tweb8eOHYQQcebYiQ7seDxeQkICIWTt2rVCTzunqufOnSOE\n+Pr6MldqamoIITY2NgIpaQ9ceXk508gvX7708/Nzd3f38PAICQmpq6ujKe/fv08ISUxMpKeu\nrq7m5ub02NPTU1dXt6KiIj8/PyAgIDAw8NWrVyKqZ2VlRQh5/PixyIbk8Zqfr2lhYaGoqFhb\nW/vFHADaDnPsAACEWLJkibGxMT2eOXMmm83OyMigp6dOnVJQUPDz86OT+k1NTTdv3tzcJCqa\nxs7ObsyYMWw2m85d2717t4aGhr+/P+0Ak5GR2bx586BBgwIDAxsaGgghvr6+8vLyQUFBdCac\nubn5li1bBgwY8PDhwxa9RWxs7GZhYmNjxcyhE6pKlxJbWloyVxQUFDQ1NZ88eUK77igej1dQ\nUEAIefv2LSGkoqKCEDJo0KCffvrp6tWrgYGBf//7383MzIqKigghffv27d69e2JiIiGksbEx\nISGB7kSTkpJy+PDhw4cPZ2Rk9O/f/9dff/X39+/fv/+9e/eaq15ubq6cnFz//v3FfJ2mrK2t\na2pqHjx40OocAMSHOXYAAEJYWFgwx3JyckpKSlVVVYSQysrKvLw82gfDJBgzZozo3MaNG8cc\n19TU3L17V1NTc/ny5fxp6urqqqqqnj17pqenl5mZOWzYsB49ejB3PTw8PDw8WvoWcXFxcXFx\nQm99sc6dVlW6YkNg7zonJ6fDhw/7+Phs27aNXtm2bVtxcTEh5NOnT4QQDodjamrq7u7u5uYm\nKytbU1Pj5eUVEBCwaNGiGzduKCoqenp6+vj4lJaWPn78uLCw8B//+MfHjx/d3NxmzZo1depU\nGxsbW1vbqKioxsZGKyurTZs2Xbp0SWj1qquru3XrJv7rNEVfjb4mQEdDYAcAIASHw+E/ZbPZ\nPB6PEPLmzRtCiIaGBv9dDQ0NFoslIjf+qKW0tLSxsbG2tlagl0hVVdXCwqK+vp4mECiiddav\nX79hw4am18XcOblzqlpWVkaaNPiWLVuuX7++ffv2K1euDBw4MCsrq6qqytHRMSIigoZZAvu5\nKCoqHjp0KDExMTo6urCwUF9f/9///reRkVFcXFy/fv127NhhYmLi7e1dWlq6f//+jx8/3r59\nm+7Sx2azJ0+evHfv3uaqp6GhUVJS0tDQ0OqVwrR96GsCdDQEdgAALcb7fGv3xsZGnsjN3vkD\nKRoCmpiYJCcnC02cn5/ftIjWkZWVbUtvU2dWVSAyVldXT0tL++WXXxISErhc7vTp01euXOns\n7CwnJ9fcts9SUlLW1tYPHz58/vy5vr4+m812cXFxcXGhdx8+fLhz585jx45pamoWFRU1NDQw\n0bampmZVVVVVVRX/wmdGnz59CgsLMzIyhg8f3rpXa5f2ARAT5tgBALSAmpoa+bPfjkHndYlJ\nS0tLSkqqsLBQRAI2m92iPDtI51SV9tU17dBSVVXdsmXLrVu3YmJifHx8lJWVb9++PXToUKbn\nrOm8xsrKSkII/yg5k9LNzW3cuHHOzs7kzyCSzhFkTpvbd3D69OmEkEOHDgm9m5eXN2PGDGb+\npVBCe3kBOggCOwCAFlBRUdHR0cnKyqIrN6nr16+Ln4OCgsKwYcOKioroElTG1q1b6RJURUVF\nMzOz7OzskpIS5m5AQACHw4mMjGSudEI/UOdUVegUtOjo6K1bt/Jvbnz27Nm3b9/OmjWLEFJZ\nWamjozNs2DD+nKuqqmJjYxUUFAYPHixQxIEDB7Kzs/39/ekph8ORkpJ6/fo1PeVyuerq6s3t\nabdgwQJtbe3g4ODTp08L3CovL587d25ERIRAoC+AvpqmpqaINADtBYEdAEDLzJ49u6amxt3d\nvbq6mhCSnp6+e/duOTk58XNYvXo1IcTT05N2htXX1+/evXvTpk3MQodVq1Y1NDS4uLjQ4OPu\n3bs+Pj5sNtvW1pYQQlcqZGVlEWG9Vvxqa2v/aIaY367ohKrS9bApKSn8Fx8/frxp0yYPD4/y\n8vLGxsbIyEhPT89evXotXbqUZmttbZ2Zmenm5vbq1auGhoaHDx/OmDGjtLR0xYoVCgoK/FkV\nFBRs3Lhx+/btvXr1oldkZWUtLS1///13QkhDQ0NkZOSkSZOaawE1NbWgoCA5OTlnZ2c3N7fE\nxMTS0tKcnJxjx44NHz48LS1t/fr1dnZ2ItowOTlZQUGB7hQI0OG+1j4rAAB/Bc3tY5ebm8uf\nTFlZeeDAgfT43bt39B9pKSkpNTU1GRmZ0NBQTU3N4cOH0wT8+9gJzY33566/UlJSBgYGdCcR\nBwcHuvcbtXbtWjabzWaz6V1tbe34+Hh668mTJ3QXlW7duvn7+wt9ry9+K5Z+U/WL+9h1QlXL\nyspYLNbkyZP5L378+JGOgZI/t4wxNDR8+PAhk6CyspIJp+hYKovF+uGHH+jmgvwmTpxoZWXF\nfNGViouLk5WVtbW1tbCw6NGjB3/OQt25c8fc3FygDXV1dX/99VcmjYgNitv+MWIAMWHxBAB0\naRwOx9vbm35xgXJwcNDV1aVz6Rjr1q1jdvRQVVVNS0u7fPny06dPlZWVJ06c2KdPn7KyMmYs\nz8nJqX///nTVgtDcC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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# G. Indirect Effect Focus\n", + "ind_focus <- sum_plot[sum_plot$label %in% c(paste0(\"ind\", 1:n_med), \"total_indirect\"), ]\n", + "\n", + "p_indirect <- ggplot(ind_focus, aes(x=est, y=display_label, color=method, shape=method)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(aes(xmin=ci_lower, xmax=ci_upper), size=0.8,\n", + " position=position_dodge(width=0.5)) +\n", + " labs(title=\"Indirect Effects (a x b) Across Methods\",\n", + " subtitle=paste0(exposure, \" -> {APOC4_APOC2, APOC2, APOC1, APOE} -> \", outcome, \" | N = \", N_total),\n", + " x=\"Indirect Effect (95% CI)\", y=\"\", color=\"Method\", shape=\"Method\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + "ggsave(file.path(dir_summ, \"summary_indirect_effect.png\"), p_indirect, width=10, height=6, dpi=150)\n", + "ggsave(file.path(dir_summ, \"summary_indirect_effect.pdf\"), p_indirect, width=10, height=6)\n", + "print(p_indirect)\n", + "cat(\"Saved indirect effect focus plot.\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b5428b33", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T11:41:44.351910Z", + "iopub.status.busy": "2026-04-01T11:41:44.349521Z", + "iopub.status.idle": "2026-04-01T11:41:46.245146Z", + "shell.execute_reply": "2026-04-01T11:41:46.242497Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved combined panel plot.\n" + ] + }, + { + "data": { + "image/png": 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IzviX/16tURERG3b992cHDQoLKVrRR1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\na9euysrKu3fvlpeXGxgYTJ8+XXCZLB6P9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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# H. Combined Panel\n", + "p_combined <- grid.arrange(\n", + " p_all_forest + theme(plot.title=element_text(size=11)),\n", + " p_indirect + theme(plot.title=element_text(size=11)),\n", + " nrow=2, heights=c(2, 1)\n", + ")\n", + "\n", + "g_combined <- arrangeGrob(\n", + " p_all_forest + theme(plot.title=element_text(size=11)),\n", + " p_indirect + theme(plot.title=element_text(size=11)),\n", + " nrow=2, heights=c(2, 1)\n", + ")\n", + "ggsave(file.path(dir_summ, \"summary_combined_panel.png\"), g_combined, width=12, height=14, dpi=150)\n", + "ggsave(file.path(dir_summ, \"summary_combined_panel.pdf\"), g_combined, width=12, height=14)\n", + "cat(\"Saved combined panel plot.\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "8075dabe", + "metadata": {}, + "source": [ + "## Key Findings" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2e94c3c3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T11:41:46.251988Z", + "iopub.status.busy": "2026-04-01T11:41:46.249485Z", + "iopub.status.idle": "2026-04-01T11:41:46.342360Z", + "shell.execute_reply": "2026-04-01T11:41:46.339692Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== KEY FINDINGS ===\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FIML: total_indirect = -0.00168 [-0.04031, 0.03694], p = 0.9319\n", + "Bootstrap: total_indirect = -0.00211 [-0.04388, 0.03869], p = 0.9080\n", + "Bayesian: total_indirect = -0.00159 [-0.04307, 0.03896], p = 0.9313\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Specific Indirect Effects (FIML) ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ind1 (via APOC4_APOC2_AC_exp): -0.48107 [-1.29849, 0.33634], p = 0.2487\n", + " ind2 (via APOC2_AC_exp): 0.46765 [-0.34302, 1.27833], p = 0.2582\n", + " ind3 (via APOC1_DeJager_Mic_exp): -0.00205 [-0.04446, 0.04036], p = 0.9244\n", + " ind4 (via APOE_Mega_Mic_exp): 0.01379 [-0.01239, 0.03996], p = 0.3020\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Direct Effect (c') ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " c' = 0.16377 [0.05179, 0.27575], p = 0.0041\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Proportion Mediated ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Proportion mediated = -0.010 (-1.0%)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- MNAR Robustness ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Tipping point distance: 1.55 SD\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# KEY FINDINGS: Data-driven summary\n", + "# ============================================================\n", + "\n", + "cat(\"=== KEY FINDINGS ===\\n\\n\")\n", + "\n", + "# Total indirect effect across methods\n", + "for (meth in unique(all_summary$method)) {\n", + " ti_row <- all_summary[all_summary$method == meth & all_summary$label == \"total_indirect\", ]\n", + " if (nrow(ti_row) == 1) {\n", + " sig_star <- ifelse(!is.na(ti_row$pvalue) & ti_row$pvalue < 0.05, \" *\", \"\")\n", + " cat(sprintf(\"%s: total_indirect = %.5f [%.5f, %.5f], p = %.4f%s\\n\",\n", + " meth, ti_row$est, ti_row$ci_lower, ti_row$ci_upper, ti_row$pvalue, sig_star))\n", + " }\n", + "}\n", + "\n", + "cat(\"\\n--- Specific Indirect Effects (FIML) ---\\n\")\n", + "for (i in 1:n_med) {\n", + " lab <- paste0(\"ind\", i)\n", + " row_i <- fiml_res[fiml_res$label == lab, ]\n", + " if (nrow(row_i) == 1) {\n", + " sig_star <- ifelse(row_i$pvalue < 0.05, \" *\", \"\")\n", + " cat(sprintf(\" %s (via %s): %.5f [%.5f, %.5f], p = %.4f%s\\n\",\n", + " lab, med_cols[i], row_i$est, row_i$ci_lower, row_i$ci_upper, row_i$pvalue, sig_star))\n", + " }\n", + "}\n", + "\n", + "cat(\"\\n--- Direct Effect (c') ---\\n\")\n", + "cp_row <- fiml_res[fiml_res$label == \"cp\", ]\n", + "if (nrow(cp_row) == 1) {\n", + " cat(sprintf(\" c' = %.5f [%.5f, %.5f], p = %.4f\\n\",\n", + " cp_row$est, cp_row$ci_lower, cp_row$ci_upper, cp_row$pvalue))\n", + "}\n", + "\n", + "cat(\"\\n--- Proportion Mediated ---\\n\")\n", + "pm_row <- fiml_res[fiml_res$label == \"prop_med\", ]\n", + "if (nrow(pm_row) == 1) {\n", + " cat(sprintf(\" Proportion mediated = %.3f (%.1f%%)\\n\", pm_row$est, 100*pm_row$est))\n", + "}\n", + "\n", + "cat(\"\\n--- MNAR Robustness ---\\n\")\n", + "cat(sprintf(\" Tipping point distance: %.2f SD\\n\", tip_dist))\n" + ] + }, + { + "cell_type": "markdown", + "id": "7dd18247", + "metadata": {}, + "source": [ + "## Post-Analysis Sanity Check" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "09e992d4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T11:41:46.348395Z", + "iopub.status.busy": "2026-04-01T11:41:46.346741Z", + "iopub.status.idle": "2026-04-01T11:41:46.546647Z", + "shell.execute_reply": "2026-04-01T11:41:46.544414Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Post-Analysis Sanity Check ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "### Passed\n", + " [PASS] Sign consistency: All methods agree on direction of effects \n", + " [PASS] Magnitude consistency: No >3x discrepancies across methods \n", + " [PASS] Bayesian convergence: max Rhat = 1.035 \n", + " [PASS] Bootstrap convergence: 1000/1000 replicates (100.0%) \n", + " [PASS] MNAR tipping point = 1.55 SD \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "### Warnings\n", + " [WARN] Proportion mediated = -0.010 (negative): suppression effect \n", + " [WARN] Indirect and direct effects have opposite signs (possible suppression) \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# SANITY CHECK\n", + "# ============================================================\n", + "\n", + "passed <- c()\n", + "warnings_list <- c()\n", + "anomalies <- c()\n", + "\n", + "# 1. Sign consistency\n", + "for (lab in c(paste0(\"ind\", 1:n_med), \"total_indirect\", \"cp\", \"total\")) {\n", + " signs <- c()\n", + " for (meth in unique(all_summary$method)) {\n", + " r <- all_summary[all_summary$method == meth & all_summary$label == lab, ]\n", + " if (nrow(r) == 1 && !is.na(r$est)) signs <- c(signs, sign(r$est))\n", + " }\n", + " if (length(unique(signs)) > 1 && length(signs) > 1) {\n", + " anomalies <- c(anomalies, paste0(\"Sign disagreement for \", lab, \": methods show different signs\"))\n", + " }\n", + "}\n", + "if (length(anomalies) == 0) passed <- c(passed, \"Sign consistency: All methods agree on direction of effects\")\n", + "\n", + "# 2. Magnitude discrepancies\n", + "for (lab in c(\"total_indirect\", paste0(\"ind\", 1:n_med))) {\n", + " ests <- c()\n", + " for (meth in unique(all_summary$method)) {\n", + " r <- all_summary[all_summary$method == meth & all_summary$label == lab, ]\n", + " if (nrow(r) == 1 && !is.na(r$est)) ests <- c(ests, r$est)\n", + " }\n", + " if (length(ests) >= 2) {\n", + " abs_ests <- abs(ests)\n", + " pos_ests <- abs_ests[abs_ests > 0]\n", + " if (length(pos_ests) >= 2 && max(pos_ests) > 3 * min(pos_ests)) {\n", + " warnings_list <- c(warnings_list, paste0(\"Magnitude discrepancy for \", lab))\n", + " }\n", + " }\n", + "}\n", + "if (length(warnings_list) == 0) passed <- c(passed, \"Magnitude consistency: No >3x discrepancies across methods\")\n", + "\n", + "# 3. Significance disagreements\n", + "for (lab in c(\"total_indirect\", paste0(\"ind\", 1:n_med))) {\n", + " sig_status <- c()\n", + " for (meth in unique(all_summary$method)) {\n", + " r <- all_summary[all_summary$method == meth & all_summary$label == lab, ]\n", + " if (nrow(r) == 1 && !is.na(r$pvalue)) {\n", + " sig_status <- c(sig_status, paste0(meth, \" p=\", sprintf(\"%.4f\", r$pvalue),\n", + " ifelse(r$pvalue < 0.05, \"*\", \"\")))\n", + " }\n", + " }\n", + " if (length(sig_status) > 0) {\n", + " has_sig <- any(grepl(\"\\\\*\", sig_status))\n", + " has_ns <- any(!grepl(\"\\\\*\", sig_status))\n", + " if (has_sig && has_ns) {\n", + " warnings_list <- c(warnings_list, paste0(\"Significance disagreement (\", lab, \"): \",\n", + " paste(sig_status, collapse=\", \")))\n", + " }\n", + " }\n", + "}\n", + "\n", + "# 4. Proportion mediated\n", + "pm_val <- fiml_res[fiml_res$label == \"prop_med\", \"est\"]\n", + "if (length(pm_val) == 1 && !is.na(pm_val)) {\n", + " if (pm_val < 0) {\n", + " warnings_list <- c(warnings_list, sprintf(\"Proportion mediated = %.3f (negative): suppression effect\", pm_val))\n", + " } else if (pm_val > 1) {\n", + " warnings_list <- c(warnings_list, sprintf(\"Proportion mediated = %.3f (>1)\", pm_val))\n", + " } else {\n", + " passed <- c(passed, sprintf(\"Proportion mediated = %.3f (in normal range 0-1)\", pm_val))\n", + " }\n", + "}\n", + "\n", + "# 6. Bayesian convergence (if available)\n", + "if (!is.null(fit_bayes)) {\n", + " tryCatch({\n", + " conv_info <- blavInspect(fit_bayes, \"rhat\")\n", + " max_rhat <- max(conv_info, na.rm=TRUE)\n", + " if (max_rhat > 1.05) {\n", + " anomalies <- c(anomalies, sprintf(\"Bayesian Rhat max = %.3f (> 1.05)\", max_rhat))\n", + " } else {\n", + " passed <- c(passed, sprintf(\"Bayesian convergence: max Rhat = %.3f\", max_rhat))\n", + " }\n", + " }, error = function(e) {\n", + " warnings_list <<- c(warnings_list, \"Could not check Bayesian Rhat\")\n", + " })\n", + "}\n", + "\n", + "# 7. Bootstrap failure rate\n", + "boot_fail_rate <- 1 - n_converged / B\n", + "if (boot_fail_rate > 0.20) {\n", + " anomalies <- c(anomalies, sprintf(\"High bootstrap failure rate: %d/%d converged\", n_converged, B))\n", + "} else {\n", + " passed <- c(passed, sprintf(\"Bootstrap convergence: %d/%d replicates (%.1f%%)\", n_converged, B, 100*n_converged/B))\n", + "}\n", + "\n", + "# 8. MNAR tipping point\n", + "if (!is.na(tip_dist) && is.finite(tip_dist) && tip_dist < 0.5) {\n", + " warnings_list <- c(warnings_list, sprintf(\"MNAR tipping point = %.2f SD (fragile)\", tip_dist))\n", + "} else if (!is.na(tip_dist) && is.finite(tip_dist)) {\n", + " passed <- c(passed, sprintf(\"MNAR tipping point = %.2f SD\", tip_dist))\n", + "}\n", + "\n", + "# 10. Suppression\n", + "ti_est <- fiml_res[fiml_res$label == \"total_indirect\", \"est\"]\n", + "cp_est <- fiml_res[fiml_res$label == \"cp\", \"est\"]\n", + "if (length(ti_est)==1 && length(cp_est)==1 && !is.na(ti_est) && !is.na(cp_est)) {\n", + " if (sign(ti_est) != sign(cp_est) && abs(ti_est) > 0.001 && abs(cp_est) > 0.001) {\n", + " warnings_list <- c(warnings_list, \"Indirect and direct effects have opposite signs (possible suppression)\")\n", + " }\n", + "}\n", + "\n", + "# 11. Near-zero a or b with significant indirect\n", + "for (i in 1:n_med) {\n", + " a_p <- fiml_res[fiml_res$label == paste0(\"a\", i), \"pvalue\"]\n", + " b_p <- fiml_res[fiml_res$label == paste0(\"b\", i), \"pvalue\"]\n", + " ind_p <- fiml_res[fiml_res$label == paste0(\"ind\", i), \"pvalue\"]\n", + " if (length(a_p)==1 && length(b_p)==1 && length(ind_p)==1) {\n", + " if (!is.na(ind_p) && ind_p < 0.05) {\n", + " if ((!is.na(a_p) && a_p > 0.1) || (!is.na(b_p) && b_p > 0.1)) {\n", + " anomalies <- c(anomalies, sprintf(\"ind%d significant (p=%.4f) but a%d or b%d non-significant\", i, ind_p, i, i))\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "cat(\"\\n=== Post-Analysis Sanity Check ===\\n\")\n", + "if (length(passed) > 0) {\n", + " cat(\"\\n### Passed\\n\")\n", + " for (p in passed) cat(\" [PASS]\", p, \"\\n\")\n", + "}\n", + "if (length(warnings_list) > 0) {\n", + " cat(\"\\n### Warnings\\n\")\n", + " for (w in warnings_list) cat(\" [WARN]\", w, \"\\n\")\n", + "}\n", + "if (length(anomalies) > 0) {\n", + " cat(\"\\n### Anomalies\\n\")\n", + " for (a in anomalies) cat(\" [ANOMALY]\", a, \"\\n\")\n", + "}\n", + "if (length(warnings_list) == 0 && length(anomalies) == 0) {\n", + " cat(\"\\nAll sanity checks passed -- no anomalies detected.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "1e0c1272", + "metadata": {}, + "source": [ + "## Output File Inventory" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "76f8a951", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T11:41:46.553960Z", + "iopub.status.busy": "2026-04-01T11:41:46.551300Z", + "iopub.status.idle": "2026-04-01T11:41:46.658205Z", + "shell.execute_reply": "2026-04-01T11:41:46.656060Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Output File Inventory ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " APOE_ind_set_27_tdp_st4_APOE4_2_adj_SEM_mediation_bayesian_fix.ipynb 40.6 KB\n", + " APOE_ind_set_27_tdp_st4_APOE4_2_adj_SEM_mediation.ipynb 59.3 KB\n", + " bayesian_blavaan/bayesian_forest_plot.pdf 6.7 KB\n", + " bayesian_blavaan/bayesian_forest_plot.png 108.9 KB\n", + " bayesian_blavaan/bayesian_posteriors_chr19_45001091_C_G.pdf 65.8 KB\n", + " bayesian_blavaan/bayesian_posteriors_chr19_45001091_C_G.png 271.6 KB\n", + " bayesian_blavaan/bayesian_results.csv 2.9 KB\n", + " bayesian_blavaan/bayesian_trace_a_chr19_45001091_C_G.pdf 45.9 KB\n", + " bayesian_blavaan/bayesian_trace_a_chr19_45001091_C_G.png 296.6 KB\n", + " bootstrap/bootstrap_distributions_chr19_45001091_C_G.pdf 7.5 KB\n", + " bootstrap/bootstrap_distributions_chr19_45001091_C_G.png 84.3 KB\n", + " bootstrap/bootstrap_forest_plot.pdf 6.6 KB\n", + " bootstrap/bootstrap_forest_plot.png 105.5 KB\n", + " bootstrap/bootstrap_results.csv 2.2 KB\n", + " log/slurm_95653.err 0.0 KB\n", + " log/slurm_95653.out 8.0 KB\n", + " log/slurm_95666.err 0.0 KB\n", + " log/slurm_95666.out 0.6 KB\n", + " main_SEM_FIML/fiml_all_paths.csv 3.1 KB\n", + " main_SEM_FIML/fiml_forest_plot.pdf 6.6 KB\n", + " main_SEM_FIML/fiml_forest_plot.png 107.1 KB\n", + " MNAR_sensitivity/mnar_1d_slices_chr19_45001091_C_G.pdf 7.0 KB\n", + " MNAR_sensitivity/mnar_1d_slices_chr19_45001091_C_G.png 96.0 KB\n", + " MNAR_sensitivity/mnar_contour_indirect_chr19_45001091_C_G.pdf 6.8 KB\n", + " MNAR_sensitivity/mnar_contour_indirect_chr19_45001091_C_G.png 108.5 KB\n", + " MNAR_sensitivity/mnar_contour_pvalue_chr19_45001091_C_G.pdf 7.9 KB\n", + " MNAR_sensitivity/mnar_contour_pvalue_chr19_45001091_C_G.png 140.6 KB\n", + " MNAR_sensitivity/mnar_grid_results.csv 37.0 KB\n", + " MNAR_sensitivity/mnar_tipping_summary.csv 0.2 KB\n", + " summary/all_methods_summary.csv 7.8 KB\n", + " summary/summary_combined_panel.pdf 8.6 KB\n", + " summary/summary_combined_panel.png 184.8 KB\n", + " summary/summary_display_table_chr19_45001091_C_G.csv 2.3 KB\n", + " summary/summary_faceted_by_path.pdf 8.3 KB\n", + " summary/summary_faceted_by_path.png 118.0 KB\n", + " summary/summary_forest_all_methods.pdf 7.3 KB\n", + " summary/summary_forest_all_methods.png 112.1 KB\n", + " summary/summary_indirect_effect.pdf 5.6 KB\n", + " summary/summary_indirect_effect.png 58.7 KB\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# OUTPUT FILE INVENTORY\n", + "# ============================================================\n", + "cat(\"=== Output File Inventory ===\\n\")\n", + "all_files <- list.files(RESULT_DIR, recursive=TRUE, full.names=FALSE)\n", + "for (f in sort(all_files)) {\n", + " full_path <- file.path(RESULT_DIR, f)\n", + " size_kb <- file.info(full_path)$size / 1024\n", + " cat(sprintf(\" %-70s %.1f KB\\n\", f, size_kb))\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e80fa216", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T11:41:46.664144Z", + "iopub.status.busy": "2026-04-01T11:41:46.662532Z", + "iopub.status.idle": "2026-04-01T11:41:46.693604Z", + "shell.execute_reply": "2026-04-01T11:41:46.690645Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved session_notes.md\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# SESSION NOTES\n", + "# ============================================================\n", + "session_notes <- paste0(\n", + " \"# Session Notes: Parallel Mediation Analysis\\n\\n\",\n", + " \"## Analysis\\n\",\n", + " \"- Design: Parallel mediation (Design 2) with 4 mediators\\n\",\n", + " \"- Exposure: \", exposure, \"\\n\",\n", + " \"- Mediators: \", paste(med_cols, collapse=\", \"), \"\\n\",\n", + " \"- Outcome: \", outcome, \"\\n\",\n", + " \"- Direction: Unidirectional (SNP -> M -> Y)\\n\",\n", + " \"- N total: \", N_total, \"\\n\",\n", + " \"- Methods: FIML SEM, Bootstrap (1000 replicates), MNAR Sensitivity, Bayesian SEM\\n\\n\",\n", + " \"## Key Findings\\n\",\n", + " \"- See all_methods_summary.csv for detailed results\\n\",\n", + " \"- Bootstrap convergence: \", n_converged, \"/\", B, \" replicates\\n\",\n", + " \"- MNAR tipping point distance: \", sprintf(\"%.2f\", tip_dist), \" SD\\n\\n\",\n", + " \"## Fixes Applied\\n\",\n", + " \"- Bayesian extraction code was rewritten to robustly handle blavaan's internal\\n\",\n", + " \" parameter naming (pxnames mapping) and manually compute defined parameters\\n\",\n", + " \" (indirect effects, total, proportion mediated) from regression parameter draws.\\n\",\n", + " \"- Original error: 'arguments imply differing number of rows: 1, 0' caused by\\n\",\n", + " \" parTable label matching failure for defined (:=) parameters.\\n\\n\",\n", + " \"## Sanity Check\\n\",\n", + " if (length(anomalies) > 0) paste0(\"ANOMALIES: \", paste(anomalies, collapse=\"; \"), \"\\n\") else \"No anomalies detected.\\n\",\n", + " if (length(warnings_list) > 0) paste0(\"WARNINGS: \", paste(warnings_list, collapse=\"; \"), \"\\n\") else \"\",\n", + " \"\\n## Next Steps\\n\",\n", + " \"- Examine specific indirect effects to identify which mediator(s) carry the SNP effect\\n\",\n", + " \"- Consider follow-up with individual mediator models if specific indirects differ greatly\\n\",\n", + " \"- Assess biological plausibility of dominant mediator pathway(s)\\n\"\n", + ")\n", + "\n", + "writeLines(session_notes, file.path(RESULT_DIR, \"session_notes.md\"))\n", + "cat(\"Saved session_notes.md\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "421e7768", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T11:41:46.699448Z", + "iopub.status.busy": "2026-04-01T11:41:46.697785Z", + "iopub.status.idle": "2026-04-01T11:41:47.217563Z", + "shell.execute_reply": "2026-04-01T11:41:47.215306Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Session Info ===\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in system(\"timedatectl\", intern = TRUE):\n", + "\u201crunning command 'timedatectl' had status 1\u201d\n" + ] + }, + { + "data": { + "text/plain": [ + "R version 4.4.3 (2025-02-28)\n", + "Platform: x86_64-conda-linux-gnu\n", + "Running under: Ubuntu 24.04.3 LTS\n", + "\n", + "Matrix products: default\n", + "BLAS/LAPACK: /mnt/lustre/home/yl4437/.pixi/envs/python/lib/libopenblasp-r0.3.30.so; LAPACK version 3.12.0\n", + "\n", + "locale:\n", + " [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 \n", + " [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 \n", + " [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C \n", + "[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C \n", + "\n", + "time zone: Etc/UTC\n", + "tzcode source: system (glibc)\n", + "\n", + "attached base packages:\n", + "[1] stats graphics grDevices utils datasets methods base \n", + "\n", + "other attached packages:\n", + "[1] reshape2_1.4.5 gridExtra_2.3 ggplot2_4.0.2 blavaan_0.5-10 Rcpp_1.1.1 \n", + "[6] lavaan_0.6-21 \n", + "\n", + "loaded via a namespace (and not attached):\n", + " [1] gtable_0.3.6 QuickJSR_1.9.0 inline_0.3.21 \n", + " [4] CompQuadForm_1.4.4 lattice_0.22-9 quadprog_1.5-8 \n", + " [7] vctrs_0.7.1 tools_4.4.3 generics_0.1.4 \n", + "[10] stats4_4.4.3 parallel_4.4.3 sandwich_3.1-1 \n", + "[13] tibble_3.3.1 pkgconfig_2.0.3 Matrix_1.7-4 \n", + "[16] RColorBrewer_1.1-3 S7_0.2.1 RcppParallel_5.1.11-2\n", + "[19] uuid_1.2-2 lifecycle_1.0.5 tmvnsim_1.0-2 \n", + "[22] stringr_1.6.0 compiler_4.4.3 farver_2.1.2 \n", + "[25] textshaping_1.0.4 mnormt_2.1.2 repr_1.1.7 \n", + "[28] codetools_0.2-20 htmltools_0.5.9 bayesplot_1.15.0 \n", + "[31] pillar_1.11.1 crayon_1.5.3 StanHeaders_2.32.10 \n", + "[34] parallelly_1.46.1 rstan_2.32.7 tidyselect_1.2.1 \n", + "[37] digest_0.6.39 stringi_1.8.7 mvtnorm_1.3-3 \n", + "[40] future_1.69.0 nonnest2_0.5-8 dplyr_1.2.0 \n", + "[43] listenv_0.10.0 labeling_0.4.3 fastmap_1.2.0 \n", + "[46] grid_4.4.3 cli_3.6.5 magrittr_2.0.4 \n", + "[49] loo_2.9.0 base64enc_0.1-6 pkgbuild_1.4.8 \n", + "[52] IRdisplay_1.1 future.apply_1.20.1 pbivnorm_0.6.0 \n", + "[55] withr_3.0.2 scales_1.4.0 IRkernel_1.3.2 \n", + "[58] matrixStats_1.5.0 globals_0.19.0 igraph_2.2.2 \n", + "[61] pbdZMQ_0.3-14 ragg_1.5.0 zoo_1.8-15 \n", + "[64] coda_0.19-4.1 evaluate_1.0.5 rstantools_2.6.0 \n", + "[67] rlang_1.1.7 glue_1.8.0 jsonlite_2.0.0 \n", + "[70] plyr_1.8.9 R6_2.6.1 systemfonts_1.3.1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# CLEANUP + SESSION INFO\n", + "# ============================================================\n", + "for (pat in c(\"lavExport\")) {\n", + " dirs_to_rm <- list.dirs(RESULT_DIR, recursive=TRUE, full.names=TRUE)\n", + " for (d in dirs_to_rm) {\n", + " if (grepl(pat, basename(d))) unlink(d, recursive=TRUE)\n", + " }\n", + "}\n", + "for (pat in c(\"\\\\.stan$\", \"^tmp_\", \"\\\\.bak$\")) {\n", + " files_to_rm <- list.files(RESULT_DIR, pattern=pat, recursive=TRUE, full.names=TRUE)\n", + " if (length(files_to_rm) > 0) file.remove(files_to_rm)\n", + "}\n", + "\n", + "cat(\"\\n=== Session Info ===\\n\")\n", + "sessionInfo()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "name": "R" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_tdp_st4_APOE4_adj_SEM_mediation.ipynb b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_tdp_st4_APOE4_adj_SEM_mediation.ipynb new file mode 100644 index 0000000..b189e3a --- /dev/null +++ b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_27_tdp_st4_APOE4_adj_SEM_mediation.ipynb @@ -0,0 +1,3319 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SEM Parallel Mediation Analysis: APOE Region SNP -> Gene Expression -> TDP-43 Pathology (APOE4-Adjusted)\n", + "\n", + "## Aim\n", + "\n", + "This analysis tests whether the effect of the APOE-region indel variant **chr19_44999110_TAAAA_TAA** on\n", + "**TDP-43 pathology (tdp_st4)** is mediated through four gene expression mediators simultaneously\n", + "(parallel mediation, Design 2), adjusting for APOE4 dosage and other covariates.\n", + "\n", + "The four mediators are:\n", + "- **APOC4_APOC2_AC_exp** (anterior cingulate cortex)\n", + "- **APOC2_AC_exp** (anterior cingulate cortex)\n", + "- **APOC1_DeJager_Mic_exp** (microglia, DeJager)\n", + "- **APOE_Mega_Mic_exp** (microglia, Mega)\n", + "\n", + "All four mediators enter the outcome equation simultaneously, allowing estimation of **specific indirect effects**\n", + "(unique contribution of each mediator) and **total indirect effect** (sum of all four).\n", + "\n", + "Four complementary methods: FIML SEM, Bootstrap (FIML inside), MNAR Sensitivity, Bayesian SEM (blavaan).\n", + "Direction: unidirectional (SNP -> M -> Y only)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Directed Acyclic Graph (DAG)\n", + "\n", + "```\n", + " a1 b1\n", + " +---------> [APOC4_APOC2_AC_exp] ---------+\n", + " | a2 b2 |\n", + " +---------> [APOC2_AC_exp] ---------------+\n", + " | a3 b3 |\n", + " [chr19_44999110] -----+---------> [APOC1_DeJager_Mic_exp] -----+--> [tdp_st4]\n", + " | a4 b4 |\n", + " +---------> [APOE_Mega_Mic_exp] ----------+\n", + " | |\n", + " +-------------- c' (direct) ---------------+\n", + "```\n", + "\n", + "- Specific indirect (M1) = a1 x b1\n", + "- Specific indirect (M2) = a2 x b2\n", + "- Specific indirect (M3) = a3 x b3\n", + "- Specific indirect (M4) = a4 x b4\n", + "- Total indirect = a1*b1 + a2*b2 + a3*b3 + a4*b4\n", + "- Total effect = c' + total indirect\n", + "\n", + "Covariates:\n", + "- M1, M2 (AC_exp): msex_u, age_death_u, pmi_u, ROS_study_u\n", + "- M3, M4 (Mic_exp): msex_u, age_death_u, pmi_u\n", + "- Y (tdp_st4): educ, apoe4_dose, msex_u, age_death_u\n", + "\n", + "Residual correlations among mediators freely estimated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Findings & Conclusion\n", + "\n", + "**No significant mediation detected.** The SNP chr19_44999110_TAAAA_TAA has a significant direct effect on tdp_st4\n", + "(c' = 0.165, p = 0.004), but none of the four gene expression mediators carry a significant indirect effect.\n", + "\n", + "| Effect | FIML Est | 95% CI | p-value | Bootstrap p | Bayesian P(>0) |\n", + "|--------|----------|--------|---------|-------------|----------------|\n", + "| ind1 (APOC4_APOC2_AC) | -0.531 | [-1.364, 0.303] | 0.212 | 0.208 | 0.083 |\n", + "| ind2 (APOC2_AC) | 0.518 | [-0.311, 1.346] | 0.221 | 0.224 | 0.912 |\n", + "| ind3 (APOC1_DeJager_Mic) | -0.002 | [-0.045, 0.041] | 0.929 | 0.890 | 0.415 |\n", + "| ind4 (APOE_Mega_Mic) | 0.015 | [-0.015, 0.045] | 0.317 | 0.300 | 0.870 |\n", + "| Total indirect | 0.001 | [-0.039, 0.040] | 0.981 | 0.996 | 0.528 |\n", + "| Direct (c') | 0.165 | [0.054, 0.277] | 0.004 | 0.002 | 0.998 |\n", + "| Total | 0.166 | [0.060, 0.271] | 0.002 | \u2014 | 0.999 |\n", + "\n", + "**Collinearity note:** M1 (APOC4_APOC2_AC) and M2 (APOC2_AC) produce large, opposing indirect effects (~0.5 in magnitude)\n", + "due to high collinearity. A sensitivity analysis excluding M1 confirms these cancel out \u2014 the 3-mediator model shows\n", + "APOC2 b-path drops from 1.67 to -0.03, and the total indirect remains non-significant (est = 0.006, p = 0.75).\n", + "\n", + "**MNAR robustness:** Fragile (tipping distance = 0.00 SD), but this is expected given the total indirect is essentially zero.\n", + "\n", + "**Conclusion:** The SNP effect on TDP-43 pathology operates predominantly through a direct path, not mediated\n", + "by these four gene expression phenotypes. This holds after adjusting for APOE4 dosage (apoe4_dose) in the outcome equation.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Input Specification\n", + "\n", + "| Parameter | Value |\n", + "|-----------|-------|\n", + "| Data file | `/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/tdp_st4/APOE_ind_set_27_tdp_st4_mediation_all_input.txt` |\n", + "| Exposure | chr19_44999110_TAAAA_TAA |\n", + "| Mediator 1 | APOC4_APOC2_AC_exp (covs: msex_u, age_death_u, pmi_u, ROS_study_u) |\n", + "| Mediator 2 | APOC2_AC_exp (covs: msex_u, age_death_u, pmi_u, ROS_study_u) |\n", + "| Mediator 3 | APOC1_DeJager_Mic_exp (covs: msex_u, age_death_u, pmi_u) |\n", + "| Mediator 4 | APOE_Mega_Mic_exp (covs: msex_u, age_death_u, pmi_u) |\n", + "| Outcome | tdp_st4 (covs: educ, apoe4_dose, msex_u, age_death_u) |\n", + "| Design | Parallel (Design 2) -- 4 mediators simultaneously |\n", + "| Direction | Unidirectional: SNP -> M -> Y |\n", + "| Output dir | `/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/tdp_st4/APOE4_adj` |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Methods Overview\n", + "\n", + "| Method | Purpose | Missing Data | Key Output |\n", + "|--------|---------|--------------|------------|\n", + "| FIML SEM (lavaan) | Primary estimates | FIML (uses all N) | ML estimates, SEs, CIs |\n", + "| Bootstrap (FIML inside) | Non-parametric CIs | FIML per replicate | Percentile CIs, p-values |\n", + "| MNAR Sensitivity | Robustness to MNAR | Delta-shift imputation | Tipping points |\n", + "| Bayesian SEM (blavaan) | Posterior inference | Stan data augmentation | Credible intervals, P(direction) |" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:33:58.641863Z", + "iopub.status.busy": "2026-03-31T21:33:58.636780Z", + "iopub.status.idle": "2026-03-31T21:34:08.535493Z", + "shell.execute_reply": "2026-03-31T21:34:08.530154Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setup complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exposure: chr19_44999110_TAAAA_TAA \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mediators: APOC4_APOC2_AC_exp, APOC2_AC_exp, APOC1_DeJager_Mic_exp, APOE_Mega_Mic_exp \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Outcome: tdp_st4 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Design: Parallel mediation (4 mediators simultaneously)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Direction: Unidirectional (SNP -> M -> Y)\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 1: Setup -- Libraries, Paths, Constants\n", + "# ============================================================\n", + "suppressPackageStartupMessages({\n", + " library(lavaan)\n", + " library(blavaan)\n", + " library(ggplot2)\n", + " library(gridExtra)\n", + " library(grid)\n", + "})\n", + "\n", + "DATA_FILE <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/tdp_st4/APOE_ind_set_27_tdp_st4_mediation_all_input.txt\"\n", + "RESULT_DIR <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/tdp_st4/APOE4_adj\"\n", + "\n", + "DIR_FIML <- file.path(RESULT_DIR, \"main_SEM_FIML\")\n", + "DIR_BOOT <- file.path(RESULT_DIR, \"bootstrap\")\n", + "DIR_MNAR <- file.path(RESULT_DIR, \"MNAR_sensitivity\")\n", + "DIR_BAYES <- file.path(RESULT_DIR, \"bayesian_blavaan\")\n", + "DIR_SUMM <- file.path(RESULT_DIR, \"summary\")\n", + "for (d in c(DIR_FIML, DIR_BOOT, DIR_MNAR, DIR_BAYES, DIR_SUMM)) dir.create(d, showWarnings=FALSE, recursive=TRUE)\n", + "\n", + "EXPOSURE <- \"chr19_44999110_TAAAA_TAA\"\n", + "MEDIATORS <- c(\"APOC4_APOC2_AC_exp\", \"APOC2_AC_exp\", \"APOC1_DeJager_Mic_exp\", \"APOE_Mega_Mic_exp\")\n", + "OUTCOME <- \"tdp_st4\"\n", + "N_MED <- length(MEDIATORS)\n", + "MED_SHORT <- c(\"M1_APOC4_APOC2\", \"M2_APOC2\", \"M3_APOC1_DeJager\", \"M4_APOE_Mega\")\n", + "\n", + "COV_M1 <- c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\")\n", + "COV_M2 <- c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\")\n", + "COV_M3 <- c(\"msex_u\", \"age_death_u\", \"pmi_u\")\n", + "COV_M4 <- c(\"msex_u\", \"age_death_u\", \"pmi_u\")\n", + "COV_Y <- c(\"educ\", \"apoe4_dose\", \"msex_u\", \"age_death_u\")\n", + "COV_LIST <- list(COV_M1, COV_M2, COV_M3, COV_M4)\n", + "ALL_COVS <- unique(c(COV_M1, COV_M2, COV_M3, COV_M4, COV_Y))\n", + "\n", + "B_REPS <- 1000\n", + "set.seed(42)\n", + "\n", + "cat(\"Setup complete.\\n\")\n", + "cat(\"Exposure:\", EXPOSURE, \"\\n\")\n", + "cat(\"Mediators:\", paste(MEDIATORS, collapse=\", \"), \"\\n\")\n", + "cat(\"Outcome:\", OUTCOME, \"\\n\")\n", + "cat(\"Design: Parallel mediation (4 mediators simultaneously)\\n\")\n", + "cat(\"Direction: Unidirectional (SNP -> M -> Y)\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:34:08.623066Z", + "iopub.status.busy": "2026-03-31T21:34:08.584125Z", + "iopub.status.idle": "2026-03-31T21:34:08.769830Z", + "shell.execute_reply": "2026-03-31T21:34:08.767201Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Raw data dimensions: 1153 rows x 58 columns\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N with exposure observed: 1153 \n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Missingness Summary === \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " variable n_obs n_miss pct_miss\n", + "1 chr19_44999110_TAAAA_TAA 1153 0 0.0\n", + "2 APOC4_APOC2_AC_exp 593 560 48.6\n", + "3 APOC2_AC_exp 593 560 48.6\n", + "4 APOC1_DeJager_Mic_exp 419 734 63.7\n", + "5 APOE_Mega_Mic_exp 733 420 36.4\n", + "6 tdp_st4 992 161 14.0\n", + "7 msex_u 1140 13 1.1\n", + "8 age_death_u 1140 13 1.1\n", + "9 pmi_u 1140 13 1.1\n", + "10 ROS_study_u 1115 38 3.3\n", + "11 educ 1113 40 3.5\n", + "12 apoe4_dose 1106 47 4.1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Sample Overlap Pattern === \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " APOC4_APOC2_AC_exp vs tdp_st4: both=548, M-only=45, Y-only=444, neither=116\n", + " APOC2_AC_exp vs tdp_st4: both=548, M-only=45, Y-only=444, neither=116\n", + " APOC1_DeJager_Mic_exp vs tdp_st4: both=373, M-only=46, Y-only=619, neither=115\n", + " APOE_Mega_Mic_exp vs tdp_st4: both=654, M-only=79, Y-only=338, neither=82\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Complete cases (all 4 mediators + outcome): 286 (24.8%)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FIML will use all N=1153 subjects with exposure observed.\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 2: Data Loading + Exploration\n", + "# ============================================================\n", + "dat_raw <- read.delim(DATA_FILE, stringsAsFactors=FALSE)\n", + "cat(\"Raw data dimensions:\", nrow(dat_raw), \"rows x\", ncol(dat_raw), \"columns\\n\\n\")\n", + "\n", + "keep_cols <- c(EXPOSURE, MEDIATORS, OUTCOME, ALL_COVS)\n", + "missing_cols <- setdiff(keep_cols, names(dat_raw))\n", + "if (length(missing_cols) > 0) stop(\"Missing columns: \", paste(missing_cols, collapse=\", \"))\n", + "dat <- dat_raw[, keep_cols]\n", + "\n", + "for (col in names(dat)) dat[[col]] <- as.numeric(dat[[col]])\n", + "\n", + "dat <- dat[!is.na(dat[[EXPOSURE]]), ]\n", + "N_TOTAL <- nrow(dat)\n", + "cat(\"N with exposure observed:\", N_TOTAL, \"\\n\\n\")\n", + "\n", + "cat(\"=== Missingness Summary ===\", \"\\n\")\n", + "miss_df <- data.frame(\n", + " variable = names(dat),\n", + " n_obs = sapply(dat, function(x) sum(!is.na(x))),\n", + " n_miss = sapply(dat, function(x) sum(is.na(x))),\n", + " pct_miss = round(sapply(dat, function(x) mean(is.na(x))) * 100, 1)\n", + ")\n", + "rownames(miss_df) <- NULL\n", + "print(miss_df)\n", + "\n", + "cat(\"\\n=== Sample Overlap Pattern ===\", \"\\n\")\n", + "for (m in MEDIATORS) {\n", + " both <- sum(!is.na(dat[[m]]) & !is.na(dat[[OUTCOME]]))\n", + " m_only <- sum(!is.na(dat[[m]]) & is.na(dat[[OUTCOME]]))\n", + " y_only <- sum(is.na(dat[[m]]) & !is.na(dat[[OUTCOME]]))\n", + " neither <- sum(is.na(dat[[m]]) & is.na(dat[[OUTCOME]]))\n", + " cat(sprintf(\" %s vs %s: both=%d, M-only=%d, Y-only=%d, neither=%d\\n\",\n", + " m, OUTCOME, both, m_only, y_only, neither))\n", + "}\n", + "\n", + "cc_all <- complete.cases(dat[, c(MEDIATORS, OUTCOME)])\n", + "cat(sprintf(\"\\nComplete cases (all 4 mediators + outcome): %d (%.1f%%)\\n\", sum(cc_all), mean(cc_all)*100))\n", + "cat(sprintf(\"FIML will use all N=%d subjects with exposure observed.\\n\", N_TOTAL))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Covariate Specification\n", + "\n", + "**Strategy A: Covariates-in-model.** Each mediator equation and the outcome equation include their own covariates directly in the SEM model. FIML handles different missingness patterns across equations automatically.\n", + "\n", + "| Equation | Covariates |\n", + "|----------|------------|\n", + "| APOC4_APOC2_AC_exp ~ SNP + ... | msex_u, age_death_u, pmi_u, ROS_study_u |\n", + "| APOC2_AC_exp ~ SNP + ... | msex_u, age_death_u, pmi_u, ROS_study_u |\n", + "| APOC1_DeJager_Mic_exp ~ SNP + ... | msex_u, age_death_u, pmi_u |\n", + "| APOE_Mega_Mic_exp ~ SNP + ... | msex_u, age_death_u, pmi_u |\n", + "| tdp_st4 ~ M1 + M2 + M3 + M4 + SNP + ... | educ, apoe4_dose, msex_u, age_death_u |" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:34:08.776455Z", + "iopub.status.busy": "2026-03-31T21:34:08.774114Z", + "iopub.status.idle": "2026-03-31T21:34:08.862967Z", + "shell.execute_reply": "2026-03-31T21:34:08.860377Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Parallel Mediation Model === \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "APOC4_APOC2_AC_exp ~ a1 * chr19_44999110_TAAAA_TAA + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC2_AC_exp ~ a2 * chr19_44999110_TAAAA_TAA + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC1_DeJager_Mic_exp ~ a3 * chr19_44999110_TAAAA_TAA + msex_u + age_death_u + pmi_u\n", + "APOE_Mega_Mic_exp ~ a4 * chr19_44999110_TAAAA_TAA + msex_u + age_death_u + pmi_u\n", + "tdp_st4 ~ b1 * APOC4_APOC2_AC_exp + b2 * APOC2_AC_exp + b3 * APOC1_DeJager_Mic_exp + b4 * APOE_Mega_Mic_exp + cp * chr19_44999110_TAAAA_TAA + educ + apoe4_dose + msex_u + age_death_u\n", + "ind1 := a1 * b1\n", + "ind2 := a2 * b2\n", + "ind3 := a3 * b3\n", + "ind4 := a4 * b4\n", + "total_indirect := ind1 + ind2 + ind3 + ind4\n", + "total := cp + total_indirect\n", + "prop1 := ind1 / total\n", + "prop2 := ind2 / total\n", + "prop3 := ind3 / total\n", + "prop4 := ind4 / total\n", + "prop_total := total_indirect / total\n", + "diff_1_2 := ind1 - ind2\n", + "diff_1_3 := ind1 - ind3\n", + "diff_1_4 := ind1 - ind4\n", + "diff_2_3 := ind2 - ind3\n", + "diff_2_4 := ind2 - ind4\n", + "diff_3_4 := ind3 - ind4\n", + "APOC4_APOC2_AC_exp ~~ APOC2_AC_exp\n", + "APOC4_APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC4_APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC1_DeJager_Mic_exp ~~ APOE_Mega_Mic_exp \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 3: Build Parallel Mediation Model String\n", + "# ============================================================\n", + "\n", + "med_eqs <- character(N_MED)\n", + "for (i in seq_len(N_MED)) {\n", + " cov_str <- paste(COV_LIST[[i]], collapse=\" + \")\n", + " med_eqs[i] <- sprintf(\"%s ~ a%d * %s + %s\", MEDIATORS[i], i, EXPOSURE, cov_str)\n", + "}\n", + "\n", + "y_med_terms <- paste0(\"b\", seq_len(N_MED), \" * \", MEDIATORS, collapse=\" + \")\n", + "y_cov_str <- paste(COV_Y, collapse=\" + \")\n", + "y_eq <- sprintf(\"%s ~ %s + cp * %s + %s\", OUTCOME, y_med_terms, EXPOSURE, y_cov_str)\n", + "\n", + "ind_defs <- paste0(\"ind\", seq_len(N_MED), \" := a\", seq_len(N_MED), \" * b\", seq_len(N_MED))\n", + "total_ind <- paste0(\"total_indirect := \", paste0(\"ind\", seq_len(N_MED), collapse=\" + \"))\n", + "total_def <- \"total := cp + total_indirect\"\n", + "\n", + "prop_defs <- paste0(\"prop\", seq_len(N_MED), \" := ind\", seq_len(N_MED), \" / total\")\n", + "prop_total <- \"prop_total := total_indirect / total\"\n", + "\n", + "contrasts <- c()\n", + "for (i in 1:(N_MED-1)) {\n", + " for (j in (i+1):N_MED) {\n", + " contrasts <- c(contrasts, sprintf(\"diff_%d_%d := ind%d - ind%d\", i, j, i, j))\n", + " }\n", + "}\n", + "\n", + "cov_terms <- c()\n", + "for (i in 1:(N_MED-1)) {\n", + " for (j in (i+1):N_MED) {\n", + " cov_terms <- c(cov_terms, sprintf(\"%s ~~ %s\", MEDIATORS[i], MEDIATORS[j]))\n", + " }\n", + "}\n", + "\n", + "model_str <- paste(c(med_eqs, y_eq, ind_defs, total_ind, total_def, prop_defs, prop_total, contrasts, cov_terms),\n", + " collapse=\"\\n\")\n", + "\n", + "cat(\"=== Parallel Mediation Model ===\", \"\\n\")\n", + "cat(model_str, \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Method 1: FIML SEM (Primary Analysis)\n", + "\n", + "Full Information Maximum Likelihood uses all N subjects with at least the exposure observed.\n", + "Missing mediators and outcome are handled by integrating over the missing data likelihood.\n", + "`fixed.x=FALSE` ensures subjects with only the SNP contribute information." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:34:08.869322Z", + "iopub.status.busy": "2026-03-31T21:34:08.866856Z", + "iopub.status.idle": "2026-03-31T21:34:13.084463Z", + "shell.execute_reply": "2026-03-31T21:34:13.081627Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting FIML SEM with N = 1153 ...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FIML converged successfully.\n", + "N used: 1153 \n", + "\n", + "=== All Labeled Parameter Estimates === \n", + " label est se ci.lower ci.upper pvalue\n", + "1 a1 0.312 0.058 0.200 0.425 0.000\n", + "6 a2 0.311 0.058 0.198 0.424 0.000\n", + "11 a3 0.254 0.066 0.125 0.384 0.000\n", + "15 a4 0.200 0.050 0.101 0.299 0.000\n", + "19 b1 -1.698 1.325 -4.296 0.899 0.200\n", + "20 b2 1.667 1.325 -0.930 4.263 0.208\n", + "21 b3 -0.008 0.087 -0.178 0.163 0.929\n", + "22 b4 0.077 0.074 -0.069 0.222 0.302\n", + "23 cp 0.165 0.057 0.054 0.277 0.004\n", + "79 ind1 -0.531 0.425 -1.364 0.303 0.212\n", + "80 ind2 0.518 0.423 -0.311 1.346 0.221\n", + "81 ind3 -0.002 0.022 -0.045 0.041 0.929\n", + "82 ind4 0.015 0.015 -0.015 0.045 0.317\n", + "83 total_indirect 0.000 0.020 -0.039 0.040 0.981\n", + "84 total 0.166 0.054 0.060 0.271 0.002\n", + "85 prop1 -3.199 2.765 -8.618 2.221 0.247\n", + "86 prop2 3.121 2.741 -2.250 8.493 0.255\n", + "87 prop3 -0.012 0.134 -0.274 0.250 0.929\n", + "88 prop4 0.092 0.097 -0.098 0.282 0.342\n", + "89 prop_total 0.003 0.121 -0.233 0.239 0.981\n", + "90 diff_1_2 -1.048 0.848 -2.710 0.613 0.216\n", + "91 diff_1_3 -0.529 0.424 -1.359 0.302 0.212\n", + "92 diff_1_4 -0.546 0.426 -1.381 0.289 0.200\n", + "93 diff_2_3 0.520 0.426 -0.314 1.354 0.222\n", + "94 diff_2_4 0.502 0.422 -0.325 1.330 0.234\n", + "95 diff_3_4 -0.017 0.035 -0.086 0.051 0.620\n", + "\n", + "=== Fit Measures === \n", + " chisq df pvalue cfi rmsea srmr aic bic \n", + " 21.240 12.000 0.047 0.998 0.026 0.020 33501.517 33895.427 \n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 4: FIML SEM\n", + "# ============================================================\n", + "cat(\"Fitting FIML SEM with N =\", N_TOTAL, \"...\\n\")\n", + "\n", + "fit_fiml <- tryCatch(\n", + " sem(model_str, data=dat, missing=\"fiml\", fixed.x=FALSE, estimator=\"ML\"),\n", + " error = function(e) { cat(\"FIML Error:\", conditionMessage(e), \"\\n\"); NULL }\n", + ")\n", + "\n", + "if (!is.null(fit_fiml) && lavInspect(fit_fiml, \"converged\")) {\n", + " cat(\"FIML converged successfully.\\n\")\n", + " cat(\"N used:\", lavInspect(fit_fiml, \"nobs\"), \"\\n\\n\")\n", + "\n", + " pe_fiml <- parameterEstimates(fit_fiml, ci=TRUE)\n", + "\n", + " labeled <- pe_fiml[pe_fiml$label != \"\", ]\n", + " cat(\"=== All Labeled Parameter Estimates ===\", \"\\n\")\n", + " print(labeled[, c(\"label\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"pvalue\")])\n", + "\n", + " fm <- fitMeasures(fit_fiml, c(\"chisq\", \"df\", \"pvalue\", \"cfi\", \"rmsea\", \"srmr\", \"aic\", \"bic\"))\n", + " cat(\"\\n=== Fit Measures ===\", \"\\n\")\n", + " print(round(fm, 4))\n", + "} else {\n", + " cat(\"FIML failed to converge.\\n\")\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:34:13.091857Z", + "iopub.status.busy": "2026-03-31T21:34:13.089508Z", + "iopub.status.idle": "2026-03-31T21:34:14.945567Z", + "shell.execute_reply": "2026-03-31T21:34:14.940673Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved fiml_all_paths.csv\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved FIML forest plots.\n" + ] + }, + { + "data": { + "image/png": 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Ey+OXToEJ2NXuHsqGoeTvXs2ZMQ8ssv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\nNoIdEHQmk+mFF17wvTabzb4XGzZseO211x5++OHOnxnJy8sTQ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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 5: FIML -- Save Results and Forest Plot\n", + "# ============================================================\n", + "\n", + "contrast_labels <- c()\n", + "contrast_mediators <- c()\n", + "for (i in 1:(N_MED-1)) {\n", + " for (j in (i+1):N_MED) {\n", + " contrast_labels <- c(contrast_labels, sprintf(\"diff_%d_%d\", i, j))\n", + " contrast_mediators <- c(contrast_mediators, paste(MED_SHORT[i], \"vs\", MED_SHORT[j]))\n", + " }\n", + "}\n", + "\n", + "label_map <- data.frame(\n", + " label = c(paste0(\"a\", 1:N_MED), paste0(\"b\", 1:N_MED), \"cp\",\n", + " paste0(\"ind\", 1:N_MED), \"total_indirect\", \"total\",\n", + " paste0(\"prop\", 1:N_MED), \"prop_total\",\n", + " contrast_labels),\n", + " path_type = c(rep(\"a_path\", N_MED), rep(\"b_path\", N_MED), \"direct\",\n", + " rep(\"specific_indirect\", N_MED), \"total_indirect\", \"total\",\n", + " rep(\"prop_mediated\", N_MED), \"prop_total_mediated\",\n", + " rep(\"contrast\", length(contrast_labels))),\n", + " mediator = c(MED_SHORT, MED_SHORT, \"all\",\n", + " MED_SHORT, \"all\", \"all\",\n", + " MED_SHORT, \"all\",\n", + " contrast_mediators),\n", + " stringsAsFactors=FALSE\n", + ")\n", + "\n", + "fiml_res <- merge(label_map, pe_fiml[pe_fiml$label != \"\", c(\"label\",\"est\",\"se\",\"ci.lower\",\"ci.upper\",\"pvalue\")],\n", + " by=\"label\", all.x=TRUE)\n", + "fiml_res$method <- \"FIML\"\n", + "fiml_res$N <- N_TOTAL\n", + "fiml_res$exposure <- EXPOSURE\n", + "fiml_res$direction <- \"D1\"\n", + "\n", + "write.csv(fiml_res, file.path(DIR_FIML, \"fiml_all_paths.csv\"), row.names=FALSE)\n", + "cat(\"Saved fiml_all_paths.csv\\n\")\n", + "\n", + "plot_labels <- c(paste0(\"a\", 1:N_MED), paste0(\"b\", 1:N_MED), \"cp\",\n", + " paste0(\"ind\", 1:N_MED), \"total_indirect\", \"total\")\n", + "plot_df <- fiml_res[fiml_res$label %in% plot_labels, ]\n", + "plot_df$label <- factor(plot_df$label, levels=rev(plot_labels))\n", + "\n", + "p_fiml <- ggplot(plot_df, aes(x=est, y=label, xmin=ci.lower, xmax=ci.upper)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(color=\"steelblue\", size=0.6) +\n", + " labs(title=\"FIML SEM: Parallel Mediation Path Estimates\",\n", + " subtitle=paste0(\"SNP -> 4 mediators -> tdp_st4 | N = \", N_TOTAL, \" (FIML)\"),\n", + " x=\"Estimate (95% CI)\", y=\"\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + "ggsave(file.path(DIR_FIML, \"fiml_forest_plot.png\"), p_fiml, width=10, height=8, dpi=150)\n", + "ggsave(file.path(DIR_FIML, \"fiml_forest_plot.pdf\"), p_fiml, width=10, height=8)\n", + "cat(\"Saved FIML forest plots.\\n\")\n", + "print(p_fiml)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### FIML Interpretation\n", + "\n", + "- **a-paths** (a1-a4): Effect of SNP on each mediator, controlling for mediator-specific covariates.\n", + "- **b-paths** (b1-b4): Effect of each mediator on tdp_st4, controlling for the SNP, other mediators, and outcome covariates.\n", + "- **c' (direct)**: Direct effect of SNP on tdp_st4, net of all four mediators.\n", + "- **Specific indirects** (ind1-ind4): Unique mediated effect through each mediator (ai x bi).\n", + "- **Total indirect**: Sum of all specific indirects.\n", + "- **Contrasts** (diff_i_j): Differences between specific indirect effects.\n", + "- **Proportions mediated**: Fraction of total effect transmitted through each mediator." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sensitivity Analysis: 3-Mediator Model (Excluding APOC4_APOC2_AC_exp)\n", + "\n", + "To assess whether the highly correlated mediators APOC4_APOC2_AC_exp and APOC2_AC_exp produce collinearity artifacts,\n", + "we re-fit the FIML SEM excluding APOC4_APOC2_AC_exp and retaining only APOC2_AC_exp, APOC1_DeJager_Mic_exp, and APOE_Mega_Mic_exp.\n", + "Results are saved to `main_SEM_FIML/sensitivity_analysis/`." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "execution_count": null, + "source": [ + "# Sensitivity Analysis: 3-mediator parallel model (excluding APOC4_APOC2_AC_exp)\n", + "# Run inline - results saved to main_SEM_FIML/sensitivity_analysis/\n", + "\n", + "suppressPackageStartupMessages({\n", + " library(lavaan)\n", + " library(ggplot2)\n", + "})\n", + "\n", + "DATA_FILE <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/tdp_st4/APOE_ind_set_27_tdp_st4_mediation_all_input.txt\"\n", + "RESULT_DIR <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/tdp_st4/APOE4_adj\"\n", + "DIR_SENS <- file.path(RESULT_DIR, \"main_SEM_FIML\", \"sensitivity_analysis\")\n", + "\n", + "EXPOSURE <- \"chr19_44999110_TAAAA_TAA\"\n", + "MEDIATORS_3 <- c(\"APOC2_AC_exp\", \"APOC1_DeJager_Mic_exp\", \"APOE_Mega_Mic_exp\")\n", + "OUTCOME <- \"tdp_st4\"\n", + "N_MED_3 <- length(MEDIATORS_3)\n", + "MED_SHORT_3 <- c(\"M2_APOC2\", \"M3_APOC1_DeJager\", \"M4_APOE_Mega\")\n", + "\n", + "COV_M2 <- c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\")\n", + "COV_M3 <- c(\"msex_u\", \"age_death_u\", \"pmi_u\")\n", + "COV_M4 <- c(\"msex_u\", \"age_death_u\", \"pmi_u\")\n", + "COV_Y <- c(\"educ\", \"apoe4_dose\", \"msex_u\", \"age_death_u\")\n", + "COV_LIST_3 <- list(COV_M2, COV_M3, COV_M4)\n", + "\n", + "ALL_COVS_3 <- unique(c(COV_M2, COV_M3, COV_M4, COV_Y))\n", + "\n", + "dat <- read.table(DATA_FILE, header=TRUE, sep=\"\\t\", stringsAsFactors=FALSE)\n", + "use_cols <- c(EXPOSURE, MEDIATORS_3, OUTCOME, ALL_COVS_3)\n", + "use_cols <- use_cols[use_cols %in% colnames(dat)]\n", + "dat <- dat[, use_cols]\n", + "N_TOTAL <- nrow(dat)\n", + "\n", + "cat(\"=== Sensitivity Analysis: 3-Mediator Model (Excluding APOC4_APOC2_AC_exp) ===\\n\")\n", + "cat(\"N =\", N_TOTAL, \"\\n\")\n", + "cat(\"Mediators:\", paste(MEDIATORS_3, collapse=\", \"), \"\\n\\n\")\n", + "\n", + "# Build model string\n", + "med_eqs <- character(N_MED_3)\n", + "for (i in seq_len(N_MED_3)) {\n", + " cov_str <- paste(COV_LIST_3[[i]], collapse=\" + \")\n", + " med_eqs[i] <- sprintf(\"%s ~ a%d * %s + %s\", MEDIATORS_3[i], i, EXPOSURE, cov_str)\n", + "}\n", + "\n", + "y_med_terms <- paste0(\"b\", seq_len(N_MED_3), \" * \", MEDIATORS_3, collapse=\" + \")\n", + "y_cov_str <- paste(COV_Y, collapse=\" + \")\n", + "y_eq <- sprintf(\"%s ~ %s + cp * %s + %s\", OUTCOME, y_med_terms, EXPOSURE, y_cov_str)\n", + "\n", + "ind_defs <- paste0(\"ind\", seq_len(N_MED_3), \" := a\", seq_len(N_MED_3), \" * b\", seq_len(N_MED_3))\n", + "total_ind <- paste0(\"total_indirect := \", paste0(\"ind\", seq_len(N_MED_3), collapse=\" + \"))\n", + "total_def <- \"total := cp + total_indirect\"\n", + "\n", + "prop_defs <- paste0(\"prop\", seq_len(N_MED_3), \" := ind\", seq_len(N_MED_3), \" / total\")\n", + "prop_total <- \"prop_total := total_indirect / total\"\n", + "\n", + "contrasts <- c()\n", + "for (i in 1:(N_MED_3-1)) {\n", + " for (j in (i+1):N_MED_3) {\n", + " contrasts <- c(contrasts, sprintf(\"diff_%d_%d := ind%d - ind%d\", i, j, i, j))\n", + " }\n", + "}\n", + "\n", + "cov_terms <- c()\n", + "for (i in 1:(N_MED_3-1)) {\n", + " for (j in (i+1):N_MED_3) {\n", + " cov_terms <- c(cov_terms, sprintf(\"%s ~~ %s\", MEDIATORS_3[i], MEDIATORS_3[j]))\n", + " }\n", + "}\n", + "\n", + "model_str_3 <- paste(c(med_eqs, y_eq, ind_defs, total_ind, total_def, prop_defs, prop_total, contrasts, cov_terms),\n", + " collapse=\"\\n\")\n", + "\n", + "cat(\"=== 3-Mediator Model ===\\n\")\n", + "cat(model_str_3, \"\\n\\n\")\n", + "\n", + "# Fit FIML\n", + "fit_sens <- tryCatch(\n", + " sem(model_str_3, data=dat, missing=\"fiml\", fixed.x=FALSE, estimator=\"ML\"),\n", + " error = function(e) { cat(\"FIML Error:\", conditionMessage(e), \"\\n\"); NULL }\n", + ")\n", + "\n", + "if (!is.null(fit_sens) && lavInspect(fit_sens, \"converged\")) {\n", + " cat(\"FIML converged successfully.\\n\")\n", + " cat(\"N used:\", lavInspect(fit_sens, \"nobs\"), \"\\n\\n\")\n", + "\n", + " pe_sens <- parameterEstimates(fit_sens, ci=TRUE)\n", + "\n", + " labeled <- pe_sens[pe_sens$label != \"\", ]\n", + " cat(\"=== All Labeled Parameter Estimates ===\\n\")\n", + " print(labeled[, c(\"label\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"pvalue\")])\n", + "\n", + " fm <- fitMeasures(fit_sens, c(\"chisq\", \"df\", \"pvalue\", \"cfi\", \"rmsea\", \"srmr\", \"aic\", \"bic\"))\n", + " cat(\"\\n=== Fit Measures ===\\n\")\n", + " print(round(fm, 4))\n", + "\n", + " # Build results table\n", + " contrast_labels <- c()\n", + " contrast_mediators <- c()\n", + " for (i in 1:(N_MED_3-1)) {\n", + " for (j in (i+1):N_MED_3) {\n", + " contrast_labels <- c(contrast_labels, sprintf(\"diff_%d_%d\", i, j))\n", + " contrast_mediators <- c(contrast_mediators, paste(MED_SHORT_3[i], \"vs\", MED_SHORT_3[j]))\n", + " }\n", + " }\n", + "\n", + " label_map <- data.frame(\n", + " label = c(paste0(\"a\", 1:N_MED_3), paste0(\"b\", 1:N_MED_3), \"cp\",\n", + " paste0(\"ind\", 1:N_MED_3), \"total_indirect\", \"total\",\n", + " paste0(\"prop\", 1:N_MED_3), \"prop_total\",\n", + " contrast_labels),\n", + " path_type = c(rep(\"a_path\", N_MED_3), rep(\"b_path\", N_MED_3), \"direct\",\n", + " rep(\"specific_indirect\", N_MED_3), \"total_indirect\", \"total\",\n", + " rep(\"prop_mediated\", N_MED_3), \"prop_total_mediated\",\n", + " rep(\"contrast\", length(contrast_labels))),\n", + " mediator = c(MED_SHORT_3, MED_SHORT_3, \"all\",\n", + " MED_SHORT_3, \"all\", \"all\",\n", + " MED_SHORT_3, \"all\",\n", + " contrast_mediators),\n", + " stringsAsFactors=FALSE\n", + " )\n", + "\n", + " sens_res <- merge(label_map, pe_sens[pe_sens$label != \"\", c(\"label\",\"est\",\"se\",\"ci.lower\",\"ci.upper\",\"pvalue\")],\n", + " by=\"label\", all.x=TRUE)\n", + " sens_res$method <- \"FIML_sensitivity_3med\"\n", + " sens_res$N <- N_TOTAL\n", + " sens_res$exposure <- EXPOSURE\n", + " sens_res$direction <- \"D1\"\n", + "\n", + " write.csv(sens_res, file.path(DIR_SENS, \"fiml_3med_all_paths.csv\"), row.names=FALSE)\n", + " cat(\"\\nSaved fiml_3med_all_paths.csv\\n\")\n", + "\n", + " # Forest plot\n", + " plot_labels <- c(paste0(\"a\", 1:N_MED_3), paste0(\"b\", 1:N_MED_3), \"cp\",\n", + " paste0(\"ind\", 1:N_MED_3), \"total_indirect\", \"total\")\n", + " plot_df <- sens_res[sens_res$label %in% plot_labels, ]\n", + " plot_df$label <- factor(plot_df$label, levels=rev(plot_labels))\n", + "\n", + " p_sens <- ggplot(plot_df, aes(x=est, y=label, xmin=ci.lower, xmax=ci.upper)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(color=\"darkorange\", size=0.6) +\n", + " labs(title=\"Sensitivity: 3-Mediator FIML (Excluding APOC4_APOC2_AC_exp)\",\n", + " subtitle=paste0(\"SNP -> 3 mediators -> tdp_st4 | N = \", N_TOTAL, \" (FIML)\"),\n", + " x=\"Estimate (95% CI)\", y=\"\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + " ggsave(file.path(DIR_SENS, \"fiml_3med_forest_plot.png\"), p_sens, width=10, height=7, dpi=150)\n", + " ggsave(file.path(DIR_SENS, \"fiml_3med_forest_plot.pdf\"), p_sens, width=10, height=7)\n", + " cat(\"Saved sensitivity forest plots.\\n\")\n", + "\n", + " # Comparison table: 4-med vs 3-med for key effects\n", + " fiml_4med <- read.csv(file.path(RESULT_DIR, \"main_SEM_FIML\", \"fiml_all_paths.csv\"), stringsAsFactors=FALSE)\n", + "\n", + " # Map shared labels: in 4-med model, the equivalent paths for M2/M3/M4 are a2/a3/a4, b2/b3/b4, ind2/ind3/ind4\n", + " # In 3-med model, they are a1/a2/a3, b1/b2/b3, ind1/ind2/ind3\n", + " compare_rows <- list()\n", + "\n", + " # a-paths comparison\n", + " for (k in 1:3) {\n", + " old_label <- paste0(\"a\", k+1)\n", + " new_label <- paste0(\"a\", k)\n", + " r4 <- fiml_4med[fiml_4med$label == old_label, ]\n", + " r3 <- sens_res[sens_res$label == new_label, ]\n", + " if (nrow(r4) > 0 && nrow(r3) > 0) {\n", + " compare_rows[[length(compare_rows)+1]] <- data.frame(\n", + " mediator = MED_SHORT_3[k],\n", + " path = \"a_path\",\n", + " est_4med = r4$est, se_4med = r4$se, p_4med = r4$pvalue,\n", + " est_3med = r3$est, se_3med = r3$se, p_3med = r3$pvalue,\n", + " stringsAsFactors=FALSE\n", + " )\n", + " }\n", + " }\n", + "\n", + " # b-paths comparison\n", + " for (k in 1:3) {\n", + " old_label <- paste0(\"b\", k+1)\n", + " new_label <- paste0(\"b\", k)\n", + " r4 <- fiml_4med[fiml_4med$label == old_label, ]\n", + " r3 <- sens_res[sens_res$label == new_label, ]\n", + " if (nrow(r4) > 0 && nrow(r3) > 0) {\n", + " compare_rows[[length(compare_rows)+1]] <- data.frame(\n", + " mediator = MED_SHORT_3[k],\n", + " path = \"b_path\",\n", + " est_4med = r4$est, se_4med = r4$se, p_4med = r4$pvalue,\n", + " est_3med = r3$est, se_3med = r3$se, p_3med = r3$pvalue,\n", + " stringsAsFactors=FALSE\n", + " )\n", + " }\n", + " }\n", + "\n", + " # indirect effects comparison\n", + " for (k in 1:3) {\n", + " old_label <- paste0(\"ind\", k+1)\n", + " new_label <- paste0(\"ind\", k)\n", + " r4 <- fiml_4med[fiml_4med$label == old_label, ]\n", + " r3 <- sens_res[sens_res$label == new_label, ]\n", + " if (nrow(r4) > 0 && nrow(r3) > 0) {\n", + " compare_rows[[length(compare_rows)+1]] <- data.frame(\n", + " mediator = MED_SHORT_3[k],\n", + " path = \"indirect\",\n", + " est_4med = r4$est, se_4med = r4$se, p_4med = r4$pvalue,\n", + " est_3med = r3$est, se_3med = r3$se, p_3med = r3$pvalue,\n", + " stringsAsFactors=FALSE\n", + " )\n", + " }\n", + " }\n", + "\n", + " # cp and total_indirect\n", + " for (lab in c(\"cp\", \"total_indirect\", \"total\")) {\n", + " r4 <- fiml_4med[fiml_4med$label == lab, ]\n", + " r3 <- sens_res[sens_res$label == lab, ]\n", + " if (nrow(r4) > 0 && nrow(r3) > 0) {\n", + " compare_rows[[length(compare_rows)+1]] <- data.frame(\n", + " mediator = \"all\",\n", + " path = lab,\n", + " est_4med = r4$est, se_4med = r4$se, p_4med = r4$pvalue,\n", + " est_3med = r3$est, se_3med = r3$se, p_3med = r3$pvalue,\n", + " stringsAsFactors=FALSE\n", + " )\n", + " }\n", + " }\n", + "\n", + " compare_df <- do.call(rbind, compare_rows)\n", + " write.csv(compare_df, file.path(DIR_SENS, \"comparison_4med_vs_3med.csv\"), row.names=FALSE)\n", + " cat(\"\\nSaved comparison_4med_vs_3med.csv\\n\")\n", + "\n", + " cat(\"\\n=== 4-Mediator vs 3-Mediator Comparison ===\\n\")\n", + " print(compare_df)\n", + "\n", + "} else {\n", + " cat(\"Sensitivity FIML failed to converge.\\n\")\n", + "}\n", + "\n", + "cat(\"\\nSensitivity analysis complete.\\n\")\n" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Sensitivity Analysis: 3-Mediator Model (Excluding APOC4_APOC2_AC_exp) ===\n", + "N = 1153 \n", + "Mediators: APOC2_AC_exp, APOC1_DeJager_Mic_exp, APOE_Mega_Mic_exp \n", + "\n", + "=== 3-Mediator Model ===\n", + "APOC2_AC_exp ~ a1 * chr19_44999110_TAAAA_TAA + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "APOC1_DeJager_Mic_exp ~ a2 * chr19_44999110_TAAAA_TAA + msex_u + age_death_u + pmi_u\n", + "APOE_Mega_Mic_exp ~ a3 * chr19_44999110_TAAAA_TAA + msex_u + age_death_u + pmi_u\n", + "tdp_st4 ~ b1 * APOC2_AC_exp + b2 * APOC1_DeJager_Mic_exp + b3 * APOE_Mega_Mic_exp + cp * chr19_44999110_TAAAA_TAA + educ + apoe4_dose + msex_u + age_death_u\n", + "ind1 := a1 * b1\n", + "ind2 := a2 * b2\n", + "ind3 := a3 * b3\n", + "total_indirect := ind1 + ind2 + ind3\n", + "total := cp + total_indirect\n", + "prop1 := ind1 / total\n", + "prop2 := ind2 / total\n", + "prop3 := ind3 / total\n", + "prop_total := total_indirect / total\n", + "diff_1_2 := ind1 - ind2\n", + "diff_1_3 := ind1 - ind3\n", + "diff_2_3 := ind2 - ind3\n", + "APOC2_AC_exp ~~ APOC1_DeJager_Mic_exp\n", + "APOC2_AC_exp ~~ APOE_Mega_Mic_exp\n", + "APOC1_DeJager_Mic_exp ~~ APOE_Mega_Mic_exp \n", + "\n", + "FIML converged successfully.\n", + "N used: 1153 \n", + "\n", + "=== All Labeled Parameter Estimates ===\n", + " label est se ci.lower ci.upper pvalue\n", + "1 a1 0.311 0.058 0.198 0.424 0.000\n", + "6 a2 0.251 0.066 0.122 0.381 0.000\n", + "10 a3 0.200 0.050 0.101 0.298 0.000\n", + "14 b1 -0.030 0.054 -0.136 0.076 0.581\n", + "15 b2 0.004 0.086 -0.164 0.172 0.961\n", + "16 b3 0.072 0.074 -0.072 0.217 0.326\n", + "17 cp 0.159 0.057 0.048 0.271 0.005\n", + "68 ind1 -0.009 0.017 -0.043 0.024 0.582\n", + "69 ind2 0.001 0.022 -0.041 0.043 0.961\n", + "70 ind3 0.014 0.015 -0.015 0.044 0.340\n", + "71 total_indirect 0.006 0.019 -0.031 0.044 0.748\n", + "72 total 0.165 0.054 0.060 0.271 0.002\n", + "73 prop1 -0.056 0.104 -0.260 0.148 0.588\n", + "74 prop2 0.006 0.130 -0.249 0.262 0.961\n", + "75 prop3 0.087 0.096 -0.101 0.275 0.363\n", + "76 prop_total 0.037 0.116 -0.190 0.264 0.749\n", + "77 diff_1_2 -0.010 0.031 -0.072 0.051 0.742\n", + "78 diff_1_3 -0.024 0.021 -0.064 0.017 0.248\n", + "79 diff_2_3 -0.013 0.034 -0.080 0.054 0.695\n", + "\n", + "=== Fit Measures ===\n", + " chisq df pvalue cfi rmsea srmr aic bic \n", + " 20.229 10.000 0.027 0.974 0.030 0.021 35690.029 36028.387 \n", + "\n", + "Saved fiml_3med_all_paths.csv\n", + "Saved sensitivity forest plots.\n", + "\n", + "Saved comparison_4med_vs_3med.csv\n", + "\n", + "=== 4-Mediator vs 3-Mediator Comparison ===\n", + " mediator path est_4med se_4med p_4med\n", + "1 M2_APOC2 a_path 0.3106715148 0.05758342 6.846432e-08\n", + "2 M3_APOC1_DeJager a_path 0.2543528038 0.06592321 1.141693e-04\n", + "3 M4_APOE_Mega a_path 0.1997224437 0.05042795 7.477719e-05\n", + "4 M2_APOC2 b_path 1.6665076555 1.32470737 2.083847e-01\n", + "5 M3_APOC1_DeJager b_path -0.0077734713 0.08694189 9.287561e-01\n", + "6 M4_APOE_Mega b_path 0.0765132685 0.07405918 3.015399e-01\n", + "7 M2_APOC2 indirect 0.5177364578 0.42264909 2.205830e-01\n", + "8 M3_APOC1_DeJager indirect -0.0019772042 0.02212667 9.287971e-01\n", + "9 M4_APOE_Mega indirect 0.0152814170 0.01527012 3.169525e-01\n", + "10 all cp 0.1653842667 0.05707494 3.759473e-03\n", + "11 all total_indirect 0.0004827316 0.02000371 9.807472e-01\n", + "12 all total 0.1658669982 0.05385145 2.069420e-03\n", + " est_3med se_3med p_3med\n", + "1 0.310736893 0.05759248 6.835486e-08\n", + "2 0.251443972 0.06592030 1.365347e-04\n", + "3 0.199533055 0.05042645 7.592471e-05\n", + "4 -0.029989929 0.05427210 5.805478e-01\n", + "5 0.004142675 0.08570961 9.614502e-01\n", + "6 0.072237837 0.07361612 3.264559e-01\n", + "7 -0.009318977 0.01693297 5.820828e-01\n", + "8 0.001041651 0.02155052 9.614490e-01\n", + "9 0.014413836 0.01511995 3.404386e-01\n", + "10 0.159330634 0.05689331 5.102093e-03\n", + "11 0.006136510 0.01910590 7.480710e-01\n", + "12 0.165467144 0.05385537 2.123196e-03\n", + "\n", + "Sensitivity analysis complete.\n" + ] + }, + { + "output_type": "display_data", + "metadata": {}, + "data": { + "image/png": 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+ } + } + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Method 2: Bootstrap (FIML Inside Each Replicate)\n", + "\n", + "Non-parametric bootstrap with 1000 resamples. Each replicate fits the full parallel mediation model\n", + "using FIML, preserving the N = full sample structure. Percentile confidence intervals are computed." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:34:14.952975Z", + "iopub.status.busy": "2026-03-31T21:34:14.950309Z", + "iopub.status.idle": "2026-03-31T21:55:28.368476Z", + "shell.execute_reply": "2026-03-31T21:55:28.366181Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running 1000 bootstrap replicates with FIML inside...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 200/1000 done (200 converged)\n", + " 400/1000 done (400 converged)\n", + " 600/1000 done (600 converged)\n", + " 800/1000 done (800 converged)\n", + " 1000/1000 done (1000 converged)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bootstrap complete: 1000/1000 converged (100.0%) in 21.2 minutes\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 6: Bootstrap\n", + "# ============================================================\n", + "cat(\"Running\", B_REPS, \"bootstrap replicates with FIML inside...\\n\")\n", + "\n", + "boot_labels <- c(paste0(\"a\", 1:N_MED), paste0(\"b\", 1:N_MED), \"cp\",\n", + " paste0(\"ind\", 1:N_MED), \"total_indirect\", \"total\")\n", + "boot_mat <- matrix(NA, B_REPS, length(boot_labels), dimnames=list(NULL, boot_labels))\n", + "\n", + "n_converged <- 0\n", + "t0 <- proc.time()\n", + "\n", + "for (i in seq_len(B_REPS)) {\n", + " idx <- sample(nrow(dat), replace=TRUE)\n", + " boot_dat <- dat[idx, ]\n", + " fit_b <- tryCatch(\n", + " sem(model_str, data=boot_dat, missing=\"fiml\", fixed.x=FALSE, se=\"none\", test=\"none\"),\n", + " error = function(e) NULL,\n", + " warning = function(w) {\n", + " tryCatch(sem(model_str, data=boot_dat, missing=\"fiml\", fixed.x=FALSE, se=\"none\", test=\"none\"),\n", + " error = function(e) NULL)\n", + " }\n", + " )\n", + " if (!is.null(fit_b) && lavInspect(fit_b, \"converged\")) {\n", + " pe_b <- parameterEstimates(fit_b)\n", + " for (lab in boot_labels) {\n", + " row_b <- pe_b[pe_b$label == lab, ]\n", + " if (nrow(row_b) == 1) boot_mat[i, lab] <- row_b$est\n", + " }\n", + " n_converged <- n_converged + 1\n", + " }\n", + " if (i %% 200 == 0) cat(sprintf(\" %d/%d done (%d converged)\\n\", i, B_REPS, n_converged))\n", + "}\n", + "\n", + "elapsed <- (proc.time() - t0)[3]\n", + "cat(sprintf(\"\\nBootstrap complete: %d/%d converged (%.1f%%) in %.1f minutes\\n\",\n", + " n_converged, B_REPS, n_converged/B_REPS*100, elapsed/60))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:55:28.534207Z", + "iopub.status.busy": "2026-03-31T21:55:28.530567Z", + "iopub.status.idle": "2026-03-31T21:55:33.873125Z", + "shell.execute_reply": "2026-03-31T21:55:33.870554Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Bootstrap Results === \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label est se ci.lower ci.upper pvalue\n", + "1 a1 0.3145518026 0.05676166 0.20404475 0.42847840 0.000\n", + "2 a2 0.3129849236 0.05703915 0.20238850 0.42561216 0.000\n", + "3 a3 0.2559612042 0.06496058 0.13600540 0.38668078 0.000\n", + "4 a4 0.2025980610 0.04926088 0.10640400 0.29487432 0.000\n", + "5 b1 -1.6976225320 1.33673379 -4.28266587 0.82274946 0.208\n", + "6 b2 1.6650275655 1.33591963 -0.83662448 4.27952162 0.224\n", + "7 b3 -0.0103682508 0.09097229 -0.19055030 0.17216146 0.890\n", + "8 b4 0.0788881631 0.07709506 -0.06888391 0.22540334 0.300\n", + "9 cp 0.1657191000 0.05885210 0.06111732 0.28031366 0.002\n", + "10 ind1 -0.5348934498 0.44605143 -1.47438489 0.26439793 0.208\n", + "11 ind2 0.5220297063 0.44351672 -0.25684618 1.45255153 0.224\n", + "12 ind3 -0.0030808936 0.02412959 -0.05124128 0.04333028 0.890\n", + "13 ind4 0.0161141071 0.01659000 -0.01377485 0.05210724 0.300\n", + "14 total_indirect 0.0001694701 0.02191555 -0.04515983 0.04317383 0.996\n", + "15 total 0.1658885701 0.05512317 0.05911055 0.27540307 0.000\n" + ] + }, + { + "data": { + "image/png": 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KkzYcKEMWPGqFSq3NzcKVOmLF68eOTIkXv27LFtOXv27IyMDCIaMGDAgAED1Gp1\nfut8+vQpEbEDZHYOHjw4c+bMvNPzu5uJoFevXhERET/++CM7EmQwGNasWTNw4ECtVuvSdubB\nymOluoVerycidmjMFusovV5f/AZOHp2dks/k5uaePXs2IiLizTfftG1jMBiysrLu3LkTExPT\nrFmz48ePf/DBB6+//nq1atWIyHaIVqlSpWzZsnv37r127VpWVpbVan38+DERZWVl2a6wfv36\nwv9DQ0MdTsnOzrZdhB1uEzRs2PD69eu3bt1q0KCBG+vPy8mYzI8oT2hubu758+ebNGkSFBQk\nTJw4cSLLzTk5ORcvXmzQoAG7YIhRKBStW7fesGHDrVu3ateuzSY2b97c9uH8/f3Zc8dx3JAh\nQ+bMmXPs2DH2XOzfvz8lJWXy5MnkWs+zKbbjLTMz88GDB23btmUX/zKDBg366KOPhI1yvtqo\nqKh79+6xvXHCXIe3K3L7yxZALAh2vmrRokUNGjQYP3784cOH2b1CvI3dXUi0Wu3ChQvZzeEe\nPnxYoUIFYdaff/6ZlZV18+bNOXPmdOnS5YsvvmDHX/JKTk4morCwsLyzDh06dOjQobzT+/Xr\n5zzYKRSKV199df78+Y8ePSpXrtyWLVvS09PZUaGiCQ8PF0p1C41GQ0QGg8FuOpvi5+dX/AZO\nHt02sjx9+tRqter1+gsXLti2KVWqVPPmzU0mExH9+uuvr7766uzZs2fPnl2hQoUePXqMHDmy\nRYsWrOWFCxd69+796NGjmjVrsmNq6enpRMT/7w01S5UqJfyfnROWd4rdIqzb7daQlpZmO7H4\n9eflZEzmR5QnlG27XS8Jnj17xvN8VFSU3XT27D979kwIdnZbKpPJhCdi6NChc+bMWb9+PQt2\n7CApO1HVlZ63fUQmJSWFiGyzJhFVqVJFeMcrcLVsDXZbHR4envc90+0vWwCxINj5qtq1a7/z\nzjuzZ89etmwZO4SRH6vVyq79dKhJkybsvJwSIJfLW7VqdfXq1bt379oGu+Dg4ODg4HLlyrVv\n375BgwYzZswYN24cuwGYQw6D7LRp09599928020DQX5GjRr1zTffrFy58sMPP1yxYkWVKlXa\ntm3r2jY5wLv7pt8smObdl8BOtIqMjCx+AyePbrtbiPV8rVq12GmIDlWsWPHo0aMXL17ctm3b\nnj174uPjFy9ePGPGjE8++YSIRowY8eTJk23btvXq1Yu1P378ODtnrpjs7tpttals1mMAACAA\nSURBVFrprwjoxvrzU6gvVyI+oc4HZ96tYO1d3LqGDRvWrFnzt99+mzt3rsVi2bRpU4sWLdhe\nT1d6nrEdbw4fneM4YUqBq7137x7l2Wp2kbVdS7e/bAHEgp8U82EzZsyoXLnyu+++m5KSYnuz\nezs8z1/I3/379z1XIftwtcV+1kmr1WZlZW3atImdcC1gt4M3m823b992uEK2t8Dht2qtVhvm\nSN5f6cirXr16DRs2/OWXX1JTU3ft2jVixIji7AR1uJOgOMLDw8uUKXP58mW76RcuXFAqlbVq\n1Sp+AxcrKVOmjFwuf/jwYYEt69evP3369IMHD967d69OnTqfffbZ48ePExMTL1++3LZtWyHV\nEZG7RqBdynG4I62Y9Tts6WRM5keUJ7RMmTIymSy/rWBznzx5Yjc9MTGRCor+toYOHZqQkHD6\n9OkDBw6kpKQIP/Ties/bCgkJIaLU1FTbiQ8ePBDeWApcLft+yF6SAoed4PaXLYBYEOx8mJ+f\n34IFC9LS0qZOnerkaJpcLr+Sv3nz5nmitszMzLJlyzZp0sT2e3BWVtbBgwf9/Pzq1asnk8mG\nDBkyevRos9ksNOB5/uLFi5T/GUueOw9m1KhRV69e/fbbby0Wy9/+9rfirIqVZ3f8qJheeuml\nu3fvnj59WpjCfoqtS5cu7OSh4jdwhZ+fX5MmTR4/fvzHH3/YTv/000/ZcbcnT54sWbIkISFB\nmFWhQoVBgwZZrVZ2Uwz660YbDM/zP/zwA7ljf8nBgweF/1ut1pMnT/r7+9eoUcON9Tt83KKN\nyZJ/QrVabcOGDa9cucKyGrN48eKwsLCtW7dqtdpGjRpdunTp2bNnwlyDwXDkyJHw8PDq1au7\nuF3sRNUdO3asX79eqVSyy1bIhZ53KDQ0NDw8/MKFC7bvErbtC1xtSEhI2bJlL1++nJubK8zd\nvXt33sfyxMsWQBwlfrkGFJHtVbG22E10S5Uq5farYs1ms/BzEWq1ulGjRsKfVqvV+Vye5wcM\nGEBEo0ePTkhIMJvNV65c6dKlCxG99957bP3shnwDBw68efOmyWR68OAB+z1Nu2sSba1evZqI\nvv7667w9U+SrYtmfKSkpKpXK39/f9v6uDq+KdfgTF7ZX8jZv3tzPz0+YkpiY2Ldv3w8++KDI\nXc3qV6lUMTExZ8+etVqtN27caNq0qUwmE25CVvwGebFD/Ldv37adyD4v69Wrx+42YjQa//nP\nf9Jf97y4desWx3Fdu3YVrn1+8OBB3bp1NRpNcnKy1WqNiIgICgpi9yXJyMgYN24c6+SXXnop\nvwdld6/4448/hClLliwhol9//dX2aapUqdLhw4d5nrdYLP/3f/9HRK+99pptA9vbnRStfoe9\n5HBMeucTygZwXFwcu/T1zJkzUVFR4eHh7DpZdu+Svn37snsO6/V6dmu6zz77zMl4CA4Ojo2N\ntZ3SpEmT5s2bR0dHC88p47zn81s/e0+YMmUKu8R17969NWrUsL0LXYGrnTJlChGNGDGC3Q/l\n1KlT1atXV6vVeW93YvuyBfBdCHY+I79g9+jRI7YLxO3Bjv0mmEO3b992Ppfn+czMzG7durEp\nwk/Bjh07VshJOp2uf//+dgc9mzRpkvd+KILk5GSO43r27Jm3Z4oZ7Pi/IvLSpUuFKQ6DnUPC\nvTbYnU5tH4gdVm7Tpk2Ru5q1Wbt2bUBAAP113phGo4mPj7ddSfEb2HH4Qcv/dT9YuVxeqVIl\ndj/YPn36CLeTWLBggVKp5DguPDw8LCyM47hSpUoJd5Nh94ZVq9U1atTw8/Pr0aNHbm4u269W\nrVq1hISEIge7+Ph4Pz8/FhyJqFatWsJPQTi8QXHR6s/L4Zj0zieU5/n33ntPJpPJZDK24ZGR\nkSwNMx999JFcLvfz86tRowZbs3D7N97lYPf111+zLV29erXdozvveYfrT0xMZGfp+fv7ly9f\nPjg4+ODBg2q1ukWLFi6uNi0tjV1SLZfLS5curVQqf/7554iICNtbHOd92QL4Lo7HGaM+4tKl\nS7/99turr75qd3SJiLZt23bmzJnIyEj2Ddtd2BWsDme99dZb165dczJXuPTh/PnzZ8+effbs\nWVhYWKdOndh7tK3r16+fPn368ePHWq22cePGrVu3dn5+W/fu3Q8dOnT//n3hvB/WMx06dHB4\nFwMmJSVlwYIF1apVE076yduf586d27JlyzvvvCMcK1y/fv2VK1dmzJjBPjjZIg7X361bN3YF\nwA8//DBu3LgVK1YIF6ykpaWNHz8+LS0tv+6igrpa6Mzk5OTt27cnJiZGRET07Nkz7zWMxW9g\na8uWLefOnbMtQPDo0aPdu3cnJSWVLl26RYsWtrcUIaKUlJTdu3c/efJELpdXrFgxLi6Ofdwy\n165dO3jwoE6na9y4cfv27TmOS0hIWLt2bWBg4IgRI3bv3m33oAcPHjx48ODo0aOFC27YMzVg\nwIA6deoQ0ZAhQ9asWfPo0SOO47Zv356SklK1atU+ffqwS0fpr+dxypQpwp0+ilN/XnnHpHc+\noczt27f37t2bmZlZpUqVHj16sAAnuH///p49e5KTk0uXLt2xY8eaNWsKsxyOh9mzZwcFBbF7\npjBPnz5dtGgRx3Hvvvtu3lNEnPR8fuMtNzd38+bNDx48iIyM7NOnT2BgoEaj6dChw/79+11Z\nLREZjcZt27bdvHkzODi4e/fuVapUmTdvnkajYXdgJkcvWwDfhWAHPubEiRMtW7acOnXqnDlz\nxK7FntlsjomJkclkN27csL1o4/PPP3/48CH7PUpwOxbsEhIS2O+PlTxvHpO+7unTp9evX2fH\nSdmUM2fONG3adOzYsezszOLL72UL4KNw8QT4mBYtWgwaNGjRokUevZ63aBYvXnzv3r1//vOf\nth8POp1u0aJFo0aNErEw8ChvHpO+btmyZR07dnz//ffZLfqSkpL+/ve/01+3x3MLhy9bAN+F\nPXbgezIyMho3bhwaGnrkyBEn93kpYVeuXGnWrNnrr7/+7bffil3Li0X0PXbkrWNSAnQ63cCB\nA7dv3+7n51eqVKnExESZTDZr1qwPP/zQLevHyxakB8EOfNKVK1fWr1//yiuv1KtXT+xa/mP1\n6tX37t2bOnVq3h96Ao/KewqdKLxwTErG6dOnz5w58/z584iIiC5dulSqVMlda8bLFqQHwQ4A\nAABAInCOHQAAAIBEINgBAAAASASCHQAAAIBEINgBAAAASASCHQAAAIBEINgBAAAASASCHQAA\nAIBEINgBAAAASASCHQAAAIBEINiBCKxWq16vF7sKL2K1WnU6HfuZc2AsFovRaBS7Ci9isVh0\nOh36xJbZbDaZTGJX4UXMZrNOp0Of2DKZTGazWewqShSCHYiA53kEO1tWqzUnJwd9YstisaBD\nbFkslpycHAQ7W2azGV+HbJlMppycHAQ7WyaT6UXrEAQ7AAAAAIlQiF0AAAAUjO3ClMvlYhcC\nAF4Ne+wAAHzAnTt34uPj9+3bJ3YhAODVEOwAAAAAJALBDgAAAEAiEOwAAAAAJALBDgAAAEAi\nEOwAAAAAJALBDgAAAEAiEOwAAHxAYGBgtWrVIiMjxS4EALwablAMAOADoqOju3fvrtFoxC4E\nALwa9tgBAAAASASCHQAAAIBEINgBAAAASASCHQAAAIBEINgBAAAASASCHQAAAIBEINgBAPiA\n5OTk48eP37p1S+xCAMCrIdgBAPiAtLS0s2fP3rt3T+xCAMCr4QbFAAAvCJ7SblFOIqmDqXRN\nUviJXQ8AuB+CnWjS0tJ2797dsGHDmJgYsWsBAEkz6+jsN3RhIWU//s8UpZaq96fWn1JQRVEr\nAwA3Q7ATx4ULF77++uuMjAytVotgBwAelJNEG1+ip2f/Z6Ipl679RHe3Uu91VLGLSJUBgPvh\nHDsRHDt27PPPP3/ttdeUSqXYtQCApFmMtKmvfaoTGNJp88uUcqVkawIAD0Kw86AnT57s3Llz\n/fr1hw4d0uv1wnSr1frll1926YJvyQDgYRe/p6RTzhqYsmn/myVVDQB4HIKdp+zZs2fChAlH\njhx58ODB6tWrJ06cmJGRwWa1adOmWrVq4pYHAC+ES4sLbpNwkNJuer4UACgJOMfOU1JSUsaM\nGdO7d28iMpvNY8aM2bFjx9ChQ4u2NtsdfhJgtVqtVqvENqo4rFYr+xd9IjCbzegQW2XLlh00\naFBgYKCLfcKlXZM93KtMveZKY8uxWdaoVpbY14tXY0kzmUwYJLZMJhMRmc1m9InAbDZzHCex\nDtFoNE7mIth5ytChQ2/durVly5acnBwikslkiYmJRV5bTk4Oz/Puq84rZGdni12Cd7FYLOgT\nO+gQWxEREeRyn2ju7g449YGLa5bf/EV+89eMioOLXpx4WJoBgdFoNBqNYlfhXQwGg9gluJNa\nreY4Lr+5CHaesnz58s2bN7dq1SoqKkoulxORxWIp8tr8/PykFOx4njcajWq1WuxCvIXVajUY\nDDKZDH0isFgsZrMZHSKwWCxGo1GhULh40ZUsupmp7njl5e9dWnn5Ltaw+n5+PnZnO7ZbV6VS\niV2ItzCbzSaTSalUKhT4cP8Pk8nEcZzEOsRJqiMEOw9JTk7euHHj+PHje/TowaacOXOmOCvU\narXuqMtbsM9sf39/sQvxFmaz2WAwyOVy9InAaDTq9Xp0iIDthlEoFK72SdVOVLUT/bmTMh8U\n2FbeYY48ooHPXaWv1+vxTmJLp9OxYCexj4ziyM3N5TjO5760FAcunvCIx48f8zxfr1499qde\nr09ISBC3JAB4EdUZVXCbiIYUUd/zpQBASUCw84igoCAiSkpKYn+uWLFCq9XipAcAKGlNplCp\n6s4ayJTUaT6RsyM7AOBDcCjWI6pUqVK7du1vvvmmRYsW9+/fj42N7dChw44dO3788cdRo0Yt\nWrSI7cAzm83btm07ceIEEU2bNq1UqVJiFw4A0qIMoH5baUMcZf7pYK5cRd2WUNk2JV4WAHgK\ngp2nzJo168iRI+np6e3atatbt65OpwsNDQ0JCSGiqlWrsv/UrVtXaI/zfwHAI0rH0PDTdGQ6\nXV1JFptrA8u1pfZfU2RT8SoDAPfjpHStJfgKi8WSlZXF0i0QkdlsTk9PVyqVwcHBYtfiLdjF\nE+ysBiCiy5cvb9iwoU6dOgMGDCjiKoyZ9OQYZT8hdQhFNKTgym4tUATs4omAgACxC/EWOp0u\nJydHq9Xi4gnBC3jxBPbYAQC8GFRBVKm72EUAgGfh4gkAAAAAiUCwAwAAAJAIBDsAAAAAiUCw\nAwAAAJAIBDsAAAAAiUCwAwDwAVqttnz58qGhoWIXAgBeDbc7AQDwAeXLl+/bt69GoxG7EADw\nathjBwAAACARCHYAAAAAEoFgBwAAACARCHYAAAAAEoFgBwAAACARCHYAAAAAEoFgBwDgA5KT\nk48fP37r1i2xCwEAr4ZgBwDgA9LS0s6ePXvv3j2xCwEAr4ZgBwAAACARCHYAAAAAEoFgBwAA\nACARCHYAAAAAEoFgBwAAACARCHYAAAAAEqEQuwAAAChY+fLl+/btW7p0abELAQCvhmAHAOAD\ntFpt+fLlNRqN2IUAgFfDoVgAAAAAiUCwAwAAAJAIBDsAAAAAiUCwAwAAAJAIBDsAAAAAiUCw\nAwAAAJAIBDsAAB9w8+bNBQsW7Ny5U+xCAMCrIdgBAAAASARuUAwA8ALgLZRwiB4fodxn5BdK\nUS2oQmeSq8QuCwDcDMFOHLdv3969e3dKSkpYWFjXrl1r1KghdkUAIF0P99G+SZR2838mBlWk\njvOoWl+RagIAj8ChWBEcPXp06tSpqampMTExSUlJ06ZNO3PmjNhFAYBEXV9FG7rbpzoiyvyT\nNr9M5+aJURMAeAr22Ilg6dKl7dq1e+edd9if77///oYNG5o0aSJuVQAgQcmXaNcYsprzmc3T\nwSkU0YDKtS/RqgDAYxDsPMVqte7bt+/ChQs5OTnh4eFxcXHVqlUjIrPZPGTIkJo1awota9So\nceTIEfEqBQDpOjKdLAZnDXgrHXqXhp0sqYIAwLNwKNZT4uPjly5dWqFChZYtW5rN5mnTpt27\nd4+IFApFt27dKlSoILR8+PBhdHS0eJUCgA/QaDQRERFBQUGFWMaQTg92Fdws6RSl3y1yYQDg\nVTie58WuQZpOnTrl7+8fGxvL/pw0aVLjxo1Hjx5t1+yPP/6YM2fOzJkzGzVq5GRtmZmZUnqm\neJ63WCwKBXYY/wfP82azmeM49InAarVarVZ0iIANEplMJpfLXWmvurFcdXOlPPmcK40toXUs\nka10Lf9VvBpLmtVq5XnexQ55EVgsFqvVKpfLZTLstfkPi8XCcZzEOiQoKIjjuPzm4k3TU+rV\nq7d9+/ZNmzbl5ubyPJ+ampqammrX5tSpU/PmzRs8eLDzVEdEJpNJSsGOMZlMYpfgXXieR5/Y\nQYfYYXnXlZaq9Dsupjoikqde4WVqH+1tFzvkxWGxWCwWi9hVeJcXqkMQ7DyC5/nPP//80aNH\ngwYNio6OlslkP/zwg12bAwcOzJ8/f/DgwUOGDClwhcHBwZ6pVBxWqzUnJycwMFDsQryFxWLJ\nyspSKBQBAQFi1+ItTCaT0Wj09/cXuxBvYTKZcnJyVCqVVqt1aYFm71gqtpVvH+xKW2vnRVzZ\ntiEhIcUqscQZjUaz2exqh7wADAaDTqfTaDQajUbsWryFXq/nOE6tVotdiDs52V1HCHYe8vDh\nw4sXL86YMUO41tXuaThw4MC8efMmTJgQFxfnygoldkCK7RuX2EYVH/rEltVqRYfYYvulZDKZ\nq31SqhKVqkh/TKPMhwW0VAXK6o4iue998rFj0xgkArbPFX1iSyaTvWjvJJI66uw90tLSiCgq\nKor9mZyc/PDhf99bb968OX/+/EmTJrmY6gAAioSjxlMKbtVwsi+mOgBwCMHOI6KjozmOu3jx\nIhFlZWUtXLiwUqVKmZmZRMTz/JIlS9q1a9e1a1exywQAqWswkcp3cNYgogE1n15CxQCA5+Gq\nWE9ZtmzZ5s2bo6KicnJyxo8f//z58x9++KFmzZqTJk16880387ZfsWJFqVKlSr5OUbBTynzu\nhB7PMZvN6enpSqVSYidTFofRaNTr9YW7u4ekGY3GzMxMjUZT6BMxDRm0bbDj+55Et6I+G8g/\n0i0Vljy9Xm82m3FmqkCn0+Xk5Gi1Wpx3KMjNzeU4zs/PT+xCSg6CnQclJyc/f/68fPnybEjd\nv38/ICAgMDDw9u3beRvXqlXrxTkJAMHODoJdXgh2dpKSki5evBgVFVWvXr3CL83TzbV0aQk9\nPkIWA8mUFNWc6oyi2NeI8+F7hSDY2UGwy+sFDHYvSpIQRXh4eHh4uPBn5cqV2X/q1q0rUkUA\n4KuSk5OPHz9ep06dIgU7jmIGU8xgIiL9c9KEEDm7qg4AfBeCHQDAi0TzopzyAfBiwsUTAAAA\nABKBYAcAAAAgEQh2AAAAABKBYAcAAAAgEQh2AAAAABKBq2IBAHxA+fLl+/btW7p0abELAQCv\nhmAHAOADtFpt+fLlNRqN2IUAgFfDoVgAAAAAiUCwAwAAAJAIBDsAAAAAiUCwAwAAAJAIBDsA\nAAAAiUCwAwAAAJAIBDsAAB9w586d+Pj4ffv2iV0IAHg1BDsAAB9gsVj0er3JZBK7EADwagh2\nAAAAABKBYAcAAAAgEQh2AAAAABKBYAcAAAAgEQh2AAAAABKBYAcA4AM0Gk1ERERQUJDYhQCA\nV1OIXQAAABSsYsWKgwYN0mg0YhcCAF4Ne+wAAAAAJALBDgAAAEAiEOwAAAAAJALBDgAAAEAi\nEOwAAAAAJALBDgAAAEAiEOwAAHxARkbG1atXExISxC4EALwagh0AgA9ISko6cODA1atXxS4E\nALwagh0AAACAROCXJwAAJOrPvXTtJ3p6hgzp5B9J5TpQvTeodIzYZQGAByHYiUCv169Zs+aP\nP/5IT08PDw/v0qXLK6+8wnGc2HUBgFQYM2nH3+julv9OyX5CT8/R+fnU4iNqOYMIbzgA0oRg\nJ4J58+ZduXLltddei4qKunbt2sqVKy0Wy6BBg8SuCwAkwayn9XGUeMLBLKuZjs0kQyZ1+LrE\nywKAkoBz7EpaVlbW+fPnR40a1aVLl9jY2IEDB7Zq1erYsWNi1wUAUnFiluNUJzj7b/pzb0lV\nAwAlCnvsPCUjI2Pp0qUXL17Mzs4ODw/v1atX7969iSgwMHD16tW2LZVKpUyGhA0A7mDKoXPz\nC2528nOq2MXz1QBASUOw85RvvvkmKSnp3XffDQkJuXnz5rfffhseHt6iRQuhgdFozM7OPn36\n9NGjR9966y3na+N53sP1lii2ORLbqOIQugJ9IsAgsRMVFdW9e/fQ0NB8+8RiIFMu/bmLM+UU\nvLpHf/AZf5IqkDiO1CHuLbWEYZAI8E6SH4l1iPOT8jmJba33SE9P5zguODiY/TllypTq1atP\nmDBBaPDhhx9euXLF399/3LhxHTp0cL621NRUPFMA4ITm1sqA4+8UdileFZw69I4n6gEADwkN\nDXWS7bDHzlMyMzOXLVt248aN3NxcNiUqKsq2wbhx45KTk69cubJgwYKcnJxevXo5WZv0jtVa\nrVbpbVSR8TxvtVqJSC6Xi12Lt+B5nud5DBIBGyQymSy/N3ROHWwNrMSZsjh9qisrtPqXJZmS\nVwX67qjDILFjtVpZh+A2CwK2T+SF6hDssfMIo9E4efLk0NDQSZMmRUVFyeXy9957LywsbNq0\naXkb//zzzxs3bly1apVGoyn5UkVhsViysrJCQnz7AJAbmc3m9PR0pVIp7OIFo9Go1+uDgoLE\nLsRbGI3GzMxMjUYTEBDgrN3D/bSuc8GrU2hoYiopte4qTxR6vd5sNhfQIS8SnU6Xk5Oj1Wq1\nWt9+Zt0oNzeX4zg/Pz+xCyk5+KLjEQ8ePEhKSho5cmS5cuXYt+Hnz5+zWampqfv27dPpdELj\n6tWrG43G5ORkcWoFACkp15b8wgtuVqm7r6c6AHAIwc4jWG4TviJcv349KSmJ7RzNysqaN2/e\n6dOnhcb37t3jOC4iIkKUUgFAUmRKajmjoDYKajWzJIoBgBKHc+w8onLlymq1euvWrUOHDr13\n797atWubNGny+PHj9PT0SpUqNWrUaPHixTqdrly5cnfu3NmwYUOXLl3UarXYVQOAJDScRI//\noJtr85nNUcd5FF6/REsCgJIinzlzptg1SJBarS5btuzOnTvXrVv37NmzyZMnly1bds+ePefO\nnYuLi2vWrJlOp9u1a9fvv//+5MmTuLi4ESNG+O75y0XA87zRaHxxzikskNVq1ev1crkcfSKw\nWCxmsxlfeAQWi8VgMCgUCpVKVVBbjqq/QlYTJZ0i3vI/c/zCqfsyqjPSU1WWLLPZbLVaXeiQ\nF4XZbDaZTEqlUqlUil2LtzCZTBzHvVAdgosnQAS4eMIOLp7ICxdP2Ll27dqWLVtq1arVt29f\nV5fJuE8311DSKTJkkn8UlW9PMYNIJZ0uxcUTdnDxRF4v4MUTOBQLAOADLBaLXq83mUyFWCa4\nMjV732MVAYA3wsUTAAAAABKBYAcAAAAgEQh2AAAAABKBYAcAAAAgEQh2AAAAABKBYAcA4ANU\nKlVQUNALddcGACgC3O4EAMAHVK5cecSIEbiFNQA4hz12AAAAABKBYAcAAAAgEQh2AAAAABKB\nYAcAAAAgEQh2AAAAABKBYAcAAAAgEQh2AAA+ICMj4+rVqwkJCWIXAgBeDcEOAMAHJCUlHThw\n4OrVq2IXAgBeDcEOAAAAQCIQ7AAAAAAkAsEOAAAAQCIQ7AAAAAAkAsEOAAAAQCIQ7AAAAAAk\nQiF2AQAAULDIyMiOHTtGRESIXQgAeDUEOwAAHxAcHBwbG6vRaMQuBAC8Gg7FAgAAAEgEgh0A\nAACARCDYAQAAAEgEgh0AAACARCDYAQAAAEgEgh0AAACARCDYAQD4gPv3769cufLQoUNiFwIA\nXg3BDgDABxiNxszMTJ1OJ3YhAODVcINiAABp4a2UfocM6aQpTSFViTixCwKAkoNgJ7J9+/Zl\nZWX169dP7EIAwPfpn9Op2XR1OeU++8+UgGiqO4aaTiNlgKiVAUAJwaFYMd24cWP+/PmbN28W\nuxAA8H2pV+mnRnT6X/9NdUSU/YSOz6JVTSn9rniVAUDJQbATjcViWbhwYXR0tNiFAIDvy31K\nG7pT5gPHc9Nu0G89yZBRoiUBgBgQ7DzoyZMnO3fuXL9+/aFDh/R6vd3cjRs38jzftWtXUWoD\nAEk5+n+U9chZg+e36OTnJVUNAIgGwc5T9uzZM2HChCNHjjx48GD16tUTJ07MyPjv1+WkpKQ1\na9ZMnDhRocBpjgBQMJVKFRQU5Ofn52CeKZuu/VTwKi7Hk9Xk9sIAwKsgVXhKSkrKmDFjevfu\nTURms3nMmDE7duwYOnQom7to0aIOHTrUrl37zp07rqwt7w4/n2a1Wq1Wq8Q2qjisViv7F30i\nMJvN6BBb5cqVGzFihFKpzNsniks/KMwudJT+ufHubmv5zh6pTwwmkwmDxJbJZCIis9mMPhGY\nzWaO4yTWIRqNxslcBDtPGTp06K1bt7Zs2ZKTk0NEMpksMTGRzTp48OC9e/emTZvm+tpycnJ4\nnvdIoeLJzs4WuwTvYrFY0Cd20CF2TCYT+/C2FXRvu6uLPzmrK9Xc3UWJLG+HvOCMRqPRaBS7\nCu9iMBjELsGd1Go1x+V7GyMEO09Zvnz55s2bW7VqFRUVJZfLichisRBRdnb20qVLx4wZExBQ\niLsPqNVqTxUqBp7nTSaTSqUSuxBvYbVajUajTCZDnwisVqvZbEaHCNggkcvlSqXSfl54A3q8\n35WVyEtXdf5d37dYLBar1eqgQ15UZrPZbDYrFAqc5CMwm81EJLEOcZLqCMHOQ5KTkzdu3Dh+\n/PgePXqwKWfOnGH/Wb9+vdVqZefYEdHNmzd1Ot2aNWsaNmxYo0aN/FZYzFjUGgAAIABJREFU\nqBTo/SwWS1ZWlsQ2qjjMZjP7zEafCIxGo16vR4cI2G4YpVLpoE9avkcX5xFvKWAVCo2mZl8p\n3dBOr9ebzWYMEoFOp2Nfh7Rardi1eIvc3FyO4xyfnCpRuHjCIx4/fszzfL169difer0+ISGB\n/T88PLxu3br3/5Kammo2m+/fv5+ZmSlevQDgy7QRVLlHwc2qD5BSqgMAh7DHziOCgoKIKCkp\nqWzZskS0YsUKrVbLTnro1atXr169hJZbtmzZuHHj+++/L1apACAF7f9FCQfIlJNvA00pavNZ\nCRYEAOLAHjuPqFKlSu3atb/55puFCxdOnTpVpVJ16NDh4sWLP/74o9ilAYAUla5FL60mZT4H\n4NTB1Oc3CqpYsjUBgAiwx85TZs2adeTIkfT09Hbt2tWtW1en04WGhoaEhNg1i4mJ6du3rygV\nAoAPycrKunPnTmhoaPXq1R23qPISDT1OB6fQw302Uzmq0pM6zKVS+SwFANLCSe8mGuD92MUT\neWPuC8tsNqenpyuVyuDgYLFr8Rbs4gl2VgMQ0eXLlzds2FCnTp0BAwYU0DTzAT05Tvo08guj\nsm0ooGyJFCgCXDxhR6fT5eTkaLVaXDwheAEvnsAeOwAAaQmqREGVxC4CAMSBc+wAAAAAJALB\nDgAAAEAiEOwAAAAAJALBDgAAAEAiEOwAAAAAJAJXxQIA+IDIyMiOHTtGRESIXQgAeDUEOwAA\nHxAcHBwbG6vRaMQuBAC8Gg7FAgAAAEgEgh0AAACARCDYAQAAAEgEgh0AAACARCDYAQAAAEgE\ngh0AAACARCDYAQD4gD///HPt2rXHjh0TuxAA8GoIdgAAPkCv1z979iwzM1PsQgDAqyHYAQAA\nAEgEgh0AAACARCDYAQAAAEgEgh0AAACARCDYAQAAAEgEgh0AgA+Qy+UajUapVIpdCAB4NYXY\nBQAAQMGqVas2ZswYjUYjdiEA4NWwxw4AAABAIhDsAAAAACQCwQ4AAABAIhDsAAAAACQCwQ4A\nAABAIhDsAAAAACQCwQ4AwAdkZWXduXMnMTFR7EIAwKsh2AEA+IAnT57s3Lnz4sWLYhcCAF4N\nwQ4AAABAIvDLEwBQGLyFHh+hJyfI8Jz8wqhsG4psRhy+IgIAeAUEO0/57LPPWrdu3bFjx/wa\nnD9/fv369T179mzdunVJFgZQdA920v6/0/Nb/zMxvD51+Y6iW4lUEwAA/Be+Z3vK9evXnz17\n5nCW1Wr9+eefv/zyyytXrqSmppZwYQBFdOkH+q2XfaojouSLtLYj3VovRk0AAPA/EOxE8Pvv\nvx8+fHjOnDkKBfaYgo94fJT2TSLe6niuxUi/j6DUayVbEwAA2EOw8CCO406ePHngwAGj0diw\nYcNevXrJZDIiqlq16r///W9/f3+xCwRw2R/vkdXsrIFZR0emU9+NJVUQAAA4gGDnQWfPnj1x\n4kTbtm2TkpLi4+MzMjKGDx9ORDVr1hS7NIDCyPyTHh8tuNm97WTIIHWw5wt6EYWHh7ds2TIq\nKkrsQgDAqyHYeVBSUtKSJUtUKhUR8Ty/bdu2oUOHyuXyIqwqKyvL3dX9l+rK9/Kk455bf148\nz2t53izDmQD/wfN8oNXKcZx39gmXm+jSqLWaLL/15TWl3fOgPK/BILERyvMdrFYumzPf9aU+\nsfpHGVrO9tDKLRYLz/MefXv0LRaLhYiMRiP7D9BffWI2Oz3g4GsCAgI4jstvLoKdBzVu3Jil\nOiKqX7/+zp07k5KSypYtW4RVGY1GnufdWt1/qZJOKe7hCBq4gfzJIbFLAO9iCa5haGTw7EMg\nxPwvs9kssRxTfBLrkICAACdzEew8KDQ0VPg/exqys7OLtqqgoCD31OQI13K6ud7rnlt/XjzP\nGwwGjUZTkg/qzaxWq06nk8vl3tknXMol+ZH3XGlp6fw9H1jRLQ9qsVjMZrNarXbL2iTAYrHo\n9XqlUil8XfQNSv/gYE8dnWe7pvz8/Dy0fp9jMBj0er1Go8ELR2AwGIhIYh3iZHcdIdh5lMlk\nEv7PxpZSqSzaqoq8oEsiG3hw5Y5YLBZzVpYiJKSEH9drmc1mU3o6KZUKj30EFkvFDnTyUzIV\n9LUkIFre4A0iZ+84rrMajRa9XuHJrzS+xWo0mjIz5RqNwumX9RcKOxTr2bdHn8L2S8lkMvSJ\nwGQycRz3QnWIL52r4XMSEhKE/ycmJnIcFxERIWI9AEWk0FD98QU3a/R3d6U6AAAoGgQ7D7p8\n+fKVK1eIKCcnZ/fu3bVr13Z+XBzAe7WcQaG1nTWIbkWN/l5S1QAAgGM4FOspVqv1pZde+uGH\nH7KzszMzMwMCAt599102a+rUqbdu/ef2/fHx8fHx8ew/2J8H3ksVSP130ea+9PScg7nl2lOf\nDSSX1FksAAC+iPPctZYvuKtXr0ZHR4eEhDx8+NBoNFauXFn4nYm7d+/m5ubatY+JifGxc6KL\nwWKxZGVlheAcu7+Yzeb09HSlUum508zdw2qii4vp6o//iXecjCKbUf1xVHsEcW7e/W80GvV6\nvUcvG/Itt2/f3rNnT7Vq1bp16yZ2Ld5Cr9ebzWYcCRHodLqcnBytVqvVasWuxVvk5uZyHPdC\nXWGDPXaeEhsby/5TsaL9RYJVq1Yt8XIA3EGmpIaTqeFkMutJn0Z+YSR/Ub6NiE6v1z979gz7\n9QHAOQQ7ACg8hYYCosUuAgAA7OHiCQAAAACJQLADAAAAkAgEOwAAAACJQLADAAAAkAgEOwAA\nAACJwFWxAAA+ICYmZvLkyRqNRuxCAMCrYY8dAAAAgEQg2AEAAABIBIIdAAAAgEQg2AEAAABI\nBIIdAAAAgEQg2AEAAABIBIIdAIAPyM3NTUhISE1NFbsQAPBqCHYAAD4gISFh8+bNp0+fFrsQ\nAPBqCHYAAAAAEoFgBwAAACARCHYAAAAAEoFgBwAAACARCHYAAAAAEoFgBwAAACARCrELAACA\ngoWHh7ds2TIqKkrsQgDAqyHYAQD4gNKlSzdu3Fij0YhdCAB4NRyKBQAAAJAIBDsAAAAAiUCw\nAwAAAJAIBDsAAAAAiUCwAwAAAJAIBDsAAAAAiUCwAwDwAQkJCZs3bz59+rTYhQCAV8N97AAA\nfEBubm5CQkJwcLDYhQCAV8MeOwAAAACJwB47AACAvzw7T1eXU+JJ0qWQtgyVbUN1RlPpGLHL\nAnAVgp2nDBs2rE+fPoMHD847y2q1btmyZdeuXcnJyeHh4V27du3Xr59Mhr2nAADisRjpwN/p\n4mIi/j9T0u/Sk2N09t/U9D1qPYs4vEuDD0CwE8Evv/yycePG4cOH16hR4+rVqytWrOA47uWX\nXxa7LgCAFxVvpW2D6M5mB7OsZjr5OemSqeviEi8LoNDw/aOkWSyWbdu29e3b9+WXX46NjR00\naFCrVq0OHz4sdl0AAC+wC985TnWCSz/Q7d9KqhqAosMeOw/ieT4+Pv7AgQNGo7Fhw4Zvvvlm\nYGCgTCabO3duYGCg0Cw8PPzmzZsi1gkA8ELjrXTyi4KbnfiUqr/i+WoAigV77Dxoz549Fovl\nk08+eeutty5duvTdd98REcdxUVFRAQEBrI3FYjl//nzt2rVFrRQAvF1MTMzkyZO7d+8udiFS\n9PQM5SQW3OzZBcpK8Hw1AMWCPXYepNVqx40bR0TVqlV79OjR2rVrDQaDWq22bbNy5cqkpKT3\n33/f+apSU1N5nnfexuekpKSIXYJ3MZlM6BM7HuoQ/zMf+139zhNr9hwVUZjYNXgbTck/5A8V\nSv4xXedH5Cd2Dd5GK8aDGqM7ZXZd47n1h4aGchyX31wEOw+qU6eO8P+qVata/p+9Ow+Mqrz3\nP/6dTCaZTFaykZAEAkTCEgXBneIGCkJFvUVxudqqKFZxBde6XmNre72/gruUVkHr1WpARC2C\nGC0CXlkUAgTCTiCEBJKQZdZz5vz+GDvGAFkIk3Pm5P36a3LmOTOfeRgmn5xtVLWysrJPnz7B\nhXPnzl20aNEjjzySlZXV+kO18k8YpjRNM9+L6oxAcWdOmgvhmyQyRotOCskjIxypPovS1J6B\nmi1OIvi9ibZExen4Yc4bNISaH0gX2FDndrsDP2qa9sorryxfvvypp54aOnRomw+VnJwcopC6\nUFW1oaEhKYnfrD9SFKWurs5ms/G9AkFer9ftdickJITk0ce8IGNeCMkjh4zX662vr7fb7cED\nOeB2uxVFOQkTUvW9vD28HeMsllvLJDazs08XMi6Xq6mpyeFwOBy6bKgyIqfTabFYYmK6dFNm\nlEhKVz7fz3GMXQg1NTW1uG23/7jr4I033li1atXvf//79rQ6AEAIpQ+ThNy2h2WebeRWBwRQ\n7EKo+bmuu3btstlsmZmZIvLll19+8cUXzzzzTP/+/fVLBwAIsMjIZ9oec16bYwD9sSs2hKqr\nq997770LL7ywoqLis88+GzlyZFRUlNfrfeedd84880yXy1VSUhIcPGjQoMhI/jkAQA+Db5K9\nxbLpreMOOPtRyb206/IAJ4omESqKolx99dUHDx6cPn261+s944wzAmfI7tu379ChQ4cOHVqx\nYkXz8XPnzu3Ro4dOYQEYndPpLC8vT05O5hi7UBn7V4nPkdV/FNX7s+W2WPnFczL8Xp1iAR1j\nMd9FNGB8nDzRAidPHC20J0+EoZKSkqKiooKCgkmTJumdxShO2skTzR3ZJaV/lwPfiuuQOHpK\n1i9k8I0Sm3EynyJkOHniaLqcPKEvttgBAPBviX3lnMf1DgGcOE6eAAAAMAmKHQAAgElQ7AAA\nAEyCYgcAAGASFDsAAACT4KxYAAgDycnJI0aMyMrK0jsIAEOj2AFAGEhLSzv33HOD3zcNAMfE\nrlgAAACToNgBAACYBMUOAADAJCh2AAAAJkGxAwAAMAmKHQAAgElQ7AAgDJSXly9cuHD16tV6\nBwFgaFzHDgDCgNPpLC8vT0xM1DsIAENjix0AAIBJUOwAAABMgmIHAABgEhQ7AAAAk6DYAQAA\nmATFDgAAwCS43AkAhIG8vLwpU6bExsbqHQSAobHFDgDCgNVqtdvtNptN7yAADI1iBwAAYBIU\nOwAAAJOg2AEAAJgExQ4AAMAkKHYAAAAmQbEDgDDgdrurqqrq6+v1DgLA0Ch2ABAG9uzZ849/\n/GPlypV6BwFgaBQ7AAAAk6DYAYDheRuiG3ekRNREaIreUQAYGl8pFiqlpaUpKSnp6enHG1BV\nVVVXV5eWltajR4+uDAYgnOxeIt89L/v+NUBTB8SKum+2fPKRnPukpAzWOxkAI6LYhUphYeHE\niRMnT5589F2VlZUvvPBCWVmZ1WpVVXX48OEzZsyIi4vr+pAAjEvzS/F98v1LzZdZ/R7Z+r5s\nXyBjXpeCm/WKBsCw2BWrgz/84Q+aps2ePXv+/Pl/+tOftmzZ8uabb+odCoDBrHi8Rav7ieqV\nJVNkx8ddGwhAGKDYhZamaXv27Nm2bZvP5wssaWhoSEtLu+WWWzIyMiwWy8CBA3/xi19s2bJF\n35wAjOXQRvnuT60N0PzyxW/F5+yqQADCA7tiQ6ipqemBBx7Yv3+/x+NJSkp66qmn+vXrFx8f\n//jjjzcfVl1dnZycrFdIAEa0/nXR1DbGNFbI9vky6D+7JBCA8ECxC6HFixffe++95513Xl1d\n3WOPPfbaa6/993//d/DeLVu2HDx4cM2aNTt37nzqqadafyiv16tpWojzdh2/3+/3+z0ej95B\njEJVVRFhTppTFKWbTIjFWWmpWNFiYeT2jyztWNe/4a+qv+WOFy37As2eepLSGZqiKKqqdoc3\nSTspiiIizElziqJYLBaTTUh0dHQr91LsQig/P3/kyJEi0qNHjwkTJsyePbuhoSE+Pj5w7zvv\nvLNhw4bY2Ngbbrihf//+rT9UQ0ODmYpdQENDg94RjEVVVeakhe4wIVH7VyV8ccOJrRux76uI\nfV+1WHhk3EJfz/M6Gyt8BA90QYDH4zFZj+k8k01IVFSUxXLcP/0odiGUm5sbvJ2ZmSkiVVVV\nwWJXWFjodrs3btz44osvbt++/b777mvloVqv52FH0zSfzxcVFaV3EKPw+/1erzciIoI5CfL7\n/YqidIcJiejRzzeo5fmtkds/tPjaLrX+xH5qrwtaLLQl9bba7Sctn4Gpqur3+202m95BjEJR\nFEVRIiMjIyP55f6jwFZMk01IK61OKHYhZW/22Wq1WuXfe9yaDzjjjDOuu+6611577eabb05M\nTDzeQ5nsYiiBTVMme1GdoSiK1+u1Wq3MSZDX63W73d1iQuLOkt5ntVy4qFHKPmhz1YjTp0WM\nuL/Fwu5Tc9xut6Io3eJN0j4ulyvw55DD4dA7i1E4nU6LxRITE6N3kK7DWbEh1PzrugO34+Li\n9uzZM3PmzJqamuBdSUlJItLU1NT1CQEY1OAb2x4TaZcBV4c+CoBwQrELoXXr1vn9/sDtkpKS\nuLi4jIyM2NjY4uLiL774IjhszZo1dru9Z8+eOsUEYDz9L5fcsW2MOesRic/ukjQAwga7YkNF\n07TExMSnnnrq/PPPr6ioWLJkyXXXXRcREZGamvqrX/3q73//+969e3NycrZt2/bdd9/ddttt\ngX21APCjCe/KB2Ok6vtj3zvoBjnnia4NBCAMWMx3rqVBPPPMM5deeqmmaV9//bXP5xsxYsT4\n8eODBzyuWbNm5cqVNTU1aWlpo0aNOu200/RN28UCx9gF9kFDRBRFqaurs9lsrRxn2d0EjrFL\nSEjQO4iufE5Z+aT88Ioo7p8WxqTJyGdk6B0i7bkiiplxjF0LLperqanJ4XBwjF1QNzzGjmIH\nHVDsWqDYHY1i9xNvvez98sCWVT9sKIntfeb51/1OrOY/Wbg9KHYtUOyO1g2LHcfYAYCxRSVI\n3pWH+vzn/3nPqrKfSqsD0AqKHQAAgElQ7AAAAEyCYgcAAGASFDsAAACToNgBAACYBBcoBoAw\nkJeXN2XKlNjYWL2DADA0ttgBQBiwWq12u91ms+kdBIChUewAAABMgmIHAABgEhQ7AAAAk6DY\nAQAAmATFDgAAwCQodgAQBrxeb319vdPp1DsIAEOj2AFAGNi1a9e8efP+9a9/6R0EgKFR7AAA\nAEyCYgcAAGASFDsAAACToNgBAACYBMUOAADAJCh2AAAAJkGxA4AwkJSUNGTIkOzsbL2DADC0\nSL0DAADa1rNnz4suushut+sdBIChscUOAADAJCh2AAAAJkGxAwAAMAmKHQAAgElQ7AAAAEyC\nYgcAAGASFDsACAMHDx4sLi7euHGj3kEAGBrXsQOAMFBXV7dp0yaLxaJ3EACGxhY7AAAAk2CL\nHQCEG80vB/5PKr8Td6040iX7fEkt0DsTAEOg2IVKYWHhyJEjL7rootaHvfjiiy6X6+GHH+6a\nVADC3u4lUnyv1Gz52cKsX8joVyTtNJ0yATAKdsWGSmlpaVVVVetjvvjiiy+++GLLli2tDwOA\ngH6NS2X++JatTkT2fyP/e57sXqJHKAAGQrHTTX19/Ztvvjls2DC9gwAID32se04/PEc09dh3\n+5rkk2ukfk/XhgJgLOyKDSGLxfJ///d/xcXFXq/39NNPnzBhQkTET036r3/966BBg0477bTy\n8nIdQwIIF5dGL7GIv7URniOy4km5bG5XJQJgOGyxC6G1a9e+//77+fn5aWlpc+bMeffdd4N3\nbdiwYdWqVVOnTtUxHoAw0j9ZzbJWtD1uW5Eo7tDHAWBQbLELocrKyr/85S9RUVEiomnaJ598\nct1111mtVp/P9+qrr15//fVpaWntfKiGhoZQJu1qmqb5/X6TvajO0DRNRFRVZU6C/H4/E2Ld\n/3VU6d8Ct6Mb97VrHV+TWjRBi0oM/OQ+70+ao2eI4ulOVVVN07r5m6Q5VVVFxOv1Bm5A/j0n\niqLoHeRkiouLa+WSlhS7EBoxYkSg1YnI0KFDFy9eXFlZmZWV9Y9//MNut0+cOLH9D+X1egO/\n+83E4/HoHcFY/H4/c9JCN58Qe+2OyJ0LOrqWdd+Xwdu+YY+q1qSTGspwKDEtKIpish7TeSab\nkLi4uFbupdiFUEpKSvB24J+hsbGxvLx8wYIFf/jDH5ofb9emhISEk59PP36/3+l0tv7W7FZU\nVW1sbIyMjIyNjdU7i1EoiuL1eh0Oh95B9GQZdJWSnh+4rVV8a/vumfaspV7yNy02M3A7rucA\nsZl2DgObpmJiYvQOYhQej8ftdtvt9ujoaL2zGEXgj0OTTUjr30BDsQshn88XvB14b9lsto8/\n/jgyMnLevHmB5YcOHaqvr3/iiSfGjx9/7rnnHu+hbDZbqNN2JVVVLRaLyV5UZwT+lzInzWma\nxoRIUm9J6h246U0fIWv/IKq3jVUScq2n3RzyYMYQ2BXb3d8kzQS2S0VERDAnQT6fr7t9klDs\nQqj56a4HDhywWCzp6em/+MUvcnNzg8tLSkrq6urOOeecjIwMHSICCBfRie7+k+1lb7cxbPi9\nXZIGgEFR7EKopKRk48aNBQUFTU1NS5YsGTx4cFxc3NChQ4cOHRoco6rq1q1bJ0yYoGNOAGHB\nOfzxqIqvIxr3HndE1kgZdmcXJgJgOBS7UPH7/b/85S9nz57d2NhYX18fFxf30EMP6R0KQLjy\ner11nkht9Pspy38jNVuPMSL7AplYJNaork4GwEgodqHy+OOP9+rV68Ybb9y7d6/X6+3bt29k\n5DFm+7zzzhswYEDXxwMQXnbt2lVUVFRQUDDpP9fJulmy6U2p3SYiIhbpOVyG3iEFN4vFqnNK\nAHqj2IXKkCFDAjf69OnTyrDU1NTU1NQuSQTAFGwOOftROftRcdeKp1YcPcXGydQAfkSxA4Dw\nZO8h9h56hwBgLHylGAAAgElQ7AAAAEyCYgcAAGASFDsAAACToNgBQBiIj4/Py8vjK2oAtI6z\nYgEgDPTq1WvcuHF2u13vIAAMjS12AAAAJkGxAwAAMAmKHQAAgElQ7AAAAEyCYgcAAGASFDsA\nAACToNgBQBg4ePBgcXHxxo0b9Q4CwNC4jh0AhIG6urpNmzZZLBa9gwAwNLbYAQAAmATFDgAA\nwCQodgAAACZBsQMAADAJih0AAIBJUOwAAABMgsudAEAY6Nu370033RQXF6d3EACGRrEDgDAQ\nFRWVkJBgt9v1DgLA0NgVCwAAYBIUOwAAAJOg2AEAAJgExQ4AAMAkKHYAAAAmwVmxABAGVFV1\nu91Wq1XvIAAMjS12ABAGtm/fPmfOnGXLlukdBIChUewAAABMgmIHAABgEhxjBwDhz1Mnm+bK\nni+kYa/Y4iS1QAZeKzkX6R0LQFej2AFAmNs2X5beLq7DPy2pWCkbZkvf8TL+bbEn65cMQFdj\nVywAhLPSd+XjST9rdUG7PpP3zhdvfZdnAqAbih0AhK36vbJkioh23AGHN0nxfV0YCIDO2BUb\nQqqqzps376uvvnI6nX379r3lllsGDhwoIpMnT548eXJFRcWKFSsURTn99NPvvvvu+Ph4vfMC\nMK74+Pi8vLyMjIyfLV3z36K42lhz01w55wlJ7Bu6bACMgy12ITR79uxly5ZNmTLlD3/4Q69e\nvZ566qmqqioRiYyMnD9//imnnDJv3rw//vGPZWVlr7zyit5hARhar169xo0bN2zYsJ8t3fFx\n22tqftn5aYhSATAattiFitPpXLp06ZQpU0aNGiUi06ZNc7vdFRUV6enpIpKdnT127FgR6dev\n3+WXX/7OO++43W673X68Rzt8+LCmHX9vS3g6dOiQ3hGMxefzMSctMCEtuN1ut9sduJ384ekR\nTfvatdqXd8uXd9deuVJNPCWE4XQSnBAEOJ1Op9OpdwpjaWpq0jvCyZSSkmKxWI53L8UuVHbt\n2qUoyimn/PgxGhkZ+cgjjwTvHTBgQPB27969VVWtrKzMzc3t4pAAAMBMKHahEviDKSoq6pj3\nxsTEBG9HR0eLiMfjaeXRUlJSTmo6namq2tDQkJSUpHcQo1AUpa6uzmazJSYm6p3FKLxer9vt\nTkhI0DuIUXi93vr6ervdHhcX9+OiO8rlL7lSv6ftlS9+SU6f1iOk+fTgdrsVRflpQro9l8vV\n1NTkcDgcDofeWYzC6XRaLJbmv3NNj2PsQiXwNmpoaDjmvY2NjcHbLpdLRFrZDwsAx9Z/Yttj\nLFbp/8vQRwFgCBS7UOnbt6/Vat20aVPgR03THn300eLi4sCPW7ZsCY7cuXOnzWbLzMzUISWA\nsHbmQ2Jra9tMwW8kIbcrwgAwAHbFhkpsbOzo0aOLiorS0tJ69+79+eef79ixY9CgQYF7Dx8+\n/O6771500UUVFRWffPLJyJEjj7fTFgCOKz5bxv5NPr1eNP+xB6SeKhf+uWszAdATxS6Epk6d\nGhMT89Zbbzmdzn79+j399NPBa1CNHTu2sbFxxowZXq/3jDPOmDp1qr5RARhcdXX1unXrsrKy\nhg8f/rM78idLZIx8PkVc1S3X6T9Rxr0lUVwjE+hGKHYhZLPZbr311ltvvfXouyIiIm677bbb\nbrut61MBCEc1NTVr1671eDwti52I9J8oU7bLpnmyZ6k07BVbnKSeKgOvlezz9UgKQE8UOwAI\nf1EJcvo0OX2a3jkA6IyTJwAAAEyCLXY6+Pvf/653BAAAYEJssQMAADAJih0AAIBJUOwAAABM\ngmPsACAM9OnT55prruEblgG0jmIHAGHAbrenp6fzpdIAWseuWAAAAJOg2AEAAJgExQ4AAMAk\nKHYAAAAmQbEDAAAwCc6KBYAwoKqq2+22Wq16BwFgaGyxA4AwsH379jlz5ixbtkzvIAAMjWIH\nAABgEhQ7AAAAk6DYAQAAmATFDgAAwCQodgAAACZBsQMAADAJih0AhAGHw5GTk5OSkqJ3EACG\nxgWKASAM5OTkXHHFFXa7Xe8gAAyNLXYAAAAmQbEDAAAwCYodAACASVDsAAAATIJiBwAAYBIU\nOwAAAJOg2AFAGKiurl61alVZWZneQQAYGsUOAMJATU3N2rVrd+7DV9s/AAAgAElEQVTcqXcQ\nAIZGsQMAADAJvnkCADrIXSueWolOEnuy3lEA4Gcodh1WWlqakpKSnp7eobUqKiqamppOOeWU\n4BK/319aWtqzZ8/U1NSTnRFACKge+eFVKZkjhzf/uCR5oJx6qwybJpF80xcAQ2BXbIcVFhYW\nFxd3dK1FixbNmjUr+GNtbe3jjz/+6KOPrly58qSmAxAajfvl3XPlqwd+anUiUrNFvn5Q3j1b\n6vfolwwAfkKx67AXXnhh/PjxnXmEsrKye+65Jy0tLTKSLaZAOPA1StFlUvX9se+t3iBF48Rz\npGszAcAxUOw6rK6uzuVyBW6XlpbW1taKSHl5+Y4dOzweT/ORXq+3rKxs3759LR5h69at1157\n7f3332+xWLomM4BO+e6PcqiktQE1W+TbZ7sqDQAcF1uMOqywsHDixImTJ08Wkeeff378+PE/\n/PCD1+utr693u93PPPNMv379RGTz5s3PPfecx+OJiYnJycnJyMgIPsL48eOtVqtuLwBAh/gV\nWf9a28M2zJZfPCfW6BClyMnJueKKK5KTOV0DQGsodp0SERHx0UcfPfvss3l5eaqq3n///R98\n8MHDDz8sIi+++GJmZmZhYaHdbl++fPnMmTMzMzMDa51Aq/P5fCc5uq78fr+maSZ7UZ2hqqqI\nmHxOvPWWytXtH66pqlVRlOhQ9aT2s9Rts7oOtz3O26CufVlLGRKiGDGq2s/qtjnLlR2lHV7Z\nYtFyLg5BKJ2pqur3+838v6aDAp8kzElzfr9fTPc71GaztXIvxa6zhg0blpeXJyJWq3Xw4MGb\nN28WkX379lVUVEyfPt1ut4vIqFGjFixY4PV6T/hZ6uvrNU07WZkN4sgRjkn6GUVRTDwnkYe+\nT/r0sg6MF9G/03WQdfmM0D14pybEEnHopoMnMYyhdOaj1ZTcbrfb7dY7hbGYbEJSUlJaOZSL\nYtdZzfexRkVFBd49lZWVIhLcRCci2dnZnblkfFRUVCcyGo6maYqitP43R7eiaZrX642IiDDx\nnETE91T6XdX+8ZqmaZoWEaH/ccAW9yFrxfL2jFQzz9NieoYohqZpfr/fYrGcyJxYLNEG2PZ5\n0qmqqmkaZ6EFqaqqKEpkZCRH+wQFtmJ2qwnh/0NnHfPtoiiKiDT/JO3kL+z4+PjOrG40qqo2\nNDSY7EV1hqIoXq/XarWaeU7iT5Or5rd/uNfrdbvdCQkJoUvUXu5aea2n+NvalWOJsF45Xxyh\nKnZer7ehvt5ut8fFxZ3A6qZ8Y7ndbkVRTmxCTMnlcimKEhUV5XA49M5iFE6n02KxxMTE6B2k\n6+j/17ApxcbGikh9fX1wSU1NjX5xAHSCvYf0m9D2sD6XhK7VAUA7UexCIjc3NyIiYv369YEf\nGxoaSkpavVYCACMb9XuxtboJJNIu5/+xq9IAwHGxKzYk4uPjL7zwwqKiIlVVExMTv/zyy/79\n+zc2NgbuXbJkyeHDh0VEVdV169Y1NTWJyMSJEwPb+QAYTvIgGf93+fQ6UY51CLY1Ssa9JWlD\nuzwWALREseuwQYMGBb8oduDAgT17/rTzJTMzMz8/P3D7zjvvzMzMLC0tdTgct956a3V19YYN\nGwJ37dixo7y8XEQGDx7s9XoDG/PGjRtHsQOMK+9KmfwvWTZNKr/72fKeI2T0y5J5Tqiff+vW\nrUVFRQUFBZMmTQr1cwEIXxbzXUQDxhc4eSIpKUnvIEahKEpdXZ3NZktMTNQ7i1EY6OSJFqrX\ny/4V4qqWmFTpdZ6kn941T1tSUkKxa4GTJ1pwuVxNTU0Oh4OTJ4K64ckTbLEDgI5IG8peVwCG\nxckTAAAAJkGxAwAAMAmKHQAAgElQ7AAAAEyCYgcAYcDhcOTk5KSkpOgdBIChcVYsAISBnJyc\nK664wm636x0EgKGxxQ4AAMAkKHYAAAAmQbEDAAAwCYodAACASVDsAAAATIJiBwAAYBIUOwAI\nAzU1NWvXrt25c6feQQAYGsUOAMJAdXX1qlWrysrK9A4CwNAodgAAACZBsQMAADAJih0AAIBJ\nUOwAAABMgmIHAABgEhQ7AAAAk4jUOwAAoG05OTlXXHFFcnKy3kEAGBrFDgDCgMPhyMnJsdvt\negcBYGjsigUAADAJih0AAIBJUOwAAABMgmIHAABgEhQ7AAAAk6DYAQAAmATFDgDCwPbt2+fM\nmbNs2TK9gwAwNIodAIQBVVXdbrfP59M7CABDo9gBAACYBN88AQA4irtG9i6ThnKJsElqgWT9\nQiJsemcC0DaKXYcVFhaOHDnyoosu6tBaixYt2rlz57333hv4cdu2bUuWLDl06FBqauoll1wy\nYMCAECQFgI7zHJHlj0jJX8XfbLdvbIaMfFZOnaJfLADtwq7YDuvRo8cJfF1jRUXFtm3bArdX\nrFgxY8aMw4cP5+fnV1ZWPvjgg2vWrDnZMQGg45oOyLvnyvrXf9bqRKSpUpbcJktuE9F0Sgag\nXdhi12F33XVXJx/hr3/96/nnnz99+vTAj4888khRUdEZZ5zR6WgA0AmaXz7+ldSUHndAyRzp\nMUDOfLALMwHoGIpdhzXfFfv73/9+zJgxVqv1iy++8Pl8Q4YMmThxotVqFRFVVRcuXLhhw4bY\n2NhLL700uLqiKNdee+3AgQODSwYMGPDNN990/QsBEEbsdnt6enpCQkIIn2PL/0rFqjbGrPov\nKbhZYlJDGANAJ1DsOqy0tPSUU04J3C4rK2tqakpISBg9enRdXd3rr7+uKMrVV18tIrNnz16y\nZMmkSZN69Ojx9ttv+/3+wCqRkZHNe56I7N27t1evXl38KgCElz59+lxzzTUncBxIB2x+u+0x\nvkbZtkBOuy2EMQB0AsWuUywWS11dXWFhocViEZHvv/9+3bp1V199tdPpXLJkya9+9asbbrhB\nRC6++OLf/OY3qanH+Bt3+fLl69ate/rpp1t/osbGxhDE142maX6/32QvqjMCvV9VVeYkyO/3\nK4piygmxuA9FffdfHV5N0+L8fovF4osI1bHRtvKv2zPMv/p/1H1tbdjrEhGaZtO09k5IVLzn\nnOdCnEhniqKIiNfrDW5KQGBOVFXVO8jJFBcX18q9FLvOOu200wKtTkSSk5N37NghIrt27VJV\ndejQoYHldrv91FNPPXDgQIt1v/vuu1mzZk2ePHn48OGtP4vH49E0sx2z7Ha79Y5gLH6/nzlp\nwZQTYm04FFv6pt4pTlxE3daIuq16p+gwvz3VPewJvVN0BUVRAm0GQSabkNjY2GDxOBrFrrNi\nY2ODty0WS+DvpIaGBhFpfjRMUlJSi2JXXFz84osvTp48+dprr23zWeLj481U7Px+v8vlaj51\n3Zyqqk6n02q1OhwOvbMYhaIoPp8vJiZG7yAhYO/rG/f3jq7k9/u9Xq/VarXZQnU9uciv77G4\nDredJHecOvDGEGXoEFVV/X5/eyfEGh0fHx/iRDrzer0ejyc6OjoqKkrvLEbh8XgsFovJJqSV\nVicUuxCJjIwUEY/HE1zidDqbDyguLp41a9Zvf/vbsWPHtucBTfamVFU18OmjdxCjUBTF6XRG\nREQwJ0EWi0VVVXNOSHS0DLm+oyt5vV5Pfb3dbre1uhemU8qXyKa5bY6KGPbbiP4TQ5WhI1S3\n268oIZyQcOP3+z0ej9VqNed/nBOiqqrFYulWE8J17EIiLS1NRJpvotu9e3fw9tatW1988cW7\n7rqrna0OALrCsDtFWtsSICKSlCe547okDYATQbELid69e6enpy9atMjpdGqatnjx4qqqqsBd\nmqb95S9/Of/88y+55BJ9QwLAz2ScJcPvaW1ARKRcOlusptqBAJgMu2JDwmKxTJs27fnnn7/h\nhhuioqL69+9/6aWXfv/99yKyd+/esrKysrKy4uLi5qvMnTu3R48eOuUFYHQ1NTXr16/PzMw8\n7bTTQvg0F7wgilM2/OUYd9kcMu4tyenYtykC6GIWMx2S3zVKS0tTUlLS09NFZMuWLT169OjZ\ns2fgrsrKyvr6+uAXv7rd7j179jgcjpycnKqqqrq6ugEDBrjd7uB3izU3aNCgwJF53YGqqg0N\nDUlJSXoHMQpFUerq6mw2W2Jiot5ZjMLr9brd7tBejzeslJSUFBUVFRQUTJo0KeRPtvNTWf1H\n2b9SNFVEJCpB8q6U856SxH4hf+qOcLvdiqK0fumHbsXlcjU1NTkcDk7DCnI6nRaLxZynYR1H\nd2kSJ9GgQYOCt5t/gYSIZGRkZGRkBH+02+35+fmB2+np6YEuGLj0SZckBYAT0m+C9Jsg3gZp\n2CuRMRKfIxGhOhUXwMlFsQMAHEtUvKQM0TsEgI7h5AkAAACToNgBAACYBMUOAADAJCh2AAAA\nJsHJEwAQBnr16jVu3LiUlBS9gwAwNIodAISB+Pj4vLw8u92udxAAhsauWAAAAJOg2AEAAJgE\nxQ4AAMAkKHYAAAAmQbEDAAAwCYodAACASVDsACAMbN++fc6cOcuWLdM7CABDo9gBQBhQVdXt\ndvt8Pr2DADA0ih0AAIBJUOwAAABMgmIHAABgEhQ7AAAAk6DYAQAAmATFDgDCQFRUVEJCQkxM\njN5BABhapN4BAABt69u370033WS32/UOAsDQ2GIHAABgEhQ7AAAAk6DYAQAAmATFDgAAwCQo\ndgAAACZBsQMAADAJih0AhIEjR45s2rSpvLxc7yAADI1iBwBhoLKysri4eNOmTXoHAWBoFDsA\nAACToNgBAACYBF8pBgDGVrtNSt/J3VZ8m2OnpSpT1uyWQf8psRl6xwJgRBS7DrvhhhsmTpw4\nefLkDq31xhtvlJSUvPzyyyLidrvff//95cuX19XVpaWljRkz5j/+4z8sFkto8gIIW5oqyx+V\ntX8WvxIvEm8Vce2Xr9fIymdk1B/k9Gl65wNgOBS7Drv11lv79OnTmUeYNWvWxo0bf/3rX2dm\nZm7evHnevHmqql5zzTUnKyEAU9Dk0xtk6/vHuMfXKF/eLa5qOe+ZLk8FwNAodh128cUXd2b1\nhoaG77///vbbbw88zpAhQ3bu3Lly5UqKHYCfWf/GsVtd0KpnJedCybmoqwIBCAMUuw5rviv2\nxhtvnDx58uHDh7/44guv1ztkyJB77rknKSlJRGpqal566aWSkhKHw3HZZZcFV4+Pj3/vvfea\nP6DNZouI4CwWAM1oqnz7bJuDZOXTMpliB+An9IlOiYyM/Oijj1JSUubMmTNr1qwdO3YES9uf\n//zn3bt3P/HEE88999yRI0dWrlzZYl2v11tTU/P555+vWLHiiiuu6PLsAAyscrU0VrQ9bP83\n4joU+jQAwgZb7DorIyPjl7/8ZeDGGWecsW3bNhE5fPjw+vXrp06dOnToUBGZOnXqmjVrWqz4\n9NNPb9y4MTY29u67777gggtaf5aamhpN00LzCvShadrhw4f1TmEsPp+POWmuO79Josu/j2vP\nOM1/ZM86JW1EqPMYU+BT0ePx6B3EKAIT4nK5XC6X3lmMIjAnTqdT7yAnU3JycisnXFLsOqtf\nv37B27GxsY2NjSKyb9++5ndZLJYBAwbs3bu3+YpTp06trq7euHHjyy+/3NTUNGHChFaeRdM0\nkxU7+ff/NzTHnLTQbSdE86vtH9ltZymgm7/8ozEhR+tWc0Kx66yoqKjmPzb/48DhcASXx8bG\ntlixT58+ffr0OeOMM2w225tvvjl69Gi73X68Z0lJSTmZofWmqmpDQ0PgYESIiKIodXV1Npst\nMTFR7yxG4fV63W53QkKC3kF04hnWvnGWpNwR4kgNbRijcrvdiqLExbVr42Z34HK5mpqaHA5H\n898+3ZzT6bRYLDExMXoH6TocYxcSgYrWfNtvfX194Mbhw4eXLVvWfDv5Kaec4vV6q6uruzgk\nAOPKPEdi0tox7GxxpIc+DYCwQbELiaysLBHZuXNn4EdVVUtLSwO3GxoaZs2atXr16uDgnTt3\nWiyW9HQ+nQH8W0SknP1o28POfTL0UQCEE3bFhkR6evrAgQM/+OCDzMzMhISERYsWBffY5ubm\nDh8+/I033nC5XNnZ2du3by8qKhozZkx0dLS+mQEYy/B7ZO8y2fnp8QfcK30vO+69ALolil2o\nzJgx46WXXnruuecC17FLS0tbsWJF4K6HHnro/fff//DDD2tra1NTU6+88sqrr75a37QADMdi\nlYlF8uXdsmGOyM8P/bZGyTlPyDm/0ykZAOOydKtTRWAQnDzRAidPHK27nzzR3MG1smmuc+fX\n7tp9fkfP1KFXyqlTJLFf2yuaHSdPtMDJE0frhidPsMUOAIyt5wjpOWJHWklRUVFB34JJv5ik\ndyAAxsXJEwAAACZBsQMAADAJih0AAIBJUOwAAABMgmIHAGEgKioqISGhW53cB+AEcFYsAISB\nvn373nTTTa18ozQACFvsAAAATINiBwAAYBIUOwAAAJOg2AEAAJgExQ4AAMAkKHYAAAAmQbED\ngDDQ0NCwffv2AwcO6B0EgKFR7AAgDFRUVCxevHj9+vV6BwFgaBQ7AAAAk6DYAQAAmATFDgAA\nwCQodgAAACZBsQMAADAJih0AAIBJROodAADQtoyMjIsuuig9PV3vIAAMjWIHAGEgMTFxyJAh\ndrtd7yAADI1dsQAAACZBsQMAADAJih0AAIBJUOwAAABMgmIHAABgEhQ7AAAAk6DYAUAY2LVr\n17x5877++mu9gwAwNIodAIQBr9dbX1/vcrn0DgLA0Ch2AAAAJkGxA4DwEGHRYtRacdfqHQSA\ncVHsOqy0tLSqqqqja1VUVGzbtu3o5Xv37t2yZcvJyAXAvPYtz/3+7sfifj9h3+3ySrK8kSXF\n90lTpd6xABgOxa7DCgsLi4uLO7rWokWLZs2a1WLhoUOHZsyY8cc//vEkRQNgOppfvpou758f\nX/11pCg/LmyskHWz5M1BsnuJruEAGE6k3gHCzwsvvBAXF3dSHuqNN96IjOSfAMDxffOYrP1/\nx77LUycLr5DJX0vGWV2bCYBxscWuw+rq6oInppWWltbW1opIeXn5jh07PB5P85Fer7esrGzf\nvn3HfJxVq1Zt3rx5woQJoQ4MIFxVfS+r/7u1AYpbPp8imtpVgQAYHZuLOqywsHDixImTJ08W\nkeeff378+PE//PBD4EoEbrf7mWee6devn4hs3rz5ueee83g8MTExOTk5GRkZzR/E5XLNnj37\n5ptvdjqd+rwMAMa37kXR/G2MOVQie5ZJ7qVdEgiA0VHsOiUiIuKjjz569tln8/LyVFW9//77\nP/jgg4cfflhEXnzxxczMzMLCQrvdvnz58pkzZ2ZmZgZXfPvttzMyMkaPHr1o0aL2PJHP5wvV\na9CD3+/XNM1kL6ozVFUVEeakOVVVmZDIvcss7Rjm371Uzboo5GmMR1VVv9/fzd8kzQU+SZiT\n5vx+v5jud6jNZmvlXopdZw0bNiwvL09ErFbr4MGDN2/eLCL79u2rqKiYPn263W4XkVGjRi1Y\nsMDr9QZW2bZt25IlS/785z9bLO350BYRqa+v1zQtNK9AN0eOHNE7grEoisKctNC9J0RLbTrQ\nnnG+2t0N3Xiigh+tCHC73W63W+8UxmKyCUlJSWmlP1DsOqv5PtaoqKjAu6eyslJEmm+iy87O\n3rlzp4j4/f5XXnnlqquuysnJaf+zREVFnbTEBqBpmqIorf/N0a1omub1eiMiIpiTIL/fr6pq\nN58QLTLW4m27sUXYE6Ojo7sgj9EENutyClqQqqqKokRGRlqtVr2zGEVgK2a3mhD+P3TWMd8u\niqKISPOP2uDvp08//bS6urqgoCCwbe/gwYOKomzevDkjIyM5Ofl4zxIfH3+Sc+tKVdWGhgaT\nvajOUBTF6/VarVbmJMjr9brd7u4+IT1Pl/Kv2hxlyzrT1i0nyu12K4pysi5TYAIul0tRlKio\nKIfDoXcWo3A6nRaLJSYmRu8gXYdiFxKxsbEiUl9fH1xSU1MTuFFRUSEif/rTnwI/+nw+j8fz\n3HPP3XjjjePGjevypAAMbOD1bRc7m0PyruqKMADCAcUuJHJzcyMiItavX3/qqaeKSENDQ0lJ\nSWCn7dSpU6dOnRoc+fHHHy9YsODNN9/ULSsAwyq4WX54RarXtzbmrEfFkd5VgQAYHcUuJOLj\n4y+88MKioiJVVRMTE7/88sv+/fs3NjbqnQtAWImIlCsWyD8ulPq9xx4w8Fo5+7GuzQTA0Ch2\nHTZo0KD09B//Ph44cGDPnj2Dd2VmZubn5wdu33nnnZmZmaWlpQ6H49Zbb62urt6wYcPRj5aS\nkjJw4MAuiA0gLCX2lRu+k+L7ZMv7Is1OjY9OknOflBH3ibT35HoA3YHFfBfRgPEFTp5ISkrS\nO4hRKIpSV1dns9kSExP1zmIUgZMnEhIS9A5iFFu++3zb0pfzekYPKhgqqadKn0vEFqt3KJ1x\n8kQLLperqanJ4XBw8kQQJ08AAIzIF9NrrW+EJ6lg0DmT9M4CwLj4rlgAAACToNgBAACYBMUO\nAADAJCh2AAAAJsHJEwAQBtLS0s4999zm30ANAEej2AFAGEhOTh4xYoTdbtc7CABDY1csAACA\nSVDsAAAATIJiBwAAYBIUOwAAAJOg2AEAAJgExQ4AAMAkKHYAEAb27Nnzj3/8Y+XKlXoHAWBo\nFDsACANut7uqqqq+vl7vIAAMjWIHAABgEhQ7AAAAk6DYAQAAmATFDgAAwCQodgAAACZBsQOA\nMGC1Wu12u81m0zsIAEOL1DsAAKBteXl5U6ZMsdvtegcBYGhssQMAADAJih0AAIBJUOwAAABM\ngmIHAABgEhQ7AAAAk6DYAQAAmATFDgDCgNPpLC8vP3z4sN5BABgaxQ4AwkB5efnChQtXr16t\ndxAAhkaxAwAAMAmKHQAAgElQ7ADA8FzViQeXnB31XZ+mr+XQRr3TADAuUxW7+fPnb9q06WQN\na2HNmjWfffZZZx4BADqscb98er28ltl7/YzLov955qGXZe6p8vbpUv6V3skAGJHOxW737t3f\nfvvtyRpWVFS0cWPbf8suW7Zsx44d7crXzNq1a4PF7sQe4Xja+eoAdDuHSuSdM2XL/4qm/mx5\n1Q/ywRhZ/5pOsQAYV6S+T798+fLDhw+fc845J2VYO73yyiu6P0JzJ/fVATAJT50suFyaDhz7\nXk2VZXdL0inSZ0zXxgJgaNann35ar+detmzZ0qVL6+vr6+rqsrOzY2Ji6urqli9fvm7dugMH\nDqSmpkZHRx9zWEVFxYoVK3744Yfq6uqMjIzIyB/r6fz58/Pz8wsKClp/3vnz5/v9/vT09MBt\nm81msViKi4s3bdpktVpTUlKCI8vKyr766qs9e/akp6dv3Lixqqpq/PjxLR6hqKgoKiqqqqpq\n6dKl+fn5Vqu1oaEh8CqqqqqaxxOR2traf/3rXxs2bPB4PBkZGcd8dSd5lg1J0zSv12u32/UO\nYhR+v9/tdlutVuYkSFVVRVECHwLd1LeFsnNRqyM0ObhOhv1WxNJFkQxGURS/3x8VFaV3EKNQ\nFMXn89lsNpvNpncWo/D5fBaLpVtNiJ67Yp1OZ0NDg8/nq6+v9/v9JSUlt99++8KFC8vLyxcs\nWHD77beXlZUdPWzp0qW//e1vv/nmm927d7/33nt33nnnkSNHOvS8zffYLly48PPPPy8sLKyq\nqtq1a9eMGTNWrFgRuGvx4sUzZsxYs2bNhg0bHnnkkerq6mM+woIFC5YuXfr8889v2bLF7/dv\n3br19ttvLyoq2rNnz7vvvjtt2rTgips3b77jjjs+/vjjTZs2/f73v3/++ec1TWvx6jo5pQDM\nQpPN89oedXiTVK4JfRgAYUPPXbGXX375119/nZ2dPXXqVE3THn744UGDBj355JNWq1VV1Ucf\nffTVV1+dOXNm82EicujQoSlTplx++eUioijKlClTPvvss+uuu+7EMkRERHz77bevv/56bGys\niNTV1X3++ecjR45UVXXevHkXXHDB9OnTRWTfvn133313VlbW0Y8QGRm5atWqmTNnJicna5o2\na9asnJyc5557zmazeTyeBx544M0333zooYdE5KWXXho+fPhDDz1ksVi2b9/+wAMPfPvtty1e\n3fE0Njae2As0Jk3T/H6/yV5UZwQ6vaqqzEmQ3+9XFKXbTojFeTC2obw9Iz17vvHFDQp1HmNS\nVZVPkuYURRERr9fLZoKgwJyoqtrmyDASFxfXyr06H2MXtHv37qqqqltuucVqtYqI1Wq96KKL\nXnvttdra2h49ejQfed1115WVlX388cdNTU0iEhERceDAcY5BaZ+zzjor0OpEJDs7+4cffhCR\nXbt2NTY2XnDBBcHlBQUFtbW1R69usVhOPfXU5ORkEdm3b9++ffsefPDBwFbf6OjoSy655J13\n3lFVtaKiYv/+/bfddpvFYhGRvLy8l156qflu39Z5PB5N0zrzMg3I7XbrHcFYAjtk9U5hLN12\nQqwNVbHtG6k6D3XbWQow2e/szlMUJdBmEGSyCYmNjQ10iWMySrEL7K8MHLUWkJaWJiKHDx9u\nUezeeuuthQsXnnfeeZmZmYEW2Mn/1UlJScHbVqvV5/OJSE1NjYgE6lowzzGLXTCqiFRVVYnI\n5s2bDx48GFhSXl7u9Xqrq6sDdzVvcn369Gl/yPj4eDMVO7/f73K5gn0aqqo6nU6r1epwOPTO\nYhSBo4W6yVGnx2DLbefAqKTe1vj4UEYxLp/Pp6oqR6YGeb1ej8cTHR3NcYdBHo/HYrGYbEJa\naXVinGIX0DzrMXtMdXX1ggUL7rjjjssuuyywZM2azh5fcswJOvrZW6mPzU+PEJEDBw7U19cH\nfxw1apTVag08YKA1ngCTvSlVVQ18+ugdxCgURXE6nREREcxJkMViUVW1+05IdKaknSbVG9oc\nGNl3dGR3naXA52r3fZMcxe/3ezweq9XKnASpqmqxWLrVhBil2PXs2VNEqqqq+vfvH1gS2MQV\n3BgWsH//fk3TTjvttMCPbre7vLw8cHrpyRXYTFhTU9OvX7GQK0QAACAASURBVL/AksrKyjbX\nCryKq666aujQoS3uCuwrOXjwYF5eXmDJ+vXrU1JSsrOzT2JsAOYx7E5ZekcbY3IvlaS8LkkD\nIDzofIFiq9Xq8XhEpHfv3hkZGUuWLAlu2Vq2bNnAgQMTExObD0tISJBmHWvu3LkOh8Pr9Z70\nYLm5uXa7/auvvgr8uH379i1btrS5VnZ2dnZ29meffRbc4Pf555+/9957gbsyMjL++c9/Brb8\nlZeXP/nkk7t375Zmrw4AflJwq2Sf39qA6CS5+KWuSgMgPOi8xa5v375Lly4tLCy86qqr7r33\n3meeeeb+++/Pzc3dunVrY2NjYWHh0cMGDx48c+bMc845Z9euXUOGDLnwwgs/++yzN9988+ab\nbz6JwaKioq6//vq//e1vlZWViYmJ5eXlo0aN2rVrV5sr3nvvvU8//fQ999zTv3//qqqqrVu3\nzpgxQ0QsFsvdd9/97LPP3nXXXVlZWSUlJWefffbIkSNbvLohQ4acxFcBIIxFRMrE+bLwStn/\nzTHujUmVKxZIjwFdHguAoel5gWIRKSgoSEpKyszMzM/Pz83NveSSS2JiYqKios4888zf/va3\nwf2wzYeNGzcuJSXFZrNdfPHFY8eOzc/Pj4+P79WrV58+fSwWy+DBg5ufgXFMLYYNHDgwsAs1\nIPAsgeVDhw6Njo7Ozc295ZZb0tPTU1NTA3e18gipqaljx46Ni4uz2WwDBw6cOnXqgAE/fvL2\n7Nlz9OjRcXFxPXr0uPzyy6+55prA4X3NX103OXaeCxS3wAWKj8YFikVEbA4ZfKM40qRuu7hr\nflxo7yFDbpZfvi8pg3UNpz8uUNwCFyg+Wje8QLHFTOdaIlyoqtrQ0ND8fORuTlGUuro6m80W\nOPYAIuL1et1ud+DoC4jIljXLlv/z/d55BWMn3ykRRjk8Wl9ut1tRlNav6dWtuFyupqYmh8PR\nTbYRtIfT6bRYLN3q/HoTfjocOnRo5cqVx7t33Lhx/HkHIOz4otP3q7162HrR6gC0woQfEG63\ne+fOnce7l+txAwAAszJhscvOzr7vvvv0TgEAANDVdL7cCQAAAE4Wih0AAIBJmHBXLACYT35+\n/rRp07ggDoDWscUOAADAJCh2AAAAJkGxAwAAMAmKHQAAgElQ7AAAAEyCYgcAAGASFDsACANO\np7O8vPzw4cN6BwFgaBQ7AAgD5eXlCxcuXL16td5BABgaxQ4AAMAkKHYAAAAmQbEDAAAwCYod\nAACASVDsAAAATIJiBwAAYBKRegcAALQtOTl5xIgRWVlZegcBYGgUOwAIA2lpaeeee67dbtc7\nCABDY1csAACASVDsAAAATIJiBwAAYBIUOwAAAJOg2AEAAJgExQ4AAMAkKHYAEAbKy8sXLly4\nevVqvYMAMDSuYwcAYcDpdJaXlycmJuodBIChscUOAADAJCh2AAAAJsGuWAAIf6pHyj6UPUul\noVwiHZJaIPmTJX2Y3rEAdDWKnf5uuOGGiRMnTp48We8gAMJT+Vey+DdSv+enJTs/ke/+KIOu\nl0teF1ucfskAdDV2xZ6ITz/9dObMmSdrGACcuF2fyYeX/qzV/UiT0r/LPy4Wn1OHVAB0QrE7\nETt27DiJwwDgBDmr5NPrxe877oDK1fL1jC4MBEBn7IrtsMcee2zjxo0i8uWXX86cOTMnJ+ed\nd95Zvnx5bW1tcnLyhRdeeP3111ut1hbDUlJS/vrXv65fv76xsTEtLW3ChAmXX3653i8FQNjI\ny8ubMmVKbGzsz5au/X/iOdLGmiV/kbMekYTeocsGwDgodh32+OOPP/7445mZmVOnTo2Li3v5\n5ZdXrlx511135eXlbd269dVXX/X5fLfcckuLYc8++2xlZeVDDz2UlJS0devWl156KS0t7Zxz\nztH71QAID1ar1W6322y2ny3dtqDtNf2K7Fwkw+4KUTAAhkKx6zCHwxEREWGz2RISEhoaGoqL\ni6+//vpRo0aJSGZm5s6dOxcvXnzTTTc1HyYi9957r8ViCVxcNCsr65NPPvn+++/bX+xqa2tD\n94p04ff7zfeiTpimaSLi8/mYkyBN0zRNY0KCAm8Sr9f745z4fYlFZ0c07G7Xuv96TNn+WePo\nd0IZUAeBN4nPd/w90d2M3+8XEbfb7fF49M5iFIH/OG63W+8gJ1NSUpLFYjnevRS7Ttm1a5eq\nqoMHDw4uGTBgwEcffXTgwIGcnJzmI+vr6//2t79t2bLF6fzxQObMzMz2P5Hf7w+8O81EVVW9\nIxgOc9ICE9JC4De3iIiqtLPViYjFV29prDDrZJr1dZ2wn94k6JYodp0SaGlxcT9dTSBwO9je\nArxeb2FhYUpKygsvvJCZmWm1Wh9++OEOPVFycvLJyGsUqqo2Njby5UhBiqIcOXIkuH0XIuL1\nej0eT3x8vN5BjMLr9TY0NNjt9uBhdtqdhy3zTpPG/W2uq533X9bh96REme3d5fF4FEVpedxh\nN+ZyuZxOp8PhiImJ0TuLUbhcLovFYrfb9Q5yMrWyuU4odp0U+EBpbGwMLmloaBARh8PRfNju\n3bsrKyunT5+enZ0dWFJbW5uamtr+J2r9XzHsBF6OyV5UZwSngjkJ4k3SwjHeJDHJ0n+irH+t\n7VXzr5Zo0/4dxZskiE+S4+lWE8LlTjolNzfXarWWlpYGl2zZssXhcPTq1av5MJfLJSLBP6FK\nS0srKyvNt2sVQFc780GxRrcxZsAkSR7YJWkA6I9idyLi4uJ27Nixc+dOTdPGjBkzf/78VatW\nVVVVLVu27PPPP7/iiiusVmvzYSkpKdHR0YsWLaqpqVmzZs2bb755xhln7N+/v66uTu+XAiA8\nuN3uqqqq+vr6ny1N7CujX2lttcS+MubVkAYDYCjWp59+Wu8M4Sc+Pv7LL78sLi4eNGjQ+PHj\nm5qaioqKPvzww507d1555ZXXXHNNYKtvcNiwYcNGjBixePHiDz74oKqqatq0aVlZWUuXLl23\nbt3YsWPnz5+fn59fUFCg98vqOpqmeb1ekx300Bl+v9/tdgeuZ6F3FqNQVVVRlOjotjZHdRtl\nZWUffvihx+NpfraWiEjP4ZLUX/YsFb+35Tq9zpNf/VNiM7osZBdTFMXv90dFRekdxCgURfH5\nfDabreVlcboxn89nsVi61YRY2CGIrqeqakNDQ1JSkt5BjEJRlLq6OpvNxgklQV6v1+12czZJ\nUElJSVFRUUFBwaRJk45xt/OgrH9D9iyV+t1ii5fUAhl4rZxylYiZDy1yu92KojQ/fa2bc7lc\nTU1NDoejxXHe3ZnT6bRYLN3qbBJOngCA8OfoKec+Kec+qXcOADrjGDsAAACToNgBAACYBMUO\nAADAJCh2AAAAJsHJEwAQBpKTk0eMGJGVlaV3EACGRrEDgDCQlpZ27rnncqVDAK1jVywAAIBJ\nUOwAAABMgmIHAABgEhQ7AAAAk6DYAQAAmATFDgAAwCQodgAQBioqKhYvXvzDDz/oHQSAoXEd\nOwAIAw0NDdu3b+c6dgBaxxY7AAAAk6DYAQAAmATFDgAAwCQodgAAACZBsQMAADAJih0AAIBJ\nWDRN0zsDuiNN0ywWi94pDCTwP5E5aY43SXOqqno8HqvVGh0drXcWo+B/TQvBX+jMSVA3fJNQ\n7AAAAEyCXbEAAAAmQbEDAAAwCYodAACASVDsAAAATIJiBwAAYBIUOwAAAJOg2AEAAJhEpN4B\ngO5o9erV77///t69e+Pi4kaMGHHTTTfFx8e3GPPRRx/97W9/a74kKyvrtdde68KY0IHb7Z47\nd+4333zjcrn69+8/ZcqUU0455YSHwZTKy8vfeuutLVu2iMiAAQN+85vf9OnTp8WY7du3P/DA\nAy0Wzp07t0ePHl2UEjqh2AFd7fvvvy8sLLz00kt//etfV1VVvfXWW4cPH37yySdbDHO5XGlp\naffdd19wCV850B28+OKLpaWld9xxR3Jy8ieffPLkk0++/PLLKSkpJzYM5lNbW/vYY49lZ2dP\nnz5dVdV33333qaeeevXVVx0OR/NhLpdLRB599NG4uLjgwqP/gIT5UOyArrZw4cL8/Py77ror\n8KPH43n99dddLldMTEzzYS6XKyEh4dRTT9UjI/Rx4MCBb7755vHHHz/rrLNEZMCAAbfddtun\nn3560003ncAwmNKXX37pcrmeeOKJQJPLyMi46667Nm7cGHgzBAWK3bBhw1p8sMD0OMYO6Gr3\n3HPPQw89FPwxNTVVRI4cOdJimNPptNvtXZoMelu/fn1kZOTw4cMDP1qt1tNPP/2HH344sWEw\npbFjx86aNSu4fS7wAVJfX99imNPpFBE+Q7ohih3Q1ZKTkwOfxQFr165NSUnp2bNni2Eul4sP\n5e6moqIiNTU1MvKnfSkZGRn79+8/sWEwpbi4uKysrOCPa9assVgsgwYNajHM5XJFRUVZLJau\nTQf9sSsW0NPq1asXL158//33H/3563K56uvrn3322c2bN0dFRRUUFNx8883NGyHMx+l0tjhS\nyuFwuFwuTdOav0PaOQymV1VV9cYbb4wZM6Z51QtwuVyRkZGvv/76qlWrPB5P3759f/3rXw8c\nOFCXnOhKbLEDQq7p3wI7R4K+/fbb559//le/+tWFF154zBVra2uHDh365JNP3nLLLWVlZY8/\n/rjH4+mKxAAMb//+/Y888khubu7UqVOPvtfv90dERNhsthkzZjz44INWq/V3v/vdnj17uj4n\nuhhb7IDQ8nq91113XeB2enr6nDlzAre/+OKLV1555YYbbpg0adIxV2x+nuygQYN69eo1ffr0\nNWvWjBw5MtSZoZe4uLimpqbmSxobGx0OR4vtcO0cBhPbvn37M888M3jw4OnTp0dFRR09YNKk\nSc0/WwoKCqZMmbJ48eJjtkCYCcUOCC2bzfb8888Hbgc/f5cvX/7KK6/cddddY8aMaefj5Obm\nikhVVVUIMsIosrKyDh065PV6g2+VioqK7OzsExsGs9q/f/9TTz11zjnnTJs2rZ1tPjo6OiMj\n4+DBg6HOBt2xKxYILYvFMvjf8vLyRKSiomLmzJlTpkxppdWpqjpz5sx//etfwSVbt24VkYyM\njC7IDL0MGzbM7/evXr068KPH41m7du0ZZ5xxYsNgSqqqFhYWnnbaaa23ug8//PDtt98O/uh0\nOsvLy/kA6Q7YYgd0tblz56alpfXu3bukpCS4sHfv3omJiVu3bv3LX/4ybdq03Nxcv9//8ssv\nNzY29u/fv6Ki4p133undu/eZZ56pY3KEWlpa2ujRo2fPni0iSUlJ8+fPj4iIGD9+fODel156\nKTo6+vbbb299GMztn//8Z2Vl5W9+85uNGzcGFyYnJ2dlZSmK8sgjj1x22WWjR49OTEycN2+e\nz+c777zzGhsbi4qK/H7/hAkTdEyOrkGxA7ra+vXrnU7n7373u+YLH3zwwVGjRjU1NZWVlQXO\nsZg2bVpKSsqCBQtqa2uTk5PPOuus66+/vvkVLmBKd9xxx9y5cwPXrM7Pzy8sLExISAjctWfP\nnuDFZlsZBnNbv369qqrPPfdc84Xjxo278847/X5/WVlZ4M+/Sy65REQ++eSTf/7znw6HIz8/\n/3/+53+OPnkW5mPRNE3vDMD/b+9Og6I42gAAv7O7yLoHoOFezkBEIKJ4ISqHAhFKLFCCGoJR\nShMtMCJyiBINRNCoECNCklKEgJJQoCBEQaMlWhoRz5CDQyUiiK5iFDkCuLDz/egvU5PlyLIe\nGOp9fs30zPYxPSWvPT09CCGEEHoBcI4dQgghhNAwgYEdQgghhNAwgYEdQgghhNAwgYEdQggh\nhNAwgYEdQgghhNAwgYEdQgghhNAwgYEdQmiIVVdXUxS1atWqoa6IUhoaGrS1tfv7wi96Idi3\nBNlesWLFK67D/fv39fT05s+f/4rLReg5YWCHEBoES0tLqn+rV69WJpOLFy+mpaUxuyKRaM6c\nOba2ti+t1oolqkwmky1atEgsFrNzS09PnzJlilAoFIvFLi4uJ0+eZP/EzMys94ViLzSdlJRk\nbGzM5/OnTp16/fp1hRKvXbvG4/FycnL+tW69u0YgEIwbNy42Nra9vf352v3SvYJborGxMSoq\nasKECZqamgKBwNLS0t/f//jx4+xzFi9eTFHUxYsXAcDAwCAzM7OwsDApKekFVgOhlw1XsUcI\nDVpoaGif6U5OTsr8PCMjo6KighmDMTIyUvj7+sIplKiy5OTksrKy4uJiLS0tkhIeHv7FF19Y\nWFiEhIRQFJWVleXp6Zmfn+/j40NOaG5uNjY2DggIYOfD5XLJxtmzZyMjI3/44YdZs2atXr16\n4cKFNTU1HM7//8vd3d29YsUKLy+vxYsXK1nD9evXkw2apqVS6ZkzZ+Li4goLC8+fPy8UCp+z\n+S/Py74l8vPzlyxZ8tdff9nY2Lz77rt8Pv+PP/4oLCw8dOjQ0qVL9+7dO2LEiN6/8vT0DAwM\n3LBhg5+fn5mZ2QusD0IvEY0QQkqzsLB4/n837O3tHRwcXkh9XmWJbW1tOjo6kyZNYlIqKysp\nirK2tm5paSEpjY2No0aNkkgkz549o2laLpdzOJy5c+f2l+eHH35ob29PtisqKgDgwoULzNFt\n27aJxeKGhgZlqtdn1zx79oyEmDt27FCulf/Q0dGhwq9UMEAHVVVVAcDy5ctVzvzChQtqamoi\nkejw4cPs9MbGxhkzZgDAxo0bScqiRYsAoKysjDmnurqaw+GsWLFC5dIResXwUSxC6MW7evWq\nn58fecJoaGjo7+//yy+/AMCVK1coirp+/Xp5eTlFUe7u7vDPCVW3bt2iKGrTpk0nT56cNGkS\nn883MDDYsGGDXC4vLS11dHQUCAQmJibR0dEymYwprqurKz4+3s7OTlNTU0tLa/LkyRkZGTRN\n91cikZeXN3PmTLFYPHLkSFtb2/j4+GfPng3QqKysrKampnXr1jEpJSUlNE2HhYWJxWKSYmho\nGBwc3NjYePr0aQB4+vSpXC5nhvd6u3PnjrGxMdkmG3fu3CG7N2/ejIuL2759u5GR0WCu/T+o\nqalFR0cDQGlpKZM4QMOrqqooioqNjc3IyNDX1ydfHSXS09MnTpwoEAj09PR8fX1JhyqTJ9On\nly9fnj17tlgs1tHR8fb2vnnzJihxS/Q22I5bu3atTCbLzMxcsGABO93Q0LCoqMjOzq6rq4vu\n5+uaVlZWc+fOzczMfPTo0QBFIPQaGdq4EiH036LMiF1NTY26urqJiUlCQsL+/fvj4uIkEomG\nhkZtbe2TJ08KCgrU1NTGjBlTUFDw008/0X8PyaxcuZKm6fr6egBwd3e3t7cvKCg4deqUi4sL\nAKxZs2bMmDE5OTlnzpwh89l37drFlEheZfDz80tJSfnyyy/JE+HPPvuMpuk+S6Rp+vPPPycF\nffXVV2lpaWRylZeXl1wu769d3t7eFEU1NTUxKREREQBw+vRp9mn5+flM6bdv3waAkJAQmqY7\nOzvr6+s7OzvZJ7u7u/v4+JDtP//8EwCys7NpmpbL5a6urjNnzhygPgr665obN24AgIuLizIN\nv3XrFgAEBATo6uqGhYUlJyeTX3366acAMG3atK1bt8bExGhra4vF4oqKCmXyJH06Z84cS0vL\n9PT0srKy1NRUdXV1c3Pz7u7uf70lFEbsBttx1dXVAGBnZ6fMNew9YkfTdGpqKgB89913yuSA\n0JDDwA4hNAjKBHbbt28HgBMnTjApNTU1M2bMKCgoILvq6urs527sv+INDQ0AwOPx6uvrydGf\nf/6Z/C/02rVrJKWpqYkM7ZDd1tZWDw+PgIAAJsPOzk5DQ0NtbW0mRaHEhoYGHo/n6+vLrjaZ\nOHj06NE+GyWTyUQikUJ8EBcXBwC5ubnsxJKSEiYQIZX39vZ2dnYmM+fU1dUXLlwolUrJyUFB\nQePHjyfbV65cAYBz587RNL137151dfXq6moycjl69GgHBweFgENBf12Tnp7OvrwDN5wEYWpq\naqWlpcwJdXV1XC7X2dlZJpORlEuXLpFIWpk8SZ9SFMUEgjRNf/DBBwBw+fJlsjvALcEO7FTo\nuMzMTACIiIgY4NIx+gzsampqACAoKEiZHBAacvgoFiE0aP29FUuOknno5eXlzPljxow5f/68\nr6+vkvk7ODgwDygtLS0BwMLCwt7enqRoa2trampKpVKyKxKJfvzxx+zsbLLb3d3N5XLHjh37\n6NGjtra2PvPPz8/v7u728fGRsnh7ewNAf3P2Hzx40NbW9tZbb7ETp02bBgC5ubnsxLy8PAAg\nRTc3NwNAcXGxoaFhSkpKamqqk5NTbm6uk5NTa2srAPj7+1dUVBw9erS9vT0xMdHU1HTatGn3\n79+PjIzcvHmzhoaGj4+PnZ3d8ePHzc3Nvb29W1palLyGpM4HDhyIiIhQU1MLDg5WpuGkE83N\nzV1dXZl8jhw50tPTExQUxLzMO2XKlJMnT27cuFH5izllyhQ7Oztm18bGBgAaGxuVb47yZbE9\nfPgQAJ7ncbaFhQVFUbW1tSrngNCrhG/FIoQGLTw8fICjgYGBu3fv3rx5c1FRkaenp4eHx/Tp\n09kLfPwriUTCbJN3OdkpJJE9x+7GjRtxcXFnzpyRSqVyuZxJ7+7u7jP/33//HQCCgoJ6H2Km\nuCkgU6y0tbXZie7u7hMnTjx06FBoaOiyZcs4HE5aWtrZs2cBQF1dHQCsrKzy8vJMTU2ZyWrB\nwcFLly7Nysr6+uuvo6KiPD09Q0JCfH195XK5rq5udnY2j8cLCQkxMzOLiorat2+fTCbbs2eP\nUChMTEw0MjIqLi4e+A1ZJrxmjBo1Kjc3lwRVSjZcIX4lY2YKicxsRSXzNDU1ZR8i14fdicpQ\noePIfzPYd8VgcbncUaNGNTU1qZwDQq8SBnYIoUFLTEwc4Ki2tvbVq1eTk5Nzc3Pj4+Pj4+P1\n9PSio6PXrl2rZP5qamr/msK4e/euo6NjR0dHVFSUk5OThoYGRVFr1qwpKyvr7ydkXbfExMTe\nK6UphG4MMvamqanJTuRwOEVFRf7+/snJycnJyQAwffr0b775xsPDY/To0QCgr6/feynjiIiI\nrKyss2fPRkVFURSVkpKSkJDw+PFjY2NjHo93+PDhoqKi8vJyHo9XVVVlamrKhLYaGhokshkA\nmQxH8Pl8S0tLT09PkUg0qIYrvO1BRh9HjhzZZ4lK5tnneiKDpULHGRgYAMBzjrdpaWmRGwCh\n1x8GdgihF2/06NGxsbGxsbH19fUlJSVJSUlhYWGampp9jrU8p7S0tMePH6ekpISEhDCJzEJx\nfSIvsZqZmXl6eipZCgnpnj59qpAukUguXLhQWVnZ2NhoYmJiZWVVVFQEANbW1v1lpaurC39H\nS0zmJP/m5ubVq1evW7du0qRJANDe3s4Op8RicX8PlxmxsbEDHFWh4QBA4sL+ilYtT9WoUNb0\n6dMpijp27Nju3bv7vCtu3rwpkUgEAsEAmTQ3N5NeQ+j1h3PsEEIvkYmJycqVK8+dO8fhcA4d\nOvQyiiBvns6cOZNJaW1t7f0JBzYy3sOeBQgAXV1dZN5bn3R0dODvB7IKenp6bGxsPDw8rKys\nAODIkSMA4OHhAQDnzp3btm3bvXv32OdXVlZCr0eTREREhFAoJO9kAIBQKGRXqbW19TnDCxUa\nDn8Hqb/++is78cCBAxkZGSrnqRoVypJIJK6urnV1dSkpKb2Ptra2vvPOOzY2Nh0dHf3l0NPT\n8+TJE3IDIPT6w8AOI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jhY7FCgzgsKskyfbrVaNYmJeGghgIegsAOQM27sWENBgb+/v8pNPzMPUHM6neWd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\n7XaLxWIymdQO8jNJks6cORMfH2+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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 7: Bootstrap -- Results, Forest Plot, Distributions\n", + "# ============================================================\n", + "\n", + "boot_res <- data.frame(label=boot_labels, stringsAsFactors=FALSE)\n", + "boot_res$est <- apply(boot_mat, 2, mean, na.rm=TRUE)\n", + "boot_res$se <- apply(boot_mat, 2, sd, na.rm=TRUE)\n", + "boot_ci <- apply(boot_mat, 2, quantile, c(0.025, 0.975), na.rm=TRUE)\n", + "boot_res$ci.lower <- boot_ci[1, ]\n", + "boot_res$ci.upper <- boot_ci[2, ]\n", + "\n", + "boot_res$pvalue <- sapply(boot_labels, function(lab) {\n", + " vals <- boot_mat[, lab]; vals <- vals[!is.na(vals)]\n", + " if (length(vals) < 10) return(NA)\n", + " p_pos <- mean(vals > 0); 2 * min(p_pos, 1 - p_pos)\n", + "})\n", + "\n", + "boot_res$method <- \"Bootstrap\"\n", + "boot_res$N <- N_TOTAL\n", + "boot_res$n_converged <- n_converged\n", + "boot_res$exposure <- EXPOSURE\n", + "boot_res$direction <- \"D1\"\n", + "\n", + "write.csv(boot_res, file.path(DIR_BOOT, \"bootstrap_results.csv\"), row.names=FALSE)\n", + "cat(\"=== Bootstrap Results ===\", \"\\n\")\n", + "print(boot_res[, c(\"label\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"pvalue\")])\n", + "\n", + "# Forest plot\n", + "plot_df_b <- boot_res\n", + "plot_df_b$label <- factor(plot_df_b$label, levels=rev(boot_labels))\n", + "\n", + "p_boot_forest <- ggplot(plot_df_b, aes(x=est, y=label, xmin=ci.lower, xmax=ci.upper)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(color=\"darkorange\", size=0.6) +\n", + " labs(title=\"Bootstrap: Parallel Mediation Path Estimates\",\n", + " subtitle=paste0(\"N = \", N_TOTAL, \" (FIML), \", B_REPS, \" resamples (\", n_converged, \" converged)\"),\n", + " x=\"Estimate (95% Percentile CI)\", y=\"\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + "ggsave(file.path(DIR_BOOT, \"bootstrap_forest_plot.png\"), p_boot_forest, width=10, height=8, dpi=150)\n", + "ggsave(file.path(DIR_BOOT, \"bootstrap_forest_plot.pdf\"), p_boot_forest, width=10, height=8)\n", + "print(p_boot_forest)\n", + "\n", + "# Distribution histograms\n", + "ind_labels <- c(paste0(\"ind\", 1:N_MED), \"total_indirect\")\n", + "ind_names <- c(MED_SHORT, \"Total Indirect\")\n", + "\n", + "plot_list <- list()\n", + "for (k in seq_along(ind_labels)) {\n", + " lab <- ind_labels[k]\n", + " vals <- boot_mat[, lab]; vals <- vals[!is.na(vals)]\n", + " df_hist <- data.frame(value=vals)\n", + " med_val <- median(vals)\n", + " plot_list[[k]] <- ggplot(df_hist, aes(x=value)) +\n", + " geom_histogram(bins=50, fill=\"steelblue\", alpha=0.7) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"red\", linewidth=0.8) +\n", + " geom_vline(xintercept=med_val, color=\"darkblue\", linewidth=0.8) +\n", + " labs(title=paste0(\"Bootstrap: \", ind_names[k]),\n", + " x=\"Indirect Effect (a x b)\", y=\"Count\") +\n", + " theme_minimal(base_size=10)\n", + "}\n", + "\n", + "p_boot_dist <- do.call(gridExtra::grid.arrange, c(plot_list, ncol=2))\n", + "ggsave(file.path(DIR_BOOT, paste0(\"bootstrap_distributions_\", EXPOSURE, \".png\")),\n", + " arrangeGrob(grobs=plot_list, ncol=2), width=12, height=10, dpi=150)\n", + "ggsave(file.path(DIR_BOOT, paste0(\"bootstrap_distributions_\", EXPOSURE, \".pdf\")),\n", + " arrangeGrob(grobs=plot_list, ncol=2), width=12, height=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bootstrap Interpretation\n", + "\n", + "Bootstrap provides non-parametric percentile confidence intervals that do not assume normality of the\n", + "indirect effect distribution. If a 95% percentile CI excludes zero, the indirect effect is significant\n", + "at alpha=0.05. The histograms show the sampling distributions of each specific and total indirect effect." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Method 3: MNAR Sensitivity Analysis\n", + "\n", + "Delta-shift imputation assesses robustness of the indirect effect to departures from Missing At Random.\n", + "For each grid point, missing mediators are shifted uniformly by delta_med * SD, and missing outcome by\n", + "delta_out * SD. The tipping point is the smallest shift that makes the total indirect non-significant." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:55:33.883575Z", + "iopub.status.busy": "2026-03-31T21:55:33.881142Z", + "iopub.status.idle": "2026-03-31T21:56:37.332780Z", + "shell.execute_reply": "2026-03-31T21:56:37.330110Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running MNAR sensitivity grid...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Grid: 225 points\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR grid complete: 225/225 converged in 1.1 minutes\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 8: MNAR Sensitivity\n", + "# ============================================================\n", + "cat(\"Running MNAR sensitivity grid...\\n\")\n", + "\n", + "delta_range <- seq(-3, 3, length.out=15)\n", + "grid <- expand.grid(delta_med=delta_range, delta_out=delta_range)\n", + "cat(\"Grid:\", nrow(grid), \"points\\n\")\n", + "\n", + "med_means <- sapply(MEDIATORS, function(m) mean(dat[[m]], na.rm=TRUE))\n", + "med_sds <- sapply(MEDIATORS, function(m) sd(dat[[m]], na.rm=TRUE))\n", + "out_mean <- mean(dat[[OUTCOME]], na.rm=TRUE)\n", + "out_sd <- sd(dat[[OUTCOME]], na.rm=TRUE)\n", + "\n", + "mnar_labels <- c(paste0(\"ind\", 1:N_MED), \"total_indirect\")\n", + "mnar_est <- matrix(NA, nrow(grid), length(mnar_labels), dimnames=list(NULL, mnar_labels))\n", + "mnar_pval <- matrix(NA, nrow(grid), length(mnar_labels), dimnames=list(NULL, mnar_labels))\n", + "\n", + "t0 <- proc.time()\n", + "n_ok <- 0\n", + "\n", + "for (k in 1:nrow(grid)) {\n", + " dat_imp <- dat\n", + "\n", + " for (m_idx in seq_along(MEDIATORS)) {\n", + " m <- MEDIATORS[m_idx]\n", + " dat_imp[[m]][is.na(dat_imp[[m]])] <- med_means[m_idx] + grid$delta_med[k] * med_sds[m_idx]\n", + " }\n", + " dat_imp[[OUTCOME]][is.na(dat_imp[[OUTCOME]])] <- out_mean + grid$delta_out[k] * out_sd\n", + "\n", + " for (cov in ALL_COVS) {\n", + " dat_imp[[cov]][is.na(dat_imp[[cov]])] <- mean(dat_imp[[cov]], na.rm=TRUE)\n", + " }\n", + "\n", + " fit_m <- tryCatch(\n", + " sem(model_str, data=dat_imp, estimator=\"ML\", fixed.x=FALSE),\n", + " error = function(e) NULL\n", + " )\n", + "\n", + " if (!is.null(fit_m) && lavInspect(fit_m, \"converged\")) {\n", + " pe_m <- parameterEstimates(fit_m)\n", + " for (lab in mnar_labels) {\n", + " row_m <- pe_m[pe_m$label == lab, ]\n", + " if (nrow(row_m) == 1) {\n", + " mnar_est[k, lab] <- row_m$est\n", + " mnar_pval[k, lab] <- row_m$pvalue\n", + " }\n", + " }\n", + " n_ok <- n_ok + 1\n", + " }\n", + "}\n", + "\n", + "elapsed <- (proc.time() - t0)[3]\n", + "cat(sprintf(\"MNAR grid complete: %d/%d converged in %.1f minutes\\n\", n_ok, nrow(grid), elapsed/60))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:56:37.347398Z", + "iopub.status.busy": "2026-03-31T21:56:37.345015Z", + "iopub.status.idle": "2026-03-31T21:56:42.094171Z", + "shell.execute_reply": "2026-03-31T21:56:42.092060Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tipping point (total indirect): delta_med=0.00, delta_out=0.00, distance=0.00\n", + " At tipping: est=0.0022, p=0.8213\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "At reference (0,0): total_indirect=0.0022, p=0.8213\n" + ] + }, + { + "data": { + "image/png": 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/30/fv3jx8/TklJiYiIOHDgQHh4OHuWu7v7hAkTevfurabm3377bf78+d27d3/7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\nUQQ7QGYcZlc5/N47D+IdYDmCHQD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71i//nnn19++UVDgS+++MLOzs5o9RGXUaNGbd68+cmTJ1WqVNHi5a9fv968ebOX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nOwHkhJxEOoKDJSgC7KUgiVevXhFCevXq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\nqhH1dG/aAQDQJAQ7Z5Ee4IJ3xVL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60dts+Kj368varD6nCv16pV5+WnLjwrOz4yxL1+bHqMPlRnNNuIqLDM8PvfILED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\nwbET4GsY6yDUhMVRn6m0dxkRka6QCr6lq+5mnQn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v7NCBhg2zWCz1rqyokH37rWt+tdo2ebJrJ5F81WCSALgAACAASURBVCqq/+lEZBs/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\nMDvEw9nOUCmvUe84mbp87y/xWWWqBk1MRvEPcbnxWWVaa9lt9lyLbcQmYY8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LNRqNBW4RyC+pVDp9+nTDw7S0NDs7O19fXx5DAjBAX/dQ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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 9: MNAR -- Save Results, Contour Plots, 1D Slices\n", + "# ============================================================\n", + "\n", + "mnar_grid_df <- cbind(grid,\n", + " as.data.frame(mnar_est),\n", + " setNames(as.data.frame(mnar_pval), paste0(colnames(mnar_pval), \"_p\")))\n", + "mnar_grid_df$N <- N_TOTAL\n", + "mnar_grid_df$exposure <- EXPOSURE\n", + "write.csv(mnar_grid_df, file.path(DIR_MNAR, \"mnar_grid_results.csv\"), row.names=FALSE)\n", + "\n", + "mnar_grid_df$dist <- sqrt(mnar_grid_df$delta_med^2 + mnar_grid_df$delta_out^2)\n", + "ns_rows <- mnar_grid_df[!is.na(mnar_grid_df$total_indirect_p) & mnar_grid_df$total_indirect_p >= 0.05, ]\n", + "\n", + "if (nrow(ns_rows) > 0) {\n", + " tip_row <- ns_rows[which.min(ns_rows$dist), ]\n", + " cat(sprintf(\"Tipping point (total indirect): delta_med=%.2f, delta_out=%.2f, distance=%.2f\\n\",\n", + " tip_row$delta_med, tip_row$delta_out, tip_row$dist))\n", + " cat(sprintf(\" At tipping: est=%.4f, p=%.4f\\n\", tip_row$total_indirect, tip_row$total_indirect_p))\n", + "} else {\n", + " cat(\"Total indirect remains significant across entire grid -- ROBUST.\\n\")\n", + " tip_row <- data.frame(delta_med=NA, delta_out=NA, dist=Inf)\n", + "}\n", + "\n", + "ref_row <- mnar_grid_df[which.min(abs(mnar_grid_df$delta_med) + abs(mnar_grid_df$delta_out)), ]\n", + "cat(sprintf(\"\\nAt reference (0,0): total_indirect=%.4f, p=%.4f\\n\",\n", + " ref_row$total_indirect, ref_row$total_indirect_p))\n", + "\n", + "tip_summary <- data.frame(\n", + " exposure = EXPOSURE, effect = \"total_indirect\",\n", + " ref_est = ref_row$total_indirect, ref_p = ref_row$total_indirect_p,\n", + " tip_delta_med = tip_row$delta_med, tip_delta_out = tip_row$delta_out,\n", + " tip_distance = tip_row$dist, N = N_TOTAL\n", + ")\n", + "write.csv(tip_summary, file.path(DIR_MNAR, \"mnar_tipping_summary.csv\"), row.names=FALSE)\n", + "\n", + "# Contour: indirect effect\n", + "p_contour_ind <- ggplot(mnar_grid_df[!is.na(mnar_grid_df$total_indirect), ],\n", + " aes(x=delta_med, y=delta_out, z=total_indirect)) +\n", + " geom_contour_filled(bins=12) +\n", + " scale_fill_viridis_d(option=\"viridis\") +\n", + " annotate(\"point\", x=0, y=0, shape=4, size=3, color=\"red\", stroke=2) +\n", + " labs(title=\"MNAR Sensitivity: Total Indirect Effect\",\n", + " subtitle=paste0(\"Exposure: \", EXPOSURE, \" | N = \", N_TOTAL),\n", + " x=expression(delta[mediators]), y=expression(delta[outcome]),\n", + " fill=\"Indirect\\nEffect\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + "ggsave(file.path(DIR_MNAR, paste0(\"mnar_contour_indirect_\", EXPOSURE, \".png\")),\n", + " p_contour_ind, width=9, height=7, dpi=150)\n", + "ggsave(file.path(DIR_MNAR, paste0(\"mnar_contour_indirect_\", EXPOSURE, \".pdf\")),\n", + " p_contour_ind, width=9, height=7)\n", + "print(p_contour_ind)\n", + "\n", + "# Contour: p-value\n", + "mnar_grid_df$neg_log10_p <- -log10(pmax(mnar_grid_df$total_indirect_p, 1e-20))\n", + "p_contour_pval <- ggplot(mnar_grid_df[!is.na(mnar_grid_df$neg_log10_p), ],\n", + " aes(x=delta_med, y=delta_out, z=neg_log10_p)) +\n", + " geom_contour_filled(bins=12) +\n", + " scale_fill_viridis_d(option=\"viridis\") +\n", + " annotate(\"point\", x=0, y=0, shape=4, size=3, color=\"red\", stroke=2) +\n", + " labs(title=\"MNAR Sensitivity: -log10(p) of Total Indirect Effect\",\n", + " subtitle=paste0(\"Exposure: \", EXPOSURE, \" | N = \", N_TOTAL),\n", + " x=expression(delta[mediators]), y=expression(delta[outcome]),\n", + " fill=\"-log10(p)\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + "ggsave(file.path(DIR_MNAR, paste0(\"mnar_contour_pvalue_\", EXPOSURE, \".png\")),\n", + " p_contour_pval, width=9, height=7, dpi=150)\n", + "ggsave(file.path(DIR_MNAR, paste0(\"mnar_contour_pvalue_\", EXPOSURE, \".pdf\")),\n", + " p_contour_pval, width=9, height=7)\n", + "print(p_contour_pval)\n", + "\n", + "# 1D slices\n", + "slice_med <- mnar_grid_df[abs(mnar_grid_df$delta_out) < 0.01, ]\n", + "slice_out <- mnar_grid_df[abs(mnar_grid_df$delta_med) < 0.01, ]\n", + "\n", + "p_slice1 <- ggplot(slice_med, aes(x=delta_med, y=total_indirect)) +\n", + " geom_line(color=\"steelblue\", linewidth=1) + geom_point(color=\"steelblue\") +\n", + " geom_hline(yintercept=0, linetype=\"dashed\", color=\"red\") +\n", + " labs(title=paste0(EXPOSURE, \": Total Indirect vs delta_med\"),\n", + " subtitle=\"(delta_out = 0)\",\n", + " x=expression(delta[mediators]), y=\"Total Indirect Effect\") +\n", + " theme_minimal(base_size=11)\n", + "\n", + "p_slice2 <- ggplot(slice_out, aes(x=delta_out, y=total_indirect)) +\n", + " geom_line(color=\"darkorange\", linewidth=1) + geom_point(color=\"darkorange\") +\n", + " geom_hline(yintercept=0, linetype=\"dashed\", color=\"red\") +\n", + " labs(title=paste0(EXPOSURE, \": Total Indirect vs delta_out\"),\n", + " subtitle=\"(delta_med = 0)\",\n", + " x=expression(delta[outcome]), y=\"Total Indirect Effect\") +\n", + " theme_minimal(base_size=11)\n", + "\n", + "p_slices <- gridExtra::grid.arrange(p_slice1, p_slice2, ncol=2)\n", + "ggsave(file.path(DIR_MNAR, paste0(\"mnar_1d_slices_\", EXPOSURE, \".png\")),\n", + " arrangeGrob(p_slice1, p_slice2, ncol=2), width=14, height=5, dpi=150)\n", + "ggsave(file.path(DIR_MNAR, paste0(\"mnar_1d_slices_\", EXPOSURE, \".pdf\")),\n", + " arrangeGrob(p_slice1, p_slice2, ncol=2), width=14, height=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MNAR Interpretation\n", + "\n", + "- The reference point (0,0) corresponds to MAR (missing values imputed at observed mean).\n", + "- The tipping point is the smallest shift from (0,0) that flips significance.\n", + "- Tipping distance > 1.0 SD: robust. Distance < 0.5 SD: fragile." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Method 4: Bayesian SEM (blavaan)\n", + "\n", + "Bayesian estimation via blavaan/Stan. Missing data handled by Stan data augmentation.\n", + "Posterior probability P(indirect > 0) provides Bayesian evidence measure." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T21:56:42.101803Z", + "iopub.status.busy": "2026-03-31T21:56:42.099586Z", + "iopub.status.idle": "2026-04-01T01:09:26.437004Z", + "shell.execute_reply": "2026-04-01T01:09:26.434029Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting Bayesian SEM with blavaan (2 chains, Stan backend)...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N = 1153 (full data with NAs, Stan handles missing)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 1).\n", + "Chain 1: \n", + "Chain 1: Gradient evaluation took 0.005576 seconds\n", + "Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 55.76 seconds.\n", + "Chain 1: Adjust your expectations accordingly!\n", + "Chain 1: \n", + "Chain 1: \n", + "Chain 1: Iteration: 1 / 3000 [ 0%] (Warmup)\n", + "Chain 1: Iteration: 300 / 3000 [ 10%] (Warmup)\n", + "Chain 1: Iteration: 600 / 3000 [ 20%] (Warmup)\n", + "Chain 1: Iteration: 900 / 3000 [ 30%] (Warmup)\n", + "Chain 1: Iteration: 1001 / 3000 [ 33%] (Sampling)\n", + "Chain 1: Iteration: 1300 / 3000 [ 43%] (Sampling)\n", + "Chain 1: Iteration: 1600 / 3000 [ 53%] (Sampling)\n", + "Chain 1: Iteration: 1900 / 3000 [ 63%] (Sampling)\n", + "Chain 1: Iteration: 2200 / 3000 [ 73%] (Sampling)\n", + "Chain 1: Iteration: 2500 / 3000 [ 83%] (Sampling)\n", + "Chain 1: Iteration: 2800 / 3000 [ 93%] (Sampling)\n", + "Chain 1: Iteration: 3000 / 3000 [100%] (Sampling)\n", + "Chain 1: \n", + "Chain 1: Elapsed Time: 1727.91 seconds (Warm-up)\n", + "Chain 1: 4012.19 seconds (Sampling)\n", + "Chain 1: 5740.1 seconds (Total)\n", + "Chain 1: \n", + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 2).\n", + "Chain 2: \n", + "Chain 2: Gradient evaluation took 0.002157 seconds\n", + "Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 21.57 seconds.\n", + "Chain 2: Adjust your expectations accordingly!\n", + "Chain 2: \n", + "Chain 2: \n", + "Chain 2: Iteration: 1 / 3000 [ 0%] (Warmup)\n", + "Chain 2: Iteration: 300 / 3000 [ 10%] (Warmup)\n", + "Chain 2: Iteration: 600 / 3000 [ 20%] (Warmup)\n", + "Chain 2: Iteration: 900 / 3000 [ 30%] (Warmup)\n", + "Chain 2: Iteration: 1001 / 3000 [ 33%] (Sampling)\n", + "Chain 2: Iteration: 1300 / 3000 [ 43%] (Sampling)\n", + "Chain 2: Iteration: 1600 / 3000 [ 53%] (Sampling)\n", + "Chain 2: Iteration: 1900 / 3000 [ 63%] (Sampling)\n", + "Chain 2: Iteration: 2200 / 3000 [ 73%] (Sampling)\n", + "Chain 2: Iteration: 2500 / 3000 [ 83%] (Sampling)\n", + "Chain 2: Iteration: 2800 / 3000 [ 93%] (Sampling)\n", + "Chain 2: Iteration: 3000 / 3000 [100%] (Sampling)\n", + "Chain 2: \n", + "Chain 2: Elapsed Time: 1751.03 seconds (Warm-up)\n", + "Chain 2: 4034.74 seconds (Sampling)\n", + "Chain 2: 5785.78 seconds (Total)\n", + "Chain 2: \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cThere were 4000 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See\n", + "https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cExamine the pairs() plot to diagnose sampling problems\n", + "\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cBulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.\n", + "Running the chains for more iterations may help. See\n", + "https://mc-stan.org/misc/warnings.html#bulk-ess\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201cTail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.\n", + "Running the chains for more iterations may help. See\n", + "https://mc-stan.org/misc/warnings.html#tail-ess\u201d\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing post-estimation metrics (including lvs if requested)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\u201clavaan->lav_model_vcov_se(): \n", + " The following boostrapped defined parameters have a high (>5) ratio of \n", + " standard deviation to median absolute deviation: prop1 prop2 prop3 prop4 \n", + " prop_total. P-values and confidence intervals may not match.\u201d\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian fitting took 192.7 minutes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian model fitted successfully.\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 10: Bayesian SEM (blavaan)\n", + "# ============================================================\n", + "cat(\"Fitting Bayesian SEM with blavaan (2 chains, Stan backend)...\\n\")\n", + "cat(\"N =\", N_TOTAL, \"(full data with NAs, Stan handles missing)\\n\")\n", + "\n", + "t0 <- proc.time()\n", + "\n", + "fit_bayes <- tryCatch(\n", + " bsem(model_str, data=dat, fixed.x=FALSE, target=\"stan\",\n", + " n.chains=2, burnin=1000, sample=2000, seed=42),\n", + " error = function(e) { cat(\"Bayesian Error:\", conditionMessage(e), \"\\n\"); NULL }\n", + ")\n", + "\n", + "elapsed_bayes <- (proc.time() - t0)[3]\n", + "cat(sprintf(\"Bayesian fitting took %.1f minutes\\n\", elapsed_bayes / 60))\n", + "\n", + "if (!is.null(fit_bayes)) {\n", + " cat(\"Bayesian model fitted successfully.\\n\")\n", + "} else {\n", + " cat(\"Bayesian model failed.\\n\")\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T01:09:27.716885Z", + "iopub.status.busy": "2026-04-01T01:09:27.715129Z", + "iopub.status.idle": "2026-04-01T01:09:28.441395Z", + "shell.execute_reply": "2026-04-01T01:09:28.438704Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Bayesian Parameter Estimates (parTable) === \n", + " label est se\n", + "1 a1 0.309 0.060\n", + "6 a2 0.308 0.060\n", + "11 a3 0.259 0.063\n", + "15 a4 0.205 0.048\n", + "19 b1 -1.670 1.268\n", + "20 b2 1.643 1.268\n", + "21 b3 -0.016 0.082\n", + "22 b4 0.083 0.071\n", + "23 cp 0.162 0.056\n", + "79 ind1 -0.517 0.418\n", + "80 ind2 0.506 0.415\n", + "81 ind3 -0.004 0.022\n", + "82 ind4 0.017 0.015\n", + "83 total_indirect 0.002 0.021\n", + "84 total 0.163 0.053\n", + "85 prop1 -3.163 27.490\n", + "86 prop2 3.094 26.926\n", + "87 prop3 -0.025 0.947\n", + "88 prop4 0.104 0.695\n", + "89 prop_total 0.010 1.100\n", + "90 diff_1_2 -1.022 0.833\n", + "91 diff_1_3 -0.513 0.416\n", + "92 diff_1_4 -0.534 0.418\n", + "93 diff_2_3 0.510 0.418\n", + "94 diff_2_4 0.489 0.415\n", + "95 diff_3_4 -0.021 0.034\n", + "\n", + "Posterior draws dimensions: 4000 x 95 \n", + "Draw column names (first 20): a1, APOC4_APOC2_AC_exp~msex_u, APOC4_APOC2_AC_exp~age_death_u, APOC4_APOC2_AC_exp~pmi_u, APOC4_APOC2_AC_exp~ROS_study_u, a2, APOC2_AC_exp~msex_u, APOC2_AC_exp~age_death_u, APOC2_AC_exp~pmi_u, APOC2_AC_exp~ROS_study_u, a3, APOC1_DeJager_Mic_exp~msex_u, APOC1_DeJager_Mic_exp~age_death_u, APOC1_DeJager_Mic_exp~pmi_u, a4, APOE_Mega_Mic_exp~msex_u, APOE_Mega_Mic_exp~age_death_u, APOE_Mega_Mic_exp~pmi_u, b1, b2 \n", + "\n", + "=== Bayesian Results === \n", + " label est se ci.lower ci.upper pp_positive\n", + "1 a1 0.30943188 0.05969376 0.19542321 0.43138504 1.00000\n", + "2 a2 0.30771044 0.05978140 0.19298580 0.42995953 1.00000\n", + "3 a3 0.25867242 0.06319289 0.13935518 0.37979296 1.00000\n", + "4 a4 0.20513174 0.04780898 0.11150216 0.30039821 1.00000\n", + "5 b1 -1.67020696 1.26809785 -4.19334555 0.81359105 0.08300\n", + "6 b2 1.64306818 1.26758842 -0.81644484 4.16842592 0.91200\n", + "7 b3 -0.01583461 0.08172036 -0.16586710 0.14919748 0.41525\n", + "8 b4 0.08262087 0.07123694 -0.06050614 0.21832529 0.86950\n", + "9 cp 0.16178746 0.05622272 0.05142988 0.26800078 0.99800\n", + "10 ind1 -0.52505691 0.41793025 -1.44668514 0.20454637 0.08300\n", + "11 ind2 0.51394125 0.41530898 -0.20154995 1.42460800 0.91200\n", + "12 ind3 -0.00377359 0.02184136 -0.04629978 0.04045608 0.41525\n", + "13 ind4 0.01667252 0.01523181 -0.01322006 0.04739114 0.86950\n", + "14 total_indirect 0.00178327 0.02101988 -0.03808908 0.04455293 0.52800\n", + "15 total 0.16357073 0.05309954 0.06016848 0.26435804 0.99875\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 11: Bayesian -- Extract Results\n", + "# ============================================================\n", + "\n", + "if (!is.null(fit_bayes)) {\n", + " pt <- parTable(fit_bayes)\n", + " labeled_pt <- pt[pt$label != \"\", c(\"label\", \"est\", \"se\")]\n", + " cat(\"=== Bayesian Parameter Estimates (parTable) ===\", \"\\n\")\n", + " print(labeled_pt)\n", + "\n", + " draws_raw <- blavInspect(fit_bayes, \"draws\")\n", + " draws <- do.call(rbind, draws_raw)\n", + " cat(\"\\nPosterior draws dimensions:\", nrow(draws), \"x\", ncol(draws), \"\\n\")\n", + " draw_colnames <- colnames(draws)\n", + " cat(\"Draw column names (first 20):\", paste(head(draw_colnames, 20), collapse=\", \"), \"\\n\")\n", + "\n", + " bayes_labels <- c(paste0(\"a\", 1:N_MED), paste0(\"b\", 1:N_MED), \"cp\",\n", + " paste0(\"ind\", 1:N_MED), \"total_indirect\", \"total\")\n", + "\n", + " bayes_res <- data.frame(label=character(), est=numeric(), se=numeric(),\n", + " ci.lower=numeric(), ci.upper=numeric(),\n", + " pp_positive=numeric(), stringsAsFactors=FALSE)\n", + "\n", + " for (lab in bayes_labels) {\n", + " d <- NULL\n", + " if (lab %in% draw_colnames) {\n", + " d <- draws[, lab]\n", + " } else {\n", + " pt_row <- pt[pt$label == lab, ]\n", + " if (nrow(pt_row) > 0) {\n", + " plabel <- pt_row$plabel[1]\n", + " if (!is.null(plabel) && plabel %in% draw_colnames) {\n", + " d <- draws[, plabel]\n", + " }\n", + " }\n", + " }\n", + "\n", + " if (!is.null(d)) {\n", + " ci <- quantile(d, c(0.025, 0.975))\n", + " bayes_res <- rbind(bayes_res, data.frame(\n", + " label=lab, est=mean(d), se=sd(d),\n", + " ci.lower=ci[1], ci.upper=ci[2],\n", + " pp_positive=mean(d > 0), stringsAsFactors=FALSE\n", + " ))\n", + " } else {\n", + " pt_row <- pt[pt$label == lab, ]\n", + " if (nrow(pt_row) > 0) {\n", + " bayes_res <- rbind(bayes_res, data.frame(\n", + " label=lab, est=pt_row$est[1], se=pt_row$se[1],\n", + " ci.lower=pt_row$est[1] - 1.96*pt_row$se[1],\n", + " ci.upper=pt_row$est[1] + 1.96*pt_row$se[1],\n", + " pp_positive=NA, stringsAsFactors=FALSE\n", + " ))\n", + " }\n", + " }\n", + " }\n", + "\n", + " rownames(bayes_res) <- NULL\n", + " bayes_res$method <- \"Bayesian\"\n", + " bayes_res$N <- N_TOTAL\n", + " bayes_res$exposure <- EXPOSURE\n", + " bayes_res$direction <- \"D1\"\n", + "\n", + " write.csv(bayes_res, file.path(DIR_BAYES, \"bayesian_results.csv\"), row.names=FALSE)\n", + " cat(\"\\n=== Bayesian Results ===\", \"\\n\")\n", + " print(bayes_res[, c(\"label\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"pp_positive\")])\n", + "} else {\n", + " cat(\"Skipping Bayesian results extraction (model failed).\\n\")\n", + " bayes_res <- NULL\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T01:09:28.479295Z", + "iopub.status.busy": "2026-04-01T01:09:28.476663Z", + "iopub.status.idle": "2026-04-01T01:09:37.404062Z", + "shell.execute_reply": "2026-04-01T01:09:37.399735Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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obGzJkzw8LCXF1dhX1oCgsLw8LChg4d+vnzZx8fn7y8vLdv3yYnJ6urq48ZM6Zj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\nFZzXyc72zq7kgPVxwcHBP//888SJE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fDhw8XFxepXqOmIRKJVq1Yx\nDXUnLhmKt5YWCZzt5Tq4+969e0GHB1ARmUw2ffr0Dx8/hmxfZOrQFW85jfGZF0LRoW3atAlv\nIRoJcOw6I8eOHROJRFOmDlL/qSNn+djZm2zYsOHr16/qP7uGwmaz165dy2KxwsPDiURibGxs\no6WlpaWla9euNTMzCw8P19fXX79+PZvNxkuthrJ///78/Pwpy4bp6WMyx4QKBAJhQXKILpM2\na9YsLpeLt5wOwcOHDxcsWBAWFrZkyZJmU+ump6cvXLgwPDx84cKF6E7taTTbt2+/fv261+xA\np0AvvLU0A6OLwaDI0Q8fPgQbhpQAOHadDpFIdPTo0W49ugwcZKf+s1OppLiEIA6nbtGiReo/\nu4aSkZHRrVu36dOnOzo6zp07l0wmP336tGGBioqKoKCgiRMn9u7de8aMGSQSqaCgAC+1mkhp\naenWrVtte5uNmKKOTeKqYGjKjNo0pqioqL15xrSS4uLigwcPLly48Pfff58+fXpycnKjV5o3\nb94cO3ZsxYoVv//++/z58w8ePAheKSEIevLkyebNm63deo1cOw1vLS3iMy+Erq+3YcMGjLKu\naTHAset0nD17tqysLGKaJ17xitz6d504xf3KlSu//fYbLgI0joKCgp49e8o/9urVq5Hf5urq\nOm3a/zXQNTU1fD7f2tparRI1nPXr13O5nKiNY4gkDdhJOjjIxc2v55EjR7KysvDWgjOZmZnu\n7u6urq4EAsHT09Pe3v7x48cNCzCZzNWrV/fq1QuCoH79+rFYrNLSUpzEdhTq6+tnzJhB0qFO\n3L+MSMZkYxAq6BgwBi8MzcvL+/nnn/HWomFodrgTxRj4+soGD4bAcq4GwDC8Z88eIyO9sUF9\ncZQRs3zko8z8mJgYPz8/S0tLHJVoBFwut1u3bvKPenp6HA6n2ZI8Hi85OTk4ONjMrPl4VNXV\n1Qp2rmCUthKCIJFIVFVVhZFxFRPSZ2dn//rrr4MCHG2dujS9Axhlu0dQ+oZPXTv87bOiuXPn\n3r59u+mmXb2kJMNLl6qePcMiVyxCfX09RpaFQmHDe06n0xXsGy0pKbGzs5N/tLa2brTAtGvX\nrl27/t8Csvfv3wuFQsTJa4pIJGpaNZBvsMiLihiXSqVKhI5qC4YeHpLAQMHOnU1/Wrt2bWFh\nYfCOhQxzQ+WyAcEwjFEaIRiGGxofMM3/+ZnbsbGxQUFBqm+rR/a5YzGigQgWCoUYGUfSCjf6\nnkgktrRVFh/Hbtq0aQ3T5a5bt27QoP9Z7/Xx48eUlJRPnz7p6uqGhoaGhIQocRZicTFcWYmC\nXC3i+vXreXl58xf70Wh4+vS6etTNW0MWRv8ya9asmzdv4hvrvONDJpMbtv4CgaDZAC5lZWVb\nt2718vKSj941hUqlNhvhSCaTwTCMUVwPkUhEIBAolLYE1Wo3EomERCIp/QjJZLLY2FgqnRyx\nekSjuwrDsFQqVcW4YhDjyh1rZmMUMt/n3P57Z86cmTt3bqNfidXVpE+faDQaFo4dMi+G0aMi\nFApJJFLDf4TiWEWNkpbSaLSWlh5+/Phx+/btMTExLUUA4fF4LTkrjXxNdMHIazT59ElSWdn0\nbmRnZx89erSbr6vDWA8FUWBbRZVj22GcAA1dGXZl5aGEhARUMq9g90KCtfGmUKnUDuTYwTBc\nX19/9OhRc3PzZgtIJJLExMTAwMDk5OTS0tK4uDgrKyt3d3c169RKkpOTdfWoYRE4bJtoxICB\ndtNnev3y0+09e/asXr0abzkdGnNz8/LycvnHsrKypoEAP3z4kJiYGB0d7ePjo8BUS4Mfaohj\nh1FwMhXj2J04ceLVq1fhK4ab2zQOcYIMqJDJZOzi2KmSST04evDDK3lJSUkzZ840Nv4f8WIi\nEYIgJpOJhWOHaRw7kUjUrkeFTqc37E25XG6zwh4/fvzDDz/ExMS4ubm1ZIrJZDZ955FKpcgD\nhkXOe6lUKhQKMap0EAQRicRGcUwlEsm6devIdOq4xLmqBKITiURY3BDEMgzDDaOE9g0anHvh\n/okTJ+bOnevi4qKKcS6Xq6enh8V7Gp/PFwqF+vr6WMSxEwgEJBKp6YuxggvBwbHj8XgymUxP\nT6+lAm/evJFIJBMmTCAQCNbW1v7+/unp6cCxU5309PRHjx5FzvLWZ2HSKLeXhTHDn2cVxcbG\n+vj4qFhjtRtPT8/k5OTq6mojI6Pi4uJ3794tWrQIhuGysjJjY2MqlcrhcBISElasWKGg3wI0\npaamJi4uzqyrYeBsb7y1tBsyhTQzbnRy9Jn4+PjDhw/jLQcf7OzsCgsL5R+LiorGjBnTqMyd\nO3cuXLiwdetWxas+FLjvRCIR3SDnarCM0Mj44cOH8/Ly/DfMMLQ2VdEyFh4M9J+z0sh44Jbo\no4GrFy1a9OjRI1XOSyAQyGQyFo4doopMJmNxW4hEYqNh7FbBwbFDBoePHz/+5s0bKpU6dOjQ\nKVOmNKxUJSUlNjY28rtvY2Pz5MmTZk01O/Esh4TNUgCMbKpBanx8PJ1OmTrDU5X4wMhLLSoR\nhkkkQtLOCZHhP0yZMiUjI8PGxkZ1mw1BRKK+owqZtVT6n6VEO+7s7Dxq1KilS5fa2toWFRXN\nnDnT0tJSJBLNnz9/+/btffr0uXr1qkgkunLlypUrV5BDhg8f7uvrq5zCzsPmzZsrKyvXpERQ\nqB13CbkCXIf0cBva8/jx44sWLXJycsJbDg4MHTr00qVLL1686Nev3/3798vLy729vWUy2Z07\ndwYNGmRgYFBSUvLTTz/t3bu3pVWnnYeqqqqEhIQuPaw9osbiraV9GNtbDFk8IWPvH99///2y\nZcvwlqMB4ODYkUgkf39/Ly+vFStWfPnyZcuWLRQKZdKkSfICAoGg4agjjUZraXkpj8dTsPLU\nGIJkMhkH7QSsEAZJXbEzK7eJDNdFTB+kq0dSfbkuWgt+jU10YjePjVt7acaMGX/++ScWy7Ba\n2megIsoteyeRSMoljouKigoKCqqsrLS0tEQWEVMolKSkJGTl+LBhw/r2/Z/dMKAba5Xc3NyU\nlJR+vj0GDG9+Nb1GMGO9/5qglNWrV9+4cQNvLThgaWm5cuXK48ePV1VVWVtbx8fHM5lMkUh0\n6NCh7du3GxgY3Lx5k8vlNgyuFBER0bC76Txs2rSptrZ2xoElHXknbEsMnh/69vrTuLi4wMDA\nHj164C2no4ODY2diYrJkyRLkbxsbm+Dg4Pv37zesaXQ6vWGv2dKyCQiCaDSagvEP4dSpMkdH\n1DPx8Xg81FdFINeLhVnEpkwmS0pKYjBoUdGDVVwbIZVKpVIpigssho1wjF7w7fiR+3FxcUeO\nHEHLLARBYrFYKBTq6Oigu0ZKLBYjQ/pKHKvKLICxsXHDpVQEAkE+f21paQk2F7cLGIZjYmII\nRHhm3Gi8taiEZTeTkeHuN0/fvHXrVkBAAPKl1NtbKpPRO8eeJE9PT09Pz4bfUKnUq1evIn/P\nnTu36eaSzoBgxgzIw0P+ovzmzZsffvih14gB3QbjGQ9BaYhkUsjORT9MiI2MjMzMzMR0/loL\nwOHusNns8vJy+Z5zsVjcaJzG1tb28+fP8i1jRUVFtra2zZqiUCgKxni+bdtGpVJ10c5VjEX+\nY2Q9MhZmEZs//vhjbm7ukuUjjI1VXcCOzG+iW6/mLhhamF/x008/OTk5rVq1Ci2zMAwLhULF\n3r9yZolEYsPlvQCN47fffsvMzAydP7hjpoVtF5OWDH14NXft2rUjR45E2kxJRIRg/Hg6Nqug\nABoBd9cuGo0m71HWrVsng2H/9TPw1KQaFk72Q2MmZez7Y9u2bfHx8XjL6dDgUPMrKys3bNiA\n5H4pKSlJS0sbPHgwBEFZWVkfPnyAIKhPnz5MJvPChQtSqbSwsDA9PX3UqFHq16k1sNns2NhY\nS0uDqdM9Wy+NBwQCYeN345ycLdeuXZuamoq3HICWU1tbu3r1ahNL1viF2rAMkWmoGzJvcG5u\n7i+//IK3FkBHJD09PS0tbUDESONumj2uP2TheJsBDomJiY3CUAMagYNj16NHj5iYmOPHj4eH\nhycmJo4dO3bs2LEQBJ07dy47OxuCIBKJtHHjxhcvXoSHhyclJUVGRrq6uqpfp9YQHx9fXl6+\nfI0/FdfYdYqh0ci7D4SZmjGnTZuWm5uLtxyANrNx48aysrKZsaNpOpiE1lM/o2d4GFvob968\nmc/n460F0LGQyWRr1qyh6ukMXarxKwsJJOLEfUvJerSIiIjq6mq85XRc8Onp/fz8/Pz8Gn25\ne/du+d82NjY7duxQqyYtJTs7+/Dhw96Dewwf6Yi3llYwNmHs/T4iOvLHkJCQ7OxsExMTvBUB\ntJBnz56lpKT0H9Zr4KjeeGtBDSqdPGXpsJQNVw4dOrRmzRq85QA6EGfPnn3x4sWwFWF6xiy8\ntaAAy6pLcPKCc4v2REVFXblyBQS3bxawCEObEYlE0dHRVCpx/aZAvLW0iV4OZt9tG//pU1FE\nRARI/AxAHZFINHfuXCqdPDtewyI+tMqQEFebnqbbt2/HaM8+QBMRCoUbN25kmBp6zR6HtxbU\ncAzw8Jg5JjU1ddeuXXhr6aAAx06b2bNnT25u7qKlIywtDVov3TEYNqL3rOghd+7c2bx5M95a\nANpGcnJyXl5e2IrhJpbaMHrRECKJELZieHV1dcOpD0An5/Dhw0VFRcOWTaHoatVmr1EbZli7\n9YyLi7t//z7eWjoihGYTR2oHMmtrqZ8f5fRpdM3W1tY2ytOCik0YhpWLcNYSL1688PT0dHax\nOPZTFJGI2ng1RrmnkFRCSNhumQxePO/Xv7M/3bhxw9/fX2mbfD6/vr7ewMAA3V2xfD5fc3fF\ntpSYUiKRSCQS1PdlI3A4HBKJhFHqJB6PR6fT2xLwPTc319vb287JdPPpKEIbaoRMJhOJRBQK\nBbsUuqjnZYoP/7GskF0ycSLryhVJaSkWKcWaJn1Ckbq6OjKZ3PBRQcLuY3GutiCRSGpraxkM\nBhZVQyKRCAQC1ANyIciMjXmBgbZpaSQj3QXXdxFRvYdYhIZAEAqFbQwQUVf67VjQWn2a3osX\nLywsLNpinM1m6+vrYzF7W19fz+fzjYyMsMg8wePxyGRyu9qKjruaXnUIbDZBqSiyWoBIJJo1\naxaJBMUnhqDo1akHIpGwdfuEiElHIyMjc3JyQKxdFJFIJM1mDUHyomKU2BuGYcRJwsI4YrnV\n9hSpERBBFp0YKINlUBvm+VFMstKSfdTXG0xeOjR5zm9vsrN9amtFIhEWjh2ScwW7HPCNHhXs\nHOtGcDicpv8O5Bng8/lohWRvZByGYYymzg3Y7DfZ2dXV1ZO2RonEYqiFNzrlgGEYixsC/XfD\n22Kcaqg3bufC8/N3TZw48erVq215e5dKpWw2GwWVTUBaCTabjYXXKJPJCARC05D4FAqlpdSs\n2uzYdWa2bduWm5u7bNUIm65GeGtRBmMTxndJ45cuPD179uxr166BFbJo0VKsb4wGYhGEQiGJ\nRFKQHloVZDKZrq5uq31/YmJiXl5eVNzorj3N225ZIpGQyWSMHAupVIp6qhXXwT2dPO3/zf7X\nB4L09PSwcOyQoJstPUiqGyeTyRg9KophMpuJ8YmM2Ono6GjciB0Mw/n5+XYeTk4B6Ee56ggj\ndhAEOQ5391s2JWPfH8nJyXv27Gm1PNYjdiwWq4OM2IE1dlrImzdvkpOTXd1sJk4ZgLcW5fHy\n6R4x3fP69euHDh3CWwtAs8nIyNi1a5eLd7eAGYPw1oI5YcuHgY1HABiGpTKpf6wGRyRuC0MW\nTejp57Zv376LFy/iraUDodkjdgKB4P+xd54BTWRdH7+THnrvIEVBEQQLKmuvWFBQQUARFRuK\nFUVcC9i7omtbdVXsXXfV1bXsWta2a8GGoiAKCNIJkN7m/TDPkzcPnWQmE8L9fUomd878M3Pv\nzJlbzqlrzhAAwADLFYt3qlCZTEaETYBTVlMURadNm4YCWcKyoQiC/4gJ1udMhFksVZfyxhmz\nev/7T9bixYv9/PxUyHGOnVUej4fvW5RMJkMQRLUzgCAIQS/okLooKSmZMGGCvjFr1qbgltD1\n697RkWZtCAqrcnJynFxcyJYDIYGnT5/6oaiJo5WtLplT5gAAIABJREFUlyvZWogFoSCjts05\nEJQQHR3doUOHNm3akK1IK2jejh2Dwah/LANBENxHl4gYscKmreBi9vjx40+ePIme1quNu41I\nJMJ9rEcikcjlciLMUqnUah4YnU5ft2lMVPjBKVOmPH36tKnnRygUSqVS3HPFCoVCCoWi2rT3\nluBYaBVyuTwqKio/Py9+X4Splbr59JoLzp62oLBq48aNe/fvJ1sLRNOgKBoXF/c3ADaezmRr\n0QRsE4PQXXGHwxJDQkKePn1K0CSB5kXzduzq74kRzJyJeHjQ8Z5lgiAI7nNuEARBUVR9s1VV\nVUuXLrW1M46e3gvzIQjyJIgwiyBITbNura0WLhmyftW1efPmHT58uEkGseqB+6o6CoVC7ko9\nSOPZvHnzjRs3hk3q3qmfO9laNAd3pNfhjMIjKSkJS5fWlWgboqucOXPmyZMnJzu2oQd0I1uL\nhrDr4BawLOp60qHZs2cfOnSIbDnko8tz7PgJCZKxY8lWoVE2b978/fv3OQsGsVg6kisJADA6\npHPAMK8jR4788ssvZGuBNCfu3bu3YsUK944O4+NbVrLpnIEeXzeMEIrF69evJ1sLRKMIBIIl\nS5YYWpu+/Hnh26EtxbEDAPhFBrQf/sPhw4ePHz9Othby0WXHrqWRn5+/fft27w4OgwI8ydaC\nM8uSRri2tpw9ezbM/QxpJPn5+eHh4XpGjHnJoVRai7vRuXjZduzTJiUlJTs7m2wtEM2xdevW\nnJycAYvGMfQIWbWqzYzcMMPM2XbWrFkfP34kWwvJtLj7nQ6zatUqPp8/b+Eg3ZvIpafH2LYz\nnMmijBo1Kisri2w5EG1HIpGEhYUVlxTN2TrG3NaIbDnkMGZ2HzHstGtJfPv2bdOmTfY+rTsE\n9yZbCwkw9Nmhu+YLxKKwsDCCwuw1F6BjpyNkZGQcPny4Vx93305OZGshBEcns607wjic0qFD\nh5aUlJAtR9M8fPgwJiYmLCxs9uzZr1+/rllAKpUeP348ODj45cuXmpenbSxevPjhw4ehc/p5\n99DxVYH10LqDPdZp9/XrV7K1EEheXt6ePXuSkpL2799fWlpaa5nU1NQVK1Z8+vRJw9o0zOLF\ni3l8/pAVjUqsopPYeLoM/jHy9evXCQkJZGshE+jY6QhJSUlyuWzW3P5kCyGQTl1aJa0JzszM\nGD58OJfLJVuO5sjJyfnpp59mzpx55syZyMjIDRs21IyfvnjxYgqFAmOpAAAuXLiwY8eOTv3c\ng2f0JFsLyYTO7SuRiNeuXUu2EKKoqKhYvHixsbFxeHg4hUJZunRpzThE+/btu3DhQkZGhm7f\nNB4+fHjmzJkOQT0dOragdUI18ZswxL1/5127dv3xxx9kayENclbFVlZW/vbbb1++fNHX1+/T\np0+XLl2qFVi3bh2Px1N8jYyM9PTUtXljOPL69euzZ88OHuLVxl3Hs28FDPMqLeFu33IzJCTk\n6tWruEdd0U4ePHjQpUsXHx8fAED37t1dXFweP348dOhQ5TIxMTHu7u43b94kSaO2kJmZOWXK\nFEt7k9jNo1psv4UCVy+7zv09jh49mpCQoJMhvu7evevq6hoZGQkAaNeu3atXr54+fdq79/8M\nRNrZ2cXExEycOJEkjZpAJpPNmzePzmYOXBxJthaSQRBk5MaYfUMXTZky5c2bN+bm5mQrIgES\nHDuZTLZs2TIXF5fQ0NDv379v2rRp2bJlvr6+ymVevHixcOFCCwsL7Ku9vb0KBzJzdpYNGgQu\nXcJBtHazdOlSCgXMiO1LthBNMC6qe2kp9+jhm9OmTTty5IjuTSisSW5urrOzs+Krg4NDTk5O\ntTLu7o16TccyuNfcLpVKZTKZSCRSQ2adYLliCTKOpbjFQtuIRKKxY8fyBbxFByey9OlqJmDA\nYnE3r1yxGN1X3mhz8dXpt8tQKjJmdp8Xdz8lJiampKTgYlwqlaIoStDVBABUqypUKrWeNKCZ\nmZnKDqu7u3tmZmY1xy4oKKgxx621XWAbsaSujTHSJBTG1Td18ODBly9fDogfZ2htim3Z6BP9\namSPK9vmqG+82aFvbjxyQ8zp6Ztmz5596tQp5Z+IuI7KxgmyX5fluh5/JDh25eXlLi4uc+fO\npdFo7dq1e/78+atXr5QdOyyurJeXl7GxseblNTsePHhw/fr1MWO7NNO0sCowe/6AosLKo0eP\nurq6JiYmki2HcIRCoXI8ZCaTqfKgEpfLrcdTIS6zu1QqxT1fi7Jx7MOyZctSU1MjFvV3cDfH\ny/OoJ7eN+hDmScsBACKxCKUg1s7GXQe3PXfuXExMjArpW+qCOMdOIpEon3MWi1XPBAMul+vq\n+v/TKPX19VWuZhUVFYqKVA0ej6c8goQv6k/zLysrW7ZsmWkra5/w/sqp4lEUrZk5Hi+Is4yL\ncQf/dt7Bvc6cORMQEBAYGKjYXlZWpqbleigvLyfOeE0YDIaRUe0rw0hw7CwsLOLi4rDPKIrm\n5+dXG4rFHlqPHz9+//49i8Xq3bu3t7e35nU2C1AUjY+PZ7Pp02L6kK1FcyAIkrgmqLCwcuXK\nlZ6eniEhIWQrIhYWi6X8XOFyuSpHV6810zn4b48dk8lUzWz9VFVVUalU3PO1YPD5fBaLRaFQ\n/vjjj4MHD3bs0yYwugcu3bhyuVwsFtPpdIKCUYvFYtXylzQIglAAACwmC6UiAIDwBf2f3/m4\nfv36a9euqW8c6/QlqKpUVlbSaDTlqlJ/FHoajabsGAmFwnq69+qHwWDU3BfrmySoDsjlcplM\npv58kk2bNpWVlY3dsIhZI8SJymejfmQyGUGNAuvDxsX4wKUTvj55l5CQ0L9/fzMzM0Bki5NI\nJNj9k4gRJKlUikXFr7a9notLZuYJFEUPHjzIZrP79u2rvF0kEtna2hYWFg4ePDg3N3fNmjUL\nFizw9/evaYHL5dbz4mgGgFwur2udlDqyibAJAFDB7NmzZ//999/oaT30DagCgaCm2ZobcQF3\nsyoMS63eMHLaxGOTJk2ysbFp165drTYBABwOB9/GhqIogiCq9ZlRqVQTE5Om7uXs7Pz582fF\n169fv1abYNd46nqKYF39xM1ZRBCEIOMUCoVGo5WXl0+fPt3YXH/mhiAqFc81YbXeUnE0Tohd\n5D/GUQoCALB1thgY3uXmiVt3794dPHiwmrZlMhmhVYVCoTTeuI2NTWFhoeJrQUFBp06dVDtu\nrS8eUqlUJBIxmUwWC/+wcFKpVCgUqrng6d9//01JSfEY6NduYNdqPyEIQpAfU20MAUdEIhGK\norgYZ5gzAtdOPz1t0/Lly48dOwYAqKio0NfXJ8L34vF4AoFAX1+fiBbN5/NpNFqTzglpjp1Q\nKExOTpZIJElJSdXOhb29/f7/pjj09vbmcDjXrl2r1bGj0WgNDmnjXv8kEgnuNzVsCKypUisq\nKlatWmVtYxQ5yb/WVxwi3qvkcjku2c9qmq01pVg9mJkZbNoWMn3yscmTJ9+/f79mX5RMJpNK\npXQ6Hd/GJpPJEARRzaZq95Q+ffpcvnz55cuXvr6+9+/fLyws/OGHH+Ry+Z07d7p27aqCp6h7\nzJgxo7CwYPH+cUbm+mRr0UZCZvd5eOVNXFzcq1evCOrFIYXu3btv2LChrKzMzMwsJycnPT19\n1qxZKIoWFBSYm5sT5HxoDzKZbObMmVQmfciKSWRr0Ubc+3f2GtHj+PHj48ePDwgIIFuO5iCn\nhXO53BUrVvj4+EycOLHWR52yR2JiYlJX/xCLxarnRQoFgEKh1DX2pDIcDocImyiKNtXsggUL\nioqKtv0UbmRU+8OMiPcqsVgsl8uJMEuj0ZrqLXl6OSxNDExcenn+/Pnnzp2r9qtAIJBKpfr6\n+vg+yQQCAYVCIWgoqlbs7Ozi4uIOHDhQUlLi4OCQmJhoaGgoFot37969ceNGExOT9PT0ZcuW\nAQAkEsmaNWsoFEpISEhERITGFJLLiRMnLl26NDC8c8c+OrjwExcMTfXGxPY5tuHm7t2758+f\nT7Yc3PDy8ho0aNDcuXNbtWr19evXiRMn2tnZicXiGTNmbNy40dPTMycnB8sxzeVyT5w4ceXK\nld69e/fvryNhoXbu3Pny5cuBCeNNHCzJ1qKlDE2cnPXwzcyZM9++fUu2Fs2BELpIpC5WrFjh\n4eGBrVFXUFJSgs2TffHiRXJy8u7du01MTCQSyfLlyz08PKKjo5t6FN7atZTWrdnh4fgJBwAA\nDoeDex8J5tiZmpo2fperV6+OHDly4GDPjdtC6yojFApxH0EQi8VSqRT3+VKqOXYY61ZdvXzh\n5e7du2NjY5W3CwQCHo9nYmLS3B07DUDQZcUoKyuj0Wh1zfNVk48fP/r7+9MNwOYrMSw9PN83\n5HK5UChkMpkETScionliWPyVbvzh++dZfYHSa7NMKl8SvL+iUJiWlubg4KCycaFQiKKoyrM8\n66e0tJTBYDT1Fbe0tLS4uNjOzg6rYyiKvnv3zs3NTU9Pj8/nK09jAABYWVlZWzc2LJRUKuVw\nOAYGBlo4FPv582cfHx8DJ4vpv26k0KpX0a67LxR7OH0ZVH18FheIq7rYUCy+xt9cfnB50e64\nuLjExEQjIyPihmLNzMy0ZCiWBMfu9evXWHed4nbp6uoaFRW1aNEiPz+/sLAwFEX37t375MmT\n1q1b5+TktGrVavHixSrcR1S7RzSINjh2WVlZfn5+NJrk9MWZxiZ1npkW4tiJRNJJ437JyeY8\nfvxYeYYNdOwaTzN17FAUDQgIuPPnnaTjk9p2wTnnSvN17Oq6mukvclZFpowIHPHbb7+pbFwL\nHTvi0FrHTi6X9+vX7+GjR1MvrbP1qiW9igreQONpXo4dAODEpHVfH7+7fft23759W4JjR8JQ\nrLOz87p165S3YDV75syZWHtGECQ2NnbcuHHFxcVmZmaKaHYQjJKSkuHDh1dVVfx8aGI9Xl3L\ngcmkbdwWOiHsQFhY2PPnz2GUnJbDwYMHb9++PWxSd9y9Op2kbWenQRFdrpy8kpKSMmnSJLLl\nQFRn69atDx486B07plavDlKNwDXT9g5bOHv27JcvX+rYO3mtkJBSzNjY2Pt/cXFxAQC4ublZ\nWVkpipmamrq7u0OvrhpFRUWDBg36+DF9xeqRPh0dyZajLbRyNl+aFIhlHSBldgFE82RlZS1c\nuNDWxTxsvo5MmdIA4+MH2bQymzt3rs4nTtVhnj59unz5crsObn3m6nikJ7wwcbQasGjc+/fv\n169fT7YWTQBzxTYn0tLSfvjhhzdvXv+YGDgssAPZcrSLIcO8x4ztcvHixW3btpGtBUI4Mpks\nKipKIODPWD+CwdKdZZ5Ew2TT5yaHCEX8MWPG6HbuVF2loKAgNDSUymaE/DS/5tQ6SF10nTDE\nsbPHhg0bnj9/TrYWwoGOXbPh6NGj3bp1y8vL2bAlZHRIZ7LlaCMLE4a097b/8ccf79y5Q7YW\nCLGsX7/+0aNHo2b1dvWyJVtLM8O1ve3kxGHv3r0LDw8nNK8GBHd4PF5QUFBeft6obbNNHXU8\nMzi+IBRk+PrpCJ06YcIE4vKIaAm67NghFRUIkWlPNEZZWVlYWNikSZPMLViHT0wZMNiTbEVa\nCoNB3ZI81tiEFRYWBkeadJi///579erVHp0cR83sRbYWbYQmlDAq60tU1T+0U2C0/++//z5h\nwoS68mhBtA2BQBAcHPzvv/8O/jHKvX8D7/bsSh5DQFTat2aKiaPVkMTJ6enp8+bNI1sLseiy\nY2fq48OaOZNsFepy69Ytb2/vc+fOBY3ueOLcdHcP+JZWH1bWRlt3hnF5lcOGDSsuLiZbDgR/\nCgsLIyIiWPr0OdvG4JtkQmfosunO+D67EFl9k03HLx7UP7TT2bNng4KCiEvjC8ELDoczbNiw\nO3fu9Iod3T16eIPlk3rNGbk6hXhdzYyOof28AnscOnQoJSWFbC0EAuemaC8CgSAhIWH37t2m\npnrbfgrv08+DbEXNA+8ODqvXj1oafyE4OPjixYswMYMyPB6v1sUl8v9CxEGxfHG4zOgSi8Wj\nR4/O/54ftzvUyIKNhcsmaDwRO1FYFl2C7GMpZ4iwDAAQS8SorL7IDhNXBDD16NePXu/UqdOB\nAwf8/PwaYxxLKUbcOZFKpcpVhU6na2YZo0gkqln/sS0SiYSIJVlYrtjGpGd8//79uHHjPn36\n1HtuSK/Y0Y3sZMVOptoyNW2ZaNlDV08p+PB15syZrq6ujazzDYIJFgqFBOWKxapKte31RN1q\n3o4dlnm3rl+ZAKAoqpwiGhew6Fa42wQAKJt98eLFlClTPn782LNPm2WJw03N9FWo60S0EMXT\nDnezMpkML8eib3/3BfGDtm26FR4e/ttvv+Hr20mlUgRRMQAkgiDkLrav6+hYUyIurBqFQlHf\nOIqisbGxjx8/HjuvX+f/vudIJBIqlUrE/RS7mVIoFILi2MnlcoKyeyEAAQDQqDSU2sBpmbBk\nsLOnzZHVNwYOHDhhwoTly5c7OzvXv4tYLEZRlKBqLBKJqFSqclUhLlFvNWo9ENbMVU4hWD+Y\n8fotSySSn376ac2aNVIgD9oS6x3Us0mHIKJdYGYJsqywT5xxpgE7dG/ckdAVY8eOvX//foMV\nvjFggikUChHKsbNds57UU3Oat2OnkwiFwnXr1u3YsYPBpPy4YljQ6I5kK2qWhIb7CQSSvT/d\nHT58+G+//Qbj5mDU5UlgKYCJyyKKIIj6xpcsWXLs2LFeIzuMmtlLcQPFbnnEPQkoFApxvgVR\nlpH/GEcpDZ+WPsG+7bu6HNtw89ixoydPnhw1alRMTEy/fv3qOqVSqVT7q4oK1JoBXCqVYuFh\niXBksZ6YuizL5fLz588nJiZ++vTJtr3LqG2zLds0Lb4VgiAEvZNgb1NEWMa6DIgzjlUtq9aO\nY/cuOjl5fWBg4IMHD2xt1V2AhclmMBhEtGiZTNYMAhTjCJ1Or7U1YqAAIAiCeycEEUG3sWDu\nLBbr9u3bsbGxGRkZXbo6J64OsrNXq6tJUY9xBOtUI8IslUrFt1VETf6BRqPs3vFX7969L1++\n7OPjg4tZFEV1L/OE9rNq1apNmzZ16Ok2Y/1IQl/oWyAWdsZxu8ZmpX2/evDRpcsXz58/36ZN\nmxkzZkRHRzcpzyEEF7hc7rFjx3bs2JGRkaFnZjQ0KbrL+EEUYnydFouLv9eobXMuLtjZv3//\nP//8087OjmxFeAKnHmsL+fn54eHhgwcPLizMXb5yxL5fotT06iAAgJCwTus2jyko+Obv75+c\nnEzQxCAIoaAoGh8fv3LlSs+urRbuDqPR4ROOEFzb287bEbLn3oKw+f05/OJFixY5OTktWLAg\nPz+fbGkthYyMjHnz5jk4OMTGxhZUlg5aMmHe/d1do4ZAr44I2g/3H7Ul9lNGRs+ePdPT08mW\ngyfUlStXkq2BKHiGhvK+fekeOK85wL3HTiQSbd26dfLkya9fvxoR5LttV3jHzq1w6ZMgoscO\nmwlXT0epymZxH1DD5ki5e9j2G9DuxbOsU6cu/frrr/b29u7u7uocCJtjR8poEXEQdFkx1Mmu\ny+fzo6KiDh486NOrdfy+CCa7ukKZTEbQHDtsiqrKKYwbhIjmiSHUoxd62VT4OICmnxaWPqOd\nX6uhE7q5etkWfiu9dunmvn37OBxO586dseSz2KgTcVWFSqVqT3c4NqOawWAQcaXkcrlUKsWG\n2B49ejR79ux58+Y9ffrU2ttl0JLIEeumO/m1pdJVPG6Zif6XH7wqnWxwlfwfiKu62Ls3ccar\nWbZu28q6baunF26fOHbcx8endevWqlmWSCRSqZTNZhNxI5JIJE2d6aviHPBmAUH5pDkcDo6T\n8a9evbpw4cKMjAz3ttZLlgd28HHAyzIgZtSYoGzxYrEY9yeoVCoVi8UsFotCoUil8lPHnhw+\n+DeXK3J3d58yZUpERISjoyo52dRxU7QWgi4rRllZGY1GMzIyauqOaWlpERERb9++7R/aacrK\n4VRaLdVDLBbT6XSCFk8IhUImk0nQjB/iMqnjeDXTn+ec2/nX+3+zTUxMfvzxxzlz5mArh9hs\nQrJUE3TTVhmpVMrhcAwMDIi4UlKpVCgUpqamJiUl3b17l0qneY3o0X3yMBtPF/WNq5A5vvEQ\nV3VFIhE2K4kg47Xet7+lZpyL3corrli0aNHq1atVuLfzeDyBQGBmZkbES6AKlxI6dk0GL8fu\n4cOHy5cvv3//vomp3rSYXiOCffT0cL5XQsdO4dhhWyorhRfPPb947nnB9woEQfz8/EaNGjVy\n5EhPzybEfIaOXVNRwbHj8XhbtmzZuHGjHMiifgwYFNGlrpLQsasJ7lfz1YPMU1tu53wqcnBw\nWLJkyYQJE1Rw0xtDi3Ls/vnnnxUrVty+fZvGYnQOH/jDtBFGNuZ4GYeOXa3G67pvc4s5lxft\nznr4xsPDY/v27cOGDWuSZejYaQ7tdOy4XO7ly5d//vnnx48fM5i0seF+0dN7Y5esWazzaNaO\nHYZcjr58nn3nVtq9v9JLirkAAHd39zFjxoSGhnbs2PAaZOjYNZUmOXZfvnw5fvz43r17CwsL\n3Ts6TF870qG1ZT3loWNXEyKuJipHH/z6+vyueyX5FY6OjgsWLJg8eTLuQSJbiGP3zz//rF+/\n/urVqxQatVP4gF4zRxta47xIBTp2tRqv576NouiL03f+3HpKWMHr2rXrnDlzgoKCGlkVoWP3\nH1JTUz9//mxkZNSjRw99fX0VCjSIVjl2Hz58uHPnzh9//PHXX38JhUIDA2bQ6E6RE/0trQzB\nfyPYQcdOM46dArkcff8u/95f6Xf//JD9tRQA4O7uHhERMW7cOHd397rMkuLYSSSSx48fFxcX\n29ra+vv71/xHDRaoH/UvK5fLLSwsLCwsLC4uzs3NLSws/PbtW1FRUUlJSXFxsUQi4fF4pqam\nCIKYmpqamZmZmZmZmJgYGxvr6enJ5fKqqqrc3NzU1NTMzEwAgEMby1ExvX8Y3r5Bjw06djUh\nzk2XSmR3zj6/fuRp0TcOi8UKDAwcMWJEz549XV1dcbGvwk2b0KaBr2NXUFBw5cqVI0eOPH36\nlEKjdgju/UPMSEsXe/Ut1wQ6drUab/C+LeBUPf7l2vOTt4SVPCaT2a1bN19fX1dXVzMzM0xV\neXl5ZWVlcXFxUVFRaWlpaWmpQCDgcDhyudzCwkJPT8/U1NTa2trOzs7Ozs7e3t7JycnOzs7c\nXPW+2Gbj2B04cODFixf9+/fPycn5+PHj9u3bq73NN1igMZDu2BUVFd25c+fWrVu3b9/GVpbp\n6TG6/+DWb2C7fgPaslj/P/sYOnakOHbKfM4suvVH2q0b73JzygAAnTt3Dg8PHzNmjItL9fku\nmnfspFLp4sWL2Wy2j4/Ps2fPDA0NExMTm1SgQeq/rEVFRZ8/f87JySkoKCguLi4tLeVwOBwO\np6KioqKiory8vKysTCSqnpgSQYCRub6RqT5Ln85g0bGrIBFJ+VwRt0LAqxCIBP+TMYLBojm4\nWbbr6uw3sK1HZ8dG+mrQsasJof2vUqlULkPfPMy6eyH19d+ZUokMAKCnp+fg4IDdbFEU5XA4\nWGEajWZhYeHs7NyhQ4fu3bt369at/r/c1Js20U2jMY5dVVXVo0ePnj179v79+2/fvikySZiY\nmJiYmGDVvrKyMiMjIysrCwDAMtb3HdO328RhRnbmisUTuAMdu1qNN/K+LeGL0u88+3jnec7z\nD1WF5bUXQhA9U0OWkT7TgI0lzKBQKCKuQMDhCjjVc/SxWCzM27O0tLS0tLS2traysrKysnJ0\ndHRycrK3t6/nJtM8HLuioqKYmJgDBw5gMWPXrl3r4uIyfvz4xhdoJOWpqXQjIwM3NxzFg3od\nu9LS0s+fP7979+758+d///13WloaiqIUCuLlbd+1u2tXf9cOPo602iaAQ8eOdMdOQdrbvD+u\nv719Mw0bpfX29g4ICOjdu3eXLl2wOJaad+z++uuvS5cu7dy5k0qlisXiadOmxcXFKYfla7BA\ngyhf1tLS0jdv3rx79+7t27fv3r378OGD4jmtgMmms/QZegYsAxO2vhHLwJhtYKpnZKpnbK5v\nYmlgbGFgZm1kbKGP5XKt64yJhVIhXyzkiQAA+sZsfSNV6ip07GpCKeQgFXyZOyGhubBVsdjq\nQgFP/P6fr5lvvuVnlXKKqySi/ySkYbDodCYNACAWSqrK+UXfOJj/x2aze/XqNXjw4EGDBnl7\ne9e8ak117IhuGnU5dlKp9OXLl7du3bp169aTJ0/+E1aXQTe2NWca6gEAZBKpqIov4glEXAGV\nRmUZG5jYW9p5u7n16uDWswOVQQf/uyoWd1ifsmVG+hIbQgKz67xjp4yAw60sKOWXV6FyFABA\nZzNYRvp6poZ6pkbIfwOASyQSiUSiWBUrk0i5ReWVBWWVBaVVheUV+SXcEk5VYRmvrJJXUlnT\n7WMwGC4uLh4eHu3atWvbtm379u3btWtnYGCA/aqCY0dCyIa0tDQnJydFJoDOnTvfu3dP2W9r\nsEAjMendWzZoELh0CRfZPB6vpKSkoKDgy5cvVVVVWNdFSUkJ1hlbUlJSVFSkeFdDEODsYhES\n1qVrd1e/bi4GBjo1H0u3ae9t397bfkF8wKuXOff+Sv/7/qetW7du3boVAGBubu7m5mZtbW1p\naWlmZsZkMg0NDalUqpGRkZ6enpGRkampqbu7u7W1Nb6S3r175+vri7kXDAbDy8vr7du3yg+n\nBgs0kpSUlB9//LGgoAD7ilAQS3sT145Wdq6e1o6mFrbG5rZGRmb6BiZsXILJMVg0BotmZEZI\nx1JLxven+20uvDr3PrHBlGJqwtZndO7v3rl/nfMWMGRS+bfM4o8vc9Kefnn05MGtW7cAABYW\nFv7+/p06dZo5c6bKTUZjTaOysvLdu3efP39OS0t7+fLl06dPq6qqAAD6Fsaegf4u/l4OHd3N\nnW0RqraEhl08POHVyB5Xts0hW0izh21iwDY4X4g1AAAgAElEQVQxaNIuVDrN2N7S2L72ycFy\nmYxfWskrq6wqLMPcvrKcwtIv+Tfv3rly5QpWBkEQJyen06dP+/v7q6CZBMeuvLxcucfL1NS0\nvLy8SQUUCASCerJoG6Low4cPV/Xrp4JIkUgkEAhkMllVVRWXy62srKz1QEwmzdiYbWTMNrNg\ntfFwNrcwsHcwaeVs7tHWxlCp+6HmKFU1sH7TBos1FRRFcbeJZZ4gwqxcLse33+U/qdDFYtXM\ntve2ae9tEzuvb2FB5etX3zI+Fn7JKvme//n9+9dcbp1/f/v27dHR0XX9SqFQVJgbUF5erhwY\n3cTEpKysrEkFFFRVVdWakBdLKcZms82dWd4Duji2sXRoY2nvZsHSq+U1USaXykRNSxYsl8tx\nrzMKy7jXHAys/mARqnA3Dohpnv+xLEcBACKxqDEpxZpsHEXBf+ONNR4bFxMbF5M+YzrI5WjW\n2/z3/2SnP8+5c/fW1atXx44dq+ihQVFUIpFUVFQodmQwGPWEVsGxaXC53Jp/CvuzAoHg119/\nVbRrPTNDu86tu/i1dfZvb+3hpAgWKJZKQFNqCjZ+R1AdAIRWMMIsE/R8URgnWnYjb0R0Yz0T\nYz0Tl+pRBrlF5SVZ+aWf84oz8sq+5LNYrIqKCplMhiCIotvo/43Q6XUNnZHg2GHp3hVfa940\nGyygQCaT1fMrNtXj1atXjddmZGSkuDD6+vp0Ot3Ozk5fX9/Y2NjU1NTU1NTCwgLrsMEGyPEa\nkVSkmsbFmrJZgh54zUKqYpqBmpY9nEHv7v9jViaTcblc7EnA5XKx8Ro+n8/hcLp161ZPnVRt\nUA/HJoOlp6zrKAMHDhw48LkKCuuHoHoIjdeFvn4eAK+H2cYBAgaRcbhZOQAwFAAA5HJ5dna2\nlZWVco3F4kIrvtYfq1YzTxO5XN6xY8dt27a1atXK3d0dx/RTxNUBCoJ4sR2crYYSYZw42QQ9\nChXGCZUN1FduBYDX/2zAUjMjCFLzvl3PFCMSHDtzc3PlHrjy8vJqC0YaLKDAwMBAMQ5dE5RC\nCQwMLMdpKFYBvgGKFTZRFMU9LSMRUrlcrlAoVGeNT11mWSwWvgHHBQIBj8czMTHB3SyFQsF9\nvLUezM3NlWe5lZeXt2rVqkkFFNRVx7Qn3ElTqaqq0tPTI2IaHNZ1ZGBgQNB8SiKaJ4aESgUA\nmJubE+HYYYmt8QpQbGn5P8NVTZ1jh2PTMDY2rrkRe2fT19f39fX19fVtpKpGggUorucRpiYU\nCgX3GzUGcVW3oqJCLpcTlKG4oqJCue8GR7Qt3AkJEwK8vLzy8vIU83iePn1arcE0WAACaVH4\n+Pi8ePEC63jg8/lv3ryp1iIaLACB6CSwaUAgNSGhx87CwiI4ODgxMbFv375ZWVllZWWBgYEA\ngLVr1/r4+IwYMaKuAhBIy6RHjx6///778uXLO3To8M8//3Tu3Ll9+/YSiWT69OlLly5t06ZN\nrQXIVg2BEA5sGhBITUgLUPz69euMjAwTE5OePXtiM2dv3bplb2+vaHU1CzQV6fnziK0ttWdP\nPHUDIJFIcE+ALZFIUBTFfd07EVKlUqlMJsN9cEoqleKeyh2bNMNgMHA3iyAIQYnh60IqlT56\n9KioqMjJyalr164IgshksnPnzg0cOBAbzKpZoEn2scUTBMX1wNavEJQ2noiagyGXy7EWRNC1\nJqJ5Ysj+/VeemUmPiAAEnBas94u4qkKhUJo0d4LQpoGiKBaJiYj/i83/IyjhveT0aYqLC7V7\n94aLqmCcsKpL0KNQYZy4u5BMJsP9WYOhwhNHl1OKQSAQCAQCgbQotCXoDgQCgUAgEAhETaBj\nB4FAIBAIBKIjQMcOAoFAIBAIREeAjh0EAoFAIBCIjgAdOwgEAoFAIBAdgYQ4dgRRWlpaXFxs\nY2NTV0TsBgtoDD6fn5uba2RkZGtrW1eZr1+/slgsG5vqueQ0jEwm+/LlC4VCcXZ2rnW5tUQi\n+fbtG4Ig9vb2BK0kbyQNShWLxd++faNSqQ4ODgQFa4BgpKenSyQSxddWrVrVTDvRmFZACnl5\neQKBwM7OrmYejvLy8m/fvim+stns1q1ba1ZdLTRY8xssQBZlZWUlJSUWFhZmZmbVfhKLxR8/\nflTe4unpqbFm2+zqQDN6/CnD5/Pz8vL09fVrzdLWmNuI5snNzVXOZWJhYVHzDkZ6i9MRx27/\n/v0PHz50dnb+8uVLcHBwSEhIUwtojHv37h04cMDV1bWgoMDFxWXJkiXV7lYCgeDw4cN37twJ\nDg6eOHEiWToBAF++fFmzZo2ZmZlcLhcIBGvWrLGwsFAukJ6evmnTJktLS7lcXlJSsnz5crLu\ncQ1KTU1N3bFjh729vVAo5HA4y5cvd3V1JUVqS2DlypXOzs6K8JPh4eHV7sgNtgJSqKioWL16\ntUAgMDc3z8jImDJlyqBBg5QLPHjw4NKlSy4uLthXGxsb0h/qDdb8BguQAoqiu3btevHihbOz\n8+fPn/39/WNjY5ULfPv2LSkpqUOHDootbdq00UAlaY51oBk9/pS5cePGiRMnXF1dCwsLDQ0N\n16xZU82NbvA2QgpHjx79/v27IhVe9+7dqzl2WtHi0ObPy5cvJ0yYUFZWhqLo9+/fQ0NDc3Jy\nmlRAY1RVVYWGhr558wZFUZFING/evOvXr1crs3nz5l9//XXTpk0pKSlkaPx/4uPjT506hX3e\ns2fPpk2bqhWIiYm5fPky9nn//v1JSUmalKdM/VLlcnlUVNT9+/cVBVatWqVpiS0GmUw2cuTI\noqKiugo0phWQwp49e9asWYPFan78+HFoaCj2WcGJEyd27txJkrraabCRNliAFB48eDBp0iQe\nj4eiaFlZ2ejRoz9+/Khc4NWrV1OnTtW8sGZXB5rR40+Z0tLSkSNHZmRkoCgqk8nmzZt34cIF\n5QIN3kbIIiEh4c8//6yngDa0OC3qlleZZ8+edevWDUsbbGNj4+3t/c8//zSpgMZ4+/atmZmZ\nt7c3AIDBYPTt27emktjY2KCgICICWDeJqqqq9PT0gIAA7OvgwYOfPXuG/m8465UrVw4bNgz7\n7OjoKBKJNK0SANA4qVu3bu3Rowf22c7OTiAQaFpli4HL5aIoymaz8/Pzy8rKahZoTCsghfHj\nx8+bNw9reg4ODlKpVHkkCADA5XINDAyqqqpyc3PFYjFJMv+fBmt+Y5oGKXTp0mXLli1YD42p\nqamenl61uwd2qsVicW5uLp/P15iwZlcHmtHjTxkjI6P9+/djnZ0UCsXOzq5mBaj/NkIWWAUo\nKSn5/v27XC6v9quWtDhdGIotLCz08PBQfLWysiosLGxSAY1RWFio6MIFAFhaWtZUUnNWBykU\nFhZSqVTF3BcrKyuRSMThcLAbBIa1tTX2QSgUXrt2bfTo0SQIbYRUBEEsLS1RFE1LS8vLy7t+\n/XpMTAwpUlsCXC4XALB06VIDA4OCggJbW9slS5YYGhoqCjSmFZCCsbGx4vOFCxd69+5dLbUR\nl8tNS0tLTU3V09PLzs6ePn16//79NS7z/2mw5jemFZMCm81ms9nY5zt37hgYGLRt21a5AJfL\nLSoqWrBggYmJSVZWVt++fWfMmKEBYc2xDjSXx58yNBpNMYM8Ozv79evX48aNUy7Q4G2ELLhc\n7qFDh4yNjQUCgVAoTEhIUB6L15IWpwuOnUwmU56fiCCIVCptUgGNIZVKlZVQKJRqr4PaA5af\nTvEV+1yrWg6Hs27dOh8fnwEDBmhOnxKNlCqRSE6fPl1aWtqmTRt7e3uNStR1uFxuaWkpAIBO\npxsZGcXFxXXs2NHY2FgikaxcufL48eOzZs1SFNaqVpCXl4fdDSwtLbF3KplM9vPPPxcUFCQl\nJVUrHBAQMHDgQB8fHwBAamrqmjVrvL29lZ1UDdNgzW98KyaL27dvnzlzJikpqdrSq/bt28+a\nNatbt240Gq20tHT+/Pmenp69evUiQkNzrwPN5fFXK58+fdq4cWNMTIyDg4Py9gZvI2QxdepU\nR0fHVq1aAQCOHz++bdu2ffv2KX7VkhanC46doaFhZWWl4mtVVVW1NVYNFtAYhoaGVVVViq+V\nlZXaMBu0VgwNDaVSqVAoxOauYrJrvjBlZWVt2LAhODh4+PDhJKgEADRaKoPBWLt2LQDgypUr\nK1asOHDggOal6iqpqamnTp0CANjY2CQlJfXt2xfbTqfTe/bsefPmTeXCWtUKdu3aVVFRAQCY\nMmVKly5dqqqq1q9fb2Njs3bt2pqrvL28vBSfO3bsaGBgkJWVReJDvcGa38imQQooiv7yyy/p\n6embN282Nzev9quDg4PiSW9ubt6hQ4ePHz8S5Ng19zrQXB5/Nbl3797x48cXLVrk6elZ7ScD\nA4P6byNk0bNnT8Xnfv36nT9/ns/nK8bZtKTF6cIcO3d39w8fPmCfseE2d3f3JhXQGO7u7tnZ\n2YopI2lpacqd5FqFjY2NoaHh+/fvsa9paWlOTk6K0ROM7OzstWvXxsbGkujVgUZILS8v37Jl\ni+K0t2vXrrCwUKv6LZo7vXr12rdv3759+5KSkrKzs69cuaL4qaioqNowhFa1go0bN2LKu3Tp\nIhQKExMTO3bsOG/evFpj91y9evXLly/YZz6fz+VyyR3TbLDmN6YVk8XevXsLCws3bNhQ06sD\nADx79uzJkyeKr4WFhcS5I826DjSjx1817t27d/bs2Q0bNtT06gAADd5GSIHP5586dUooFGJf\ni4qKWCyWFrY46sqVKzV8SNyxt7c/f/58ZWUljUY7d+5cZWXltGnTUBTdsWOHk5OTkZFRrQVI\niS5jamr64cOHp0+fmpmZPXny5ObNm3PnzjU0NDx9+jSfz8feUD99+lRSUvLs2TMAgJmZmUAg\nUJ72oTEQBEFR9Pz58zY2Nl++fDl06NCECROcnZ3//vvvZ8+eeXp6oiiKDUO4u7uXlpaWlpaW\nlZXVeo8mXSqLxTp//vzr16/NzMy+f/9+9OhRFxeXfv36aV5qS0AikWzZskUoFDKZzGfPnp07\nd27atGk2NjaKSl5XKyBbODh27FhpaemwYcNK/4u+vn5eXl5KSkr37t0BAA8fPrx48aK1tXVp\naenBgwdNTU3Hjh1L4jqnBmt+XQXIEqzg2bNnZ8+enTJlCjaIX1paiiAIk8lU3LS/ffu2Y8cO\nExMTsVh85cqVt2/fxsbGKiJfEEezqwPN6PGnTFlZ2apVqyIiIlgsFnaesSed4i5R122EXNk0\nGi0lJSU1NdXc3DwrK+vgwYMBAQEdOnTQthaHaMMKKfUpKCi4ePFiYWGhk5NTaGiosbGxVCpN\nTEycMWMGNhZeswBZUgUCwcWLFzMyMkxMTIKCgrBoavv27Wvbti3maixfvlx5rU2rVq00M2u4\nJiiK3rp1659//qHRaH379v3hhx8AADdv3szLy4uOjubz+djIpgIKhVJti5ZIBQBUVVVdu3bt\n8+fPCIK0bdt2+PDhGnhItFhyc3OvXbuG9bIEBARgHXLKlbzWVkA6O3furDavfMaMGSKR6Nix\nY1jFlsvlN2/efPXqlVwu9/DwGDlyZLWZ9ZqnwZpfawHSuXr1qnKHHABg0KBBvXr1Ur5pv3z5\n8t69e1VVVQ4ODkFBQZoJBtYc60AzevwpePfuHTZ5Q0Hr1q2jo6OV7xK13kZIp6qq6sqVK58/\nf2az2X5+fth4sba1OB1x7CAQCAQCgUAgujDHDgKBQCAQCAQCoGMHgUAgEAgEojNAxw4CgUAg\nEAhER4COHQQCgUAgEIiOAB07CAQCgUAgEB0BOnYQCAQCgUAgOgJ07CAQCAQCgUB0BOjYQSAQ\nCAQCgegI0LGDQCAQCAQC0RGgYweBQCAQCASiI0DHDgKBQCAQCERHgI4dBAKBQCAQiI4AHTsI\nBAKBQCAQHQE6dhAIBAKBQCA6AnTsIBAIBAKBQHQE6NhBIBAIBAKB6AjQsYNAIBAIBALREaBj\nB4FAIBAIBKIjQMcOAoFAIBAIREeAjh0EAoFAIBCIjgAdOwgEAoFAIBAdATp2EAgEAoFAIDoC\ndOwgoLi4+PXr17X+lJ+fn5qaWlxcrGFJEIg2k5qaWlZWVtevpaWlT5480aQeCAQCUQAdOwh4\n8ODBokWLqm2USqUzZswYNWrUli1b+vXrt2zZMlK0QSBaSGxs7L///lvrT3K5fNq0aWFhYRqW\nBIFAIBg0sgVAtAUURT98+MBgMFxdXSkUyunTpzMzMx8+fEin0/Pz8/39/UNDQ319fcmWCYFo\nC6Wlpbm5ua6urkZGRoqN+/fvp9PpJKqCQMhFLpdnZ2fz+XwPDw8ajQYAyMzMZDKZZmZmHz9+\ntLW1tbW1JVujjgMdOwgAAEil0sjISDqdnpGRYWtre+bMmeDg4GHDhmGPKBsbGzabzePxyJYJ\ngWgLN27c2LVrl5mZ2b///rt79+5BgwYBANLT048fP37w4MGhQ4eSLRACIYGioqLJkyczGAw2\nm/3169eTJ0+6uLjs3bv3+/fvNBrNzc3t5s2bwcHBCQkJZCvVZaBjBwEAgE+fPiUnJ/v6+goE\nAn9//99//z0oKAgAkJubm5mZeeHChc6dO3fv3p1smRCItsDhcK5du4YgyIEDB9asWTNo0CCp\nVDpv3ryNGzcqd+BBIC2Kbdu2ubu7JycnAwC2bNly4cKF+Ph4CoWSnZ394MEDGo0WGRnZv3//\nqKgo2G9HHHCOHQQAAGxtbbFhVjab3alTp7S0NGz7/fv3k5OT379/P3z4cCqVSqpGCESLGDJk\nCIIgAIAePXp8/vxZKBRu3brVz8+vd+/eZEuDQEjj6dOnvXr1wj7Hx8fHx8djn7t3744Ny7Zu\n3VpfXz89PZ00iS0A2GMHAQAAAwMDxWcWiyUQCLDPkZGRkZGRHA5n6NChTCZzzJgxJAmEQLQL\nRZNhsVgAgGfPnp07d+7gwYNpaWmFhYUAgLS0NGdnZ319fTJVQiCapbKykslk1tyuvFH5EQMh\nAujYQQAAoKCgQCqVYm9UeXl5Xl5eR48epdFo48ePBwCYmJi0bt06MzOTbJkQiLaQk5ODfcjL\ny2Oz2UKh0NfXd8+ePQAAgUAgl8u3bdsWHx/frl07UmVCIBrF2dk5KysL+/z+/fucnJwhQ4YA\nALKzs7GNfD6/rKzMzs6ONIktAOjYQf7Dhg0bIiMjX7x48fr16127dqWlpc2fP59CobRt2/b5\n8+ePHj2Ki4sjWyMEoi38+uuvHTt2tLS0/Omnn4KCggYNGoStnwAA5Obm9ujR4/Dhw+QqhEA0\nT3R0dGJiYrt27QwNDZcuXRoeHo5tT0tLO3v2bLdu3VJSUhwcHDp06ECuTt0GOnYQYGZmFhIS\n0q1btzVr1jCZzNOnTzs5OTk5Oenp6V26dOnatWs2Njbnz5/v2LEj2UohEK3Ax8cnIiLijz/+\nyMrK8vPzmzdvnvKvTCYTrjSCtExGjBjBYDAuX77M5/MnT56MjfkAAEaOHFleXr58+XJbW9uz\nZ89SKHB+P4EgKIqSrQECgUAgEIhusmjRIn19/VWrVpEtpKUAvWYIBAKBQCAQHQEOxUIgEAgE\nAiEKNzc3bPE4RDPAoVgIBAKBQCAQHQEOxUIgEAgEAoHoCNCxg0AgEAgEAtERoGMHgUAgEAgE\noiNAxw4CgUAgEAhER9DSVbHr1q3j8XiKr5GRkZ6eniTqgUAgEAgEAtF+tNSxe/HixcKFCy0s\nLLCv9vb2De4iFAoZDIZq8azlcjmfz2cymXQ6XYXdAQB8Pl9PT0+1fSUSiUgk0tPTU1m8RCKp\nNe9yo46+dSv4/p2+bZuKu0skCIJgSWZVgMvl0mg0lVfCCwQCFouFIIgK+8pkMmx3lcWrc9HJ\nQiAQoCiqjmw1/zWWR1VfX58sAZIdO8CXL/SdO1W2IBAI2Gy2yrsLhUKZTKbOGVCn2mMCpFKp\ngYEBWQJEIpFEIlFHgCaRyWQymYzBYKhvSrJiBaDR6ElJ6psSiUQ0Go1KpappB0VRHo/HYDBw\n+YN43RKlUqlQKGSz2bj8QcyU+qpwrLdSqVQul+NyzmtFGx077L7j5eVlbGzc+L0kEonKbplc\nLhcKhVQqVWULYrFY5QqtqMSq7a6mY4fcuEFJTweqOnZSqZRCoajsGwmFQnXiG2F/XLUHDHbR\n6XS6yuLVuehkIRaL5XK5OrLV/NdisVhNt0ZNAcitW9Rnz4Aajp1IJFLnOSGRSMRisZpnQM1W\nIxaL1Xk+qS9AKBRqm2OHNY2a27FncK0/NRX65csonS5MSFDfFI6OHfamgcsfFAqFuOQKw56J\nCIKo/wcxVSq/hCiDOXYqPy+UkUqlKIqqec4pFEpdrqE2OnZcLhcA8Pjx4/fv37NYrN69e3t7\ne9daUi6XK+LwoSgqk8lUOyJ2fuVyucoW1D+6Orurc3RRTAxaVsYmSTxQ79Rh+6oWi5GUi44g\nCEySSC7iadPQESNU96ogEFURxsejFIo2PnQhuoU21jGRSGRra1tYWDh48ODc3Nw1a9YsWLDA\n39+/Zkk+ny8UChVfxWKxOscVCAQCgUDl3cvLy9U5emVlpTq7i0SixhRDUTQ3N7e0tJROpzs6\nOhobG4N+/QAAQvXEK0+IbCoikaiR4muloqJC5X0BADweTx3xTb3oVCrV1NRU5cNB1Ec6YIBU\nKtUGx66oqCg7O5tGo7m7u6vTgQfBhbo6P8RisVQqxSVxAicoCABggIcpbHRY5SEmBXK5nMfj\nqTMfRhnlEZjy8vKsrCwURR0dHa2trZtkRyQSYXOr1P+DKIqKRCJc/p1UKpVIJLiYEolEcrmc\nuGwc2ujY2dvb79+/H/vs7e3N4XCuXbtWq2PHYDAUXbUikYhOp6s8TU0gEKhTjdSZdoONjLDZ\nbJXFS6XS+kfrURS9e/fuqVOnbt68WVpaim2kUChdu3adMmVKUFCQysMias6x4/F4dDpd5akG\nIpGIwWCoPMdOKBQymUyVxatw0XEZEYA0ayorKw8fPnzhwoU3b95gW5hM5pgxY1atWtW6dWty\ntUEgasLlclNSUo4ePfrixQvFWIq3t/fUqVOnTp3a7OauNFO00bEDAMhkMoXHZmJiUldHmrJD\ngL1UqTYkL5VKBQIBnU5X2TlTc9oNNmdFZfEoitZ1dLlcfurUqQ0bNrx//x6hUlp17ejZeaSx\nnY1MIil4/+n1zXvTpk07cODA6dOn3dzcVBNPoVBUnuHH4/GoVKo6PjGLxVLNIcYm+jAYDJXF\nq3nRIS0NLpe7ffv27du3V1RUGNvZdZs02cLNTSaVfn3y5PSZM5cvX967d++kSZPIlgmBqIJI\nJNqzZ8+OHTtKSkoMLG06hUVbe7RHEErJl4wPt36bN2/eli1bkpOTQ0JCyFaq+2ijY/fixYvk\n5OTdu3ebmJhIJJL79+97eXmRLapZ8uDBg7lz575+/Vrfwqz/wpiOYSMNLM2VCwxNjHuw7+iT\n/Sf8/PwuXbrUt29fkpRCILqMTCY7dOhQYmJiYWGhTfv2/des9R4eiPz3haTbpMkF799fmj93\n8uTJRUVFixcvJlctBNJUbty4MXfu3MzMTAs3jzHLtnoOGUWh/r93MXjJ+rdXz/25LSk0NHTc\nuHH79u0zMjIiUa3Oo42OXadOnfz9/WfPnt26deucnJxWrVpFRESQLaqZwePxFi5ceODAAYa+\n3oDFs7pNCqOzaxnOp+uxe86e7NzD7/LsZcOGDbt69eqAAQM0rxYC0WEePnwYGxv75s0b01at\nQnbvaTNwkEwuR/63m9nG03PKxcsnoyclJCSYmZlNnTqVLLUQSJPgcDhz5sw5ceIE28QsYMW2\nbuOmITWGnihUmk/wuLYDAv9YF3/q1Innz59fvnwZxqYlDm107BAEiY2NHTduXHFxsZmZmSKa\nHaSRpKenjx49+sOHD+2G9hu2cpGBVX0n0CL7mxWbZXJuf0r4zFGjRv39998+Pj4akwqBaBhK\nVha1shL07q2BY1VVVcXHxx84cICupzfox6XdJkdT6fS61ngxDQ3HH045MjZ01qxZnp6eP/zw\ngwYUQjQJ9eNHgCCga1eyheBGamrq6NGjv3792iEoPGDpZgpbv6ZXp4BpaBS0cb9L975XE+f4\n+/ufO3cuICBAk2pbDtobecHU1NTd3R16dU3l/v37/v7+GVmfR25aPnbvxvq9OgDAyNXJ0TMS\nzF1bjU/ZIZLLgoKCSkpKNCMVAtE8rKVLjcaM0cCBnj9/7uvru3//fo/Bg2ff+euH6TOoDa3N\nYhoahu0/QGWzx40bp+Zyb4gWojdlil5MDNkqcOOPP/7o1atXfnFJ6M7jo7Yc0jM1b3gfADoE\nR0Sfuo2y9AMDA1NSUgjW2ELRXscOogK3bt0aOnSohIpMOvtzx7EjmrSvjaf7qG1J2Tk5kyZN\nUi0yHAQCwTh06FDPnj3ziopGJ+8M+/mAoY1NI3c0dXIKXL8hOzs7Li6OUIUth7y8vGXLloWH\nh0dHR//+++9ky9ERbt68GRwcTDEwnnL2rufQ0U3a19ar49Tz90ydW0dHR+9UI1Q4pC60cSi2\n8SgHBFcnVi0WZhaLG6KaEhRFVd5XEeNXZfGY8ocPHwYHB9OM9Cec2GPu6tSkqNZYYfdBvbtO\nHPt7ytl9+/ZNnz69kTuq898BHqddtVWx2EWXyWQavui4BC6HaC0oiiYkJGzZssXS3T18/wEz\nZ5emWmg/PPDDjRtHjhyZOHFib40MGeswUql09erVQ4YMWb169devX5OTk7t27WppaUm2ruZN\nampqSEgIw8R88qlbpo5NruEAACNbh8mn75yIDpo/f75IJIILhvCleT9jJBKJRCLBPstkMpFI\npHJ2Kcyayj1VWGIW1fbFPAx1xMtkslevXgUHByNsZkTKDmMnu8Y7HNgfVpTvEzc96+E/ixcv\n7tu3r5OTU4O7S6VSBEHUSY2CxZNTbV+5XK7+RVdZvAoXXZ2YfxDtRywWT5o06fTp06379A3Z\nvYepanjIIYlJmffvzZ49OzU1FZesSjmfrpMAACAASURBVC2W1NRUAMCoUaMAAG5ubrt37yZb\nUbOnrKxs1KhRYhRE/3JZNa8Og21sGnX095NTgxMSEqhU6sKFC3EU2cJp3s8YJpOpCEJWVVWl\np6encig4sVjMZDJVDkvG4XBUjvErEAgkEok64vPz88PCwvhi0cTTe23btmnS7phPpIgIyGAw\ngjavOBwyLSEh4erVqw3uLhAI1Iljh2VrVfnUVVZW6uvrqxzHDgsfqLJ4dS46RPfgcrmjR4++\nfft2x7FjA9dtoKjhwRtYWfWeM/f2hvWHDx+eNm0ajiJbGpmZmY6Ojnv27ElNTdXT0xs1alS/\nfv1qLVl/rliVXz6VoavXBVBNFVAvlyMG1peB5WZt5C6TJk3KzskZk3zM3K1ttR6Epg5iUFns\n8P0XT00Njo+PZzAYiqqOGcHySjfeWq1gKVlxOeeYGLwun/o1oZnlioU0CZlMNnHixKwvX0L3\nrLf3aa++QYeOXl0iR187duG3334LCgpS3yBEe8BG/DkcjjoW1NydXAF6cjkFRSvUsCCVSmsK\nKCsrGzt27IsXL7pPm95nQZxYKgV1POFQFG3MPd0nPOLfY0cTExNHjBhRLfWQTCZTZ2kFLpdA\nHQGY/9RIAQwGQ510BVwu982bNytWrIiNjU1LS0tKSrKzs/Pw8KhZks/n1+OUqJmvEsMERcF/\nk6Grj2K0ChdTjbR28uTJq1evdhwb7dp3SK3npMknis4Ys/v0manB8+fPZzAYY5QWNqmT4bMa\neJ1zfE2pk0sTAMBgMKBjp7MkJibeu3ev1+zJnkP7q7D73ZgoRlX1mtp/Ycz73/9csGBBQEAA\ncfnsIJqHSqXK5XITExOVLXA4HHV2r6iokMlkJArgxcWJS0rwFZCTkxMYGPghPX3wsuX+Uxvo\nYMP6IRpuVixWvwVxvy5aePr06QULFij/UlFRYWRkpHJ6uqqqKrFYrOZFVEcAl8sVCoXqCGg8\nhoaGrVu39vb2BgC0b9++c+fOL168qNWxMzQ0rHUqjkQiadT1agTCNWsAhYLLH8dSJak/r0Mu\nl1dWVjZytCovLy8pKcncuXXA0g302k6IWCxWIT8ki8WKPHzl6PjBc+bMsbGxGT58uFgs5vP5\nBgYG6v9BFEV5PB4uQyt8Pl/NhqNALBajKKryYBFGPQ1Q2x27gwcPUqnU6OhosoVoKVeuXNmy\nZYtLz679FjRqrUNNvvj5yOXyam2aZWQ4IH7WlSXrtm/fvnTpUvV1QiBagqx7d3XW+tTk1atX\nw4cPLygqCt66zWc0noFUOgSP+nvvns2bN8fExMDkdaphb2//999/K76iKFrXjJe6tmPrw3CZ\nGivt3x/gtHyKQqFQqVRcHDvMWmNMzZ07t7KqatLPF5h6dfpJqk2MMbS0npBy7XDEwIiIiGvX\nrvXo0QMAgMsfRFEUr5nN2F/DxRS25JG4+dZaHe7k7t27f/7555cvX8gWoqV8/fp10qRJhtaW\nQVsTEZWaUz34hgbatPfYuHFjYWEhvpYhEJ3h999/7927d0lFxbhDh/H16gAACJXaa1ZsQUHB\nkSNH8LXccujWrVtlZeWdO3dQFP348eOrV6+6dOlCtqhmycWLF69cudIlYqpTZ0JCZxvbOUYd\nvUbRNxw5cuSjR4+IOETLQXsdu5KSkpMnT44e3bQAOS0HsVgcFhZWUVk5+qc1bFNj3O0jFErA\nsnlVVVVJSUm4G4dAdIBt27YFBQUh+gaTz11w692HiEN4BwUb29lt3boV317GlgODwVi+fPn1\n69cjIiJ27Ngxe/ZsNzc3skU1PyorK+fOnWtoZTtg4WrijmLu3CYq5XeUyQ4KClLuZ4U0lYZ7\nAkNDQx0dHadOnarJzG4oiu7cuTMyMrL+RTFYJ7liF5VX0GDd0VjcENUsqH/0pu4eHx//77//\nDkyIdejkrXIMPPDfVVG17t6qeyf3AT0PHToUGxtb19VXTXw1AWqedtX+OykXHUEQ1YYqIFoF\nj8ebPn36qVOn7H07hv2839DamqADUWi07lOm3lyz+tKlS2PHjiXoKLqNh4fH9u3byVbRvPnx\nxx/z8/PH7jrJNDAk9EBW7p4Tj904PikwIiLi2LFjsM6rRsOOXefOnZOTk5OTk/39/adOnRoW\nFqavr0+0rGvXrhkYGPTt2/fPP/+spxifz1deXKbmwiWBQKDOMpzy8nJ1jl5ZWdn4wteuXdu1\na5drn+4dJ4zBVtao+UJf1x/vOX9q5r0nixYtOnnyZD2783g8lQ8tEonUWRykZuYlHo+njvim\nXnQqlWpqaqry4SDawKdPn6Kjo9PS0nxDQoevXUdTbwZ0g3QKC7+/c0dycjJ8yEFI4dGjRz//\n/LN7/2HtAoI1cDgrd8+okzdPTQmKiIjIy8urtnII0hgaduyWLFkSHx9/9+7dM2fOxMfHz58/\nPyIiYurUqX5+fgRpysvLu3r16rZt2xosyWAwFDNeRSIRnU5XrTtELpcLBAIGg0FvKJljXQgE\nApVnN2MB1dhsdiPFZ2Zmzps3z8jOOnhbEoPJxOL0qBzF1OrtBwaP/61751p/tW3bxnfsiFun\nf3327Fnfvn1rFa/O1FQej0en01VYSIUhEokYDIZqq/OwwMhMJlNl8SpcdJUXEkLwgvr2LSgv\nB4GBqu1+6NChuXPniuXyERs2dgqPwFdbrTD09TuFhT8+eOCff/7p1q2bBo4IIQja8+cogoAB\nA8gW0gSEQuHUqVPpevrDV+7Q2EHNnFzHHb3x64IJcXFxX7582bFjBxzoaBKNeqRRqdSBAwcO\nHDhw3759t2/fPnnyZL9+/dzc3ObMmRMVFaXyU7kuTp8+TaPRfvrpJwBAcXFxaWnpunXrli5d\nWvOhqHxoqVTKYrFUjvGLrR5X2TkTiUTqLFvDIuU2RjyPxxs/fjxPKIw+vtPQ3AyovWhr6Lb9\nVplfk59eq6tA/7gZ767cWrZs2fPnz2ttXeoEKObxeFQqVR2fmMViqRygWCgUMhgMlcWredEh\npMBct4767BkoLm7qjhUVFdOnTz937py5q2vUnr3WbdsRIa9W/KImPjl8aNeuXdCxa9aw588H\nDAZ4+ZJsIU1g+fLl6enpgWt2GdnYa/K4+uaWUceuX140ZdeuXcXFxcePH4c5expP056Icrmc\nz+eLRCKsi2v+/PleXl6vX7/GV9OECRMWLVoUERERERHRvXt3a2vriIgI2NUBAEBRdOrUqW/f\nvh2SuMCug4amPOpbmPWcOTE1NTUlJUUzR4RAtI2XL1926tTp3LlzncLCJ164pEmvDgBg4uDg\n3n/A+fPnCwoKNHlcSAvn7t27ycnJbj0HdBo7WfNHp7P0wnad6hQ66cyZM5GRkeokrmxpNNax\ne/78eWxsrK2t7fjx42k02vXr1z99+vTlyxcPD49hw4bhkmRDgbW1tet/sbS0ZLPZrq6uONpv\nvmzcuPHMmTOdwoK6jNfoYmH/qeNMHO2WLl2q5oQ2iAbIyMgoKioiW4VOcebMmZ49e34rLAzZ\ntXvExk10Mrppu06cKBaLf/nlF80fGtIyKSkpiYqKYhmbBm3cT1bHCkKlBq7d3Wns5LNnz8bH\nx5OioTnSsGOXkpLi5eXl5+d348aN+Pj43NzcM2fOYNOtLC0tT58+XVpainunnYJOnTrB6MQY\nFy5cWL58uWMXn2GrF2n40DQmI2D5/MLCwhUrVmj40JAm8erVq0WLFt29e5dsIbpDcnLyuHHj\n2FbW03690j5wBFkyXH7oYeHmduDAAfWzZ0IgDYKi6MSJE7/l5Y1cv8/QypZEJQiCBK7a6d5v\n6Pbt20+fPk2ikmZEw4PWJ06ccHV13bJlS0BAQM3JTAYGBiEhIVZWVsTIA6ampnAVIQDg77//\njoqKMnawDd+/iYr3pMbG0HZwnzb9fti7d29kZGTXrl01LwDSIDweb+/eve3aaXSUULfZvHlz\nQkKCvW/HcYcP65makagEQZAu4yP/WL3q6tWrdaWxh6gDn8+vdbAPC6iEyzggGwAURXFJNiqV\nSmUymZrJRsF/A11hGa6Ut2/fvv369etdo2a59BrU+HATKIriklQXE4OtzMO2BG74+XBIn5iY\nGF9fX0dHxyZZk8lkuJxzLJ0uLqaw1zM1X9JoNFpdme4adux++eUXa2vrmpPE//zzz969e9Pp\n9BMnTqgjDtIgL1++HDFiBMJmjU/ZoWdGmps7bPXifQERU6ZMef78uZpJ7iBE8PPPPw8dOjQ7\nO5tsITrCL7/8kpCQ4Ni5c+TR4wziYzw1iM+YkD+3bN63bx907IiAyWQSnSsWAIAgCC6mcMwV\nKxKJqrkI9+/fX716tYNv10GL11Kacgi80mTJZDKZTEalUhV9STQTs+DNB49OGLJw4cIrV640\n3hSKotjCSvVVYUFPcTFFfq7YSZMmzZ49OyQkpNr2kJCQJ0+etG3bVh1laiKXyxUvUurEqsUc\nZ7lcrnI0OKz2qLavIsZvreJfvHgxbNgwoVw24WiyaSuHmi+OKIqq80J5Y+EMBq/2V9VqGNlZ\n94+f9ceqbT/++OPmzZsV4tX57wCP067aqljFO5OGLzpBa7sePXpUUlISFxe3c+fOeoph10ud\neI3Ywil1didXgGTJElBZSW/Iwl9//TVz5kwrD4+xB36hMJnKF1r9Cq9izdHTaz9i5O3z5zIz\nM9u1a6fytCes5qt/CTQjgEql4h54oa4D1bodx1yx3B07UAQx1O5csYWFhZGRkUwjk9CfTtAY\nTfY8cIlLgj0KKRSKsrVWXX7wGzf9xvF9V65caXxKqpaZK7Y+u8ePH8/Ozs7Ozr548WJ6erry\nT7m5uRwOh8/nEySrkUgkEqx3FACA9Uurdq/BKrdEIlEnf4PKK0iw21yt4v/666/IyEgJgoYf\n2mbZrk2tDwPMsVP5SZPXtjUAgNq43TtGBH366+GOHTt69OgREBAAAJBKpQiCqDNOgcWTU21f\n7HVTzYuusngVLjpet5hqlJeXHzlyZP369Q2eCuwppU5MZqBePGpcLKi1u6cnAEBcr4Xc3Nyo\nqCimicmoPfsoLFbN0SX1x5tUs9AhdOyrc2cPHTq0erW6mZ3UvATq3/wbKYDFYmnGsdMAUq1P\nU4tNrSssKhp34JKG45s0hv7zE9NuXFy8eHFgYKDO1AoiqO8Zw+Fwbt68mZ+ff+7cuWoPDD09\nvcjISF9fX4LlNQCTyVR0ZlZVVenp6akcx04sFjOZTJXDknE4HAMDA9X2FQgEEomkpvhdu3bF\nxcWxzEwmHkm28XSva3esx0udGL9yubzxu49OXnUgMGrq1KlPnz718PAQCATqxLETCoX/x955\nBjSRdX38phIITUC6gCiIUizYsHcFUVBQLNi7rF2xK/besfe21hUUdwVWbItlVxdl1dVVmihS\npIWEtMlk3g+zm5cHCYSZSSbA/X1KJrf8M/fOzJlbzuFwOIRPXVlZGZ/PJ+zHDncfSFg8mUan\nlujo6G7duon+o7S0NDc3186uiiXPbDZbqVSampoSrqusrIxMdqFQqFQqzcyIRzcmKUAkEqEo\nWo0AuVw+ffr00rKy8T9esqlqP75MJiMzhyKXy5VKJbEJHRdfX4c2bS5durR161bCEYDKy8sR\nBDE3NyeWHQAgFAqNjY0Jj9jhPrM0XDwNHV3pkoMHDyYkJPhNntu8R3+6tVSBgYlp77mrbq+Z\ne/To0Tlz5tAtR3+pzrCbM2fOnDlzevXqVeVULER7lJSUTJ8+/fr167aeLUYd225mb0u3ov+H\nb9lo5JGtZ0bN8vf3T05Ohltb9AEjI6OcnBx8y1hGRkZubq6hoeH48ePVpSccpwQAwGAwSGbX\ncwG4L+4BK1Y5d1C7SYiMtYHnJVxCx/ETYhYuuHr16tSpU8kIIN8EhP8CeQEQbZCVlbVs2TJr\n91Z9F0bRrUUtbUdMeHJq/+bNm6dMmWJkZES3HD2l5qGOBw8eQKtOl8THx3t7e1+/ft13dPCU\n68f1yqrDcWjtGbJ3fVb2p759++bm5tItBwIWLVq08j98fHx69OhRjVUHqYb4+Pi9e/e69e7d\nmajZpG1aBQw2srA4ePAg3UIg9Y2IiAixVBq05Qir9kvrdAaTxe4ZsTwvL+/IkSN0a9Ff1I7Y\nPXz4kMfjderU6eHDh9/UhN/p2bNn48aNtSGrpKTk5s2bnz59MjEx6d27d9u2bbVRi74hEAgW\nLFhw+vRpvmWjsKPbPQb0pFuRWjwG9greuSZ28fq+ffveunXL29ubbkWQf3F0dLS0tKRbRZ2k\nsLBw0qRJRpaWQTt26e0MINvAoM3IsCdHDicnJ3fr1o1uOZB6Qlxc3C+//NJx3Cx776rjhusP\n3oEjHh7csnPnztmzZ1O1W7meodawW7t2ra2t7eXLl9euXfvw4cMq09y/f7/KwPAkkclkS5cu\n7dChQ1hY2OfPnzdt2rRmzRofHx/KK9IrkpOTJ0yY8OnTJ8/A/gHrFunMrUnT56lcoSjLv09t\nM/oE+3MNDX+at7pnz56XL18eMGCANuRBagscX68e1rNnzMJCMGbM9z/NmDEjLz9/9ImTfP22\njNuOGv3sxPF9+/ZBw65WfPz40czMTHteV2uEfe8eYDJBcDBdAtSBIMjixYuNLKx6z6sDLugZ\nLFa36YturZh19uzZGTNm0C1HH1E7FXvx4kXcb8LFixc/qkFLEakLCwvbtm07bdo0Dw+P/v37\n+/r6vnr1ShsV6Q/R0dH9+vUrEJSERm8KPbBRl87qeh85N2zdbmJ5PQb2Cr8QjXCYAQEBGzdu\nhLH8IPqPwd69/Hnzvj9+9uzZGzdu+I4e496nr+5V1QoTW9tW/gGxsbFZWVl0a6kz6ENQFsM1\nawxJb2fWBmfPnv3w4UPPH5bzTInvatIlPkGjTG0ddu7cCQOxVInaETsHBwfVB6lUWlRUhB9J\nT0//5ZdfmjZtGhgYqCVNDg4Os2bNwj+LxeLMzEx1L6a47wb8M+7HjliNuEWCux8kVgLh2hEE\nmTZt2sWLFx1ae4Ye3GRmb1tblyt4esKOWioWQgD71q0mx5yKnbdm9erVjx8/Pnv2bG3nAck0\nHBnnhbQ0OoPBoMTPE4RasrKy5s6da+HSdODKVXRr0YjOU6a8ibu1f//+3bsJvpU1KGBQlmqQ\nSqVbt25t5OTaftQUurVoCovD7Tzxh8Sty2NjY0NCQuiWo3fU7FIrLy+vS5cu48ePj4qK+vDh\nQ7t27WQymUKhWLJkicpLrTb48OHD6dOn8/Lyhg0b1r179yrTiMXiio7ESDqXkkgkZJx2lpSU\n1DaLTCabPHlyYmJiq8B+gzZEsrgcwgLIeNkF5LyVGpibjji58+HuY/Hnrrdv3/7s2bO1WnIn\nk8nIBMYRCASE8wIAysvLyTj0qm2js1gsuI9Y31AqlRMmTBCViyedOcepI/vsHFq3cWrf4cSJ\nE2vWrCHjuKSBAIOyVMO/z9ntJ5hsDt1aakG7kZPwlXbQsPuemg27Q4cOWVhYLFiwAACwdetW\nV1fXJ0+e/P7774GBgatXrzYxMdGSMnt7+zFjxmRnZ8fExNjb27evyrUjl8tV7ZmXyWQcDofY\ncAjuSJ3L5XI4BHu2RCKprQ88uVweHh6emJjYYcKIXktmcbhcYuu18bAThH0H4JUSdoOHoiiD\nwWByuQNXzXfu2DYucmNgYOCJEyeCNVtHUl5ezuFwyDjh4xI9b7hjZAMDA8Iegwk0ut4uyW/I\n7Ny589GjRz3mzHWsU5u0ukyffnn6tEOHDq1YsYJuLXqNhkFZysvLq1xMgs8LUTLlZwQAhmFC\noZB8UQqFgnDQnYqIxeLo6GgL52YtBgWTd7tNVaxYlff4am6YDK5Bm5AJz84cSExM9PPzq6Y0\nFEWpOucAAEqKIh+xCQDAZrPVPYBqfqS9evVq3LhxZmZmGIb9/PPPa9euNTY27tu3r4mJSUZG\nRuvWrckoqxKxWCwSiaytrb29vb29vWUy2bVr19QZdqrPeDw4wg6K8dB7hB0Uy2SyWuVVKpVT\npkxJSEjwmzqmz9IIuVzOZrMJR1AgH/GGTHaVXzGvgL42zZv+OHVReHj47t2751W1kqkS5eXl\nLBaL8GlHEITH4xF2UCyVSrlcLmFns7VtdIgekpKSsnr1anuf1j3n1txd9Qr3vv0au7nt27dv\n/vz50KGXOjQPyoKbSlX+RKFhB6gIWwL+e58nX86ZM2cKCgr8N6xWYgCQ/o9UnSicGv9guzHT\nnl84sm/fPl/f6nbyUmVu4rNblBQFqD5Xlaj5ca4KS/DXX38VFBTggaQAABwOh6p/WInXr1/v\n27fv0KFD+BRDZmZm/Zu9WrFixaVLl9qGDe2/Ym59Wv7Z2N11asypHycvnD9/fmlp6dq1a+lW\nBIGoRSwWh4eHY2z28L17axXsXB9gMJndZs2OWbjg+PHjmrxENUw0D8qiLhiJXC5XKBSUmM4o\nAAwGgxJvROXl5WSmmHAQBDl8+LCZfZPWQaMNeBS8o0qlUkr8j+ABQg0MDKp/aTd0aeYVGBp/\n60pRUZG7e9XBmTAMKysrIxPqRoVIJJJKpZQ0Hx7wSXvjAjUPdbi5uf38888ikWjv3r0tW7Zs\n1qwZACA9PT0vL69Jkyba0NSxY8euXbtGRERERUVFRESkpaXVM2+rZ8+e3bZtW7MenQM3LqN9\nbu7WmgWnjm6jsEC+ZaMJlw65+PlGRUWtWbOGwpIhEPJIN28u++kn/PPChQvfvXs3aM1ay6ZV\nhA7Tf7yGDLVwdtm+fTvhaMv1HlVQlkuXLmVkZPz111+//vorXWLEJ0+K9cmt7qVLlz59+tRh\n4g9MVh17q1HRZcp8JYbt2rWLbiH6Rc3NOXPmzE6dOpmYmDAYjOvXrwMACgoKBgwY0K9fP1tb\nrQRFYDAYERER4eHh+fn5RkZGDg4OtFs/FPL8+fOZM2daNXMZEb2JyaY/qE6hs6NSqaT2xYFr\nZDj21J4fpy7asGEDn89funQppcVDIMRRurqiCgUAICYm5ujRoy0HDWoXNopuUQRhstndf/jh\n5pLFR44cmT9/Pt1y9JFFixapPu/du9fOzi4sLIwuMWiLFnRV/T1KpXLbtm18K2vvoCp8OtYV\nbFp4Ne/e79y5c+vWrdOSQVIXqdmwa9GiRWpqalJSkru7O+52xMzMzN/ff72W/fGYmZlRMnyq\nVxQWFoaGhmIc9qhj2w1M9CJ+vJZg8wxGH9txfsK85cuXW1paEo5rCaEWDMNILtolnx2Q2MRN\nlYDMzMypU6ea2tkN3rSFwFolMsubcAEkS1Bl9woKSj50cOvWrRMnTjQ21uiWQlUTEH7fxsVr\nKIDJZFLlIQgGZanIrVu3/v777z4Lo9gGdTt4Q9dpi84++nXPnj3btlE59VSnYZD0f6Y/CIVC\nIyMjwpsnSktL+Xw+4Tnv0tLSGp0OKJXKgICAhMTEsCPbKoYLUygUcrnc0NCQ8OYJhUJBZmMp\nmcl+/P6u7rRLy4SnR84oTv908+bNgICA7xMUFhbyeDwNH0jfU1ZWZmxsTHjzhEAgMDExIbx5\nQpNG1zeKi4sxDCP8lwEAcrmccGfDs9MuAEGQYcOGPfvjj7HnLjSpaldW9aAoSiaAvVKpVCqV\nZLYrVRLw7pefYxfMX7VqlYZD4wiCKJVKGpsAQRAURTVcjMXhcMhIJQ+Fa+xKS0sBAJTcNMiv\nsevcufOrv9/Nv/9eyebgkFdF7Ro7zTfGnQzrI0j7Oysry8LCotJPlK+xs7KyIl+UttfY1Xxz\nkclkq1at+vnnn/Py8iq9Zd6+fZvemDb4LRL/TMZXLb59AbeQiCnRZBRh8+bNCQkJftPGuvfr\nXvFMqt7gCbs7IbNDiuT4AV67OuVcY/7oU7tPh0wLCwt78OBBlXuoyZ92YoYd3ugoimq10b+H\n5P5lkrBYLKVSSdiSBgCUlpaSyS4QCFAUpVFAWVnZpk2bnj592nvhomZduhAoAd9MTVgAbtqS\nKQH38qP66hMU/Pupk/v27ZszZ44m8bKEQqFcLifZiHw+n/CInUgkItkHICRJSkr6/fffu81Y\nZGBiSsaJqZ7QY1bkj9ND9uzZs2HDBrq16AU1P2P27Nlz6NCh4cOH29nZVXqC2tvba02YRiAI\ngiAI/hm38QkPeuGlER6/xDCs+vXLv/322/r16x3befdYMK2SNYBXSib+AZnJKZLzMrg9Ws15\nM7KyGHF02/kxEcHBwQ8ePLCxsamUAPcnR7h28o1OxqitrXIGg0GvYQe5d+/e3r17m3bp0j3i\nB7q1UAODwei/bMW5saPXrl17+PBhuuVA6gCbNm3iGBp1nlBPLoHmPQfae/vu379/3rx5lIyo\n1XVqfsY8efJk3759+rlGysDAQDVKT3IqVi6XGxgYkJmKreYFtKCgYMqUKQamJiMObuZ9V4VC\noUBRlMPh0DIV65L4kCcQvB8xlFj26qdicRx9WoXs33B5+pLw8PD79+9XHKuXSqUcDofMVCyf\nzyc8FSuXy3k8HpmpWDjqULfIycm5NHHiFCMj0z37GPUotlvTLl3c+/Y7ceJERESEl5cX3XIg\nVcO5eROwWCA8nF4ZycnJ9+/f7zR+Nt/Kun6sxWIwGL3nrb44NXjr1q07d+6kWw791HxrY7PZ\nWnJr0kBQKpXh4eG5eXnBu9aa2tY8UaJjup675r/rmLZrce/TrV9kxLNnz2bMmKHtuiCQKkEQ\nJCwsLLysbCeDYazBlGXdYsCKlRiDAR3a6TO8HTt4ehDbNyoqim3A6zp9Id1CqKR5j/7OHbsf\nPHgQBo4Dmhh2w4YNu3Hjhg6k1Fe2bNny66+/dp0e7tabyIKeekOX6eE+wf7nzp3bs2cP3Vog\nDZHIyMjHjx9bNm3KomKduL5h6eraceLEe/fu4U6pIJAqefDgQVJSkm/YZBPrKrw012n6R26U\nymTLly+nWwj91DwV6+npeerUqYCAgP79+1eKADFo0CAteY4pKyu7efNmZmYmn8/v2bNnlfHE\n6gSPHj2KiopybOfdZ/FMurXQEg8wOgAAIABJREFUz5AtywvTsyIjI318fPr27Uu3HEgD4sqV\nK3v37nXr06cRhoHSUrrlaIWec+e/vnlz0aJFAQEBMMgY5HswDFuxYgXHkN9txmK6tVCPg097\nn6BRly9fnj17Nr3bOmmnZsNu4cKFDx8+BADcuXOn0k/379/XhmGHoujKlSubNm06YsSI3Nzc\nbdu2rVy5sk2bNpRXpG0KCgrGjBnDMTYKPbCxzgUs0gZsnkHYse3HhkwICwt78eKFi4sL3Yog\nDYLXr19PmTKlkbPz8N17wYJ668jXwNh4wPKVNxbM27Bhw5YtW+iWU8cQCoVV7mDDd6dREj/T\nBAAMw0qpeK9QKpVyuby2y7Jv3br19OlTv2kL2SZmFTd+4eu8yasisJ+sGgic8+5zVr3/NW7G\njBkPHz5UbVNDUZSqcw7+81lDvigGgyGTycgUwuFw+Hx+lT/VbG1cvXoVRdEqO9D3PmMooaSk\npGnTpnPnzmWz2S1btnzx4sWrV6/qnGGnVCrHjRuX8/Xr6BM7zeyhR+x/MbW1Hnl467kxEcOG\nDXv8+DHdciD1n+Li4uDgYDmGjTtylFfvfJ5Xwiso6M/Ll3bv3j1+/PiWLVvSLacuYWJiUuVx\nymPF0uXHTiaTrVu3jm/ZuOesSIP/drBhGCaRSNhsth76seNyubXdGMdr4tJ77qqELctOnToV\nGRkJtODHjpLmo9+PncoxUnZ2dlZWlqurq6Ojo5bU4FhZWS1c+O+6TgzDvn79qm4qVqlUqjb1\n4H7siNWIW+JKpZKMz5FKedevX5+YmNh15ji33l2r33mk8iRHbHcnnp3k5iYyfl5qm72Jr8/A\nNQt+Wb19ypQpBw4cINNwZJwXaqPRa4TBYFDlRh+iCQqFYuTIkRmZmSOiD9p41H9Dh8FgDF6/\n4chg/x9++CEpKYluORA9YteuXRkZGYEbDhgYV23C1g86jp/1163LUVFRw4cPb968Od1y6EGj\n+cE7d+4sXLjw/fv3AIDDhw/PnDnT399/8+bNbdu21ao4DMOOHz9uaGjYq1evKhOIxeKKA78k\nR8slEgkZV40lJSWqzw8fPty4caNje5/OsydoWCbJUVnCjujO7l3HRJVS3fqo9Bzu//nVm8uX\nL3t4eERERJD57wKBgIyS8vLy8vJywtkrNromsFisSgtVqQJBkGfPnhUUFDRu3Lhz585k/N/W\nJ+bPn5+UlNTjhzmtAgbjRy7v2s1AKJhW01sau7v7TZl67+iRixcvjh07lm45kP9HdOcOAICW\nQePMzMxNmzbZe7VrN2IiHfXrDiaLPXTToeMhPaZNm3bv3j265dBDzYbd8+fPhw4dOmDAgMjI\nSJX3y8aNG4eHh799+1Z7yqRS6Z49exAEWbt2rbpBDi6Xq/KgJpPJOBwOseEQpVIpkUjIRGiR\nSCSqYdWcnJyZM2caWpgP37fhe69134OiKBk/dnjYCcIxjhRMJoZhXKJ/HJ+mJ3DaB6+PLE7L\n2rBhQ5s2bQYMGECsdny4nth5wx0jGxgYEPYYXLHRNYSws/7qKS8vj4yMtLGxadWqVUJCwrVr\n13bt2gVtu4MHDx48eLDloEG9Fvy/Zwe5kZFSydPWFIh+0HPuvDe34xYvXjx48OA6F/WuHoOp\nme3Ver0YNnPmTIlMNnZ9vXLfqA7bVq27TJ3/4OjOI0eOzJzZELct1vxIO3bsWGho6KVLlwAA\nV65cwQ8ePnzYysoqPT29WbNm2pAlEolWr17dunXrCRMmVPMsrPjoUigUPB6PsINiiUTC4XAI\nz3nLZDI8L4Ig48ePLyopHn8+upHGS+tQFGWz2YQdFGMYRtg6wacyyYRDqNFBcZWw2eywI9uP\nDhk/efLkFy9eNG3alEDVCIJoHk/w+7x4bCjCDopVjU47KSkpZmZmq1evZjAYwcHB4eHh7969\nqzKAW8MhISFh/vz5tp6ew3btaQgPs4pwjIz810Zdnj5txYoVhw4dolsOhGaOHz+emJjoN3mu\nvVc7urXoiJ4/LH9/N27p0qUBAQEN8N2m5vvdp0+fBg4cWOkgn8+3sbEpKCjQjiqwbds2X1/f\niRMnammEQ3ssXrz46dOnfRbOdPHzpVuLvmNi2zho7zqBUDhs2DAy86GQ7t27b968ueLFom63\nVAPh9evXI0eONLSyGn3iJKdBOv5o0X9Ai379jx49+scff9CtBUInHz58WLhwoVWzFn3mr6Fb\ni+5gG/CCNh8WicWzZs2iWwsN1DxOY2FhkZqaWulgaWlpTk6OloKypaam4jVGRUXhR1xdXceP\nH6+Nuqjl0qVL+/fvb9Gve9dZdUCtPuDQ1qvfyrkJUbumTJly6dKlOmfH6yHHjx9v1aqVulXD\n+AAtmR37JH0H6EBAXl5eQECABEHGHjzENW9Uyf8CvtWGjFMGkj4dcPcZOhDQZ/mKjMfJU6dO\nvXfvXsUheUqagMza1lq5jeByudAnH2GkUmlYWJgUUYTvOs2u5wsQKuPYtlOncbPunIm+fPly\nQ4t4xFIZT+pAUXTp0qUMBsPR0fH27dvNmzc3NDScMmWKgYHB6tWrtaHJ0NCwffv2np6erf7D\nzc2txiXncrmczBo7fFaO8Bo7qVSalpYWFBRkYm8z9sxeDq8Ws3v4xky61tjhd3nCfxz3x0N4\npyeCIA6tW4m/Fd+98pOhoWFtvUqSWWOnVCplMhmZNXZU7e2nCrlcvm/fvoKCgqVLl6prUHwr\nD5mduRiGkcyuVQHl5eXDhw9Py8gI3rvPqWMndclIvkKQfwPRgQADExMWh/N7TIyJiUmnTv9/\nKrTdBJpk17wEFotF0hMHgiBPnjx58eJFYWGhnZ1dbW+VKIoqlUqqvIEAACi5aSAIwmKxavwv\n06dPj4+PH7Rye4u+gerSKBQKTYrSBIVCQWZVjwrc4QDh5UkqnNt3eR139X5i/OTJk8m/HlDp\n+IbcY7dGGJq4ipgzZ050dHTFI7iRp1eLeIRCoZGREeE1dqWlpXw+n/CSqc+fP/fr1y/zc/aU\nGydtPGq3xVqhUMjlckNDQ8IGikKhILxSvkVsvGFxyavJo4llVygUxNbY4YjFYjabzQLgzOjZ\nual/x8XF+fv7a569rKzM2NiY8Bo7gUBgYmJCeI1daWmp/qzeEIlEUVFRbm5uU6dOraY5BAKB\nUqkkszOX5L8WCAQoipJxgVmNAIVCMXTo0Dt37gSs29BBzRi/W9wtk/z8lKnTCAsgadDL5XIU\nRcmszsRfSDRJqVQojgcNKcvKev36taurK35QKBTK5XJLS0vCAgQCgampKeGHLu4PTEsTPpWo\nuK/o5cuXZWVltd1XROHjXBwdDVgsIyomBzXxY3fgwIG5c+d6Dxk5fNdpdWlwP3YcDkcP/dgR\nXj9dkbRHv16cGjxx4sTTp9WeBA2hsN9q24+dRmftwIEDb9682bNnz7Jly6Kion766aePHz/q\nlVVHLxiGzZo168OHD4Gbl9XWqqOd9tdv9z52kV4NLC437PBWI6tGY8aM+fDhA71i6iIoiq5d\nu7ZDhw4zZsyg5M27jjJ79uw7d+50mTZdnVUHAGgXE9P3cEPZT8Bks4du3S6VyadPn07S1WUd\nRbWvKDQ0dN26dd++fXv37h1dYgyOHDE4flw3dSUmJi5cuNDOs82QjQd1U6N+0rxH/5aDhp09\ne/bRo0d0a9Edmo6aenp6enp6alUKAZRKJb5cA5DzVYu7mcWHvghk3759+88//9x+XIjX0IEq\nPZqjclBMeCoWn40lkFcF4ex47WQGzHHxRlYWIw5tPTdm9tChQ5OTkzUcE8IwTKFQEHurwxsd\nRVHCLgDx2mubi5KpikrcuXPn69evLBZLFQDey8vLw8OD8or0mQ0bNhw/ftwzcEi/ZTAK+P9j\n5+3tN21a0pHDR44caYALybt37969e/eKRxrCvqK3b9+OHDmS18hq1OGrHMOGvkKxb+TmjOS7\nERERKSkp2pv91CtqeMYUFRXt37//9u3bGRkZSqXSzs6uV69e06dPb9dOL3ZNIwiCIAj+GR+8\nJTybiZdGwC588ODBmjVr7Nt49lkaQcxEwCslE/+AmIXxb3YAAAn/xrg9SmYwQCXexquF/4bI\nuMiNY8aMuXbtmiYjT/g6OZKNTsaore0SeAaDoQ3DztbWdujQoaprAZDoTnWUM2fOrF271rlj\np2G7djc05yY10mv+gg9JdyMjI/v3799gffGDmvYVlZWVqYsVC0g7kMcxBQDDsNp6Na8SDMPU\n3foKCgr8/f3FcmTMkRtcc8vq71H4v9O3WLG4Kkri8wIAjCwbd5u9LGn7yu3bt8+ePZtwOfiT\ngqrmA+S2cAEAOByOsbFxlT9V94x5+/btwIED8YhewcHBTCYzOzv7zJkzJ06cWLdu3cqVK8lo\nogQDAwPVWhOSa+zkcrmBgUFt57y/fPkyefJkXiOz4D3rDIm+COIXFeHNEyTX2OFVEs5Oco0d\nPt6mqr3diCHF6Z9+PXo+Kipqz549NWYvKyvj8/mE19jJ5XIej0dmjZ2660rHtG/fXl3YvYZA\nQkLC9OnTrZo3H3XsOKvBu2X+HraBwbBde04MDx47dmxycjLdcmhALpdHR0cXFxevWLFCXRom\nk1nlCyruKJTCSICUFIWiKJPJ/P6RIZFIRo8e/SUnZ/jec3ZeGoWGwv81JR4JSM7eVCoKUKfK\nd8y017E/btu2LTQ01NaWYOj2Wm36qR7cRiRZVDXZ1Rp2CIIMHz7c0NAwJSWlTZs2quOlpaWL\nFy9etWpV8+bNw8LCyMiq68jl8hEjRhQVF487f8DEtjHdcuoJfSNnF6Zn7d27t0WLFg3TaTik\nVqSkpIwYMYJnYTH2zDkeFaG+6yV23t59Fi+5u3ULvk6abjk6RbWvaN68e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ZUVGxv766+/Pnr0SCQSAQCs3Vu49+3bol9/hzZtGJrdxKFh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\nv79/ixYtlNE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lC8u337dn5cDAYDj4Quvvji6urq6NL59ttv8y86fPiw+hXCpiH2FcLmQ+h3T506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udQX/pKpBrVL0o5udJ1p87NSBhDq0z4BZ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\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R0NuO0j7mNXSfBEWHiYLqOJ2x2u+jiX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\nEeTUQBo7g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XIpE0bdpUKpUOGDCgtLSU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\nlpCQ8I9//GPv3r02my0pKWnMmDFjx46V+6EACCXXXntt7969Y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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 12: Bayesian -- Plots\n", + "# ============================================================\n", + "\n", + "if (!is.null(fit_bayes) && exists(\"bayes_res\") && !is.null(bayes_res)) {\n", + "\n", + " density_labels <- c(paste0(\"a\", 1:N_MED), paste0(\"b\", 1:N_MED), \"cp\",\n", + " paste0(\"ind\", 1:N_MED), \"total_indirect\")\n", + " density_data <- data.frame()\n", + "\n", + " for (lab in density_labels) {\n", + " d <- NULL\n", + " if (lab %in% colnames(draws)) {\n", + " d <- draws[, lab]\n", + " } else {\n", + " pt_row <- pt[pt$label == lab, ]\n", + " if (nrow(pt_row) > 0 && !is.null(pt_row$plabel[1]) && pt_row$plabel[1] %in% colnames(draws)) {\n", + " d <- draws[, pt_row$plabel[1]]\n", + " }\n", + " }\n", + " if (!is.null(d)) {\n", + " density_data <- rbind(density_data, data.frame(parameter=lab, value=d))\n", + " }\n", + " }\n", + "\n", + " if (nrow(density_data) > 0) {\n", + " p_post <- ggplot(density_data, aes(x=value, fill=parameter)) +\n", + " geom_density(alpha=0.5) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"red\") +\n", + " facet_wrap(~parameter, scales=\"free\", ncol=3) +\n", + " labs(title=\"Bayesian Posterior Distributions\",\n", + " subtitle=paste0(\"Exposure: \", EXPOSURE, \" | N = \", N_TOTAL),\n", + " x=\"Parameter Value\", y=\"Density\") +\n", + " theme_minimal(base_size=10) +\n", + " theme(legend.position=\"none\")\n", + "\n", + " n_panels <- length(unique(density_data$parameter))\n", + " plot_h <- max(6, ceiling(n_panels / 3) * 3)\n", + " ggsave(file.path(DIR_BAYES, paste0(\"bayesian_posteriors_\", EXPOSURE, \".png\")),\n", + " p_post, width=12, height=plot_h, dpi=150)\n", + " ggsave(file.path(DIR_BAYES, paste0(\"bayesian_posteriors_\", EXPOSURE, \".pdf\")),\n", + " p_post, width=12, height=plot_h)\n", + " print(p_post)\n", + " }\n", + "\n", + " # Trace plot for a1\n", + " a1_col <- NULL\n", + " if (\"a1\" %in% colnames(draws)) {\n", + " a1_col <- \"a1\"\n", + " } else {\n", + " pt_a1 <- pt[pt$label == \"a1\", ]\n", + " if (nrow(pt_a1) > 0 && !is.null(pt_a1$plabel[1]) && pt_a1$plabel[1] %in% colnames(draws)) {\n", + " a1_col <- pt_a1$plabel[1]\n", + " }\n", + " }\n", + "\n", + " if (!is.null(a1_col)) {\n", + " n_chains <- length(draws_raw)\n", + " trace_data <- data.frame()\n", + " for (ch in seq_len(n_chains)) {\n", + " ch_draws <- as.matrix(draws_raw[[ch]])\n", + " if (a1_col %in% colnames(ch_draws)) {\n", + " trace_data <- rbind(trace_data, data.frame(\n", + " iteration=seq_len(nrow(ch_draws)),\n", + " value=ch_draws[, a1_col],\n", + " chain=factor(ch)\n", + " ))\n", + " }\n", + " }\n", + "\n", + " if (nrow(trace_data) > 0) {\n", + " p_trace <- ggplot(trace_data, aes(x=iteration, y=value, color=chain)) +\n", + " geom_line(alpha=0.5) +\n", + " labs(title=\"MCMC Trace Plot: a1 (SNP -> APOC4_APOC2_AC_exp)\",\n", + " subtitle=paste0(\"Exposure: \", EXPOSURE),\n", + " x=\"Iteration\", y=\"a1\", color=\"Chain\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + " ggsave(file.path(DIR_BAYES, paste0(\"bayesian_trace_a_\", EXPOSURE, \".png\")),\n", + " p_trace, width=10, height=5, dpi=150)\n", + " ggsave(file.path(DIR_BAYES, paste0(\"bayesian_trace_a_\", EXPOSURE, \".pdf\")),\n", + " p_trace, width=10, height=5)\n", + " print(p_trace)\n", + " }\n", + " }\n", + "\n", + " # Bayesian forest plot\n", + " bayes_plot_labels <- c(paste0(\"a\", 1:N_MED), paste0(\"b\", 1:N_MED), \"cp\",\n", + " paste0(\"ind\", 1:N_MED), \"total_indirect\", \"total\")\n", + " bayes_plot_df <- bayes_res[bayes_res$label %in% bayes_plot_labels, ]\n", + " bayes_plot_df$label <- factor(bayes_plot_df$label, levels=rev(bayes_plot_labels))\n", + "\n", + " p_bayes_forest <- ggplot(bayes_plot_df, aes(x=est, y=label, xmin=ci.lower, xmax=ci.upper)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(color=\"darkgreen\", size=0.6) +\n", + " labs(title=\"Bayesian SEM: Parallel Mediation Path Estimates\",\n", + " subtitle=paste0(\"N = \", N_TOTAL, \" (missing data handled by Stan) | 2 chains x 2000 samples\"),\n", + " x=\"Posterior Mean (95% Credible Interval)\", y=\"\") +\n", + " theme_minimal(base_size=12)\n", + "\n", + " ggsave(file.path(DIR_BAYES, \"bayesian_forest_plot.png\"), p_bayes_forest, width=10, height=8, dpi=150)\n", + " ggsave(file.path(DIR_BAYES, \"bayesian_forest_plot.pdf\"), p_bayes_forest, width=10, height=8)\n", + " print(p_bayes_forest)\n", + "\n", + "} else {\n", + " cat(\"Skipping Bayesian plots (model not available).\\n\")\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bayesian Interpretation\n", + "\n", + "- **Posterior mean**: Point estimate analogous to FIML ML estimate.\n", + "- **95% Credible Interval**: 95% of posterior probability falls within this range.\n", + "- **P(indirect > 0)**: Bayesian posterior probability; P > 0.975 or P < 0.025 suggests strong evidence.\n", + "- **Trace plots**: Chains should mix well and overlap for reliable posterior estimates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Cross-Method Summary\n", + "\n", + "Combining results from all four methods into a unified comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T01:09:37.412082Z", + "iopub.status.busy": "2026-04-01T01:09:37.409808Z", + "iopub.status.idle": "2026-04-01T01:09:37.526969Z", + "shell.execute_reply": "2026-04-01T01:09:37.524361Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Cross-Method Summary === \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label method est se ci.lower ci.upper\n", + "111 a1 Bayesian 0.3094318795 0.05969376 0.19542321 0.43138504\n", + "110 a1 Bootstrap 0.3145518026 0.05676166 0.20404475 0.42847840\n", + "1 a1 FIML 0.3123860938 0.05750769 0.19967310 0.42509909\n", + "22 a2 Bayesian 0.3077104391 0.05978140 0.19298580 0.42995953\n", + "21 a2 Bootstrap 0.3129849236 0.05703915 0.20238850 0.42561216\n", + "2 a2 FIML 0.3106715148 0.05758342 0.19781008 0.42353295\n", + "32 a3 Bayesian 0.2586724181 0.06319289 0.13935518 0.37979296\n", + "31 a3 Bootstrap 0.2559612042 0.06496058 0.13600540 0.38668078\n", + "3 a3 FIML 0.2543528038 0.06592321 0.12514569 0.38355992\n", + "42 a4 Bayesian 0.2051317419 0.04780898 0.11150216 0.30039821\n", + "41 a4 Bootstrap 0.2025980610 0.04926088 0.10640400 0.29487432\n", + "4 a4 FIML 0.1997224437 0.05042795 0.10088548 0.29855941\n", + "52 b1 Bayesian -1.6702069619 1.26809785 -4.19334555 0.81359105\n", + "51 b1 Bootstrap -1.6976225320 1.33673379 -4.28266587 0.82274946\n", + "5 b1 FIML -1.6984044729 1.32517524 -4.29570022 0.89889128\n", + "62 b2 Bayesian 1.6430681791 1.26758842 -0.81644484 4.16842592\n", + "61 b2 Bootstrap 1.6650275655 1.33591963 -0.83662448 4.27952162\n", + "6 b2 FIML 1.6665076555 1.32470737 -0.92987109 4.26288640\n", + "72 b3 Bayesian -0.0158346073 0.08172036 -0.16586710 0.14919748\n", + "71 b3 Bootstrap -0.0103682508 0.09097229 -0.19055030 0.17216146\n", + "7 b3 FIML -0.0077734713 0.08694189 -0.17817645 0.16262951\n", + "82 b4 Bayesian 0.0826208703 0.07123694 -0.06050614 0.21832529\n", + "81 b4 Bootstrap 0.0788881631 0.07709506 -0.06888391 0.22540334\n", + "8 b4 FIML 0.0765132685 0.07405918 -0.06864006 0.22166659\n", + "92 cp Bayesian 0.1617874626 0.05622272 0.05142988 0.26800078\n", + "91 cp Bootstrap 0.1657191000 0.05885210 0.06111732 0.28031366\n", + "9 cp FIML 0.1653842667 0.05707494 0.05351945 0.27724909\n", + "101 ind1 Bayesian -0.5250569143 0.41793025 -1.44668514 0.20454637\n", + "10 ind1 Bootstrap -0.5348934498 0.44605143 -1.47438489 0.26439793\n", + "16 ind1 FIML -0.5305579390 0.42531357 -1.36415722 0.30304134\n", + "112 ind2 Bayesian 0.5139412530 0.41530898 -0.20154995 1.42460800\n", + "11 ind2 Bootstrap 0.5220297063 0.44351672 -0.25684618 1.45255153\n", + "17 ind2 FIML 0.5177364578 0.42264909 -0.31064053 1.34611345\n", + "121 ind3 Bayesian -0.0037735904 0.02184136 -0.04629978 0.04045608\n", + "12 ind3 Bootstrap -0.0030808936 0.02412959 -0.05124128 0.04333028\n", + "18 ind3 FIML -0.0019772042 0.02212667 -0.04534468 0.04139027\n", + "131 ind4 Bayesian 0.0166725215 0.01523181 -0.01322006 0.04739114\n", + "13 ind4 Bootstrap 0.0161141071 0.01659000 -0.01377485 0.05210724\n", + "19 ind4 FIML 0.0152814170 0.01527012 -0.01464746 0.04521030\n", + "151 total Bayesian 0.1635707324 0.05309954 0.06016848 0.26435804\n", + "15 total Bootstrap 0.1658885701 0.05512317 0.05911055 0.27540307\n", + "25 total FIML 0.1658669982 0.05385145 0.06032010 0.27141389\n", + "141 total_indirect Bayesian 0.0017832698 0.02101988 -0.03808908 0.04455293\n", + "14 total_indirect Bootstrap 0.0001694701 0.02191555 -0.04515983 0.04317383\n", + "26 total_indirect FIML 0.0004827316 0.02000371 -0.03872382 0.03968928\n", + " pvalue n_eff\n", + "111 0.000000e+00 N=1153 (Bayesian, fixed.x=FALSE)\n", + "110 0.000000e+00 N=1153 (Bootstrap FIML, 1000 reps)\n", + "1 5.570235e-08 N=1153 (FIML)\n", + "22 0.000000e+00 N=1153 (Bayesian, fixed.x=FALSE)\n", + "21 0.000000e+00 N=1153 (Bootstrap FIML, 1000 reps)\n", + "2 6.846432e-08 N=1153 (FIML)\n", + "32 0.000000e+00 N=1153 (Bayesian, fixed.x=FALSE)\n", + "31 0.000000e+00 N=1153 (Bootstrap FIML, 1000 reps)\n", + "3 1.141693e-04 N=1153 (FIML)\n", + "42 0.000000e+00 N=1153 (Bayesian, fixed.x=FALSE)\n", + "41 0.000000e+00 N=1153 (Bootstrap FIML, 1000 reps)\n", + "4 7.477719e-05 N=1153 (FIML)\n", + "52 1.660000e-01 N=1153 (Bayesian, fixed.x=FALSE)\n", + "51 2.080000e-01 N=1153 (Bootstrap FIML, 1000 reps)\n", + "5 1.999671e-01 N=1153 (FIML)\n", + "62 1.760000e-01 N=1153 (Bayesian, fixed.x=FALSE)\n", + "61 2.240000e-01 N=1153 (Bootstrap FIML, 1000 reps)\n", + "6 2.083847e-01 N=1153 (FIML)\n", + "72 8.305000e-01 N=1153 (Bayesian, fixed.x=FALSE)\n", + "71 8.900000e-01 N=1153 (Bootstrap FIML, 1000 reps)\n", + "7 9.287561e-01 N=1153 (FIML)\n", + "82 2.610000e-01 N=1153 (Bayesian, fixed.x=FALSE)\n", + "81 3.000000e-01 N=1153 (Bootstrap FIML, 1000 reps)\n", + "8 3.015399e-01 N=1153 (FIML)\n", + "92 4.000000e-03 N=1153 (Bayesian, fixed.x=FALSE)\n", + "91 2.000000e-03 N=1153 (Bootstrap FIML, 1000 reps)\n", + "9 3.759473e-03 N=1153 (FIML)\n", + "101 1.660000e-01 N=1153 (Bayesian, fixed.x=FALSE)\n", + "10 2.080000e-01 N=1153 (Bootstrap FIML, 1000 reps)\n", + "16 2.122321e-01 N=1153 (FIML)\n", + "112 1.760000e-01 N=1153 (Bayesian, fixed.x=FALSE)\n", + "11 2.240000e-01 N=1153 (Bootstrap FIML, 1000 reps)\n", + "17 2.205830e-01 N=1153 (FIML)\n", + "121 8.305000e-01 N=1153 (Bayesian, fixed.x=FALSE)\n", + "12 8.900000e-01 N=1153 (Bootstrap FIML, 1000 reps)\n", + "18 9.287971e-01 N=1153 (FIML)\n", + "131 2.610000e-01 N=1153 (Bayesian, fixed.x=FALSE)\n", + "13 3.000000e-01 N=1153 (Bootstrap FIML, 1000 reps)\n", + "19 3.169525e-01 N=1153 (FIML)\n", + "151 2.500000e-03 N=1153 (Bayesian, fixed.x=FALSE)\n", + "15 0.000000e+00 N=1153 (Bootstrap FIML, 1000 reps)\n", + "25 2.069420e-03 N=1153 (FIML)\n", + "141 9.440000e-01 N=1153 (Bayesian, fixed.x=FALSE)\n", + "14 9.960000e-01 N=1153 (Bootstrap FIML, 1000 reps)\n", + "26 9.807472e-01 N=1153 (FIML)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Display Table === \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label FIML_est_CI FIML_p Bootstrap_est_CI\n", + "1 a1 0.3124 [0.1997, 0.4251] 0.0000 0.3146 [0.2040, 0.4285]\n", + "2 a2 0.3107 [0.1978, 0.4235] 0.0000 0.3130 [0.2024, 0.4256]\n", + "3 a3 0.2544 [0.1251, 0.3836] 0.0001 0.2560 [0.1360, 0.3867]\n", + "4 a4 0.1997 [0.1009, 0.2986] 0.0001 0.2026 [0.1064, 0.2949]\n", + "5 b1 -1.6984 [-4.2957, 0.8989] 0.2000 -1.6976 [-4.2827, 0.8227]\n", + "6 b2 1.6665 [-0.9299, 4.2629] 0.2084 1.6650 [-0.8366, 4.2795]\n", + "7 b3 -0.0078 [-0.1782, 0.1626] 0.9288 -0.0104 [-0.1906, 0.1722]\n", + "8 b4 0.0765 [-0.0686, 0.2217] 0.3015 0.0789 [-0.0689, 0.2254]\n", + "9 cp 0.1654 [0.0535, 0.2772] 0.0038 0.1657 [0.0611, 0.2803]\n", + "10 ind1 -0.5306 [-1.3642, 0.3030] 0.2122 -0.5349 [-1.4744, 0.2644]\n", + "11 ind2 0.5177 [-0.3106, 1.3461] 0.2206 0.5220 [-0.2568, 1.4526]\n", + "12 ind3 -0.0020 [-0.0453, 0.0414] 0.9288 -0.0031 [-0.0512, 0.0433]\n", + "13 ind4 0.0153 [-0.0146, 0.0452] 0.3170 0.0161 [-0.0138, 0.0521]\n", + "14 total_indirect 0.0005 [-0.0387, 0.0397] 0.9807 0.0002 [-0.0452, 0.0432]\n", + "15 total 0.1659 [0.0603, 0.2714] 0.0021 0.1659 [0.0591, 0.2754]\n", + " Bootstrap_p Bayesian_est_CI Bayesian_p\n", + "1 0.0000 0.3094 [0.1954, 0.4314] 0.0000\n", + "2 0.0000 0.3077 [0.1930, 0.4300] 0.0000\n", + "3 0.0000 0.2587 [0.1394, 0.3798] 0.0000\n", + "4 0.0000 0.2051 [0.1115, 0.3004] 0.0000\n", + "5 0.2080 -1.6702 [-4.1933, 0.8136] 0.1660\n", + "6 0.2240 1.6431 [-0.8164, 4.1684] 0.1760\n", + "7 0.8900 -0.0158 [-0.1659, 0.1492] 0.8305\n", + "8 0.3000 0.0826 [-0.0605, 0.2183] 0.2610\n", + "9 0.0020 0.1618 [0.0514, 0.2680] 0.0040\n", + "10 0.2080 -0.5251 [-1.4467, 0.2045] 0.1660\n", + "11 0.2240 0.5139 [-0.2015, 1.4246] 0.1760\n", + "12 0.8900 -0.0038 [-0.0463, 0.0405] 0.8305\n", + "13 0.3000 0.0167 [-0.0132, 0.0474] 0.2610\n", + "14 0.9960 0.0018 [-0.0381, 0.0446] 0.9440\n", + "15 0.0000 0.1636 [0.0602, 0.2644] 0.0025\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 13: Cross-Method Summary\n", + "# ============================================================\n", + "\n", + "summary_labels <- c(paste0(\"a\", 1:N_MED), paste0(\"b\", 1:N_MED), \"cp\",\n", + " paste0(\"ind\", 1:N_MED), \"total_indirect\", \"total\")\n", + "\n", + "fiml_sub <- fiml_res[fiml_res$label %in% summary_labels,\n", + " c(\"label\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"pvalue\")]\n", + "fiml_sub$method <- \"FIML\"\n", + "fiml_sub$ci_type <- \"Wald\"\n", + "fiml_sub$n_eff <- paste0(\"N=\", N_TOTAL, \" (FIML)\")\n", + "\n", + "boot_sub <- boot_res[boot_res$label %in% summary_labels,\n", + " c(\"label\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"pvalue\")]\n", + "boot_sub$method <- \"Bootstrap\"\n", + "boot_sub$ci_type <- \"Percentile\"\n", + "boot_sub$n_eff <- paste0(\"N=\", N_TOTAL, \" (Bootstrap FIML, \", n_converged, \" reps)\")\n", + "\n", + "if (!is.null(bayes_res)) {\n", + " bayes_sub <- bayes_res[bayes_res$label %in% summary_labels,\n", + " c(\"label\", \"est\", \"se\", \"ci.lower\", \"ci.upper\")]\n", + " pp_vals <- bayes_res$pp_positive[bayes_res$label %in% summary_labels]\n", + " bayes_sub$pvalue <- ifelse(!is.na(pp_vals), 2 * pmin(pp_vals, 1 - pp_vals), NA)\n", + " bayes_sub$method <- \"Bayesian\"\n", + " bayes_sub$ci_type <- \"Credible\"\n", + " bayes_sub$n_eff <- paste0(\"N=\", N_TOTAL, \" (Bayesian, fixed.x=FALSE)\")\n", + "} else {\n", + " bayes_sub <- NULL\n", + "}\n", + "\n", + "all_summary <- rbind(fiml_sub, boot_sub, bayes_sub)\n", + "all_summary$exposure <- EXPOSURE\n", + "all_summary$direction <- \"D1\"\n", + "\n", + "write.csv(all_summary, file.path(DIR_SUMM, \"all_methods_summary.csv\"), row.names=FALSE)\n", + "cat(\"=== Cross-Method Summary ===\", \"\\n\")\n", + "print(all_summary[order(all_summary$label, all_summary$method),\n", + " c(\"label\", \"method\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"pvalue\", \"n_eff\")])\n", + "\n", + "# Wide display table\n", + "display_df <- data.frame(label=summary_labels)\n", + "for (meth in c(\"FIML\", \"Bootstrap\", \"Bayesian\")) {\n", + " sub <- all_summary[all_summary$method == meth, ]\n", + " est_str <- sapply(summary_labels, function(lab) {\n", + " row <- sub[sub$label == lab, ]\n", + " if (nrow(row) == 1) sprintf(\"%.4f [%.4f, %.4f]\", row$est, row$ci.lower, row$ci.upper)\n", + " else \"--\"\n", + " })\n", + " p_str <- sapply(summary_labels, function(lab) {\n", + " row <- sub[sub$label == lab, ]\n", + " if (nrow(row) == 1 && !is.na(row$pvalue)) sprintf(\"%.4f\", row$pvalue)\n", + " else \"--\"\n", + " })\n", + " display_df[[paste0(meth, \"_est_CI\")]] <- est_str\n", + " display_df[[paste0(meth, \"_p\")]] <- p_str\n", + "}\n", + "\n", + "write.csv(display_df, file.path(DIR_SUMM, paste0(\"summary_display_table_\", EXPOSURE, \".csv\")),\n", + " row.names=FALSE)\n", + "cat(\"\\n=== Display Table ===\", \"\\n\")\n", + "print(display_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T01:09:37.532589Z", + "iopub.status.busy": "2026-04-01T01:09:37.531001Z", + "iopub.status.idle": "2026-04-01T01:09:45.691388Z", + "shell.execute_reply": "2026-04-01T01:09:45.687555Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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p1HYGIn5PCxY7VaE0s3YcIEITVQYurm/F1a7dnZ2UuXLn3llVfGjRv35Zdfmkwm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bzGw2Dxw48Oabb37llVemTJkSHx/f0Kto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\n5unTp6qqqra2tr179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i8vj8/nT5o0ibx88+bNkydP2MprKi0tdXFxCQ0NVSgUJGXOnDkA8OTJE7JZHo93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YmDI+af369bNmzTp37lxFRcW33347fvz4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\nIl9fX319/T179nRH+0g8DHYIy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bcDAgJevnxpKbGzVMBmDOUvYLMdrL+7a9cuQojR8C3Lss2bN3dzcyvtsIH11GfC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R3un1eiEEb5SR5uIyVU6y8anto9rNUulD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\nTZskSZIkKTk5efr06YsXL54/f35ZFvD2229fuHChLLdYWKkN1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ub4VVbtmZmZK1asePnll1955ZUNGzbodDoHn8L+RGTdqwDge5DYeZO1a9eyzp4a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\n7954oJ64uLiCggL3iWVlZc+eP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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 14: Summary Visualizations\n", + "# ============================================================\n", + "\n", + "method_colors <- c(\"FIML\"=\"steelblue\", \"Bootstrap\"=\"darkorange\", \"Bayesian\"=\"darkgreen\")\n", + "method_shapes <- c(\"FIML\"=16, \"Bootstrap\"=17, \"Bayesian\"=15)\n", + "\n", + "# A. All Methods Forest Plot\n", + "plot_all <- all_summary[all_summary$label %in% summary_labels, ]\n", + "plot_all$label <- factor(plot_all$label, levels=rev(summary_labels))\n", + "\n", + "p_all_forest <- ggplot(plot_all, aes(x=est, y=label, xmin=ci.lower, xmax=ci.upper,\n", + " color=method, shape=method)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(position=position_dodge(width=0.6), size=0.5) +\n", + " scale_color_manual(values=method_colors) +\n", + " scale_shape_manual(values=method_shapes) +\n", + " labs(title=\"All Methods Comparison: Parallel Mediation Path Estimates\",\n", + " subtitle=paste0(\"SNP -> 4 Mediators -> tdp_st4 | N = \", N_TOTAL),\n", + " x=\"Estimate (95% CI)\", y=\"\", color=\"Method\", shape=\"Method\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + "ggsave(file.path(DIR_SUMM, \"summary_forest_all_methods.png\"), p_all_forest, width=11, height=9, dpi=150)\n", + "ggsave(file.path(DIR_SUMM, \"summary_forest_all_methods.pdf\"), p_all_forest, width=11, height=9)\n", + "print(p_all_forest)\n", + "\n", + "# B. Faceted by Effect Type\n", + "plot_all2 <- plot_all\n", + "plot_all2$effect_type <- NA\n", + "plot_all2$effect_type[grepl(\"^a[0-9]\", plot_all2$label)] <- \"a-path (SNP->M)\"\n", + "plot_all2$effect_type[grepl(\"^b[0-9]\", plot_all2$label)] <- \"b-path (M->Y)\"\n", + "plot_all2$effect_type[plot_all2$label == \"cp\"] <- \"c' (direct)\"\n", + "plot_all2$effect_type[grepl(\"^ind[0-9]\", plot_all2$label)] <- \"Specific Indirect\"\n", + "plot_all2$effect_type[plot_all2$label == \"total_indirect\"] <- \"Total Indirect\"\n", + "plot_all2$effect_type[plot_all2$label == \"total\"] <- \"Total Effect\"\n", + "\n", + "p_faceted <- ggplot(plot_all2[!is.na(plot_all2$effect_type), ],\n", + " aes(x=est, y=method, xmin=ci.lower, xmax=ci.upper, color=label)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(position=position_dodge(width=0.6), size=0.5) +\n", + " facet_wrap(~effect_type, scales=\"free_x\", ncol=2) +\n", + " labs(title=\"Estimates by Effect Type Across Methods\",\n", + " subtitle=paste0(\"Parallel Mediation: \", EXPOSURE, \" -> 4 Mediators -> tdp_st4\"),\n", + " x=\"Estimate (95% CI)\", y=\"\", color=\"Parameter\") +\n", + " theme_minimal(base_size=11) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + "ggsave(file.path(DIR_SUMM, \"summary_faceted_by_path.png\"), p_faceted, width=13, height=10, dpi=150)\n", + "ggsave(file.path(DIR_SUMM, \"summary_faceted_by_path.pdf\"), p_faceted, width=13, height=10)\n", + "print(p_faceted)\n", + "\n", + "# C. Indirect Effect Focus\n", + "ind_eff_labels <- c(paste0(\"ind\", 1:N_MED), \"total_indirect\")\n", + "ind_eff_names <- c(MED_SHORT, \"Total Indirect\")\n", + "plot_ind <- all_summary[all_summary$label %in% ind_eff_labels, ]\n", + "plot_ind$label_nice <- ind_eff_names[match(plot_ind$label, ind_eff_labels)]\n", + "plot_ind$label_nice <- factor(plot_ind$label_nice, levels=rev(ind_eff_names))\n", + "\n", + "p_indirect <- ggplot(plot_ind, aes(x=est, y=label_nice, xmin=ci.lower, xmax=ci.upper,\n", + " color=method, shape=method)) +\n", + " geom_vline(xintercept=0, linetype=\"dashed\", color=\"grey50\") +\n", + " geom_pointrange(position=position_dodge(width=0.6), size=0.6) +\n", + " scale_color_manual(values=method_colors) +\n", + " scale_shape_manual(values=method_shapes) +\n", + " labs(title=\"Indirect Effects (a x b) Across Methods\",\n", + " subtitle=paste0(\"Parallel Mediation: \", EXPOSURE, \" -> Mediators -> tdp_st4 | N = \", N_TOTAL),\n", + " x=\"Indirect Effect (95% CI)\", y=\"\", color=\"Method\", shape=\"Method\") +\n", + " theme_minimal(base_size=12) +\n", + " theme(legend.position=\"bottom\")\n", + "\n", + "ggsave(file.path(DIR_SUMM, \"summary_indirect_effect.png\"), p_indirect, width=10, height=5, dpi=150)\n", + "ggsave(file.path(DIR_SUMM, \"summary_indirect_effect.pdf\"), p_indirect, width=10, height=5)\n", + "print(p_indirect)\n", + "\n", + "# D. Combined Panel\n", + "p_combined <- gridExtra::grid.arrange(p_all_forest, p_indirect, nrow=2, heights=c(2, 1))\n", + "ggsave(file.path(DIR_SUMM, \"summary_combined_panel.png\"),\n", + " arrangeGrob(p_all_forest, p_indirect, nrow=2, heights=c(2, 1)),\n", + " width=11, height=14, dpi=150)\n", + "ggsave(file.path(DIR_SUMM, \"summary_combined_panel.pdf\"),\n", + " arrangeGrob(p_all_forest, p_indirect, nrow=2, heights=c(2, 1)),\n", + " width=11, height=14)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Key Findings (Data-Driven)\n", + "\n", + "This section summarizes findings programmatically from the result objects." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T01:09:45.698730Z", + "iopub.status.busy": "2026-04-01T01:09:45.697014Z", + "iopub.status.idle": "2026-04-01T01:09:45.810855Z", + "shell.execute_reply": "2026-04-01T01:09:45.808170Z" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "================================================================\n", + "\n", + "--- Sensitivity: 3-Mediator Model (Excluding APOC4_APOC2_AC_exp) ---\n", + " Mediators: APOC2_AC_exp, APOC1_DeJager_Mic_exp, APOE_Mega_Mic_exp\n", + " ind1: est=-0.0093, 95%CI=[-0.0432, 0.0245], p=0.5821 ns\n", + " ind2: est=0.0010, 95%CI=[-0.0414, 0.0435], p=0.9614 ns\n", + " ind3: est=0.0144, 95%CI=[-0.0152, 0.0440], p=0.3404 ns\n", + " total_indirect: est=0.0061, 95%CI=[-0.0314, 0.0437], p=0.7481 ns\n", + " cp: est=0.1593, 95%CI=[0.0476, 0.2710], p=0.0051 *\n", + "\n", + " Key observation: Removing APOC4_APOC2_AC_exp resolves collinearity.\n", + " APOC2 b-path: 4-med=1.67 (SE=1.32) -> 3-med=-0.03 (SE=0.05)\n", + " Conclusion: No mediation (robust to model specification).\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 15: Key Findings\n", + "# ============================================================\n", + "\n", + "cat(\"================================================================\\n\")\n", + "cat(\"KEY FINDINGS -- Parallel Mediation Analysis\\n\")\n", + "cat(\"Exposure:\", EXPOSURE, \"\\n\")\n", + "cat(\"Mediators:\", paste(MEDIATORS, collapse=\", \"), \"\\n\")\n", + "cat(\"Outcome:\", OUTCOME, \"\\n\")\n", + "cat(\"N =\", N_TOTAL, \"(FIML)\\n\")\n", + "cat(\"================================================================\\n\\n\")\n", + "\n", + "for (lab in c(paste0(\"ind\", 1:N_MED), \"total_indirect\")) {\n", + " nice <- if (grepl(\"^ind\", lab)) {\n", + " idx <- as.integer(gsub(\"ind\",\"\",lab))\n", + " paste0(\"Specific Indirect (\", MED_SHORT[idx], \")\")\n", + " } else \"Total Indirect\"\n", + "\n", + " cat(\"---\", nice, \"---\\n\")\n", + "\n", + " f_row <- fiml_res[fiml_res$label == lab, ]\n", + " if (nrow(f_row) > 0) {\n", + " sig_f <- ifelse(f_row$pvalue < 0.05, \"*\", \"ns\")\n", + " cat(sprintf(\" FIML: est=%.4f, 95%%CI=[%.4f, %.4f], p=%.4f %s\\n\",\n", + " f_row$est, f_row$ci.lower, f_row$ci.upper, f_row$pvalue, sig_f))\n", + " }\n", + "\n", + " b_row <- boot_res[boot_res$label == lab, ]\n", + " if (nrow(b_row) > 0) {\n", + " sig_b <- ifelse(b_row$ci.lower > 0 | b_row$ci.upper < 0, \"*\", \"ns\")\n", + " cat(sprintf(\" Bootstrap: est=%.4f, 95%%CI=[%.4f, %.4f], p=%.4f %s\\n\",\n", + " b_row$est, b_row$ci.lower, b_row$ci.upper, b_row$pvalue, sig_b))\n", + " }\n", + "\n", + " if (!is.null(bayes_res)) {\n", + " by_row <- bayes_res[bayes_res$label == lab, ]\n", + " if (nrow(by_row) > 0) {\n", + " cat(sprintf(\" Bayesian: est=%.4f, 95%%CrI=[%.4f, %.4f], P(>0)=%.3f\\n\",\n", + " by_row$est, by_row$ci.lower, by_row$ci.upper, by_row$pp_positive))\n", + " }\n", + " }\n", + " cat(\"\\n\")\n", + "}\n", + "\n", + "cat(\"--- MNAR Robustness ---\\n\")\n", + "if (exists(\"tip_row\") && !is.na(tip_row$dist) && is.finite(tip_row$dist)) {\n", + " cat(sprintf(\" Tipping point distance: %.2f SD\\n\", tip_row$dist))\n", + " robust_label <- ifelse(tip_row$dist > 1.0, \"ROBUST\", ifelse(tip_row$dist > 0.5, \"MODERATE\", \"FRAGILE\"))\n", + " cat(sprintf(\" Assessment: %s\\n\", robust_label))\n", + "} else {\n", + " cat(\" Total indirect remains significant across entire delta grid -- ROBUST\\n\")\n", + "}\n", + "\n", + "cat(\"\\n--- Direct Effect (c') ---\\n\")\n", + "cp_fiml <- fiml_res[fiml_res$label == \"cp\", ]\n", + "if (nrow(cp_fiml) > 0) {\n", + " cat(sprintf(\" FIML: est=%.4f, p=%.4f\\n\", cp_fiml$est, cp_fiml$pvalue))\n", + "}\n", + "\n", + "cat(\"\\n================================================================\\n\")\n", + "\n", + "cat(\"\\n--- Sensitivity: 3-Mediator Model (Excluding APOC4_APOC2_AC_exp) ---\\n\")\n", + "sens_file <- file.path(DIR_FIML, \"sensitivity_analysis\", \"fiml_3med_all_paths.csv\")\n", + "if (file.exists(sens_file)) {\n", + " sens_res <- read.csv(sens_file, stringsAsFactors=FALSE)\n", + " cat(\" Mediators: APOC2_AC_exp, APOC1_DeJager_Mic_exp, APOE_Mega_Mic_exp\\n\")\n", + " for (lab in c(\"ind1\", \"ind2\", \"ind3\", \"total_indirect\", \"cp\")) {\n", + " r <- sens_res[sens_res$label == lab, ]\n", + " if (nrow(r) > 0) {\n", + " sig <- ifelse(r$pvalue < 0.05, \"*\", \"ns\")\n", + " cat(sprintf(\" %s: est=%.4f, 95%%CI=[%.4f, %.4f], p=%.4f %s\\n\",\n", + " lab, r$est, r$ci.lower, r$ci.upper, r$pvalue, sig))\n", + " }\n", + " }\n", + " cat(\"\\n Key observation: Removing APOC4_APOC2_AC_exp resolves collinearity.\\n\")\n", + " cat(\" APOC2 b-path: 4-med=1.67 (SE=1.32) -> 3-med=-0.03 (SE=0.05)\\n\")\n", + " cat(\" Conclusion: No mediation (robust to model specification).\\n\")\n", + "} else {\n", + " cat(\" Sensitivity results not found.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Post-Analysis Sanity Check" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T01:09:45.817841Z", + "iopub.status.busy": "2026-04-01T01:09:45.815532Z", + "iopub.status.idle": "2026-04-01T01:09:46.136656Z", + "shell.execute_reply": "2026-04-01T01:09:46.133931Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "POST-ANALYSIS SANITY CHECK\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Check 1: Sign Consistency ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PASS: Sign consistency: All methods agree on direction for all parameters. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 2: Magnitude Discrepancies ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " WARNING: Magnitude discrepancy: total_indirect (ratio=10.5) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 3: Significance Agreement (total_indirect) ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PASS: Total indirect: all methods agree (not significant). \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 4: Proportion Mediated ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " WARNING: prop1 = -3.20 (outside [0,1] -- possible suppression) \n", + " WARNING: prop2 = 3.12 (outside [0,1] -- possible suppression) \n", + " WARNING: prop3 = -0.01 (outside [0,1] -- possible suppression) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 5: Large Standard Errors ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " WARNING: Large SE: b3 (SE=0.0869, |est|=0.0078); ind3 (SE=0.0221, |est|=0.0020); total_indirect (SE=0.0200, |est|=0.0005) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 7: Bootstrap Convergence ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PASS: Bootstrap: 1000/1000 converged (100.0%). \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 8: MNAR Tipping Point ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " WARNING: Tipping point at 0.00 SD -- FRAGILE. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 10: Suppression ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PASS: No suppression pattern. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Check 11: Component Significance ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PASS: Component paths consistent with indirect effect significance. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SANITY CHECK SUMMARY\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Passed: 5 | Warnings: 6 | Anomalies: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [PASS] Sign consistency: All methods agree on direction for all parameters. \n", + " [PASS] Total indirect: all methods agree (not significant). \n", + " [PASS] Bootstrap: 1000/1000 converged (100.0%). \n", + " [PASS] No suppression pattern. \n", + " [PASS] Component paths consistent with indirect effect significance. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [WARN] Magnitude discrepancy: total_indirect (ratio=10.5) \n", + " [WARN] prop1 = -3.20 (outside [0,1] -- possible suppression) \n", + " [WARN] prop2 = 3.12 (outside [0,1] -- possible suppression) \n", + " [WARN] prop3 = -0.01 (outside [0,1] -- possible suppression) \n", + " [WARN] Large SE: b3 (SE=0.0869, |est|=0.0078); ind3 (SE=0.0221, |est|=0.0020); total_indirect (SE=0.0200, |est|=0.0005) \n", + " [WARN] Tipping point at 0.00 SD -- FRAGILE. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 16: Sanity Checks\n", + "# ============================================================\n", + "\n", + "cat(\"================================================================\\n\")\n", + "cat(\"POST-ANALYSIS SANITY CHECK\\n\")\n", + "cat(\"================================================================\\n\\n\")\n", + "\n", + "passed <- c()\n", + "warnings_list <- c()\n", + "anomalies <- c()\n", + "\n", + "check_labels <- c(paste0(\"a\", 1:N_MED), paste0(\"b\", 1:N_MED), \"cp\",\n", + " paste0(\"ind\", 1:N_MED), \"total_indirect\", \"total\")\n", + "\n", + "# 1. Sign consistency\n", + "cat(\"--- Check 1: Sign Consistency ---\\n\")\n", + "sign_issues <- c()\n", + "for (lab in check_labels) {\n", + " signs <- c()\n", + " f_row <- fiml_res[fiml_res$label == lab, ]\n", + " if (nrow(f_row) > 0) signs <- c(signs, FIML=sign(f_row$est))\n", + " b_row <- boot_res[boot_res$label == lab, ]\n", + " if (nrow(b_row) > 0) signs <- c(signs, Boot=sign(b_row$est))\n", + " if (!is.null(bayes_res)) {\n", + " by_row <- bayes_res[bayes_res$label == lab, ]\n", + " if (nrow(by_row) > 0) signs <- c(signs, Bayes=sign(by_row$est))\n", + " }\n", + " if (length(unique(signs[signs != 0])) > 1) sign_issues <- c(sign_issues, lab)\n", + "}\n", + "if (length(sign_issues) == 0) {\n", + " msg <- \"Sign consistency: All methods agree on direction for all parameters.\"\n", + " passed <- c(passed, msg); cat(\" PASS:\", msg, \"\\n\")\n", + "} else {\n", + " msg <- paste0(\"Sign disagreement for: \", paste(sign_issues, collapse=\", \"))\n", + " anomalies <- c(anomalies, msg); cat(\" ANOMALY:\", msg, \"\\n\")\n", + "}\n", + "\n", + "# 2. Magnitude discrepancies\n", + "cat(\"\\n--- Check 2: Magnitude Discrepancies ---\\n\")\n", + "mag_issues <- c()\n", + "for (lab in check_labels) {\n", + " ests <- c()\n", + " f_row <- fiml_res[fiml_res$label == lab, ]; if (nrow(f_row)>0) ests <- c(ests, f_row$est)\n", + " b_row <- boot_res[boot_res$label == lab, ]; if (nrow(b_row)>0) ests <- c(ests, b_row$est)\n", + " if (!is.null(bayes_res)) { by_row <- bayes_res[bayes_res$label==lab,]; if(nrow(by_row)>0) ests <- c(ests, by_row$est) }\n", + " if (length(ests) > 1 && min(abs(ests)) > 1e-6) {\n", + " ratio <- max(abs(ests)) / min(abs(ests))\n", + " if (ratio > 3) mag_issues <- c(mag_issues, sprintf(\"%s (ratio=%.1f)\", lab, ratio))\n", + " }\n", + "}\n", + "if (length(mag_issues) == 0) {\n", + " msg <- \"No magnitude discrepancies >3x between methods.\"\n", + " passed <- c(passed, msg); cat(\" PASS:\", msg, \"\\n\")\n", + "} else {\n", + " msg <- paste0(\"Magnitude discrepancy: \", paste(mag_issues, collapse=\"; \"))\n", + " warnings_list <- c(warnings_list, msg); cat(\" WARNING:\", msg, \"\\n\")\n", + "}\n", + "\n", + "# 3. Significance agreement\n", + "cat(\"\\n--- Check 3: Significance Agreement (total_indirect) ---\\n\")\n", + "sig_fiml <- fiml_res$pvalue[fiml_res$label==\"total_indirect\"] < 0.05\n", + "sig_boot <- (boot_res$ci.lower[boot_res$label==\"total_indirect\"] > 0 |\n", + " boot_res$ci.upper[boot_res$label==\"total_indirect\"] < 0)\n", + "sig_bayes <- if(!is.null(bayes_res)) {\n", + " pp <- bayes_res$pp_positive[bayes_res$label==\"total_indirect\"]\n", + " !is.na(pp) && (pp > 0.975 | pp < 0.025)\n", + "} else NA\n", + "\n", + "sig_agree <- c(FIML=sig_fiml, Bootstrap=sig_boot)\n", + "if (!is.na(sig_bayes)) sig_agree <- c(sig_agree, Bayesian=sig_bayes)\n", + "if (length(unique(sig_agree)) == 1) {\n", + " msg <- paste0(\"Total indirect: all methods agree (\", ifelse(all(sig_agree), \"significant\", \"not significant\"), \").\")\n", + " passed <- c(passed, msg); cat(\" PASS:\", msg, \"\\n\")\n", + "} else {\n", + " msg <- paste0(\"Total indirect significance disagrees: \", paste(names(sig_agree), sig_agree, sep=\"=\", collapse=\", \"))\n", + " warnings_list <- c(warnings_list, msg); cat(\" WARNING:\", msg, \"\\n\")\n", + "}\n", + "\n", + "# 4. Proportion mediated\n", + "cat(\"\\n--- Check 4: Proportion Mediated ---\\n\")\n", + "prop_rows <- fiml_res[grepl(\"^prop\", fiml_res$label), ]\n", + "prop_ok <- TRUE\n", + "if (nrow(prop_rows) > 0) {\n", + " for (r in 1:nrow(prop_rows)) {\n", + " pv <- prop_rows$est[r]\n", + " if (!is.na(pv) && (pv < 0 | pv > 1)) {\n", + " msg <- sprintf(\"%s = %.2f (outside [0,1] -- possible suppression)\", prop_rows$label[r], pv)\n", + " warnings_list <- c(warnings_list, msg); cat(\" WARNING:\", msg, \"\\n\")\n", + " prop_ok <- FALSE\n", + " }\n", + " }\n", + "}\n", + "if (prop_ok) {\n", + " msg <- \"All proportions mediated within [0, 1] or total near zero.\"\n", + " passed <- c(passed, msg); cat(\" PASS:\", msg, \"\\n\")\n", + "}\n", + "\n", + "# 5. Large SEs\n", + "cat(\"\\n--- Check 5: Large Standard Errors ---\\n\")\n", + "se_issues <- c()\n", + "for (lab in check_labels) {\n", + " f_row <- fiml_res[fiml_res$label == lab, ]\n", + " if (nrow(f_row)>0 && abs(f_row$est)>1e-6 && f_row$se > 5*abs(f_row$est)) {\n", + " se_issues <- c(se_issues, sprintf(\"%s (SE=%.4f, |est|=%.4f)\", lab, f_row$se, abs(f_row$est)))\n", + " }\n", + "}\n", + "if (length(se_issues) == 0) {\n", + " msg <- \"No excessively large standard errors.\"\n", + " passed <- c(passed, msg); cat(\" PASS:\", msg, \"\\n\")\n", + "} else {\n", + " msg <- paste0(\"Large SE: \", paste(se_issues, collapse=\"; \"))\n", + " warnings_list <- c(warnings_list, msg); cat(\" WARNING:\", msg, \"\\n\")\n", + "}\n", + "\n", + "# 7. Bootstrap convergence\n", + "cat(\"\\n--- Check 7: Bootstrap Convergence ---\\n\")\n", + "fail_rate <- 1 - n_converged / B_REPS\n", + "if (fail_rate <= 0.20) {\n", + " msg <- sprintf(\"Bootstrap: %d/%d converged (%.1f%%).\", n_converged, B_REPS, n_converged/B_REPS*100)\n", + " passed <- c(passed, msg); cat(\" PASS:\", msg, \"\\n\")\n", + "} else {\n", + " msg <- sprintf(\"High bootstrap failure rate: %.1f%%.\", fail_rate*100)\n", + " anomalies <- c(anomalies, msg); cat(\" ANOMALY:\", msg, \"\\n\")\n", + "}\n", + "\n", + "# 8. MNAR tipping\n", + "cat(\"\\n--- Check 8: MNAR Tipping Point ---\\n\")\n", + "if (exists(\"tip_row\") && !is.na(tip_row$dist) && is.finite(tip_row$dist)) {\n", + " if (tip_row$dist < 0.5) {\n", + " msg <- sprintf(\"Tipping point at %.2f SD -- FRAGILE.\", tip_row$dist)\n", + " warnings_list <- c(warnings_list, msg); cat(\" WARNING:\", msg, \"\\n\")\n", + " } else {\n", + " msg <- sprintf(\"Tipping point at %.2f SD.\", tip_row$dist)\n", + " passed <- c(passed, msg); cat(\" PASS:\", msg, \"\\n\")\n", + " }\n", + "} else {\n", + " msg <- \"Significant across entire grid -- robust.\"\n", + " passed <- c(passed, msg); cat(\" PASS:\", msg, \"\\n\")\n", + "}\n", + "\n", + "# 10. Suppression\n", + "cat(\"\\n--- Check 10: Suppression ---\\n\")\n", + "total_est <- fiml_res$est[fiml_res$label == \"total\"]\n", + "total_ind_est <- fiml_res$est[fiml_res$label == \"total_indirect\"]\n", + "cp_est <- fiml_res$est[fiml_res$label == \"cp\"]\n", + "if (length(total_est)>0 && length(total_ind_est)>0 && length(cp_est)>0) {\n", + " if (abs(total_ind_est) > abs(total_est) && abs(total_est) > 1e-6) {\n", + " msg <- sprintf(\"|total_indirect| (%.4f) > |total| (%.4f) -- suppression.\", abs(total_ind_est), abs(total_est))\n", + " warnings_list <- c(warnings_list, msg); cat(\" WARNING:\", msg, \"\\n\")\n", + " } else if (sign(total_ind_est) != sign(cp_est) && abs(total_ind_est)>1e-6 && abs(cp_est)>1e-6) {\n", + " msg <- \"Indirect and direct have opposite signs -- possible suppression.\"\n", + " warnings_list <- c(warnings_list, msg); cat(\" WARNING:\", msg, \"\\n\")\n", + " } else {\n", + " msg <- \"No suppression pattern.\"\n", + " passed <- c(passed, msg); cat(\" PASS:\", msg, \"\\n\")\n", + " }\n", + "}\n", + "\n", + "# 11. Component significance\n", + "cat(\"\\n--- Check 11: Component Significance ---\\n\")\n", + "comp_issues <- FALSE\n", + "for (i in 1:N_MED) {\n", + " a_p <- fiml_res$pvalue[fiml_res$label == paste0(\"a\", i)]\n", + " b_p <- fiml_res$pvalue[fiml_res$label == paste0(\"b\", i)]\n", + " ind_p <- fiml_res$pvalue[fiml_res$label == paste0(\"ind\", i)]\n", + " if (length(ind_p)>0 && !is.na(ind_p) && ind_p < 0.05) {\n", + " if ((length(a_p)>0 && !is.na(a_p) && a_p > 0.1) || (length(b_p)>0 && !is.na(b_p) && b_p > 0.1)) {\n", + " msg <- sprintf(\"ind%d significant (p=%.4f) but a%d p=%.4f, b%d p=%.4f.\", i, ind_p, i, a_p, i, b_p)\n", + " warnings_list <- c(warnings_list, msg); cat(\" WARNING:\", msg, \"\\n\")\n", + " comp_issues <- TRUE\n", + " }\n", + " }\n", + "}\n", + "if (!comp_issues) {\n", + " msg <- \"Component paths consistent with indirect effect significance.\"\n", + " passed <- c(passed, msg); cat(\" PASS:\", msg, \"\\n\")\n", + "}\n", + "\n", + "# Summary\n", + "cat(\"\\n================================================================\\n\")\n", + "cat(\"SANITY CHECK SUMMARY\\n\")\n", + "cat(sprintf(\"Passed: %d | Warnings: %d | Anomalies: %d\\n\", length(passed), length(warnings_list), length(anomalies)))\n", + "cat(\"================================================================\\n\")\n", + "if (length(passed) > 0) for (m in passed) cat(\" [PASS]\", m, \"\\n\")\n", + "if (length(warnings_list) > 0) for (m in warnings_list) cat(\" [WARN]\", m, \"\\n\")\n", + "if (length(anomalies) > 0) for (m in anomalies) cat(\" [ANOM]\", m, \"\\n\")\n", + "cat(\"================================================================\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Output File Inventory" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T01:09:46.143348Z", + "iopub.status.busy": "2026-04-01T01:09:46.141088Z", + "iopub.status.idle": "2026-04-01T01:09:46.239486Z", + "shell.execute_reply": "2026-04-01T01:09:46.236775Z" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Output Files ===\n", + " .claude/settings.local.json (1.2 KB)\n", + " APOE_ind_set_27_tdp_st4_APOE4_adj_SEM_mediation.ipynb (1337.5 KB)\n", + " MNAR_sensitivity/mnar_1d_slices_chr19_44999110_TAAAA_TAA.pdf (7.0 KB)\n", + " MNAR_sensitivity/mnar_1d_slices_chr19_44999110_TAAAA_TAA.png (81.7 KB)\n", + " MNAR_sensitivity/mnar_contour_indirect_chr19_44999110_TAAAA_TAA.pdf (6.8 KB)\n", + " MNAR_sensitivity/mnar_contour_indirect_chr19_44999110_TAAAA_TAA.png (109.6 KB)\n", + " MNAR_sensitivity/mnar_contour_pvalue_chr19_44999110_TAAAA_TAA.pdf (7.8 KB)\n", + " MNAR_sensitivity/mnar_contour_pvalue_chr19_44999110_TAAAA_TAA.png (137.7 KB)\n", + " MNAR_sensitivity/mnar_grid_results.csv (54.9 KB)\n", + " MNAR_sensitivity/mnar_tipping_summary.csv (0.2 KB)\n", + " bayesian_blavaan/bayesian_forest_plot.pdf (5.8 KB)\n", + " bayesian_blavaan/bayesian_forest_plot.png (55.5 KB)\n", + " bayesian_blavaan/bayesian_posteriors_chr19_44999110_TAAAA_TAA.pdf (71.1 KB)\n", + " bayesian_blavaan/bayesian_posteriors_chr19_44999110_TAAAA_TAA.png (267.5 KB)\n", + " bayesian_blavaan/bayesian_results.csv (2.0 KB)\n", + " bayesian_blavaan/bayesian_trace_a_chr19_44999110_TAAAA_TAA.pdf (26.8 KB)\n", + " bayesian_blavaan/bayesian_trace_a_chr19_44999110_TAAAA_TAA.png (188.6 KB)\n", + " bootstrap/bootstrap_distributions_chr19_44999110_TAAAA_TAA.pdf (7.4 KB)\n", + " bootstrap/bootstrap_distributions_chr19_44999110_TAAAA_TAA.png (77.9 KB)\n", + " bootstrap/bootstrap_forest_plot.pdf (5.7 KB)\n", + " bootstrap/bootstrap_forest_plot.png (51.2 KB)\n", + " bootstrap/bootstrap_results.csv (2.1 KB)\n", + " log/slurm_95659.err (0.0 KB)\n", + " log/slurm_95659.out (0.4 KB)\n", + " main_SEM_FIML/fiml_all_paths.csv (4.5 KB)\n", + " main_SEM_FIML/fiml_forest_plot.pdf (5.7 KB)\n", + " main_SEM_FIML/fiml_forest_plot.png (51.2 KB)\n", + " main_SEM_FIML/sensitivity_analysis/comparison_4med_vs_3med.csv (1.7 KB)\n", + " main_SEM_FIML/sensitivity_analysis/fiml_3med_all_paths.csv (3.7 KB)\n", + " main_SEM_FIML/sensitivity_analysis/fiml_3med_forest_plot.pdf (5.5 KB)\n", + " main_SEM_FIML/sensitivity_analysis/fiml_3med_forest_plot.png (49.8 KB)\n", + " session_notes.md (3.4 KB)\n", + " summary/all_methods_summary.csv (7.6 KB)\n", + " summary/summary_combined_panel.pdf (8.3 KB)\n", + " summary/summary_combined_panel.png (130.0 KB)\n", + " summary/summary_display_table_chr19_44999110_TAAAA_TAA.csv (1.8 KB)\n", + " summary/summary_faceted_by_path.pdf (10.1 KB)\n", + " summary/summary_faceted_by_path.png (96.8 KB)\n", + " summary/summary_forest_all_methods.pdf (7.0 KB)\n", + " summary/summary_forest_all_methods.png (65.0 KB)\n", + " summary/summary_indirect_effect.pdf (5.5 KB)\n", + " summary/summary_indirect_effect.png (50.5 KB)\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 17: Output File Inventory\n", + "# ============================================================\n", + "\n", + "cat(\"=== Output Files ===\", \"\\n\")\n", + "all_files <- list.files(RESULT_DIR, recursive=TRUE, full.names=FALSE)\n", + "for (f in sort(all_files)) {\n", + " full_path <- file.path(RESULT_DIR, f)\n", + " size_kb <- round(file.info(full_path)$size / 1024, 1)\n", + " cat(sprintf(\" %s (%.1f KB)\\n\", f, size_kb))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T01:09:46.245523Z", + "iopub.status.busy": "2026-04-01T01:09:46.243793Z", + "iopub.status.idle": "2026-04-01T01:09:46.274734Z", + "shell.execute_reply": "2026-04-01T01:09:46.272059Z" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "session_notes.md saved.\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# Section 18: Save session_notes.md\n", + "# ============================================================\n", + "\n", + "session_notes <- paste0(\n", + " \"# Session Notes: SEM Parallel Mediation Analysis\\n\\n\",\n", + " \"## Date: \", Sys.Date(), \"\\n\\n\",\n", + " \"## Analysis\\n\",\n", + " \"- **Exposure:** chr19_44999110_TAAAA_TAA\\n\",\n", + " \"- **Mediators (parallel):** APOC4_APOC2_AC_exp, APOC2_AC_exp, APOC1_DeJager_Mic_exp, APOE_Mega_Mic_exp\\n\",\n", + " \"- **Outcome:** tdp_st4\\n\",\n", + " \"- **Design:** Parallel mediation (Design 2) -- 4 mediators simultaneously\\n\",\n", + " \"- **Direction:** Unidirectional (SNP -> M -> Y)\\n\",\n", + " \"- **N =** \", N_TOTAL, \" (FIML, all subjects with exposure observed)\\n\",\n", + " \"- **Covariates:** Strategy A (covariates-in-model)\\n\",\n", + " \" - M1, M2 (AC_exp): msex_u, age_death_u, pmi_u, ROS_study_u\\n\",\n", + " \" - M3, M4 (Mic_exp): msex_u, age_death_u, pmi_u\\n\",\n", + " \" - Y (tdp_st4): educ, apoe4_dose, msex_u, age_death_u\\n\",\n", + " \"- **APOE4 adjustment:** apoe4_dose included as covariate in outcome equation\\n\\n\",\n", + " \"## Methods\\n\",\n", + " \"1. FIML SEM (lavaan, missing='fiml', fixed.x=FALSE)\\n\",\n", + " \"2. Bootstrap (1000 replicates, FIML inside each)\\n\",\n", + " \"3. MNAR Sensitivity (15x15 delta grid, delta-shift imputation)\\n\",\n", + " \"4. Bayesian SEM (blavaan, 2 chains x 2000 samples, Stan backend)\\n\\n\",\n", + " \"## Key Findings\\n\",\n", + " \"- See key findings section in notebook and all_methods_summary.csv for detailed results.\\n\",\n", + " \"- Residual correlations among mediators were freely estimated.\\n\\n\",\n", + " \"## Sensitivity Analysis: 3-Mediator Model (Excluding APOC4_APOC2_AC_exp)\\n\",\n", + " \"- Removed APOC4_APOC2_AC_exp to assess collinearity impact with APOC2_AC_exp.\\n\",\n", + " \"- Removing M1 resolves the collinearity -- APOC2 b-path drops from 1.67 (SE=1.32) to -0.03 (SE=0.05).\\n\",\n", + " \"- a-paths remain stable; total indirect changes from 0.0005 to 0.006 (still non-significant).\\n\",\n", + " \"- Direct effect and total effect remain essentially unchanged.\\n\",\n", + " \"- Conclusion: The 4-mediator model's large opposing ind1/ind2 are collinearity artifacts.\\n\",\n", + " \"- Results in: main_SEM_FIML/sensitivity_analysis/\\n\\n\",\n", + " \"## Sanity Check\\n\",\n", + " \"- Passed: \", length(passed), \"\\n\",\n", + " \"- Warnings: \", length(warnings_list), \"\\n\",\n", + " \"- Anomalies: \", length(anomalies), \"\\n\\n\",\n", + " if (length(warnings_list) > 0) paste0(\"### Warnings\\n\", paste0(\"- \", warnings_list, collapse=\"\\n\"), \"\\n\\n\") else \"\",\n", + " if (length(anomalies) > 0) paste0(\"### Anomalies\\n\", paste0(\"- \", anomalies, collapse=\"\\n\"), \"\\n\\n\") else \"\",\n", + " \"## Next Steps\\n\",\n", + " \"- Compare with unadjusted (non-APOE4) model if available.\\n\",\n", + " \"- Test individual mediators in separate models (Design 1) for comparison.\\n\",\n", + " \"- Examine pairwise contrasts to determine if any mediator dominates.\\n\",\n", + " \"- Consider serial mediation if biological ordering among mediators is known.\\n\"\n", + ")\n", + "\n", + "writeLines(session_notes, file.path(RESULT_DIR, \"session_notes.md\"))\n", + "cat(\"session_notes.md saved.\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-01T01:09:46.281028Z", + "iopub.status.busy": "2026-04-01T01:09:46.278811Z", + "iopub.status.idle": "2026-04-01T01:09:46.739070Z", + "shell.execute_reply": "2026-04-01T01:09:46.736885Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Session Info === \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in system(\"timedatectl\", intern = TRUE):\n", + "\u201crunning command 'timedatectl' had status 1\u201d\n" + ] + }, + { + "data": { + "text/plain": [ + "R version 4.4.3 (2025-02-28)\n", + "Platform: x86_64-conda-linux-gnu\n", + "Running under: Ubuntu 24.04.3 LTS\n", + "\n", + "Matrix products: default\n", + "BLAS/LAPACK: /mnt/lustre/home/yl4437/.pixi/envs/python/lib/libopenblasp-r0.3.30.so; LAPACK version 3.12.0\n", + "\n", + "locale:\n", + " [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 \n", + " [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 \n", + " [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C \n", + "[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C \n", + "\n", + "time zone: Etc/UTC\n", + "tzcode source: system (glibc)\n", + "\n", + "attached base packages:\n", + "[1] grid stats graphics grDevices utils datasets methods \n", + "[8] base \n", + "\n", + "other attached packages:\n", + "[1] gridExtra_2.3 ggplot2_4.0.2 blavaan_0.5-10 Rcpp_1.1.1 lavaan_0.6-21 \n", + "\n", + "loaded via a namespace (and not attached):\n", + " [1] gtable_0.3.6 QuickJSR_1.9.0 inline_0.3.21 \n", + " [4] CompQuadForm_1.4.4 lattice_0.22-9 quadprog_1.5-8 \n", + " [7] vctrs_0.7.1 tools_4.4.3 generics_0.1.4 \n", + "[10] stats4_4.4.3 parallel_4.4.3 sandwich_3.1-1 \n", + "[13] tibble_3.3.1 pkgconfig_2.0.3 Matrix_1.7-4 \n", + "[16] RColorBrewer_1.1-3 S7_0.2.1 RcppParallel_5.1.11-2\n", + "[19] uuid_1.2-2 lifecycle_1.0.5 tmvnsim_1.0-2 \n", + "[22] compiler_4.4.3 farver_2.1.2 textshaping_1.0.4 \n", + "[25] mnormt_2.1.2 repr_1.1.7 codetools_0.2-20 \n", + "[28] htmltools_0.5.9 bayesplot_1.15.0 pillar_1.11.1 \n", + "[31] crayon_1.5.3 MASS_7.3-65 StanHeaders_2.32.10 \n", + "[34] parallelly_1.46.1 rstan_2.32.7 tidyselect_1.2.1 \n", + "[37] digest_0.6.39 mvtnorm_1.3-3 future_1.69.0 \n", + "[40] nonnest2_0.5-8 dplyr_1.2.0 listenv_0.10.0 \n", + "[43] labeling_0.4.3 fastmap_1.2.0 cli_3.6.5 \n", + "[46] magrittr_2.0.4 loo_2.9.0 base64enc_0.1-6 \n", + "[49] pkgbuild_1.4.8 IRdisplay_1.1 future.apply_1.20.1 \n", + "[52] pbivnorm_0.6.0 withr_3.0.2 scales_1.4.0 \n", + "[55] IRkernel_1.3.2 matrixStats_1.5.0 globals_0.19.0 \n", + "[58] igraph_2.2.2 pbdZMQ_0.3-14 ragg_1.5.0 \n", + "[61] zoo_1.8-15 coda_0.19-4.1 evaluate_1.0.5 \n", + "[64] viridisLite_0.4.3 rstantools_2.6.0 rlang_1.1.7 \n", + "[67] isoband_0.3.0 glue_1.8.0 jsonlite_2.0.0 \n", + "[70] R6_2.6.1 systemfonts_1.3.1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ============================================================\n", + "# Section 19: Cleanup + Session Info\n", + "# ============================================================\n", + "\n", + "temp_patterns <- c(\"lavExport\", \"*.stan\", \"tmp_*\", \"*.bak\")\n", + "for (pat in temp_patterns) {\n", + " files <- list.files(RESULT_DIR, pattern=glob2rx(pat), full.names=TRUE, recursive=TRUE)\n", + " if (length(files) > 0) {\n", + " file.remove(files)\n", + " cat(\"Removed\", length(files), \"temp files matching\", pat, \"\\n\")\n", + " }\n", + "}\n", + "\n", + "lav_dirs <- list.dirs(RESULT_DIR, recursive=TRUE, full.names=TRUE)\n", + "lav_dirs <- lav_dirs[grepl(\"lavExport\", lav_dirs)]\n", + "for (d in lav_dirs) unlink(d, recursive=TRUE)\n", + "\n", + "cat(\"\\n=== Session Info ===\", \"\\n\")\n", + "sessionInfo()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_31_SEM_mediation.ipynb b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_31_SEM_mediation.ipynb new file mode 100644 index 0000000..b93b8d7 --- /dev/null +++ b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_31_SEM_mediation.ipynb @@ -0,0 +1,3147 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6122afec", + "metadata": {}, + "source": [ + "# Parallel Mediation SEM: BLOC1S3 Expression (DLPFC, AC, PCC) Mediating SNP Effect on Age at First AD Diagnosis\n", + "\n", + "## Aim\n", + "Test whether BLOC1S3 gene expression in three brain regions (DLPFC, AC, PCC) **jointly mediates**\n", + "the effect of SNP chr19_45114770_ACCC_AC on age at first AD diagnosis (`age_first_ad_dx_num`).\n", + "\n", + "This is a **Design 2 (parallel mediation)** analysis: all three mediators enter the outcome equation\n", + "simultaneously, allowing us to estimate the **specific indirect effect** through each region while\n", + "controlling for the others.\n", + "\n", + "**Direction:** Unidirectional only (SNP -> Expression -> AD age of onset).\n", + "\n", + "## Methods\n", + "| Method | Description |\n", + "|--------|-------------|\n", + "| FIML SEM (lavaan) | Full-information ML; uses all N=1153 subjects including those missing one or more phenotypes |\n", + "| Bootstrap (FIML inside) | 1000 resamples of full data; FIML fit inside each replicate for non-parametric CIs |\n", + "| MNAR Sensitivity | Delta-shift imputation grid to assess robustness to missing-not-at-random |\n", + "| Bayesian SEM (blavaan/Stan) | Posterior distributions via MCMC; handles missing data via data augmentation |\n" + ] + }, + { + "cell_type": "markdown", + "id": "51bcad8d", + "metadata": {}, + "source": [ + "## Directed Acyclic Graph (DAG)\n", + "\n", + "```\n", + " Covariates (X\u2192M):\n", + " msex_u, age_death_u,\n", + " pmi_u, ROS_study_u\n", + " \u2502\n", + " \u25bc\n", + " \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", + " a1 \u2502 BLOC1S3_DLPFC_exp \u2502 b1\n", + " \u250c\u2500\u2500\u2500\u25b6\u2502 (M1) \u2502\u2500\u2500\u2500\u2510\n", + " \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502\n", + " \u2502 \u2502 \u2191 \u2502\n", + " \u2502 \u2502 \u2502 (~~) \u2502\n", + " \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502\n", + " \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2510 a2 \u2502 BLOC1S3_AC_exp \u2502 b2 \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", + " \u2502 SNP \u2502\u2500\u2500\u2500\u25b6\u2502 (M2) \u2502\u2500\u2500\u2500\u253c\u2500\u25b6\u2502 age_first_ad_dx_num\u2502\n", + " \u2502 (X) \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502 \u2502 (Y) \u2502\n", + " \u2514\u2500\u2500\u2500\u252c\u2500\u2500\u2518 \u2502 \u2502 \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2518\n", + " \u2502 \u2502 \u2502 (~~) \u2502 \u2502\n", + " \u2502 \u2502 \u2193 \u2502\n", + " \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502 Covariates (X\u2192Y):\n", + " \u2502 a3 \u2502 BLOC1S3_PCC_exp \u2502 b3 \u2502 educ, apoe4_dose,\n", + " \u2514\u2500\u2500\u2500\u25b6\u2502 (M3) \u2502\u2500\u2500\u2500\u2518 apoe2_dose, msex_u\n", + " \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n", + " \u2502 \u2502\n", + " \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 c\u2032 (direct) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n", + "```\n", + "\n", + "**Note:** Double-headed arrows (~~) between mediators indicate freely estimated\n", + "residual covariances. These account for shared unmeasured causes across brain regions\n", + "(e.g., common regulatory mechanisms affecting BLOC1S3 expression in multiple regions).\n", + "\n", + "**Indirect effects:**\n", + "- **ind1** = a1 \u00d7 b1 (SNP \u2192 DLPFC \u2192 Y)\n", + "- **ind2** = a2 \u00d7 b2 (SNP \u2192 AC \u2192 Y)\n", + "- **ind3** = a3 \u00d7 b3 (SNP \u2192 PCC \u2192 Y)\n", + "- **Total indirect** = ind1 + ind2 + ind3\n", + "- **Total effect** = c\u2032 + total indirect\n" + ] + }, + { + "cell_type": "markdown", + "id": "0bba0a23", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "**Model:** Parallel mediation SEM with freely estimated residual covariances among the three mediators\n", + "(BLOC1S3 expression in DLPFC, AC, and PCC). This accounts for shared unmeasured causes across brain regions.\n", + "\n", + "**Overall:** The SNP chr19_45114770_ACCC_AC has a significant total effect on age at first AD diagnosis\n", + "(total = -3.05, p = 0.004, FIML). The total indirect effect through BLOC1S3 expression is marginally\n", + "significant (est = -0.70, p = 0.045, FIML), accounting for ~23% of the total effect.\n", + "\n", + "**Region-specific findings:**\n", + "\n", + "| Path | Estimate (FIML) | p-value | Interpretation |\n", + "|------|----------------|---------|----------------|\n", + "| **ind3: SNP -> PCC -> Y** | **-0.971** | **0.018** | **Only individually significant indirect effect** |\n", + "| ind1: SNP -> DLPFC -> Y | 0.203 | 0.332 | Non-significant (positive direction) |\n", + "| ind2: SNP -> AC -> Y | 0.068 | 0.769 | Non-significant |\n", + "| Total indirect | -0.701 | 0.045 | Significant; driven primarily by PCC |\n", + "| Direct effect (c') | -2.354 | 0.033 | Significant; substantial residual effect |\n", + "\n", + "**Impact of mediator correlations:** Compared to the model without residual covariances, the direct\n", + "effect (c') is now significant (p = 0.033 vs. 0.099), and the total indirect effect is smaller in magnitude\n", + "(-0.70 vs. -1.08) but still significant. The PCC indirect effect (ind3) remains the dominant mediator.\n", + "The DLPFC path (ind1) flipped sign to positive, suggesting suppression effects when correlations are modeled.\n", + "\n", + "**Cross-method consistency:** The PCC-mediated indirect effect (ind3) is the largest across\n", + "FIML, Bootstrap, and Bayesian methods. Bayesian posterior probability of ind3 < 0 is 99.7%.\n", + "Bootstrap CIs are wider (p = 0.26), reflecting uncertainty in the correlated-mediator model.\n", + "\n", + "**MNAR sensitivity:** Results are robust to moderate deviations from MAR assumptions.\n", + "\n", + "**Key takeaway:** BLOC1S3 expression in the **posterior cingulate cortex (PCC)** is the primary mediator\n", + "of the SNP's effect on AD age of onset. Accounting for inter-regional correlations in gene expression\n", + "reduces the total indirect effect but confirms the PCC pathway as the key mechanism.\n" + ] + }, + { + "cell_type": "markdown", + "id": "033d1b16", + "metadata": {}, + "source": [ + "## 1. Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b1148acd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:00:09.136438Z", + "iopub.status.busy": "2026-03-31T16:00:09.131674Z", + "iopub.status.idle": "2026-03-31T16:00:19.248160Z", + "shell.execute_reply": "2026-03-31T16:00:19.245909Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setup complete.\n" + ] + } + ], + "source": [ + "suppressPackageStartupMessages({\n", + " library(lavaan)\n", + " library(blavaan)\n", + " library(ggplot2)\n", + " library(gridExtra)\n", + " library(reshape2)\n", + "})\n", + "\n", + "# \u2500\u2500 Paths \u2500\u2500\n", + "DATA_FILE <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set31/APOE_ind_set_31_mediation_all_input.txt\"\n", + "RESULT_DIR <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set31\"\n", + "\n", + "dir_fiml <- file.path(RESULT_DIR, \"main_SEM_FIML\")\n", + "dir_boot <- file.path(RESULT_DIR, \"bootstrap\")\n", + "dir_mnar <- file.path(RESULT_DIR, \"MNAR_sensitivity\")\n", + "dir_bayes <- file.path(RESULT_DIR, \"bayesian_blavaan\")\n", + "dir_summ <- file.path(RESULT_DIR, \"summary\")\n", + "\n", + "for (d in c(dir_fiml, dir_boot, dir_mnar, dir_bayes, dir_summ))\n", + " dir.create(d, recursive = TRUE, showWarnings = FALSE)\n", + "\n", + "# \u2500\u2500 Variables \u2500\u2500\n", + "exposure <- \"chr19_45114770_ACCC_AC\"\n", + "med_cols <- c(\"BLOC1S3_DLPFC_exp\", \"BLOC1S3_AC_exp\", \"BLOC1S3_PCC_exp\")\n", + "outcome <- \"age_first_ad_dx_num\"\n", + "med_covs <- c(\"msex_u\", \"age_death_u\", \"pmi_u\", \"ROS_study_u\")\n", + "out_covs <- c(\"educ\", \"apoe4_dose\", \"apoe2_dose\", \"msex_u\")\n", + "all_covs <- unique(c(med_covs, out_covs))\n", + "\n", + "cat(\"Setup complete.\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "4c3ec4e2", + "metadata": {}, + "source": [ + "## 2. Data Loading and Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0f88f5cf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:00:19.291440Z", + "iopub.status.busy": "2026-03-31T16:00:19.253351Z", + "iopub.status.idle": "2026-03-31T16:00:19.390607Z", + "shell.execute_reply": "2026-03-31T16:00:19.388476Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dimensions: 1153 x 51 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Missingness pattern:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Variable N_obs N_miss Pct_miss\n", + " chr19_45114770_ACCC_AC 1153 0 0.0\n", + " BLOC1S3_DLPFC_exp 777 376 32.6\n", + " BLOC1S3_AC_exp 593 560 48.6\n", + " BLOC1S3_PCC_exp 441 712 61.8\n", + " age_first_ad_dx_num 388 765 66.3\n", + " msex_u 1115 38 3.3\n", + " age_death_u 1115 38 3.3\n", + " pmi_u 1115 38 3.3\n", + " ROS_study_u 1115 38 3.3\n", + " educ 1113 40 3.5\n", + " apoe4_dose 1106 47 4.1\n", + " apoe2_dose 1106 47 4.1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Total N (all have SNP): 1153 \n" + ] + } + ], + "source": [ + "dat <- read.delim(DATA_FILE, header = TRUE, stringsAsFactors = FALSE)\n", + "cat(\"Dimensions:\", nrow(dat), \"x\", ncol(dat), \"\\n\")\n", + "\n", + "# Keep only the columns we need\n", + "keep_cols <- c(exposure, med_cols, outcome, all_covs)\n", + "missing_cols <- setdiff(keep_cols, names(dat))\n", + "if (length(missing_cols) > 0) stop(\"Missing columns: \", paste(missing_cols, collapse=\", \"))\n", + "\n", + "dat <- dat[, keep_cols]\n", + "# Convert all to numeric\n", + "for (col in names(dat)) dat[[col]] <- as.numeric(dat[[col]])\n", + "\n", + "cat(\"\\nMissingness pattern:\\n\")\n", + "miss_tbl <- data.frame(\n", + " Variable = keep_cols,\n", + " N_obs = sapply(keep_cols, function(v) sum(!is.na(dat[[v]]))),\n", + " N_miss = sapply(keep_cols, function(v) sum(is.na(dat[[v]]))),\n", + " Pct_miss = sapply(keep_cols, function(v) round(100*mean(is.na(dat[[v]])), 1))\n", + ")\n", + "print(miss_tbl, row.names = FALSE)\n", + "\n", + "N_total <- nrow(dat)\n", + "cat(\"\\nTotal N (all have SNP):\", N_total, \"\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "76a54bda", + "metadata": {}, + "source": [ + "### Sample Overlap" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "91978585", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:00:19.396645Z", + "iopub.status.busy": "2026-03-31T16:00:19.394964Z", + "iopub.status.idle": "2026-03-31T16:00:19.437478Z", + "shell.execute_reply": "2026-03-31T16:00:19.435323Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pairwise overlap counts:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " BLOC1S3_DLPFC_exp BLOC1S3_AC_exp BLOC1S3_PCC_exp\n", + "BLOC1S3_DLPFC_exp 777 543 413\n", + "BLOC1S3_AC_exp 543 593 365\n", + "BLOC1S3_PCC_exp 413 365 441\n", + "age_first_ad_dx_num 262 188 133\n", + " age_first_ad_dx_num\n", + "BLOC1S3_DLPFC_exp 262\n", + "BLOC1S3_AC_exp 188\n", + "BLOC1S3_PCC_exp 133\n", + "age_first_ad_dx_num 388\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Subjects with at least one mediator OR outcome observed: 942 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Subjects with all 3 mediators AND outcome observed: 98 \n" + ] + } + ], + "source": [ + "# Overlap matrix\n", + "obs_mat <- !is.na(dat[, c(med_cols, outcome)])\n", + "cat(\"Pairwise overlap counts:\\n\")\n", + "overlap <- crossprod(obs_mat * 1)\n", + "print(overlap)\n", + "\n", + "cat(\"\\nSubjects with at least one mediator OR outcome observed:\",\n", + " sum(rowSums(obs_mat) > 0), \"\\n\")\n", + "cat(\"Subjects with all 3 mediators AND outcome observed:\",\n", + " sum(rowSums(obs_mat) == 4), \"\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "45cf3445", + "metadata": {}, + "source": [ + "## 3. Model Specification (Parallel Mediation)\n", + "\n", + "```\n", + "SNP -> BLOC1S3_DLPFC_exp (a1) -> age_first_ad_dx_num (b1)\n", + "SNP -> BLOC1S3_AC_exp (a2) -> age_first_ad_dx_num (b2)\n", + "SNP -> BLOC1S3_PCC_exp (a3) -> age_first_ad_dx_num (b3)\n", + "SNP -> age_first_ad_dx_num (cp, direct)\n", + "\n", + "Specific indirects: ind1 = a1*b1, ind2 = a2*b2, ind3 = a3*b3\n", + "Total indirect = ind1 + ind2 + ind3\n", + "Total effect = cp + total_indirect\n", + "```\n", + "\n", + "**Covariate strategy: Covariates-in-model (Strategy A)**\n", + "- Each mediator equation includes: msex_u, age_death_u, pmi_u, ROS_study_u\n", + "- Outcome equation includes: educ, apoe4_dose, apoe2_dose, msex_u\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bfdf74fd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:00:19.443960Z", + "iopub.status.busy": "2026-03-31T16:00:19.442167Z", + "iopub.status.idle": "2026-03-31T16:00:19.535789Z", + "shell.execute_reply": "2026-03-31T16:00:19.533669Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model specification:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BLOC1S3_DLPFC_exp ~ a1 * chr19_45114770_ACCC_AC + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "BLOC1S3_AC_exp ~ a2 * chr19_45114770_ACCC_AC + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "BLOC1S3_PCC_exp ~ a3 * chr19_45114770_ACCC_AC + msex_u + age_death_u + pmi_u + ROS_study_u\n", + "age_first_ad_dx_num ~ b1 * BLOC1S3_DLPFC_exp + b2 * BLOC1S3_AC_exp + b3 * BLOC1S3_PCC_exp + cp * chr19_45114770_ACCC_AC + educ + apoe4_dose + apoe2_dose + msex_u\n", + "BLOC1S3_DLPFC_exp ~~ BLOC1S3_AC_exp\n", + "BLOC1S3_DLPFC_exp ~~ BLOC1S3_PCC_exp\n", + "BLOC1S3_AC_exp ~~ BLOC1S3_PCC_exp\n", + "ind1 := a1 * b1\n", + "ind2 := a2 * b2\n", + "ind3 := a3 * b3\n", + "total_indirect := ind1 + ind2 + ind3\n", + "total := cp + total_indirect\n", + "prop_med1 := ind1 / total\n", + "prop_med2 := ind2 / total\n", + "prop_med3 := ind3 / total\n", + "prop_med_total := total_indirect / total\n", + "diff_1_2 := ind1 - ind2\n", + "diff_1_3 := ind1 - ind3\n", + "diff_2_3 := ind2 - ind3 \n" + ] + } + ], + "source": [ + "# Build model string\n", + "med_cov_str <- paste(med_covs, collapse = \" + \")\n", + "out_cov_str <- paste(out_covs, collapse = \" + \")\n", + "\n", + "n_med <- length(med_cols)\n", + "\n", + "# Mediator equations\n", + "med_eqs <- sapply(seq_len(n_med), function(i)\n", + " paste0(med_cols[i], \" ~ a\", i, \" * \", exposure, \" + \", med_cov_str))\n", + "\n", + "# Outcome equation with all mediators\n", + "y_med_terms <- paste0(\"b\", seq_len(n_med), \" * \", med_cols, collapse = \" + \")\n", + "y_eq <- paste0(outcome, \" ~ \", y_med_terms, \" + cp * \", exposure, \" + \", out_cov_str)\n", + "\n", + "# Defined parameters\n", + "ind_defs <- sapply(seq_len(n_med), function(i)\n", + " paste0(\"ind\", i, \" := a\", i, \" * b\", i))\n", + "total_ind_def <- paste0(\"total_indirect := \", paste0(\"ind\", seq_len(n_med), collapse = \" + \"))\n", + "total_def <- \"total := cp + total_indirect\"\n", + "prop_defs <- sapply(seq_len(n_med), function(i)\n", + " paste0(\"prop_med\", i, \" := ind\", i, \" / total\"))\n", + "prop_total_def <- \"prop_med_total := total_indirect / total\"\n", + "\n", + "# Pairwise contrasts\n", + "contrasts <- c()\n", + "for (i in 1:(n_med - 1)) {\n", + " for (j in (i + 1):n_med) {\n", + " contrasts <- c(contrasts, paste0(\"diff_\", i, \"_\", j, \" := ind\", i, \" - ind\", j))\n", + " }\n", + "}\n", + "\n", + "# Residual covariances among mediators\n", + "med_covars <- c()\n", + "for (i in 1:(n_med - 1)) {\n", + " for (j in (i + 1):n_med) {\n", + " med_covars <- c(med_covars, paste0(med_cols[i], \" ~~ \", med_cols[j]))\n", + " }\n", + "}\n", + "\n", + "model_str <- paste(c(med_eqs, y_eq, med_covars, ind_defs, total_ind_def, total_def,\n", + " prop_defs, prop_total_def, contrasts), collapse = \"\\n\")\n", + "\n", + "cat(\"Model specification:\\n\")\n", + "cat(model_str, \"\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "7f051c7d", + "metadata": {}, + "source": [ + "## 4. FIML SEM (Primary Analysis)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b2392a68", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:00:19.541796Z", + "iopub.status.busy": "2026-03-31T16:00:19.540152Z", + "iopub.status.idle": "2026-03-31T16:00:24.937694Z", + "shell.execute_reply": "2026-03-31T16:00:24.935477Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model converged: TRUE \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N used: 1153 \n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== All Parameter Estimates ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label est se z pvalue ci.lower ci.upper\n", + "1 a1 0.553 0.119 4.629 0.000 0.319 0.787\n", + "6 a2 0.514 0.165 3.110 0.002 0.190 0.838\n", + "11 a3 0.645 0.174 3.708 0.000 0.304 0.987\n", + "16 b1 0.366 0.370 0.989 0.323 -0.360 1.093\n", + "17 b2 0.132 0.444 0.296 0.767 -0.739 1.002\n", + "18 b3 -1.505 0.477 -3.152 0.002 -2.440 -0.569\n", + "19 cp -2.354 1.106 -2.129 0.033 -4.521 -0.186\n", + "79 ind1 0.203 0.209 0.969 0.332 -0.207 0.612\n", + "80 ind2 0.068 0.230 0.294 0.769 -0.383 0.518\n", + "81 ind3 -0.971 0.409 -2.373 0.018 -1.773 -0.169\n", + "82 total_indirect -0.701 0.350 -2.001 0.045 -1.388 -0.014\n", + "83 total -3.055 1.063 -2.874 0.004 -5.138 -0.972\n", + "84 prop_med1 -0.066 0.074 -0.893 0.372 -0.212 0.079\n", + "85 prop_med2 -0.022 0.076 -0.292 0.770 -0.171 0.126\n", + "86 prop_med3 0.318 0.176 1.805 0.071 -0.027 0.663\n", + "87 prop_med_total 0.229 0.137 1.673 0.094 -0.039 0.498\n", + "88 diff_1_2 0.135 0.326 0.413 0.679 -0.505 0.775\n", + "89 diff_1_3 1.174 0.513 2.289 0.022 0.169 2.179\n", + "90 diff_2_3 1.039 0.547 1.897 0.058 -0.034 2.112\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Fit Measures ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chisq df pvalue cfi tli rmsea srmr aic \n", + " 421.335 12.000 0.000 0.397 -0.910 0.172 0.089 33217.375 \n", + " bic \n", + "33611.285 \n" + ] + } + ], + "source": [ + "set.seed(42)\n", + "fit_fiml <- sem(model_str, data = dat, missing = \"fiml\", fixed.x = FALSE, estimator = \"ML\")\n", + "\n", + "cat(\"Model converged:\", lavInspect(fit_fiml, \"converged\"), \"\\n\")\n", + "cat(\"N used:\", lavInspect(fit_fiml, \"nobs\"), \"\\n\\n\")\n", + "\n", + "pe <- parameterEstimates(fit_fiml, ci = TRUE)\n", + "cat(\"=== All Parameter Estimates ===\\n\")\n", + "print(pe[pe$label != \"\", c(\"label\", \"est\", \"se\", \"z\", \"pvalue\", \"ci.lower\", \"ci.upper\")])\n", + "\n", + "# Fit measures\n", + "fm <- fitMeasures(fit_fiml, c(\"chisq\", \"df\", \"pvalue\", \"cfi\", \"tli\", \"rmsea\", \"srmr\", \"aic\", \"bic\"))\n", + "cat(\"\\n=== Fit Measures ===\\n\")\n", + "print(fm)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f9e049fb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:00:24.944347Z", + "iopub.status.busy": "2026-03-31T16:00:24.942522Z", + "iopub.status.idle": "2026-03-31T16:00:24.989124Z", + "shell.execute_reply": "2026-03-31T16:00:24.987001Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FIML results saved. N = 1153 \n" + ] + } + ], + "source": [ + "# Save FIML results\n", + "N_fiml <- lavInspect(fit_fiml, \"nobs\")\n", + "\n", + "# All labeled params\n", + "fiml_res <- pe[pe$label != \"\", c(\"label\", \"est\", \"se\", \"z\", \"pvalue\", \"ci.lower\", \"ci.upper\")]\n", + "fiml_res$N <- N_fiml\n", + "fiml_res$method <- \"FIML\"\n", + "fiml_res$ci_type <- \"Wald\"\n", + "write.csv(fiml_res, file.path(dir_fiml, \"fiml_all_paths.csv\"), row.names = FALSE)\n", + "\n", + "# Fit measures\n", + "fm_df <- data.frame(measure = names(fm), value = as.numeric(fm), N = N_fiml)\n", + "write.csv(fm_df, file.path(dir_fiml, \"fiml_fit_measures.csv\"), row.names = FALSE)\n", + "\n", + "cat(\"FIML results saved. N =\", N_fiml, \"\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "f0e98d67", + "metadata": {}, + "source": [ + "### FIML Forest Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1cd94bc2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:00:24.995160Z", + "iopub.status.busy": "2026-03-31T16:00:24.993451Z", + "iopub.status.idle": "2026-03-31T16:00:27.598606Z", + "shell.execute_reply": "2026-03-31T16:00:27.595997Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FIML forest plot saved.\n" + ] + }, + { + "data": { + "image/png": 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DYzM9Pc3FxdXb28vBwv6du3L41GO378OJ/P5/F4gYGBOA4TBDSLFy9GCB05cgRX\nsLi42MvLCyEUHh5OsdhbtmxBCEVGRgqWkAM7CwsLCwuL3r17f/r0icvl8vn8r1+/0un0gQMH\n4rMpn8+/ceMGg8Hw8/PDq+B/ehJdihXbDgMHDkQInT59Gr+9du2avr6+RIGd2Bw2bNiAELp2\n7RpBEGw2u1OnTiYmJiUlJRTzt7Ky0tDQGDx4sKDM8+bNQwhdvXoVvxUb2OEcpkyZggetvn//\nnk6n6+vre3h44NCnoKBAXV1d9PUZioEdlZOOkCdPnjg07ty5c2KbqLHDsuhPjYyMnJ2dP3z4\nsG3btrlz565YseLevXsitkLlcN18ILCTc/hXjRCytbWdN29eSEhI/UHZgj9PxD9XCQX/eygG\ndvjuy8mTJ0tdzufPn3t7e+M/+lOnThV7RBgyZAj+izZ16tS+ffsqKSlNnTqVfFFVdGyRkJAg\ndDSpb9asWYKf5Zs3bxBCw4cPl6hSIo4gVL4XLC8vLyAgYNGiRba2tlpaWkeOHBGxxR49eigr\nKwv1m4puilGjRiGE3r59KzaliARCO4DYfCRqT7wTBgUFkReyWCx1dXVdXV3cKZWfn89gMNq3\nby+oexM3QaWm1Dchxf7To0cPVVVV6unro/6bSkpKEurq+P333xFC0dHRBEF8/vwZISQUZQ4Z\nMkQQjpSUlCgpKY0dO5acoLCwkE6nkzufRBMd2OFhlOTRCw8fPkQICcaTYI8ePUpPT8evpQjs\nRLdDTk4O7v0iJ8B/pSgGdlRy4HA43bp1MzMzq6io2Lx5MyINuqUCNxT5Uum9e/cQQmvXrsVv\nqQR2CKGMjAxBAnt7e0S6S4AgCHzHqOCCeH3Ue+wIcScdmZMusFNWVlZXV2cwGOrq6u3bt8e3\nR0ycOFHoni2JDtfNB6Y7kXPHjx+fM2fOuXPn7t+/f+LEiRMnTiCEevbsuW7duvHjx9dPHxQU\ndOvWrUWLFr1580Zwt6aQkJAQ/DcLIVReXv7y5cv4+HgjI6PAwMAG09fW1paVlQneqqurq6ur\nC6Xp1atXaGjo7t279+zZs2fPnp49ezZ2nzyWmZmZl5eHECIIoqioCCGUl5eXkJCA/3OLVVVV\nhUsiIg2+a7WkpIRieolQ/17y8/M3bdqEEFJWVvbz8xPceNWggoICAwODBmfZaIyqqir6p4IU\nUdwByMkElJWV16xZI1F74hMPjiQELly4UFlZuWTJEjzHjZGR0ciRI8PDw+/evQAvn0QAACAA\nSURBVDtixAgk4VfW4CbE1pT6JqTYf4yMjJKTkysrKxtcS7a/qR49ehQXF1+4cOHz5884EkpM\nTEQIsVgshNCrV68QQrgLUGDKlClRUVH49bNnz9hs9pcvXxYsWEBOo6qqKmLWG0nR6XR8ORtz\ncHBQU1M7cuRI+/btJ0yYgPu9hAopKdHtkJKSghDq06cPeRV3d/fg4GCK+VPJQVFRMSQkpHfv\n3nPnzo2IiPj5559Hjx4tUS2UlZXxFVhMT09PUAWKVFVVra2thXLAw47JSyorK9XU1CQqW4Oo\nnHRaF5fLtba2VlVV3bZtm4eHB41Gy8nJ8fb2vn79emBgoL+/vyClRIfr5gOBnfxzcXFxcXFB\nCBUUFMTHx0dERNy4cWPChAl79uzBY1bIDAwMdu7cOX/+/G3btuGRrfXhK4kYg8EwNTX18/Pb\nsGGDqalpg+mvXbvm6+sreOvv799gznl5eUeOHPnzzz8JgsDjY0Q4c+YM+SifnZ29dOnScePG\nnTt3bsaMGaLXRQjhKU7Ip8b68Kd4cgqcHgd5skLxe3F0dCwtLcW3sm7cuPHYsWN///13Y8eL\noqIifNsmdd+/f0cISTTfEsUdgJxMQEtLa82aNRK1Z1RUlIGBAflchRA6duwYQgj3qmKzZs0K\nDw8/ceIEDuyavgkkrqbUNyHF/oNXKSwsbDCwk+1v6ty5c35+fnV1dd26dTMwMFBQUBD8a0II\n4T9OQrO04H4dwVZwMhy4CDg4ODCZTEq1pUBPT488u6Surm5YWNjs2bP9/PwWLFjQtWtXLy+v\nuXPnWlhYSL0J0e1QWFiI/vleBOpPXiMCxRy6d+++evXqrVu3Ghoa7tu3T9Ja6OnpkeenxH/z\ncBUo0tHRIb+l0+kKCgrk/UeKPEWgctJpXQwGIzU1lbykffv2ly9ftrCwOHXqFDmwk+hw3YwF\nbuHtgVZkZGQ0adKkSZMmBQQEODk5rV+/funSpQyG8D4wd+7ckJCQ3bt3+/j4NHgyePDgATmo\nEqtr164BAQGCt3iUCdmLFy/27dt35coVGo02ZcqUZcuW9ezZk3r+CCELC4sLFy7o6Ohs3ryZ\nSmDXoUMHOp3+4sULEWlSUlJoNBo+gZmamiorKyclJdXV1SkqKkpUNrFEfy8KCgra2tra2trW\n1taurq4dOnRYtWoVvm+gQUJzDotWW1ublJSkoaHRWFDeIIo7wM2bN/v37y+0EJ8SqLcnQRDR\n0dH4X7Jg4cuXL5OSkhQVFfGFUayurg4hFBERkZ+fb2xs3MRNUKkp9U1Isf+IPmvK8DeVl5c3\nb948PT29hw8fCsK1HTt2rFu3jlwSocYhv8WvfXx8tm/fTqVq0qkfI3p4eGRlZcXFxUVGRt69\ne3fr1q1BQUF//fWXdBPbim0HTOh74fF4km6ISg44RC4pKcnKynJ2dpZ0Ez8csScdgbdv3+Lr\nvA3y9fXFl+BbgJmZWceOHTMzM/l8vuAiiaSH62YCjxSTWwRBfPjw4d27d/U/6tixY79+/aqq\nqvCYDyE0Gu3o0aN8Pn/BggX1wz4pdO3adSOJ4O8Lj8e7evWqq6trr169YmJi/P39c3Jyzp07\nJ2lUh6mrqxsZGWVlZfH5fLGJdXR03NzcsrOzBZeThKSkpLx69crV1RUPEldSUhoxYkR5eXlo\naGiD6a9evbp48WJ8T4ZoFL+X7Ozsq1evpqenkxO0a9euXbt2Da6L6enp4V4BikJDQ1ks1vjx\n42XyRQvR0tLSrwc/WoN6e6amphYUFAhdJMXddaampikkb9++NTU15XK5ISEhTd8EFdQ3IcX+\ng/vJ6s+ejcnwNxUbG8tms+fNm0fuhPv06ZPgNZ5mr7i4mLwWOQH+V5Cdnd3YJpoPvsUkKCgo\nNTU1Li5OQUFh2bJl0mUlth1wPxb+XgS+fftGfRMUc/jzzz9v3bq1a9cuY2PjmTNncjgc6psQ\ni0ajCUWWEh0xmgn1kw6LxUppXLPWpf6ZpaKiQklJiU6nS3e4bj4Q2Mmt169f29rajh8/vra2\nVuij2tra169fKyoqNnbacHR0/PXXXx8+fHjlypVmKt67d+86duyI7706f/58dnZ2QEBAUx7A\n8uXLl9zcXBMTE4ojzFatWoUQ8vPzw1dbyMrKymbPno0QWrNmjWDhb7/9RqfTf/vtt7dv3wql\nf/369cKFC/HEeGJR/F4yMjKmTJki1AVSVFT07ds3Ea1kZGRUWFhIJbRFCKWmpq5atYrJZK5c\nuZJKetmi2J71R7+xWKyLFy+qqamlpKS8+W9xcXE0Gu3UqVP41CX1JmReC4lSYgUFBSoqKhIN\ny5PuN4XbitxNUlxcfP36dcFHnTt3Rgjh0WYCV69eFbx2cXFRUlK6detWZWWlYGFNTc0vv/zy\n/Plz6uWXyKtXrw4fPkze1QcOHOji4pKZmUkOXKhfLhTbDo6Ojgihp0+fktfCtyZQRCWHvLy8\n5cuX9+/ff+XKlQcPHnzz5g0esyUr6urqQkMCmu87kgjFk06fPn3eNI7K5RopxMTEaGlp/fbb\nb+SFz58/LygowMNppDtcNx8I7ORWt27dJk6c+OHDB3d391u3bhUXF/P5/JKSkvv37w8fPvzb\nt2+zZ88W0emN5zGS7TGFrKioaMCAAc+fP3/27JmPj4+k1zc5HE7tP/Ly8iIjI728vIh/psAQ\nqKysLKsHX/sYMWLEypUrP3786OTkdODAgYyMjLKysqysrJMnTzo7OycnJ69atWrkyJGCrFxd\nXTds2FBaWtq3b98tW7a8fv36+/fvr1692rRpk6ura1VV1ZkzZ3D3Xl1dHS4Y/qvN4/HwWzab\njSh/L25ubnZ2dmfPnt2yZUtBQUFdXV1ycvL48eO5XK6Ig1efPn1qa2uFhjoJtVhFRUVqampA\nQEC/fv3Kysr27duHzzdUGk2GKLZnVFSUtbU1eeAUvm3i559/xj1JZNbW1p6enpmZmfiOB6k3\nIfNaSJQSIVRZWfnmzRuhUfZiSfebwoPir127Vl1djRD69OnTmDFjhg4dihD6+PEjQsje3r5T\np06RkZHXrl1DCPH5/KCgIHI/hJaW1ty5cysqKhYsWIBvEykpKZk5c+bBgwcbvCYgE4mJiUuW\nLFmzZo0gmnz48OHz58979OiBLw3jm5/w0Cgq4Z3YdrC2tnZycoqLiwsODsZTrpw7d66xLv8G\nUcnBz8+vqqrq+PHjNBpt3Lhx48aNCwwMFD1oRCLdu3f/+vWrIJh78uRJ8/17l1SznnREH5ZF\nf9q/f39NTc1Dhw7t37+/oqKitrY2Kipq+vTpCKHVq1cjhKQ7XDejFrr7FrSGqqqqBnu2mUzm\nihUrhJ4liu88J8Nz8SNqExS3mMYe4qmgoLB8+XLBbBciHleamJgoyO3kyZNmZmZCCczNzfHc\n6/WdO3cOP62BzMnJ6enTp4I0jQ3yMDMzwwkofi8ZGRl4FmUyX19fQYL6cAfDrl27qLSYjY0N\n+cljVBqN4g5AfT8R3Z5sNltVVXXBggXkVZycnGg0WmMzKdy+fRshNH369KZsQqIqiN2EFCnx\nBMU7duygsvWmw3+H8MAgBQWFgICA7OxsBoPBZDIHDRpEEERsbCzuO8TX0+3t7fGetmbNGpxD\ndXX1pEmTEEKqqqoWFhZ4jhXytIJiiZ3uRPDzwerq6vDtIwwGw8TEBIdxNjY2grkhT506hRCi\n0+mqqqoi5t2QqB1evHiBxxLgzlQjIyMcFQkmahZLdA74Zh3yA3O/fv2qqanp4OBAca7p+g2F\n51pfvHgxfhsfH6+lpaWiouLh4dG7d28LCwscrwu+rPo5uLm5KSgokJf4+PgghAQPSqlPoulO\nyOqfdGRF9GFZ7EH7zZs3NjY2eKHgUbBHjx4V5C/F4br5wM0T8kxVVTU4OHjLli0PHz78+PFj\nTU2NmpqalZWVm5sbubdjzJgx7dq1qz82ecyYMQcPHiwqKhL0HOCUHTp0aLEq1IfLQF6ipKSE\nJ0nHU7o3lkyAfKPAnDlzZs2aFR8fn5GRUVhYqK+vb2tr6+rq2tj13BkzZnh7e8fHx3/48KG4\nuNjY2Lhr16744UXkNA32teBzD6L8vVhbWycnJyckJLx79w6Xzd3dHc+M35hhw4bp6elduHAB\nX2husCnwTLn29vb154YQ22gUdwDq+4no9iwtLV21atWYMWME6VksFr75sbGZOzw9Pbdt20b+\n+iTdhKRVELsJKVJeuHABPxyMytab7tixY9OmTUtOTmYymUOGDMFt++jRowcPHuD9zc3N7cOH\nD+Hh4aWlpZ06dRo9enRcXBxCSDA5hYqKytWrV1+9ehUfH89isUxMTIYOHVr/L5MIAwcODAgI\nIM+ysXLlSmVlZfz6l19+4XK55PQMBuPs2bP+/v4JCQmFhYWampqdOnUaPHiw4KufPXu2kZHR\n27dvzczM6gfT0rUDnqI2PDw8Ly+vXbt2Y8aModFoAQEBffv2pVhN0TmUlZVt2bKF/OM1MzO7\nePHi8+fP09PT69+1XV/9hjI2Ng4ICOjduzd+6+rq+v79+7t37+ICeHl58fn8gIAA/EjGBnOY\nOXOm0H2dEyZMsLa2bsr0T9RPOrIi+rAs9qDt4ODw/v376Ojo9PR0FotlYWExfPhwPO0LJsXh\nuvkIj6MEAPzQdu7cuXbt2lu3buGZh8EPJzMz087O7qeffjp9+nRrl+X/4WkjBwwYIFgSFBS0\natWq0NBQb2/vViwYaJvwUSgtLU30dKSgmcAYOwDkytKlSy0tLf/44w/Z3kwHWsy6devU1NTw\no6XaiMWLFw8cOPDixYu4IyAxMTEoKEhbWxv+PADQBsGlWADkipqa2rVr1/r167dmzRryNG/g\nh3Dy5MmrV69evXq1dQc8CAkODh4+fLi3t/eiRYsYDEZRUZG2tvalS5e0tLTErhseHh4TEyM6\nzZYtW8TOSd4ULVCG5t5EW2hG8KOAwA4AeePk5HTt2rWXL1+Wl5dTOfWCNoLP55eWlp45cwbf\niNB2dOzY8f379/fu3UtPT6+pqbG0tBwxYgTFXSs/Px8/KlcEoUFdMtcCZWjuTbSFZqTO1dU1\nICAAP+cNtDwYYwcAAAAAICdgjB0AAAAAgJyAwA4AAAAAQE5AYAcAAAAAICcgsAMAAAAAkBMQ\n2AEAAAAAyAkI7AAAAAAA5AQEdgAAAAAAcgICOwAAAAAAOQGBHQAAAACAnIDADjQXDofD4/Fa\nuxRtXU1NTW1tbWuXoq3jcrl1dXWtXYq2rra2tqamBh4mJBqPx2Oz2a1diraOw+HU1NTAAVw0\ngiDa5tEbAjvQXNhsdtt5dmHbRBBEVVVVdXV1axekraurq4PATqyampqqqioI7ETj8/kQ2InF\nZrOrqqogsBONIIi2efSGwA4AAAAAQE5AYAcAAAAAICcgsAMAAAAAkBMQ2AEAAADgXzExMWfP\nns3JyWntggBpQGAHAAAAgH/V1NRUVFTA3W8/KAjsAAAAAADkBAR2AAAAAAByAgI7AAAAAAA5\nAYEdAAAAAICcgMAOAAAAAEBOQGAHAAAAgH8xGAxlZWUFBYXWLgiQBqO1CwAAAACANmT48OHu\n7u6ampqtXRAgDeixAwAAAACQExDYAQAAAADICRlfiq2srPz06ZONjY2ysjLFVWpqagoLC2tr\na7W1tQ0NDckfvXv3jsFgdOrUibywvLw8JyfH0dFRkIbH4+HXdDpdT0/P0NCQTpdZwCrIn0aj\nqaqqmpqa1q/au3fvdHR0TExMRKzeWPHICQQUFRXt7OzIS8rLywsKCphMpqGhoaqqatPrBQAA\nAPxw2I8fV4Ve4CS+4JeW0rW1mT2d1aZPVxo4oLXL1YbQCIKQVV7p6em7du0qLCzct29fx44d\nxaavrKw8efJkXFwcQojBYLDZ7Hbt2i1durRz5844wbRp06qrqwMDAwVLEELx8fG7d+8ODw8n\npyFnq6+vv2DBgt69e8ukUkL5Kygo9OjRY+bMme3btyen8fDwmDdvntjV6xevfgKEkJ6e3pkz\nZ/Dr7Ozs48ePv3nzBn9TCgoK/fr1mz9/vpaWlizq14xYLBaTyVRSUmrtgrRdBEEUFxfT6XRd\nXd3WLkubVlNTQxAE/KURrbS0lMfj6erqyvCfrfypq6urqamB0WOisVgsNputqanJZDJbuyz/\nImprS1esrLkZVv8jldGjdfb+h9ayhwg+n19WVtYGj94y67G7d+/e8ePHXV1dY2JiKK5y+PDh\nt2/fbtmyxcHBgUaj5eXlHThwYOPGjSdPntTQ0MBpVFVVjx8/vmfPHhqN1lg+Xl5eOKji8/l5\neXmnTp3asWPH/v37ybFXU+D8CYKorKx89+7dhQsXVq5cuXXrVqGuRKmL5+XlNXv2bPIqgspm\nZ2evXr3awMAgICDAzs6Ow+GkpKScOnVq1apVBw4coN4tCgAAAPzAeLySefNrYx40+GHNrVv8\n8nL982cRA24JlXyMHYfD+fz5c2ZmZlVVFXl5enr6pk2bRowYUX+VrKysT58+1V/+4sWLYcOG\ndenSBccxJiYmy5cvd3NzKysrE6Tx8vL6/PlzVFQUlbLR6XQzM7Ply5fzeLwnT56ISBkZGXns\n2LHc3Fwq2WI0Gk1DQ8PFxWXXrl0mJiZ79+6VtLNTRPEU/pvgD/eRI0dUVVV37Njh5OSkqqqq\nra3t7u6+adOmsrKy5ORkibYOAAAA/KCqzp1vLKrD2PHxlf9c6fofJ1lgd+fOnZ9++mnNmjWb\nNm3y9fW9evWq4KNFixY5ODg0uFZwcPDp06frL1dTU8vOziaHR4aGhosWLTI3NxcsMTIyGj16\n9Llz5+pfr2yMhoYGk8kUijuFODo6lpSULF68ePPmza9evaKYM6akpOTt7f3t27c3b95ItCL1\n4mG5ublpaWnjx49XV1cnL+/YseOFCxf69u0rxdYBAAAA0fLy8up337QmgmAdCRabqvJIMOLz\nW6A4bZwEnZa1tbVhYWETJkyYPHkyjUZ7/PhxYGBg7969LSwsEEIiZjL08vJiNNQ7OmLEiNDQ\n0NWrV3t4eDg6OpqamtZPw+fzp0+fHhsbe/ny5VmzZlEp58ePHzkcjpmZmYg07dq1W7t2bX5+\nflhY2LZt24yMjMaMGePm5kZxPEHXrl0RQpmZmYJ7OKijUjwsMzMTIdTgJhpsTzKCIPitvX/j\nMtS/NQQICP7VQCuJxufzCYKAVqKCx+PJcOS0/IF9iYqXL1+mp6dramo202BEPovFLymlnp77\n6TPv2zexyXjfC6vu3WN0sqWeM11Hmy5tHVtxXxI9d7QEgZ2ysnJwcHBVVVVGRgaHw1FRUSEI\nIisrCwd2Iri5uTW4fMqUKVpaWhEREYcPH0YI6evru7q6jh8/XkdHh5xMVVXVx8fn2LFjw4cP\nbzD4Kyoqwr1ufD4/Pz//+vXrRkZGjW2UzNjY2M/Pz8fH586dOxcuXDh79mxAQIC1tbXYFVVU\nVBgMBovFEpuSSvEKCgoSExPJq2hra9vY2OD8pbtJgsvllpeXS7GibHE4nDb0n6+t4vP5paUS\nHOP+Z9XW1rZ2EX4AbeGH3/bBL0403C9QW1vbTA3FOx/K3R3UHDmXzWngLkYRFJYuYcyd05Qt\ntsq+pKenJ+LGA8mGGYaEhISFhRkbG2tra+MlbDZb6pLRaDRPT09PT8/v37+/evXq5cuXt27d\nevDgwX/+8x+heU+GDx8eGRl58uTJDRs21M/n+fPnggFnenp6zs7OPj4+QjcWfPny5ds/8b6V\nlZWBgYHgI3V1dS8vLyUlpZCQkKKiIiqBHYfD4XK5QldIGyO2eImJiS9evCCv0rNnzz/++APf\nAyhdC9NoNLG9es2Nx+PR6XQROx8Q/Ntr9S+rjcOnGbjZUzQul4tgXxIHX0mAh2WJho/bzXce\noRka8h3sJVihqor/OZtSzu3Naf/cfEkFw9i4KXXkcrlt8BcnQYFevXp148aNVatWDRgwACHE\n5XInTJggk0IYGhoOHTp06NChWVlZK1asuH379syZM8kJaDTavHnz1q1bl5SUVH/1kSNHNjjV\nCFlsbOz169fx619++WXw4MH4dWlp6d9//x0ZGamiojJjxoxu3bpRKfDHjx8RQg1OXCdF8UaP\nHt1gAiMjI4TQp0+fjI2NqWyIjMFgCILv1gLTnYglmO6k1b+sNg6mO6ECT3eiqakJEbAIMN0J\nFXgXUlFRaa5Dk/d05D2denJefn5+z96IwhgDo8uXFdqbi00mE3i6kzZ49JYgsHv37p2GhgaO\n6hBC3yhc8BahsLAwKSlpyJAh5H9OVlZWpqamhYWF9dN36dKlX79+J0+enD5dgr1BwNfX19fX\nl7wkOzv75s2bcXFxNjY2ixYt6tu3L/Wj4Z07d5SVlZ2cnKQoCXV2dnaampp37twRuk+CIIgt\nW7YMHToU7p8AAAAg9xSMjZnOzpz/vrRVn2JXxxaL6toyCf7YKSgocLlcwTjBsLAwGo0m9Qj9\nwsLCw4cPh4X910yD2dnZeXl5jQ3amz179vfv36Ojo6XbIllUVNTy5cu5XO6uXbsCAwP79+9P\nPaq7e/fugwcPpkyZ0tx9UXQ6ferUqcnJyZcvXyaPsj969OjLly/19fWbdesAAABAG6G55nck\n+jRNo2mtWdNSxWnTJOix69WrV2ho6KFDh7p37/706dP27dubmJg8ffrUysrKxsbmypUrCKGi\noiKE0J07d3R0dNTU1MaMGYMQWr9+vbKysr+/Pzk3e3t7Ly+vkJCQFy9edO7cmclk5uXlPX78\nuEOHDqNHj26wAIaGhmPHjiXPsSI1a2vrkydPUpwwOj09/eLFiwihqqqqt2/fZmVljRgxQugy\ndFpa2p9//kleMnHiRIqD8EQYPXp0bm5uaGjow4cPu3btyuVyU1JSSkpKVq5caWNj08TMAQAA\ngB+CUt++mmtWV2zf0VgCzRW/KbkNbMkitVkSBHaWlpYbNmyIiopKSEhwcXEZMmSIlZXVnTt3\nsrOzra2tU1NTcbIuXbp8/fr169evgptbTU1NG+zcmjdvnqura0JCQk5ODo/H09PTW7ZsGfmS\nqL29vVDsNXny5I8fP5LvJ7C3t2/wVlnROnToQDGlvb19bW0trp2ysrKdnZ2fn5/Qg1xxmvT0\ndPLCuro6KsUTnYBGo/n5+Q0aNCguLi4/P5/BYAwaNGjYsGHQXQcAAKCZ2Nra6ujotLWHZWks\nXqRgbFy+aTO/uJi8nK6jo7VhveqUya1VsLZGls+KBYAMbp4QC54VSxHcPEEFPCuWCrh5goq2\n+axYjGBV1ty9y0lM5JeW0bW1mD17Ko/wpEtyJ6ysyP+zYgEAAAAAmhVNQ1110kTVSRNbuyBt\nF/yxAwAAAACQExDYAQAAAADICQjsAAAAAADkBAR2AAAAAAByAgI7AAAAAAA5AYEdAAAAAP4V\nHx9/5cqVr1+/tnZBgDQgsAMAAADAvyoqKr5//87hcFq7IEAaENgBAAAAAMgJCOwAAAAAAOQE\nBHYAAAAAAHICAjsAAAAAADkBgR0AAAAAgJyAwA4AAAAA/2IwGMrKygoKCq1dECANRmsXAAAA\nAABtyPDhw93d3TU1NVu7IEAa0GMHAAAAACAnILADAAAAAJATENgBAAAAAMgJCOwAAAAAAOQE\nBHYAAAAAAHICAjsAAAAAADkBgR0AAAAA/lVYWPjly5fq6urWLgiQBgR2AAAAAPjX8+fPw8LC\n8vPzW7sgQBowQTEAAAAA2i4+i1Vz/UZtfDw/v4CmoqLYpYvKuDHM7t1bu1xt1I8R2LFYrPPn\nzycmJlZWVrZr127atGm9e/cWvQpBENHR0Xfu3MnPz6+trdXW1nZ2dp4+fbq2tjZOMG3aNC6X\ne+TIEUNDQ8Fa8fHxu3fvDg8PF6QR9EXTaDQ9PT0HBwdfX1/yKk3H5/Nnz55dUlISHBxsZmYm\n9Gltbe3NmzcfPXqUl5fHZDJNTU09PDw8PT1pNJoMywAAAAC0QTV/3y5bs5ZfUiJYwn7ypPLE\nCZWxY3R276KpqbVi2dqmHyCwIwhi+/btBQUFP/30k56e3r1797Zt27Z79+5OnTqJWOvcuXN/\n/fXXlClTunbtymQyP336FBoampqaevDgQcHz77hc7pkzZ1avXi0in379+o0aNQohxOfz8/Ly\nbty4sWLFikOHDmlpacmqgsnJySwWy8zMLCYmxtfXl/wRi8Xy9/fPz88fNWpU586d2Wx2SkrK\n0aNHk5KS1q1bB7EdAAAAOVZ97Vrpr78hgqj/UU1YOC87R//6VZqycssXrC37AQK7jIyMt2/f\nbtq0qUePHgghe3v7169fJyQkiA7s7t275+npOX36dPzWxsbG3Nz8wIEDHz9+tLGxwQvd3d1j\nYmLevn3r4ODQWD56enqOjo74dbdu3RwcHBYvXhwXFzdmzJjGVnnx4gWbze7bty+dTmkIY3R0\ndI8ePWxsbO7duzdjxgxyuHby5Mnc3Nxdu3Z17NgRLxkwYECXLl327t378OFDNzc3KvkDAAAA\nPxxudnbZqtUNRnUYJyWlYmeg1saAlixV29eGbp7IyMhYv369t7f3lClTVqxY8erVK7y8Q4cO\nhw8f7tq1K36roKCgq6tbXl6O365cuXL9+vX1c+PxeEJxVefOnYODgwVRHULI0dGxV69eJ06c\nIBrfb4SYm5szmczCwkIRaRgMxvHjx+fPn//XX39VVVWJzrCqqur58+dubm5ubm6FhYVv3rwR\nfFReXv7w4UMvLy9BVIcNGjRo165dAwcOpFhmAAAA4IdTeegwweGITlMV8ie/qKhlyvOjaCuB\nHYfD2bhxo6qq6tatW//zn/84ODhs27atuLgYIcRkMs3NzQXXT4uKirKzs+3t7fFbe3t7W1vb\n+hn26tXr1q1bZ8+e/fbtW2Mb5fP5c+bMycnJuXfvHsVylpaWcjgcHR0dEWm6d+9+6tSp6dOn\nx8TEzJo169ixY3l5eY0lfvjwoaKioouLi7Gxsb29fXR0tOCjtLQ0Ho/n+7nA5wAAIABJREFU\n4uJSfy07Ozu4DgsAAECO1dwVf2om6upqYx60QGF+IG3lUiyDwdi/f7+6urqysjJCyNvb++bN\nm2lpaa6uruRkdXV1QUFBJiYmgwcPxktmz57dYIYLFizg8XjXr1+/du0avpzq6uraq1cvcjxE\nEISpqeno0aPPnz/v6uqq1sgYTB6PhxPn5eWdPHlSSUlJqFQNVsfDw8PDwyMlJeXmzZsLFizo\n2bOnn59f/bsuoqOjBwwYwGQyEUIeHh4nTpxYuHChkpISQqi0tBQhJN2NGnV1dRUVFVKsKEME\nQXA4nMrKytYtRtvH5/PxfxjQGNynXlNT09oFadNwK+HjBhCBIAj4xYlmZWWlo6PDZDIlbqja\nWvaQYbIpBEEQLBaVhGVr1pYFbJTJNhVm/syYO4d6+tbal3R1dUV07rSVwI5Op2dmZl65ciUn\nJ4fzT9cr67+/1Nra2m3btn3//n379u2KioqiM1RVVV21atXcuXOTkpJevXqVkpISGxvr4OAQ\nEBCg/N8DLadNm/bgwYOLFy/OnTu3fj4RERERERGCt+3atdu4caNQsMXhcOrq6vBrJSUlBuPf\nVu3evXv37t0TEhL27t378eNHoRW/fv364cOHWbNm4dixb9++x48ff/Lkibu7O0II58Pn80XX\ntDHUry83n7ZQhh8CNBQV0EpUQCtRAa0kmqWlpaWlJZKiofh8osX7FAg2G7HZsspK0iq3wX2p\nrQR2OTk5gYGBI0aMWL9+vba2Np/PHz9+PDlBRUXFpk2bampqdu7cSb0TS0dHB/ec8Xi8O3fu\nHDt27Pbt2xMmTCCnUVVV9fX1DQ4O9vT0rJ/DwIEDx40bh1/r6ek1eBH24sWL169fx6+XLVvm\n4eEh+OjVq1d//fVXcnKys7Mz/p2QRUVFIYTWrl1LXhgdHY0DOz09PYRQbm6uvr4+xfoKKCoq\nSrGWbLFYLCaTiXsfQYPwvz06na6rq9vaZWnTampqCIJQVVVt7YK0aaWlpTweT1dXl+JtW/+b\n6urqampqNDU1W7sgbRqLxWKz2ZqamvhqkmS+fZFVMfIcHPllZWKT6fwnSHXaVFltlDo+n19W\nVtYGj95tJbB78eKFoqLinDlz8Fg6oasJbDZ706ZNBEEEBgZqaGiIzY0giNzcXPKccAoKCqNG\njQoLC/v48WP99EOHDr19+/bJkyeHDh0q9JGWlpa1tbXozY0YMaJXr174Nd4ol8t9+PDhzZs3\n8/PzPTw85s+fb2pqKrQWn8+PjY318vIaNGiQYGFGRsbRo0eLi4v19PTs7e2ZTGZCQoLgxhGB\nGzduODs7W1hYiC4YAAAA8INSHja0+spVMYkYDKXBg8Sk+R/TVv7Y1dTUMJlMwR0SMTEx5E+P\nHTtWXV29adMmKlEdQujJkycLFy5MSkoiL6yuri4vL2+wy41Go82bNy8pKen169dSFN7Q0ND+\nH1paWikpKXPmzAkNDXV3dz9z5oyfn1/9qA4hlJycXFJS4unpaU0ydOhQVVXV2NhYhJCysrKH\nh8e9e/cENwhjsbGxISEhnz9/lqKoAAAAwA9BY8liGkNM95Oaj7eCTB8ZIAfaSo+dra3t5cuX\n79+/7+zs/OTJk+zsbB0dnU+fPlVXVxcUFERHR8+YMYMcyigpKeF57EJCQphMpre3Nzk3FxeX\nzp07BwYGjhs3rnPnzkwmMzc3Nzw8nE6njxw5ssECODg49O/fn/rtsSJwudz58+eLnccuOjra\nwsLC3NycvJDBYLi4uMTExEycOBEh9PPPP2dlZW3cuHHYsGHdunWrq6tLSkqKjY0dOXIkTGIH\nAABAjjGsrLS2bilbs7axBIoODlp/+LdkkX4IbSWw69mz56RJk86ePXvq1CkXF5clS5aEhYXd\nuHFDUVHRyMiIIIhz586R05uZmQUHByOE3rx5o6KiIpSbgoLCpk2bwsPDHz16FBYWxuPx9PX1\nu3XrNmHCBBHj82bNmpWYmIjvY2hiXcSmwdPXTZo0qf5Hrq6uMTExmZmZ1tbWqqqq27dvv3Xr\nVlxc3IMHDxgMhoWFxe+//96/f/8mFhIAAABo49R8Z9BUVMr/WM+vd4es8rChOvv30WDobT20\nNnhDB5APcPOEWHDzBEVw8wQVcPMEFXDzBBVNunmiGfBLSqovXa6Nj+fl5tHV1RUd7FXGj1Pq\n27eVSwU3TwAAAACg7Xv69GlOTs6QIUPayC16dF1d9UUL1RctbO2C/Bjgjx0AAAAA/lVcXPzl\nyxeYEvwHBYEdAAAAAICcgMAOAAAAAEBOQGAHAAAAACAnILADAAAAAJATENgBAAAAAMgJCOwA\nAAAAAOQEzGMHAAAAgH+NGjVqyJAhMI3zDwp67AAAAAAA5AQEdgAAAAAAcgICOwAAAAAAOQGB\nHQAAAACAnIDADgAAAABATkBgBwAAAAAgJyCwAwAAAMC/CgsLv3z5Ul1d3doFAdKAwA4AAAAA\n/3r+/HlYWFh+fn5rFwRIAwI7AAAAAAA5AYEdAAAAAICcgMAOAAAAAEBOQGAHAAAAACAnILAD\nAAAAAJATENgBAAAAAMgJRmsXAAAAAABtiJWVlZqamra2dmsXRDIEl8t+GM9+8oRfWETX0lTs\n1k156BC6hkZrl6ultX5gx+Px7t+/n5qaWltba25uPnr0aH19fbFr5eXl3bt3Lz8/v6amRkdH\nx8nJqX///nT6/3dAbt68mSCI1atXKysrC1Z5/fr1pUuXtm/fLkhTW1uLX9NoNH19fXt7+6FD\nhwoyaSKK+b99+zYhISE/P5/JZJqYmHh4eJibmwtlRSUNAAAAIBN2dnaWlpaampqtXRAJsB89\nKlu9lvvpE3khXVtbc83var6+rVWqVtHKl2L5fP6WLVvOnj2ro6NjZWWVmJi4dOnSgoIC0WvF\nx8cvXLjww4cPHTp06Nq1K4/H27Nnz+7duwUJ3r179/Lly6tXr5LXKi8vf/PmDTkNh8NxdHR0\ndHS0t7fncrlHjx5ds2YNl8uVSdXE5s/n8w8cOLB27dqMjIx27dppa2s/ffp0yZIlN2/eFGRC\nJQ0AAADwv6wmPKLIe4ZQVIcQ4peVla1ZV75pc6uUqrW0co9dampqUlLSzp077e3tEUJjx46d\nPXt2VFSUj4+PiLXOnTvXq1cvf39/wRJHR8eDBw+mpaV17twZL3FwcAgLCxs2bJiRkVFj+dja\n2k6fPl3w9sWLF5s3b46Pjx80aFBjq3z+/FlTU1NXV5dK7UTnf/Xq1aioqCVLlgwbNgwnIAji\n+PHjp0+ftrGxcXBwoJgGAAAA+J/FzcoqXf4barxTpvL4CWaPHipjvFqyVK2ohQI7FosVGRmZ\nlZVVV1fXvn37sWPH6ujoIISsrKyCgoI6deqEk6mpqeno6LBYLPw2ODhYSUlp9uzZQrmVlpa6\nubmRl3h4ePTs2RPnKVhSWVl5+vTptWvXUiyks7OzoqLip0+fRAR2mZmZR44ccXV1HTNmjLW1\nNcWc6+fPZrP/+uuvAQMGCCI2hBCNRpszZ46urq6enh5CiEoaAAAA4H9ZxZ69xD8DnxpTvn27\nyuhRSEZDrdq4lqgkQRB//PFHXFycvb19z54909PTV61ahcefqaurC6I6hNDjx4/z8vJ69+6N\n3379+vXbt2/1M7S2to6MjExKShIsodPp5KgOb3Tu3LlPnjxJTU2lWM66ujoul6uioiIizZAh\nQ/bs2UOn03///fc1a9Y8fvyYz+dLkf/79++rq6s9PDyE0jAYjMmTJxsbG1NMAwAAAPzPItjs\n2nv3xSbjffnKSUlpgfK0BS3RY8fhcMaNG2dra2tqaooQ6t+/v6+vb2pqaq9evQRpfvvtt9LS\nUmVlZX9/fycnJ7xw27ZtDWa4aNGiTZs2bdy4UVdX19HRsWvXri4uLkLDPAmC6Natm4uLy/Hj\nx/fv3y/2lgiCIC5evEgQhLOzs+iUHTp0+PXXX3/++edbt24dPnz49OnTo0eP9vT0JN+oITZ/\n/HBl0fdAUEnTGC6XW1lZKcWKMsTj8bhcbk1NTesWoy0jCAIhxOfzy8rKWrssbRr++8ThcFq7\nIG0aj8dDCFVUVLR2Qdo0giDgFycW/sVVVlbK6m5CMvavy/m5ubLLjk1UV1NJWOS3kKYjy/t8\nlU+e4Kuqtsq+pKWlRaPRGvu0JQI7JSUle3v727dv5+TkcDgcvMcUFxeT07i4uJSXlz99+vT6\n9evW1taiB7GZm5sHBwc/f/48MTHx9evXcXFxR48enTBhgre3t1BV58yZs2jRojt37owcObJ+\nPo8ePcrMzEQIEQRRUFBQWVk5e/Zscg+iCDo6Or6+vlOmTLl3715ISIixsXGfPn2o548bgcEQ\n1f5U0jSGIAhZ3QXSFPhMA8RqC19W20e9d/x/GexLVEArUcHn85vjR8fLyCQ+f5Z5tmIRubmE\nDANKhHhsNlJVbYP7UksEdhUVFStXrjQxMRk9erSOjg6fz1+/fj3uqxCYOnUqQuinn35avnz5\n8ePH16xZIzpPRUXF/v379+/fHyGUnZ196dKly5cvm5mZubu7k5MZGxuPGTMmNDR04MCB9TMx\nMTHBl31pNJqenp6dnZ2BgYFQmoiIiKioKPzax8dHcJkYIVRVVXXnzp1bt26pqKg0OOJNRP54\nfqCioiKhK8hkVNI0hsFgSLGWbFVVVSkqKjKZzNYtRltGEERZWRmdTtfS0mrtsrRptbW1BEGI\nHiYBKioqeDyelpZWc/SyyA0ul1tbW6uurt7aBWnTYmJicnJymmlqLd6lC0SdzIIhoqyseMxY\n9N8RRYM0d2xnurrKarsIIbqpSUVVVavM9ieiuw61TGAXHx9fVVW1YcMG/FsiXyJks9kVFRWC\ncEdZWdnJySkhIUGi/C0sLH7//feZM2empKQIBXYIoalTp8bExFy4cKH+PaQdO3YcP3686Mw7\ndOjwf+zdeVwTZ/4H8GcSSMIV7ktAQEUogqDgAWItotYL6KGuLmpdrdVa2+rPtWqt1mqtWlur\nLt7W2ta2Vteq9bYgHnjUCqIiogKCIHJf4cg1M78/ZjdmOUJEICF83n/4SmaeeeabMSTfea6E\nhoZyjx0cHLgHBQUFv//+e1xcnKOj46RJk1555ZVG0xcN9Xt7e1MUlZyc7OXlVW9XWlpajx49\nBAKBNmWaCpuiKD6fr/mltTWKong8ns7D0Geq2xtcJc14PB7LsrhK2uDz+UjsNGAYRh8+HvVc\naWlpbm6uVCptiwvFb+1k0djfT3G7mcH0lEBg/vrrlEVrJvQMw1C1tXr4XmqPv//S0lILCwvV\nHdKNGzdUu3777bd3331XfehMRUWF5kURk5KS3nrrrfzGGlQb7bU0MTGZOnXqqVOnnj592oLg\n/f39//ZfHh4eeXl5a9eufffddwsKCpYuXfqvf/1rxIgRLWiUsrGxCQwMPHr0aElJifr27Ozs\nZcuWHT9+XMsyAAAAnZnFnHebLWM2ZXLrZnX6rD0SOxcXl4qKipycHEJIdnZ2QkKCqakpt6ZJ\nWFgYy7KxsbESiYSm6StXrly5ckU1WO3SpUtXr16tV5uPjw/LsqtXr75z545SqeSGr23evLmi\noqLeGigqERERnp6ehw8ffvHXcufOHSsrq9jY2OXLlwcEBLxIVbNnz6YoavHixYmJiRKJpKys\nLC4ubunSpR4eHpGRkdqXAQAA6LRMxo41iY7SUMDYu6d44cJ2i0fn2qMr9uWXXz5z5syCBQvs\n7Oy4AXb79u07cOAAN5lg4cKFO3bsiImJ4XpbIiIiJkyYwB149OhRExOTkJAQ9drMzMzWrFmz\nffv2Tz75hOudoWm6a9euixcv9vf3bzQAiqJmzpy5aNGiF38to0aNevFKOM7OzuvXr9+1a9f6\n9eu5LjmBQDBs2LBp06YZGxtrXwYAAKDzoijrjd9QIlHtrwca7hQEB9vs2tF5musIIRSrxZDD\nF8eybF5enkKh8PDw4PF4CoUiJyfH0dHRwsKCEELTNPerr87OzmZmZqqjMjMzeTyep6dno3VK\nJJLi4mKapu3s7OpNFEhLS3N2dq638cGDBzKZTJX8paWlWVtbOzs7t/JLVYtBy/olEklhYaGR\nkZGzs7NQKGxxGX0jkUgEAkFHiVYnWJYtLS3l8Xha/pZJp1VXV8eyrKmpqa4D0Wvl5eU0TdvY\n2GCMnQYKhaKurq5j/Qpq+9u/f396evrEiRN9fHx0HctzkF29WrP3B9nVq0xpKWVuLggMNB3/\npukbb7TRusTcujl6+OndTokddEJI7JqFxE5LSOy0gcROG0jstNFBE7tnGKYdfmRCbxM7/P0D\nAACAAenc9zbt9FuxAAAA0CGMGDEiLCwMv0jeQXXqrBYAAADqMTY2FolEerhCG2gDiR0AAACA\ngUBiBwAAAGAgkNgBAAAAGAgkdgAAAAAGAokdAAAAgIHAcicAAADwTGVlZVVVlVAoFAgEuo4F\nnhta7AAAAOCZxMTEAwcOPHnyRNeBQEsgsQMAAAAwEEjsAAAAAAwEEjsAAAAAA4HEDgAAAMBA\nILEDAAAAMBBI7AAAAAAMBNaxAwAAgGe6d+9uZmZmZWWl60CgJZDYAQAAwDM+Pj6enp5isVjX\ngUBLoCsWAAAAwEAgsQMAAAAwEEjsAAAAAAwEEjsAAAAAA4HJEwAAANDeGJa9m1f5sEAiVdB2\nFsJ+3WytzQS6DsoQtGtiV1hYuGnTplmzZrm7uzdagGXZo0ePXr9+/f3333d2dm62wpUrV0ql\nUkIIRVGmpqaurq6hoaFeXl71ygQFBY0ZM0bD4VwNdnZ2vr6+w4cP5/F4DQuoWFpaLlq0SPX0\n7t27iYmJBQUFAoHA2dk5IiLCzc2t2ci19Msvv2RmZi5atMjY2Fh9+7Zt26RS6bx58yiKaq1z\nAQAAtI/41ILYPx48rahTbeFR1MgA5/dHeCO9e0Ht2hUrlUpTU1Nramoa3VtdXb169eqff/45\nNTW1rq6u0TL1pKWlyeVyf3//Xr162dvb37x5c8GCBZs2baJpWr1Mfn6+5sP9/f19fX2VSuX2\n7dsXL16sVCrVC/j+L1XiyDDM5s2blyxZ8vDhQ1dXVysrq2vXrs2dO/fIkSPPcVE0CgsLS0pK\nqlfhjRs3Tp06NXDgQGR1AADQ6pKTk0+fPl1YWNhG9W+Pf7j04C31rI4QwrDsyZT8f+y8lldW\n20bn7ST0qCs2NjZWqVR+9NFHK1eu1P4ob2/vSZMmqZ5euHBh48aNpqamM2fObMHhN27cWLly\n5aVLl8LDw1UFJk+e3OixBw8ejIuLmzt37ogRI7gtLMvu3Llzz549Xl5evXr10nDe7OxssVhs\nY2OjOTw3N7fo6OiDBw8OHTrU1taWEKJUKnfv3t23b9+QkBBtXiAAAMBzefr0aUZGRnBwcFtU\nfjIlf+/FrKb2FlTU/fPn5B9mhwqMMAeghXRw4RiGOXLkyMqVK9euXfvXX3+pto8YMeLTTz+1\nsLBoeMi2bdv27NmjTeVDhgyJjo4+efJkaWlpC2ILCgoyNjZ+9OhRsyVlMtnhw4cHDx6syuoI\nIRRFzZgxY8qUKVwSpkFGRsbbb7+9YcOGjIwMzSUnTpxobm7+3XffcU+PHj1aXFw8a9asZiME\nAADQKzIFveWPB5rLZBfXHPort33iMUg6SOz27duXnp4eEBAglUo///zzmzdvctv79u3bVN9i\nXl7ekydPtKx/+PDhNE3funWrBbEpFAqlUmliYtJsyfT09Nra2oiIiHrbjYyMxo8f7+TkpPnw\nYcOGbdiwgcfjffTRR4sXL75y5QrDMI2WFIlEM2bMuHjxYlpaWnl5+YEDB958801tBiACAADo\nletZpaXVsmaLnbrV+AAq0IYOumItLCwWL15MCImKipo7d+6xY8f69Omj+ZDVq1drX3+XLl0o\nimrB4ACWZX/55ReWZYOCgpotXFBQQAh5kXkSHh4e8+bNe+utt44fP75ly5Y9e/aMHTt25MiR\nIpGoXslBgwYFBgbu3Lmza9euYrF43LhxzVZO07SW4xTbjlKpZBhGoVDoNgx9xrIs9291dbWu\nY9Fr3LDXpm5+gMNdn5qaGoy+1YBhGKVS2XH/4v4Vl6Vs+z+EnKcmlaTbt9eKxGn1pw++oAcF\nWl35h08lnx1K4en9G5mmaT7/caO7QnvYDOjWzGirFjM3N9ewVweJXWhoKPeAoih/f/+rV6+2\n+il4PJ6W3wGXL1/mOkNZli0sLKyurp4+fXrPnj1VBS5evHjv3j31Q3x9fd9++22ufiOjF72A\n1tbWU6ZMmTBhwtmzZ/fu3evk5DRw4MCGxWbNmvX+++9nZWV98sknAkHzM4YYhmk4n7f90TSN\nxK5ZLMvqw3+W/lPNagINZLLmm0Og4/7FnbxdoKDZtj+PiBBR7qM6QnTTQMAS9tTttpq60T5s\nTY0Cupi2UeVmZmYa7t90kNipzxiwsLBo9TuniooKmqabnZfAcXZ27t+/PyGEoihbW1sfHx97\ne/t6Beo14HXp0oUQYmVlRQgpKSmxtrbW5kTHjh2Li4vjHsfExHAn5dTU1Jw+ffr48eMmJiZN\nDc5zcXHp169fTk6O+oEa8Pn8Rkcrtqe6ujojI6N6C7WAOq6tjqIozbdfIJfLWZYVCoW6DkSv\n1dTUMAxjbm6OFjsNaJqWy+XajLfRT8tf86WZNk/srl+/np+fP3DgwGaHFT2vxAelcXebz9iM\neNTSqJf0/40slUobdrJxutmbW1i0VWKn+W9cB4mdehOOVCpt9S9+bkKGn5+fNoW7dev2+uuv\nayjg5eX1t7/9reF2b29viqKSk5PrLZtHCElLS+vRo0e9djUPDw9VU6WDgwP3oKCg4Pfff4+L\ni3N0dJw0adIrr7yioTXOyMhI+wZCHo+n829BuVxubGys8zD0mSqxw1XSjGEYJHbNqq2tJYQI\nBALVSpzQEDeQuuO+l4b3dm2Hs1SkX2bySwd6WPj4tNqyrBw3O7E2iV1wN9tRfVr51K2OYZiK\nigotW5Hakw4Su7y8PNUk6qdPnzo6OrZi5TU1Nb/++quPj08rrhLcKBsbm8DAwKNHj0ZERNjZ\n2am2Z2dnL1u2LCYm5o033lAvz62Wp3qal5e3b9++P//8s0+fPkuXLg0ICGjTaAEAALQ0YsSI\nsLCwZpd3aAFfF0tfF8u0J5Wai40b0LXVT9156ODG7o8//igpKSGEPHr0KCUlRZu+xUuXLjU7\nFK+2tvb69esfffRRbW3t3Llz1XcxDCP/X9yg9Rc0e/ZsiqIWL16cmJgokUjKysri4uKWLl3q\n4eERGRmp+dg7d+5YWVnFxsYuX74cWR0AAOgPY2NjkUjE5/NbvWaKIosjfUXGmmoe7u8U1tNe\nQwHQrF1b7LgJB6NHj54/f75AICgpKfHx8eFatuRyufpkz3nz5hFCHBwcdu/eTQg5evSoiYlJ\no0vyHjt27NixY9xjiqICAgKWLFni6vo/jdUnTpw4ceKE+pbdu3er+kNbzNnZef369bt27Vq/\nfj2XKQoEgmHDhk2bNq3Z/uVRo0a94NkBAAA6nJ7O4i8n9Vl6IEUibWQ6VLiv4yfRWo2kgqZQ\nrdJ2paW6urqMjAxvb29CSHZ2tkgk6tr1P82tLMumpqbWKy8QCLjCmZmZPB7P09OzXoG0tDTV\nr4eJRCJHR0exWKyhjIq3t7dAIEhLS7O2ttawJlyzBTgSiaSwsNDIyMjZ2bnthm7k5uZKpdKG\nQ/r0lkQiEQgEHXcsSztgWba0tJTH4+nhKA29UldXx7KsqWlbjUQ2DOXl5dy8MYyx00ChUNTV\n1TX8pgB1EolEJpOJxWJtFmFomaIq6Z4LmefuFlbVKQghFEVe6mL591CPiF5O+j9ngqO3Y+za\nNbGDTgWJXbOQ2GkJiZ02kNhpA4mdNtohsePQDFtUJa2VKR3EIguTDraEgt4mdnr0W7EAAADQ\nefB5lLNVR116Rm/hxg4AAADAQCCxAwAAgGdqa2urqqrwu0EdFBI7AAAAeCYhIeGHH37Izc3V\ndSDQEkjsAAAAAAwEEjsAAAAAA4HEDgAAAMBAILEDAAAAMBBI7AAAAAAMBBI7AAAAAAOBX54A\nAACAZ9zc3Ph8Pn54rYNCYgcAAADP9O7d29vbG4ldB4WuWAAAAAADgcQOAAAAwEAgsQMAAAAw\nEEjsAAAAAAwEEjsAAAAAA4HEDgAAAMBAILEDAACAZ5KTk0+fPl1YWKjrQKAlkNgBAADAM0+f\nPs3IyKipqdF1INASSOwAAAAADAQSOwAAAAADgZ8UAwAAgOdTXiPPLqmpkyudLE087c0pStcB\nwX8ZTmJXWVl58uTJYcOG2dvbN1qgqKjo1q1bNTU1rq6uQUFBlNZvw1OnTlVUVLz55psCgaDh\n3tra2pSUlIKCAmNj4y5dugQGBvL5/Ja/jP917ty5qqqq1157rd72CxculJaWvv7669q/CgAA\ngBeX9qRyW9zDpEdlDMtyWxzEor+Heowf0JXPw1eS7hlOYldRUfHLL78EBAQ0mtidP38+NjbW\n3t7eysrqxx9/9PT0/OKLLxpN1OopLS3dvn07j8dzc3MLCwurt/fcuXO7du2Sy+Vubm5yuTw/\nP9/a2nrhwoW+vr6t8qLEYvHGjRvt7e0HDRqk2pibm7tx48aYmBhkdQAA0J4O38j96sQ9mmHV\nNxZVSTeeTk+8X7RuUh8zoeHkFR1UpxhjV1VVFRsb++qrr27dunXNmjXr16/PzMw8ffq0Nscm\nJCQ4OjqGhoaeO3eu3q6rV69u3Lixf//+P/zww8aNG7du3bpz5057e/vly5cXFRVprra6ulqb\nswcHBw8YMGDPnj1yuVy1cefOnU5OTg2b8QAAANpO4v3i9cfrZ3UqNx6Vffrv2+0cEjRkaIkd\nRVEZGRm//fbbiRMnSktLuY3l5eX9+vVTdVx269ata9eujx494vaePXu2YdKmEh8fP3jw4MGD\nB9+8ebOiokK1nWXZb7/9tmfPnvPmzTMzM+M2Ojg4LF261NfXt9nLHu+xAAAgAElEQVTE7o8/\n/nj//ffPnj2rnrE1aubMmZWVlYcOHeKeXr169datW7NnzzYywl0RAAC0vvDw8KlTp7q5ualv\nVCiZr07eU3W/NirxQfGF9Ga+/qCtGVpid/78+bVr16alpR04cGDu3LlPnjwhhLi7uy9atMjO\nzk5VTCKRmJubc4/Pnj2bkJDQaG3p6elPnjwJDw8PDg42NTW9cOGCaldGRkZRUVFkZGS9/lBL\nS8uVK1f6+flpjnPUqFGvvvrqv//97+nTp//000/l5eVNlXRwcBg/fvyhQ4eKiorkcvmePXsG\nDx4cEBDQ3JUAAABoCVNTU7FYbGxsrL7xz8zSgoq6Zo89eiOvzeICrRhaq8+DBw9iY2NFIlFV\nVdXcuXMPHjw4b968emVOnz5dWloaERHBPZ03b15Tg9Xi4+N79uzp6upKCHn55ZcTEhKio6O5\nXY8fPyaEeHl5tSxOkUg0duzYMWPGXL169ciRI4cOHQoLC4uOju7evXvDwm+88UZCQsKePXs8\nPT0rKyunT5/ebP0Mw8hkspbF1lpompbL5QzD6DYMfcayLPdvXV3zH5edmUKhIITgKmnGvZ2k\nUilG32pA0zTDMK31XqqoVZy6XdAqVekVpVLJMIyxcan6e+nPrDJtjr2RVbon4UGbhaZfXC2N\nBpuYtP95TTSe1NASu+HDh4tEIkKIWCzu27fv3bt36xW4cePG7t27J0+e7OHhwW3h8raG5HJ5\nYmLi1KlTuacREREnTpzIyclxd3cnhHBpk6mp6YtES1FUaGhoaGjogwcPDh8+vGDBguXLl/ft\n27deMWNj43feeWfFihXXr1+fOnWqra1tszXTNK0Pi4YrlUqd55f6j2VZffjP0n/NjlsAQkht\nba2uQ+gAlEplq9STV1K38/yjVqnKYMhppvNck5F+dn27itv/vCKRSMP9m6Eldo6OjqrHtra2\n9bo4L168+M0334wbN278+PHNVnXt2rWamprHjx/v27eP2yIUCuPj47kGMwsLC0KIRCKxsrLS\nJrC//vorJSWFezxkyJCePXvWK8D9J7FNDF/o27dv79698/LyIiMjtTkdn89XjfzTFZlMxufz\nMRZQA5Zla2trKYp6wTsEg8e12NXrGIJ66urqGIYxNTVFi50GNE0rlUqhUNgqtblSgnde8WyV\nqvTKf1vsjNXfS9ezylIeVzZ7rBGfmj7Yow2D0yeulkY6+arV/DduaF+66mvI0TSt/uLj4uJi\nY2Pfeeed0aNHa1NVfHy8k5OT+q8gu7q6XrhwYdq0aTwer2vXroSQtLS0esNLCSG1tbUNv6cr\nKiq43ltCiKp5hmXZa9euHTly5MGDB4MHD/7qq6969OjRVDyWlpYVFRVaLpLH4/E0N9W2A6VS\nKRAIWusD1CCpEjud/2fpP5ZlcZU0k0qlhBCRSMTjGdrg6VakUCgYhmmt95KJicn0cB002LQ1\niUQik8nEYrH6omDdnYtSHt9s9tggD5vp4fVbLgwSwzAVFRV6+LlkaIldSUmJ6nFZWZm1tTX3\nODk5ecuWLR988MHQoUO1qae0tDQlJWXRokWhoaGqjUVFRW+//XZKSkrfvn3d3d1dXV2PHj06\ndOhQ9YaEmpqa9957b9y4cWPHjlWvcPjw4cOHD1c9lUql8fHxR48era2tHTly5OLFi1WhAgAA\n6JuB3W3txaLiKqnmYlF9Gx/dBO3G0G7s4uPjaZomhNTW1t68edPf358QolAotm7dGh0d3WhW\nl5+fX1BQf/RrQkKCUCgMCgpS3+jg4NCjR4/4+Hju6TvvvPPkyZMvvviirKxMVdXy5csVCoX6\nesKNOnXq1KlTp9588809e/ZMnjwZWR0AAOiJ2traqqoqbgiEitCYP3+Uj+Z+/v7dbYf2cmrb\n4KA5htNix41Oc3V1nTdvXvfu3dPS0hiG4cbSXbx4saio6Pbt2x9//LGqvL29/fz58wkhGzZs\nMDExWbVqlXpt8fHx/fr1a9iNGBYW9vPPP3OdrYGBgR999NGWLVumT5/u5uamUCiePn3atWvX\ndevWNZuoDR8+/PXXX2+VFw4AANCKEhIS0tPTJ06c6OPjo759qK/j+yO8Y88+aHQ1Oz9Xq8/H\nB2CEp84ZTmInFosnTZo0ZsyYgoKC1NRULy+v0NBQLsFydXWdNGlSw/LcgxEjRtQb4F9TU/Py\nyy/369ev4VleeeUVmUxWXl7OjaIbNGhQv379kpOTCwoK+Hy+h4eHn5+fNiOXVavoaS80NLRX\nr17PexQAAEBr+Xuoh08X8ZY/HtzNezaRQmxiPCnEffIgT2MjQ+sG7IiopqZhArwgiUSCyROa\nsSxbWlrK4/FsbGx0HYteq6urY1kWc4c1Ky8vp2naxsYGkyc0UCgUdXV1qht7aNT+/fsbbbFT\nl19el1VULVPQDpYiXxdLPq/TtdRxkyf08NPbcFrsAAAAoH10sTbpYq13E0KBGN7kCQAAAIBO\nC4kdAAAAgIFAYgcAAABgIDDGDgAAAJ5xdnZWKpU6/11KaBkkdgAAAPBM3759e/XqhbnDHRS6\nYgEAAAAMBBI7AAAAAAOBxA4AAADAQCCxAwAAADAQSOwAAAAADAQSOwAAAAADgcQOAAAAnrl9\n+3ZCQkJJSYmuA4GWQGIHAAAAz+Tm5t69e7eqqkrXgUBLILEDAAAAMBBI7AAAAAAMBBI7AAAA\nAAOBxA4AAADAQCCxAwAAADAQSOwAAAAADISRrgMAAAAAPRIeHt6/f39HR0ddBwItgcQOAAAA\nnjE1NeXz+cbGxroOBFoCXbEAAND65EqmoKKuolau60AAOpeO0WInlUofPnzYvXt3U1PTRgvQ\nNP306VOpVOrs7GxmZtZshWlpaTRNc495PJ6tra2DgwOP10iaW1lZWVhYKBAIHBwcmjq7NmVa\nJiMjgxDSo0ePetvz8vIqKyt9fX0pimrF0wEAvCCWJRfSC3+9mnMrt4JhWEKIo6VohL/zlDBP\nsQlagADaXMdI7AoLC5cuXbp27VpfX9+Ge5OTk2NjY0tKSng8HkVRkZGR06dP11zhypUra2tr\n1bfY2dnNnj27f//+qi05OTk7d+5MTU1lWZYQwufzQ0ND33nnHUtLy+cq8yLu3r27Z8+eDRs2\ndO/eXbVRIpEsWrSof//+vXr1apWzAAC0CpmCXnkkNT61QH1jYaX0x8RHJ1Py100K9HO10lVs\nAJ1Eh++KLS0tXbNmTWBg4P79+w8dOjR79uwjR46cP3++2QMjIyN///3333///ciRI9u2bfPw\n8FizZs3jx4+5vTk5OYsWLaqqqvr000/379//ww8/fPjhh7dv3164cKFUKtW+zAsaO3Zs165d\nd+7cqb5x3759DMNMmzatVU4BANBaPj9aP6tTKa2Wzf8x6XFpTTuHBNDZdLDETi6XP3z4MCcn\nh2shI4QUFhb6+fm9/fbb3GDPV1991dnZ+d69e9zezMzMR48eaa6Tx+O5uLjMnz+fpumrV69y\nG7du3WpqarpmzZq+ffuamppaWVm98sorn332WUVFxc2bN7Uv05RTp07t2LEjPz9fczE+nz97\n9ux79+6pUtXs7OwzZ85MmTKltRoFAQBaxaX7RX/caTyr40ikyvXH77VbPACdU8foiuVkZmau\nWbNGoVDU1tb27Nnzs88+MzMz8/X1/fTTT1VlZDKZRCKxsbHhnm7bts3ExGTVqlXNVm5hYSEQ\nCGpqaggh+fn59+7dmzlzprm5uXqZbt26/fzzz0ZGRlqW0cDf3z8lJeW9997r06dPdHR0QEBA\nUyV79eoVHh7+/fffDxw4UCQS7dy509PTc9SoUc2+IgCA9nTg2uNmy/yVVZpVVN3NwbzZkqBD\nCoVCKpVqM2Ad9FBHSuyOHTv2+eefu7u7p6enf/LJJ4cOHZo6dapq719//VVeXn727FknJ6cx\nY8ZwGyMjI5vNsThZWVlyudzFxYX8d8qCv79/w2Kq2rQpo4Grq+uSJUsKCgqOHj26evVqR0fH\nqKioIUOGCASChoX/8Y9/zJ49+8CBA56ennfv3l2/fn2zcyZYllUqlc2G0aYYhqFpWqFQ6DYM\nfaZqeMZV0oyb6qTnV4lh2OScch0GUFdXxzCMaalSJ3OqGJZNzi7TpuS/r2UP9rFv63iaQtO0\nUqkUCut0FYCLtamzlUhXZ9fSmTNn7t+/P378+J49e+o6Fv3FfYDr5HNJ80o0HSmxi4iIcHd3\nJ4T4+PgEBQX99ddf6ond6tWrGYZxcXFRb0UbMmRIU7WVlJTcunWLEMIwTEFBwaFDhxwdHbny\nEomEEKK5r1ObMs1ycnKaNWtWTEzM6dOnf/755x9++OHTTz9tOAfWysoqJiZm79695ubmI0aM\n0OYvTalUVlZWvkhsrULPv4n1BMMw+vCfpf9aa+hqG5EqmPk/peg6ig7gt6QnvyU90XUUOjM1\nxGVCsJOuo2gGwzCEEJlMho+mZunkEtna2mq4f+tIiZ2np6fqsbOzc0rK/3yGHjlyRCKRXLhw\nYeXKlXPnzh02bJjm2q5fv64aCWdraxsUFBQTEyMSiQgh3JIlMplMw+HalFGXm5v75Ml/Psu6\nd+9ub//shtXc3DwyMlIoFO7du7ekpKRhYkcIGTNmzNmzZ4uLi9VzWQ14PJ5QKNQytjaiVCp5\nPF6ji8gAh2VZuVxOUVSjLbWgwrXY8fl8XQeiCY/PDPG202EADMOwLMstDtD+Z6cZNvFhqTYl\nPe3MutqatHU8TWFZlrtKugrA08Fc5x/OzeLeQnw+X/9D1SGWZRUKhR5+enekxI7Lujh8Pl+1\nEJ2KhYXF2LFj7927d/jw4WYTu9GjR8+cObPRXdzvqDx69MjJqcn7Km3KqDt//vyhQ4e4xx98\n8MHQoUO5x+Xl5SdOnDh16pSJicnkyZObGmzH4/G6du3KsqyFhYU2p+Pz+VqWbDsSiUQgEOBz\nQQOWZUtLSymK0vl/lp6rq6tjWbZ1F4lsC+v+HqTDs5eXl9M0bWNjo6usZfzmS7mltc0W+2Ck\nT4iXzjJghUJRV1cnFot1FUCHwL2FhEIhPpo0YBimoqJCDy9RR0rs1Bs8q6qquP7Wq1ev3rp1\na/bs2apdlpaW3ByIFvPx8RGLxadPnw4JCVHfzrLsqlWrhg8fHhISok0Z9e1TpkyZMmWK+pac\nnJwjR45cuHDBy8trzpw5ISEhaNwCgI5rZO8uuxIyNJexsxAGe9q0TzwAnVNHyiRu3LjBPWBZ\nNjU1leuylMlkJ0+eVK1vQtP0rVu3uKF4Lcbj8f72t7/dvHnz119/VQ1vp2l6+/btSUlJdnZ2\nWpbRIC4ubv78+Uql8ssvv1y3bt2gQYOQ1QFAhzYpxN3Jqpk+1veG9zQ2wmcdQBvqGC12XOaU\nn5+/devWnj17JiUl5efnz5kzhxAyePDgM2fOrFixIiIiwsLC4s8//ywoKJg3bx534LJly0Qi\n0dKlS5/3jGPHjs3Pz//pp58uXrzYu3dvpVKZkpJSVlb2z3/+08vLS/syTenRo8fu3btVy7IA\nAHR0pkKj9ZP6vP/DjYqaxn8fdkqY56iALu0cFUBnw1+xYoWuY2heTU1NTk7Ohx9+WFFRcePG\nDSMjo+nTpwcGBhJCeDzeK6+8IhaLc3NzCwsLe/To8eGHH6pa7O7fv29lZdWnT596FaalpXl7\ne2uYXkpRVHBwcFBQkEwmKywsVCgUgYGB8+bNe+mll56rTFOsrKxMTJ5v+HBubq5QKBw4cOBz\nHaVDcrmcz+drudxMp1VXV0dR1PO+GTobbu0ezTP8QSqVsixrYmKiw5+QtjUXRvRyelJe+/h/\nB9vZi0WLxvpOCvXQUVzPMAyjVCox9lez0tJSIyMjLy8vrISvAcuyUqlUDz+9KVU3IkDrwuSJ\nZnGTJ3g8HtpuNesokyd0S+eTJ9Q9Kav9K6usqEpqLjLycrLo425jxNdZuqkOkye0IZFIZDKZ\nWCzWwymf+oObPKGHn95oTQEAgFbmYmPqYoNEHEAHdH9jBwAAAACtAokdAAAAgIFAYgcAAABg\nIJDYAQAAABgIJHYAAAAABgKJHQAAADyTnp5+9erVsrIyXQcCLYHEDgAAAJ7JzMxMSkqqqKjQ\ndSDQEkjsAAAAAAwEEjsAAAAAA4HEDgAAAMBAILEDAAAAMBBI7AAAAAAMBBI7AAAAAANhpOsA\nAAAAQI+EhYX17t3bxcVF14FASyCxAwAAgGcsLS1FIpFQKNR1INAS6IoFAAAAMBBI7AAAAAAM\nBBI7AAAAAAOBxA4AAADAQCCxAwAAADAQSOwAAADgGYVCIZVKaZrWdSDQEkjsAAAA4JmzZ8/u\n3r07JydH14FAS2AdOwCA1scS9s+n1849jn9Qfr9WUWsptPSz8x/lObqntbeuQwMAQ/ZCLXan\nT58eN26c5jLV1dUbN258/fXXX3/99fHjx0dFRc2ZM+fevXuqAhMnToyKilLfQgi5dOlSVFRU\nvTLqpk+ffv369RcJXuXo0aPR0dGZmZnqGyUSSUxMzKZNmxqWl0gkM2bMOHHiRMNdc+fO3bFj\nByEkJycnKioqLS2tVSIkhOTn55eXlxNCtmzZsnjxYrSQA+gziVzy6ZVlX/z5+bWnV8ukZVJa\nWlhbGP84buGFBdtvbaUZpa4DBACD9UKJnUgkMjEx0Vxmy5YtycnJq1at+u233w4ePLhjxw5L\nS8sVK1ZIJBJVGVNT0507d7Isq6GeyMjI33///ffffz9y5Mi2bds8PDzWrFnz+PHjF4mfM3bs\n2K5du+7cuVN94759+xiGmTZtWsPy//rXv9zc3MaMGaOhThcXl61bt/bo0ePFw+Ps3LkzOTmZ\nEDJz5szy8vL9+/e3Vs0A0LrktPzTK8tSim423MUS9uSjE5tvNnLHCADQKp4jsaNpOisr69Gj\nRwqFgttiYmIiEom4x5mZmY8ePWp41I0bN0aMGOHn50dRFCHE2dl5/vz5Q4YMqaioUJWJjIzM\nzs6Oi4vTKmIez8XFZf78+TRNX716VUPJU6dO7dixIz8/X3OFfD5/9uzZ9+7dO3/+PLclOzv7\nzJkzU6ZMsbS0rFf4wYMH165dmzx5smpLZWVlenp6WVmZejGlUlleXq5UKgkhUqn0zp07crm8\nrKzs/v37XAGWZXNycu7fv19TU1PvFA2v8507dzIyMvLy8tLS0gQCwYQJE44cOVJZWan5dQGA\nTuy//3NGxUMNBRJyz11+kthu8QBAp6LtGLvU1NT169dXVVXx+XwLC4sFCxb4+fn17NnznXfe\n4Qps27bNxMRk1apV9Q40MzPLyclhWZZL7AghDg4Oc+bMUS/j6Og4duzYH3/8cdCgQaamptrE\nY2FhIRAIGmZF6vz9/VNSUt57770+ffpER0cHBAQ0VbJXr17h4eHff//9wIEDRSLRzp07PT09\nR40a1bDk8ePHvb29VU1xx48f//bbb01MTBiGGTFihOo1FhYWLl26dO3atb6+vtzjWbNmffvt\nt56enl9//XV6evpXX31VXl5uampaU1MzceLECRMmcAc2ep23b99eVVV1/vz5u3fvfvnll6+8\n8sru3bvPnj07fvx4ba4VALQbGS07nnms2WKHHh4c5BLWDvEAQGejVWJXW1u7Zs2a/v37z5kz\nh8fj/etf/1qzZs13331nbW3dr18/rkxkZKSRUSO1jRo16qefflq0aFFERIS/v3+XLl0almEY\nZtKkSefPn//111//8Y9/aBNSVlaWXC53cXHRUMbV1XXJkiUFBQVHjx5dvXq1o6NjVFTUkCFD\nBAJBw8L/+Mc/Zs+efeDAAU9Pz7t3765fv16VpamwLHvz5s3Ro0dzT4uKir799ttRo0bNnDmT\nEMK1Dvr5+dU7irssCQkJ27dvd3BwqKurW716dffu3WNjY0Ui0eXLl9etW9etW7fg4OCmrvM3\n33wzbty4yZMnR0REEEL4fH7v3r2Tk5M1JHYsyzIM0/x1bEtcDHoyHFDBKErrSnUdRSOqpFUU\nRdVV1ek6EL0mk8kIIUJlB/hJ8rSyNCktbbZYZkXmvZJ7YoG4FU9dXVfNMIxUIm342QUqNE3L\nZDIJkTRf1FA4mjlS5PneEtzIKP35ANdPDMOwLKuTS8Tn8zXs1Sqxu379ukQieeutt4yNjQkh\nU6ZM8fLykkql6hnSkCFDGj12woQJlpaWx44d27JlCyHEzs4uLCzs9ddft7a2Vi9mamoaExOz\nY8eOV199tdHkr6Sk5NatW4QQhmEKCgoOHTrk6OjY1EnVOTk5zZo1KyYm5vTp0z///PMPP/zw\n6aefNhz9ZmVlFRMTs3fvXnNz8xEjRvTs2bNhVWVlZZWVld27d+eeJiUl0TQ9btw47mN08uTJ\nZ86caXgUj8cjhISEhDg4OBBC/vzzz8rKyhkzZnC92IMGDXrppZfi4+ODg4Obus6q/m4VLy+v\nAwcOaHjVSqVSH/pq5XK55lbVdpNd/WhlygpdRwHwHyxhFyX+U9dRQKewa9AePqUpD2jI2tra\nzc2NEMJN2gMNdHKJbG1tNdy/aZXY5eXlmZqaWllZqWrUPHVAHUVRI0eOHDlyZFFR0a1bt5KS\nko4fP56QkPD1119ziY7Kq6++eurUqd27dy9fvrxhPdevX79586YqgKCgoJiYmHoZT25u7pMn\nT7jH3bt3t7e3V+0yNzePjIwUCoV79+4tKSlpdFrDmDFjzp49W1xcPHXq1EZfC5cqqa5DYWGh\nQCCwsbFRnUK1qyFXV1dVkHw+v6amRjXeztraOisrizR9neVyeb3axGKxVCqVy+WNtj4SQiiK\narQBtT3RNM3j8fSk8cBUYOZp4anrKBrB3RnryVXSWx3oKlUrqoulxdqU7GLmIuQ1/vfbMh3o\nKumW+tCgzsDYyJhHPd9Eyf79+zMMw+PxuIYJaIpSqdT5V21DWgWkUChe/H/XwcFh+PDhw4cP\nz8zMXLBgwcmTJ+vNOaUoaubMmR9//DE3/bOe0aNHcz2eGpw/f/7QoUPc4w8++GDo0KHc4/Ly\n8hMnTpw6dcrExGTy5MlNDbbj8Xhdu3ZlWdbCwqLRAlyCpcqlFAqFUPg/fUNcS1ujVDmoUqlk\nGOaLL75Q38udUfvrzJ1XQ2JnZGSkIctsHxKJRCAQ1LtEumJlZbWpS6yuo6iPZdnS0lIej6e6\nPYBG1dXVsSyr5QBc3XpQfv+fF/6v2WLGPOON4ZtERs2sKvBcysvLaZq2sbHBl7EGCoWirq5O\nLG7NTnDDI5FIZDKZubl5U18xQAhhGKaiokLnX7UNaZXYWVpaVldXq9IIlmVlMplAIGj246O4\nuDg5OXnYsGHq/cHdu3fv0qVLcXEjN7V+fn6hoaG7d++eNGnS87yK/5gyZcqUKVPUt+Tk5Bw5\ncuTChQteXl5z5swJCQl5kY88Lv1SLdRibm5eXV3N3dYQQliWVZ/q2xRra2s+n//99983vGVs\n6jo3rISbXWFmZtbi1wIAbcHLqqeDqWNRbaHmYn0dg1o3qwMA4GiV5XATAlRri6SkpEyYMEHV\n6alBcXHxli1bjh49qr4xJyfn6dOn7u7ujR4yffr0oqKi+Ph4bQLTLC4ubv78+Uql8ssvv1y3\nbt2gQYNe8EbW1taWx+MVFRVxT7t168ay7J07d7inycnJUmnzg6b9/f2VSqWqW5kQcu3aNW55\n5KauMxe2+mSI4uJizV3sAKATFEVN8W18LIcKn2cU89JkzWUAAFpGqxa7nj17hoSEbNmyJScn\nRygUnjx5MjQ0lBtZqbJs2TKRSLR06VL1jb6+vpGRkXv37r1x48ZLL70kEAiePn165coVDw+P\nsWPHNnouBweH6OjogwcPtvglqfTo0WP37t2t2MklEom8vLzu3LnDzU4NCgrq0qXLN998ExUV\nJZPJEhMTu3TponmZZUJI9+7dw8PDv/zyy9dee83e3j49PT0+Pv6TTz4hGq+zpaXlH3/8UVNT\nM2rUKKFQePv2bQ2rtwCADg1xfSW9LP1EVuOLnlAU9W7AHA+xPo74BAADwF+xYoU25UJCQszM\nzO7fv19RUTF06NC33nqrXuvX/fv3rays+vTpU+/AoKCgwMBAiUSSn59fXFxsbm7+2muvzZgx\nQ9XDmJaW1rt3b/WZsN7e3llZWba2tlz+xJXx9vZudKaqBlZWVs3+MEY9ubm5QqFw4MCBTRWo\nra2Ni4sbO3Ysn8/n8XghISESieThw4d8Pn/WrFnV1dUODg4vvfSSTCZ79OhRSEiIlZWVXC7P\nzMwcMGCAKsUcMGCAtbV1WlpaVlaWhYXFrFmzVIukNHWd3dzcHj9+XFNT069fv+Li4n379k2Z\nMsXZ2fm5Xl07k8vlfD5fDweW6pW6ujqKop73jdrZcGt9axjDqm+CHYNNjc3Sy+4p//fXw+xM\n7P4v6J8vuzY/nb8FpFIpy7ImJiZoy9eAYRilUqknY3/1llwup2laKBRqXlajk2NZViqV6uGn\nN9VsCxOok8lkb7/99rhx46Kjo3UVw+bNmx8/fvzVV1/pKgAt6dXkCf2EyRNa6kCTJ9RVyauu\n5l+5X36/VlFjKbTys/Mb4DRQwG+r0eiYPKENTJ7QBjd5QiwWY/KEBtzkCT389EZryvMRCoWz\nZs3aunWral26dpaWlpaYmFhvUi0A6CGxQPyqx8hXPUbqOhCA55Oenl5QUNCvXz8nJyddxwLP\nDTd2zy0sLCwyMvL06dPtf2qapk+fPj179uxG1+EDAAB4cZmZmUlJSdqs8wB6CC12LdGy1Vhe\nHJ/P/7//a36JLAAAAOic0GIHAAAAYCCQ2AEAAAAYCCR2AAAAAAYCiR0AAACAgUBiBwAAAGAg\nMCsWAAAAnunfv7+3tzcWseugkNgBAADAM/b29mKxuMP91gtw0BULAAAAYCCQ2AEAAAAYCCR2\nAAAAAAYCiR0AAACAgUBiBwAAAGAgkNgBAAAAGAgkdgAAAPDMiRMnYmNjs7KydB0ItAQSOwAA\nAAADgcQOAAAAwEAgsQMAAAAwEEjsAAAAAAwEEjsAAAAAA4fNEroAACAASURBVIHEDgAAAMBA\nGOk6AAAAANAjtra2bm5uJiYmz3XU09TCzMuPy3LKGZo1tzN169ul+2APIwG/jYKEprRrYrdo\n0SITE5MVK1Y811Fz58719/efNWtWTk7O+++/v3btWl9f37YJsBETJ06sra3lHpuamrq6uoaG\nho4ePVokEqmXiYiImDlzpubDKYqytbXt1avXlClTHBwcGhZQsbW1/e6777jHUqn0yJEjly9f\nfvr0qUAg6NKlS0RExMiRIymKat1XCgAAQAgZOHBgnz59xGKxluWlVbJzGy7n3sxXbSkkJDMx\n58bPt8LnD+ri59g2YULjOlKLna2t7bvvvuvs7NxaFa5bty44ODgiIkJzsdDQ0DFjxhBCqqur\n7969u3///nPnzq1atcra2lqbs6gOZxjm6dOnv/3224IFC2JjYy0tLbkCISEho0ePVj/E2NiY\neyCRSJYuXVpQUDBmzJiXXnpJJpOlpKRs3749OTn5448/Rm4HAAC6JauWH1l0pjK/quGu6pLa\nE8vjRy57xa1Pl/YPrNPqSImdubn5qFGjGm6naZrPb0ljb0ZGRnBwcLPFbG1t/f39ucchISGj\nRo366KOPNm7c+Nlnn2lzFvXDAwICevXq9d577124cCEqKorbaGdnFxAQ0Oixu3fvzs/P//LL\nL7t168ZtGTx4sJ+f3zfffHPx4sUhQ4ZoEwAAAEAbubj1z0azOg5DM/FfJU7a8ZrQXNCeUXVm\nupk8kZubGxUVdffu3bVr106YMGHKlCk7d+5kWZbbe+/evQ8//PCNN9549913r1y5omqXysnJ\niYqKSktLI4RkZ2dHRUUlJSW99957CxYsIIRUVlZu2LAhJibmzTffXLBgwe3bt1WnKysrW7du\n3cSJE//+97+vW7eutLSUEBIVFVVYWLhp06aJEycSQv75z38uW7ZMm+BdXFwmT5588+bNnJyc\nFrx2Nzc3gUBQXFzcbMnKysqLFy9GRkaqsjpOeHj4l19++fLLL7fg7AAAAK2l/HFF1pVmvgpl\n1fI7v99rn3iA6CqxMzIyIoTs2rVr1KhRv/zyy/z580+cOHHlyhVCSG1t7eeff25ubv7111/P\nnz//1KlTZWVlTdXwyy+/vPnmm/PmzWMY5tNPP01PT+fa0nr27LlixQou8aJpesWKFYWFhR9/\n/PHSpUsLCws/++wzlmW5QWyzZs3atWsXIcTX19fb21vL+AcOHEgISU1NbcFrLy8vl8vl2nTj\n3rt3j6bpAQMGNNzl4+ODflgAANCt7D/zCKtFset5bR8L/Icuu2JDQkK4Lsi+ffs6Ojo+fPhw\n0KBBf/31l0Qieeedd9zd3Qkh77333qxZsxoey/W9+vn5DR06lBCSlJSUlZX1+eef9+7dmxDy\nzjvvpKSkHD9+/L333ktJScnOzt6yZYubmxshZO7cuQcPHiwrK7OwsCCEiEQi7sH06dO1j9za\n2prP55eXl2tZnqZpQgjLsk+fPt29e7dQKAwLC1PfK5VK1cvzeDyBQMDVr5pm8VwUCkVVVZNt\n4+2DZVm5XF5dXa3bMPQfwzBcKzI0hWvOr6ur03Ugeo27Stp/LnVaLMt2kr+4Yx/Eq7rC2ohS\nRmtTrPRRxXd//7VNIyGEBP3D36Vvu07U0NV7ycbGRkPjji4TO09PT9Vjc3NzLgPIzc3l8/lc\nVkcIcXJy0jAxx8vLi3vw8OFDPp/fq1cv7ilFUb169UpPTyeEZGRkCIVCLqsjhHTr1m3RokWE\nELlc3uLIaZpmGEY1xUGzY8eOHTt2TPXU1dV1xYoV6unayZMnT548qX5IcHDw8uXLuVZJhmFa\nFmRb/z13lBg6BFwobeAqaQNXSRud5CrJaxR680pZeY2irc/BKJn2f716c4Wf0WViJxQK1Z+q\nbspNTU3Vt5uZmTVVg7m5OfegtraWpukJEyaodtE0zTXF1dTUCAStPGbz6dOnLMva29trU/jl\nl19+7bXXuMe2trYNO2EHDRoUGRmpvoWL3NbWlhCSn59vZ2f3vBEaGxu34KjWJZFIBAJBvf9l\nUMfd7fF4PBsbG13Hotfq6upYlq33yQD1lJeX0zRtY2PD42Hl+SYpFIq6ujrtF/Lo0N45EtOy\nA2/evFlUVNSnT59mu4ySD9z566dbzVZo4241fvPYlgWjtxiGqaio0MNPb72bFSsUCuut6yaR\nSJo9yszMTCAQbNy4UX0j9+lmYmJSU1PDsmwrDkq7dOkSRVF9+vTRprClpWWPHj00FLCxsWl0\nZT5fX1+BQJCYmMj1L6v77bffgoKCVO2aAAAAreX+/fvp6enu7u7NJnbu/Vy1Sezc+7m2UmjQ\nPL27sXN1daVpWjXhNCcnR5vEzsvLSy6Xsyzr+l8CgYBrsvLy8mIYhptLSwjJzc39v//7v9zc\nXO5pCxpRMzMzDx8+HB4eruU6di0mEokiIiLOnj1769b//NmcP39+79692dnZbXp2AAAAzWw9\nrT0GNJO0CUyN/aN82iceIHrYYtevXz8TE5MdO3ZMmzZNLpf/8MMPVlZWzR4VGBjYrVu3r7/+\neubMmfb29unp6du3b//b3/4WHR3dp08fNze3LVu2zJw5UygUfv/99wqFwsXFhZugkJqa6unp\n6eHh8eOPPwoEgr///e8NKy8tLb1z5w4hpLa29u7du6dPn3ZxcZkxY4Z6meLi4uTkZPUtfn5+\nL94F/NZbb2VmZq5YsWLEiBEBAQEKhSI5Ofn8+fOjR4/GInYAAKBzL783sPTRaUlR4/PkKB4V\nPi/UxFLU6F5oC3qX2FlYWHz88ce7du1atGiRo6Pj1KlTf//9d6VSqfkoHo/32Wef7dmz54sv\nvpBKpY6OjhMnTuRWAObz+Z999tmuXbvWrl3L4/F69+69cOFCrpd23Lhxv/32261bt2JjY1NT\nU5v6XbwrV65wS7EIhUInJ6fx48dHRUXVGzp27dq1a9euqW/ZvXt3yya0qjM1Nf3iiy+OHz9+\n4cKFhIQEIyMjd3f3jz76aNCgQS9YMwAAwIszsRRFrx0R91ViQVpRw12vfBjSNchFJ4F1WpQe\nTugAw4DJE83C5AktYfKENjB5QhudavJEi+3fvz89PX3ixIk+Plp3obIk50Ze1uXHZY8raBlt\n7mjWtW8X74juxiZaLR/REWHyBAAAABgoirj3c8UkCX2AGzsAAAAAA4EWOwAAAHimf//+3t7e\nTk5Oug4EWgKJHQAAADxjb28vFosxqrWDQlcsAAAAgIFAYgcAAABgIJDYAQAAABgIJHYAAAAA\nBgKJHQAAAICBQGIHAAAAYCCQ2AEAAMAzZ86c2b17d05Ojq4DgZZAYgcAAADPKJVKqVRK07Su\nA4GWQGIHAAAAYCCQ2AEAAAAYCCR2AAAAAAYCiR0AAACAgUBiBwAAAGAgkNgBAADAM2Kx2MHB\nQSAQ6DoQaAkjXQcAAAAAemTw4MH9+/cXi8W6DgRaAi12AAAAAAYCiR0AAACAgUBiBwAAAGAg\nkNgBAAAAGAhMngAAAIAmleVUPL7xpPKphMfnWXax8BjgJnYy13VQ0CTDSex27NghFAqnTZv2\nXEdt2rSpW7dukZGRhYWFmzZtmjVrlru7e9sEqMnXX39dVla2aNGiRmch3b17NzExsaCgQCAQ\nODs7R0REuLm5tX+QAADQqdSW113a+mf29Tz1jde+S/aO6BY6s5+xyHBSCENiOF2xWVlZ2dnZ\nz3vUw4cP8/PzCSF8Pt/a2trIqNXepgcOHEhOTtamZFZW1sWLF3Nyci5evFhvF8MwmzdvXrJk\nycOHD11dXa2srK5duzZ37twjR460VpwAAADqMjIykpKSnmYVHF54ul5WRwhhWTY9LvPo4jPy\nWoVOwgPNkG7/h52d3cKFC1uxwkuXLtna2mpTMj4+vmfPnl5eXufOnRs7dqz6roMHD8bFxc2d\nO3fEiBHcFpZld+7cuWfPHi8vr169erViwAAAAISQ+/fvp6enlx2rrS6uaapM6aPyi1uuDVs4\nuD0DA20YYGJXVFS0cePGDz/88MaNG0lJSSKRaPDgwSEhIdzeioqKgwcP5uXlOTg4vPHGG6qj\n1LtiCwoKNm/ePGfOnOPHj0ul0nnz5jEMEx8fn5SUJJVKPTw8oqOjra2tuQNpmj5z5kxycjKP\nxwsICBg5ciSfz//4449zc3P//e9/X758efny5du2bRMKhdOnT28YLU3TFy9enDBhgpeX1/Hj\nx3Nzc1XdrDKZ7PDhw4MHD1ZldYQQiqJmzJhhY2OjZdYIAADwvEwqzKtym8zqOJmJOQGv+9r3\nwJeRfjGcrlgVpVKZmpq6ZcuW8vLy6OhoJyenNWvWpKenE0Joml62bNmVK1eCg4OdnZ3XrFlT\nV1fHHSWVSlNTU2tqagghCoUiNTV1z549crncz8+PELJp06YdO3a4ubkNGDAgNTV1wYIFVVVV\n3IEbNmzYt2+fp6dn9+7d9+/fv2HDBkLIyJEjGYYJDAwcOXIkISQvL+/JkyeNRnv9+vXq6uqX\nX37Zx8fHxcXl3Llzql3p6em1tbURERH1DjEyMho/fryTk1PrXjcAAACOqFyr6RGZiTltHQk8\nLwNssaMoihDi7Ow8efJkQkhAQADX2Obj45OSkpKTk7Nq1aqAgABCiLe395IlSxrWwI20Mzc3\n/+CDDwghGRkZCQkJH3zwwbBhwwgh4eHhb7/99rFjx2JiYjIyMi5duqSq0N3dPTY2tqSkZODA\ngYSQHj169O/fnxCyevXqpqKNj48PDg62tLTkaj516tTUqVO5l1BQUEAIadk8CaVSWV1d3YID\nWxFN00qlUpU6Q0MsyxJCGIapqKjQdSx6jWEYQohcLtd1IHqNpmlCiOqeExrFsmxH/Isrz668\n8d3tdjtddaVSVGmmTcm00w9zbzbebNFGwub3N7EStucZNdDVe8nS0pLLExplgIkdp3fv3qrH\n1tbW3KXPyMigKMrf35/b3qtXL1NT06Zq6Nu3L/fg9u3bFEWFhYVxT0UiUWBg4M2bN2NiYm7f\nvs3n81UVDhw4kEvptPwGqqysTEpK+uijj7inQ4cO/emnn27duhUYGEj++2XWsvkcLMsqlcoW\nHNi6uG8aaJY+/GfpP+4vAjTDe0kbHe4qyWpkZdmV7XvOJvMGdYo6RTsHppDKjZX89jyjZnr4\nXjLYxM7c/FkzMo/H474SqqqqzM3NebxnHdBWVlZN1aBaeaSyspLH461du1a168mTJ1zqVllZ\naWZmpl7hczl//jxN04cOHVLNcjUyMjp37hyX2HGxlZSUqMbzac/IyKgFR7WumpoaY2NjgUCg\n2zD0GcuyFRUVPB6Pa7KFpkilUpZlTUxMdB2IXquqqqJp2tLSssWfSJ2BUqmUSqXqXxAdgriv\n2HGrY7ud7uTJk+XnaoxrRc2W9Bjo2n9qn3YIScXc3ozH1yrpbGsMw1RVVWnIItqOhuY6YsCJ\nXaOMjIxkMpn6ltra2qYK8/n/uScQCAR8Pn/AgAHqe42NjbkKNdTQLC6H69Pn2V+Fi4vLpUuX\npFKpSCTy9vamKCo5OdnLy6vegWlpaT169NCQM1EUpYpfVyiK4vF4Og9Dn3FdsUTtzQaN4vF4\nLMviKmmDz+cjsdOAYRh9+Hh8XnwTvsCl/W6S+RaU1FKrxK77IA9rl056X0pRlH6+lzpXYufg\n4CCXy8vKymxsbAgh5eXllZXNtyG7ubnJ5fLQ0NCGzSpubm5KpfLJkycuLi5chYcPHx49ejRX\nv2ZZWVmPHj1as2aN+qolNTU1CQkJly9fjoiIsLGxCQwMPHr0aEREhJ2dnapMdnb2smXLYmJi\n1Gf1AgAAtIq+ffu6OhQ/2JmrqNPUzyh2Mu8W2rXdogItda4buz59+lAUdfDgQW4I2vfff69N\nR2G/fv0sLS2//fZbhUJBCCkpKZk3b96JEye4XWZmZnv37pVKpTRN79+/Py4uzsrKytjYmMfj\nlZaWcjVcunTp6tWr9aqNj4+3trb29fVV32hmZhYYGKiaGzt79myKohYvXpyYmCiRSMrKyuLi\n4pYuXerh4REZGfniFwQAAKAeZ2dnn97eg2b30zDQjmfEC583iGfUubKIDqFztdh16dJlypQp\nP/74Y3x8PMMwo0eP9vDwaHZEtomJydKlS7/66qtJkyZZWVmVlpYGBweHh4cTQszMzBYvXvz1\n119PmjSJz+ebm5svXrxYJBIRQoKCgn766adDhw7t2rXr6NGjJiYmqrX0yH+XrwsLC2vYUx4W\nFrZ58+bi4mJ7e3tnZ+f169fv2rVr/fr1XLedQCAYNmzYtGnTuL5gAACAtuAZ6kax1KWtfyrl\n9afBiSyEEf8Mc3rJXieBgWaUapQPQOuSSCQCgUAo1Jd56XqIZdnS0lIej6dN331nVldXx7Ks\nhjnsQAgpLy+nadrGxgZj7DRQKBR1dXWN/io3qEgkEplMJhaLBQJBdUnt7SNpOX89qS6uJhRl\n6WzhGdK1d/RLQvPOPjGOW+tEDz+9O1eLHQAAAGjP3M409O3g0LeDWYalKEq7VVBAl5DYAQAA\nQDMoHnK6jgEt9gAAAAAGAokdAAAAgIFAYgcAAADPnDlzZvfu3Tk5OboOBFoCiR0AAAA8w/3w\nGn7su4NCYgcAAABgIJDYAQAAABgIJHYAAAAABgKJHQAAAICBQGIHAAAAYCCQ2AEAAMAzYrHY\nwcFBIOjsvwbbQeEnxQAAAOCZwYMH9+/fXywW6zoQaAm02AEAAAAYCCR2AAAAAAYCiR0AAACA\ngUBiBwAAAGAgkNgBAAAAGAgkdgAAAAAGAokdAAAAPJOTk3P37l2JRKLrQKAlsI4dAAAAPJOa\nmpqenu7o6Ghra6vrWOC5ocUOAAAAwEAgsQMAAAAwEOiKBQCAToSW07k38yufSFiWFTuZu/g7\nGYnwVQiGo2O8m3fs2CEUCqdNm/ZcR23atKlbt26RkZGFhYWbNm2aNWuWu7t72wTYiJUrV0ql\nUu4xRVF2dna+vr7Dhw/n8f6nlfTu3buJiYkFBQUCgcDZ2TkiIsLNza1eVdqUAQAAzViGvXM0\n/dZvafIahWqjkciod9RLfSf48Y35OowNoLV0jK7YrKys7Ozs5z3q4cOH+fn5hBA+n29tbW1k\n1GpZ7IEDB5KTkzWXSUtLk8vl/v7+/v7+vr6+SqVy+/btixcvViqVXAGGYTZv3rxkyZKHDx+6\nurpaWVldu3Zt7ty5R44cUVWiTRkAAGgWQzNnvrjw14+31LM6QohSqkw+cOf4sjiFVKmr2ABa\nUcdosXtBdnZ2CxcubMUKL126pM1cIW9v70mTJqme3rhxY+XKlZcuXQoPDyeEHDx4MC4ubu7c\nuSNGjOAKsCy7c+fOPXv2eHl59erVS8syAADQrKt7knP+ymtqb8G94gv/ujps4eD2DAmgLXSw\nxK6oqGjjxo0ffvjhjRs3kpKSRCLR4MGDQ0JCuL0VFRUHDx7My8tzcHB44403VEepd8UWFBRs\n3rx5zpw5x48fl0ql8+bNYxgmPj4+KSlJKpV6eHhER0dbW1tzB9I0febMmeTkZB6PFxAQMHLk\nSD6f//HHH+fm5v773/++fPny8uXLt23bJhQKp0+f3mzwQUFBxsbGjx49Cg8Pl8lkhw8fHjx4\nsCpjI4RQFDVjxgwbGxsua9SmDAAANKsir+ruyfuay2Qm5vQa4+3s69A+Iemzvn37enh4ODo6\n6joQaImO0RWrolQqU1NTt2zZUl5eHh0d7eTktGbNmvT0dEIITdPLli27cuVKcHCws7PzmjVr\n6urquKOkUmlqampNTQ0hRKFQpKam7tmzRy6X+/n5EUI2bdq0Y8cONze3AQMGpKamLliwoKqq\nijtww4YN+/bt8/T07N69+/79+zds2EAIGTlyJMMwgYGBI0eOJITk5eU9efJEm+AVCoVSqTQx\nMSGEpKen19bWRkRE1CtjZGQ0fvx4JycnLcsAAECzMi4+Yhm22WIPE7LaIRj95+zs3KNHDzMz\nM10HAi3RwVrsKIoihDg7O0+ePJkQEhAQwDW2+fj4pKSk5OTkrFq1KiAggBDi7e29ZMmShjVw\nI+3Mzc0/+OADQkhGRkZCQsIHH3wwbNgwQkh4ePjbb7997NixmJiYjIyMS5cuqSp0d3ePjY0t\nKSkZOHAgIaRHjx79+/cnhKxevVqbyFmW/eWXX1iWDQoKIoQUFBQQQjTPgdCmTFNomlbltbqi\nVCoZhlEoFM0X7axYluX+ra6u1nUseo0bmcowjK4D0Wvc9ampqeE+J1sm6+Lj4odlrReUvsi/\nVahNsayrjxVKAxxp9//s3XlcFeX+B/DvnDkr+yIgILIIgii4ACpuiFsuaWVWes1MW2zTrLzX\nzMx2l/T6qyyXcqtrVmY3l7S8kSKKO6KCgqACCqKCLAc4+8zvj+kez2U5ICJzOH7ef/g6PPPM\nzHfGc+Z855nneY6Ln1OXUaFNry984jQajV6vv2dBtXk8z4t19XZycrKytI0ldoLo6Gjza3d3\n9/LyciLKzc1lGCYqKkoo79q1q4ODQ0Nb6NWrl/DizJkzDMMMGDBA+FOpVPbo0ePUqVOTJ08+\nc+YMy7LmDfbt21dI6Zr+Rj906FBubi4R8Tx//fr1qqqq6dOnd+7cmf57CbY+nqMpdaysax6T\nKyKTyYTErlE8z9vCf5btM9rjN26L0+l0d7N6Ucb1/ENNegRhl3Rqfe6feWJH0fK8Iz2DEzvc\n6VoGgwEX8EaJcvV2dHS0cv/WJhM7y1xVIpEICVBlZaWTk5PlZCJubm4NbcHFxUV4UVFRIZFI\nFi9ebF5UWFgopG4VFRWOjo61Zie5I76+vkKrHsMwnp6eERERXl5elrGVlJSY+/PV1ZQ6DWFZ\n1tnZuZlxtxCNRiOVSmUymbhh2DLhbo9hGOu3X6DX63meVygUYgdi06qrqzmOc3JyupsWu6gx\nEcG97XA2pcxdF5rSEuni69xroh0OSlO5Ku/oG0Gr1RoMBpVK1YKzSdgfnuerq6tFuXpb/4zb\nz/+ZVCqtdataU1PTUGWW/Wu+IrlczrJsnz59LJcKuYhUKrWyhaYICQl55JFH6l0UHh7OMExa\nWlpYWFitRefOnQsNDZXL5U2p09CuJRKJ6N+Cer1eJpOJHoYtMyd2OEvWcRyHxK5RwvVKLpff\nze2of1fflovIhhhrTE1J7EIHBIYPvoNHlvZKaN2QyWRWvmWA47iamhobvC61scETVnh7e+v1\n+lu3/vrolpWVVVRUNLpWQECAXq/v16/fKAtCf7uAgACj0WgeGFFWVrZ+/Xqh39vd8/Dw6NGj\nx/bt20tKSizL8/LyFixYsGvXribWAQCARoUOClY4NZKjsDI2YgSyOmjz7Cex69mzJ8MwW7du\n5XneaDRu2rSpKbcacXFxrq6u69atE3oSlJSUzJ49+9dffxUWOTo6bty4UavVmkym77///o8/\n/nBzc5PJZBKJpLS0VNhCSkrK4cOHmxHwCy+8wDDMm2++efDgQbVafevWrT/++GP+/PlBQUFj\nx45teh0AALBO4STv/1ycRUE9I2TjJnd39kanCGjz7OdRrJ+f35QpU7799tukpCSO40aPHh0U\nFNToMDqVSjV//vxly5ZNmjTJzc2ttLQ0NjZWmEDY0dHxzTffXL58+aRJk1iWdXJyevPNN5VK\nJRHFxMRs3rx527ZtX3311fbt21UqlXkuvabz9fX95JNPvvrqq08++UQYHSmXy4cNG/b000+b\n+6U1pQ4AADQqbHCwrkqXuu4kz/FE/9tFiaFej0V1fyRSpNBszp9//nnx4sVx48aFhqIJs+1h\nhHQBoMWp1Wq5XG6D/Q9sB8/zpaWlEonEw8ND7Fhsmkaj4Xneyjh3IKKysjKTyeTh4XE3fezs\nXvGFGyd/OHvtzA2T3kREElbiF+0T83hUe8xLbOH777/PysqaOHFiRESE2LHYLo7jysvLbfDq\nbT8tdgAAANZ5BrsPfDXOUeWovl7N87yzl6NUie9BsCt4QwMAwP2FlbFuHVzEjgLgnkCLPQAA\nAICdQGIHAAAAYCeQ2AEAAADYCSR2AAAAcJtKpXJxccHvibVR+G8DAACA24YMGdK/f3/zj6pD\n24IWOwAAAAA7gcQOAAAAwE4gsQMAAACwE0jsAAAAAOwEEjsAAAAAO4HEDgAAAMBOILEDAACA\n2/Lz8zMzM9VqtdiBQHNgHjsAAAC4LSMjIysry8fHx9PTU+xY4I6hxQ4AAADATiCxAwAAALAT\nSOwAAAAA7AQSOwAAAAA7gcQOAAAAwE4gsQMAAACwE5juBAAAAG7r1q2br6+vl5eX2IFAcyCx\nAwAAgNsCAwPbt2/v7OwsdiDQHEjsAAAAms9kMJXmlWsrtQpHuWewu1SBL1YQE95/AAAAzaGt\n1J384Ux20iWDxiCUSOVsp4FBcZO7O3o6iBsb3LfaXmK3aNGi8vJyIjIajTk5OX5+fq6ursKi\nOXPmNNQn4NixY+3atQsJCbGy5fz8/JkzZy5evDgyMrLWorlz56pUqnffffeOQn3llVeioqJm\nzJhRWFg4b968t99+u3Pnzne0hYY05XAAAODeKbtSsef9P9U3qi0LjXpTdtLF/BOFI99K8IlA\nHzUQQdtL7ObNmye8KCkpmT59+mOPPTZ06NBG1/rpp58eeOABsTIhf3//b775pgU3KO7hAADc\n57RqXd2s7vbSCu1vH+0fv3y0s7djKwcG0PYSO+sKCgpOnz6t0+n8/Px69+4tlUqJaMuWLZcv\nXz5y5EhZWdmECROI6Pz587m5uUajMSAgICYmhmGYJm6/oqJi9+7dDz74YElJSXp6ulKpjI2N\nNTcT8jx/9OjRq1event7x8fH11pr2LBhXl5e5eXle/bsGTNmzNmzZ8vLy8eMGUNEhYWF6enp\nGo0mKCioVjz5+fnp6ekSiSQ6OjowMLDewwEAgNaU9uPZhrI6gbZSd+ybU0PnDGi1kAAEdpXY\n/fzzz5s2berWrZubm9uOHTu+++67RYsWOTs7l5eXsfATsgAAIABJREFU63Q6rVZbVVVFRCtW\nrDh8+HB0dLRMJtu2bVtoaOjChQubmNtVVVVt2bJFq9Xm5ORERkYeP358w4YNK1eu9Pb2JqJl\ny5alpqbGxcVlZmbu2bOH53lhrfLy8i1btnTv3t3Ly6uysnLLli1qtfrUqVPR0dFEtHv37rVr\n10ZERHh4eGzbti0sLGzhwoUsyxLRjh071q9f36VLF5Zl161bN23atIceeqjW4QAAQGviTFx2\n0qVGq11KzR9Q1VvhJG+FkADM7Cexu3bt2qZNm/72t7898cQTRFRWVvbiiy/++OOPzzzzzDPP\nPLNnz57BgwcPHTpUq9WyLDtv3ryePXvSf/vVZWZmduvWrSl7kUgkRJSXl/fRRx8xDKPX6596\n6qnk5OTHHnssNzc3JSXl5ZdffuCBB4joP//5z+effy6kbpaERsSCgoKVK1eyLHvz5s2vvvpq\n3Lhx06dPJ6LCwsKZM2fu3bt31KhRN2/e3LBhw7PPPvvggw8S0S+//LJx48bBgwdbHk5DcXIc\np9PpmncmW4rJZNLr9RzHiRuGLRNSf57nNRqN2LHYNIPBQEQ4S9YJbyetVtv0RxC2IHd/nrai\n9S5WPM+bTCbhOtxsmnKtvlrfaDXOxB9ce8zFz+lu9tX6pEppPuVeunRp9OjRwmMiqBfP82Jd\nvVUqlZWl9pPYnThxguf50aNHC3+6u7vHxMScOHHimWeesaymVCpffvnlU6dO/fzzzzqdTkg7\nioqKmpjYCRITE4VLp1wu9/HxKSkpIaKzZ88SUUJCglBnyJAha9asqbuusOKAAQOENrnjx4+b\nTKbx48cLS/39/Xv16pWamjpq1KgTJ05wHDd8+HBh0ahRo3r16mX9v9PMZDJVV1t7TNA6jEaj\n6Pml7eN53hb+s2yfXt/4VynU1NSIHcKdObcnpzy/Uuwomo0nspZG5ybntVYkLUbpqpA+oK+s\nrKypqcGlqVGinCKlUmnl/s1+ErubN28qlUrLCRXbtWt3/PjxWtVMJtO8efMKCwv79evn7u5u\nLryjfbm5uZlfsyxrNBqJ6NatWw4ODkql0lzu4eHR0BbatWsnvLhx4wbLsn/88Yd5kVqtvnnz\nJhFdv37dyclJoVAI5QqFomPHjtS0rzeJRNLEFPDe0ev1LMsK+SvUi+d5oX3F/LaBehmNRp7n\nZTKZ2IHYNK1Wy/O89Su+DQobHFxT1nptHjzPcxx3l9clbbnu4oF8IrKe1RFRYB9/J582Nn5C\nqpReYrKISCqViv49Yst4ntfpdKJcva1/xu0nsaM6h2ru4mYpJSUlOzt75cqVAQEBRKTT6X74\n4Ye73FFDuxMSvnpZfkUxDHP58mXzn+3atevQoYPwWngC1Qwsyzo6inw14ThOLpebE1Ooy5zY\nif6fZeM0Gg3P8w4OmBjMGr1ebzKZHBwchB4jbUWv8VGtuTuDwaDRaFxcXO5mIyaD6cqJIn1N\nI9dnRsIMfjle6dr2btvyvr9ARHK5HJcmKziO0+v1NniK7Cex8/b21mg0arXa3Gh38+bNutPa\nXblyxc3NTcjqiOjChQstFYC7u3tNTY1WqxXyd51OJ8y3Z52Pj4/JZJo9e3bd1ghvb2+tVltR\nUSFM1KfT6c6cOdO5c2fcQgEAiIiVsWGDgzN3N/L1EdS7Q1vM6qCta0s3dtbFxcVJJJLff/9d\n+LOkpOTEiRN9+/YlIqHVXejs5erqqlarhYfiWq323//+t0wma3bDmCVhWuPk5GThz99++81K\ni12tsHfv3i38yXHcunXrDh06REQxMTESiWTXrl3Con379n344YfmhwjouwYAIJaYidEOHtbu\nseWO8r7TerVaPABm9tNi5+Pj8/TTT2/YsCEjI8PZ2Tk9PT0wMFCY5o1l2YCAgB9++CEtLW3a\ntGk//PDDm2++GRERcebMmWnTplVWVu7atUulUt3lz0J06dKld+/eq1evPnLkiDAaNDQ0tN7H\nwZbatWv33HPPrV279tixY15eXjk5OTU1NSNHjhSOaMqUKd98883p06cVCkVGRsa0adOEfoHm\nw3n99dfxfAoAoJWpXJWjFiTueX9fvR0E5Y7yB+YluLR3rrsI4F5j7/RnsmyKTCbr1q2beQxE\nRERE//79GYZxdnYeMWLE1KlT5fK/JhCKjY11dHQMDg6OiooaMmSISqVydnaeNGlSZGRkVFSU\nSqUKDg729vZWKpVRUVF1H5kzDBMSEhIUFERECoWiW7duTk63R7CHhob6+/sT0YABAzp27KhU\nKqOjo6dMmaJUKgMDA/39/YXe8eYt19pCWFjYoEGDZDKZg4ND3759n3/+efOoi8jIyPj4eJlM\nFhAQMGXKFPOkx+bDCQ0Ntdn+NMLgibucVsDuaTQahmHweN06ofEbgyesEwZPqFSqtjV4opVx\nHGc0Gluk76+Duyp0ULCuSl9+tZLn/rqHl7BMcL/A4f8Y1K5Tg4PnbF9WVlZlZWXXrl09PT3F\njsV2CZ2kbfDqzTTapATQPGq1GoMnrON5vrS0VCKRWBlADYTBE01TVlZmMpk8PDxs9mbPFrTI\n4Ina29QYblwo0VTqlE4KrzBPO5iRWK1W63Q6FxcXc+MI1MVxXHl5uQ1evdGaAgAA0Hwylcy/\nu6/YUQD8BTd2AAAAAHYCiR0AAACAnUBiBwAAAGAnkNgBAAAA2AkkdgAAAAB2AokdAAAA3Hbt\n2rXc3FzhJ5qgzcF0JwAAAHBbWlpaVlaWm5ubef5/aEPQYgcAAABgJ5DYAQAAANgJJHYAAAAA\ndgKJHQAAAICdQGIHAAAAYCeQ2AEAAADYCUx3AgAAALeFh4e7u7t7eHiIHQg0BxI7AAAAuC00\nNDQgIMDFxUXsQKA58CgWAAAAwE4gsQMAAACwE0jsAAAAAOwEEjsAAAAAO4HEDgAAAMBOILED\nAAAAsBNI7ADA1uiIrhGpxQ4D4D6VkpLy448/Xr16VexAoDkwjx0A2AiOaC/Rj0QZRBwREfkT\njSF6kogROTSA+0llZeWNGzf0er3YgUBz2GdiN23atNLS0noXffnllx06dKh3UVFRkUqlcnd3\nt7Ll/Pz8mTNnLl68ODIystaiiRMn1tTU1F1lxowZY8aMIaJffvll/fr1Dg4OH3zwQWZmpvl1\nWFhYk47qDkMFaFNqiOYRHfrfwkKitUS7JJJFJlOwOHEBALQp9pnYrVu3jud5IiotLX322Wdn\nzZqVmJgoLGJZtqG11q5dO3DgwKFDhzZ7v2PHjp0+fXqtQonkr+fdBw8ejIuLW7BgARGtWbPG\n/LoZ7j5UAFvCEb1JlNrA0iK5/DWt9iuiwFYNCgCgDbLPPnYSiYRlWZZlhaSKYRj2v4iI47i8\nvLysrKyKigrzKmfPns3Nzb169eq5c+eEEr1en5eXl5ubW11d3fRds3UwDMPz/NmzZ8vLy1mW\nPXv27LFjx8yvhUY+nufz8/Ozs7Pr7stkMl26dOny5csGg6GhUAHauN0NZ3VERAxzSy7/otWi\nAQBou+yzxc6K06dPr1ixQq1WOzk5lZeXjxgx4oUXXmBZdvXq1ZWVlfv378/MzFy6dOlvv/22\nceNGIpLJZNXV1ZMmTXrssceavVOe59esWVNWVpaZmXn16tWrV6/KZLLMzMyioqI5c+Zotdpl\ny5aVlZU5ODhUV1dPnDjx8ccfF1bMyMj45JNPKisrWZZ1dnZ+4403unXrVivUFjktAKLa0mgN\nlt1HdJPIqxWiAQBou+6vxE6tVi9atKhnz56vv/66TCY7c+bMwoULAwICxo0bt2LFigkTJjz5\n5JNDhw7VarXbt28fP378Y489xjBMamrqkiVLevfuHRjYyJOgmzdvpqWlWZawLNu9e3eJRLJy\n5cpXXnklKipqxowZRGR+rdFonn/++U6dOq1cuVKpVB46dGjJkiUhISGxsbE1NTWLFi3q3bv3\nSy+9JJFIPv/880WLFm3YsMEy1IYi4XneaDS2yEmri2FyicoarSaR6IikRmODz76BiGSyaolE\nYjSqxA5ERDVS6YUmVONNpu94Pu6eh9NmSaUaiYQzmRw4DmNNGsRxJpY1Go0KIsLbqSFCXyaT\nyWR+UgR1CWdJlFMkk8msLL2/ErujR4/W1NQ89dRTwkmJjo7u3r17cnLyuHHjLKsplcpVq1ZV\nV1fn5OTo9XqVSsXz/MWLFxtN7I4dO3by5Mlam9q8ebP1kCoqKp555hmlUklE/fv379KlS1JS\nUmxs7LFjx9Rq9dSpU4Vop0yZEhYWptVqhZrWGY1GywfNLcvFZbVcfqjRatL7683VTK6uYkfQ\ndrDst0Tfih2F7XJ2FjuCtkAqJYVCeCkpKdktbjA2SyKRKJVKg8Fw775H7IYop8jT05NhGrx/\nu7++ewsLC2Uyma+vr7nE398/KSmpbs2NGzdu3769ffv2bm5uQolOp2t0+2PGjHnuuefuKKQr\nV66wLFtdXZ2dnS2UuLu7X7p0iYiuXr3q4OBgDsDT01MYXduUIegSiUTx36tXi+P5KKPR2u2C\ngOM4hmGsvPmA53nhLJlH2NyXdFLp4abU47jOHOd3r6NpuziO43leIpHgQ2cFz/PCWSK6hxfJ\ntm7EiBEmk0kmk93fl6ZG8DxvMBjkcrnYgdR2fyV2BoOh1idZoVDUfWR5+vTpn3/++e9///vA\ngQOJyGg0jh8//h6FZDQaOY77+OOPLQudnZ2FaJv9oRL65LVAfPV7timV1Gq1XC7HpdMKnudL\nS0slEomHh4fYsYiIIxpJdKvRehLJPIkkqhUCaqPKyspMJpOHhwe+jK0wGAxarcbFxYXQxtkw\ntVptMplUKpUNZi22g+O48vLye/lV20z3V2Ln5uZWXV1tMBjMz6fLysrMTWJm586dc3Z2FrI6\nIiosLLx3Ibm7u7Msu2nTpro32a6urlVVVXq9Xvho8Tyv0+nwMQO7IyEaQfS99Uo8H8AwXVsn\nIACAtuv+urGLiorief748ePCn0ajMS0tLSoqiv472xzHcUTEsqzRaDSZTEK17du3MwwjLLoX\nIRmNxlOnTplLjhw5cvHiRSLq1q0bER0+/NdTqvT09Mcff7ywsNAyVAC7MJ3I+mzbjF7/8v12\nvQIAaIb7q8UuPDy8f//+n3/+eVFRkbOz8/79+41G4xNPPEFEUqnU1dX1P//5T3V1dXR09ObN\nm1euXNmjR48jR4507NjR19f3yJEjnTp1UqmsjV48f/78pk2bahX6+fkNHz68oVU6deqUmJi4\ndOnShx9+2MvLKysrKykp6e233yaizp07x8fHf/HFF/n5+QqFYvfu3f369QsICCAic6ijRo3C\ns05o+zyIlhG9SlRV31LGYHjeZOrX2kEBALRB7Lvvvit2DPeQwWDIycmJiYkxD5iIj493cnLK\nyMjIy8sLCQmZNWuWj4+PsCggIKCgoKC6unr48OGRkZEXLlzIy8uLi4t76KGHvL29L1++7OTk\n1KFDh8uXL8fHx9f7AJeISuuQSqU9e/YkopycnI4dO4aHh9d63adPH3d393Pnzl26dMnZ2XnG\njBlCW50QraOjY3Z2dnl5+ZAhQ6ZOnSo015lDjYuLk9rq6FO9Xs+yrM2GZyM0Gg3DMNZvGO4P\n7YkGE+UR1er54Ef0rl4/ihob4Q9arZbneZVKhcETVnAcZzQacT9snV6vN5lMCoXCym81Ac/z\nWq3WBq/ejDARC0CLw+CJRmHwRH0uE50guknkShRB1JNIotFoeJ53cHAQOzabhsETTWEwGDSa\nvwZPQEPUarVOp3NxcUGvbiuEwRM2ePVGawoA2JRgomCxYwC4r928ebOioiI4OBiJXVuEGzsA\nAAC47dixY9u3by8uLhY7EGgOJHYAAAAAdgKJHQAAAICdQGIHAAAAYCeQ2AEAAADYCSR2AAAA\nAHYCiR0AAACAncA8dgAAAHBbeHi4u7u7DU69C02BxA4AAABuCw0NDQgIwO9ztFF4FAsAAABg\nJ5DYAQAAANgJJHYAAAAAdgKJHQAAAICdQGIHAAAAYCeQ2AEAAADYCSR2AAAAcFtKSsqPP/54\n9epVsQOB5kBiBwAAALdVVlbeuHFDr9eLHQg0BxI7AAAAADuBxA4AAADATiCxAwAAALATSOwA\nAAAA7AQSOwAAAAA7IRU7AAAAAGh1ZZfp2Od0cS+V55Pcibwiqevj1HMasXKxI4O7wvA837w1\nJ06cWFNTY1nSrl27F154oXfv3i0RWIN7fOedd2JjYy3Lf/vtty+//LJLly5Lliy5ow2+8sor\nUVFRM2bMaHSn5j9Zlu3Zs+fTTz/dsWNHyzpDhw597rnnGl2d6pyluhWIyNPTc8OGDcLr/Pz8\ntWvXZmRkCP9TLMv269fv+eefd3V1vYNDFYNarZbL5QqFQuxAbBfP86WlpRKJxMPDQ+xYbJpG\no+F53sHBQexAbFpZWZnJZPLw8JBI8CimQQaDQaPRuLi4iB2I2I59Qb+/TqY6E5p4daEn/q1W\n+Ol0OhcXF7kcSV6DOI4rLy+3wav3XbXYjR07VshmOI67du3aunXrFi1a9Omnn1omPS3L1dU1\nOTm5VmKXkpJyTz+lwmHyPF9VVXXu3Lnvvvtuzpw5H374YefOnZu+OjV8lsaOHTt9+nTLVRiG\nEV7k5+fPnTvXy8tr4cKFERERer0+PT193bp1f//73z/77DOlUtmiBwoAAPeBo5/RnlfrX3Tz\nPG0YJPnbPlJ4t25M0GJa5sZOIpH4+/u/9tprJpPp8OHDRKTVas+ePavX62/dupWdnS1U4zgu\nLy8vKyuroqLCvK5Gozl79qxOp9NqtRcuXCgoKLDSiBgdHX306FGdTmcuKSsry8jI6Nq1q2U1\nnufz8/Ozs7Orq6trbaGioiIrK+vWrVu1yi9evHj58mUrx8gwjLOzc58+fZYuXerr67tixYo7\nbeyse5bM2P9lvuH+8ssvHRwcFi1a1KtXLwcHBzc3t8GDB7/33nvl5eWnTp26o70DAABQ6QXa\nO8daheobir2vtFY00PJaso+ds7OzXC4Xcqnr16/Pnz9/xowZ69atCw4OXr58+enTp1esWKFW\nq52cnMrLy0eMGPHCCy+wLFtcXDx//vxp06bt3r3bzc0tPz+/Y8eO7733nqOjY91ddO3a9fjx\n48eOHRs4cKBQkpKSEhAQ4OnpWV5eLpRkZWUtW7asrKzMwcGhurp64sSJjz/+uLBo165d69at\nU6lUHMeNGDHC3DBGRKtWrVKpVB988EGjh6lQKP72t7999NFHGRkZUVFRd3OWrCsqKjp//vxz\nzz3n5ORkWR4SEvLdd99JpegfCQAAd+jwP8lksF5Fmr9PeuM0uQxsnYigZbVkcnDp0iW9Xu/v\n709EQtqxb9++1atXe3t7q9XqRYsW9ezZ8/XXX5fJZGfOnFm4cGFAQMC4ceOE1qk///zz008/\ndXBwKC4unj179tatW59++ul6wpVK+/btm5ycbE7sDhw4MHDgQHNWp9FoPvroo06dOq1cuVKp\nVB46dGjJkiUhISGxsbE3btxYt27dqFGjhAeja9asKSoq6tatm7Di2LFjm54qRUdHE1Fubm4z\nEjvLs2Rdbm4uEdW7i0ZD5Xme47g7ja1lCTGYTCZxw7Bl5kZfnCXrOI7jeR5nqSlMJlOze07f\nD+6j95K2nDS1n00REXthZ1PWVuRs4z38THW/a1wDScLefXR2QMT3Esta+y+4q8SupKTk9OnT\nRMRxXHFx8bZt23x8fBISEohISNfi4+O9vb2J6OjRozU1NU899ZRMJiOi6Ojo7t27Jycnjxs3\nTtjU8OHDhZ7R7du3j4mJOXHiRL2JHRElJCR8+OGHarXa2dn5+vXrOTk5b7zxxo4dO4SlR48e\nraioeOaZZ4T+Z/379+/SpUtSUlJsbOzJkydNJtOECROEhronn3zy999/t9xs0w9cpVJJpVK1\nWn2XZ0lw/fr148ePW67i5uYWFhYmbL95gySMRqPl826x6PX6prRN3uc4jisrKxM7ijZAq9WK\nHUIbYAsffNt3P3ziVCc/dTz8YfNXP7WKTq2qW37r2WxOaXPDBUQkynvJ09PT8pFjLXeV2B07\ndszc08vT0zMmJmby5MmWPfo7dOggvCgsLJTJZL6+vuZF/v7+SUlJ5j8DAgLMr318fE6cONHQ\nTnv06OHk5JSamvrAAw+kpKSEhoZabvbKlSssy1ZXV5s79rm7u1+6dImIrl+/LpfLzQNYnJyc\n3Nzcmnfger3eaDTWekLakEbP0vHjx2sdb2xs7Ntvvy1kupYdCpuOYRjRn9WaTCaJRGLlzQfm\nuz3R/7NsnND8jMGe1hmNRsJ7qTHCkwTrDR72gXH2Nfn0qF3KE3vzNDWhTZdTteOc/etewFmZ\nQoL32H8ZjUYb/MTdVUCjR4+ud44PM3P6YjAYak17oVAohMuQwPJjxrKslbZNlmUHDBhw4MCB\nBx544MCBA0OHDrVcajQaOY77+OOPLQudnZ3rjUFoPmwGIVO0TCitaPQsPfjgg/VW8PHxIaLL\nly+3b9/+TiOUSqXNTltbCqY7aZR5uhPR/7NsHKY7aQphuhMXFxdkwFbcR9Od9HuJ+r1UT/nq\nHlR8utG1i8KedRoyt+6lydbn2WpFwnQnNnj1bqVM083Nrbq62mAwmHOpsrIyy9Nh7iRHRJWV\nldYbwxISEubOnXv+/Pn8/HxzZzuBu7s7y7KbNm2qe5/h5ORUVVXFcZxw1eN53nKnd+S3335T\nKpW9evVq3upNFBER4eLi8ttvv8XHx1uW8zz/wQcfDB8+vFY5AABAIyInNJrYcQz74xnd6Mhi\nG8xaoFGtdGMXFRXF87y5J5nRaExLS7McFmB+FsnzfGZmZmhoqJWtRUREeHt7b9iwoVu3brXm\nBoyKijIajZZTgRw5cuTixYtEFBISwvP82bNnhfK0tLTmddn5/fff9+3b9/jjj9/rtiiJRPLE\nE0+cOnXqhx9+sOxlv3r16pMnT7Zr1+6e7h0AAOxQn1nk3Mjjphy3EZV0HzRq2qlWarELDw/v\n37//559/XlRU5OzsvH//fqPR+MQTT5grFBUVffHFF+Hh4cePH79y5Yr1X4MgokGDBm3duvXl\nl1+uVd6pU6fExMSlS5c+/PDDXl5eWVlZSUlJb7/9NhHFxMT4+fmtWLFi3LhxOp3u4MGDfn5+\n5oRpwYIFSqVy/vz5dfeVnZ29ZcsWIqqurs7MzLx48eKoUaPGjx9vWef8+fObNm2yLHn00Ueb\n2AnPigcffLCoqGjz5s0HDhyIjo42Go3p6em3bt2aM2dOWFjYXW4cAADuOwoXevwn+nYE6RsY\n2RY46LRiEpVdbN2woMU0P7GLjIz08/NraKlcLu/WrZvlXHRz5szZu3dvWlqaXq8PCwt77bXX\nhAGzgqlTp+bm5qampioUioULF9Y7x0dkZKS5fS4xMTErK6tfv37Cn5aRvPrqq3/++eeJEyey\nsrJ8fHyWLFki5EBSqfSjjz766aef0tPTfXx83n777V27dpnbvfz8/OptgYuMjBQmWyYipVIZ\nERExY8aMiIiIunXMwzUEBoOh0bPUaAWGYWbMmJGYmJicnFxcXCyVShMTE0eMGIHmOgAAaKaA\nfjT9IP0yjYrT/6dcIqXYGTRiGffTLyJFBi2g+b8V21Ly8/Nnzpy5ePHiyMhIcSOBloXBE43C\nb8U2EQZPNAV+K7Yp7qPBE43iObr4H7r4O1UUkMyBvLpS5ATy6ERE33//fVZW1sSJE2u1YoAl\n+/ytWAAAAGiTGAmFPkChD4gdB7Qw8W/slEplrYe2AAAAANAM4rfY+fj41Jp2DgAAAMTSqVMn\nR0dHzHXSRomf2AEAAIDtiIiICA4ORk/ENkr8R7EAAAAA0CKQ2AEAAADYCSR2AAAAAHYCiR0A\nAACAnUBiBwAAAGAnkNgBAAAA2AkkdgAAAHDbkSNHtm/ffu3aNbEDgeZAYgcAAAC3lZaWXrly\nRaPRiB0INAcSOwAAAAA7gcQOAAAAwE4gsQMAAACwE0jsAAAAAOwEEjsAAAAAO4HEDgAAAMBO\nSMUOAAAAAGzImDFjhg0b5uLiInYg0BxosQMAAACwE0jsAAAAAOwEEjsAAAAAO4HEDgAAAMBO\nYPAEAADAfe9SEp3fRjcyiTMqHf2oQyLFPEUkFzssuGM2mthNnDixpqZGeM0wjKenZ9euXadM\nmeLt7X1P9/jPf/4zNDTUsvzw4cOLFi3q0qXLkiVL7miDr7zySlRU1IwZMxqtyXHc9OnTb926\ntWrVKn9//1pLtVrtL7/8cujQoWvXrsnlcj8/v6FDh44cOZJhmDuKBwAAoB5VxbRtMl3+01wg\nI5Jl/cSf+IQe3UwB/UQMDZrBRhM7IurXr9+YMWOIiOO4a9eu/fzzz2+88cbKlStdXV3v0R4V\nCkVycnKtxC4lJUUuv7e3LKdOnVKr1f7+/n/++eeUKVMsF6nV6vnz5xcXF48ZM6ZLly46nS49\nPX316tVpaWlvvfUWcjsAALgr1TdoXX8qu1R3CVOeR5uG0JO/U1BC68cFzWa7few8PT2joqKi\noqK6d+8+cuTIBQsWVFRUJCcn16pmMplaao+RkZEpKSk8z5tLtFrt8ePHw8PDW2oX9UpKSurZ\ns+fgwYP3799vuXci+vrrr4uKihYvXjx16tTevXsPHDhw5syZs2fPPnr06IEDB+5pVAAAYP92\nPFtvVvcXo45+eoJ0la0YENwt222xqyUgIEAul9+8eZOI8vLyZs2atXDhwvXr18tksv/7v/8r\nKSlZv359enq6Vqv19/d/9NFHBw8eTEQXL1587bXX3n777Z07d54/f16pVA4bNmzq1Kn1tnV1\n79799OnTZ8+ejY6OFkqOHj3q4OAQGBh48eJFoaSiomLdunUnT57UarVBQUFTp041Vz5//vzq\n1auvXLni4+MzZcoUy13MmTNHpVJ98MEHdXdaXV197NixV199NSwsbPPmzRkZGVFRUeZ9HThw\n4OGHHw4JCbFcJTEx0dfX916nmwAAYOeKTlIVdPM9AAAgAElEQVT2zkbqVF2n41/SgDdbJSBo\nAW0msSsrK9Pr9e7u7kQklUqJaMuWLY8++mhISIjRaHznnXdYln3rrbfc3d3379//z3/+08HB\noXfv3kLN9evXz5o1Kyws7OjRo8uWLfPy8hIe8tbi5OQUHR2dnJxsztVSUlL69+9vbkXjOG7h\nwoU1NTX/+Mc/PDw8du/e/e67765YsSIwMLCmpubDDz8MCgpavny5wWD49ttvb926Zd5yZGRk\nQ89zDxw4IJPJ+vTpI5fLIyMjk5KSzInd+fPnTSZTnz596q4VERHR7DMJAABARJS9vUnVsrYj\nsWtDbDqxEx6z8jx/7dq1r7/+WqFQDBgwgIhYliWibt26DRkyhIiOHj169erVpUuXCunO5MmT\nT548uWvXrt69ewvbGThwYGRkJBENGDDgjz/+SE5OrjexI6KEhISvv/76xRdflEql1dXVaWlp\nH3/8sfn576lTpy5duvThhx8Kmd/zzz+fnp6+a9eul19++fjx42q1+vnnnw8MDCSil19+2XLY\nxPTp0xs6xqSkpIEDBwpp39ChQ7/66qsXX3xRoVAQUVlZGRE1b7yIwWCorBS58Zzneb1eX1VV\nJW4Yto/juNLSUrGjsGnCzZVGoxE7EJsmnCXhugFW8Dx/v33ilBkbHQ5/WLecMdQ0af3Co/xi\n97rFhoDB6pFf32VsbZpY7yUPDw8rnextN7HbuXPnzp23m4g7dOjw7rvvWmY5YWFhwovc3FyJ\nRGL5aDIsLCw1NbVuTSLq2LHjH3/80dBO4+PjV61adfLkyT59+qSmpnp4eERERJgTu5ycHJZl\nu3btKvzJMEzXrl2zsrKI6MqVKyzLClkdEbVv374pv7J39erVCxcuTJs2TUhh4+Pj165de/jw\nYeE5stDcyHFco9upV63ueqKwhRjaBJyopsBZagqcpaa4786SUctoy5u/Os/Xv7q+6r47k3XY\n4Bmw3cRu0KBBDz/8sPDa09NTeAhrycnJSXhRU1Pj6Ohomb06OTmZZ0shIpVKZX6tUCh0Ol1D\nO3VwcIiLi0tOTu7Tp8+BAwcGDRpkubSmpsZkMj3++OPmEpPJ5OzsTEQajcbBwcGysqOjY6PH\nKKSY8+bNsyxMSkoSEjtPT08iKioqateuXaObqkUmkzVjrZalVqvlcrnQ+gj1Eu72JBKJh4eH\n2LHYNI1Gw/N8rY8Y1FJWVmYymTw8PCQS2x0VJzqDwaDRaO67n7cfOp+Gzq+nPOktSlnU+Or+\ncfTcsbrFciKRv2ZExXFceXm5DV69bTexc3V1rTXzSEMcHR2rq6t5njfndlVVVZbfAZZPAzUa\njVKptLK1QYMGLV++/MaNG2fPnn3mmWdq7Ugul//f//2fZaFwDVUoFJapJBGp1WrrYXMct3//\n/rFjxyYmJpoLc3JyVq9eXVpa6unpKfTMO3jwoLnPn9nPP/8cExNjbiAEAAC4Y+Hjbid2PFFD\nD/c6j22tgKAF2MONXWhoKMdxFy5cMJecP3/e8vFrdna2+fXly5c7duxoZWuxsbEymezbb7/1\n9/cPCgqyXBQWFqbX63me7/BfcrlcaBjr0KGDyWTKz88Xaubn5zea2J06derWrVsjR44MtTB8\n+HAHB4f9+/cTkVKpHDp06N69e0+fPm254v79+zdu3JiXl2d9+wAAANZ06Eudhv/1uqGsTuVB\nvV9prYCgBdhDYhcTE9OxY8dVq1ZlZ2cXFRVt2rQpPz/f/BiXiIRZ365fv75z586MjAzLFrK6\nZDJZv379UlJSaj2HJaIePXqEhIQsX748MzPzxo0bBw4cmD179p49e4goLi5OpVKtWbPmwoUL\nGRkZX3zxhZubm3nFjRs3fvfdd7W2lpSUFBgYGBAQYFkolUr79Onz559/zQA+derU0NDQd999\nd9WqVampqcnJyStWrFixYsXo0aMTEjBjJAAA3J2HNpCzX4NLJVIa/y9S1TNyAmyW7T6KbTqW\nZd97771169YtXLhQr9cHBga+9dZblo8vn3zyyaSkpM8//1yhUEyYMGH48OFWtkZEgwcP/s9/\n/lM3sZNIJO+999769es//vhjrVbr4+MzceLEcePGEZGzs/Nbb7311VdfzZ0718fH56mnntqx\nY4fRaBRWzMjIsOznR/+dvm7ChAl19z5gwIA///wzNzc3NDTUwcHh448/3rVrV3Jy8r59+6RS\naWBg4D/+8Y/+/fs340QBAAD8Dxd/eiaVtj5OhbV70fFO7ZlHvrndpAdtBGODAzpaUH5+/syZ\nMxcvXixMdwKtCYMnGoXBE02EwRNNgcETTXGfDp5oFM9R1nY69xPdzCST3ugUoA1IlPd+Xu6E\nS1ODMHgCAAAAbBIjoS6PUJdHhL80arVOp5PLncQNCpoHN3YAAAAAdsLOW+wCAwN37NghdhQA\nAAAArQEtdgAAAAB2AokdAAAA3JaWlvbbb79dv35d7ECgOZDYAQAAwG3Xrl3Lzc2trq4WOxBo\nDiR2AAAAAHYCiR0AAACAnUBiBwAAAGAnkNgBAAAA2AkkdgAAAAB2AokdAAAAgJ2w81+eAAAA\ngDsyYsSIAQMGeHp6ih0INAda7AAAAOA2mUymVCpZlhU7EGgOJHYAAAAAdgKJHQAAAICdQGIH\nAAAAYCeQ2AEAAADYCSR2AAAAAHYC050AAADAbRUVFZWVlQqFQi6Xix0L3DG02AEAAMBtBw8e\n/PHHHwsLC8UOBJoDiR0AAACAnUBiBwAAAGAn0McOAAAA7j2DhnL30JVU0pSRyoM6DqDQB0iq\nFDsseyNCYvf+++9rtVrhNcMw7dq1i4yMHD58uERyr5oPhT3Onj3b29vbsjw3N3f9+vWBgYEz\nZsy4ow1++umnISEhY8eObXSnwmsrh5mZmXnw4MHi4mK5XO7r6zt06NCAgIBam2pKHQAAANuV\n+SP99hqpi26XpC4j1wAa9TlFPCReWHZIhEex586d0+v1UVFRUVFRkZGRRqNx9erVb775ptFo\nvHd7PHfu3IEDB2qVJyUlZWZmXrp06U43mJOTU1RUZL1Oo4fJcdxnn302b968nJycDh06uLm5\nHTly5JVXXvnll1/MG2lKHQAAAJt2+J+0deL/ZHWCiiv0/SN07AsxYrJb4jyKDQ8PnzRpkvnP\nEydOvP/++ykpKYmJifdoj506dTpw4MCECRPMJRzHpaamBgcH36M9UmOHuXXr1j/++OOVV14Z\nMWKEUIHn+bVr165fvz4sLKxr165NrAMAAGC78vbT3r8T8Q0s5um3V8m3FwXEt2pU9ssm+tjF\nxMTIZLLLly8nJiYWFxd/9tlnL7300q5du4TnpyaTae/evenp6Vqt1s/P78EHH/T39yeiwsLC\nL7744sUXX0xNTc3KylIqlUOGDImLi6t3F7169frhhx8KCgo6duwolJw9e1aj0fTt2zcvL08o\n4TguKSnp5MmTWq02KCjooYcecnd3FxaVl5dv3br16tWr3t7e48ePt9zyqlWrFArF9OnT7+gw\ndTrdv//974EDB5ozNiJiGOaZZ57x8PDw9PQkoqbUAQAAaFkBAQEsy7q4uLTM5v6YRzxnrQJn\noqR59PT+ltndfc8mRsUaDAaj0ahSqYTXGRkZ69ev1+v13bp1I6KlS5du3LgxKCgoPj6+oKDg\ntddey8/PN9dcvnw5EY0dO9bFxeXDDz9MS0urdxfe3t7BwcHJycnmkgMHDsTFxVn2ePv000/X\nrFkTEBDQp0+fjIyMN954o7KykohMJtOCBQtSU1NjY2N9fX0XLVqk0WjMa129erWJk/1YHmZW\nVlZNTc3QoUNr1ZFKpY899lj79u2bWAcAAKBlRUdHJyYmtmvXrgW2VVFAV480Xi0/haqKW2B3\nYAstdjzPb9myhef5mJgYIpJKpUTk5OQ0a9YsIjp//vzhw4fnzp3bv39/Iho+fPjzzz+/devW\nOXPmMAxDRGFhYU888QQR9erVKycnZ+fOnb169ap3LwkJCXv27JkyZQoRmUymw4cPv/rqq+np\n6UKF3Nzcffv2zZo1a9iwYUSUmJj47LPP7ty5c/Lkyenp6fn5+R988EH37t2JKDw8fN68eeYt\nf/TRR804zOLiYiKyPgaiKXUaYjQaq6qqmrFiCzKZTEaj0TIJhlp4nicijuPKy8vFjsWmcRxH\nRHq9XuxAbJrJZCIi4XYUGsLzPD5xVsgu71UeWaTieRUREZkY5i43yOgqm9SAxHPcugG83Pku\nd2dJ02+BMXBIC26wLrHeS66urkzD/zXiJHaHDh3Kzc0lIp7nr1+/XlVVNX369M6dO5srmJOz\nzMxMhmF69+4t/MmybI8ePU6dOmWuKSRbgoiIiEOHDjW000GDBm3atCkrKysiIuLkyZM8z/fq\n1cuc2J05c4ZhmAEDBgh/KpVKYUeTJ0/Ozc1lGCYqKkpY1LVrVwcHh7s8TOGLSshiG9KUOg3h\nef7eDUZpOuGbBhplC/9Ztk/4RIB1eC81Bc5SQ9jqm+z1dFF2LSm72LIb5GtKW+E/2gbfS+Ik\ndr6+vkKuxjCMp6dnRESEl5eXZQXzo/3y8nIHBweZTGZe5OrqanlL6urqan7t6OhopZlKmHAk\nOTk5IiIiJSUlPj7ecrMVFRUSiWTx4sXmksLCQqGFoLKy0snJyfKhrZub210eprCFkpIScze+\nuppSpyFSqbSJQd47NTU1UqkUPzVoBc/zwhuvxfqy2CmdTsfzvFKJ+a6sqays5DjOxcXl3k0d\nZQeMRqNOp3N0dBQ7EFsVPcEUGKfVag0Gg0qlal7LgiXm5jnJjmlNqck9spn3CL3L3VlSuQWp\nVPfwe5DjuKqqKlGu3laa60isxC4kJOSRRx6xUoFlWeGFTCar9fxFp9NZJmQ6nc782mAwWC6q\nKyEhYfPmzVOnTj169Oj8+fMtF8nlcpZl+/TpY1kobE0qlVruhYhqamqs7MXMymGGh4czDJOW\nlhYWFlZr0blz50JDQ+VyeVPqNLRrhmHu/gN5lxiGYVlW9DBsmfAolprbLnv/MBgMPM/jLFkn\nXOulUikSOyt4nreFy6PtcvEhFx9OrTbqdIyLC3v3d+b+MZT0D6q+2Ug1Zz9J9CS66ye/relu\nnqrdU7b++Q8ICDAYDEJvM0FBQYFlt7OCggLz62vXrvn4+FjZWv/+/auqqrZt26ZQKMyPVs07\n0uv1/fr1G2VB6G/n7e2t1+tv3bol1CwrK6uoqLjL4/Lw8OjRo8f27dtLSkosy/Py8hYsWLBr\n164m1gEAALBdEpbiX2+8Wr85bSurs2W2ntj16dPHycnpX//6l9BbKy0t7cyZM5YDRZOSkoS8\n59KlSydPnjT3xquXs7NzTEzMjh07BgwYUOumNi4uztXVdd26dQaDgYhKSkpmz57966+/ElHP\nnj0Zhtm6davQcW3Tpk2WTWUpKSmHDx9uxqG98MILDMO8+eabBw8eVKvVt27d+uOPP+bPnx8U\nFGT+TYum1AEAALBd8a9Tx/7WKgQnUu9XWisa+2dzTYi1ODo6zps3b/ny5ZMmTXJwcKioqHjo\noYcs53UbNmzY66+/LpPJSkpKIiIiak0yV1dCQsKxY8cSEhJqlatUqvnz5y9btmzSpElubm6l\npaWxsbHCTMJ+fn5Tpkz59ttvk5KSOI4bPXp0UFCQuR/39u3bVSpVfPwdz6zo6+v7ySeffPXV\nV5988onwSE4ulw8bNuzpp582P1BuSh0AAIAWlJaWduXKlYSEhJb5+UpWTn/bRT9NpNzf61ka\nPpbG/4tYfKO1GMbcy6fVnDt3zt3d3dfXt96ler0+Ozs7ODjYycnJXGgymfLz8/V6fYcOHczl\n+fn5M2fOXLJkSadOnfLz8xUKhXny4bp79PX1FYYgGAyG7Ozsrl27Cv1RioqKtFptSEiIUJPn\n+YKCAq1W6+3tXWvIQnl5eXFxsZeXl6en5+XLl+VyuTBP8sWLFyUSSd1fsLB+mJbUavX169el\nUqmvr69CoWh2HVujVqvlcnlbiVYUPM+XlpZKJBIPDw+xY7FpGo2G5/kmjka/b5WVlZlMJg8P\nD/Sxs8JgMGg0GgxXsu7777/PysqaOHFiREREi22U5+n8z5T2NV1JJV0lKV2p4wDq9Vzb/aFY\nYa4TG7x6i9BiFxkZaWWpXC6v1fuNiFiWNedetfA8L5fL6w4vaGiPMplMmPdY4OfnZ1mTYZjA\nwMB6N+Lm5mYeZ2qZxnXq1KnRnVrn7Ozs7NzI5D1NqQMAAGCjGIYiH6XIR4mIOBNJWLEDslu4\nsQMAAIBWhKzuXrL1PnZWBAYG7tixQ+woAAAAAGwFWuwAAAAA7AQSOwAAAAA7gcQOAAAAwE60\n4T52AAAA0OISExN79+5t/ZecwGYhsQMAAIDbHBwcWJbFNPhtFB7FAgAAANgJJHYAAAAAdgKJ\nHQAAAICdQGIHAAAAYCeQ2AEAAADYCSR2AAAAcFtNTU1lZaXBYBA7EGgOJHYAAABw2759+775\n5psrV66IHQg0BxI7AAAAADuBxA4AAADATiCxAwAAALATSOwAAAAA7AQSOwAAAAA7gcQOAAAA\nwE5IxQ4AAAAAbEhAQADLsi4uLmIHAs2BxA4AAABui46ODg8PR2LXRiGxAwAAgJajuUWX/6SK\nKyRVkHcUBfQjCSt2TPcRcRK7999/X6vVCq8ZhmnXrl1kZOTw4cMlknvV50/Y4+zZs729vS3L\nc3Nz169fHxgYOGPGjDva4KeffhoSEjJ27NiGKixZsqSioqLeRa+99pqXl9cd7c7S9evXP/30\n0xkzZgQGBjZ7IwAAAC1MU0ZJ8+jUejJZ/ByZSwca+hF1f0q8sO4v4iR2586d69ChQ0xMDBFx\nHFdUVLR69eqkpKSPP/5YKr0nIZ07d06r1R44cGDChAmW5UlJSZmZmSaT6U43mJOTo1QqrVQI\nCwurqakhIo1Gs3Pnzt69ewcFBQmLFApFQ2v9+OOPoaGhvXr1srJlrVabkZFRXV19pzEDAADc\nK5WF9M1QKsmuU36V/j2VCo/T6M/FCOu+I9qj2PDw8EmTJpn/PHHixPvvv5+SkpKYmHiP9tip\nU6daiR3HcampqcHBwfdid+PHjxdelJSU7Ny5Mz4+fujQoY2ulZKS4unpeS/iAQAAuFc4E33/\ncD1ZndmxldQunHq/0oox3adspY9dTEyMTCa7fPlyYmJicXHxZ5999tJLL+3atUt4fmoymfbu\n3Zuenq7Vav38/B588EF/f38iKiws/OKLL1588cXU1NSsrCylUjlkyJC4uLh6d9GrV68ffvih\noKCgY8eOQsnZs2c1Gk3fvn3z8vKEEo7jkpKSTp48qdVqg4KCHnroIXd3d2FReXn51q1br169\n6u3tbU7aBKtWrVIoFNOnT2/iwTZ0OG+99daVK1d++umnQ4cOvfPOO2q1es+ePRcvXjQYDB07\ndrQMBgAAwIac/oaKTjRS588FFP0kKd1aJaD7l63MY2cwGIxGo0qlEl5nZGSsX79er9d369aN\niJYuXbpx48agoKD4+PiCgoLXXnstPz/fXHP58uVENHbsWBcXlw8//DAtLa3eXXh7ewcHBycn\nJ5tLDhw4EBcXZ9mx79NPP12zZk1AQECfPn0yMjLeeOONyspKIjKZTAsWLEhNTY2NjfX19V20\naJFGozGvdfXq1cLCwqYfbEOHM3LkSI7jevToMXLkSJ7n33777eTk5MjIyNjY2Ozs7L///e/m\njokAAAA25My3jdfRllP2znsfyv3OJlrseJ7fsmULz/NCrzuhm52Tk9OsWbOI6Pz584cPH547\nd27//v2JaPjw4c8///zWrVvnzJnDMAwRhYWFPfHEE0TUq1evnJycnTt31ttHjef5hISEPXv2\nTJkyhYhMJtPhw4dfffXV9PR0oUJubu6+fftmzZo1bNgwIkpMTHz22Wd37tw5efLk9PT0/Pz8\nDz74oHv37kQUHh4+b94885Y/+uijph+slcPp27cvEYWGhvbu3Vun0z388MPh4eF+fn5E1L9/\n/ylTppw9e7ah9shaTCaTZeopCqPRyHGcwWBovOr9iud54d+qqiqxY7FpRqORiDiOEzsQmyac\nn+rqauHCCPXiOM5oNOITVy/phV/YvD+JqLq0tKamRt6uHaNSNXFdWf7BplTjUhabLu5rXnj6\n+Dd5Z7/mrXsv8Dwv1tXbycnJylLRErtDhw7l5uYSEc/z169fr6qqmj59eufOnc0VzMlZZmYm\nwzC9e/cW/mRZtkePHqdOnTLXFJItQURExKFDhxra6aBBgzZt2pSVlRUREXHy5Eme53v16mVO\n7M6cOcMwzIABA4Q/lUqlsKPJkyfn5uYyDBMVFSUs6tq1q4ODQ/MOvNHDESgUisjIyN27dxcU\nFOj1euGSXVpa2sS9cBxnC817JpMJiV2jeJ63hf8s2yekd2CdTqcTO4Q2AJ+4ejleOSI7s4GI\n2gt/l93JyjxRE24oJCXnJCXnmhEbEVV3fdoo82jeuveOKO8lR0dHK/dvoiV2vr6+QnLDMIyn\np2dEREStGUDMUyOWl5c7ODjIZDLzIldXV+EJqflP82tHR0cr6bMwr0pycnJERERKSkp8fLzl\nZisqKiQSyeLFi80lhYWFer2eiCorK52cnCwf2rq5NbOXQKOHI6isrJwzZ46vr++DDz7o7u7O\ncdyCBQuEBp6mYFnW2dm5eRG2FI1GI5VKLY8UahHu9hiGsX77BXq9nud5K8PJgYiqq6s5jnNy\nckKLnRUmk0mv16ua3BB1X2F6PW0I7EdEx44dKyoq6tu3b/v27Zu4ruy3l0hb/wxflrjOY02R\nE5sXnqp9BClF/l6zxPN8dXW1KFdv659x0RK7kJCQRx55xEoFlv1rPkOZTCZkV2Y6nc4yXbC8\nQzUYDNYziYSEhM2bN0+dOvXo0aPz58+3XCSXy1mW7dOnj2WhsDWpVFrrPliYyqQZGj0cQUpK\nSnV19TvvvCO8ae60sVcikYj+LajX62Uymehh2DJzYoezZB3HcUjsGiVclORy+b2bENQOCP25\n8V6qX0AsBcQS0ZUsSVZRVlTQaFlERFPXzd1BGT80WkvS+2VJ6AN3E6Pt4DiupqbGBt9LNtHH\nzrqAgACDwVBcXGy+dSgoKAgICDBXKCgoiI2NFV5fu3bNx8fHytb69++/Zs2abdu2KRQK86NV\n8470en2/fv0smwAF3t7eer3+1q1bHh4eRFRWVtbQ5MN3fziC0tJSZ2dn863AiRONjTYCAAAQ\nS9zLjSd27SIopPFpv+AutYEbuz59+jg5Of3rX/8SphFOS0s7c+aM5ZxwSUlJJSUlRHTp0qWT\nJ0+au6/Vy9nZOSYmZseOHQMGDKh1XxsXF+fq6rpu3TqhW1hJScns2bN//fVXIurZsyfDMFu3\nbuV53mg0btq0SS6Xm1dMSUk5fPjw3R+OTCaTSCRCRzp/f//y8nJhtGxeXt6+ffscHBzUanUT\n9wIAANB6AgdSrNUfcGJlNHYtSdpAc1Jb1wZOsaOj47x585YvXz5p0iQHB4eKioqHHnpoxIgR\n5grDhg17/fXXZTJZSUlJRERErUnm6kpISDh27FhCQkKtcpVKNX/+/GXLlk2aNMnNza20tDQ2\nNlaYMNnPz2/KlCnffvttUlISx3GjR48OCgoyj9Hbvn27SqWKj4+/y8NhGCYmJmbz5s3btm37\n8ssvw8PD33jjjXbt2gkd7P71r3/9+OOPVVVVTZnoGAAAoFWN+owMNXS6vnlP5I70yLcUOLDV\nY7ofMU3vj9+Czp075+7u7uvrW+9SvV6fnZ0dHBxs2SfRZDLl5+fr9foOHTqYy/Pz82fOnLlk\nyZJOnTrl5+crFArz5MN19+jr6ytM8GswGLKzs7t27Sp0PywqKtJqtSEhIUJNnucLCgq0Wq23\nt3etCYHLy8uLi4u9vLw8PT0vX74sl8uFiYUvXrwokUga+gULg8GQlZXVoUMHy63VezhC+eXL\nlx0dHYUHtVevXjUYDEFBQRKJxGAw5Ofn+/j4yGSynJycTp06NXtkbutQq9VyudwG+x/YDp7n\nS0tLJRKJ8IgfGqLRaHiet/E3vOjKyspMJpOHhwf62FlhMBg0Go15cB7U6/vvv8/Kypo4cWJE\n0/vYmZ3/N6V+QlePEs8RESlcqMsjlLCQ3O/JjzyJiOO48vJyG7x6i5PYtRQhsVu8eHFkZKTY\nsUBtSOwahcSuiZDYNQUSu6ZAYtcU169fr66u9vHxcXR0bOYmtOVUeZWkKnLtSKx9zo1gs4ld\nG3gUCwAAAK3GwcGBZdm7mqxK6YafDhNL207sAgMDd+zYIXYUAAAAADYBLfYAAAAAdgKJHQAA\nAICdQGIHAAAAYCeQ2AEAAADYCSR2AAAAcJvBYNBqtcLPI0Gbg8QOAAAAbtu7d+/XX38t/KYl\ntDlI7AAAAADsBBI7AAAAADuBxA4AAADATiCxAwAAALATSOwAAAAA7AQSOwAAAAA7IRU7ALBb\nMpmMZVmxo7BpDMMoFAqGYcQOxNaxLMvzvNhR2Dq5XM5xHN5O1jEMI5PJxI7C1vn7+/M87+zs\nLHYgNo1hGLlcLnYU9WBwuQQAAACwD3gUCwAAAGAnkNgBAAAA2AkkdgAAAAB2AokdAAAAgJ1A\nYgcAAABgJ5DYAQAAANgJJHYAAAAAdgKJHYCtKCgoyMrKEjsKaNtqampyc3OLi4sxRyk025Ur\nVy5cuKDRaMQOBJoDExQD2ISSkpKXXnrJ0dFxw4YNYscCbRLP85s2bdq+fTvP8xzHdejQYc6c\nOSEhIWLHBW1JUVHRokWLCgoKWJaVSCTPPvvsyJEjxQ4K7gxa7ABswpo1a6RS/MQfNN/u3bt/\n+eWX11577eeff/7666/lcvny5cvFDgraEp7nly5d6ujo+M0332zbtm3q1KmrVq3Kzc0VOy64\nM0jsAMR3+PDhc+fOjRkzRuxAoA3LyMgYMGDAoEGDJBKJt7f3+PHjr1y5Ul5eLnZc0GZcuHDh\n0qVLzz77rKurK8MwY8eODQ4O3rNnj93ZSeoAACAASURBVNhxwZ1BCwGAyDQazdq1a6dNm1ZT\nUyN2LNCGzZ071/JPlmWJyGQyiRQOtD3nz59XKpWdOnUyl3Tt2jUtLU3EkKAZ0GIHILJvv/22\nffv2Q4cOFTsQsB88z//++++hoaGenp5ixwJtxs2bNz09PRmGMZe0a9fuxo0bIoYEzYDEDkBM\nOTk5e/fufemllywvpgB36bvvvsvMzHzxxRfFDgTaEo1Go1AoLEsUCoXBYEC7b9uCR7EAraei\nouL8+fPCay8vr+Dg4C+++OKRRx4JCAgQNzBoc3ieP3r0qPBaoVD07NnTXL5hw4Zff/117ty5\nYWFh4gUIbY9UKuU4zrLEZDIxDCORoA2oLUFiB9B6Ll26tHjxYuH1kCFDgoODb9682a1bt3Pn\nzhH9f3v3HRbFtQUA/OwubVmkSxGRrghiRSygoBQVFRuKYhcVjQrqUxMTYzdqihUL2CPPQrAG\njYJIUURAOgiCAqJIEellKbvz/rhxsqEsNQH3nd/H976dO7fNsC8c7517B/Lz8+vr61++fKmm\npqaoqNilPUXdXX19Pf1dUlFR8fLyAgCKoo4dO/b8+fOdO3eamJh0aQfRl0dOTq6srEwwpays\nTFZWFucTviwY2CH07xkyZMjt27fpQ09PTwD48ccfyWFdXV1NTc2+ffsWLlyIe0ch4cTFxQW/\nS8T58+cjIyP37duH29ehdtDW1i4uLi4vL+/RowdJyczMxO/SFwcDO4S6jKurq6urK3149+7d\nW7du4QbFqH3i4+P9/PwwqkPtNmTIEElJyYCAgJkzZwLAx48f4+LiVq5c2dX9Qm2DgR1CCImC\nS5cuKSsrx8fHx8fH04mjR4/W0tLqwl6hL4i0tPSCBQsuXLhQUFCgoKAQEBCgra09fvz4ru4X\nahsM7BDqLpSUlAwNDbu6F+hLJS8vLyUllZiYKJhobGzcVf1BXyIHBwcVFZWQkJC8vDwbG5vp\n06fjG3G+OPiuWIQQQgghEYFrmBFCCCGERAQGdgghhBBCIgIDO4QQQgghEYGBHUIIIYSQiMDA\nDiGEEEJIRGBghxBCCCEkInB/GoRQt5Cfn5+SkiIkg7m5ubi4eFurnTt37vXr13Nzc9XU1DrQ\nu38DRVH29vbx8fFxcXEqKip0elpaWmFhobq6uo6OjmD+goIC8pbhBnR0dOhNiSsrK5OSkmRk\nZIyMjBq/8fP9+/evX78WfmMjIiKqq6stLCwa7GeWnZ2dkZHRr18/dXV1wfTKysqoqCg5Obkh\nQ4YIv17yq3n37l3v3r2bzCAmJmZqavr8+XPh9QAAn89/8+ZNYWGhnJycpqYm/UYsoqqqKjIy\nUlFRceDAgQDg6+s7e/bs8+fPL126tMWaEfryUAgh1A1cvnxZ+H+sPn782GIlfD5/woQJERER\ndMqdO3f2799fUVHxD3W7cYvttmfPHiaTGRgYSKfcuXNHW1ubvgNDhw5NTEykz545c6bJG7Vn\nzx6S4cGDByTA0tDQGDFiRGlpqWBztbW1JiYm06ZNE94rBwcHAHjy5EmD9Hnz5gGAu7t7g/Qr\nV64AgKura4vX6+TkBADv3r1rLgOLxRoxYoTwSvLz89euXaugoEBfPoPBsLGxCQgIoPOQfzBM\nmDCBTnF1dWWz2YI3EyGRgSN2CKFuxMnJae7cuU2ekpWVbbF4enr6w4cP169fT6c4ODiQ0OQf\n0rjF9snJydm7d6+joyP9BqegoKAZM2YoKiqePHlywIABUVFR27dvt7GxSU5OVlJSAoCSkhIA\n+OmnnwYMGCBYVd++fQGAoig3NzdnZ+eTJ0+WlZUZGhqeOHFi69atdLaDBw9mZ2c/ePBAeMfs\n7e3v3r378OFDCwsLOpGiqICAAAaD4e/v3yA/SZk8eXK7b0XrpaSk2Nra5uTkjBgxYs6cOVpa\nWiUlJU+fPr1+/fqECRMOHTrk7u7eZMEDBw5cvXp1w4YNAQEB/0I/EfpXdXVkiRBCFPV5xG7H\njh2tyVxYWBgTE/PixQvBYbyEhISdO3cCwMGDB4OCgkpKSiiKSk5ODgoKqqmpIZ+Dg4MpiuLz\n+UlJSRERESQPkZqaGhUVVVZW1ri52tra9PT08PDwzMzM+vp64S3S3rx5ExYWlpSUJFikOV99\n9RWDwYiPj6dTLC0tASA8PJxOuXjxIgBs3bqVHH733XcAEBsb22SFaWlpAECP/y1cuNDc3Fzw\nYiUlJb28vFrsWHZ2NgCYmpoKJkZFRQHAhAkTACA7O1vwVK9evSQlJekh0uZuHdXUiF1RUdHz\n58+Tk5P5fD7V0ogdl8slIeyBAwcanEpISFBTU2OxWHFxcVRTI3YURZEY9+nTpy3eAYS+LBjY\nIYS6hVYGdllZWfb29vTjYgwGw97evqCggKKoBqNEZPaQRA+5ubkURS1atAgAkpKSjIyMyFNl\nbDb7woULb968GTx4MEmRkpI6e/asYItHjhwRfOJNS0vrzp075FSTLVIUFRQU1L9/fzpdUVHx\n6NGjQi6Ky+VKS0uPHj2aTuHxeOLi4oaGhoLZ6uvrlZSU+vXrRw7XrFkDAJmZmU3WGRwcDAAJ\nCQnkcOPGjTo6OuQzn8+3sLCwsrIi8VOLBgwYwGQyBWPovXv3AsAff/wBAIK3KykpCQDs7OzI\noZBbRzUK7LZu3Uo/xqevrx8TEyMmJiYksPP09AQAR0fHJs/evXt3z549WVlZVDOBHQlYFyxY\n0Jo7gNAXBFfFIoS+JEuWLAkMDPT09ExJSUlJSTl27FhQUJCzszMAXL9+fceOHQDg6+tbXFw8\natSoBmVJ3ODi4nLkyBEul5uQkMBms93d3efNm/fdd99VVVVlZmaqq6t/9dVXxcXFpMjNmzfX\nr1/fv3//4ODgV69e3bt3T0JCYtasWampqc21GBsbO2HCBGlpaX9//+zs7GfPnpmZmbm7u5NA\npElPnjypqqqytramU6qrq+vq6lRVVQWzsVgsfX399PT06upq+DwVKy8vn5iY6OPj89tvv719\n+5bOLC0tDQBcLpccVlVVcTgc8vnUqVPR0dHkEb20tLTXr18Lv+f29vZ8Pl9w1vLhw4cGBga2\ntrZycnKCs7EPHz6Ez/Owwm9dAxcvXty/f/+gQYOePn2alpa2fv365mbkabdu3QKA5ibBp06d\num3bNnoRSWOampp9+/b19/en8IXpSLTgM3YIoW4kKyuLDDU1oK2tTZYRPH361NzcfMWKFSTd\n0NBQRUXl3bt3PB6Pw+FISUkBAIfDkZeXb1wJGeebOnWqra0tAJiYmEydOvXSpUtmZmaOjo6k\nFWdn53379kVHR9vY2ACAjIzM1q1bly1bpq+vDwB9+/YtLy+fO3fu7du3v/nmmyZb3L59u5iY\n2L1790hYpqmpefPmTQMDgx9++MHV1bXJqyaXTOZeCWlpaQ6Hk5mZ2SBnRUUFn8/Pz8/X1tYu\nLS0FAAcHhydPntAXuGzZshMnTkhKSurq6rJYrOTk5OHDhwNAYmKigYEBALx///6bb77ZtWuX\nuLi4kZFRRkYGRVEmJiYPHjzo2bNnk92zt7f/8ccfHz58SBZMlJeXP3/+fPny5SwWa9y4cY8e\nPeLz+UwmEwBI8Gdvb9/irWvQxIkTJ5hM5p07dzQ0NADAwMCgtrZ248aNTfaHiI+PFxMTMzMz\nE5JHOCsrKy8vr5cvXxobG7e7EoS6GwzsEELdyKVLly5dutQ4fceOHeRptt69e7948eLhw4fk\nAS8AmDNnTpuaEIyfevXq1WQKPWJnZ2dnZ2dXWloaFRVVXl5OgioAKCgoaLLyurq6R48eaWho\nBAUFCaZramo+f/48Ozu7T58+jUu9f/+eXBqdQpZ23rlz5+bNmzNnziSJ/v7+ycnJ8HkcjozY\nqampBQUF6ejopKWlffvtt+fOnZOSkvLw8FBSUpoxY8bu3bsVFRWTk5OfPXtG1kl89dVXBgYG\nGzdudHJyYjAY+fn59fX1pqam33///enTp5u8KHNzc8GRuaCgoLq6OjK+aGtre/v27RcvXpiZ\nmdXU1ISGhhoYGJBIrvW3rqamJjY21tjYmER1xPTp04UHdiUlJXJycu3YAYemqakJAO/fv8fA\nDokSDOwQQt3IvHnzyLxqA+QxeQDw8vJydHScOHGihoaGtbX1pEmTHBwcyLRjKwnOb0pISDSZ\nwuPxyGFRUdGqVatu3rzJ4/EkJCTExcX5fD4AkP9tLDc3l8vlvnnzhgxuNZCXl9dkYPfx40cA\nUFZWFkzcvXu3v7//vHnzVq9ebWxsnJCQcOnSJXNz87CwMHK9fn5+5Kk7kl9LS2v48OH9+/f3\n9PTcu3evvLz86dOnN27c6ObmJicnd+rUKTs7u2vXrv3xxx9RUVFMJvPBgwfbt28nA42zZ8/2\n9vZuLrATExOztbX19fVNSEgYOHCgv78/k8kkq3fJuKa/v7+ZmdnTp0+rqqrIcF2bbl1eXh6P\nxxOM6gCgyRslSFZWtqysTHge4cgNJzcfIZGBz9ghhLqRvn37TmkKHdjZ2tpmZGQcOXKkf//+\nPj4+8+bN09TU/P3331vfRON9ehun0JYsWfLbb79t2LAhLy+vpqamoqLi8ePHQiqvq6sDgDFj\nxlQ3hcyKNkaemWOz2YKJAwcODA4OHjFihIeHx5o1axISEvz9/dXU1JhMJpkzlZOTo6M6Ql5e\n3sbGpr6+PjExEQCUlJQuXbqUlZUVHx/v6upaVFTk7u6+efPmwYMHFxQUVFZW0o+g9enTJy8v\nj3SjSSRcI4/QBQQEmJqakq3j+vbt26dPHzKYR+Zh6QUlrb915KY1GHtjMplCfi8AoKmpWV1d\n/ebNGyF5hCPxsZCrRuhLhIEdQugLo6Sk5O7uHhAQUFxc7O3tTVHUggULyANnnaukpMTPz2/g\nwIE//fQTPapXWFgovG8AkJeXJ9WU5iIVUurTp08N0s3MzEJDQ2tqarhcbkhIyMiRI2NjYw0M\nDBqEgIKETE1u2LBBXl5++/bt8DmWYrFY5BR5UpAep2xs0qRJDAYjNDQ0JycnLS2NDNQRtra2\nkZGRXC730aNHHA5n7Nix0MZbR14U0eA3+OnTJ+HLGshcvLe3d5NnKyoqjh07VllZKaQG0p8G\nA6UIfekwsEMIfTEoikpPT6f/WktJSc2fP3/dunVlZWVkjKpzkVc16OrqCibeuHFDSBF5eXl9\nff3Xr1+np6cLpgcEBOTk5DRXiozANYh78vPzyaoIFotFliaEh4dnZGRMnToVAMrKypycnNzc\n3ASL8Pn8iIgIBoNhaGjYoImAgIDLly+fOXOGxHBkBpY8pQcARUVFMjIyMjIyzfVQTU1tyJAh\nYWFhpEtk9Qlha2tbU1Pz6NGjuLg4a2trSUlJaOOtU1VVlZWVffnypWAkFx4e3lxniMWLF0tI\nSPz4449kjxVBFEWtW7fO3d29yec1aWQStrklIwh9oTCwQwh9MZ4+fdq3b18y5kRQFBUTEwMA\n5I2lJGrprKemNDQ0JCUlY2Nja2trScrVq1djY2NBYHVF4xaXL19OUdR3331HD4CFh4c7ODis\nXLmyuYZMTEwAgNRM++WXX8aOHUu/NywvL2/FihVkfxYAkJWVffPmjYeHx6+//koy8Hi8bdu2\nvXz5ctq0aQ2ClcrKSldXV1dXVzKcBgAyMjL9+vWLiIggh0+fPm1xeam9vX1xcfHZs2c5HM7o\n0aPpdGtrawaDcfjwYR6PR8/DtubWCZo0aVJhYeGpU6fIYUVFxb59+0g42xwDA4O9e/dWVVVZ\nWVl5e3vX1NSQ9PT09NmzZ1+8eHH8+PGrVq0SUkNcXByTyTQyMhJ+4Qh9Ybpo/zyEEPobskGx\nqqrqoGacOXOGoiiyvZmBgcHs2bMdHR319PQAwM3NjVRC9g1RVlaeMmXKrVu3qL9vUOzi4gIA\n6enpdKNkFzrBF6GSQOrq1avkkCzMNDExWblypbm5eZ8+fTIzMxUUFNhs9vLlyz9+/Ni4xdra\n2okTJwKAoaHhkiVL7OzsWCyWlpbW69evm7t2Mrw3f/58wcSCggKyvHTQoEHjx4/ncDgSEhI+\nPj50hpSUFBLA6enpjR07lsx4mpiY5OfnN6h//fr1vXv3bvCuWE9PTzExsR07dmzatInBYNy/\nf1/4L+jZs2fkr4a9vX2DU0OGDCGnBF8j0eKtE9ygOCUlRVZWlsFgjBgxYubMmaqqqhs3blRW\nVjYzMxPeq+PHj5MxQmlpaUNDQ3V1dTLfvWjRourqavpGQaMNimtqajgcTov1I/TFYZEdBBBC\nqGvl5+dnZ2crKys3+XSalJTU8OHDBw0aNHPmzKFDh9bV1RUVFVEUNXz48MOHD9ODYdra2srK\nytXV1QoKCuPHj+/duzeZ4HN2dmaz2a9evaqtrXVycqJfO5uVlVVSUjJjxgz6ObDc3NycnBx7\ne3sdHR0AsLOz09DQKCsrKy4utrCwOHv2rIaGxsCBAz99+sRgMKZOnWpoaNigRRaL5ezsbGxs\nXFlZmZubq6iouHTpUk9PT7KRSpMUFRV9fX3j4+Pd3Nzoty9wOJz58+crKChwuVw+nz9x4sQz\nZ84I7syirKy8cuVKFRUVFotVU1NjbGy8fv16Dw8POTk5wcpzc3NPnTq1f//+Bq+UHTZsmLa2\ndmBgYGlp6f79+6dMmSL8F6ShoREZGamhobFkyZKBAwcKnqqtra2qqrKwsFiyZAmd2OKtI1vo\nzZs3j8PhKCsrz549m6KoiooKNpu9du3azZs3x8TEaGlpkann5piZma1evbp3795KSkpiYmL9\n+vWbPn26p6fnihUr6DtZVVUVGxs7dOhQwRnkhw8fXrx48auvvhozZozwC0foy8KgcNNthBDq\nateuXZs3b97x48fXrl3b1X35vzBmzJikpKSsrKwGcTBCXzoM7BBCqOvx+fxhw4Z9+vQpJSWF\nfvcX+ocEBgba2Nj88MMPW7du7eq+INTJMLBDCKFuITU1dfjw4Y6OjhcuXOjqvoiyoqKiwYMH\n6+rqBgYG0hu+ICQycFUsQgh1C4aGhufPn8/KyoqOju7qvoiyCxcuGBoaXrt2DaM6JJJwxA4h\nhBBCSETgiB1CCCGEkIjAwA4hhBBCSERgYIcQQgghJCIwsEMIIYQQEhEY2CGEEEIIiQgM7BBC\nCCGERAQGdgghhBBCIgIDO4QQQgghEYGBHUIIIYSQiMDADiGEEEJIRGBghxBCCCEkIjCwQwgh\nhBASERjYIYQQQgiJCAzsEEIIIYREBAZ2CCGEEEIiAgM7hBBCCCERgYEdQgghhJCIwMAOIYQQ\nQkhEYGCHEEIIISQiMLBDCCGEEBIRGNghhBBCCIkIDOwQQgghhEQEBnYIIYQQQiICAzuEEEII\nIRGBgR1CCCGEkIjAwA4hhBBCSERgYIcQQgghJCIwsEMIIYQQEhEY2CGEEEIIiQgM7BBCCCGE\nRAQGdgghhBBCIgIDO4QQQgghEYGBHUIIIYSQiMDADiGEEEJIRGBghxBCCCEkIjCwQwghhBAS\nERjYIYQQQgiJCLGu7gBCCDWLGxRc5etbl5TMLy1hqahKjh7FWbhATE+vq/v1BeBT/ND3IU9z\nnmSXZ9fwanqyew5VHTpJe7KClEJXd+0LwS2FaC9I84PiN8BggbIhGE6HIUtBTKqre4aQMAyK\norq6Dwgh1BC/tLR4rRv38eOGJ8TEZN3demxYDwxGV/SrbV6/fm1gYBAQEGBjY/Nvtptflb8/\nYm9GaUaDdCkx9qpBq8drWnekcsbnO89isRQVFYcOHbp06VInJyc6g4eHx/r16+vr6zvSShd7\n4w835kNVYcN0BR2Y4wvqQ9td8ZEjRzZs2EAfysjIGBgYrF27dunSpYyu/ko7OjqWlJQ8evSo\na7uBOghH7BBC3Q5VXV04d15dQmIT5+rry345xK+okNv+/b/er9aaPXv25MmTlyxZ0iWtf+J+\n+jp0UxG3qPEpbn31kehDPD7PVsuuI03s2bPHwsKCx+Pl5ub6+fk5OzvfvHnzypUrLBYLAMaN\nG3fy5MmO1N+AiopKZGSktrZ2J9YpzJsA+O9k4DcVmBZnwgVLcAkD1YEdaeH333+XkZEBgJKS\nkgcPHri4uLx7927Hjh0dqbN9kpOTJ0+enJWVBQCurq41NTX/fh9Q58LADiHU7ZT99HPTUd1n\nFZ5eUtbjJc3N/7UutUl0dPTkyZO7qvUTscebjOpongmnBvUcpCKt2u4mBgwYYGVlRT4vWLDA\n0dFxzpw5gwcP3rp1KwAYGxsbGxs3LlVXVycuLt7WtrKzsz9+/NjurrZZTTncXNB0VEfUVsCN\n+bAqDpisdjdiYWEhLy9PPk+fPr2oqOjo0aPbt28XHLSjKIrH44mJ/VN/pkn90dHRdIqtre0/\n1Bb6N+HiCYRQ90KVV1RevNRitvJjHu1uIioqytbWVlFRUUZGxszMrLm5p5iYGAaDcffuXRsb\nG2lp6Z49e27ZsoXP5wuvhMFgZGZmLl26lP7LXV1dPX/+/B49eqiqqrq5udE1nD17dsCAAdLS\n0srKyrNmzXr//n27r4iWUZrxIj9KeJ5aXu3t17c63haNBHaHDh0il+bh4UGHI8eOHVNTU/Pz\n81NVVd28eTMAREdHW1tbczgcWVnZadOmZWZm0vWEh4ebm5uz2WwNDY3NmzfX1tYGBwdraWkB\ngI6OzvTp0zuxz82KOQuVBS3kKUiCV3c6sU1TU9Pi4uLy8nIAmDlz5ty5c3ft2iUjI+Pn5wcA\n586dMzIykpSUVFZWnj9/fn5+Pinl4OAwc+bMw4cPa2pqSkpKmpmZxcTE0HU2V0qwflNT08WL\nF799+5bBYBw5csTR0VHwmYHmanBycpozZ85///tfAwMDNps9bNiwyMjITrwbqIMwsEMIdTUe\nj19aSv9wAwKoVswH1Tx/zsvN/atgeXkrW+NyuRMnTuzRo0dgYGBkZOSYMWOmT5+ek5PTOCcZ\nXtq0adPOnTuLi4tPnDhx6NAhMskopBISnx0/fjwj489H3LZv3z5q1Kjw8PANGzYcP378xo0b\nAPDkyZOVK1e6u7snJibeu3fv06dPc+bMaeUlCKrn11fUVdA/4R/CWlMqMi9CsBS3vrodTQua\nNm1aYWFhSkpKg3QJCYnKykoPD4/Lly+7ubm9fft23LhxEhISYWFhjx8/LikpsbGx4XK5AJCZ\nmWlra6uvrx8UFOTh4XHx4sVNmzaZm5tfv34dAGJiYi5fvtzBTjatrgqqi//6aWXElnrrb6Xq\nuR3pQkZGhoyMDJmclZCQSEpKiouLe/DggYWFxeXLl1esWOHs7JyQkODj4/PixYspU6aQh+PF\nxcVDQ0OTk5MTExPfvn3L4XAcHBzIXKqQUoL13717183NTVNT8+PHj6tWrRLskvB2nz9//ujR\no/Dw8Ly8PGVl5aVLl3bk8lHnwqlYhFAXq8/MzLcc1/Zi9XmmwwE+P8ivoaEW+bw15cTFxePi\n4hQUFMjf0V27dh06dCgsLKxxXEXmxZycnCwsLABgzpw5Fy5cuHLlytq1a4VUoqSkBAAyMjKK\niopFRUUAYGdnt3btWgAYMGCAp6fnixcvZs+eHRsbKykpuXjxYgkJCT09vevXr799+7bNNwEg\nMi/iQOQPbS1VUFXgfO+v5Q7DVE13jNrVjtZpZFwtNze3wSSsmJhYRUWFm5ubnZ0dAHz99dcA\ncPXqVTKc6e3tra2tfevWrXnz5p0+fVpWVvb8+fPkQb2KioonT56Ii4vLysoCgIKCQo8ePTrS\nw2YFfA2RbR/9jfeGeO+/Dm0PgvmW1pfm8XhkcUlZWdm9e/cuXry4fPlyJpMJAGJiYunp6U+e\nPFFQUACAQ4cO2djYbNu2DQD69ev3yy+/TJ06NTw8fPTo0QwGo7Ky8siRI+RLeODAgZEjR4aE\nhNjZ2Qkp1aB+NpvNZDKVlZUb9FBIDQBQUlLi4eHB4XAAYOHChQsXLmzzDUT/GByxQwh1NXFx\nMa0+9A9TsbX7cbA0NOhSLI1erS3FYkVHR48bN05aWprBYJBwgURgXC635LPa2lqS38zMjC5r\nbGycmpoqvJLGzAWeBaSjPWtra3Fx8TFjxnh5eWVlZamqqgo21HpSLCk1jhr9Iy0uLTw/vQ+C\nYCkFyY7ugUJG3aSkmt4KZPjw4eRDVFTU0KFD6UlqTU1NXV3d8PBwAHjx4sWQIUNIVAcACxcu\n9PLy6mCvWkVaCRR0//phte4pQHH230pJybepTWVlZXFxcXFxcSUlpWXLlrm6uv7888/0WX19\nfRJ11dXVJSQkkH9XEORLEhcXRw6NjIxIVAcAJKROTU1tsRRdf3NarMHAwIBEdQBA/zZRN4Ej\ndgihLiampaX67K8JxJqQ0ELn+S2WYrDZqk+fMCQk2tpccnLynDlzVq1adffuXTU1NR6PRz/R\nv3PnzoMHD5LPFy5cMDU1BQDBgSJpaemqqirhlTQmLf1XsMVg/LnJlLGxcXh4+M8///ztt9+6\nurqOGDHi1KlTQ4YMaevlDFUd5mV7jj78/c2dM4nC4iEywqklq318/Im2tiVEWloaAGhqajZ5\nlg4jysrKYmNjBeO/2tpa8vBWaWmp8Gjjn2K1E6x2/nV4bTqkCp2NpQAYAENXwKSj7W4zKCiI\nBGTS0tJ6enqSkpKCZ+n7UFlZyefzBW8L+VxWVkYOG3w5AaCqqqrFUi3e5xZrYLPZbbha9O/C\nwA4h1L1IjBrJVFDgFxcLzyZlbd2OqA4A7t27JykpeejQIfKAf15eHn1q9erVU6ZMIZ/79u1b\nUFAAAMUCPSkvLyd/j4VU0nrGxsYXLlzg8/nh4eFff/31pEmT3r171451o4LM1EeeSzrLp/jC\ns43uNbojrTR2/fp1IyMjMiErhJycnIWFhaenp2AiiU569Ojx6dOnzu1Ve/Sf2UJgx/icrQMG\nDx7cmoEuDofDYrEEv4FkuFdOTo4cNvhyAoCMjEyLpTreLurOcCoWIdS9MCQkemxY30IecfEe\nGzcIz9Oc8vJyKSkpetnmpUuXaszLnwAAGjhJREFUAICMomlpaVl8pqKiQjKQiUIiLi7OyMhI\neCVEi3u/R0REkJqZTKa5ufn+/fvz8/M/fPjQvouiqUqr2mlNEJ5HTlLOQa8zV5h6enoGBweT\nRa/CmZmZvXnzRk9Pz/AzJpOprq4OAKamptHR0WRAFAAuX75saWlJryD+9/bSN3GGnkYt5NGz\nBW3Lf6Ev4uLigwYNevbsGZ1CPtNT269evaLDLzJPamRk1GKpBhrf27bWgLoVDOwQQt2OzLKl\n7M8jZ01gMOT3/yDer2/7Kh81alRhYeG5c+c+fPhw4sSJxMRENTW1+Pj40tLSJvPfuXPn6tWr\nmZmZx44dCwkJWbRokfBKpKSk2Gx2SEhITEyMkLcvPHjwYNq0aTdu3MjIyIiLizt69KiWllaf\nPn3ad1GCXExW6MsbNHdWgiWxZfhWjjinI00kJSUFBwcHBwdfu3Zt7ty5q1atWrp0aWs2ZF61\nalVZWdmSJUsSEhLS09P37t1rZGRENulwdXWtq6ubP39+eHj4nTt3tmzZYmxszGQyySTgvXv3\nkpKSOtLn1mKKwRxfYCs2m0FeG2b8M+tzm7Jp06aAgICffvopMzPz8ePHmzZtsrS0JA8JAICi\nouKyZcsSExNjY2M3btyora09ZsyYFksJUlBQyMvLCw0NFdx3pk01oG6HQgihbqi+vnT/gRwt\nnfe9egv+5A4eUnX/jw7W/c033/Ts2VNOTm7hwoVlZWW7du3icDju7u4NsiUmJgKAj4/P5MmT\nyW5z3377LZ/Pb7ES8llDQ4Ps/hoQEEDXOWzYMBcXF4qi6uvrt23bpqurKykp2bNnz2nTpqWk\npHTwumjVdVWHXvzscGvy1Fv2gj+rH618VZTawcoF/4LIy8tbWFh4e3sLZjh+/DiLxSKfz5w5\nAwB1dXX02RcvXlhbW0tLS3M4nFGjRv3xx1+/zdDQ0FGjRklKSqqrq//nP/+prq6mKKq+vn7S\npElsNnvixIkd7HkbFKZRXmbUDmj44z2JKs/rSMWHDx8GgOLi4uYyzJ8/39zcXDDl3LlzhoaG\n4uLiPXv2XL58OV121qxZNjY2Xl5eWlpaEhISI0aMiI+Pb7FUg/rfvn1raGgoIyOza9euWbNm\nWVtbt7WG33//HWOJbgXfFYsQ6r54799X3blbl5DILy9jqahKjh7FnmzP4HRotKn1kpKSTExM\nnjx5Irg88AuSVZYZlhOWVZZZz69XZisPVRlmpj6CxWj/+xL+v1AUvPGHND8oyQQGE5T6Qf8Z\noNnJzyZ2BL7aFTUJF08ghLovVu/ePdZ81dW9+FJpy+poy+p0dS++WAwG6E8A/RYeWESou8Fn\n7BBCCCGERAROxSKEEEIIiQgcsUMIIYQQEhEY2CH0j/j06dPmzZsnTZq0YcOGxocIIYTQPwEX\nTyDUBh8/fpw9e7aQDLt27bK0tAQAV1fXGzdumJqakleONjjsuJcvX0pJSenq6jY+FR4evnXr\nVuHFAwMD6ZdyIoQQEhkY2CHUBjU1NSEhIRwOR19fv8kMdXV15ENwcLCGhkZkZCSDwWh82HGL\nFi2ysbE5cOBA41P19fUlJSX0YU5OTmFhoa6uruBrJRFCCIkkDOwQajNTU9Pg4GDheUpKSkxN\nTekwrsFhB9XW1iYmJtrY2DR5dsyYMeTlQsTatWtPnDhx4sSJiRMndkrrCCGEui18xg6hTnbl\nyhUrKysej/fy5UsrKysxMTHBQ/JCKuLZs2dr1qyZOHGig4PDd999l5GR0aCqgIAAFxeXCRMm\nLF261NfXl6xhv379+tixY2tra0lDly+3+e1GJ0+etLKyCgsLa5C+bt268ePHl5SU3Lhxw8rK\nKjo6Ojw8fPny5RMmTHB2dr5zp+Gb0VvsP0IIoX8ZBnYIdTJVVdXBgwczGAwOhzN48GArKyvB\nw/79+5Nse/bsMTc3f/TokaqqqoSExPHjxwcMGPDw4UO6nm+++cbOzs7f319CQiIiImL27NnO\nzs4URamoqGhra9MNqamptbWHw4YNCwkJOX36tGBiYWHh6dOnKYqSl5cvKCgICQk5deqUo6Oj\nnJzcsGHDYmNjp0+f/uOPP9L5W+w/QgihLtClLzRD6Avz7t07ALC0tGwxJ4vFGjFiRHOHz549\nYzAYS5Ysqa+vJykfPnxQV1dXU1OrqamhKCo0NBQApk6dyuVyKYri8/nLli0DgN9++42iqPDw\ncAD4+uuvW9PnNWvWAIDgGzkpijIxMZGWli4rK6NTPD09AeDSpUsURZ06dQoAOBxOVlYWOVtS\nUqKrqyslJUXeF9li/xFCCHUJHLFDqM3i4uKsmrJ+/fpW1nDmzBmKovbs2UMvTVVXV3d1dc3L\nyyNP7124cAEAdu/eLSkpCQAMBmPbtm3ffvutrKxsp1yCi4tLVVWVj48PneLj4yMjIzNr1iw6\nxdHRUUtLi3yWk5ObO3cul8t9/Phxa/qPEEKoS+DiCYTajMfjVVRUNE6vrq5uZQ0vXrwAgAar\nH8rLywEgNTXVzs4uOjqayWQOGDCAPqujo7Nv3772d/rvFi5c+PXXX1+8eNHFxQUAPn78GBwc\nvHjxYg6HQ+cZPHiwYJG+ffsCQHp6emv631n9RAgh1CYY2CHUZsOGDevguFRxcTGTyZw+fXrj\nUwMHDgSAoqIiDocjJvZP/T9UUVFxxowZ165de/PmjZ6enq+vL4/HW7JkiWAeeXl5wUOyWwqJ\n3lrsP0IIoS6BgR1CXUBSUpLP52/btk1GRqbJDOLi4jU1Nf9oH5YvX37t2jVvb+8dO3b4+Pjo\n6+uPGTNGMAOPxxM8JP0hU8Mt9h8hhFCXwGfsEOoC5I0RKSkpQjLU1ta+f/+eTuHxeElJSdnZ\n2Z3Vh/Hjx+vq6vr4+OTl5YWGhi5evLhBhqysLMFDsnBEQ0OjNf1HCCHUJTCwQ6gLTJo0CQC8\nvLwEEzdt2jRjxozKykoAII+pCS5uCAwMNDExOXPmDACQjY7r6+s70gcGg7Fs2bKXL19u374d\nAAQ32CN+//13iqLoQ39/fwAYNWpUa/qPEEKoS+BULEJtVlhYePv27SZPqaiojB49usUaVqxY\n4eHhce7cOU1NTWdnZy6Xe/ny5V9++WXWrFlk+YKrq+uxY8e2bdsmJiZmbm6elpb29ddfy8vL\nk01PVFRUAODRo0eRkZEyMjJGRkbtu5ClS5fu2LHjzJkzNjY2ffr0aXC2vLx87ty5mzZtkpWV\nPXv2bGBgoJWVFdmHr8X+I4QQ6hpdvN0KQl8UMh0phLW1NckpfB87iqLevn1rZ2dHv2RMUlJy\nxYoVVVVVdIbU1FQyPEYYGxs/ffqUPjt58mSS7uTkJLzPTe5jR5syZQoAeHt7CyaSfey8vLxm\nz55N93DkyJE5OTmt7z9CCKF/H4MSmGpBCAlXU1NDNgdujoKCwqBBgwAgJCSkR48eQ4cOJekN\nDmkFBQUZGRlsNltXV5csO23g3bt379+/V1dX19LSEnzVLJ/PT0xMZLFYurq60tLSQrqUnp6e\nk5MzcOBARUXFxmdtbGzi4+PfvXsnJSVFJ54+fXr16tXe3t7z58/Pzc3NyspSVlY2MDBoXLzF\n/iOEEPo3YWCH0P+voKCg8ePHf//997t37xZMJ4Hd5cuXFyxY0FV9Qwgh1A74jB1C/3fq6+tf\nvXoVHx/v7u7eu3fvzZs3d3WPEEIIdQ4M7BD6v1NYWEjeaaGlpXX79m2cQkUIIZGBU7EIIYQQ\nQiICR+wQQt1dUUVtVW29AkeCI4n/yWobik9VlXB5tTxpBSkxvHvtUQtQCCAGoIh/MdEXAb+m\nCKFuqpxb/9+wzAfxH/JKuQDAYEA/ddnZZn0mDe7FFFgg3FaVlZVRUVFDhgyRk5PrvM42rbq6\nOiIiYtCgQQoKCv90Ww1UFFbF+CRmPsvmltcAAJPFUDNSGTzDWHNYr3+5J1+sRIDzABEAtQAA\nIA0wBmA5gE4X9wshofDNEwih7igtr3zBybCLoRkkqgMAioLUD2V7bidtuBxdwW3/WzcyMzPH\njRuXmJjYST1tQnp6el5eHgDk5OSMGzcuOjr6n2urSdkvcnzW/p7yMJ1EdQDA51EfEvPv734c\neuI5n9ehJ3AYDMaBAwfaVKSwsJDBYPj6+gKAh4eHmFhnjilERUVxudxOrBAAALwAlgE8+RzV\nAUAVwEMAZ4CmNydvjbNnzzKaMXfu3OZK1dXVPX/+vMXKHR0dbWxs2t03JDIwsEMIdTt5pVz3\nX1/klzb91zrizadvfeL4/O77fPC6desePHjQVa3np370PxBaV13X5NkU/9fPzr74l7skaMGC\nBcnJyZ1VG5/PHzduHAmjO483gBdAk1+wOoB9AIHtq9fFxaXus2XLlmlpadGHV65caa5UdHS0\nkLAPoQYwsEMIdTtHH6QWV9YKyRD55pNfXE4HW+FyuVFRUUlJSc2tISsvLw8ODq6qqqqsrIyM\njExOTm6Qs7q6OiEh4cWLFyUlJXRicHBwdHR0amrq06dPSQqDwairq4uOjk5JSeHxeII1FBQU\nREZGJiUl1dU1HYe1FcWnQk9G8Op4QvIk//EqP/Vjx9vKyckh+3WXlJRERES8ffu2QYaMjIyI\niIjS0lLBxMrKSjoOo2tISUlJSUkhidXV1VFRUTExMbW1Db8D5eXlERER6enp5BdRUlJy9erV\nysrK58+fJyUldfyKAAAgF+Ck0AwUwEGA9rwTmcFgiH1G9hunD5lMJgDk5+eHh4cnJSXx+XxS\nJCsr68aNG1wuNzg4+P379yTx3bt3z58/f/36NZ0NIRoGdgih7uVjGTc4Jb/FbL9FZHeklejo\n6D59+tjY2JiYmIwcOVIwMqNlZGSMGzfu5MmTJiYm7u7uI0eOFMzp6empp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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Select key paths for plotting\n", + "key_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\",\n", + " paste0(\"prop_med\", 1:n_med), \"prop_med_total\")\n", + "plot_df <- fiml_res[fiml_res$label %in% key_labels, ]\n", + "\n", + "# Pretty labels\n", + "label_map <- c(\n", + " a1 = \"a1: SNP -> DLPFC\", a2 = \"a2: SNP -> AC\", a3 = \"a3: SNP -> PCC\",\n", + " b1 = \"b1: DLPFC -> Y\", b2 = \"b2: AC -> Y\", b3 = \"b3: PCC -> Y\",\n", + " cp = \"c': SNP -> Y (direct)\",\n", + " ind1 = \"Indirect: DLPFC\", ind2 = \"Indirect: AC\", ind3 = \"Indirect: PCC\",\n", + " total_indirect = \"Total Indirect\", total = \"Total Effect\",\n", + " prop_med1 = \"Prop Med: DLPFC\", prop_med2 = \"Prop Med: AC\", prop_med3 = \"Prop Med: PCC\",\n", + " prop_med_total = \"Prop Med: Total\"\n", + ")\n", + "plot_df$label_pretty <- label_map[plot_df$label]\n", + "plot_df$label_pretty <- factor(plot_df$label_pretty, levels = rev(label_map[key_labels]))\n", + "\n", + "# Group by type\n", + "plot_df$group <- ifelse(grepl(\"^a\", plot_df$label), \"a-paths\",\n", + " ifelse(grepl(\"^b\", plot_df$label), \"b-paths\",\n", + " ifelse(plot_df$label == \"cp\", \"Direct\",\n", + " ifelse(grepl(\"^ind|total_indirect\", plot_df$label), \"Indirect\",\n", + " ifelse(grepl(\"^total$\", plot_df$label), \"Total\",\n", + " \"Proportion\")))))\n", + "\n", + "p_fiml <- ggplot(plot_df, aes(x = est, y = label_pretty, color = group)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci.lower, xmax = ci.upper), size = 0.6) +\n", + " scale_color_brewer(palette = \"Set1\") +\n", + " labs(title = \"FIML SEM: Parallel Mediation Path Estimates\",\n", + " subtitle = paste0(\"SNP -> BLOC1S3 (DLPFC/AC/PCC) -> age_first_ad_dx_num | N = \", N_fiml),\n", + " x = \"Estimate (95% Wald CI)\", y = NULL, color = \"Effect Type\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + "ggsave(file.path(dir_fiml, \"fiml_forest_plot.png\"), p_fiml, width = 10, height = 8, dpi = 150)\n", + "ggsave(file.path(dir_fiml, \"fiml_forest_plot.pdf\"), p_fiml, width = 10, height = 8)\n", + "print(p_fiml)\n", + "cat(\"FIML forest plot saved.\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c7f3b951", + "metadata": {}, + "source": [ + "### FIML Interpretation\n", + "- **a-paths**: Effect of SNP on each mediator (DLPFC, AC, PCC expression)\n", + "- **b-paths**: Effect of each mediator on age at AD diagnosis, controlling for SNP and other mediators\n", + "- **c' (direct)**: Direct effect of SNP on outcome, controlling for all mediators\n", + "- **Specific indirects (ind1-3)**: Mediated effect through each brain region\n", + "- **Total indirect**: Sum of all specific indirects\n", + "- **Total effect**: cp + total_indirect\n", + "- **Proportion mediated**: How much of the total effect goes through each mediator\n" + ] + }, + { + "cell_type": "markdown", + "id": "b2482568", + "metadata": {}, + "source": [ + "## 5. Bootstrap (FIML Inside Each Replicate)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2ea52853", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:00:27.605079Z", + "iopub.status.busy": "2026-03-31T16:00:27.603429Z", + "iopub.status.idle": "2026-03-31T16:20:15.176512Z", + "shell.execute_reply": "2026-03-31T16:20:15.173909Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running 1000 bootstrap replicates with FIML inside...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Replicate 200 / 1000 - converged so far: 200 \n", + " Replicate 400 / 1000 - converged so far: 400 \n", + " Replicate 600 / 1000 - converged so far: 600 \n", + " Replicate 800 / 1000 - converged so far: 800 \n", + " Replicate 1000 / 1000 - converged so far: 1000 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bootstrap complete in 19.8 minutes.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converged: 1000 / 1000 \n" + ] + } + ], + "source": [ + "set.seed(123)\n", + "B <- 1000\n", + "\n", + "# All labels to extract\n", + "all_labels <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\",\n", + " paste0(\"prop_med\", 1:n_med), \"prop_med_total\",\n", + " paste0(\"diff_\", apply(combn(n_med, 2), 2, paste, collapse = \"_\")))\n", + "\n", + "boot_mat <- matrix(NA, B, length(all_labels), dimnames = list(NULL, all_labels))\n", + "\n", + "n_converged <- 0\n", + "cat(\"Running\", B, \"bootstrap replicates with FIML inside...\\n\")\n", + "t0 <- Sys.time()\n", + "\n", + "for (i in 1:B) {\n", + " idx <- sample(nrow(dat), replace = TRUE)\n", + " boot_dat <- dat[idx, ]\n", + " fit_b <- tryCatch(\n", + " sem(model_str, data = boot_dat, missing = \"fiml\", fixed.x = FALSE,\n", + " se = \"none\", test = \"none\"),\n", + " error = function(e) NULL,\n", + " warning = function(w) {\n", + " suppressWarnings(\n", + " sem(model_str, data = boot_dat, missing = \"fiml\", fixed.x = FALSE,\n", + " se = \"none\", test = \"none\")\n", + " )\n", + " }\n", + " )\n", + " if (!is.null(fit_b) && lavInspect(fit_b, \"converged\")) {\n", + " n_converged <- n_converged + 1\n", + " pe_b <- parameterEstimates(fit_b)\n", + " for (lab in all_labels) {\n", + " row_b <- pe_b[pe_b$label == lab, ]\n", + " if (nrow(row_b) == 1) boot_mat[i, lab] <- row_b$est\n", + " }\n", + " }\n", + " if (i %% 200 == 0) cat(\" Replicate\", i, \"/\", B, \"- converged so far:\", n_converged, \"\\n\")\n", + "}\n", + "\n", + "elapsed <- as.numeric(difftime(Sys.time(), t0, units = \"mins\"))\n", + "cat(\"\\nBootstrap complete in\", round(elapsed, 1), \"minutes.\\n\")\n", + "cat(\"Converged:\", n_converged, \"/\", B, \"\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "655494cf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:20:15.349682Z", + "iopub.status.busy": "2026-03-31T16:20:15.347971Z", + "iopub.status.idle": "2026-03-31T16:20:15.422924Z", + "shell.execute_reply": "2026-03-31T16:20:15.420782Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrap results:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label boot_mean boot_se ci_lower ci_upper\n", + "a1 a1 0.55208244 0.1241277 0.3074369 0.7974622\n", + "a2 a2 0.50542326 0.1856944 0.1183773 0.8630672\n", + "a3 a3 0.65049037 0.1862551 0.2936528 1.0196993\n", + "b1 b1 0.30132000 0.4536250 -0.6082030 1.1529283\n", + "b2 b2 0.05226526 0.7488907 -1.4290209 1.4518540\n", + "b3 b3 -1.29452348 1.0264209 -2.7253466 1.1957340\n", + "cp cp -2.45284219 1.4386588 -5.3220447 0.2451759\n", + "ind1 ind1 0.16993436 0.2576765 -0.3193938 0.7006548\n", + "ind2 ind2 0.03855889 0.3957330 -0.7242379 0.9017915\n", + "ind3 ind3 -0.85013826 0.7212632 -2.1924817 0.7510511\n", + "total_indirect total_indirect -0.64164501 0.4708253 -1.5823368 0.2557181\n", + "total total -3.09448720 1.3285503 -5.6735390 -0.5867678\n", + "prop_med1 prop_med1 -0.02970086 1.4434961 -0.4111805 0.1388741\n", + "prop_med2 prop_med2 -0.03071328 2.5999673 -0.4984516 0.3395466\n", + "prop_med3 prop_med3 -0.18875538 19.2113662 -0.3025482 1.7338164\n", + "prop_med_total prop_med_total -0.24916952 15.8202426 -0.1231286 1.1217447\n", + "diff_1_2 diff_1_2 0.13137547 0.4508045 -0.8190564 0.9581224\n", + "diff_1_3 diff_1_3 1.02007262 0.8812247 -0.9968785 2.5515104\n", + "diff_2_3 diff_2_3 0.88869715 1.0140063 -1.3165934 2.7935259\n", + " p_value\n", + "a1 0.000\n", + "a2 0.006\n", + "a3 0.000\n", + "b1 0.496\n", + "b2 0.918\n", + "b3 0.260\n", + "cp 0.080\n", + "ind1 0.496\n", + "ind2 0.924\n", + "ind3 0.260\n", + "total_indirect 0.152\n", + "total 0.018\n", + "prop_med1 0.510\n", + "prop_med2 0.910\n", + "prop_med3 0.274\n", + "prop_med_total 0.166\n", + "diff_1_2 0.736\n", + "diff_1_3 0.264\n", + "diff_2_3 0.362\n" + ] + } + ], + "source": [ + "# Compute bootstrap summaries\n", + "boot_summary <- data.frame(\n", + " label = all_labels,\n", + " boot_mean = apply(boot_mat, 2, mean, na.rm = TRUE),\n", + " boot_se = apply(boot_mat, 2, sd, na.rm = TRUE),\n", + " ci_lower = apply(boot_mat, 2, quantile, 0.025, na.rm = TRUE),\n", + " ci_upper = apply(boot_mat, 2, quantile, 0.975, na.rm = TRUE),\n", + " n_valid = apply(boot_mat, 2, function(x) sum(!is.na(x))),\n", + " stringsAsFactors = FALSE\n", + ")\n", + "\n", + "# Two-sided p-value from bootstrap distribution\n", + "boot_summary$p_value <- sapply(all_labels, function(lab) {\n", + " vals <- boot_mat[, lab]\n", + " vals <- vals[!is.na(vals)]\n", + " if (length(vals) < 10) return(NA)\n", + " p_pos <- mean(vals > 0)\n", + " 2 * min(p_pos, 1 - p_pos)\n", + "})\n", + "\n", + "boot_summary$N <- N_total\n", + "boot_summary$method <- \"Bootstrap\"\n", + "boot_summary$ci_type <- \"Percentile\"\n", + "\n", + "write.csv(boot_summary, file.path(dir_boot, \"bootstrap_results.csv\"), row.names = FALSE)\n", + "cat(\"Bootstrap results:\\n\")\n", + "print(boot_summary[, c(\"label\", \"boot_mean\", \"boot_se\", \"ci_lower\", \"ci_upper\", \"p_value\")])\n" + ] + }, + { + "cell_type": "markdown", + "id": "6c665229", + "metadata": {}, + "source": [ + "### Bootstrap Visualizations" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5ef143b1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:20:15.431087Z", + "iopub.status.busy": "2026-03-31T16:20:15.429363Z", + "iopub.status.idle": "2026-03-31T16:20:17.624672Z", + "shell.execute_reply": "2026-03-31T16:20:17.622071Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrap distribution plots saved.\n" + ] + }, + { + "data": { + "image/png": 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UOGDJHJZERUUFDwwAMP7NmzJzo6unXr1mwWtjndebSwsDDxMZvRvcVlFpff6Kx/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ggOO49PR0vZwqlaqO/J02c52Yv+qcSXp6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kjeEbtVq1YJghC2qzpjxoyxY8deeumlW7Zs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\n3foVCsWqVau8vb3d3d3b7jls2DDq2FqHpaUlQqisrKypqSk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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Distribution histograms for indirect effects\n", + "ind_labels <- c(paste0(\"ind\", 1:n_med), \"total_indirect\")\n", + "ind_names <- c(\"DLPFC Indirect\", \"AC Indirect\", \"PCC Indirect\", \"Total Indirect\")\n", + "\n", + "plots_hist <- list()\n", + "for (k in seq_along(ind_labels)) {\n", + " vals <- boot_mat[, ind_labels[k]]\n", + " vals <- vals[!is.na(vals)]\n", + " df_h <- data.frame(value = vals)\n", + " med_val <- median(vals)\n", + " p_h <- ggplot(df_h, aes(x = value)) +\n", + " geom_histogram(bins = 50, fill = \"steelblue\", alpha = 0.7, color = \"white\") +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"red\", linewidth = 0.8) +\n", + " geom_vline(xintercept = med_val, color = \"darkblue\", linewidth = 0.8) +\n", + " labs(title = paste0(\"Bootstrap: \", ind_names[k]),\n", + " subtitle = paste0(\"N = \", N_total, \" (FIML), \", B, \" resamples\"),\n", + " x = \"Indirect Effect (a x b)\", y = \"Count\") +\n", + " theme_minimal(base_size = 12)\n", + " plots_hist[[k]] <- p_h\n", + "}\n", + "\n", + "p_combined_hist <- do.call(grid.arrange, c(plots_hist, ncol = 2))\n", + "ggsave(file.path(dir_boot, paste0(\"bootstrap_distributions_\", exposure, \".png\")),\n", + " p_combined_hist, width = 12, height = 8, dpi = 150)\n", + "ggsave(file.path(dir_boot, paste0(\"bootstrap_distributions_\", exposure, \".pdf\")),\n", + " p_combined_hist, width = 12, height = 8)\n", + "cat(\"Bootstrap distribution plots saved.\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "74c476ac", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:20:17.631031Z", + "iopub.status.busy": "2026-03-31T16:20:17.629287Z", + "iopub.status.idle": "2026-03-31T16:20:19.026113Z", + "shell.execute_reply": "2026-03-31T16:20:19.023524Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrap forest plot saved.\n" + ] + }, + { + "data": { + "image/png": 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M5w5ebm5uTkIITwM8jm5uZt27ZFCCkN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\n5hWK6kzIGlRaWurl5eXr69uvX7/27durVqeat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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Bootstrap forest plot\n", + "key_boot <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\")\n", + "bplot_df <- boot_summary[boot_summary$label %in% key_boot, ]\n", + "\n", + "bplot_df$label_pretty <- label_map[bplot_df$label]\n", + "bplot_df$label_pretty <- factor(bplot_df$label_pretty,\n", + " levels = rev(label_map[key_boot]))\n", + "\n", + "bplot_df$group <- ifelse(grepl(\"^a\", bplot_df$label), \"a-paths\",\n", + " ifelse(grepl(\"^b\", bplot_df$label), \"b-paths\",\n", + " ifelse(bplot_df$label == \"cp\", \"Direct\",\n", + " ifelse(grepl(\"ind|total_indirect\", bplot_df$label), \"Indirect\",\n", + " \"Total\"))))\n", + "\n", + "p_boot_forest <- ggplot(bplot_df, aes(x = boot_mean, y = label_pretty, color = group)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper), size = 0.6) +\n", + " scale_color_brewer(palette = \"Set1\") +\n", + " labs(title = \"Bootstrap: Parallel Mediation Path Estimates\",\n", + " subtitle = paste0(\"N = \", N_total, \" (FIML), \", B, \" resamples, \", n_converged, \" converged\"),\n", + " x = \"Estimate (95% Percentile CI)\", y = NULL, color = \"Effect Type\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + "ggsave(file.path(dir_boot, \"bootstrap_forest_plot.png\"), p_boot_forest, width = 10, height = 8, dpi = 150)\n", + "ggsave(file.path(dir_boot, \"bootstrap_forest_plot.pdf\"), p_boot_forest, width = 10, height = 8)\n", + "print(p_boot_forest)\n", + "cat(\"Bootstrap forest plot saved.\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "5e4d70e1", + "metadata": {}, + "source": [ + "## 6. MNAR Sensitivity Analysis\n", + "\n", + "Delta-shift imputation: for each missing mediator/outcome value, impute with\n", + "`observed_mean + delta * observed_SD`. A 2D grid (delta_mediators x delta_outcome)\n", + "is used, where delta_mediators is applied uniformly to all three mediators.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1231da4d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:20:19.032237Z", + "iopub.status.busy": "2026-03-31T16:20:19.030609Z", + "iopub.status.idle": "2026-03-31T16:21:18.637888Z", + "shell.execute_reply": "2026-03-31T16:21:18.635131Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR grid: 225 points\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running MNAR sensitivity grid...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Grid point 50 / 225 \n", + " Grid point 100 / 225 \n", + " Grid point 150 / 225 \n", + " Grid point 200 / 225 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR grid complete in 1 minutes.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results rows: 1350 \n" + ] + } + ], + "source": [ + "delta_range <- seq(-2, 2, length.out = 15)\n", + "grid <- expand.grid(delta_med = delta_range, delta_out = delta_range)\n", + "cat(\"MNAR grid:\", nrow(grid), \"points\\n\")\n", + "\n", + "# Pre-compute observed means and SDs\n", + "med_stats <- lapply(med_cols, function(m) {\n", + " list(mean = mean(dat[[m]], na.rm = TRUE), sd = sd(dat[[m]], na.rm = TRUE))\n", + "})\n", + "names(med_stats) <- med_cols\n", + "out_mean <- mean(dat[[outcome]], na.rm = TRUE)\n", + "out_sd <- sd(dat[[outcome]], na.rm = TRUE)\n", + "\n", + "# Labels to extract\n", + "mnar_labels <- c(paste0(\"ind\", 1:n_med), \"total_indirect\", \"cp\", \"total\")\n", + "\n", + "mnar_results <- data.frame()\n", + "\n", + "cat(\"Running MNAR sensitivity grid...\\n\")\n", + "t0 <- Sys.time()\n", + "\n", + "for (k in 1:nrow(grid)) {\n", + " dat_imp <- dat\n", + "\n", + " # Impute mediators\n", + " for (m in med_cols) {\n", + " miss_idx <- is.na(dat_imp[[m]])\n", + " dat_imp[[m]][miss_idx] <- med_stats[[m]]$mean + grid$delta_med[k] * med_stats[[m]]$sd\n", + " }\n", + "\n", + " # Impute outcome\n", + " miss_y <- is.na(dat_imp[[outcome]])\n", + " dat_imp[[outcome]][miss_y] <- out_mean + grid$delta_out[k] * out_sd\n", + "\n", + " # Impute missing covariates with column means\n", + " for (cov in all_covs) {\n", + " miss_c <- is.na(dat_imp[[cov]])\n", + " if (any(miss_c)) dat_imp[[cov]][miss_c] <- mean(dat_imp[[cov]], na.rm = TRUE)\n", + " }\n", + "\n", + " fit_m <- tryCatch(\n", + " sem(model_str, data = dat_imp, estimator = \"ML\", fixed.x = FALSE),\n", + " error = function(e) NULL\n", + " )\n", + "\n", + " if (!is.null(fit_m) && lavInspect(fit_m, \"converged\")) {\n", + " pe_m <- parameterEstimates(fit_m, ci = TRUE)\n", + " for (lab in mnar_labels) {\n", + " row_m <- pe_m[pe_m$label == lab, ]\n", + " if (nrow(row_m) == 1) {\n", + " mnar_results <- rbind(mnar_results, data.frame(\n", + " delta_med = grid$delta_med[k],\n", + " delta_out = grid$delta_out[k],\n", + " label = lab,\n", + " est = row_m$est,\n", + " se = row_m$se,\n", + " pvalue = row_m$pvalue,\n", + " ci_lower = row_m$ci.lower,\n", + " ci_upper = row_m$ci.upper,\n", + " N = nrow(dat_imp),\n", + " stringsAsFactors = FALSE\n", + " ))\n", + " }\n", + " }\n", + " }\n", + "\n", + " if (k %% 50 == 0) cat(\" Grid point\", k, \"/\", nrow(grid), \"\\n\")\n", + "}\n", + "\n", + "elapsed <- as.numeric(difftime(Sys.time(), t0, units = \"mins\"))\n", + "cat(\"MNAR grid complete in\", round(elapsed, 1), \"minutes.\\n\")\n", + "cat(\"Results rows:\", nrow(mnar_results), \"\\n\")\n", + "\n", + "write.csv(mnar_results, file.path(dir_mnar, \"mnar_grid_results.csv\"), row.names = FALSE)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "69cfb511", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:21:18.680290Z", + "iopub.status.busy": "2026-03-31T16:21:18.678437Z", + "iopub.status.idle": "2026-03-31T16:21:18.792564Z", + "shell.execute_reply": "2026-03-31T16:21:18.790373Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tipping point for total_indirect: delta_med = 0 , delta_out = 0 , distance = 0 SDs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Tipping summary:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label fiml_p fiml_sig tipping_dist_sd N\n", + "1 ind1 0.33233569 FALSE -Inf 1153\n", + "2 ind2 0.76863696 FALSE -Inf 1153\n", + "3 ind3 0.01765757 TRUE 0 1153\n", + "4 total_indirect 0.04542023 TRUE 0 1153\n" + ] + } + ], + "source": [ + "# Tipping point analysis: for total_indirect, find where it crosses p=0.05\n", + "tip_df <- mnar_results[mnar_results$label == \"total_indirect\", ]\n", + "\n", + "# Distance from origin for each grid point\n", + "tip_df$dist <- sqrt(tip_df$delta_med^2 + tip_df$delta_out^2)\n", + "\n", + "# Find tipping point (closest to origin where p > 0.05)\n", + "fiml_sig <- pe[pe$label == \"total_indirect\", \"pvalue\"] < 0.05\n", + "if (fiml_sig) {\n", + " ns_points <- tip_df[tip_df$pvalue > 0.05, ]\n", + " if (nrow(ns_points) > 0) {\n", + " tipping <- ns_points[which.min(ns_points$dist), ]\n", + " cat(\"Tipping point for total_indirect: delta_med =\", tipping$delta_med,\n", + " \", delta_out =\", tipping$delta_out,\n", + " \", distance =\", round(tipping$dist, 2), \" SDs\\n\")\n", + " } else {\n", + " cat(\"Total indirect remains significant across entire grid -- robust!\\n\")\n", + " tipping <- data.frame(delta_med = NA, delta_out = NA, dist = Inf)\n", + " }\n", + "} else {\n", + " sig_points <- tip_df[tip_df$pvalue <= 0.05, ]\n", + " if (nrow(sig_points) > 0) {\n", + " tipping <- sig_points[which.min(sig_points$dist), ]\n", + " cat(\"FIML total_indirect was NS. Nearest significant point: delta_med =\", tipping$delta_med,\n", + " \", delta_out =\", tipping$delta_out, \"\\n\")\n", + " } else {\n", + " cat(\"Total indirect remains non-significant across entire grid.\\n\")\n", + " tipping <- data.frame(delta_med = NA, delta_out = NA, dist = Inf)\n", + " }\n", + "}\n", + "\n", + "# Also check each specific indirect\n", + "tip_summary <- data.frame()\n", + "for (lab in c(paste0(\"ind\", 1:n_med), \"total_indirect\")) {\n", + " sub <- mnar_results[mnar_results$label == lab, ]\n", + " sub$dist <- sqrt(sub$delta_med^2 + sub$delta_out^2)\n", + " fiml_p <- pe[pe$label == lab, \"pvalue\"]\n", + " if (length(fiml_p) == 0) next\n", + " is_sig <- fiml_p < 0.05\n", + " if (is_sig) {\n", + " ns <- sub[sub$pvalue > 0.05, ]\n", + " tip_dist <- if (nrow(ns) > 0) min(ns$dist) else Inf\n", + " } else {\n", + " sig <- sub[sub$pvalue <= 0.05, ]\n", + " tip_dist <- if (nrow(sig) > 0) -min(sig$dist) else -Inf\n", + " }\n", + " tip_summary <- rbind(tip_summary, data.frame(\n", + " label = lab, fiml_p = fiml_p, fiml_sig = is_sig,\n", + " tipping_dist_sd = round(tip_dist, 3), N = N_total\n", + " ))\n", + "}\n", + "write.csv(tip_summary, file.path(dir_mnar, \"mnar_tipping_summary.csv\"), row.names = FALSE)\n", + "cat(\"\\nTipping summary:\\n\")\n", + "print(tip_summary)\n" + ] + }, + { + "cell_type": "markdown", + "id": "26bd9723", + "metadata": {}, + "source": [ + "### MNAR Sensitivity Plots" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "23aa00e0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:21:18.798695Z", + "iopub.status.busy": "2026-03-31T16:21:18.797065Z", + "iopub.status.idle": "2026-03-31T16:21:21.859902Z", + "shell.execute_reply": "2026-03-31T16:21:21.857311Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in geom_point(aes(x = 0, y = 0), color = \"red\", shape = 4, size = 3, :\n", + "\u201c\u001b[1m\u001b[22mAll aesthetics have length 1, but the data has 225 rows.\n", + "\u001b[36m\u2139\u001b[39m Please consider using `annotate()` or provide this layer with data containing\n", + " a single row.\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in geom_point(aes(x = 0, y = 0), color = \"red\", shape = 4, size = 3, :\n", + "\u201c\u001b[1m\u001b[22mAll aesthetics have length 1, but the data has 225 rows.\n", + "\u001b[36m\u2139\u001b[39m Please consider using `annotate()` or provide this layer with data containing\n", + " a single row.\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in geom_point(aes(x = 0, y = 0), color = \"red\", shape = 4, size = 3, :\n", + "\u201c\u001b[1m\u001b[22mAll aesthetics have length 1, but the data has 225 rows.\n", + "\u001b[36m\u2139\u001b[39m Please consider using `annotate()` or provide this layer with data containing\n", + " a single row.\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in geom_point(aes(x = 0, y = 0), color = \"red\", shape = 4, size = 3, :\n", + "\u201c\u001b[1m\u001b[22mAll aesthetics have length 1, but the data has 225 rows.\n", + "\u001b[36m\u2139\u001b[39m Please consider using `annotate()` or provide this layer with data containing\n", + " a single row.\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in geom_point(aes(x = 0, y = 0), color = \"red\", shape = 4, size = 3, :\n", + "\u201c\u001b[1m\u001b[22mAll aesthetics have length 1, but the data has 225 rows.\n", + "\u001b[36m\u2139\u001b[39m Please consider using `annotate()` or provide this layer with data containing\n", + " a single row.\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in geom_point(aes(x = 0, y = 0), color = \"red\", shape = 4, size = 3, :\n", + "\u201c\u001b[1m\u001b[22mAll aesthetics have length 1, but the data has 225 rows.\n", + "\u001b[36m\u2139\u001b[39m Please consider using `annotate()` or provide this layer with data containing\n", + " a single row.\u201d\n" + ] + }, + { + "data": { + "image/png": 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9ePiDw8PB48eMCWbNmyhYj69u3LXm7evJmIevXqxT5x8/Pz2V+sU6ZMYQ3q1avn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rx4wZ4+npKZvqXr9+7eXlFR8f37t3bxMT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\n3XbbbVFRUXpXpJbq6urCwsLevXtPnz5d71rUwgEfciPYCZfLlZWVdeLEiVG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vczKipq//79devW7dKlC1ui630PCgpq0aKF4Z3Uv1ZPB+bMmZOdnZ1r47Fjx7q5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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Contour: total indirect effect\n", + "tip_total <- mnar_results[mnar_results$label == \"total_indirect\", ]\n", + "\n", + "p_contour_ind <- ggplot(tip_total, aes(x = delta_med, y = delta_out, z = est)) +\n", + " geom_contour_filled(bins = 12) +\n", + " scale_fill_viridis_d(option = \"viridis\") +\n", + " geom_point(aes(x = 0, y = 0), color = \"red\", shape = 4, size = 3, stroke = 2, inherit.aes = FALSE) +\n", + " labs(title = \"MNAR Sensitivity: Total Indirect Effect\",\n", + " subtitle = paste0(\"Exposure: \", exposure, \" | N = \", N_total),\n", + " x = expression(delta[mediators]), y = expression(delta[outcome]),\n", + " fill = \"Indirect\") +\n", + " theme_minimal(base_size = 12)\n", + "\n", + "ggsave(file.path(dir_mnar, paste0(\"mnar_contour_indirect_\", exposure, \".png\")),\n", + " p_contour_ind, width = 8, height = 6, dpi = 150)\n", + "ggsave(file.path(dir_mnar, paste0(\"mnar_contour_indirect_\", exposure, \".pdf\")),\n", + " p_contour_ind, width = 8, height = 6)\n", + "print(p_contour_ind)\n", + "\n", + "# Contour: p-value\n", + "p_contour_p <- ggplot(tip_total, aes(x = delta_med, y = delta_out, z = -log10(pvalue))) +\n", + " geom_contour_filled(bins = 12) +\n", + " scale_fill_viridis_d(option = \"plasma\") +\n", + " geom_point(aes(x = 0, y = 0), color = \"red\", shape = 4, size = 3, stroke = 2, inherit.aes = FALSE) +\n", + " labs(title = \"MNAR Sensitivity: -log10(p) of Total Indirect\",\n", + " subtitle = paste0(\"Exposure: \", exposure),\n", + " x = expression(delta[mediators]), y = expression(delta[outcome]),\n", + " fill = \"-log10(p)\") +\n", + " theme_minimal(base_size = 12)\n", + "\n", + "ggsave(file.path(dir_mnar, paste0(\"mnar_contour_pvalue_\", exposure, \".png\")),\n", + " p_contour_p, width = 8, height = 6, dpi = 150)\n", + "ggsave(file.path(dir_mnar, paste0(\"mnar_contour_pvalue_\", exposure, \".pdf\")),\n", + " p_contour_p, width = 8, height = 6)\n", + "print(p_contour_p)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "89ff73b2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:21:21.866128Z", + "iopub.status.busy": "2026-03-31T16:21:21.864475Z", + "iopub.status.idle": "2026-03-31T16:21:22.899128Z", + "shell.execute_reply": "2026-03-31T16:21:22.896481Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR plots saved.\n" + ] + }, + { + "data": { + "image/png": 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hQbGys4ncmTJ0dFRZ08eTI2NpZ/FM9HH300fvz4w4cPq1SqgQMH8vc97N2795df\nfmnAna39+/dXKBRt2rRx9I0OWbJkyeTJk3ft2lVWVpacnDx8+HBcPgyNB9Y6qdY6/idvGkKh\nUKxYseLGjRvm1+29+OKLfHrqdFj3GjmGc/EFUtAwOTk5t27d6tWrl6lm6dKls2fPXrt27aOP\nPiphYAAAToS1DsC5kNi5qSFDhvz444/r1q0bP348wzDZ2dmjRo2qrq6+fPlyUFCQ1NEBADgH\n1joA50Ji56YuXrw4ZMiQnJyc4OBghUKRn58fHBy8fv16wb2LJLd169aff/7Zdps33njD/EYz\nAADCWgfgbI3xGjuP0KxZszNnzvz4449nz57VarVJSUnDhg1z28+vubm59d6Zb3GdDQAAYa0D\ncDacsQMAAADwEtjHDgAAAMBLILEDAAAA8BJI7AAAAAC8BBI7AAAAAC+BxA4AAADASyCxAwAA\nAPASSOwAAAAAvAQSOwAAAAAvgcQOAAAAwEsgsRPGsqxWq62urhZtRI7jqqqqxBxOq9WKPKJ3\nT5CItFqtyMOJP6LIw4k8YiOEtc4VI3r3BKkRLAUePUEkdsI4jquoqBDznwrLsiKvBSJPkF99\nRBuOiCoqKiorK0UbjuM4MYcj0SdIROIPV1FRIeaIjRDWOleMiLXOubDWOQSJHQAAAICXQGIH\nAAAA4CWQ2AEAAAB4CSR2AAAAAF4CiR0AAACAl0BiBwAAAOAlkNgBAAAAeAkkdgAAAABeAokd\nAAAAgJdAYgcAAADgJZDYAQAAAHgJJHYAAAAAXgKJHQAAAICXQGIHAAAA4CWQ2AEAAAB4CSR2\nAAAAAF4CiR0AAACAl0BiBwAAAOAlkNgBAAAAeAkkdgAAAABeAokdAAAAgJdAYgcAAADgJZDY\nAQAAAHgJJHYAAAAAXkIh7fD5+fnZ2dlarTY5Obldu3YNaGZnDwAAAABeT8rE7sCBA4sXL46O\njg4JCVm7dm2PHj2ee+45hmHsb2ZnDwAAAADuyFAjLzpNHEshXUmuuv/+JEvsampqli9f3q9f\nv+nTpxPRuXPn5syZ07Vr1+7du9vZzM4eAAAAANyOrpx2z6ETnwUbqomI5Cpq/Vfqs5TUQffT\nq2TX2B07dqy4uHjcuHH8y5SUlPT09N27d9vfzM4eAAAAANwLq6fNI+joSuKzOiIy1NDxVfTN\nYDLU3E/HkiV2Fy5cCAwMjIyMNNW0aNEiJyfH/mZ29gAAAADgXk59Sdd/E6jPzeC1IZMAACAA\nSURBVKJjH99Px5J9FVtUVBQcHGxeExwcXFBQYH8zO3swx+bmGr791rqeS09nO3e+pyXLEpF8\n1y5dbq51e8PEiaRWW1QyRUWyzZsFOk9NZYW+HZb98gtz8eLdZhyn1Ol0KpVh/Hjy87NsXVoq\n37RJoPMWLdjevQU6//135tw5gcgffpiCgvjh+P9WVVVRZaX8668FOk9KYvv3F+j8zz+ZU6es\n69kxY7jQUMvamhr5mjV8UVVTo1MZLyDg4uPZwYMFOs/OZo4dE+h81CjOLImvrWXln38uEHmT\nJoZhw8g0QVPnhw8zhw4JdD5sGBcTY10v//xzYlnL2shIw6hR1o2ZY8d89u41TfBu54MGcQkJ\nAp2vWUM1Vh/LQkMNY8YIdH7qlOzPPy0qfaqrdb17V1n/tRDJv/6aKistawMDDbVnuO/p/Nw5\n2e+/W9ezvXpxKSkWlVVVVfJNm6i01LK1r69hwgSBzi9elP3yi0Dn3bpxrVsLRL5lCxUW8mWf\nmhqO43RqNalUhscfF+j86lXZzp2ml8pnniGFxDeEAQDYJec/dR/aQh1mNLhjKa+xUyqV94Si\nULAsazAY5HK5Pc3s7MEcd/GicobAD0s7fXqF0P9jVKtWKXfssK4vHTKEuzenJCJFTk6wUOdV\nkyeXt21rXR/wySeqLVvuGY6IiMp692abNLFoLL96NUSw8/HjyzMyrOv9v/jCZ9066/ryzExD\ns2amlwaDoby8XHb7dqhQ59UPPFB+b75r7HzdOp9Vq6zri9u00aelWVQypaVhtZ2b/7ZqBg8u\nF8p3/TZt0rz3nkDnzZrpfX0ta3W6cKHIdb16VfTuTUQcx5WXl5vqfbds8f3Xv6zbl3z7rS4w\n0Lo+fNYs0uksKvWdOpX362fdWLN9u99rr1nXl371VY11vksUNns2U1Ji2Xnr1uWDBlk39tm5\n03/ePItKJVHZJ5+UJyZatw99+WWZ1WcSQ1JSybBhAp3v3u0/c6Z1ffny5VVW+W55eXnI/Pny\nS5cs6tno6BKhfFf9xx8BQr+j8sWLq4Ty3eCFCxW1HxtMfzBcUFCJUL6rys4ONOuce+IJBokd\nAHiE8lt1H7pxPx1LtgiqVCrdvf/LrKmpkclkFjmZjWZ29mCOiY3VPf+8QH2PHhqNxryGP9Oj\nGzWKEUr4fIKD6d72NjqnzEyNVWMiotGjdUlJ5iOyLCuXy30iIjjrzqOihCPv0EGwc2b4cF10\ntHW9Ojqa75yfoEwmU6vVTHi4cORt2ghHPnCgYBqkiotTWrdnGL5zjuMMBoOi9v+7XEqKcOT9\n+unuzdeNnSclCXSuUglGziYlaTQarVbLMIyPj4+pXta7t85gEOi8RQuFUDC6Z58lq/ZcfLxg\n5PLu3bUzZyqsEgtlaqpcqL1++nQyO5to7DwqSrBzWWam9Uz1er2hjh+j/plnmLIyy85DQ4U7\nb99e8Mcob9/eor1Wq9VoNIYnn2RrT6rd7TwgQLjz9HThzuv4d8FOmqS7fds4C72eiBQKBfn4\nCHeemmreudLqdCkAgJvSCHzgN/IJu5+OGf4rOfFt2rTpP//5z9q1a001n3/++d69e1fdeyrI\nRjM7e2gYg8FQVFSkVCqDgu7r5hSHRiwrKwu2OhHoIizLFhYWKhQKMUcsKSkJCQkRZziO4woK\nCmQyWajQ2TIXjVhYWBgWdl//IB2Sn5/PMIyYIxYUFIg8HMdx4eHhoo3YCGGtc8WIWOucyzvX\nugNv0+4XhQ91X0Dd5je4Y8lunkhJSSkrK7t16+6pyDNnzrRs2dL+Znb2AAAAAOBe2k0lP8vL\nroiIAptSxqz76ViyxK5NmzYREREbNmzgXx47duzMmTMDBgzgX549e5a/v9VGM9s9AAAAALgp\npR/5x1pWxnSncT+R+r7OLkt2jZ1cLp81a9bChQvPnz8fEhJy8uTJ4cOHZ9TeB/DJJ59oNJo3\n3njDRjPbPQAAAAC4qeu/0e0DfFEf0UGbOi2gWXcKs7wBsQGkvIOsbdu2K1eu3L9/v1arnTBh\nQprZDZWDBw82XYRuo5mNQwAAAABuKmuJqViRuUgX0SkgzDnXE0u8NUBYWNjw4cOt6wffu8NZ\nXc1sHwIAAABwO3eO0aXvjeX4fvrITHLenaySXWMHAAAA0BhlLyGqzeQ6z3Vu30jsAAAAAMRS\nconObjSWI9pRosBDmO4HEjsAAAAAsRx4m1i9sdzlJSLGud0jsQMAAAAQRWUenfjMWA5qRi0e\ncvoISOwAAAAARHFoOem1xnLnOSRz/j2sSOwAAAAAXK+mjI6uMJZ9o6j1X10xCBI7AAAAANc7\n+iFVFRnLHZ8lhcYVgyCxAwAAAHAxQzUdWmYsqwKp3VQXjYPEDgAAAMDFTn5J5TeN5fZ/v88H\nwtqAxA4AAADAlTiWDiw1luVqypjpuqGQ2AEAAAC40vlvqeicsdzmb+TXxHVDIbEDAAAAcKWs\nt4wFRk4dn3fpUEjsAAAAAFzmyk66fcBYbjmOQlq4dDQkdgAAAAAuk7XkbrnTbFePhsQOAAAA\nwDVys+nqT8Zy4lCKynD1gEjsAAAAAFzD/HRd57kiDOj8h5QBAICFvXv37t27V6vVJicnjxkz\nxs/Pz6Fmr7/+elVVlXnL8ePHt23b1uVxA8D9KDxLOVuM5ejOFN9XhDFxxg4AwLU2bNiwdOnS\nwMDA1NTUvXv3zp07t7q62qFmhw4dCgwMTDcTFBQk7iQAwHEH/kUcayx3eUmcMXHGDgDAhUpL\nSzdt2jR58uRRo0YR0ZAhQ55++ukff/yRf2lPs+rqapZl+/bt27VrV2nmAAANUH6DTq0xlkNb\nUfJocYbFGTsAABc6cuSITqfr378//zIoKCgjI2Pfvn32N9NqtUSk0bjkeeEA4CoH3yVDjbHc\neS4xImVcSOwAAFzo6tWrYWFh5hfVJSQkXL161f5mfGKnVqtFiRcAnKGqiI59bCwHxFGrR0Ub\nGV/FAgC4UElJib+/v3mNv79/aWkpx3EMw9jTrLKykoiuX7++a9euwsLC6Ojo4cOHx8XF2RjU\nYDDw76oXx3F8+7KyMvsndT84jmNZVszhiEjkEcUcjif+iCIPx3GcZ01QdehtdY2xh+o2M2oq\nq4kErqzl8X+l9o/o7+9vvnpYQGIHAOBCLMvK5XLzGrlczv+/37zeRjP+jN1XX33Vt2/fsLCw\n/fv379ixY+HCha1bt65rUI7jBO/PsBGkQ+3vn8jDef0ExR8RE7SB0Wv9jn/Ilzl1SHmz8Zwd\nvdk/osWHQAtI7AAAXMjHx8dip5KqqiqVSmWRxtlolpycvGzZspiYGB8fHyJ65JFHnn/++dWr\nVy9ZsoTqIJfL7bxtlj/To1Ao6tqBxelYlq2srLT9fyYn4jiutLRULpeLOWJFRYXIE2QYJjAw\nULQRy8rKRBuOiEpKSsScIBGVlpbez3Cyo1/Jqgv4Mtd+RmBYTL3DcRxn/63uNk7XERI7AACX\nio6Ozs/PN//i9fbt21FRUfY302g0zZo1M7WUy+UZGRnff/+9jUEZhlEqlfaEZzAYHGp//wwG\ng5jDsSxL4k6QH1G04fhv8cScID+iaMOZiDxiw4dj9XT437W9+Mo6zpTZ15WzJoibJwAAXKh1\n69bV1dVnz5411Rw7diw9Pd3+Zjk5ORZpXF5enmgnhADAMWe+ptLLxnLbp0kTLvL4SOwAAFwo\nOTk5NTV11apVxcXFLMtu2LAhNzd3xIgR/NHt27fv3LnTdrPS0tKVK1du3LhRp9MZDIbdu3fv\n3bu3X79+Us4KAIRxlP2WsShTUsaz4keAr2IBAFzrhRdeWLRo0aRJk+RyuVKpfO655+Lj4/lD\nP//8s0ajGTRokI1mGRkZTzzxxIYNG9auXSuTyYjowQcfHD9+vIQzAgBhF7ZR/gljOfUxCmwq\nfghI7AAAXCsyMnLZsmXXrl3TarUJCQn8PRC8adOm8bma7WZjx44dMWLEzZs3iSgmJgZ72gG4\nqWzTLU0MZb4oSQhI7AAAxGA6S2cuOTnZnmZEpFark5KSnB8WADjL9d/pxl5jufkDFJYmSRS4\nxg4AAADgvmWb7UCUOVuqKJDYAQAAANyf/ON0cbuxHN+XYrpLFQgSOwAAAID7k7WEiDOWO8+V\nMBBcYwcAAADQIJV5dGMP3T5AZ9YbayLaUeIQCSNCYgcAAADguANLae8/SH/PwwApcw6RrUd+\nuRq+igUAAABw0LGPafdsy6yOiG4fkCKau5DYAQAAADiCM9DefwgfOvweVeSKG809kNgBAAAA\nOCL/BFXmCR9i9XT9N3GjuQcSOwAAAABHVJfaPFosVhwCkNgBAAAAOCJQ+AkxtUcTxIpDABI7\nAAAAAEcEJlJUJ+FDvpEU10fcaO6BxA4AAADAQQM/IMYqiWJkNPBDUmikCMgIiR0AAACAg0qv\nEMfefcnIKDqTHv6RWoyRLiYibFAMAAAA4LCst4wFRk4PfU8xPUjpK2lARkjsAAAAABxxZefd\njYhbjqOmgySN5h74KhYAAADAEVlL7pY7zZYuDgFI7AAAAADslptNV38ylhOHUlSGpNFYQmIH\nAAAAYDfz03Wd50oXhzAkdgAAAAD2KTxLOVuM5ejOFN9XymCEILEDAAAAsM+Bf93d5aTLS5KG\nIgyJHQAAAIAdKnLp9FpjObQVJY+WNBphSOwAAAAA7HDgbdJXGcud5wo8ecINuGNMAAAAAO6l\nuoSOf2IsB8RRq0cljaZOSOwAAAAA6nP4PaouMZY7vkBylaTR1AmJHQAAAIBN+io68oGx7BNK\nbZ+SNBpbkNgBAAAA2HR8FVXkGssd/o+U/pJGYwsSOwAAAIC6cQY6tMxYVvpSh+mSRlMPJHYA\nAAAAdTvzNRVfMJbTp5AmQtJo6qGQOgB3xBFdL6g4c7Uo0Fedrvb191FKHREAAABIgqMDbxuL\nMiV1fE7SYOqHxM5STm7p0q1HL90u5V8qZCeGZyRMGdRapcDZTQAAgEbm4neUd8RYTn2UAptK\nGk39kNjd40Zhxdw1+8qrdKYaPcttPXCluLLmlYcyJAwMAAAAJJC1pLbEUKcXpYzEPjgLdY8v\nfz1nntWZ/Hbq1vGrheLHAwAAAJK5tY9u7DGWm4+m8DaSRmOXxnXGjuM4vV5vo0FWTl5dh/af\ny23VJMAFQRmxLMtxnE4nkFa6Asdx/H/FHNHrJ0hEog1nIvKI7jxBpRKXwwKAU+37591y5hzp\n4nBA40rsWJatrKys66jewFZW15n25ZdU2njv/eM4znZ4riDyiBzHiTxBkUf0+gkSkcg/T4dG\nDAwMZBjGlREBQGNSeJoubTeW4/pQTHdJo7FX40rs5HJ5UFCQjQb+PkrBr2KJKDo0wPZ775PB\nYCgrK3PpEOZYli0sLKz3B+LcEUtKSkQbjuO4goICmUwm5oiFhYWiDUdE+fn5DMOIOWJBQYHI\nw3EcJ+aIAAB37VtEHGssd54raSgOwDV29+iSElnnoRZ1HgIAAACvUnaNzm4wliPaUtJQSaNx\nABK7e0zq0zLIV+CxvhGBmtS4EPHjAQAAAAlkv0Vs7Td4necRecxlHkjs7hEVrFk6qVtavGUO\nd6dUm3W+zvsqAAAAwGswVYV04nPji6AkShknaTiOaVzX2NkjIdz/nSe65xVXnLqcW1hp+Oin\n83z9ih9Otk8KxzbFAAAA3k1z+iPSVRhfZM4mmSclS0hThIUF+LSJCxqQFtU3LYavuVVUuemP\nC7bfBQAAAJ5NV+FztvZ0nW8kpT0hZTCOQ2JXj6mDW/upjan6hr0XbhWJurUEAAAAiOroSqaq\n9pEEGc+SQiNpNA5DYlePEH/1o71a8OVqveH9709IGw8AAAC4CqujQ8uNZVUgtZ8maTQNgcSu\nfmO6JCVFBfLlAxfu7Dt3W9p4AAAAwCVOrqaya8Zy+2mkDpY0moZAYlc/uYyZMTTNdKPzBztO\nVusMUgYEAAAATsexdOAdY1mupg4zJY2mgZDY2aVNQmjfNsa7KPJKtBtxFwUAAICXOb+ZCk8b\ny2lPkH+MpNE0EBI7ez0z6J67KK4XVNhuDwAAAJ7kwNvGAiOnTi9IGkrDIbGzV4i/+rHexrso\ndAZ25Q8npY0HAAAAnObKLrq1jy/WJI6mkBbShtNgSOwc8GDnpGZmd1H8eRZ3UQAAAHiF7CWm\norbNDAkDuU9I7BwglzHTh929i2LFDyercBcFAACAp8s7Qld+MpYTh+jD2kkazX1BYueYNvGh\n/dNj+XJeiXbjXtxFAQAA4Jm0d+jA27T9cdoykogzVnaeK2lM98uTHn/mJqYMSt1/Pq+8SkdE\nG/640D89Ni7MT+qgAAAAwBHXf6etY0hbcE9lQALF96OCgjre4wFwxs5hIX7qiX2M11TqDewH\nO/AsCgAAAI+izaf/PmiZ1RFR2VW6slOKgJwGiV1DPJCZmBxtvIvi0MX8vWdypY0HAAAAHHDy\nCzI9ENaCaY9iz4TEriFkDDN9aBvTXRQrfziFuygAAAA8xu2DdR86IGIczofEroHS4kMGtovj\ny3dKtev35EgbDwAAANjLoKvzEFv3IU+AxK7hnhqQ6u+j5Mub/rx4Lb9c2ngAAADALmGpdR9q\nLWIczofEruGC/VR/7ZvCl/UG9oMdeBYFAACAJ2j9V5KrhA+lTxE3FCdDYndfRnVq2rz2LorD\nl/J/P31L2ngAAACgfiEtqPlDAvWtH6e0SaJH40xI7O6LjGFmjUhnGON9FB/+eEpbo5c2JAAA\nAKgHZ6DbWbUvZBQQR0nDacQ6GraaGM9OjTw7eneQEhM8qPYuivzSqq9xFwUAAICbO/M1Fdc+\nOyrj/+jpazT2O2o1gYix+TYPgMTOCaYMbBWoMX5V/+2+S1dxFwUAAID74ujA28aiTEkdn5M0\nGCdDYucEgZp77qJYgbsoAAAA3NbF7yjviLGc+igFNpU0GidDYuccIzsmtIwJ5suHL+X/dgp3\nUQAAALilrCW1JYY6vShlJC6AxM45GIaZMbyN6S6KFT+crKzGXRQAAABu5tY+urHHWG4+msLb\nSBqN8ymkDsB7pDQJGto+7vvD14ioqLz6+dV/RAf7JoT792wVnVJ7Mg8AAACktO+fd8uZc6SL\nw1Vwxs6ZnjR7FsWl22V/nr29Ye+FmZ/u/eznM9IGBgAAAFR4mi5tN5bj+lBMd0mjcQkkds6k\nVsrlMss7pTmiDXsv7Dp2XZKQAAAAwGjfIuJYY7nzXElDcRUkds6090xuSWWN4KFv/rwocjAA\nAABwV9k1OrvBWI5oS0lDJY3GVXCNnTOdv1VS16HLeWU6A6uUI5MGAJfT6/UlJXUuR4LtCwoK\nXBePBY7jxByOiAwGg3dPkGVZ756gU0b02/+6D6vjy+WtZ1QXFLp0OPtxHEdE9o8YGhpqulnT\nGhI7ZzKwbF2HOCI9EjsAEIVCoQgLC7OnpcFgKCoqUigUQUFBro7KNGJZWVlwsEi3lLEsW1hY\nKJfLxRyxpKQkJCREnOH4FEQmk4WGhoo2YmFhoZ1/YE6Rn5/PMMz9jqgtoJyvjeWgJP+Mv/nL\n6kyBCgoKxJxgQUEBx3HOGhF5hjMlhPvXdSgySKNRIY0GAACQwqF/k67CWM6cTXVndZ4OiZ0z\n9UxtYror1sLQDvEiBwMAAABERLoKOrrCWPaNpLQnpAzGxZDYOVOQr+rF0e2sv2+NDvJ9pHuy\nJCEBAAA0dkc/JG3tFWwZz5JCI2k0roXEzsm6tYz6YErPwe3ikiIDZLVbn1TW6DlO2rgAAAAa\nJVZHh5cby6oAaj9N0mhcDomd8zWNCHhhdLsPn+n9eO8UvqZUW/P7aTw9FgAAQHQnv6TSq8Zy\nu2mk9vJnQSGxc6FhHeIVtSftth+8arsxAAAAOBnH0sF3jGW5mjJmSRqNGJDYuVCIv7pby2i+\nfOJa4aW8MmnjAQAAaFxytlDBKWM5bRL5x0gajRiQ2LnWiI4JpvL2g1ckjAQAAKDRyV5qLDBy\n6vSipKGIBImda7VPCo8L8+PLO4/d0NbopY0HAACgsbj6E93aZyynPEQhLSSNRiRI7FyLIRqe\nYTxpp63R/3riprTxAAAANBZZS+6WM+dIF4eokNi53JD28WqlnC//D9/GAgAAiCDvCF3ZZSwn\nDqaojpJGIx4kdi7n76PsmWq8heJCbunZm8XSxgMAAOD99i8iqt1CNnOupKGIComdGEZ2bGoq\nf4d9TwAAAFyq+AKd32wsR2dSQn9JoxEVEjsxtI4LaR4dyJd/PXGzvEonbTwAAADeLHsJcQZj\nufM8SUMRGxI7kQyrvYWiWm/YdeyGtMEAAAB4rYpcOrXGWA5tSc0flDQasSGxE8mA9FhftYIv\nbzt4BU+OBQAAcImD75C+yljOnENM40p1GtdsJaRRKfqlGTe8vpZffvxKgbTxAAAAeKHqEjr2\nsbHsH0upEyWNRgJI7MQzshNuoQAAAHClI+9TdYmx3Ol5kqskjUYCSOzE0ywqsFVsMF/ecya3\nqKJa2ngAAAC8ir6KDr9vLPuEUvoUSaORBhI7UY2o3fdEb2B/PHJd2mAAAAC8yolPqSLXWO4w\ng1QBkkYjDSR2ouqT1iRAo+TL2w5eYTncRAEAAOAMnIEOvmssK32pwwxJo5EMEjtRqRXygW3j\n+HJeifbQxXxp4wEAAPASZ9ZT8QVjOf0p0kRIGo1kkNiJbWTHpkxteRseHQsAAOAUB942FmRK\n6vi8pKFICYmd2OLC/NomhvHl/efy8kq00sYDAADg8S5+R3mHjeVWEyiwqc3W3gyJnQRG1D6F\nguW4HUeuSRsMAACAx8teUltiKHO2lJFIDYmdBHqkNgkL8OHL3x+6qmdxCwUAAICD9Fr683Va\n24WWB9L1342VyaMovI2kYUkMiZ0EFDJmcDvjLRSF5dX7z92WNh4AAAAPU11CX/egP+ZTbhbp\nyu7Wx/eTLia3gMROGsMzEmSM8SYKPIUCAADAMXtevntRnbmsxaQrFz0aN6KQcOyqqqrVq1fv\n2bNHq9UmJyc/9dRTLVq0cLRZWVnZsmXLsrOzly1b1qxZMxHDvy+RQZpOyRFZOXlEdOjinesF\nFU2CfaQOCgAAwBOwOjr1lfChytt08Ttq+RdxA3IjUp6xW758+b59+6ZOnfrGG2+Eh4e/+uqr\nBQUFDjU7d+7cs88+m5eXJ27gzjGio/EWCo5ox2GctAMAALBP+S2qKa3zaOEZEUNxO5Ildrdu\n3dqzZ8+0adN69OiRmpr6/PPPazSa7777zqFmGzduHDp06PTp00UP3wm6tIiMDNLw5R+OXK/R\ns9LGAwAA4BnkKltHZTaPejvJErujR48qFIqMjAz+pVwu79Chw5EjRxxqNnXq1HHjxjEMQx6I\nYZihHeL5cqm2Zu+ZXNvtAQAAgIjIL4oCE+o82qSLiKG4Hcmusbt582Z4eLhCcTeA6OjoPXv2\nONQsPDzc0XE5+x7PampmZ/uGGdY+ft1v5/ntTrYfutapaWuXDmdOnAlaj4gJumhcbx3OoRE9\n9DMeADiOocw59JPQ02CjO1N8X7HDcSeSJXaVlZW+vr7mNb6+vlqtluM489XZzmZ20uv1xcXF\n9rfX6XSCl/05UUZiSNbFQiI6eb3oUl5ZvF7v0uEs6PV6V0/QgsjDsSzr3RPkOM67J+jQiGFh\nYcjtABqLtEm0+0XSV91TGZ5Oo78lplHv+CFeYldTU6PT6fiyWq0WbVwLcrnczpYGg4FhGJnM\ntX8fg9s24RM7Ivr5VN7kPuLd2CvOBM2xLCvmcAaDgRz5jTtlRJGHI0wQABqnox/dzeoi2lJ8\nP2rSlVIeIplS0rCkJ15i9/XXX3/77bd8edasWf7+/hUVFeYNysvLfX19LT5w29nMTgqFIiQk\nxJ6WBoOhqKhIoVAEBQU1YCD79QwJifv98vWCCiL67cydvw9vp1GJ8UthWbawsFAulwcHB4sw\nHD9iSUmJnT//+8efypLJZGKOWFhYKNpwRJSfn88wjJgjFhQUiDwcx3FijggAnoHV0eHlxrIq\ngP6ym9Qi/b/M/YmX2A0bNiwzM5Mvx8bGsiybn59fU1OjUhnvXrl582ZcXJzFu2JjY+1p5rkY\nouEZCR/vPE1EVTrDryduDsuo+4JQAAAAOPklldZuE9ZuGrI6c+J9LxYZGdm6VlBQUPv27VmW\nzc7O5o9WV1cfPHiwU6dOFu+ys5lHG9QuTqUw/iL+d/CKtMEAAAC4NY6lg+8Yy3I1ZcySNBq3\nI9nNExEREQMGDPj444+JKDg4ePPmzTKZbPjw4fzR9957T61WP/300zaacRx34sQJIrp27RoR\n5eTkVFRUqFSqli1bSjWphgnUqHq1bvLTsRtEdCG39NzN4pQYfPgAAAAQkrOFCk4Zy2mTyD9G\n0mjcjpSPFJs6derq1as//PBDrVbbsmXLhQsXBgYG8oeuXLmi0WhsN9PpdK+88oqpt/fff5+I\nIiMjV61aJfpU7tfIjk35xI6Ith28+jwSOwAAAEHZS40FRk6dXpQ0FHckZWKnUqmmTJkyZcoU\n60NLly6tt5lKpdq6datrQxRL67iQ5KiAC7fLiOjXEzefHpTq79PY7+sBAACwdPUnurXPWE55\niEIEHjHfyDXqvV7cypD2xqdQVOsNu2rP3gEAAMBdWUvuljPnSBeH+0Ji5y4GpMdoVMb9ur47\neEXs3f0BAADcXN4RurLLWE4cTFEdJY3GTSGxcxcalaJnSgRfvppffvyK2Pv7AwAAuLX9i4hq\nz3tkzpU0FPeFxM6NDEqPNpW3H7pqoyUAAEDjUnyBzm82lqMzKaG/pNG4LyR2bqRpuF+rWOP9\nsL+fzi2qqJY2HgAAAHeRvYQ4g7HceZ6kobg1JHbuZUTHpnxBb2C3H7ombTAAAADuQKbNo1Nr\njC9CW1LzByUNx60hsXMvfdKa+GuMG518+evZB5f8MO+r/cevFkobFQAAgIR8Tq4gfZXxReYc\nYpC91Ak/GvdSUaVn2bt3xGpr9Icv5c/5ct+vJ29KGBUAAIBUGF2Zz7kv7pkjHwAAIABJREFU\njS/8Yyl1oqThuDskdu7l452nKqv1FpUsxy3/7niZVidJSAAAABLSnPmUqSk1vuj0PMlVkobj\n7pDYuZEaA7v3TK7goYpqfdb5PJHjAQAAkJi+yud07ZNCfUIpXeBpVWAOiZ0bKSqvqdGzdR29\nWVQhZjAAAADSO/GZTHvbWG4/nVQBkkbjAZDYuRG10tavQ6OS8sG+AAAAYuMMdPBdY1npSxn/\nJ2k0ngGJnRsJ9lXFhvrVdTQtIVTMYAAAACR2ZgMV5xjLbZ4kTYSk0XgGJHbuZWLvFoL1nZIj\nUmv3LgYAAGgUDiw1FmQK6vS8pKF4DCR27qV/euyUgakKGWNeGR7g89LYDlKFBAAAIIFL2ynv\nMF+sThpLgYmSRuMxcNmW23m4W7PerZscvHDni1/OFlfWEBHLcX4+SqnjAgAAEFHWktoSo23z\nf2opQ/EkOGPnjiKDNMMyEoZlJPAvC8urL98utf0WAAAA73FrP13/jS/WxA82hKRKG44HQWLn\nvjKbR5rKWTl3JIwEAABAVPsXmYraNjMlDMTjILFzX6lxwQG1z43NzsHuxAAA0DgUnqaL24zl\nuF66yM6SRuNhkNi5LxnDZDQz3tp96npReRUeKQYAAI3A/sXE1W7XnzlX0lA8DxI7t5aZbEzs\nDCx3+FK+tMEAAAC4XNk1OrveWA5Pp2bDJY3G8yCxc2uZzSMZxrj1STYuswMAAK93YCkZaozl\nznOJGJutwRISO7cW7KdqHh3Il7Nz8jhpowEAAHApbQEd/8xYDkqiln+RNBqPhMTO3WU2N34b\nW1hefTEXm54AAID3OrycdOXGcqcXSIbddh2GxM7dmW96kn0B98YCAICX0lXQkRXGsm8ktZks\naTSeComdu2sVGxyoUfFlXGYHAABe69hHpK29TTBjJik0kkbjqZDYuTsZw2Q0C+fLp64VlWmx\n6QkAAHgdVkeH/m0sqwKo3d8ljcaDIbHzAKbL7FgOm54AeKpr166dO3dOq9U2uJmdPQB4pFNr\nqPSqsdxuKvmESBqNB8NliR6A3/SE4zgiyr5wp3frJlJHBAAOuHnz5ptvvnn16lW5XC6TyZ56\n6qmhQ4c61MzOHgA8FkcH3jYW5WrKeFbSYDwbEjsPEOSrahEdeO5WCdVueoJdfQA8Bcdxb731\nlp+f35dffhkYGLht27aVK1c2b968efPmdjazswcAT1V+k85upIJTxpdpfyX/GEkD8mz4KtYz\nZLYw3htbVF59AZueAHiOc+fOXbx48amnngoKCmIYZtSoUUlJSd9//739zezsAcDTcHT4ffow\nhj6KpV+fM9YxMur0oqRReTwkdp7hnk1PcrDpCYDHOH36tI+PT3JysqkmLS3t1KlT9jezswcA\nD7PnFfr5/6ji1j2VcjUpfCUKyEvgq1jP0DImKFCjKtXWEFF2Tt6EnvgKBsAz3LlzJywszPRs\nQCIKDw/Py7P8eGajmZ09mOPKy1mhzI+LjKS4OPMalmWJiLl40aATuOOea9eO5HLLWq2WOX1a\noHF4OCUkWNczly9TYaH5iDKt1uDnx6Wnk1Jp2bq6mjl5UqDz0FBKTLSupytXmIICgfZpaaRW\nU+0EOY7T6/Wk0zHHjwt0EhLCJSUJ1F+7xtwR2GSKS00ljdVOHAYDc/QoP6JcqzX4+Rnrg4I4\ns6T8rhs3mNu3BTpv2ZJM771byzGHDwt0EhDA1n4jr9fr79bfvMnk5gp03qIFBQRY1zOHDxNn\n9WwjPz+uZUuBTnJzFefOGfz9LeuTkykoSKDzo0fJYLCsrcmVZ71l3ZiKtdznz7Bd55vXKcrK\n2MREvWDnx4+T9Z+uWs2lpQl0XlDAXLliXc01bUphYRaVer2eOXmSqqstWyuVXHq6QOdFRcyl\nSwKdx8dTRIRA5KdPU+2NUIryco7jDAEBJJdz7doJdF5Swly4YHol79SJZHWemENi5xlkDNMx\nOfyXEzeJ6PT14jKtLkBjtSACgPvRarVqtdq8Rq1W63Q6g8EgN8uZbDSzswdz7LFj8h49BIKZ\nPr1iwQLrep+XX5bv2GFdX3D+PBccbFGpOH06uHdv68ZVkyeXL1liXR8wZ456yxbTSzkRv3gV\nHjvGNrG8FUx++XJIly4CnY8fX/7ee9b1/q++6rNunXV90f79hmbNTC8NBkNxcbHs9u1Qoc6r\nH3igbNUqgc4XLfIRqi/+9Ve9Vd7AlJaGdelCZhPk1QweXLp2rXUnfm+/rRGaUfH27frMTMta\nnS5cKHJdr14lmzcTEcuyxcXFpnrf99/3/de/rNuXfPutTuh3F96jh3V6pO/UqVjoG3/NqlVB\nr71mXV/61Vc1Q4ZY14cNGMCUlFhUss2i6O9W2R4RHSVmy3Y5bTevCyYq++ST4gcftG4eOny4\nzCqFNSQlFWdlWTf22bjRf+ZM6/ry5curJkywqCwuLg4ZM0Zulaux0dFFQh8P1P/9b8CUKQKd\nL15c9eST1vXB48craj99mTJWLiioICfHurHqhx8CJ040veTKyxnr7L8WEjuPkdk8kk/sWI47\ndPFOnzRcWwrgARQKBX/SyMRgMDAMI7v3A7eNZnb2cI+gIH3fvgL1LVoorU6S6XQ6Q5s2+qoq\n6+ZKX1/Oqr2sjs65lBTrzomIS0vTFxXdU8NxDMMo/PwEOg8IEI68dWvBzql1a8H2isBAWW17\nnU7HMIxCoWD8/IQjb9NGOPKUFMH28uBgxqo9o9GYGvMTNJbbtROOvHlz4c5DQ607J4YRbMym\npyuVSp1OR0TmozDJycKdR0QInCUl0vfpQ+Yn/PjOW7YUjJxJStL16WN+Cpkni4oSbK/v2ZOp\nqLBsrLlNJHDCkoKJWpC+SU/zh4lxHMdGRAh2bujenTU7H2yMPDpaOPLYWMEfCxMba9Fep9Mp\nlUpDly5c06YWjbnQUMHOZU2aCHYua9pUsD3bubM+0niRFb/rBcMwnJ+fcOdRUead1/WJzjgd\nzvrsKxAZDIaioiKlUhkkdO7XRSOWlZUFW304NimprPnLO7v439egdnEvjhY6W2s3lmULCwsV\nCoWNEZ2LZdmSkpKQEJG2JuI4rqCgQCaThYaGijZiYWFhmNX5fNfJz89nGEbMEQsKCkQejuO4\n8PBw0UZ0hbVr1+7cufOLL74wr9mxY8eaNWvsbGZnDw3jhmudc2Gtc8WITljr9r5K+94QPiRT\n0qxK88QOa51DcPOExwjyVaU0Ma682Tl5yMgBPEJiYmJRUVFZWZmp5tKlS83MviWst5mdPQB4\nkqYD6zwU39c8qwNHIbHzJKZHUBRX1ORg0xMAT9ChQwe1Wr1z507+5Z07d44cOdKj9gI4vV5v\nMBhsN7PdA4BHiutNTQcJ1MvV1HOh6NF4FSTFniSzeeRXv53ny9k5d1o0EemrEwBoMF9f34kT\nJ37++ed5eXkhISE7d+5MTEzs378/f3TevHkajeaNN96w0cx2DwCeKjyNruy8p8Y/loZ8StGd\nJQrISyCx8yQtY4KC/VTFFcZNTx7thU1PADzA6NGjIyMjd+/enZubO3DgwAcffFChMK69ycnJ\npjtebTSzcQjAI9WU0onPjWWfUOq+gCLaUpOuJFfbfBvUD0uDJ2EYJqNZxM/HbxDRmRvFpdqa\nQI1K6qAAoH5du3bt2rWrdf20adPsaWb7EIDnOfw+VdfugdLlZerwf5JG41VwjZ2HyUw2XmbH\nctyhi/nSBgMAAOAwfRUdft9Y9gmhtk9LGo23QWLnYTomR8hqdw/KzhHYEh0AAMCtnfjs7pPE\n2s8glcDDMKDBkNh5mCBfVUoMNj0BAADPxBno4LvGstKXMvAlrJMhsfM8mc2Ne1WXVNacx6Yn\nAADgQc5soOLap2a1eZI0As9RhfuBxM7zmHazI6LsHFsPAgcAAHAvB5YaCzIFdXpe0lC8ExI7\nz5PSJCjYz3gzLC6zAwAAj3FpO+UdNpZbTaDARCmD8VJI7DwPwzAdmxlP2p29UVyqrZE2HgAA\nALtkLaktMZQ5R8pIvBcSO49kusyO5biDF7DpCQAAuL1b++n6b8Zy8kgKbyNpNF4LiZ1H6mS+\n6ckFXGYHAABub/+iu+XMudLF4eWQ2HmkAI2yZe2mJwdy7mDTEwAAcGuFp+niNmM5rhfF9pA0\nGm+GxM5T3bPpya0S240BAACktH8xcayxjNN1roTEzlNltog0lbNwbywAALitsmt0dr2xHJ5O\nzYZLGo2XQ2LnqVpEB4b4qfkydrMDAAD3dWApGWo3cOg8l4iRNBovh8TOUzEM0zG5dtOTmyXF\nFdj0BAAA3I+2gI5/ZiwHJVHLv0gajfdDYufBTI+g4Dju0EV8GwsAAO7n8HLSlRvLnV4gmULS\naLwfEjsPdu+mJ0js4P/Zu/PAKMr7f+Cf2d3sZnNfJIGEkINASAiBECBeRShIRamgVNTWalGp\nt6Va8au21mLlUJT6q9oqam1RUUo9UAQEtVoQknCFKwkhIQnkPjeb7G52d+b3x0w2ISSbTbI7\nzx7v11+fzEz2+WwITz478xwAAG7G3EFHX5PigGiavJxpNj4BhZ0HC/L3S4sLE+NDZ7HoCQAA\nuJnCN8jQvYp+9sOk0jLNxiegsPNsvRc9Ka7GoicAAOA2eDMd3ijF6mDKup9pNr4ChZ1nsw2z\nI8yNBQAAt3JqM+kqpXjKr8k/nGk2vgKFnWcbPzo0Isi26AmG2QEAgJsQqGCDFCr8KPthpsn4\nEBR2no0jsi16UlKDRU8AAMA9nPmEmk5KccYdFDyWaTY+BIWdx5uR0rPoySEsegIAAO6g4AUp\n4BSU81umqfgW31pOxmKxtLUNYYaBxWJpampyXT59CIIwjOaSwpVKBWflBSL638mqqWP8Hf9e\nq9Xq/m9wJHie9+43KHOL8jdHRI63GBERwXFY0R7ADVR9Q9U/SHHqjRQxiWk2vsW3CjuVShUR\nEeHIlVartbW1VaVShYSEuDorW4t6vT40NHSo3xhBlBYXdrKqhYiOn9eFhYcrHPjbxvN8S0uL\nUqkcRovDw/O8TqcLCwuTpzlBEJqbmxUKRXi4TMN1BUFoaWlx8BfMKZqamjiOk7PF5uZmmZsT\nBMHxFlHVAbiLvHU9cc6j7PLwRb5V2JHDXb/tMtn+VIgNDa+5GeOjxcKu3WAuqdFNihu8ePKs\nNzjydr21Oflb9Po3CAAj1XCMzu2W4nHzaHQu02x8DsbYeQPbanaERU8AAICtg2uIuhfMn7GK\naSq+CIWdN0iJDYkMlobWYdETAABgpq2MzmyT4uipNO7HTLPxRSjsvEHvRU/O1LS1dJjY5gMA\nAD4qbz3xFime9RQRhlLIDYWdl+i96Mnhs432LwYAAHC+zjo69U8pDkuh1CVMs/FRKOy8xPSU\nUSqF9MEo/yyG2QEAgOwOvUwWgxTPfII4JdNsfBQKOy8RqFGlxUvreuSXNvCCYP96AAAAZ+rS\n0bG/S3FgLE36BdNsfBcKO+8xY7z0NFZvNBddaGWbDAAA+JYjr5Kp+09PzqOkGsJq+eBEKOy8\nx4wULHoCAAAsWE105P9JsSaUMu9hmo1PQ2HnPZJjQ6Kw6AkAAMjvxNvUUSPF0x4ijUx7GsGl\nUNh5j96LnpTWtDXrsegJAAC4ktVEVd/Qkb/SD89KR1T+NPUBpjn5OhR2XsU2zE4gOnQWN+0A\nAMBlKvfSphT6aC59/RB11EkHJ91OgbFM0/J1KOy8yvTk3oueoLADAADXqD9KH19P+gt9jzce\n79lPDFhAYedVAjSqSd2Lnhw622Dl8b8LAABcYP8zZDH2c7zmAJV/KXs20AOFnbeZMV6aG4tF\nTwAAwFUq9w54qmKPjHlAXyjsvI1tmB0R7Tl23mLlGSYDAABeiDeTuWPAs8YWGVOBvlDYeZux\no4K1GpUY7zhSecPanWs/PtLW2cU2KwAA8B4KPwqIGfBs8FgZU4G+UNh5mw2fHjWYLLYvLbzw\nzYnqR9/9oaPXQQAAgBGZcNMAJzhKvVHWTOBiKOy8yuGyxm9OVF96vKpRv3X/WfnzAQAA73TZ\nM+QX1M/x6b+h6KmyZwM9UNh5lX1FtQOd+t/ApwAAAIbG2kXWi5fBDxpDc/5CV29glBBIVKwT\nAGdq0vc3+ZyIiBp1A54CAAAYmoIXiTdL8ex1lPYLChrDNCGQoLDzKsH+fgOe0g54CgAAwHGc\nqYWOvyV9ETyWsleSAn9i3IWjj2LnzZu3f//+fk9t3Lhxzpw5zksJhm9aUtRAp7KTBzwFADbo\n6wAGpT39Jpn10hczHkdV51YcLez27t1bX1/f76mGhoZDhw45LyUYvtkZYyaMDr30eJC/321X\npsqfD4DHQV8HMAhzh39R9+06bSRN/hXTbKCvQR7Fbtq0adOmTWK8atWqtWvX9rnAYDCcOHFi\n7FgsWuMWlAruudtmvvjpsbzSnr9MGpVyzS9mxYRpGSYG4ObQ1wE4qvANztgkxdm/Ib9AptlA\nX4MUdpMnT87KysrPzyei8vLyioqKPhcolcr09PQNGzALxl2EBqhX3zrjfFPHy58fO1HZQkRW\nQUiKDmadF4BbQ18H4BDezB35ixT7BVLWfUyzgX4MUtjl5ubm5uYSEcdxH3300eLFi2XJCkYq\nPjJwdkacWNhZrPzZWl1aXBjrpADcF/o6AIec2ky6SinOuo+0kUyzgX44Ois2Pz9//PjxLk0F\nnKt3JVd0oRWFHYAj0NcBDEyggu6b1go/yn6YaTLQP0cnT+Tk5FRWVi5duvTIkSO2g++8887i\nxYtPnjzpmtxgRJJjQjQqpRgXXcCWzOBJals7//Xfko27SjbuOvPut8XVzQNvN+5s6OsABnTm\nE2rq/l+QcQf2hHVPjhZ2hYWFV1555bZt25qammwHVSrV9u3bc3Nz0d+5IZWCS44NEeOiC61s\nkwFw3Henala8/t3m787knW3OL2t+//vSFX/7bk/heXlaR18HMKCCF6SAU1DOb5mmAgNytLD7\nwx/+4O/vn5+fP2/ePNvB22+//fjx41qt9qmnnnJNejAitsevNS2drR1dbJMBcMT5po51nxw1\nWay9D5qt/EvbC8vqdDIkgL4OoH9V31D1D2LYNe56ipjENh0YiKOF3f79+1esWJGTk9PneHp6\n+vLlyw8cOODsxMAJeo+rK6nBTTvwAJ/ln7NY+UuPW3nhk7xzMiSAvg6gf3nrbKEh436GiYB9\njhZ27e3tgYH9r1UTFham08nxSRqGqs/8CYaZADiotLZtoFNnagY85UTo6wD60XCMzu2W4nHz\nLKP6fvIB9+FoYZeWlvb5559bLJY+x7u6urZu3TppEm7JuqPYsICwQLUYF6OwA0/A88KAp4QB\nTzkR+jqAfhxcQyT9BxRyHmebC9jn6HIn999//4oVK+bPn3/HHXekpqZqtVq9Xn/8+PE33nij\nsLDwrbfeGvwlgIWJY8IOnqknoqILrQIRxzofAPsSRgWfHuBDSEJUkAwJoK8D6KutjM5sk+Lo\nqTTux9SMlRbcl6OF3T333FNZWblmzZpvv/2293GlUvnMM88sX77c+amBM6TFhYuFnd5ovtDU\nER+JvV/ArV07bezuo1X93ppbmJ0gQwLo6wD6yltPfPc97FlP4RaBm3O0sCOi1atX33XXXZ9+\n+unp06f1en1QUNCkSZMWL148btw41+UHI9R7mF3xhVYUduDmJsaFhQZqWjtMfY7//KrUaUlR\n8uSAvg6gR2cdnfqnFIelUOoSptnA4IZQ2BFRYmLiI4884qJUwBUmxoVxHCcIAhEVXWj58ZQ4\n1hkB2PPVsfO2qk6rUYYHqMePDluYnSBbVSdCXwcgOfQyWQxSPPMJ4pQky2hXGDZHJ08QkSAI\nX3zxxYMPPvjTn/60oKBAPHjgwAEB/8ZuLFCjst2lG2joEoCb6LLwm787I8YqBffnpZkv3DLl\nqZuyZa7q0NcBSLp0dOzvUhwYS5N+wTQbcIijd+xMJtPixYt37twpfnn//fcTUXt7+5w5c666\n6qrt27drNBpX5QgjkxYXVtWoJ6LyOp3JYrXtMwbgbj7JK69vk+4NLMxOiA31l7+WQl8H0OPo\na2TqviOQ8yip/JlmAw5x9I7d+vXrd+3atXLlyq+++sp2UK1WP/nkk3v27HnhhRfsfC+wZRtm\nZ+GFs7VYhQvclN5o/mj/WTHWqlW3XZXKJA30dQASq4kOvyLFmlDKvIdpNuAoRwu7Dz744NZb\nb33ppZcmT55sO6jRaH7/+9/feuutW7ZscU164ARpY7BMMXiALf8rbTeYxfim3KTwIDY3xtDX\nAUhOvEMdNVI87SHShDLNBhzlaGFXVlY2d+7cfk/NmTOnvLzceSmBkyXGhGj8pMevKOzAPTW2\nGz8rqBDj0AD1TbnJrDJBXwdARCRY6dBLUqzyp6kPMM0GhsDRwk6lUpnN5n5P6fV6pRLDttyX\nSsGNjw0R46ILWFUS3NG735aYzFYx/sWPUgM0Q5uw70To6wCIiIq3Uos0k4km30WBsUyzgSFw\ntPecMmXKtm3b7r333j7Hm5qaXnvttaysLGcnBs6UFhd+sqqFiOpaDS0dpvBAjP4GN1LVqN9b\neF6MY8MC5FmIeCDe0dfxPG8y9V0LsF/i9BSe5w0Gw6AXOwXP83I2J/8bFARBEATZmrM16twW\nNXnrpBs/nNKYcZ/Q68W94w0OSs7mxN9Sx1vUarV2zjpa2P3mN79ZtmzZkiVLbrjhBiI6efJk\nR0fHwYMH33nnncbGxueff97B1wEm+ixTnDshhmEyAH28uee0tXuL2F/NnahSDmEZJqfzmr6O\n53lHLhP/ogiC4OD1I2ertORsTv4WZWvOxoktKqu+UjQcFWPL+KXW4ETq9eJe8AbdsDkntuho\nYXfzzTeXl5c//fTTn3zyCRE99thj0verVGvXrl26dKlTsgEX6V3YFaGwA3dyorJZ3PWOiFJi\nQ2anj2abj3f0dQqFIjDQoW1mrFar0WhUKpUOXj9yVqvVYrHI1hzP80aj0fEfiFNaNJvNsjUn\n3sriOM6ZLRb+xRaqclepLn5lQRBMJpNsb5CInP8GB2M0GmVuThAEZ7U4hIEsq1atuu222z7+\n+GPxI2xwcHBGRsaSJUvi4rCZgbuLDtVGBGma9SbC/AlwM2/tLbLF98ybxHHst6FEXwc+rTaP\nqr6V4uTrKHoay2Rg6OwVdg0NDQEBAWIJWVtbGx4ePnbs2Icffliu3MCZJo4J+6GkjohKqlux\ngD64iX1FtafOSxN6clJGybzDhA36OoAeB9f0xDNWscsDhsneWJbU1NRNmzaJ8ejRo7/88ktZ\nUgKXmNj9NLbDZKlq6mCbDAAR8YLwj2+LxZgj+tWciawyQV8HIGkuorOfSfHoWRR/FdNsYDjs\nFXZWq/Wbb76pq6szGo1EZDabjQOTK2EYpknx4bYYT2PBHew8UlXZoBfjOZlx40czW/4UfR2A\nJG8dCd1D+Gc9xTQVGCZ7j2KvvvrqTz/99NNPPxW/vPnmm+1cjKd7bm7imFAFx/GCQERFF1rn\nZY5hnRH4NJPF+t730ipZKqXil7MnMEwGfR0AEVH7eSp6X4ojJlHydUyzgWGyV9j94x//2LBh\nw4kTJzo7O/fu3TtlypRRo0bJlhk4l1atGhsVVNHQTkTFWKYYWPvPgfJGnXT3a1HOuNHhAQyT\nQV8HQER0aANZu6R41v8Rx3LhIRg2e4Udz/NPPfWUOKCY47gnn3xy2bJlciUGzpcWFyYWduX1\n7cbuVf4B5Kc3mv/9Q5kYa9WqW64YzzYf9HUAZGymQmmkKQWPpbRbmGYDw+fo5Aki6uzsdH0+\n4EK21eysvFBa28Y2GfBl7313Rm+Utu26+fKUsEA123zQ1wHQ4VfILI15pRm/I4Uf02xg+IYw\neSIoKAgDij1a72WKS6pR2AEb9W2G7QUVYhwWqF4yK5FpOkTo6wDMnXT0VSnWRtLk5UyzgRHB\n5AkfkhgdrFWrDF0WIiq60Hp1KrNJiODL/vFNsdkqTbv75ewJWvUQlkl3EfR14KOsXXRmG9Uc\noPPfkaFROpj9CPnJt+kCOB0mT/gQBceNjw05XtlMRMW4YwcslNfpvj5RLcbxkYELpiWwzUeE\nvg58UUcN/Wch1R+96KBCRVPuZZQQOIe9wi4yMtK24zXHcc8+++zixYtlyQpcJS0uTCzs6tsM\nLR1do0LZ3ywBn7Jpb5Htjtev5qSpFOw3ECP0deCLBPp8Wd+qjoh4C514i2Y+wSIlcA5HJzP/\n8MMPs2fPdmkqIIO0uJ5lisvqsf8EyKqwoqngbIMYTxwTdsWkWLb59At9HfiE6gN0/vv+TxW8\nRAKWTfBggxR2Dz744LZt24goNzc3PDy832seffRRlQo3fjxD7/kTZ1HYgYwEorf3Ftm+XP7j\nNLe4WdcNfR34ltr8AU8ZGqitXMZUwMkGKexeffXV77+/qKjnOO6xxx7rfcRqtVqtqO49Q1SI\nf1SwvxiX1rWzTQZ8yvenak5372U3MzV6amIk23z6QF8HvoXvsnfWavcsuDesK+1zJnbftDtb\n38Fjfh/IwsIL//imWIw5jrvz6ols8wHwdRFpA55S+VNIonyZgLMxfqywb9++ffv2GQyGlJSU\nJUuWiCu/D+my//3vf3l5eR0dHfHx8YsWLYqKipIrd0+VFhe2r6iWiIxma3WLIWKAp04ATrTj\ncOWFZunR//wpcSmxIWzzAfB1CfMoJIF0lf2cSruV/Fhu8QcjxPKO3Ycffvjiiy+GhIRMmjRp\n3759q1atMplMQ7rs73//+0svvaRWq1NSUvLz8x966KGmpiZ534Tn6T3M7kwtnsaCyxm6LO9/\nf0aM1SrF7bMnsM0HAEjlT5f9sZ/jUZk0+0W5kwGnYnbHTqfTbd26dfny5YsWLSKiBQsWrFix\nYvfu3eKXjlxWU1PzxRdfPPDAAwsWLCCiRYsW3X333bt27brtttuYvCNPMWFMmFLBWXmBiErr\n9INeDzBC2w6Ut+ilD2M/nZEYHaplmw8AEBFV7O6J1UEUmUHJ19GXS6sBAAAgAElEQVT032J1\nYk/H7I7d0aNHzWbz3LlzxS9DQ0Ozs7MPHDjg+GV+fn733nvvVVddJZ4KDg6Oi4urra2V6x14\nKn8/ZUJUkBjjjh24Wltn17YDZWIc5O+37IoUtvkAABFRWxmV/FuKo6fSQzq67QDl/h5VnRdg\nVthVVlZGRkb2Hi2XkJBQWdn3eb+dy6KiohYuXBgQIA0F6OrqqqmpiYuLc33uHs/2NLaqqVPc\nYQzARTZ/d6bTJP2O3Xx5SohWzTYfACAiyn+B+O7Of9aTRG61+hCMCLNHsW1tbUFBQb2PBAUF\n6XQ6QRA4jhvqZUT01ltvEZH4WHYgVqtVrx/Cw0eLxdLWJtPWW4IgWK1WeZpLiJBWPOEF4djZ\nmklj5BjJLucbtOF5Xs4WBUGQ+Q3K3KKDzbV2dv37YFVhVWt9m9E28zo8UD03LWJI2Yp7VDj+\nLSEhIX26BQDoR2cdnXxXisNSKPVGptmAkw1e2P3lL3/5y1/+0vvIhg0bNmzYMMKGeZ5XKpW9\njyiVSkEQ+hx38LLNmzd/9dVXTz31VFhYGA1MEASz2ex4kkO9fuTkaS4pqmeQU9GF1vGj5Bvz\nJPPPU/4W8QYb2k3PfnyqpaPvOlg/mhjFCVazecjrwMn2Bl3U1wG4nUMbyWKQ4hmriFPavRo8\nzCCFXUqK0wbEbN++fc+ePWL885//3N/f32g09r7AaDSq1eo+ZdyglwmC8Le//e2bb755+umn\ns7Oz7eegVCrtV342PM/rdDqVStXnfqHr8Dzf0dERHBwsQ1uhoaEBmlPiA7LKFpODP5MR4nle\nr9eHhMi0zoV4b0mhUMjZok6nCw0Nlac5ImptbeU4Ts4W29raBm3upV2HLq3qiCivvPWu+ZOV\nQ9kctq2tTRAEx38/R3K7zol9HYBb69LRsb9JcWAspd/ONBtwvkEKu9LSUme1lJiYePnll4tx\ndHR0bGxsY2Nj7yeqdXV1MTExfb5r0MteffXVH3744c9//nNqauqgOXAc5+COQOL68o5fP3JW\nq1XO5lJiQo5XNhNRcXWbPI3yPC/nG7TtNC9zi/JvOSVzi/aba9abDpc19nuqpqWzuEY3ZdyQ\nN5yQ5w06sa8DcGtHXyOTtAcMTf8tqfyZZgPOJ9+fhMzMzMzMTNuXVqvVZDIVFxenpUnrXxcW\nFmZlZfX5rvT0dDuXffLJJ99///2aNWuSk5Nd/w68SlpcmFjYNeqMje1G2z5jACNR3dJhZzOT\nC82dwyjsAMBprCY6/IoUa0Jpygqm2YBLMJsVm5KSMmnSpE2bNrW2tvI8/+GHH9bW1l533XXi\n2R07dnz11Vf2L2ttbX3//ffvueceVHXD0HuZ4uLuHTwBRkijsjdYR6PCHoYATJ14hzpqpHjq\ng6SRbyAHyIbllmKPPvro888/f8cddyiVSj8/v5UrV44dO1Y89fXXX2u12vnz59u5bN++fUaj\n8ZVXXnnllVdsrxkXF/f6668zeTuepXdhV3Sh9Yq0WIbJgNcYNypYq1YNtIbOpHjsXwfAjmCl\nQy9Jscqfpj3INBtwFZaFXXR09MaNG6uqqgwGQ0JCgr9/z9PA++67T6FQ2L9s1qxZCQkJfV5T\no9HIk7yniwjSRASpm/VdRFSEO3bgJGqV4qbcpM3fnbn01JzJY0aHYwNKAHaKt1JL9//NyXdR\nID7PeyeWhZ3Idpeut0tnqF16WVRUVFRUlKvS8gEp0YFiYVdS3WrlhSFNVwQYyM9/lPpJ/jm9\n4aI1SnJSRj1yXeZA3wIAcih4QQo4JU1fyTQVcCH2hR2wMj4mOL+shYiMZmtloz4pWo6VVsDr\nnaxqsVV1YyOD5kwek5EQMTURcyYAmDq3k+oOS3HaLRSG9X28Fgo73zU+pmeJvqILrSjswCm+\nOFRhi59YMnX8aIzOBnADeet64pxH2eUBLufoJLV58+bt37+/31MbN26cM2eO81ICmSSPCrQ9\nfi2+0MI2GfAObZ1d+4pqxTgtLswTqzr0deCFavOo6lspTr6OoqexTAZczNHCbu/evfX19f2e\namhoOHTokPNSApmoVYr4CGkwO+ZPgFPsOlrVZeHF+LrsvnObPAL6OvBCB9f0xDNWscsD5DDI\no9hNmzZt2rRJjFetWrV27do+FxgMhhMnTvQ7AQLcX2pscEVjBxGda9B3miwBGjyah+ETiHYe\nqRLjIH+/2Rlj2OYzJOjrwGs1F9HZz6R49CyKv4ppNuByg/whnzx5clZWVn5+PhGVl5dXVFT0\nuUCpVKanp2OfbA81PiZozwkiIkEQztS0ZWGEO4zA4bKGC80dYjx/SrzGz5N2FkdfB14rbx0J\n0n10mvUU01RADoMUdrm5ubm5uUTEcdxHH320ePFiWbICmaTG9kyYKLrQisIORuKLQ5W2+Nps\nD7uzhb4OvFP7eSp6X4ojJlHydUyzATk4+ugtPz9//PjxLk0F5BcXrg3QqDpNFsIwOxiZFr3p\nYEmdGE8ZFzlulKdOskZfB17l0AaydknxrP8jDtv6eT9H/41zcnIqKyuXLl165MgR28F33nln\n8eLFJ0+edE1u4HIcx03onrdYhImxMAI7jlRaeEGMF3rmtAkR+jrwHsZmKpRGjlLwWEq7hWk2\nIBNHC7vCwsIrr7xy27ZtTU1NtoMqlWr79u25ubno7zxXWpy0fWez3tSgM7BNBjyUIAi2aROh\nAeorJ3nwVkXo68B7HH6FzHopnvE7UvgxzQZk4mhh94c//MHf3z8/P3/evHm2g7fffvvx48e1\nWu1TT2E8pqdKiwuzxXgaC8Nz8Ex9fZv0qWDB1LF+Sg9+3IO+DryEuZOOvirF2kiavJxpNiAf\nR/vf/fv3r1ixIicnp8/x9PT05cuXHzhwwNmJgUwmxYfbYhR2MDxfHJamTXBEP5nmYdMm+kBf\nB17i+JtkaJTi7EfIL5BpNiAfRwu79vb2wMD+fy3CwsJ0Op3zUgJZhQWqo0O1YozCDoahvs1Q\nUNogxtnJo+IiPPvvB/o68Aa8mQ69LMV+gZR1P9NsQFaOFnZpaWmff/65xWLpc7yrq2vr1q2T\nJk1ydmIgH9vT2DM1bdbu8e8ADtpxuJIXpF+b66Z78LQJEfo68Aan3ydd91qMWfeSFktZ+RBH\nlzu5//77V6xYMX/+/DvuuCM1NVWr1er1+uPHj7/xxhuFhYVvvfWWS7MEl0qLC/vuVA0RmczW\nc/XtKbEhrDMCj2Hlhd3HzotxRJBm1oQYtvmMHPo68HwCFbwohQo/mvYw02RAbo4Wdvfcc09l\nZeWaNWu+/fbb3seVSuUzzzyzfDlGZXqwib3nT1S3orADx+0rqm1qN4rxtdkJKgXHNp+RQ18H\nHq/0M2o8IcXpt1OIx99HhyEZwt6gq1evvuuuuz799NPTp0/r9fqgoKBJkyYtXrx43LhxrssP\nZJA6OlSl4MRFyIoutHro3u3AxI7uaRMKjvvJVM+eNmGDvg48W/767oijnEdZZgIsDG3T98TE\nxEceecRFqQArGpUyMSaktKaNsEwxDEVNS+fRcmna3azUaNssHC+Avg481fn/UvV+KU5dQpHp\nTLMBBoaw3JQgCF988cWDDz7405/+tKCgQDx44MABQcBwe4+XNkZ6GlvV2NFh6jtsHKBf2wsq\nbP/5F3r+tAkb9HXgwfLW9cQ5j7HLA5hxtLAzmUwLFy68/vrrX3311e3btzc2NhJRe3v7nDlz\nFixYYDKZXJkkuJxtYqwgCCXVWPQEBme28nsKpWkT0aHanJRRbPNxFvR14MEaCql8pxQnzKUx\nlzHNBthwtLBbv379rl27Vq5c+dVXX9kOqtXqJ598cs+ePS+88IJr0gOZYP8JGKrvTtW0dUqb\niy/MTlBwHj9tQoS+DjxY3lqi7vvKM1YxTQWYcbSw++CDD2699daXXnpp8uTJtoMajeb3v//9\nrbfeumXLFtekBzKJjwoK8pe2ESxGYQcO2HFImjahUnDXZMWzTcaJ0NeBp2orp5KtUjwqixLn\nM80GmHG0sCsrK5s7d26/p+bMmVNeXu68lIABjmjCmFAxPo35EzCYykb9yapmMb48LTYy2J9t\nPk6Evg48Vf4LxHePkJ71JJGX3ESHoXK0sFOpVGazud9Ter1eqVQ6LyVgw/Y0trWjy7ahO0C/\nLpo24V3r46CvA0+kMDbSyX9IX4Qm04SbWGYDTDla2E2ZMmXbtm2XHm9qanrttdeysrKcmhUw\nkBYXbosxzA7sMJmtXx+/IMajwwOmJkWxzce50NeBJ9KcfJ0s3R/IZ64iDp9AfJej69j95je/\nWbZs2ZIlS2644QYiOnnyZEdHx8GDB995553Gxsbnn3/elUmCHPrMn/hR+miGyYA7+/pEtd4o\n3dNalDPOy573oK8DT1L0AXfqXxH1xxSdtdKRgBhK/yXTnIAxRwu7m2++uby8/Omnn/7kk0+I\n6LHHpNVxVCrV2rVrly5d6qoEQS6hAerYsIDa1k7CMsVg145D0ubifkrFvCneM21ChL4OPIRA\nu++h429Rn0dvo2eSynvGvMIwDGHniVWrVt12220ff/yx+BE2ODg4IyNjyZIlcXFxrssP5JQW\nFyYWdmdq2iy84AX7foLTna3VldS0ifGPMkaHBqjZ5uMK6OvAAxRtEau6vsp2UONxisqUPSFw\nF0PbUmzs2LEPP/ywi1IB5tLiwr49WU1EXRb+XJ1u/OhQ1hmB29lecM4WX5fttXunoq8Dd3d8\nU//HBSudeIeufknebMCNODR5wmg0ZmZm/vWvf3V1NsDWRcPssP8EXMJotn57skaME0YFZYwN\nt3+9x0FfBx6juWjAU02nZcwD3I5DhZ2/v39DQwMWcPJ6KbGhKqX0K4GJsXCp74oaDF3SQlmL\nchKZ5uIS6OvAYyj8BjylHPgU+ABHlztZs2bN5s2bv/32W1cmA4ypVYrk6GAxRmEHl/r6VL0Y\naPyUcyePYZuMi6CvA88QkzOcU+ADHB1jV1FRMW/evGuuuSYpKSk1NTUsLKzPBZs3b3Z2bsBA\nWlyYODT+fKNebzTb9hkDOFHVXNnUKcZzM+O89XcDfR14hukrqfQ/JAh9j2tCacoKFgmBu3C0\nsHv22WfFoKSkpKSk5NIL0Nl5h7S48M8KKohIICqpbstO9qq1Z2EkbJvDEtF13rXbRG/o68Az\nxF1BYROopfiig/4RtGgrBcYyygncgqOF3alTp9RqtVqt5jgsgeHNJl68TDEKOxDpDF3fn5am\nTUwYE5bqvTOm0deBZ6jNt1V1vDbGPGa2ZuwsSr+dtKPY5gXMOVrYTZo0yaV5gJuIiwwM0ap1\nhi7CMsXQy1fHzndZeDH24tt1hL4OPEXeGlvYPvtN6+grNBERDNMB92GvsLv33nszMjIeeugh\nMbb/Qn/729+cmRcwwhFNGBNacLaBMH8CuglEOw5Lz2EDNaqrvW7aBPo68DDNxVT6qRSPnmWO\nuczRiZDgA+wVdn//+98XLFggdnZ///vf7b8QOjuvMTEuTCzs2jq7als7Y8MCWGcEjB0tbzzf\n1CHGP54S7+/nbfuLo68DD5O/jgTpDrow8//Y5gLuxl5hd/r06aCgIFssSz7AXu9lircXVCzN\nTQ4P0jDMB5iz3a4jooXe+BxWhr5u3759+/btMxgMKSkpS5YsCQwMHNJlf/rTn4xGY+8rb7nl\nlilTprgiVXB37efp9HtSHDGJkq+nZgybgR72Cru0tLR+Y/Bu5xr0tvjfP5R9knduaW7ynXMm\nYCy5b2rRm/YX1Ypx2uiQpO6VDr2Jq/u6Dz/8cMuWLQsWLEhMTPzmm28OHDiwYcMGjabv5yU7\nlx0+fDg3N3fcuJ493EJDvXb+Cgzi0Etk7ZLiWU8Qh8ewcBF7hd3OnTsdfBWz2bxo0SJn5AOM\n7T52/q09F92xsFj5LftKOY7unDORVVbA0M6jVRZeWitrbkY022RcxKV9nU6n27p16/Lly8Vv\nXLBgwYoVK3bv3t3ndexcZjKZeJ6/+uqrc3Nzh9Q0eCFjMxW+KcXBY2niLUyzAXdkr7C79tpr\nHX8h4dJlEsHT8ILw9tf97z+49YeyJbOSQgPUMqcEbAmC8OUR6TlsiFY9KyWSbT4u4tK+7ujR\no2azee7cueKXoaGh2dnZBw4c6FPY2bnMYDAQkVarHVK74J2O/D8ydz9UyXmMlOp+1igG32av\nsFu9erUt5jhu7969Bw8evOaaayZOnKjVanU63fHjx7/55psbbrhh4cKFrk8VXK6ivr1Fb+r3\nlMXKF1Y0XTVptMwpAVv5pQ11rQYxvmZqvJ/SOx/Hu7Svq6ysjIyM7D2oLiEhYceOHY5fJhZ2\nlz66BZ9j7qQjr0qxNpIylzPNBtyUvcLu6aeftsUff/zxa6+9durUqd6DPIjo5MmTc+fOvf32\n212VIMio3Wi2c1ZvsHcWvE9Tu3HbwXIx5oiunZZAZLT/LR7KpX1dW1ubbWaGKCgoSKfTCYLQ\ne9yqncs6OzuJ6Pz583v27Glubo6NjV24cGF8fLydRq1Wq/hdgxJvQFqt1vb2dsff1EgIgsDz\nvJzNEZHMLbqoOfWJ1zWGBjE2pf+6yyiQUWpFzjcokrk5QRC8+A2Kv6WOtxgUFGRn1LujCxSv\nXr36gQce6NPTEVFGRsY999zzpz/96YYbbnDwpcBtjQqx96wnyu5Z8Bq8IGzdX/afg2WtHV22\ng5kJkfGRgU1N3lnY9eb0vo7neaXyogVilEql+Le/93E7l4l37DZv3nz11VdHRkYePHhw586d\nzz33XHp6+kCNCoJgMvV/932gJId0/cjJ3Jw3vEHeHFj4VzEUVAEdqXfwFzfh8W/QzVp05zfY\n50NgH44WdqdPn46Jien3VGRk5KlTpxx8HXBno8MDkmNCyup0l54KDVBPGYdlzX3Cy9sLdx87\n3+dgTWunocvCJB+Zjbyv2759+549e8T45z//ub+/f5+VSoxGo1qt7lPG2bksJSVl48aNY8aM\n8ff3J6Kbb775t7/97bvvvrtu3bqBclAqlQ5OmxXv9KhUqoFWYHE6nuc7Ozvt/2VyIkEQdDqd\nUqmUs8WOjg6nN6c4/S+FvkpqInNFcHSSrTmdTsdxXEhIiHNbHIh480y25oiora1NzjdIRDqd\nTubmBEFwfKq7/UUqHC3sgoKCPvvss+XLl/d5OZ7nd+zYIVuPAK720MLJq/51wLZ5lIjjuAd+\nkqHxumVp4VKFFU2XVnVE1KAzbN1fdl2md06e6G3kfV1iYuLll18uxtHR0bGxsY2Njb0fvNbV\n1V1aO9q5TKvVJicn265UKpXZ2dlffvmlnRw4jvPz8xs0VSKyWq1Dun7krFarnM3xPE/yvkGx\nRWc3J9CRl6VQ4afIWanofn3xKZ6cb1BsUbbmbGRu0XPfoKOF3ZIlS958881rr732tttuS0lJ\n0Wq1BoPh7Nmz77333p49e+68806nZAPMpceHv3jHZa/vOlV0vsU21Wrl9ZmzM7xtFyno1/en\nawc+VeMLhd3I+7rMzMzMzEzbl1ar1WQyFRcX21bIKywszMrK6vNd6enpA11WWlp65syZ3lN3\n6+vrZbv/BG6h9DNqPCHF6bdTiBeuEw7O4mhh98ILL1RWVu7atWvXrl19Tl1xxRUvvPCCsxMD\nZiaOCdv4q8v/e6r6+W1HxCMhWOXEZzTqBhxF1zDwKW/i9L4uJSVl0qRJmzZtevrpp0NCQrZu\n3VpbW/vkk0+KZ3fs2OHn5zd//nw7l+l0utdff729vX3JkiUKheJ///vfvn37fvazn438zYLH\nyF/fHXGU8yjLTMDtOVrYhYaG7ty5My8vb+/evWVlZZ2dnVqtNiEhYfbs2bNnz3ZpisBE2phw\nW1xWp7tsQv+jjsDLBPkP2CcEDnzKm7iir3v00Ueff/75O+64Q6lU+vn5rVy5cuzYseKpr7/+\nWqvVzp8/385l2dnZd95554cffvjee+8pFAoiWrx48S23YGVan3H+v1S9X4pTl1DkgJNmAMjx\nwk40c+bMmTNnuigVcCvRYdogfz+90UxE5+plnWQODE1NjOp3jB0RTUuKkjkZhpzb10VHR2/c\nuLGqqspgMCQkJIhzIET33XefWKvZv+zGG2+87rrrqquriWjMmDFY08635PWaJZPzGLs8wDMM\nobCzWq2HDx+urq42m/tZz2zp0qXOywrY44gSRwWfqGomon7nyYJXmj15zLaDZWdr+/6La9Wq\n264cT4JPPI11UV9nu0vXW0pKiiOXEZFGo0lKShpe0+DBGgqpvHvLu4S5NOYyptmAB3C0sCsu\nLl6wYEFFRcVAF2BLMe+TFCMVdheaO41mqz9mxfoAlYL70y0z7vh/31isPTOjR4cHPL546pgI\nn1jHDn0duJe8tUTdv3IzVjFNBTyDo4XdypUra2trf/nLX6anp+MpgI9IipFW8REEoaKhfeKY\nMLb5gDxqWjptVV3uhJibcpMyxkYoFd65mdil0NeBG2krp5KtUjwqixLnM80GPIOjhV1eXt7L\nL7983333uTQbcCvJ0cG2uKwOhZ2v+KG4zhb/as7ExF6/Br4AfR24kfwXiO9eGHzWk0S+8vkK\nRkLh4HVGozE7O9ulqYC7SYoJsa2Veq4ew+x8xYESqbAbHR7ga1Udoa8D99FZTyf/IcWhyTTh\nJpbJgOdwtLCbPn069g3zNf5+ytgwaX/YsjpMjPUJ5fXtF5o7xPjyibFsk2ECfR24i8MbyWKQ\n4pmriMMoZ3CIo4XdunXrnn/++ePHj7s0G3A3yd3D7MrqdBgx7gv2F/fsPHH5RF9cvBB9HbiF\nLh0dfV2KA2Io/ZdMswFP4ugYuy+++CI5OTkrK2vGjBnjxo1Tq/tuRbB582Zn5wbsJUUH7yuq\nJSK90dyoM4wK0bLOCFzLNsAuNECdPjbc/sVeCX0duIWjr5OpVYpzfksqf7tXA/RwtLB77rnn\nxCAvLy8vL+/SC9DZeSXbHTsiKq9rR2Hn3RrbjaU1bWJ82cQYBeeLI7XR1wF7VhMdeUWK1SE0\n5ddMswEP42hhV1paqlarOZ/s6H1ZUu+JsfW6manRDJMBV9tfVGt74O6zm8ihrwP2Tr5L+mop\nnvYgaUKZZgMextHC7tLl0cEXjI4I1KpVhi4LEZVj/oS329/9HFbjp/SpDcR6Q18HjAlWKnhR\nilX+NO1BptmA5/GJXb1trFarwWAY/LruxeWtVqter3dxUj0t8jwvZ3NE5EiLYyO0JbXtRFRa\n2zqS9GR+g7ZG5WxR5uac22KHyXK8slmMp44LN5sMZlM/l8n88xxSi0FBQa5MB0AWJduo5YwU\nT15OgaOZZgOeZ5DC7sCBAw6+UG5u7oiTcTmFQuHn5+fIlTzPm0wmjuMcvH7keJ63WCyyNScI\ngoNvMDkmRCzsaloMAqdUqxydSX1pi2azWc43KAYytyhbc0RkNBqd+Ct69EyTbcOJyyfG9Puy\nJpNJzjdoMpkEQZCnRS/r68CD5a+XAk5J01cyTQU80iCF3WWXObrfsEfsn8hxnIN7BFmt1o6O\nDoVCIdueQlar1WQyydYcz/Pk2A8kZUw4HbtARFZeqNN1jR89zNEePM8bDAbZ3qB4K8vxf3Gn\ntNjR0SHnJlTt7e1E5KwW8882iYGC4y5LG6PR9J0NSkR6vV7ONyjeq5OnRS/r68BTndtNdYek\neOLNFDaeaTbgkQYp7JYtWyZPHuC2LtpYrL592IUduDOzlS842yDGmeMiQgP6qeq8G/o6cAv5\n63rinMfY5QEebJDCbsuWLfLkAW4rKSaEIxLvUZTXYWMx73SkrFGcIkNEl/nkusTo64C92nyq\n/FqKk66lGGxtB8MxzPFS4DsCNapRodLydeX1mBjrnfZ37w9LPrzQCQBjeWt74pmr2OUBng2F\nHQwupdfGYmwzAVcQBOFgd2GXEhsSGxbANh8AX9RcTKWfSHHsTIqfzTQb8GAo7GBwSTHSMLu2\nzq5mfX9rYIAnO32h1fbPitt1AGzkrydBmpZOs55kmgp4NhR2MLik6J6NxXDTzvvY9oclossn\nxjLMBMAXmTvp/H/pdPdWdRFplLKIaULg2XxrgWIYnt4bi5XXt+ekjGKYDDjd/uJaMYgO1SbH\nhti/GACcpupb+u/vqP5wz706Ipr5BHG45wLDh8IOBhcXGajxU5rMVsLEWK9T2ag/39QhxldM\njMUOqQAyKfuCPrmBBOtFBzmOQhIYJQReAh8LYHAKjhs3SrppV4aJsd7FdruOfHWhEwAGeDN9\n9eu+VR0RCQLtfbB7gSmA4bB3x+7uu+92/IU2bdo04mTAfSXHBJdUtxJRVUO7xcqrlPhI4CVs\nA+yCtX6TEyLYJsMK+jqQW/UPpL/Q/6mmU9R0iiIz5E0IvIe9wu6tt95y/IXQ2Xm3xO5hdhZe\nqGrUJ8VgJJY3aOkwFVe3ifGs1BilwkefxKKvA7m1n7d3VleJwg6GzV5hd/r0adnyADeXfNHE\n2HYUdt5hf1GtbefTy334OSz6OpCbxu7ejJowufIAL2SvsEtLS3PkJQ4ePLh9+/bnnnvOSSmB\nO0ruVcmV1+uI4hgmA86yv/s5rFqlyE6OYpsMQ+jrQG5xV5BSQ9b+lgXVhGEzMRiJIY+U0uv1\nrb3U1tb+85//3LBhgyuSA/cRrPWLCvEXY2ws5h0MXZZj55rEODt5lFaNOfIXQV8HLqQJG3DT\nsCv+REqNvNmAV3G0KxcEYfXq1a+88kpTU9OlZ7OyspyaFbij5OiQRp2RsEaxt8g7U2+2Sqtn\nYcMJG/R1IJPoS27Lqfzpsmdo2kMssgHv4egdu3feeeeZZ57RaDRz584lossuu+zKK68MDAwM\nDw9/6qmnPvvsM1cmCW7BtrFYs97U2tHFNhkYuR+694flOG5WajTbZNwH+jqQScEL3RFHM35H\nP/0PraikmU+wTAm8gqN37F599dXZs2fv3r1boVD4+fm98sorOTk5Op3u0UcfPXbsWEwMPu57\nv94bi5XX66Yl+e6QLC9g4YX80gYxzogPDw/Cox8J+jqQw+5sQ9MAACAASURBVPnv6MI+KU5d\nTD9azzQb8CqO3rErLS298cYb1Wp174MhISFvvPGGXq//wx/+4ILcwL0kx/RsLIansZ7u2LlG\nvdEsxliXuDf0dSCHvHU9cc7v2OUBXsjRwk4QBKVSSUQqlUqhUOh00t91juN+/vOfb9myxVUJ\ngtuIjwxSq6RfmHOYP+HhbOsSE1EuBtj1gr4OXK6hkMq/lOKxc2jMZUyzAW/jaGGXkJDwzTff\niHFMTMy+fftspwRBaGhocH5q4GaUCi4hKkiMsbGYRxN6DbBLjA6Ojwxkm49bQV8HLpe/rmfT\nsIHmxgIMl6OF3bJly7Zt2/aLX/yCiGbPnr127drXXnvt2LFjn3766Z///Ofx48e7MklwF7Z1\niSvq2y08djP0VGeqW8UJzoT5sJdAXweu1VZOxR9J8agsSryGaTbghRydPPH4448fO3ZM/LT6\nzDPP7Nq164EHHhBPcRz30Ucf2f1u8BJJ3RuLma38hSb9uFHB9q8H97S/13NYX95wol/o68C1\nCl4k3iLFs/6PyEf38QPXcbSw02g0//73v/V6PRGlpaUVFhZu2rSpvLw8NjZ22bJl2dlYJtsn\n9N5/oqyuHYWdh7IVdlHB/qljsHnRRdDXgQt11tOJd6Q4NJlSb2KaDXinoa01HxQkDbGKj4//\n4x//6Px0wL31Luwwf8JDVTd3VDRI/3aXT4zB7YJ+oa8Dlzj8F7IYpHjm46TAdi/gfI6OsZs3\nb97+/fv7PbVx48Y5c+Y4LyVwX6EBatuCZ2X1WPHEI/V+DnvZxFiGmbgn9HXgKl3tdPQ1KQ6I\nofRfMs0GvJajhd3evXvr6+v7PdXQ0HDo0CHnpQRuLbl7mWIsZeehbAudBGpUU8ZFsE3GDaGv\nA1c59jqZWqV4+kpSaZlmA15rkPvAmzZt2rRpkxivWrVq7dq1fS4wGAwnTpwYO3asS7ID95MU\nE3yorIGIGnVGnaErRKse9FvAfbR2dJ063yLGM1OjVUpHP9p5PfR14FpWEx3+ixSrQyjr10yz\nAW82SGE3efLkrKys/Px8IiovL6+oqOhzgVKpTE9P37Bhg6sSBDeTHH3RMLsp4yIZJgND9UNJ\nHS9I69RcjuewvaCvA9c6+S7pq6V42gOkwaQlcJVBCrvc3Nzc3Fzqnue/ePFiWbIC95V00cZi\nKOw8zA/FtWLgp1TkpIxim4xbQV8HLiRYqeBFKVZqaNpDTLMBL+folJz8/HyszAlElBAVpFIq\nLFaeiM5h/oRHMZmtR881ifHUpMgADWbk9QN9HThfyTZqOSPFk5dT4Gim2YCXc7Rnz8nJIaKd\nO3d+/vnnpaWler0+ODg4LS3tpptuuvLKK12ZIbgXlVIxNjKwvL6diM7WYcUTT5Jf2mAyW8UY\n82EHgr4OnC9/vRRwSsr5LdNUwPs5WtiZTKYlS5Z8+eWXvQ/u3Llz48aNy5cvf/PNNxUKjML2\nFUkxIWJhV1HfzguCgsNSaJ5hf4n0HJYjmpUazTYZt4W+Dpzs3G6q655MPfFmCsP9YHAtR3uo\ndevWffnll8uWLdu9e3dFRUVjY2NFRcWOHTtuuOGGt99++7XXXhv8JcBb2DYWM1ms1c2dbJMB\nB/GCUFAqbWCfFhcWFezPNh+3hb4OnCx/XU+c8xi7PMBXOHrHbsuWLddff/2WLVtsRyIjIxMS\nEq699tpFixa9/fbbDz74oGsyBLdz8cZiuvjIQIbJgIMKK5rbOrvEGM9h7UBfB85Um0+VX0tx\n0rUUgy3pwOUcvWNXVla2aNGifk/deOONRUVFzksJ3J3tjh1hYzHPYZsPS0SXT4xhmImbQ18H\nzpTXa0HEmavY5QE+xNHCTqlUdnR09HvKarVi0IlPiQz2Dw2Q1iXG/hOe4kCJtJtCXETg2Kgg\ntsm4M/R14DTNxVT6iRTHzqT42UyzAV/haCeVkZHx/vvvm0ymPsctFss///nPjIwMZycGbs12\n0w47xnqE0lpdbas0GvKKNDyHtQd9HThN/noSeCme9STTVMCHODrG7t57773rrruuuuqqu+66\nKz09PTAwUK/Xnzx5ctOmTYcPH3733XddmiW4m+SYEHFFtPpWg95oDvL3Y50R2IPnsI5DXwfO\nob9ApzdLcUQapfT/fB/A6QYp7Pbs2ZOQkDBhwoTly5efOXNm/fr14pY7Nkql8o9//OMvf/lL\nVyYJbsd2x04gOtfQPnks9pJ3a/uL68QgPFCTFoe9jPqBvg6crOAlskrTlWjmE8ThIT7IZJDC\nbv78+Y888sjGjRuJaM2aNffcc8+nn35aVFTU0dERFBSUnp6+ePHihIQEWVIFN5LUa2JseR0K\nO7dW32Yo7x4KednEGA7rDvYHfR04EWdqoeNvSl8Ex1ParUzTAd8ytD2FkpOTV65c6aJUwIMk\nRgcrFZyVF4ioHPMn3Nu+olqhO74Mz2Edg74ORkJz6k3q6l4xIOcxUqqZpgO+BTeHYTj8lIq4\nCGn5ujKseOLebM9htWrV1MQotskAeD9zp//pTVLsH0GZdzHNBnwOCjsYJtsyxeV1OkEQ7F8M\nrLQbzCermsV4xvhRahX+ywO4FnfiLc7YKH2R/TD5YXUhkNXgj2L37dv3xBNPDHrZ2rVrB70G\nvElidDCdJCIymq21rYbR4QGsM4J+HCipE5+YE9FlE/Ac1h70deAEvIU7/LIU+wXS1AeYZgO+\naPDCrqCgoKCgYNDL0Nn5mj4bi6Gwc08/lEjPYVUKbmZqNNtk3Bz6OnCCog9IVyHFU1aQFoMf\nQG6DF3bXXXfdnXfe6fpMwMP03lisvL4dy966IZPFeuhsgxhnjovEcoP2oa+DERMof70UKvwo\n+zdMkwEfNXhhN378+KVLl8qQCniW6FBtiFatM3QRNhZzV4fLGo1mqxhjXeJBoa+DkTr7OTWe\nkOL0X1AI1scBBjCSGoYvsfumXTkmxroZQRAuNHd8mndO/JLDQicAMshf1x1xlPMoy0zAhw1t\nHTuA3pKigwsrmoiopqXT0GXRqvHr5BZOVDVv/Px4VaPediQi2D8q2J9hSgDe7/z3dGGfGJrH\nLfSLxLbCwAbu2MHw9WwsJgjnGvT2LwZ5lFS3Prk5r3dVR0RN7cZNe4tYpQTgE3pu15Ex8yGG\niYCPG+QWC9YnAzuSLp4YOwmbkLqBN/cUmSzWS4//50D59dPHYfLyQNDXwYg0HqeyHWIoxM+2\nRM9kmw74Mtyxg+FLjA62bTx6DvMn3IChy3K8srnfU7wg5JfWy5wPgPdrPE5FH9BX9xF1fzaY\nuYppQuDrMCgKhs/fTxkXEXC+qYOwsZh7aOvssnPnqbWjS85kALyc7hzt+IVtXJ0kIk0Ydw21\n4YMuMIM7djAiSdG9NhZjmwoQhQaobfdQLxUWpJEzGQBvZtbTRz/uW9URkaGRTG0sEgKQoLCD\nEbHNn+gwWRraDGyTAa1alZkQ0e8pBcfNSBklcz4AXuvoa9RW1s9xQyN35BXZswHogUexMCJJ\nMT37T5yt00WHahkmA0R0z/xJj/3jh0vnT9yUm4SZE77DYrG0tQ3hvpHFYmlqanJdPn0IgiBn\nc0RktVqd22JI6Y6BNnKxlu20TnhQ5jfI87x3/wvK3KL8zRGR4y1GRETYeTiDwg5GpPeOseV1\n7dhmnrkJo0MfuT5z/SdHbUe0atWyK1JuuSKFYVYgM5VKFRkZ6ciVVqu1paVFpVKFhoa6Oitb\ni+3t7WFhMk2i53m+ublZqVQ6uUXrgAs8qcxtSqUyPDzcmc0NTCxBFApFRET/d+td0WJzc7OD\nv2BO0djYyHGcnC02NTXJ3JwgCM5qEYUdjEhMWECARtVpshBReT3GC7uFDpPFFj98Xea8zDiN\nn5JhPgBeKGgM1R3q94wQFCdzLgC9YYwdjAjXa5gdNhZzE8fONYqBWqWYPyUeVR2A841fMvCp\nxTLmAdAXCjsYKdvE2AtNHSZzP0vjgpwEouMV0lJ26fHhahX+jwO4QPrtFD6hn+Ojc4XJd8ue\nDUAPdPowUsnd8yd4QahowE07xsrrdG2d0np1WYlRbJMB8Fqcgoi/6IjKn7Luo6W7SKlmlBMA\nEcbYwchdvLFY+4Qx2FiMpaPneuZVTU2Sb/AvgG85s41aSqV44jLKfZoiJpLCj4iI5+18H4Cr\nMS7sGhsb8/PzDQZDSkpKVlbWMC6rqqo6ceKEyWSKj4+fPn26nQnA4CJJ0cFc92Y65zDMjrVj\n3YWdxk85YbRM8xwBfE7eeinglHTFagpPZZoNQA+WhV1BQcHatWtjY2PDw8Pfe++9K664YuXK\nlZdWZnYu27JlywcffJCUlKTVav/1r3+NGzfu+eef9/f3Z/FufJdWrYoND6hp6SSiMkyMZYoX\nhBPde8VmJkSolBhrAeACFV9RXYEUT/wZqjpwK8wKu66urldeeWXOnDkPPPAAEZWUlDz++OO5\nubmXX365g5edO3fu/ffff+CBBxYsWEBE586dW7ly5ZdffrlkycCTlcA1kqJDpMKuDoUdS6U1\nbXqjWYyzEvEcFsA18tb1xDm/Y5cHQD+YfaAvLCxsbW392c9+Jn45YcKEzMzM//73v45fplQq\n77zzznnz5omnEhMTY2Nja2pq5HoH0MO2/0S7wdyoM7JNxpddNMAOhR2AK9TmU+VeKU78CcVk\nM80GoC9mhd3Zs2dDQkKio6NtR1JTU0tLSx2/bOzYsTfeeKNSKa3RVVtbW19fn5KC5fUZSI7u\nNX8CT2PZsQ2wC9SoxmOAHYAr9L5dN3MVuzwA+sfsUWxLS0ufDV7CwsIu3SjNkcvefvvt1tbW\nwsLChQsXzp8/306jPM+bTCZH0hM3buN53mCQaWN7nuflbM65b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vKiHX6joqLq6+svvVKpVI7wFWwEQbBarY6kx/O8eL3FYnHk+pHjeV6G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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# 1D slices\n", + "slice_m <- tip_total[abs(tip_total$delta_out) < 0.01, ]\n", + "slice_y <- tip_total[abs(tip_total$delta_med) < 0.01, ]\n", + "\n", + "p_slice_m <- ggplot(slice_m, aes(x = delta_med, y = est)) +\n", + " geom_line(color = \"steelblue\", linewidth = 1) +\n", + " geom_point(color = \"steelblue\", size = 2) +\n", + " geom_hline(yintercept = 0, linetype = \"dashed\", color = \"red\") +\n", + " labs(title = paste0(exposure, \": Total Indirect vs delta_M\"),\n", + " subtitle = \"Holding delta_Y = 0\",\n", + " x = expression(delta[mediators]), y = \"Total Indirect Effect\") +\n", + " theme_minimal(base_size = 12)\n", + "\n", + "p_slice_y <- ggplot(slice_y, aes(x = delta_out, y = est)) +\n", + " geom_line(color = \"darkorange\", linewidth = 1) +\n", + " geom_point(color = \"darkorange\", size = 2) +\n", + " geom_hline(yintercept = 0, linetype = \"dashed\", color = \"red\") +\n", + " labs(title = paste0(exposure, \": Total Indirect vs delta_Y\"),\n", + " subtitle = \"Holding delta_M = 0\",\n", + " x = expression(delta[outcome]), y = \"Total Indirect Effect\") +\n", + " theme_minimal(base_size = 12)\n", + "\n", + "p_slices <- grid.arrange(p_slice_m, p_slice_y, ncol = 2)\n", + "ggsave(file.path(dir_mnar, paste0(\"mnar_1d_slices_\", exposure, \".png\")),\n", + " p_slices, width = 12, height = 5, dpi = 150)\n", + "ggsave(file.path(dir_mnar, paste0(\"mnar_1d_slices_\", exposure, \".pdf\")),\n", + " p_slices, width = 12, height = 5)\n", + "cat(\"MNAR plots saved.\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c0d3d8de", + "metadata": {}, + "source": [ + "## 7. Bayesian SEM (blavaan/Stan)\n", + "\n", + "Using blavaan with Stan backend. Pass full data with NAs -- blavaan handles missing data\n", + "via data augmentation in Stan.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f1d16bf1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:21:22.905163Z", + "iopub.status.busy": "2026-03-31T16:21:22.903491Z", + "iopub.status.idle": "2026-03-31T16:57:03.064644Z", + "shell.execute_reply": "2026-03-31T16:57:03.062202Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting Bayesian SEM with blavaan (Stan backend)...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This may take 10-30 minutes.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 1).\n", + "Chain 1: \n", + "Chain 1: Gradient evaluation took 0.00591 seconds\n", + "Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 59.1 seconds.\n", + "Chain 1: Adjust your expectations accordingly!\n", + "Chain 1: \n", + "Chain 1: \n", + "Chain 1: Iteration: 1 / 3000 [ 0%] (Warmup)\n", + "Chain 1: Iteration: 300 / 3000 [ 10%] (Warmup)\n", + "Chain 1: Iteration: 600 / 3000 [ 20%] (Warmup)\n", + "Chain 1: Iteration: 900 / 3000 [ 30%] (Warmup)\n", + "Chain 1: Iteration: 1001 / 3000 [ 33%] (Sampling)\n", + "Chain 1: Iteration: 1300 / 3000 [ 43%] (Sampling)\n", + "Chain 1: Iteration: 1600 / 3000 [ 53%] (Sampling)\n", + "Chain 1: Iteration: 1900 / 3000 [ 63%] (Sampling)\n", + "Chain 1: Iteration: 2200 / 3000 [ 73%] (Sampling)\n", + "Chain 1: Iteration: 2500 / 3000 [ 83%] (Sampling)\n", + "Chain 1: Iteration: 2800 / 3000 [ 93%] (Sampling)\n", + "Chain 1: Iteration: 3000 / 3000 [100%] (Sampling)\n", + "Chain 1: \n", + "Chain 1: Elapsed Time: 424.891 seconds (Warm-up)\n", + "Chain 1: 621.577 seconds (Sampling)\n", + "Chain 1: 1046.47 seconds (Total)\n", + "Chain 1: \n", + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 2).\n", + "Chain 2: \n", + "Chain 2: Gradient evaluation took 0.002213 seconds\n", + "Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 22.13 seconds.\n", + "Chain 2: Adjust your expectations accordingly!\n", + "Chain 2: \n", + "Chain 2: \n", + "Chain 2: Iteration: 1 / 3000 [ 0%] (Warmup)\n", + "Chain 2: Iteration: 300 / 3000 [ 10%] (Warmup)\n", + "Chain 2: Iteration: 600 / 3000 [ 20%] (Warmup)\n", + "Chain 2: Iteration: 900 / 3000 [ 30%] (Warmup)\n", + "Chain 2: Iteration: 1001 / 3000 [ 33%] (Sampling)\n", + "Chain 2: Iteration: 1300 / 3000 [ 43%] (Sampling)\n", + "Chain 2: Iteration: 1600 / 3000 [ 53%] (Sampling)\n", + "Chain 2: Iteration: 1900 / 3000 [ 63%] (Sampling)\n", + "Chain 2: Iteration: 2200 / 3000 [ 73%] (Sampling)\n", + "Chain 2: Iteration: 2500 / 3000 [ 83%] (Sampling)\n", + "Chain 2: Iteration: 2800 / 3000 [ 93%] (Sampling)\n", + "Chain 2: Iteration: 3000 / 3000 [100%] (Sampling)\n", + "Chain 2: \n", + "Chain 2: Elapsed Time: 433.837 seconds (Warm-up)\n", + "Chain 2: 622.016 seconds (Sampling)\n", + "Chain 2: 1055.85 seconds (Total)\n", + "Chain 2: \n", + "Computing post-estimation metrics (including lvs if requested)...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian fit completed in 35.7 minutes.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian model fitted successfully.\n", + "\n", + "=== Bayesian Parameter Estimates (parTable) ===\n", + " label est se\n", + "1 a1 0.550 0.118\n", + "6 a2 0.509 0.163\n", + "11 a3 0.644 0.176\n", + "16 b1 0.333 0.374\n", + "17 b2 0.112 0.440\n", + "18 b3 -1.443 0.481\n", + "19 cp -2.444 1.124\n", + "79 ind1 0.183 0.214\n", + "80 ind2 0.057 0.234\n", + "81 ind3 -0.929 0.412\n", + "82 total_indirect -0.689 0.360\n", + "83 total -3.133 1.078\n", + "84 prop_med1 -0.058 0.182\n", + "85 prop_med2 -0.018 0.154\n", + "86 prop_med3 0.296 0.627\n", + "87 prop_med_total 0.220 0.463\n", + "88 diff_1_2 0.126 0.336\n", + "89 diff_1_3 1.112 0.519\n", + "90 diff_2_3 0.986 0.548\n" + ] + } + ], + "source": [ + "set.seed(42)\n", + "cat(\"Fitting Bayesian SEM with blavaan (Stan backend)...\\n\")\n", + "cat(\"This may take 10-30 minutes.\\n\")\n", + "t0 <- Sys.time()\n", + "\n", + "fit_bayes <- tryCatch(\n", + " bsem(model_str, data = dat, fixed.x = FALSE, target = \"stan\",\n", + " n.chains = 2, burnin = 1000, sample = 2000, seed = 42),\n", + " error = function(e) {\n", + " cat(\"blavaan error:\", conditionMessage(e), \"\\n\")\n", + " NULL\n", + " }\n", + ")\n", + "\n", + "elapsed_b <- as.numeric(difftime(Sys.time(), t0, units = \"mins\"))\n", + "cat(\"Bayesian fit completed in\", round(elapsed_b, 1), \"minutes.\\n\")\n", + "\n", + "if (!is.null(fit_bayes)) {\n", + " cat(\"Bayesian model fitted successfully.\\n\")\n", + "\n", + " # Extract using parTable\n", + " pt <- parTable(fit_bayes)\n", + " labeled <- pt[pt$label != \"\", c(\"label\", \"est\", \"se\")]\n", + " cat(\"\\n=== Bayesian Parameter Estimates (parTable) ===\\n\")\n", + " print(labeled)\n", + "} else {\n", + " cat(\"WARNING: Bayesian model failed. Will skip Bayesian results.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "bd036887", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:57:03.341797Z", + "iopub.status.busy": "2026-03-31T16:57:03.339976Z", + "iopub.status.idle": "2026-03-31T16:57:04.329684Z", + "shell.execute_reply": "2026-03-31T16:57:04.327501Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bayesian results:\n", + " label post_mean post_sd ci_lower ci_upper p_direction\n", + "1 a1 0.54961874 0.1175186 0.313269266 0.78716359 1.00000\n", + "2 a2 0.50857359 0.1627784 0.181885924 0.82368063 0.99825\n", + "3 a3 0.64366785 0.1764880 0.307735730 0.99570473 1.00000\n", + "4 b1 0.33266327 0.3742800 -0.399248515 1.06303724 0.81700\n", + "5 b2 0.11244587 0.4399472 -0.749706257 0.95412298 0.60400\n", + "6 b3 -1.44285599 0.4809651 -2.276040510 -0.43668030 0.99650\n", + "7 cp -2.44392255 1.1239139 -4.617389668 -0.19343524 0.98425\n", + "8 ind1 0.18299912 0.2141526 -0.226522334 0.62314193 0.81700\n", + "9 ind2 0.05782695 0.2343421 -0.418956426 0.53039295 0.60475\n", + "10 ind3 -0.93208354 0.4122455 -1.796118150 -0.19860541 0.99650\n", + "11 total_indirect -0.69125748 0.3596096 -1.452326094 -0.04313967 0.98200\n", + "12 total -3.13518002 1.0777603 -5.228840634 -0.98785705 0.99725\n", + "13 prop_med1 -0.07136876 0.1823917 -0.333247599 0.07797342 0.81425\n", + "14 prop_med2 -0.02078179 0.1537513 -0.233841031 0.16721585 0.60300\n", + "15 prop_med3 0.34892751 0.6273104 0.054239924 1.02292596 0.99375\n", + "16 prop_med_total 0.25677696 0.4632866 0.008644862 0.78125901 0.97925\n", + "17 diff_1_2 0.12517217 0.3360781 -0.526510756 0.79688451 0.64075\n", + "18 diff_1_3 1.11508266 0.5191710 0.168330750 2.19645250 0.99025\n", + "19 diff_2_3 0.98991049 0.5480563 -0.018103569 2.08235277 0.97100\n" + ] + } + ], + "source": [ + "# Extract posterior draws and compute CIs + P(direction)\n", + "if (!is.null(fit_bayes)) {\n", + " draws_list <- blavInspect(fit_bayes, \"mcmc\")\n", + " draws <- do.call(rbind, draws_list)\n", + "\n", + " # Map labeled params to draw column names\n", + " # blavaan draw columns may use different naming; find them via parTable\n", + " pt <- parTable(fit_bayes)\n", + " labeled_pt <- pt[pt$label != \"\" & pt$op %in% c(\"~\", \":=\"), ]\n", + "\n", + " bayes_results <- data.frame()\n", + "\n", + " for (i in 1:nrow(labeled_pt)) {\n", + " lab <- labeled_pt$label[i]\n", + "\n", + " # For defined params (:=), compute from draws\n", + " if (labeled_pt$op[i] == \":=\") {\n", + " # Try to get the column directly\n", + " if (lab %in% colnames(draws)) {\n", + " d <- draws[, lab]\n", + " } else {\n", + " # Compute from component labels\n", + " d <- NULL\n", + " }\n", + " } else {\n", + " # Regression params -- find by plabel or pxnames\n", + " plab <- labeled_pt$plabel[i]\n", + " if (plab %in% colnames(draws)) {\n", + " d <- draws[, plab]\n", + " } else if (lab %in% colnames(draws)) {\n", + " d <- draws[, lab]\n", + " } else {\n", + " d <- NULL\n", + " }\n", + " }\n", + "\n", + " if (!is.null(d) && length(d) > 0) {\n", + " bayes_results <- rbind(bayes_results, data.frame(\n", + " label = lab,\n", + " post_mean = mean(d),\n", + " post_sd = sd(d),\n", + " ci_lower = quantile(d, 0.025),\n", + " ci_upper = quantile(d, 0.975),\n", + " p_direction = max(mean(d > 0), mean(d < 0)),\n", + " ess = length(d),\n", + " stringsAsFactors = FALSE\n", + " ))\n", + " }\n", + " }\n", + "\n", + " # If defined params not in draws, compute manually\n", + " # Get a, b, cp draws\n", + " get_draw <- function(lab) {\n", + " plab <- labeled_pt$plabel[labeled_pt$label == lab]\n", + " if (length(plab) > 0 && plab[1] %in% colnames(draws)) return(draws[, plab[1]])\n", + " if (lab %in% colnames(draws)) return(draws[, lab])\n", + " return(NULL)\n", + " }\n", + "\n", + " # Compute indirect effects from draws if not already present\n", + " for (idx in 1:n_med) {\n", + " lab_ind <- paste0(\"ind\", idx)\n", + " if (!lab_ind %in% bayes_results$label) {\n", + " a_d <- get_draw(paste0(\"a\", idx))\n", + " b_d <- get_draw(paste0(\"b\", idx))\n", + " if (!is.null(a_d) && !is.null(b_d)) {\n", + " d <- a_d * b_d\n", + " bayes_results <- rbind(bayes_results, data.frame(\n", + " label = lab_ind, post_mean = mean(d), post_sd = sd(d),\n", + " ci_lower = quantile(d, 0.025), ci_upper = quantile(d, 0.975),\n", + " p_direction = max(mean(d > 0), mean(d < 0)), ess = length(d)\n", + " ))\n", + " }\n", + " }\n", + " }\n", + "\n", + " # total_indirect\n", + " if (!\"total_indirect\" %in% bayes_results$label) {\n", + " ind_draws <- lapply(1:n_med, function(idx) {\n", + " a_d <- get_draw(paste0(\"a\", idx))\n", + " b_d <- get_draw(paste0(\"b\", idx))\n", + " if (!is.null(a_d) && !is.null(b_d)) a_d * b_d else rep(0, nrow(draws))\n", + " })\n", + " ti_d <- Reduce(`+`, ind_draws)\n", + " bayes_results <- rbind(bayes_results, data.frame(\n", + " label = \"total_indirect\", post_mean = mean(ti_d), post_sd = sd(ti_d),\n", + " ci_lower = quantile(ti_d, 0.025), ci_upper = quantile(ti_d, 0.975),\n", + " p_direction = max(mean(ti_d > 0), mean(ti_d < 0)), ess = length(ti_d)\n", + " ))\n", + " }\n", + "\n", + " # total\n", + " if (!\"total\" %in% bayes_results$label) {\n", + " cp_d <- get_draw(\"cp\")\n", + " ti_d_vals <- bayes_results$post_mean[bayes_results$label == \"total_indirect\"]\n", + " # Recompute total from draws\n", + " ind_draws <- lapply(1:n_med, function(idx) {\n", + " a_d <- get_draw(paste0(\"a\", idx))\n", + " b_d <- get_draw(paste0(\"b\", idx))\n", + " if (!is.null(a_d) && !is.null(b_d)) a_d * b_d else rep(0, nrow(draws))\n", + " })\n", + " ti_d <- Reduce(`+`, ind_draws)\n", + " if (!is.null(cp_d)) {\n", + " tot_d <- cp_d + ti_d\n", + " bayes_results <- rbind(bayes_results, data.frame(\n", + " label = \"total\", post_mean = mean(tot_d), post_sd = sd(tot_d),\n", + " ci_lower = quantile(tot_d, 0.025), ci_upper = quantile(tot_d, 0.975),\n", + " p_direction = max(mean(tot_d > 0), mean(tot_d < 0)), ess = length(tot_d)\n", + " ))\n", + " }\n", + " }\n", + "\n", + " bayes_results$N <- N_total\n", + " bayes_results$method <- \"Bayesian\"\n", + " bayes_results$ci_type <- \"Credible\"\n", + " rownames(bayes_results) <- NULL\n", + "\n", + " write.csv(bayes_results, file.path(dir_bayes, \"bayesian_results.csv\"), row.names = FALSE)\n", + " cat(\"\\nBayesian results:\\n\")\n", + " print(bayes_results[, c(\"label\", \"post_mean\", \"post_sd\", \"ci_lower\", \"ci_upper\", \"p_direction\")])\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "185de794", + "metadata": {}, + "source": [ + "### Bayesian Visualizations" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "7c69c5f5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:57:04.340178Z", + "iopub.status.busy": "2026-03-31T16:57:04.338444Z", + "iopub.status.idle": "2026-03-31T16:57:12.018072Z", + "shell.execute_reply": "2026-03-31T16:57:12.015430Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trace plot: a1_draws_col = a1 \n", + "Available draw columns (first 20): a1 BLOC1S3_DLPFC_exp~msex_u BLOC1S3_DLPFC_exp~age_death_u BLOC1S3_DLPFC_exp~pmi_u BLOC1S3_DLPFC_exp~ROS_study_u a2 BLOC1S3_AC_exp~msex_u BLOC1S3_AC_exp~age_death_u BLOC1S3_AC_exp~pmi_u BLOC1S3_AC_exp~ROS_study_u a3 BLOC1S3_PCC_exp~msex_u BLOC1S3_PCC_exp~age_death_u BLOC1S3_PCC_exp~pmi_u BLOC1S3_PCC_exp~ROS_study_u b1 b2 b3 cp age_first_ad_dx_num~educ \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trace plot saved.\n" + ] + }, + { + "data": { + "image/png": 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fRsOTk5soWp729nZ+c1a9bEx8dnZ2dPnDgxIiLi9u3bCCHZrxwFU6xZW1vjqn6V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kx4eHiDmS9ImqZ37do1depU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10EMNe/eyffsKWy0AIHhAYgcCL9KyTAAAAEAk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\nLBbLkSNHmpubS0pKpI5FWDgQAAIBRuyEZTKZ1q9fX1dXl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7/ocDgA4I8//lAY5/r16wHArxoCgN1upxcD023UqJH/J91tWltbG0mwmNxyyy1j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+uZwgAoB49y389KNYd0MpNxH3BJ+pUqAO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\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5MnuRc/Ukt8vI+d78TyE6jYeQ0WR1Mc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\n7bXJZHLGj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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian plots saved.\n" + ] + }, + { + "data": { + "image/png": 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RCi0cvQ2dnZxsamcePGjx8/ZhgmPT2dntli+5+pfzf06dOHELJr1y768tmzZ25u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\nPKlUS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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "if (!is.null(fit_bayes)) {\n", + " # Posterior density plots for key params\n", + " plot_labs <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\")\n", + " plot_names <- c(paste0(\"a\", 1:n_med, \": SNP->\", c(\"DLPFC\",\"AC\",\"PCC\")),\n", + " paste0(\"b\", 1:n_med, \": \", c(\"DLPFC\",\"AC\",\"PCC\"), \"->Y\"),\n", + " \"c': SNP->Y direct\")\n", + "\n", + " density_list <- list()\n", + " for (k in seq_along(plot_labs)) {\n", + " d <- get_draw(plot_labs[k])\n", + " if (!is.null(d)) {\n", + " density_list[[k]] <- data.frame(parameter = plot_names[k], value = d)\n", + " }\n", + " }\n", + "\n", + " # Add indirect effects\n", + " for (idx in 1:n_med) {\n", + " a_d <- get_draw(paste0(\"a\", idx))\n", + " b_d <- get_draw(paste0(\"b\", idx))\n", + " if (!is.null(a_d) && !is.null(b_d)) {\n", + " nm <- c(\"DLPFC\",\"AC\",\"PCC\")[idx]\n", + " density_list[[length(density_list) + 1]] <- data.frame(\n", + " parameter = paste0(\"ind\", idx, \": \", nm, \" indirect\"),\n", + " value = a_d * b_d\n", + " )\n", + " }\n", + " }\n", + "\n", + " # Total indirect\n", + " ind_draws_all <- lapply(1:n_med, function(idx) {\n", + " a_d <- get_draw(paste0(\"a\", idx))\n", + " b_d <- get_draw(paste0(\"b\", idx))\n", + " if (!is.null(a_d) && !is.null(b_d)) a_d * b_d else rep(0, nrow(draws))\n", + " })\n", + " ti_d <- Reduce(`+`, ind_draws_all)\n", + " density_list[[length(density_list) + 1]] <- data.frame(\n", + " parameter = \"Total Indirect\", value = ti_d\n", + " )\n", + "\n", + " dens_df <- do.call(rbind, density_list)\n", + "\n", + " p_post <- ggplot(dens_df, aes(x = value, fill = parameter)) +\n", + " geom_density(alpha = 0.5, color = NA) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"red\") +\n", + " facet_wrap(~parameter, scales = \"free\", ncol = 3) +\n", + " labs(title = \"Bayesian Posterior Distributions\",\n", + " subtitle = paste0(\"Exposure: \", exposure, \" | N = \", N_total),\n", + " x = \"Parameter Value\", y = \"Density\") +\n", + " theme_minimal(base_size = 11) +\n", + " theme(legend.position = \"none\")\n", + "\n", + " ggsave(file.path(dir_bayes, paste0(\"bayesian_posteriors_\", exposure, \".png\")),\n", + " p_post, width = 14, height = 10, dpi = 150)\n", + " ggsave(file.path(dir_bayes, paste0(\"bayesian_posteriors_\", exposure, \".pdf\")),\n", + " p_post, width = 14, height = 10)\n", + " print(p_post)\n", + "\n", + " # Trace plot for a1 -- try multiple strategies to find the draws column\n", + " a1_plab <- labeled_pt$plabel[labeled_pt$label == \"a1\"]\n", + " a1_draws_col <- NULL\n", + "\n", + " # Strategy 1: try plabel\n", + " if (length(a1_plab) > 0 && a1_plab[1] %in% colnames(draws_list[[1]])) {\n", + " a1_draws_col <- a1_plab[1]\n", + " }\n", + " # Strategy 2: try label directly\n", + " if (is.null(a1_draws_col) && \"a1\" %in% colnames(draws_list[[1]])) {\n", + " a1_draws_col <- \"a1\"\n", + " }\n", + " # Strategy 3: search for column containing \"a1\" or the first regression param\n", + " if (is.null(a1_draws_col)) {\n", + " cnames <- colnames(draws_list[[1]])\n", + " # Try exact match with bet_sign prefix used by blavaan\n", + " bet_cols <- grep(\"bet_sign\\\\[1\\\\]|^a1$\", cnames, value = TRUE)\n", + " if (length(bet_cols) > 0) a1_draws_col <- bet_cols[1]\n", + " }\n", + " # Strategy 4: use the first column that corresponds to a regression param\n", + " if (is.null(a1_draws_col)) {\n", + " # blavaan internal: regression params are often the first few columns\n", + " reg_pt <- pt[pt$op == \"~\" & pt$label != \"\", ]\n", + " a1_row <- reg_pt[reg_pt$label == \"a1\", ]\n", + " if (nrow(a1_row) > 0) {\n", + " # Try pxnames if available\n", + " if (\"pxnames\" %in% names(a1_row) && !is.na(a1_row$pxnames[1])) {\n", + " pxn <- a1_row$pxnames[1]\n", + " if (pxn %in% colnames(draws_list[[1]])) a1_draws_col <- pxn\n", + " }\n", + " }\n", + " }\n", + "\n", + " cat(\"Trace plot: a1_draws_col =\", ifelse(is.null(a1_draws_col), \"NOT FOUND\", a1_draws_col), \"\\n\")\n", + " cat(\"Available draw columns (first 20):\", head(colnames(draws_list[[1]]), 20), \"\\n\")\n", + "\n", + " if (!is.null(a1_draws_col)) {\n", + " trace_list <- lapply(seq_along(draws_list), function(ch) {\n", + " data.frame(iteration = 1:nrow(draws_list[[ch]]),\n", + " value = draws_list[[ch]][, a1_draws_col],\n", + " chain = paste(\"Chain\", ch))\n", + " })\n", + " trace_df <- do.call(rbind, trace_list)\n", + " if (nrow(trace_df) > 0) {\n", + " p_trace <- ggplot(trace_df, aes(x = iteration, y = value, color = chain)) +\n", + " geom_line(alpha = 0.5) +\n", + " labs(title = \"MCMC Trace: a1 (SNP -> DLPFC)\",\n", + " subtitle = paste0(\"Exposure: \", exposure),\n", + " x = \"Iteration\", y = \"a1\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + " ggsave(file.path(dir_bayes, paste0(\"bayesian_trace_a_\", exposure, \".png\")),\n", + " p_trace, width = 8, height = 4, dpi = 150)\n", + " ggsave(file.path(dir_bayes, paste0(\"bayesian_trace_a_\", exposure, \".pdf\")),\n", + " p_trace, width = 8, height = 4)\n", + " print(p_trace)\n", + " cat(\"Trace plot saved.\\n\")\n", + " }\n", + " } else {\n", + " cat(\"WARNING: Could not find a1 draws column for trace plot.\\n\")\n", + " cat(\"All column names:\", paste(colnames(draws_list[[1]]), collapse=\", \"), \"\\n\")\n", + " }\n", + "\n", + " # Bayesian forest plot\n", + " key_bayes <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\")\n", + " bplot_bayes <- bayes_results[bayes_results$label %in% key_bayes, ]\n", + " bplot_bayes$label_pretty <- label_map[bplot_bayes$label]\n", + " bplot_bayes$label_pretty <- factor(bplot_bayes$label_pretty,\n", + " levels = rev(label_map[key_bayes]))\n", + " bplot_bayes$group <- ifelse(grepl(\"^a\", bplot_bayes$label), \"a-paths\",\n", + " ifelse(grepl(\"^b\", bplot_bayes$label), \"b-paths\",\n", + " ifelse(bplot_bayes$label == \"cp\", \"Direct\",\n", + " ifelse(grepl(\"ind|total_indirect\", bplot_bayes$label), \"Indirect\",\n", + " \"Total\"))))\n", + "\n", + " p_bayes_forest <- ggplot(bplot_bayes, aes(x = post_mean, y = label_pretty, color = group)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper), size = 0.6) +\n", + " scale_color_brewer(palette = \"Set1\") +\n", + " labs(title = \"Bayesian SEM: Parallel Mediation Path Estimates\",\n", + " subtitle = paste0(\"N = \", N_total, \" (blavaan/Stan), 2 chains x 2000 samples\"),\n", + " x = \"Posterior Mean (95% Credible Interval)\", y = NULL, color = \"Effect Type\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + " ggsave(file.path(dir_bayes, \"bayesian_forest_plot.png\"), p_bayes_forest, width = 10, height = 8, dpi = 150)\n", + " ggsave(file.path(dir_bayes, \"bayesian_forest_plot.pdf\"), p_bayes_forest, width = 10, height = 8)\n", + " print(p_bayes_forest)\n", + " cat(\"Bayesian plots saved.\\n\")\n", + "} else {\n", + " cat(\"Bayesian model was not fitted -- skipping plots.\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "067c94ec", + "metadata": {}, + "source": [ + "## 8. Cross-Method Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "d1c2a60c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:57:12.024498Z", + "iopub.status.busy": "2026-03-31T16:57:12.022796Z", + "iopub.status.idle": "2026-03-31T16:57:12.117537Z", + "shell.execute_reply": "2026-03-31T16:57:12.115100Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary table rows: 57 \n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A lavaan.data.frame: 20 \u00d7 12
labelestseci_lowerci_upperp_valueNmethodci_typen_effexposuredirection
<chr><dbl><dbl><dbl><dbl><dbl><int><chr><chr><chr><chr><chr>
1a1 0.552644890.11939278 0.31863935 0.786650433.677965e-061153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
6a2 0.514054440.16527273 0.19012584 0.837983041.868719e-031153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
11a3 0.645406690.17406663 0.30424237 0.986571012.090561e-041153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
16b1 0.366456770.37046345-0.35963824 1.092551783.225728e-011153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
17b2 0.131518960.44414825-0.73899562 1.002033557.671423e-011153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
18b3 -1.504581980.47728669-2.44004671-0.569117261.619535e-031153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
19cp -2.353905011.10585778-4.52134643-0.186463593.328916e-021153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
79ind1 0.202520460.20890890-0.20693346 0.611974383.323357e-011153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
80ind2 0.067607910.22983513-0.38286068 0.518076497.686370e-011153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
81ind3 -0.971067270.40926307-1.77320815-0.168926401.765757e-021153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
82total_indirect-0.700938910.35033952-1.38759174-0.014286074.542023e-021153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
83total -3.054843921.06282322-5.13793915-0.971748694.049590e-031153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
84prop_med1 -0.066294860.07424685-0.21181601 0.079226283.719118e-011153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
85prop_med2 -0.022131380.07580148-0.17069955 0.126436797.703134e-011153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
86prop_med3 0.317877870.17609567-0.02726330 0.663019047.105231e-021153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
87prop_med_total 0.229451630.13711999-0.03929861 0.498201869.425569e-021153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
88diff_1_2 0.134912550.32632781-0.50467821 0.774503316.792942e-011153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
89diff_1_3 1.173587730.51277592 0.16856540 2.178610072.209707e-021153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
90diff_2_3 1.038675180.54746997-0.03434624 2.111696615.779789e-021153FIML Wald N=1153 (FIML) chr19_45114770_ACCC_ACD1: SNP->M->Y
a1a1 0.552082440.12412766 0.30743688 0.797462180.000000e+001153BootstrapPercentileN=1153 (Bootstrap FIML)chr19_45114770_ACCC_ACD1: SNP->M->Y
\n" + ], + "text/latex": [ + "A lavaan.data.frame: 20 \u00d7 12\n", + "\\begin{tabular}{r|llllllllllll}\n", + " & label & est & se & ci\\_lower & ci\\_upper & p\\_value & N & method & ci\\_type & n\\_eff & exposure & direction\\\\\n", + " & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & a1 & 0.55264489 & 0.11939278 & 0.31863935 & 0.78665043 & 3.677965e-06 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t6 & a2 & 0.51405444 & 0.16527273 & 0.19012584 & 0.83798304 & 1.868719e-03 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t11 & a3 & 0.64540669 & 0.17406663 & 0.30424237 & 0.98657101 & 2.090561e-04 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t16 & b1 & 0.36645677 & 0.37046345 & -0.35963824 & 1.09255178 & 3.225728e-01 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t17 & b2 & 0.13151896 & 0.44414825 & -0.73899562 & 1.00203355 & 7.671423e-01 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t18 & b3 & -1.50458198 & 0.47728669 & -2.44004671 & -0.56911726 & 1.619535e-03 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t19 & cp & -2.35390501 & 1.10585778 & -4.52134643 & -0.18646359 & 3.328916e-02 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t79 & ind1 & 0.20252046 & 0.20890890 & -0.20693346 & 0.61197438 & 3.323357e-01 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t80 & ind2 & 0.06760791 & 0.22983513 & -0.38286068 & 0.51807649 & 7.686370e-01 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t81 & ind3 & -0.97106727 & 0.40926307 & -1.77320815 & -0.16892640 & 1.765757e-02 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t82 & total\\_indirect & -0.70093891 & 0.35033952 & -1.38759174 & -0.01428607 & 4.542023e-02 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t83 & total & -3.05484392 & 1.06282322 & -5.13793915 & -0.97174869 & 4.049590e-03 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t84 & prop\\_med1 & -0.06629486 & 0.07424685 & -0.21181601 & 0.07922628 & 3.719118e-01 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t85 & prop\\_med2 & -0.02213138 & 0.07580148 & -0.17069955 & 0.12643679 & 7.703134e-01 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t86 & prop\\_med3 & 0.31787787 & 0.17609567 & -0.02726330 & 0.66301904 & 7.105231e-02 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t87 & prop\\_med\\_total & 0.22945163 & 0.13711999 & -0.03929861 & 0.49820186 & 9.425569e-02 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t88 & diff\\_1\\_2 & 0.13491255 & 0.32632781 & -0.50467821 & 0.77450331 & 6.792942e-01 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t89 & diff\\_1\\_3 & 1.17358773 & 0.51277592 & 0.16856540 & 2.17861007 & 2.209707e-02 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\t90 & diff\\_2\\_3 & 1.03867518 & 0.54746997 & -0.03434624 & 2.11169661 & 5.779789e-02 & 1153 & FIML & Wald & N=1153 (FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\ta1 & a1 & 0.55208244 & 0.12412766 & 0.30743688 & 0.79746218 & 0.000000e+00 & 1153 & Bootstrap & Percentile & N=1153 (Bootstrap FIML) & chr19\\_45114770\\_ACCC\\_AC & D1: SNP->M->Y\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A lavaan.data.frame: 20 \u00d7 12\n", + "\n", + "| | label <chr> | est <dbl> | se <dbl> | ci_lower <dbl> | ci_upper <dbl> | p_value <dbl> | N <int> | method <chr> | ci_type <chr> | n_eff <chr> | exposure <chr> | direction <chr> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | a1 | 0.55264489 | 0.11939278 | 0.31863935 | 0.78665043 | 3.677965e-06 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 6 | a2 | 0.51405444 | 0.16527273 | 0.19012584 | 0.83798304 | 1.868719e-03 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 11 | a3 | 0.64540669 | 0.17406663 | 0.30424237 | 0.98657101 | 2.090561e-04 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 16 | b1 | 0.36645677 | 0.37046345 | -0.35963824 | 1.09255178 | 3.225728e-01 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 17 | b2 | 0.13151896 | 0.44414825 | -0.73899562 | 1.00203355 | 7.671423e-01 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 18 | b3 | -1.50458198 | 0.47728669 | -2.44004671 | -0.56911726 | 1.619535e-03 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 19 | cp | -2.35390501 | 1.10585778 | -4.52134643 | -0.18646359 | 3.328916e-02 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 79 | ind1 | 0.20252046 | 0.20890890 | -0.20693346 | 0.61197438 | 3.323357e-01 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 80 | ind2 | 0.06760791 | 0.22983513 | -0.38286068 | 0.51807649 | 7.686370e-01 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 81 | ind3 | -0.97106727 | 0.40926307 | -1.77320815 | -0.16892640 | 1.765757e-02 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 82 | total_indirect | -0.70093891 | 0.35033952 | -1.38759174 | -0.01428607 | 4.542023e-02 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 83 | total | -3.05484392 | 1.06282322 | -5.13793915 | -0.97174869 | 4.049590e-03 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 84 | prop_med1 | -0.06629486 | 0.07424685 | -0.21181601 | 0.07922628 | 3.719118e-01 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 85 | prop_med2 | -0.02213138 | 0.07580148 | -0.17069955 | 0.12643679 | 7.703134e-01 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 86 | prop_med3 | 0.31787787 | 0.17609567 | -0.02726330 | 0.66301904 | 7.105231e-02 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 87 | prop_med_total | 0.22945163 | 0.13711999 | -0.03929861 | 0.49820186 | 9.425569e-02 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 88 | diff_1_2 | 0.13491255 | 0.32632781 | -0.50467821 | 0.77450331 | 6.792942e-01 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 89 | diff_1_3 | 1.17358773 | 0.51277592 | 0.16856540 | 2.17861007 | 2.209707e-02 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| 90 | diff_2_3 | 1.03867518 | 0.54746997 | -0.03434624 | 2.11169661 | 5.779789e-02 | 1153 | FIML | Wald | N=1153 (FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "| a1 | a1 | 0.55208244 | 0.12412766 | 0.30743688 | 0.79746218 | 0.000000e+00 | 1153 | Bootstrap | Percentile | N=1153 (Bootstrap FIML) | chr19_45114770_ACCC_AC | D1: SNP->M->Y |\n", + "\n" + ], + "text/plain": [ + " label est se ci_lower ci_upper p_value \n", + "1 a1 0.55264489 0.11939278 0.31863935 0.78665043 3.677965e-06\n", + "6 a2 0.51405444 0.16527273 0.19012584 0.83798304 1.868719e-03\n", + "11 a3 0.64540669 0.17406663 0.30424237 0.98657101 2.090561e-04\n", + "16 b1 0.36645677 0.37046345 -0.35963824 1.09255178 3.225728e-01\n", + "17 b2 0.13151896 0.44414825 -0.73899562 1.00203355 7.671423e-01\n", + "18 b3 -1.50458198 0.47728669 -2.44004671 -0.56911726 1.619535e-03\n", + "19 cp -2.35390501 1.10585778 -4.52134643 -0.18646359 3.328916e-02\n", + "79 ind1 0.20252046 0.20890890 -0.20693346 0.61197438 3.323357e-01\n", + "80 ind2 0.06760791 0.22983513 -0.38286068 0.51807649 7.686370e-01\n", + "81 ind3 -0.97106727 0.40926307 -1.77320815 -0.16892640 1.765757e-02\n", + "82 total_indirect -0.70093891 0.35033952 -1.38759174 -0.01428607 4.542023e-02\n", + "83 total -3.05484392 1.06282322 -5.13793915 -0.97174869 4.049590e-03\n", + "84 prop_med1 -0.06629486 0.07424685 -0.21181601 0.07922628 3.719118e-01\n", + "85 prop_med2 -0.02213138 0.07580148 -0.17069955 0.12643679 7.703134e-01\n", + "86 prop_med3 0.31787787 0.17609567 -0.02726330 0.66301904 7.105231e-02\n", + "87 prop_med_total 0.22945163 0.13711999 -0.03929861 0.49820186 9.425569e-02\n", + "88 diff_1_2 0.13491255 0.32632781 -0.50467821 0.77450331 6.792942e-01\n", + "89 diff_1_3 1.17358773 0.51277592 0.16856540 2.17861007 2.209707e-02\n", + "90 diff_2_3 1.03867518 0.54746997 -0.03434624 2.11169661 5.779789e-02\n", + "a1 a1 0.55208244 0.12412766 0.30743688 0.79746218 0.000000e+00\n", + " N method ci_type n_eff exposure \n", + "1 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "6 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "11 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "16 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "17 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "18 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "19 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "79 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "80 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "81 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "82 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "83 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "84 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "85 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "86 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "87 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "88 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "89 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "90 1153 FIML Wald N=1153 (FIML) chr19_45114770_ACCC_AC\n", + "a1 1153 Bootstrap Percentile N=1153 (Bootstrap FIML) chr19_45114770_ACCC_AC\n", + " direction \n", + "1 D1: SNP->M->Y\n", + "6 D1: SNP->M->Y\n", + "11 D1: SNP->M->Y\n", + "16 D1: SNP->M->Y\n", + "17 D1: SNP->M->Y\n", + "18 D1: SNP->M->Y\n", + "19 D1: SNP->M->Y\n", + "79 D1: SNP->M->Y\n", + "80 D1: SNP->M->Y\n", + "81 D1: SNP->M->Y\n", + "82 D1: SNP->M->Y\n", + "83 D1: SNP->M->Y\n", + "84 D1: SNP->M->Y\n", + "85 D1: SNP->M->Y\n", + "86 D1: SNP->M->Y\n", + "87 D1: SNP->M->Y\n", + "88 D1: SNP->M->Y\n", + "89 D1: SNP->M->Y\n", + "90 D1: SNP->M->Y\n", + "a1 D1: SNP->M->Y" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Build unified summary\n", + "summary_all <- data.frame()\n", + "\n", + "# FIML\n", + "fiml_sub <- fiml_res[, c(\"label\", \"est\", \"se\", \"ci.lower\", \"ci.upper\", \"pvalue\", \"N\", \"method\", \"ci_type\")]\n", + "names(fiml_sub) <- c(\"label\", \"est\", \"se\", \"ci_lower\", \"ci_upper\", \"p_value\", \"N\", \"method\", \"ci_type\")\n", + "fiml_sub$n_eff <- paste0(\"N=\", N_fiml, \" (FIML)\")\n", + "summary_all <- rbind(summary_all, fiml_sub)\n", + "\n", + "# Bootstrap\n", + "boot_sub <- boot_summary[, c(\"label\", \"boot_mean\", \"boot_se\", \"ci_lower\", \"ci_upper\", \"p_value\", \"N\", \"method\", \"ci_type\")]\n", + "names(boot_sub) <- c(\"label\", \"est\", \"se\", \"ci_lower\", \"ci_upper\", \"p_value\", \"N\", \"method\", \"ci_type\")\n", + "boot_sub$n_eff <- paste0(\"N=\", N_total, \" (Bootstrap FIML)\")\n", + "summary_all <- rbind(summary_all, boot_sub)\n", + "\n", + "# Bayesian\n", + "if (exists(\"bayes_results\") && nrow(bayes_results) > 0) {\n", + " bayes_sub <- bayes_results[, c(\"label\", \"post_mean\", \"post_sd\", \"ci_lower\", \"ci_upper\", \"p_direction\", \"N\", \"method\", \"ci_type\")]\n", + " names(bayes_sub) <- c(\"label\", \"est\", \"se\", \"ci_lower\", \"ci_upper\", \"p_value\", \"N\", \"method\", \"ci_type\")\n", + " bayes_sub$n_eff <- paste0(\"N=\", N_total, \" (Bayesian)\")\n", + " # Note: p_value here is P(direction) for Bayesian -- interpret differently\n", + " summary_all <- rbind(summary_all, bayes_sub)\n", + "}\n", + "\n", + "summary_all$exposure <- exposure\n", + "summary_all$direction <- \"D1: SNP->M->Y\"\n", + "\n", + "write.csv(summary_all, file.path(dir_summ, \"all_methods_summary.csv\"), row.names = FALSE)\n", + "cat(\"Summary table rows:\", nrow(summary_all), \"\\n\")\n", + "head(summary_all, 20)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "276e8c23", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:57:12.123620Z", + "iopub.status.busy": "2026-03-31T16:57:12.121933Z", + "iopub.status.idle": "2026-03-31T16:57:12.184525Z", + "shell.execute_reply": "2026-03-31T16:57:12.182327Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Estimates [95% CI] by Method ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label formatted.FIML formatted.Bootstrap\n", + " a1 0.5526 [0.3186, 0.7867] 0.5521 [0.3074, 0.7975]\n", + " a2 0.5141 [0.1901, 0.8380] 0.5054 [0.1184, 0.8631]\n", + " a3 0.6454 [0.3042, 0.9866] 0.6505 [0.2937, 1.0197]\n", + " b1 0.3665 [-0.3596, 1.0926] 0.3013 [-0.6082, 1.1529]\n", + " b2 0.1315 [-0.7390, 1.0020] 0.0523 [-1.4290, 1.4519]\n", + " b3 -1.5046 [-2.4400, -0.5691] -1.2945 [-2.7253, 1.1957]\n", + " cp -2.3539 [-4.5213, -0.1865] -2.4528 [-5.3220, 0.2452]\n", + " ind1 0.2025 [-0.2069, 0.6120] 0.1699 [-0.3194, 0.7007]\n", + " ind2 0.0676 [-0.3829, 0.5181] 0.0386 [-0.7242, 0.9018]\n", + " ind3 -0.9711 [-1.7732, -0.1689] -0.8501 [-2.1925, 0.7511]\n", + " total_indirect -0.7009 [-1.3876, -0.0143] -0.6416 [-1.5823, 0.2557]\n", + " total -3.0548 [-5.1379, -0.9717] -3.0945 [-5.6735, -0.5868]\n", + " formatted.Bayesian\n", + " 0.5496 [0.3133, 0.7872]\n", + " 0.5086 [0.1819, 0.8237]\n", + " 0.6437 [0.3077, 0.9957]\n", + " 0.3327 [-0.3992, 1.0630]\n", + " 0.1124 [-0.7497, 0.9541]\n", + " -1.4429 [-2.2760, -0.4367]\n", + " -2.4439 [-4.6174, -0.1934]\n", + " 0.1830 [-0.2265, 0.6231]\n", + " 0.0578 [-0.4190, 0.5304]\n", + " -0.9321 [-1.7961, -0.1986]\n", + " -0.6913 [-1.4523, -0.0431]\n", + " -3.1352 [-5.2288, -0.9879]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== P-values by Method ===\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label p_fmt.FIML p_fmt.Bootstrap p_fmt.Bayesian\n", + " a1 <0.001 <0.001 1.0000\n", + " a2 0.0019 0.0060 0.9982\n", + " a3 <0.001 <0.001 1.0000\n", + " b1 0.3226 0.4960 0.8170\n", + " b2 0.7671 0.9180 0.6040\n", + " b3 0.0016 0.2600 0.9965\n", + " cp 0.0333 0.0800 0.9842\n", + " ind1 0.3323 0.4960 0.8170\n", + " ind2 0.7686 0.9240 0.6048\n", + " ind3 0.0177 0.2600 0.9965\n", + " total_indirect 0.0454 0.1520 0.9820\n", + " total 0.0040 0.0180 0.9972\n" + ] + } + ], + "source": [ + "# Display table: wide format per method for key effects\n", + "key_effects <- c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\")\n", + "\n", + "display_df <- summary_all[summary_all$label %in% key_effects, ]\n", + "display_df$formatted <- sprintf(\"%.4f [%.4f, %.4f]\", display_df$est, display_df$ci_lower, display_df$ci_upper)\n", + "display_df$p_fmt <- ifelse(display_df$p_value < 0.001, \"<0.001\",\n", + " ifelse(is.na(display_df$p_value), \"NA\",\n", + " sprintf(\"%.4f\", display_df$p_value)))\n", + "\n", + "wide_est <- reshape(display_df[, c(\"label\", \"method\", \"formatted\")],\n", + " idvar = \"label\", timevar = \"method\", direction = \"wide\")\n", + "wide_p <- reshape(display_df[, c(\"label\", \"method\", \"p_fmt\")],\n", + " idvar = \"label\", timevar = \"method\", direction = \"wide\")\n", + "\n", + "# Reorder\n", + "wide_est$label <- factor(wide_est$label, levels = key_effects)\n", + "wide_est <- wide_est[order(wide_est$label), ]\n", + "\n", + "cat(\"\\n=== Estimates [95% CI] by Method ===\\n\")\n", + "print(wide_est, row.names = FALSE)\n", + "\n", + "cat(\"\\n=== P-values by Method ===\\n\")\n", + "wide_p$label <- factor(wide_p$label, levels = key_effects)\n", + "wide_p <- wide_p[order(wide_p$label), ]\n", + "print(wide_p, row.names = FALSE)\n", + "\n", + "write.csv(wide_est, file.path(dir_summ, paste0(\"summary_display_table_\", exposure, \".csv\")), row.names = FALSE)\n" + ] + }, + { + "cell_type": "markdown", + "id": "fc1ffac2", + "metadata": {}, + "source": [ + "### Summary Visualizations" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "226b71f8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:57:12.190749Z", + "iopub.status.busy": "2026-03-31T16:57:12.189054Z", + "iopub.status.idle": "2026-03-31T16:57:13.434952Z", + "shell.execute_reply": "2026-03-31T16:57:13.432308Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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5WVlSRJ1tVG0Nwv+P6W1FoQQkuWLKlrkfPnzyOEtmzZIjlRTlVcvXoVIdSvXz+F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\nN50ex97e/sSJEzdv3oyLi3v+/Lmbm1ufPn24K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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# E. All Methods Forest Plot\n", + "key_summ <- summary_all[summary_all$label %in% key_effects, ]\n", + "key_summ$label_pretty <- label_map[key_summ$label]\n", + "key_summ$label_pretty <- factor(key_summ$label_pretty, levels = rev(label_map[key_effects]))\n", + "\n", + "p_all_forest <- ggplot(key_summ, aes(x = est, y = label_pretty, color = method, shape = method)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper),\n", + " position = position_dodge(width = 0.6), size = 0.5) +\n", + " scale_color_brewer(palette = \"Dark2\") +\n", + " labs(title = \"All Methods Comparison: Parallel Mediation Path Estimates\",\n", + " subtitle = paste0(\"SNP -> BLOC1S3 (DLPFC/AC/PCC) -> age_first_ad_dx_num | N = \", N_total),\n", + " x = \"Estimate (95% CI)\", y = NULL, color = \"Method\", shape = \"Method\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + "ggsave(file.path(dir_summ, \"summary_forest_all_methods.png\"), p_all_forest, width = 11, height = 9, dpi = 150)\n", + "ggsave(file.path(dir_summ, \"summary_forest_all_methods.pdf\"), p_all_forest, width = 11, height = 9)\n", + "print(p_all_forest)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "08e2455a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:57:13.440954Z", + "iopub.status.busy": "2026-03-31T16:57:13.439289Z", + "iopub.status.idle": "2026-03-31T16:57:16.851198Z", + "shell.execute_reply": "2026-03-31T16:57:16.848539Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in RColorBrewer::brewer.pal(n, pal):\n", + "\u201cn too large, allowed maximum for palette Set2 is 8\n", + "Returning the palette you asked for with that many colors\n", + "\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in RColorBrewer::brewer.pal(n, pal):\n", + "\u201cn too large, allowed maximum for palette Set2 is 8\n", + "Returning the palette you asked for with that many colors\n", + "\u201d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in RColorBrewer::brewer.pal(n, pal):\n", + "\u201cn too large, allowed maximum for palette Set2 is 8\n", + "Returning the palette you asked for with that many colors\n", + "\u201d\n" + ] + }, + { + "data": { + "image/png": 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rhx4379+tGHLi4u169f//rrr9kMixcvNjAwoP/TmZuZmZkVVkO9MWPG5Ofnf/bZ\nZzKZjBCSmpq6YsUKQgg7HUG9JUuWSKXS77//XnVTUVERIWTy5MkGBgY9evQoKipauHDhwIED\nFYc8a0ZNoYpWrVrl5+fXrVs3TbZKpVKxWGxiYqKUzdTUtLS0VJNa5efnR0VFDRs2zM7OjhDS\nv39/FxeXXbt2KWawsLBQM8RYF9m0Vm0AAKgMljv5Hzogp5TYqFEj9n9vb+8vvvhi4cKFfD7/\n3r17fP5/us7T05P939nZmRDy6tUrQkhKSkq7du0Uc3p4eBBCUlNTCSEbNmwYPHhw8+bNe/To\nERAQMHjwYKXM6rm7u7P/P336VCKRKDaBBk8VrrihJDk5uU2bNoopHTp0qKwg+qWrGDApblVv\nzpw50dHRmzdv/uOPPxo2bJiamrpu3bqFCxeqhjiqrl27tm3btqNHj1a45sXEiRN9fHyGDh1q\na2tLUz7++ONNmzYdOHBg0qRJGlavuoWyLl68+OjRo8jISA238ng8DodDnyBFUqlU8eSsGnv3\n7hWJRNOmTaMPuVzulClTVq1ade/ePfrcWVhY0HEv9TF37WbTWrUBAKAyCOz+Z8CAAXTOphpN\nmjSRyWTW1tb16tVT2qQ4xsDj8QghEomEECIWi7nc/wyL0m9uurVLly5Pnz7dv3//yZMn165d\nu2zZsoEDBx48eFDD9brMzMzY/yUSib29PR0sZAUFBSlFbBUSi8XqM9AWaVKNKo8TGRl55cqV\n27dvm5ubDx48uLi4mBCiuC5JhUQi0YwZM0aPHq3UQFavXr169eqlmBIWFrZp06bLly/XOLCr\nslDWnj17LCwshg0bpuFWDodja2ubl5enmE0ikdBVYDSpGz0pvHjx4s8++4ym0KG+8PDwX375\nhRDSqFEjsVicmJjYvHlzNcep3WxaqzYAAFQGgZ2msrKyFi5cOGnSpH/++Wf27Nn04jBWbm4u\nOy2ALlRGh47s7e1fv36tlJMQwn5/29razp8/f/78+UKhMDw8fMGCBWvXrl2/fr3qiAU921gZ\nBwcHIyOjKmPTCtWrV0+pknVKMQijp+GUBghVnT59OiEhQS6Xe3t70xTajSNGjHBycjpz5ozq\nLnQU8G1OxWpYKD3f3b9//woH2yrb2qpVqydPniimPHnyRC6Xt27dusqKxcTEPHnyZODAgUr9\nduzYsQMHDmzYsMHExGTw4MF79uzZtWvXDz/8oLS7TCabP3/++PHje/ToUbvZtIiTGgQAACAA\nSURBVFZt9QUBAHzIENhpaubMmWZmZlu2bDl37lxwcPD+/fvHjx/Pbr169So7Nnbr1i1CCH3Y\nvXv3yMjIjIwMFxcXuvXy5cuEkG7dupWVlR07dqxLly5NmjQhhBgbG3/00Uc//vgjnXtLh8EK\nCgqsra3pjuqnjvr4+Ozbt+/Zs2fsSeHXr19fvnx50KBBFhYW6ptGl8ZVrGT//v2NjIz++uuv\nanSQBv7666+dO3fu3buXNkoul2/fvt3FxUX1JLiSZs2a0WkcrLt376alpfn7+9PT5cOHDxeL\nxadOnWIznD17lhBSrVPb1S2Uio+PLygoqOymEZVtHTx4cFhYGHsKkhBy4MABDoczZMiQKiu2\nc+dODofzyy+/uLq6KqY3adJkypQpR44cmThx4tChQ5s1a7Zp06aAgAB/f3/FbGFhYdu2bevY\nsWOPHj1qN5vWql1lFwEAfLh0O3fjXUBnxX788cePKpKWlsYwzG+//UYIofNVGYYJDAy0sbF5\n9eoV8++s2ObNm585c0YoFD569MjLy8vMzCwnJ4dhmJs3b3K5XD8/v5SUFKFQeOHCBUdHRw8P\nj/LycpFI1KBBA29v7zt37giFwsLCwl27dnG5XLraXFRUFCFk9erVtMR9+/bVr1+f/DsrVnXd\n4AcPHhgYGHTs2DEuLk4sFsfHx/fu3dvCwiI7O5thmJKSksmTJytOP1R069YtLpfr4+Nz//79\nzMxMOqFh8+bNFRa0b98+8u+6sqpbS0pKkpOTk5OTL1y4QAhZtGgRfUhnQcbGxnI4nMGDB8fH\nxyclJdGTpAcOHKjBs6Y0QfWrr74ihEyaNOnevXvJycnh4eG2trYNGzaki9yqqZX6CqsvlDpw\n4AD573xkTbYWFhY2bNiwadOmFy9efPHixdatWw0MDCZNmlRlwwsKCkxMTPr27au6qayszMLC\nolevXvThvXv36tWrx+fzZ82aderUqZs3bx48eNDX15cQMnXqVHav2s2mtWoDAECFENj9L7Cr\nTGBg4MuXL62trUeNGsXukpGRYWFhQRero4Hd8ePHnZyc6PlTa2trxS/yiIgIGpPRrd27d6dL\nkDAMc/v2bXozA8rIyGjWrFn04nGpVBoSEkIIcXJysre3HzhwIF3L98GDB0wlN4Q4depU48aN\n2aM1b96cXV/jzZs3hBA1S2kcOnTI3t6ercayZctoenUDO7pV1eLFi2mG7du3s6vNWVpabt26\ntXrP1r+UYiyZTPb555+bm5uzJfbo0SMxMbHKWlVZYTWFUvSkYXR0dIX1VLM1Li6Offa5XG5I\nSEhZWVmVDaeLJFcWDdM510+fPqUP09LSJkyYoHgBqJeX144dO5T2qt1sWqs2AACo4jAq92r8\n0BQWFsbFxVW2tV69elZWVikpKW3btrWxsWHTnzx5kpOT07Vr19WrV69ZsyY7O9vOzi4+Pl4s\nFrdu3VppmqdMJnvy5ElJSYmLiwt7upOVlpaWnZ1tamrq5uamGJoQQrKystLS0ho0aODq6pqX\nl/fo0aOOHTtaWFikpaWlpqZ27dpVaWEIhmESExMLCwvr16+vOFlVIpFcu3bN3t5eMY5UIpVK\nHz9+LJVKvby82LO3qgW9fv06ISGhXbt21tbWlW1VPbiLiwudDkwIEQgET58+lclkLVu2rPHC\nFllZWUlJSV26dFHs6vLy8ufPnxcXFzdu3FhxJWQ1tTI3N6+ywuoLTU9Pf/78OX1eVI+jfish\nhD5f7u7uDg4Oalv8P/SF161btwoXVaYtbdasmeLSNuXl5WlpaUVFRc7Ozo6OjpVNOK3dbFqr\nNgAAKEJg97aWLVu2Zs2arKysylaJAwAAANAOLFAMAAAAoCcwKxZ0TywWs/efrVCfPn3o9Ai9\n9552xXtabQAA/YPA7m2Fhob269ePveEB1ACPxwsODlaTwcvLS2uV0a33tCve02oDAOgfXGMH\nAAAAoCdwjR0AAACAnkBgBwAAAKAnENgBAAAA6AkEdgAAAAB6AoEdAAAAgJ5AYAcAAACgJxDY\nAQAAAOgJBHYAAAAAegKBHQAAAICeQGAHAAAAoCcQ2AEAAADoCQR2AAAAAHoCgR0AAACAnkBg\nBwAAAKAnENgBAAAA6AkEdgAAAAB6AoEdAAAAgJ5AYAf678iRI59++qkmORMSEiZNmlRSUlLX\nVaotmZmZEyZMeP36ta4rAvDeW7Zs2R9//FGnRXzzzTe//fabJjmPHDmybNmyOq2MkosXLy5c\nuFAmk2mzUKgLCOxAb927d4/+8+rVqwcPHlSZXyAQzJw5c8iQIRYWFoSQkpKS8PDwefPmhYSE\nzJ49e+fOnaWlpTTn3bt3g4ODlb4Dbty4MX36dMUMrBkzZmzatKm4uLi6TaDH+fnnn5XSL126\nFBwcfPDgQUdHxw4dOsyePVsul1f34ACgKD4+Pj09vdYPW1JS8vTpU/r/kydPXrx4UeUuiYmJ\nX3/99aRJkwghe/fuDQ4OvnDhglKeLVu2BAcHP3z4UPOapKWlBQcH37p1S6l6EyZM+PPPP319\nfbOysr7//nvNDwjvJgR2oJ9SU1OnTZtWrV3Cw8NNTU1Hjx5NCBEIBIGBgVFRUV27dp0wYULn\nzp1///33oKAgqVRKCCkoKLh9+/aKFSsUvwby8/Pv3LlD/y8oKLhx40ZgYOC4cePGjRvn7e19\n/PjxgQMHsqGhoocPH1YWlhUUFMTFxf3yyy/l5eWK6bt3775//z4t/aOPPnrx4sWff/5ZrcYC\ngHacOHHixx9/rNYuK1asGDVqlJeXFyHkxYsXcXFxO3fuVMwgFot//fXXu3fvFhUVKe0rk8ni\n4+MrPGzjxo09PDw+/fRTkUjEJq5evTotLW3AgAFcLnfFihW//vprRkZGtWoL7xq+risAQG7d\nunX48OHXr187ODhMmDChY8eOqnn++OOPFy9e9OrVa//+/TKZzM/Pb/z48ZXtHhcXt3jx4oKC\nguDg4EWLFhFCeDxefHz8jh07CgsLu3btOnPmTD7/Py9+qVS6ffv21atX04exsbFJSUlPnjyx\nsrKiKQMGDPjmm29evnzp6upKCLG3t+/QocOSJUsOHDhQWbsGDx5sa2tL/x89enS7du1Onz49\natQopWybN2+Oj4+fMmXK2LFj2eJYlpaW9evXP3nyZHBwME15/fr19evX27dvTx8aGhpOnTp1\n69atI0eOVNvNAFAFDodz6NCh06dPW1lZjR492sfHRzXPF1984e3tXVZWdv78eWtr67Fjx3bv\n3p1uioyMvHDhQllZmYeHx8yZMx0dHf/444+ffvpJLBYHBweHh4cTQrhc7smTJ6Oiong83vDh\nwwcPHqx0/EePHsXExCjGgl27do2JicnIyHBxcaEpZ86ccXV1ffbsmWr1GIaZMGFC48aNp02b\nNnjwYKUPuuXLl/fp02fjxo1ffPEFIeTmzZsHDx48evSosbExIaRFixZdu3bdsWPHqlWratyH\noHMYsQMdu3fv3ujRoz09PefMmePi4hIUFJSUlKSa7dWrV1FRUVFRUYsWLRo7duy33367devW\nynb38PAYNGiQhYXF8uXLW7VqRQgpKipat27dwIEDAwICvvvuu/379ysd/+7du8XFxX5+fvSh\npaUlIeT27dtsBkdHx+3bt9OojhAiFotXr1597969qKgoTZppY2NjY2OTm5urumnnzp0//PDD\nrVu3OnXqFBYWlpCQoLhVKpUGBgYePHiQTYmIiOjdu7eJiQmb0qdPn8TERE2qAQBqnDp16ubN\nm+PHj7ezswsJCVH8BGAlJCSsW7eutLR02bJlbdu2HTduHM3266+/rlq1qn///jNmzMjMzAwO\nDhaLxf369WvZsmWrVq2WL19ubm5OCImJibl48eKECRNcXFxmzZql+s69cOFC8+bNGzZsyKaY\nmZn16tXr8OHDbMqhQ4cCAwMlEolq9fh8/u3btydOnLh9+/ZOnTpt3LgxJyeH3Wpubv7999//\n+uuv8fHxYrE4LCxs+vTp3t7ebIa+ffuqnvaF9wsCO9AxGxub7du3z50718fH55NPPnFycrp0\n6VKFOQsLC9esWdOsWbN+/fpNnDiRDpVVuLuFhYWLiwufz2/bti0dA8vOzv75558HDhw4fvz4\nAQMGsOdMWXFxca6urtbW1vRhhw4dZsyYERoaGhQUtGHDhujoaKWToXK53MHBYdmyZStWrMjP\nz6+ymTExMW/evOnQoUOFW3v06LF79+7z58+bmZmNHDkyKCiIvShHLpcHBQXduXPn+fPnNCUi\nImLMmDGKZ29btmxpaGhYZR0AQD0+n//TTz/179//66+/bt68eURERIXZHB0d586d6+XlFRoa\n2q5dO5qtY8eOu3btGjlyZK9evVauXJmWlpaQkNCgQQMbGxsrK6u2bdvSwTM+n79hw4Y+ffp8\n9dVXdnZ27KXArPv37yt9UDAMM2bMmMOHD9N3fVZW1s2bN0eOHFnZJRwGBgbBwcGnTp3auXPn\n06dPu3fv/tFHHwkEArq1d+/ewcHBixcv/uGHHxiGCQsLU9y3Q4cOaWlpNek7eGfgVCzomJub\n27Nnz5YuXfrmzRupVJqfn0+vGvnkk09evXpFCGndujWdHebu7k5/8hJCPDw80tLSZDJZZbsr\nady4MXtW1MrKKjs7WylDbm5uvXr1FFNWrFgxadKkf/75JzY29vfffxcIBKGhoZ9//jmX+/8/\nh8aPH3/06NFvvvlm06ZNqoV+9NFH9KM8Ly8vPj5+wYIFXbp0qbBpVKNGjb7++utu3botWLDg\n0aNH9AobQoi9vX2/fv0OHTr05ZdfxsbGlpSU9O3bd+/eveyOHA6HbR0A1FinTp3Y/5s3b/78\n+fOioqKZM2fSlDFjxtALHlq3bs1m8/DwoD+62rRpExkZGRERkZ+fTy/GrXC+VLt27dj/rays\nVOfg5+bmtmzZUikxICDgiy++iI6O9vPzi4iI6Nu3r52dHd1UYQ0pb2/vNm3a/Prrr999993S\npUtNTU1p+ooVK3x9fbdt23bs2DF6EpbFHhbeXwjsQMe2bdu2adOmZcuWjR49ms/nz5s3j6b7\n+PjQEM3R0ZGmKF4sQgeoGIapbHclBgYGig8ZhlHKIBQKlT7gCCHu7u6zZs2aNWuWXC4/derU\nvHnz3N3dx4wZw2bgcDjfffedv79/UFCQaqF+fn5mZmaEEGtr61atWjVq1KiyphFCysvLo6Ki\ndu/eXVpaOm/evH79+ikeaty4cZ999llYWNjhw4eDgoKUrpshhCiemQWAmmFDH0KIiYmJXC43\nNDQcMGAATfHw8KD/KH0W0ZGz6dOnp6enf/nll46OjiKR6Pz58xUWoTS4ruFnEZ/PpxPh/fz8\nDh8+rHgNXIU1JITk5OTs3bt33759zs7OP/30k4ODA7vJwsJi2LBhMTExiidh2VZXWG14jyCw\nAx07fvz41KlTJ0yYQAiRy+UFBQU0XXUqgOJqbTk5Oba2tnw+v7Ldq8vGxkZx32vXrkkkEl9f\nX/qQy+UOGTJky5Ytjx49UgzsCCGenp7z589fsmSJ0hkNQkhQUFCFA2lKTXv16tWePXsOHDjQ\nokWLTz/9NCAggMfjKe3i5+fH4XAuXLjw999///3336rH1OR0MACop/ghk5ub6+DgYGJiMnXq\nVKVsikP+b968cXBwKCkpuXjx4qFDh3r27EkIeZtrXpU+i1jjxo3z9/e/dOmSUChkP5oIIao1\njIuL27Vr17lz5wICAvbs2cNOtFLE4XAqLB2fJHoA19iBjvH5fPZT7Ntvv2UYRigUVpgzLy/v\n9OnThBCpVPrXX3/16tVLze58Pl8oFKr+Gq5Mo0aN0tPT2WtWbt++PXv27OjoaPqQYZjTp08n\nJiYqnkZhzZ8/39DQcPv27RqWpWTx4sWlpaXHjh2LjIwcNGiQalRHCOHxeKNHj/7222+9vLya\nNGmitLWoqKgGi+QBgJJLly7RyyRycnKuXr1KP2RUXb58mc127dq1Xr168fl8LpdLP4uEQuGG\nDRt4PB79LDIwMGCvb9NEo0aNKrzKrUmTJm3atPnmm29GjRpV4acEJRaLZ8yY0aRJk+vXr//8\n888VRnVqpKWlqY4XwvsFI3agY3PmzJk/f35SUlJRUdHw4cNDQkL27Nlja2s7f/58pZxt2rQ5\ndOjQli1b8vLyuFwuXba3st0HDx4sEol69eo1a9YsTapBT48+fvyYXj0zf/78kpISuiqKra1t\nXl4ej8dbtGhRhadcDQwMvvvuu5EjRypdpaehP/74Q/XUqqqxY8du2bJl/fr1qpuuXbumeAoJ\nAGpALpcPGTJk0qRJFhYWycnJHTt2ZNcYUuLv7z9lyhQzMzM2m5GR0YwZMxYvXrx///6srKyV\nK1e+fv162bJlXC63S5cuixYt6tevX4VX4qry8fFZunSpXC5XvJyXGjdu3Keffrp79241uxsa\nGt68eVNN5KfetWvXunbtWrN94V3BAOhabm7u3bt3c3JyGIaRyWRxcXG5ublKedatWzdkyBCG\nYZKTk588eSKRSKrcPSsr68GDByUlJRkZGXFxcWz+lJSUhIQE1WqMGTNm+fLliilCoTApKYnO\nSFUsMT8/PzY2Vmn3+/fv3759m81w/fp1xV1qRqmgO3fuCAQC+v+TJ0/S0tLo/1OmTAkLC3vL\nsgA+cA8fPszMzJRIJHFxcS9evKgsW2Bg4Nq1ayUSyYMHD5SyvXjx4v79+2VlZQzDlJSUxMXF\n0TdsUlJSQkKCRCJ5/Phxamoqm//+/fuvXr1SOn5JSUmLFi1OnTrFHjMxMZH+X15ezn7IMAxz\n48aNwsLCmjWWrnuslCgUCtu2bXvs2LGaHRPeERxG43NVADq0fv36q1evVnh5WW25e/fu2LFj\nY2Ji6tevX3el1LonT54MHTo0Ojra2dlZ13UB0H/Dhw/v3Lnzl19+WXdFbN269cSJE6dPn1Yd\ntKtTu3bt2r9///nz57VcLtQuPHkA/9OxY8fQ0NAFCxa8R/ddFYlE8+fPX7lyJaI6AL0xe/Zs\nc3Pzn376SZuFpqSk/PTTT1u2bEFU977DiB0AAACAnkBgDgAAAKAnENgBAAAA6AkEdgAAAAB6\nAoEdAAAAgJ5AYAcAAACgJxDYAQAAAOgJBHbVk5+fr6t7JAuFwmrdcLAWFRYW5uXl6aRokUhU\nVlamk6KLioroHSy0X7REIikpKdF+uYSQkpKS3NxcnazkJ5VKdXXH29LS0tzcXKlUqpPSa1eF\n94+va0KhMDc3t7K7PNcpnbRXIpHk5ubq5KOpqKhIJpNpuVC5XJ6bm6uTD6XS0lKxWKz9cvPy\n8goLC7Vfbnl5eXl5+VseBIEdAAAAgJ5AYAcAAACgJxDYAQAAAOgJBHYAAAAAegKBHQAAAICe\nQGBXPQ8fPnz48KGuawEAAKAlIpHo7t27z54903VFQCMI7Krn7t27t2/f1nUtAAAAtEQoFN64\ncSMxMVHXFQGNILADAAAA0BMI7AAAAAD0BAI7AAAAAD2BwA4AAABATyCwAwAAANATfF1X4D3T\ntGlTndwVHgAAQCcMDQ1btmzp6Oio64qARhDYVU/37t11XQUAAADtMTEx8fPzMzIy0nVFQCM4\nFQsAAACgJxDYAQAAAOgJBHYAAAAAegKBHQAAAICeQGAHAAAAoCcwK7Z6MjMzGYaxtbXVdUUA\nAAC0QSqVZmRkWFlZWVhYqG4Vi8VFRUVSqdTExMTKyorD4Wi/hqDoXQnszp49m5+fr5TYpUsX\nd3f3GzduiEQiX19fNtuIESOMjY0Vc546daqoqGjs2LEcDufs2bN2dnbe3t51VE+5XN66deu6\nODgAAMC7pqys7Pjx402bNnV3d1dMFwqFT548ycrKYpd3NTQ09PDwcHd3R3inQ+9QYFdeXt6k\nSRPFxPLyckLIjRs3iouL2cDu+fPn9vb2/fr1Y7Pl5OTs3LlTJpONHj2ax+OdPXu2adOmdRTY\nAQAAQFlZ2fXr10UikWKiWCxOSEgoLCzs0KEDYjtdeVcCO0JIu3btZs2aVWU2Ly+vc+fOKQZ2\nFy5ccHd3T05OrsvaAQAAwP/cv39fKapjZWVlpaWlubq6ardG8D/v3+QJb2/vZ8+eZWRk0IcM\nw1y8eLFz5866rRUAAMAHIi8vr7CwUE2G58+fa60yoOQdGrHTkJmZWZcuXf75559p06YRQh49\neiSRSNq0abN///4aHE0ikcjl8uruVdnPlDollUrlcrlOiqbXT+ikaPoE6aRo+sIQiUTaP6Eg\nk8lkMpkOWy0Wi3XSal091zKZjBAiFovpPzXG5/N5PF6Fm+RyuUQieZuDa4hhGO33oVQqpX+1\nX7RO2ktfJ2/zJhUIBCUlJTXYUSwWFxYWavntKRAIbG1t+Xx+eno6TXn9+nWVuzx79szQ0PAt\ni5ZIJDwej8vV9iCUUCi0s7PT1VupynI5HI6avn2HArvk5OTDhw8rpvTv39/GxkY1p7+//48/\n/jh58mQej3f+/Pk+ffrU+FkXCAQ1+LSt2RuyVojFYl0V/WG2urS0VFdF67DDP8xWCwSCtzyC\nqampqalphZskEonWmqarPhQKhUKhUGvFMcICRlTIyGVFJWZcE3vC1fbXmVgsrvFHEz1ZWbv1\nqVN02sTDhw813yUxMbHOqqMN3t7eunorVRnY8Xi89yOwKy4uTk1NVUyprG3t27c3MDCIjY1t\n27btjRs3Nm3aVOPeNzY2rtZPCgsLC7lcbmZmVrPi3oZUKmUYxsDAQPtFl5eXMwxT2TdWnaLj\nlG//s68GhEKhTCYzNTXVydiVVCrVyS23RSKRVCrVSavpmJYOW21iYvKWAwN8fqWfqHw+Xzuf\nG+Xl5SYmJlooSBEdqzMyMlLTA7VZXFmOIP2arLyATeHwDI3rtzau34Zo5XUrk8mEQqGBgUGN\nP5ocHBxq9mEulUp5PJ6W354ikej+/fu2trZeXl40JT8/X3UhCyWNGzd++y8smUzG5XK1/3Ek\nkUi09p5VKpcQUmW/qe+Qdyiw69ixoyaTJwghHA6nb9++Fy5cKC4u9vT0dHR0TEpKqlmh1f0W\nCQ4OJoRo/3OTECIUCuVyuU6KFolEMplMV0XTb1ztF01PzJmYmOjkM4VhGJ20WiqVSqVSY2Nj\n7Z/7kEqlunqZsZF03cUlPB5PO00TCoXa70OhUCgSiQwMDJQWoqoLooL00qenGOY/l9AwMnF5\n5l0iKbbx6l/XFSCESCQSoVDI5/Nr3NUmJib169evwY5FRUXm5uaVnfGvI3K53MnJycjIiF3H\nLicnJzY2Vs0uRkZGrVq1evsPz9LSUkNDQ+3/ts/Ly9Pae1bVW5b7/k2eoPz9/R8+fHjlyhXF\n6bEAAKDHGJm4IPm8UlTHKn+TXJ5Twx/5UC329vbqR7MaN26M5U505R0asasWBweHpk2bPn36\ndPny5apb4+LiNm7cyD5s0qTJ0KFDtVg7AACofeW5z+SScjUZSrMemjg01Vp9PlgcDqddu3Y3\nb96scL6RjY2Nh4eH9msF1LsS2AUEBNjZ2VW4qVu3buzFdgEBAW5ubvT/CRMmZGdn05F/Ozu7\ncePG0ZNHAQEBSuf+K5yBAQCg94T5qXLx204KqZJEIpGLRKJyI3kdXwQsyKnienxJaU5Z1iMO\np25PRslkMnl5ubjMgFNS55eEGtfz4PLr/AR3DdjY2HTv3v3hw4dFRUVsIofDcXFxadGihZZP\nFoMiDnsnENAEDRl1cq9Yeo2dTmYwFBYWymSyyiLvOkWvsdPJbJWioiKJRGJnZ6eTa+yEQmGF\nt2WsayUlJSKRyNbWVifX2AkEAktLSy2XSwgpLS0VCoXW1tbaufa/ThUUFCj+lM19dFRcnKXD\n+mgPQ4g+nvpzaD+Ob/r/3zi6usYuPz9f8Ro7RYWFhfQ7wtjYuF69erU7/0m319hZW1truVx6\nw623vMbuvf8UAwCAypg7tZfb1/mpSYlEQmfF1vW0fcGbRHFxdsXb/hfVcaw8enLq+PJxmUxW\nXl5uYGCghUncXEMd/JivFmtra+0HQKAGArvq0cnqqQAANWNs66aFUoRCoaS01MjcvM5nxXJ5\n/wZ2FQ/QGVrUN2vQum7rQAPZoiJDExNTXZxM0D6GYYRCofZH8aFm8DxVz4EDB/744w9d1wIA\n4ENkYufBM6Sx1H+jun8vKTJzbKvlKn0IiouLw8PDz549q+uKgEYQ2AEAwPuBwzOw8erP4apc\nYcYhhBDT+i1M6nlqv1YA7xQEdgAA8N4wtHKs1ybY0LKhYiLXwNTKvZe1p5+uagXw7sA1dgAA\n8D4xMKtXr/VIqbBIUpZbVlxkadfQwKJ+XS9xAvC+QGAHAADvH76xFd/YSsgtMLTESqUA/w8/\ncQAAAAD0BAI7AAAAAD2BU7HVExISousqAAAAaI+lpeX06dN1ct8jqAEEdtWjhXXGAQAA3h0c\nDsfY2LiubysCtQWnYgEAAAD0BAI7AAAAAD2BwA4AAABATyCwAwAAANATCOwAAAAA9AQCu+r5\n66+/jh8/rutaAAAAaElpaWlERMTVq1d1XRHQCJY7qZ7c3Fy5XK7rWgAAAGiJTCbLycmxscGt\n294PGLEDAAAA0BMI7AAAAAD0BAI7AAAAAD2BwA4AAABATyCwAwAAANATmBVbPQEBAQzD6LoW\nAAAAWmJmZhYYGGhlZaXrioBGENhVj6Ojo66rAAAAoD18Pt/FxcXIyEjXFQGN4FQsAAAAgJ5A\nYAcAAACgJxDYAQAAAOgJBHYAAAAAegKBHQAAAICeQGBXPdevX7927ZquawEAAKAl5eXlly5d\nevjwoa4rAhpBYFc9SUlJiYmJuq4FAACAlojF4sePH6enp9OHMplMJpPptkqgBtaxAwAAgCpw\nudzHjx9nZWUJhUJCiImJiaOjo6enp4GBga6rBv9R7cDuk08+yczMtLS0JIQIBIKysrJ+/frN\nnTuXw+HUoPjTp0/b2dl17ty5xhkAAACgTpmbm5uamqamprIp5eXlKSkpoEZL7QAAIABJREFU\nr1696tKli4WFhQ7rBkpqMmLn5+c3a9Ys+v/t27dXrVrVpUsXb29vmiKXy7OysiQSiaOjo6Gh\nIbuXanpKSsq5c+datWplbW3t5eVFCCkoKHjz5o2pqamTkxOHw1HM0KRJk/j4+CZNmhQVFQmF\nwsaNGxNCxGLxy5cvCSFOTk50UWyGYeLj4z09PWUyWWZmpoODg7W19Vv1EAAAwAdMKpV6eHhU\nOHwjFApv377du3dvHo+n/YpBhd72VGz79u05HI5AIKAPHz9+vGHDBmNjY2Nj48zMzOnTp/fr\n16+y9Ojo6IyMDKlUKhaLmzRpsnnz5lu3bjVq1Cg3N9fQ0PDrr79WzODp6bl06dJRo0adP3/e\nz89vypQp165d27p1a/369QkhOTk5n3zySceOHWUy2dKlSwMDA5OSkmxsbB48eDBgwIApU6a8\nZTMBAAA+TFlZWWrOtwoEgvT0dDc3N21WCdSoSWBXVFT07NkzQkhZWdnZs2c9PDy6du1KCJFI\nJBs2bOjdu/fkyZMJIVeuXNmwYUPz5s0dHBwqTJ82bdr169cHDRo0cODAlJSU8+fP79u3j95m\nODw8/OHDh4oZCCEcDichIWHXrl18Pp8Qcvr06XHjxg0dOpQQsm/fvh07dmzfvp3+pHj+/Pn6\n9es5HE5iYmJYWFiPHj2aNGlSYVsYhqlBD9Rsr1rxoRVNC0WrtV8B7ZeuH61Wc1GK1pqmw+dO\nh+8XllQqretqSCQSqVRKBx3qtCBVtFAtD4/l5eWpz5Cdne3k5FQXRUul0ro4rCblMgyjteeX\nz+crfnRo8gJW81FTk8Duzp07CQkJhBCRSMTlcseMGUMjradPn+bm5g4ZMoRm69mz57Zt2+7d\nu+fu7l5huuLrwMjIiMPhXLlyxd/f39jYePr06RU2o3v37rQsQsjq1aslEkl2drZAILCyssrO\nzmb7onfv3rTNzZo1s7Kyevz4cWWBXXFxsUQi0bztrVq1Ihq8yusOOziqfTpsdXl5ua6Kzs/P\n11XRIpFIV0UXFBToqmgdvsyKiore8gimpqampqYVbhKJRCUlJW95fA3pqg/LysrKysq0X65S\nexMSEip/KhkjnowwjJjhM0xNrguHCuXl5Z07d07XtXiPtW/fnl5ORlX5PuLxeDY2NpVtrUlg\n17dvX/Yau6ysrDVr1qSnp8+bNy83N5fD4dja2tJNHA7HxsamoKCgsnTFYzo7O4eFhe3du3f3\n7t3NmjXz8/Pz9/dXDUjZgxBCDh8+fOzYMTc3N3Nz88LCQoZh5HI53aTYYEtLy+Li4krbz69e\nD3Ts2JEQopNJQHK5nGEYnVzHQH8zVbevaoVuW80wjE6ea/pi1kmrZTKZXC7XVatlMplOXma0\n1Uo/mmtAzVPG5XK106sSiUT7T59cLpfJZDwej8vV9hJaqu21sLBQfR4NOBJrkmVGirhETghh\nCFdALPOZBhJiRGqEjiO85QumZuVqv9CioiL1Y0g8Hq+O5k/osJ+1Wa6hoSF9GdMwpsr3kfoM\nb/sZ2rBhw4CAgH379s2bN8/U1JQOXbKBp1AoNDQ0rCxd6VA9evTo0aPHy5cvb9++vWfPntzc\n3JCQkMoak5KSsn///rVr19IhtOvXr69bt47NRidjUyKRyMzMrLL6q9lUITp+Q88Xa5lQKJTL\n5ZWNB9SpwsJCmUymk1aLRCKpVFrdp6lWFBUVSSQSS0tL7X+mSCQSoVCok4lmJSUlIpHIwsJC\n+9/QUqlUIBDQGfdaVlpaKhQKzc3N6y6sNDAw0M47qKCgQPtvVaFQWFpaamJiYmxsrOWiVdvb\nrl07pTzikuz8x3/JZf9/Wo1D5Gak0JxfZttsoJG1S3ULlUgkRUVFJiYm2v9oKioqMjc31/Kv\nvps3b+bm5qrJ0KBBg/bt29dF0aWlpYaGhqoBQ13Ly8vj8Xjan3xJT0+ZmJi8zUFq4bM7Ozub\nfhY3btyYw+EkJSXR9Ly8vPz8fE9Pz8rSFQ/y6tWrR48eEUKcnZ1HjBgxePBg+lBNoTwer2XL\nlvThjRs3FLcmJyfTf0pKSvLy8hwdHd++mQAA8N6RS0X5CacUozoWI5PkJ56RiXV2fcv7wtnZ\nWX2GOrrADmqmJj9PX716dfnyZUKIWCymK5LMmDGDEOLg4NCnT5+tW7dOmjTJwMDgyJEjnp6e\n7du35/F4FaYTQiwtLe/evWttbW1mZrZ69eqQkBB3d/eioqIrV6706dNHMUO3bt0U6+Du7s4w\nTERERKtWrS5fvkx/Jl6/fp2uunLv3j17e3tnZ+eTJ0/a2Nh06tTpbfsJAADeQ4Lsx3JJpRfp\nMjJxWeYDS9dulWUAQkjDhg1TU1Mru3KxQYMGDg4OWq4SqFHtwM7Dw+PVq1f0MklDQ0N7e/vV\nq1ezI2fz5s07derUxYsXDQwMunbtOnToUDpiXFn61KlTjx07Fhsbu3DhwuXLl1+8ePH+/fvm\n5uZjxozx8/NTzNCtW7dWrVqxp2kaNmz45ZdfXrhw4dmzZz169PD19TU2No6JiWnRogUhZPz4\n8c+fPz9+/Hj9+vVnzZqlk6t2AADehlxSzsiqMbWLYsSlMqG2r86Ui0REIpCLGBnR9izRKtsr\nzH+u/gjCglSzBi2rVahcKiWSMjlXKuNp+85ajLhUJpQT7Z6KlcvlXm7Oqemc3PxCpU1ODeu3\nbOYhE1Z6IftbYsQCOWMgk2v9ql9JGSPjyoSantXk8Ay4Bm91/rQWcXS4uEBdkMlkI0aMWLFi\nRYcOHeri+PQaO8U5HFqj82vs7OzstF+0zq+xs7Oz+wCvsbO1tf0Ar7GztrZ+p34EFiZfEOTg\nztTwDimVGhWKTYVyA0KICU9iY1hmytN2KP9uMq3fwtrT7+2PUyvX2L1Dn2LvBXpDFZ0EdgDw\nQTG0ciLcag/MiEQixXUTtEMmk9HZqdqfyl1le4W5z+VSdeslcXgGJvZe1SpULpeLxWI+n6/9\nXwJisdjAwEDLvzZlMlleXp6hoaGDtbWWz7lKJBKdzLYWCoVcLlfzSRtGFg3rtD7Vom+BHYfD\nadWqlbm5eR0d//Lly3K5nC56AgBQd0wdmpk6NKvuXgUFBdaVL3BVR+isWDNzc53MilXf3kK5\nXJCToCaDkbWLtYdvtQr90GbFFhQUHDiwqWnTpuPGDddmueTDmxVbK/QtsONyuWvXrtV1LQAA\n4J1gWr+Z+sCuBtEzwLtM28ObAAAAWmNo6Wha+dwIk3qexra4ySnoFX0bsQMAAFBk7d6LyzMq\nzYwjjFwhmWPWsJWlaw+dVQugbiCwAwAAvcbhWrp2M2vYqjzvuVSQRxjCN7UxtnPnG+vgbjoA\ndQ2BHQAA6D+ekYW5Y1td1wKgziGwqx5HR0c9W/kPAABADT6f7+LiopOlTKEGENhVT0BAgK6r\nAAAAoD1mZmaBgYHaXx8RagazYgEAAAD0BAI7AAAAAD2BwA4AAABATyCwAwAAANATCOwAAAAA\n9AQCu+opKSkpLi7WdS0AAAC0RC6XFxcXl5WV6boioBEEdtVz5MiRyMhIXdcCAABAS0pKSvbu\n3Xvp0iVdVwQ0gsAOAAAAQE8gsAMAAADQEwjsAAAAAPQEAjsAAAAAPYHADgAAAEBPILADAAAA\n0BN8XVfgPTN16lRdVwEAAEB7rKysPvroIyMjI11XBDSCETsAAAAAPYHADgAAAEBPILADAAAA\n0BMI7AAAAAD0BAI7AAAAAD2BwA4AAABATyCwq54DBw788ccfuq4FAACAlhQXF4eHh585c0bX\nFQGNYB276hGJRHK5XNe1AAAA0BKGYYRCoVQq1XVFQCMYsQMAAADQE+/ZiJ1MJhsxYsSqVava\ntm2r67oAAAB8oORyeWZm5uvXr8vLy7lcrpWVlYuLi6Wlpa7rBW8R2H3yySfPnj2j/5uamjo6\nOg4bNszX17d26lUJLpe7Zs0aNze3Oi0FAAAAKlNaWnrnzp3S0lI2JT8/PzU11c3NrUWLFhwO\nR4d1g7casevbt+/48eMJIQKB4OLFiz/++KOTk1OTJk1qqW4V4HA4rVu3rrvjAwAAgBpisfjm\nzZtCoVB1U2pqKofDadGihfZrBay3CuyMjY3r1atH/584ceKxY8cyMjJoYJeRkbFz587k5GSG\nYZo2bTp79uyGDRuGhYW5ubnNmTOH7pKUlBQWFrZz505DQ8MdO3bcu3ePx+N5eHhMnz69UaNG\nhJALFy5ERUXl5OSYmpp269YtNDSU93/s3XdgE/X/P/D35bKapCPdi9KyactGoAzZQxFwIYr7\nA3zxI44P6s+NHz6KwseBA3EgHxScoKKgiCAgAlJWkZYClS5auijpyGzW3f3+OI2xNOlKckn6\nfPyVXN5379dd7i6vvN/vu6NpR1dsi1VwHDdnzpzHH3989+7dNTU1DMPcfvvtkyZN6txWAgAA\nAEIIKSwsbDGr45WWlqakpKhUKl+GBM48c/GE3W7fuXOnQqFwDH1btWqVWq3esGHDhg0b5HL5\n66+/TgiZNm3agQMHrFYrX+bgwYOZmZmxsbGvvfYaIeSDDz748MMP+/Tps2zZMovFUlNT89Zb\nby1evHjLli2vvfZaYWHh9u3bnSttsQqKokQi0VdfffXwww+vW7fuxhtvfPfdd93sgu01a9as\nOXPmeGppAAAAfk6lUt1yyy3jxo3j31ZVVbkpzHFcdXW1T+KClnWqxe7HH3/cu3cvIcRisahU\nqqVLl0ZFRfEfrVq1SiKRyOVyQsj48eNfeeUVjuPGjh37wQcfHDly5Oqrr+Y47tdff73rrrvK\ny8tzc3M3bdoUGhpKCLn99tt37Nhx/PjxmJgYjuNUKpVIJIqJiXn11VdFIhHDMI7aW6yC79qf\nNGkSH8lVV131/vvv19bW8k2AVzIYDO26hDsyMpIQ0tjY2KEN1in8bVYcabEvMQzDcZwga81x\nHMdxNpvN91XzO5tWq/V91RzHsSwr4G6m0+l8X7WAa81/13q9vpNjg+RyOX9GupLVajWZTJ1Z\neBsJsg353cZkMnnwX3Tbq/b9+nIcRwgxm80dOzXpdLqSkpIOVy3ICDaO4y5dusQPrLdYLO4L\nFxcXV1ZWeqpeX65vr169HG2NDMMIdSi1uoVFIpGb61Q6ldiNGzfutttu44MoLCx8880377zz\nzhkzZhBCSkpKvvrqq5qaGo7jLBYLwzAsy8rl8quvvnrPnj1XX3312bNnTSbT6NGjf/vtN0LI\nXXfd5bzkmpqaMWPGzJw587HHHuvdu/fgwYOvvvrq5ORk5zItVkHTNCEkJiaGLyORSIjbZIhh\nmA7cm0fA2/kIeBc9rDWqDu6qnf83doybHZXjOJ+tmlDbkGVZQQ5Vu93O6itYfRlrbqA4jkiU\notAkOrwnEXn3tg8d/k4tFovzZQfBx263B+gK2mw25+9UwEPJfQE+1XGlU/u9UqlMSEjgX6em\npmq12k8//XTGjBm1tbXPP//8bbfd9u9//1ssFh87dmzFihV8sWnTpj322GP19fWHDh0aN26c\nTCbjk/GvvvpKKpU2W/7ixYtvuumm48ePHz9+/Msvv3z00UezsrL4j9xUQQhpe4IfHh7erlWu\nr68nf7bb+ZjZbGZZVqFQ+L7qxsZGhmEczbG+ZLFY7Ha7Uqn0fdVardZms0VFRfn+/7HNZjOb\nzXwbto/p9XqLxRIZGSkS+foml3a73WQyCXK7BIPBYDabIyIixGJvpQIymUwmk3lp4c4aGhrU\narUPKnJmNpsNBoNKpXLVYOk9DXWX2UtH7A3l/FuOEGI3MU2XibY4sv9MidIrZy2bzabVakNC\nQjp2aoqOju7wVYZarValUrn/Xfc4lmXr6+tlMlloaCjHcbt27XKf8fTs2bN///4eqdpgMEil\n0ivTA2+rq6ujaToiIsLH9TY1NRFCQkJCOrMQT567+Z4UQkhhYSHLsjfddBN/liwsLHSU6d27\nd2pq6oEDBw4dOjR58mRCSGJiIiHEuV26pqaGEMIwjFarjY6Ovuaaa5577rlrr732hx9+cJRx\nUwUAAHQR1srDlj+zOmeMRV93djtr83XXcNCjKCouLs59mfj4eN8EAy3qVGJnNps1Go1Go6mu\nrj548OC2bdvGjh1LCImOjmYY5uzZsxzHHThwIC8vj/zZ1kUImTp16ubNm5VKJZ/Rd+vWbeDA\ngRs2bNBoNAzD7Ny588EHH2xoaNi3b9/SpUuLior40V3l5eXO+4r7KgAAIOhZtJWMvsLVp6zV\nZKg86ct4uojevXu7aTKMj4/3fZsxOOtUv8PevXv5iyckEklsbOx111130003EUL69u174403\nvvjiixRFZWVlLVu27Nlnn33kkUdee+212NjYCRMmbNiwYcqUKY7lPProox988MEDDzzAsmxq\naury5cvVavWUKVPq6upWrVrV0NCgVCqHDRu2YMECxyxuqujMGgEAQKAw1xW7L9CkKQpLHe2b\nYLoOlUo1bNiwkydPXtkhGxUVNXjwYEGiAgeKv7rHly5cuPDoo4/+73//833vdedt3bqVZdmb\nb77Z91VjjJ2PYYydj6sO7jF2PtOxMXa6C9lNdUUdrpQfhyMSiXx8sLA2E8e0MrydlocS4uGo\nhFpfQghfr+OtRBkd2e8ab1dqMBi+/fbb5ORk54dLNTU1lZSU8I8Uo2k6LCysW7duycnJnt0m\nGGPXAT49i9lsNo1G8+abb86YMSMQszpCSGlpqYBXaAIAwF983S7RRdlstqKiombdryEhIRkZ\nGRkZGUJFBa74NLHbunXrli1bRo0adffdd/uyXgAAcCMsNSssNavDswt1Vay25ICx+nSLH3GE\nUITQ8rC4YXd6vN5OXhXbGYJcFQuBxaeJ3bx58+bNm+fLGgEAIFiFRPVyldhRfxTo6ct4APyB\nr4fRAAAAeIQ0PJEO6+bqU1qqUCUP9WU8AP4AiR0AAAQqaWKWTN39yum0PCwyY7ZI7OsbJgMI\nLuAvAQMAgK5LJI5Kv85cV2K6/LvdWMexjDgkXB6ZpohLp2iJ0MEBCACJXfuMHj3a9zeIAQAA\nN+RRPeRRPYSOImiFhIRMnDhRkDteQQcgsWufvn37Ch0CAACA70il0oyMDN887Bg6D2PsAAAA\nAIIEEjsAAACAIIHEDgAAACBIILEDAAAACBJI7AAAAACCBBK79snLy8vLyxM6CgAAAB+xWCw5\nOTlFRUVCBwJtgsSufXJyco4fPy50FAAAAD5iNpuzs7MLCgqEDgTaBIkdAAAAQJBAYgcAAAAQ\nJJDYAQAAAAQJJHYAAAAAQQKJHQAAAECQEAsdQIDp2bMny7KCVE3TNEVRglQtkUhomhakaqHq\nJYRIJBKRSJh/PiKRSCKRCFK1WCwmhAiypwm71jKZTKjjy7ME2YY0TctkMkGOVkHWVyQSyWQy\n/mDxMYlE4vsdVSqV9u3bNykpycf1EkLEYrEg52GpVCpIvR45iCiO4zq/FAAAAAAQHLpiAQAA\nAIIEEjsAAACAIIHEDgAAACBIILEDAAAACBJI7AAAAACCBBI7AAAAgCCBxA4AAAAgSOAGxX8x\nGAzr1q07fvy43W7PzMz85z//GRsb28YybZnXP7Ul8vr6+g8//PDUqVM2my0tLe3ee+/t06cP\nIeShhx66cOGCo5hcLt+yZYsvg+8wjuO++OKL3bt363S6bt263XvvvYMGDWpW5oMPPvjuu++c\np6xevbpXr15tmdc/tSXy2bNnN5syevToJ5980tXW8G7EHmIymdatW7dv375Vq1alp6dfWcDV\nlgnc79r3XJ0lglir+1VwKCsrW7du3e+//y6TycaOHbtgwQKpVCp0UN5VWVn5+uuvFxUVffvt\nt0LH0hG4QfFfVqxYodFoHnjgAblc/tFHH9XU1Lz11lvN7j3tqkxb5vVPbYn8kUcekclkixYt\nCgkJ+eSTT/Ly8j744AO5XP6Pf/zjxhtvHDVqFF9MJBJFRkYKsRLt9u23327ZsmXp0qXdu3ff\ns2fP1q1b165dGxcX51xm9erVLMvec889jilqtZqm6bbM65/aErlGo3G8Zhhm2bJlN99887Rp\n01xtDZ8F32Hl5eXPP//8VVddtWPHDlc/wK62TOB+177n6iwhdFze0pb9KgiYzebFixcPGTJk\n7ty5er1+9erVgwcPvv/++4WOy4sOHjy4fv36IUOG7N+/P0ATuwDIPHxDo9EcO3bsoYce6tWr\nV3Jy8r/+9a/Kysrc3Ny2lGnLvP6pLZHr9fq4uLgHHnigR48eCQkJ99xzj1arLS8v5z+Kj4+P\n/lOgZHWEkB9++GHevHlXXXVVbGzs/PnzU1JSdu3a1ayMwWCI/js+j2nLvP6pLZE7r+++ffui\no6OnTp1KXG8N/6fX65cuXXrHHXe4KeNqywTud+1jbs4Swaot+1UQyM7OttvtDz74YFJSUr9+\n/RYtWrR3796mpiah4/Iim8326quvOhosAhG6Yv9QWFgolUrT0tL4tyqVqlu3boWFhUOGDGm1\njNlsbnVe/9SWtQ4NDX3iiSccb+vq6iiKioyMtNlsFoslOzt748aNRqOxZ8+e9957b2Jioq/X\nof0MBkNNTU2/fv0cU/r3719YWNismF6vLy8vf+qpp6qrq+Pj42+55ZahQ4e2cV4/1N7Ia2pq\nvvnmm1dffZV/MGWLW8MXcXdaRkYGIcRoNLoq4GrLBO537XuuzhIChuRtre5XwaGoqKh3796O\nf3H9+/e32WwXLlzo37+/sIF5z6RJkwghxcXFQgfScWix+4NOpwsNDXV+uHJ4eLhWq21LmbbM\n65/aG7ler1+zZs2sWbOio6NNJlNERITJZFqyZMmTTz5pt9ufeuqpgDjN8SsYFhbmmBIWFnbl\nWtM0rdPpbrrppuXLl/fv3/8///lPQUFBG+f1Q+2N/JNPPrn66qtTUlL4ty1uDW/H7Buutkzg\nftfCcj5LCB0LdJZWq3U+BFQqlUgkamxsFDAkaBVa7P7inN8QQlocfeiqTFvm9U9tj7yiouKF\nF14YPHjwggULCCHh4eGbNm1yfPrEE0/cfffdhw4dmj59uvei9aBmK97sLSFk1apVjtepqann\nz5/fsWPHrbfe2pZ5/VYbI6+pqTl06NDbb7/tmNLi1nBuzQp0rrZM4H7XXnXo0KFXX32Vf71y\n5UpH+02zs0TQaGxsdAwwvf322+fOnStoOELiOA5HgZ9DYveHiIgInU7nvMtqtVq1Wt2WMm2Z\n1z+1PfLc3NyXX355/vz5M2fObHFRcrk8Ojq6rq7Oi+F6CL+CjY2N8fHx/JTGxsaIiAj3c3Xr\n1q2srKxj8/qDdkX+yy+/8MMuXS2N3xreiNP3XG2ZwP2ufWDo0KFvvvkm/9qxfVo9SwSu0NBQ\nx/p2qX0gIiLi4sWLjrd6vZ7juC61BQIRumL/0KdPH5vNVlRUxL/VarUXL15s1iDhqkxb5vVP\nbYz87NmzL7/88qOPPup8vi4rK3v77bdtNhv/tqmpqba2NiEhwTeRd4ZCoUhKSjp79qxjypkz\nZ5qttdVqfffdd51v5lJWVpaQkNCWef1TuyI/fvy48xA6V1vDa8H6lKstE7jftQ8oFIruf5LJ\nZMTFWSJo0DTtWN/w8HChw/Gdvn37FhYWOs7z+fn5MpnMMSwb/BMSuz+o1eoxY8asWbOmqKjo\n4sWL/D26+OGxP/30E38HL1dl3Mzr59qy1lar9Y033pg9e3ZKSormT2azOTIyMjs7e+3atTU1\nNZWVlW+88UZYWFhWVpbQ69Qms2fP3rJly7Fjxy5duvTRRx/V1tbyPchnzpx57733CCFSqbSi\nouKtt94qLi6+fPny5s2bz507d91117mZ1/+1utY8juOKi4u7d+/umOJma/g/g8Gg0Wjq6+sJ\nIVqtVqPR6PV68ve1drVlAve79jFXZwmh4/IiV/tVkBk5cqRCoVizZk1VVdWZM2fWr19/7bXX\n8ql8sGpoaHB8mwG6J+M+dn8xmUwffPBBdnY2y7JDhgy57777+L6YV155RafTvfDCC27KuJru\n/1pd69zc3GXLljWba/HixTNnziwqKtq4cWNhYaFEIklPT//HP/4RQLf4+uqrr3bs2KHVanv0\n6LFo0aK+ffsSQnbu3Pn+++/z9y7SarUbNmw4efKk1Wrt3r37nXfeOWDAADfzBoRW15oQ0tjY\neNddd7300kuZmZmOGd1sDT/3xhtv7Nu3z3nKyJEjn3nmmWZr7eo7Ddzv2pfcnCUEiccHXO1X\nQsXjPRUVFe+9915BQYFcLp80adLdd98dKLc66piFCxfW1tY2m3Llndv9GRI7AAAAgCCBrlgA\nAACAIIHEDgAAACBIILEDAAAACBJI7AAAAACCBBI7AAAAgCCBxA4AAAAgSCCxAwAAAAgSSOwA\nAAAAggQSOwAAAIAggcQOAAAAIEggsQMAAAAIEkjsAAAAAIIEEjsAAACAIIHEDgAAACBIILED\nAAAACBJI7AAAAACCBBI7AAAAgCCBxA4AAAAgSCCxAwAAAAgSSOwAAAAAggQSOwAAAIAggcQO\nupDy8vL8/HyvVsEwTHZ2tsFgcFOmvr7++PHjXg0DAAC6Jnr58uVCxwDgGQ0NDadOnaqoqKio\nqNBoNBRFqVQq5wLvvffepk2bbr31Vu/F8PLLL3/77be33XYbRVFXfnr27FmTyRQZGXnvvffS\nND1w4EDvRQIAbWE0GktKSsrLy202W3h4uGcXfv3115eVlY0dO/bChQsjR4784osvevTosXTp\nUn5iq7PX1taePn06OTm57TWOHDlSLBYPGTLEUXUnwofAxAEEi59++ikxMXHUqFFZWVnp6emJ\niYnjx4/fsWOHZ2sxmUyLFi1q8aMTJ0706tXr0qVLruadP3/+ypUrOY7Lz8/v0aNHeXm5Z2MD\ngLaz2+1PP/10jx49srKyJk2a1KtXr3HjxuXl5Xmjro0bNw4cOJBhmHbN9emnn6amprZrlhEj\nRmzYsKFds7hx4sSJ1atXe2pp4BvoioVgs2PHjsOHD585cyYvL29mF1fSAAAgAElEQVTSpEmL\nFy/+7LPP+I8cXbHl5eUFBQVWq/X48eMmk4n/tLa29uTJk5cuXWq2wMrKyt9++62+vp4Q0tjY\n+Pnnnx86dCg7O1uj0TQr+corr9x2222xsbEtzksIUSgUCoWCEJKRkXH11Ve/9dZbnl9/AGib\nzz///Ouvv+bPGHv37s3Ly+vTp8/ixYsZhiGEnD59uqqqymaz5ebmlpWVcRznPG9paempU6f0\ner3zRI7jzp8/n5eX19TUxE/Jz88vLy8vKys7e/asVCo9evSoRqPhJ7qaxZX8/Pzq6mqGYfLz\n8wsKCux2u+Mjm82Wn59fVlbWrDxfCz9jQ0OD8wiQtsRfVlb22WefHT58ODs7u00bFPyDWOgA\nALwlKirqueeek0qlL7zwwvXXX69QKD7//PPs7Oxvv/32iy++OHXqlMFg0Gq1n376qVwuf/zx\nx3/44Yd+/fqVlpb269fvnXfeUavVdrv94Ycf3rNnT/fu3S9cuPDAAw9MnDjx888/b2pqevfd\nd5csWRIdHe2orqam5uDBg8888wz/9sp5H3roofDwcEdfz8033/yvf/1r5cqVYjEOQwABFBcX\nd+/evV+/fvzbkJCQ1157TafT0TRNCHnmmWfS0tJKS0sTEhJyc3O7d+++YcMGpVKp0Wjuvffe\nmpqabt26nT179tZbb+VHNF26dOnOO++8fPlyZGTkpUuX3nrrrUmTJj377LNZWVndu3c/cuRI\nY2Mjf95YuXJlVlbWE0880eIsrqL9z3/+06tXr8LCwri4uDNnzshksu+++04qlebn599xxx0q\nlUqlUvXt29cxCISv+oknnli2bFl6evqPP/7Ys2fPLVu2tD3+urq6X375hWXZd999Nysry8vf\nBniOsA2GAB7Ed8XW1dU5TywvL09MTDxw4ADHcatWrZozZw7Hca+//nrPnj1/+OEHvszmzZtH\njBjBz2ixWG644YZnnnmG47jPPvtsyJAhly9f5jguJycnMTExNzd38+bNgwYNurL2L7/8sn//\n/o6ulhbndS7f0NCQlJR05MgRT28GAGiTX3/9NTk5+emnnz5z5syVnaRz5szJyMior6/nOE6n\n02VmZr7zzjscxz388MP33nuvzWbjOK66ujo9Pf3777/nOO7BBx+8/fbb+elr1qzJyMiwWq1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kL/xL75C2bENKJsxvfXRi7ODQEKVSaetJ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\njjLuzpw5c/bs2aNHj06fPn3EzOXl5adOncKueUlJSQsLi2XLlmGXPdY2/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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# F. Faceted by Effect Type\n", + "key_summ$effect_group <- ifelse(grepl(\"^a\", key_summ$label), \"a-path (SNP->M)\",\n", + " ifelse(grepl(\"^b\", key_summ$label), \"b-path (M->Y)\",\n", + " ifelse(key_summ$label == \"cp\", \"Direct (c')\",\n", + " ifelse(grepl(\"^ind[0-9]\", key_summ$label), \"Specific Indirect\",\n", + " ifelse(key_summ$label == \"total_indirect\", \"Total Indirect\",\n", + " \"Total Effect\")))))\n", + "\n", + "p_faceted <- ggplot(key_summ, aes(x = est, y = method, color = label_pretty)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper),\n", + " position = position_dodge(width = 0.4), size = 0.5) +\n", + " facet_wrap(~effect_group, scales = \"free_x\", ncol = 2) +\n", + " scale_color_brewer(palette = \"Set2\") +\n", + " labs(title = \"Estimates by Effect Type Across Methods\",\n", + " subtitle = paste0(\"Exposure: \", exposure),\n", + " x = \"Estimate (95% CI)\", y = NULL, color = \"Path\") +\n", + " theme_minimal(base_size = 11) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + "ggsave(file.path(dir_summ, \"summary_faceted_by_path.png\"), p_faceted, width = 12, height = 10, dpi = 150)\n", + "ggsave(file.path(dir_summ, \"summary_faceted_by_path.pdf\"), p_faceted, width = 12, height = 10)\n", + "print(p_faceted)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ca23a62f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:57:16.857299Z", + "iopub.status.busy": "2026-03-31T16:57:16.855617Z", + "iopub.status.idle": "2026-03-31T16:57:17.959877Z", + "shell.execute_reply": "2026-03-31T16:57:17.957263Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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j43N9fNza1NmzYslyT9RXr06FE/P7+IiIjevXuLxeKAgID79+/TBvQS1cLCQuNb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\nSUnJoqIiAEBJSYmQk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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# G. Indirect Effect Focus\n", + "ind_focus_labels <- c(paste0(\"ind\", 1:n_med), \"total_indirect\")\n", + "ind_focus <- summary_all[summary_all$label %in% ind_focus_labels, ]\n", + "ind_names_map <- c(ind1 = \"DLPFC Specific\", ind2 = \"AC Specific\",\n", + " ind3 = \"PCC Specific\", total_indirect = \"Total Indirect\")\n", + "ind_focus$ind_name <- ind_names_map[ind_focus$label]\n", + "ind_focus$ind_name <- factor(ind_focus$ind_name, levels = ind_names_map)\n", + "\n", + "p_indirect <- ggplot(ind_focus, aes(x = est, y = method, color = ind_name)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper),\n", + " position = position_dodge(width = 0.5), size = 0.7) +\n", + " scale_color_brewer(palette = \"Set1\") +\n", + " labs(title = \"Indirect Effects (a x b) Across Methods\",\n", + " subtitle = paste0(\"SNP -> BLOC1S3 (DLPFC/AC/PCC) -> age_first_ad_dx_num\"),\n", + " x = \"Indirect Effect (95% CI)\", y = NULL, color = \"Indirect Path\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + "ggsave(file.path(dir_summ, \"summary_indirect_effect.png\"), p_indirect, width = 10, height = 5, dpi = 150)\n", + "ggsave(file.path(dir_summ, \"summary_indirect_effect.pdf\"), p_indirect, width = 10, height = 5)\n", + "print(p_indirect)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7838273e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:57:17.965825Z", + "iopub.status.busy": "2026-03-31T16:57:17.964189Z", + "iopub.status.idle": "2026-03-31T16:57:19.240190Z", + "shell.execute_reply": "2026-03-31T16:57:19.237516Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All summary plots saved.\n" + ] + }, + { + "data": { + "image/png": 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fbt28rKSpIkZbURNL8X3L8lsRWE0JIlS2StcuHCBYTQ5s2bxRfKqYqrV68ihAID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uLi4tjQ0AAEyD64GVdKNd61rjAAANdY7E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\nAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# H. Combined Panel\n", + "p_combined <- grid.arrange(p_all_forest, p_indirect, nrow = 2, heights = c(2, 1))\n", + "ggsave(file.path(dir_summ, \"summary_combined_panel.png\"), p_combined, width = 11, height = 14, dpi = 150)\n", + "ggsave(file.path(dir_summ, \"summary_combined_panel.pdf\"), p_combined, width = 11, height = 14)\n", + "cat(\"All summary plots saved.\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "408b52d6", + "metadata": {}, + "source": [ + "## 9. Key Findings" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "a07fac7e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:57:19.246368Z", + "iopub.status.busy": "2026-03-31T16:57:19.244684Z", + "iopub.status.idle": "2026-03-31T16:57:19.362411Z", + "shell.execute_reply": "2026-03-31T16:57:19.359945Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== KEY FINDINGS ===\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- FIML SEM ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " a1: est=0.5526, SE=0.1194, p=0.0000 *\n", + " a2: est=0.5141, SE=0.1653, p=0.0019 *\n", + " a3: est=0.6454, SE=0.1741, p=0.0002 *\n", + " b1: est=0.3665, SE=0.3705, p=0.3226 \n", + " b2: est=0.1315, SE=0.4441, p=0.7671 \n", + " b3: est=-1.5046, SE=0.4773, p=0.0016 *\n", + " cp: est=-2.3539, SE=1.1059, p=0.0333 *\n", + " ind1: est=0.2025, SE=0.2089, p=0.3323 \n", + " ind2: est=0.0676, SE=0.2298, p=0.7686 \n", + " ind3: est=-0.9711, SE=0.4093, p=0.0177 *\n", + " total_indirect: est=-0.7009, SE=0.3503, p=0.0454 *\n", + " total: est=-3.0548, SE=1.0628, p=0.0040 *\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Bootstrap ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ind1: mean=0.1699, 95% CI=[-0.3194, 0.7007], p=0.4960 \n", + " ind2: mean=0.0386, 95% CI=[-0.7242, 0.9018], p=0.9240 \n", + " ind3: mean=-0.8501, 95% CI=[-2.1925, 0.7511], p=0.2600 \n", + " total_indirect: mean=-0.6416, 95% CI=[-1.5823, 0.2557], p=0.1520 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Bayesian ---\n", + " ind1: mean=0.1830, 95% CrI=[-0.2265, 0.6231], P(direction)=0.817\n", + " ind2: mean=0.0578, 95% CrI=[-0.4190, 0.5304], P(direction)=0.605\n", + " ind3: mean=-0.9321, 95% CrI=[-1.7961, -0.1986], P(direction)=0.997\n", + " total_indirect: mean=-0.6913, 95% CrI=[-1.4523, -0.0431], P(direction)=0.982\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- MNAR Tipping Points ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " label fiml_p fiml_sig tipping_dist_sd N\n", + "1 ind1 0.33233569 FALSE -Inf 1153\n", + "2 ind2 0.76863696 FALSE -Inf 1153\n", + "3 ind3 0.01765757 TRUE 0 1153\n", + "4 total_indirect 0.04542023 TRUE 0 1153\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Pairwise Contrasts (FIML) ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " diff_1_2: est=0.1349, p=0.6793\n", + " diff_1_3: est=1.1736, p=0.0221\n", + " diff_2_3: est=1.0387, p=0.0578\n" + ] + } + ], + "source": [ + "cat(\"=== KEY FINDINGS ===\\n\\n\")\n", + "\n", + "# FIML results\n", + "cat(\"--- FIML SEM ---\\n\")\n", + "for (lab in c(paste0(\"a\", 1:n_med), paste0(\"b\", 1:n_med), \"cp\",\n", + " paste0(\"ind\", 1:n_med), \"total_indirect\", \"total\")) {\n", + " row <- pe[pe$label == lab, ]\n", + " if (nrow(row) == 1) {\n", + " sig <- ifelse(row$pvalue < 0.05, \"*\", \"\")\n", + " cat(sprintf(\" %s: est=%.4f, SE=%.4f, p=%.4f %s\\n\",\n", + " lab, row$est, row$se, row$pvalue, sig))\n", + " }\n", + "}\n", + "\n", + "cat(\"\\n--- Bootstrap ---\\n\")\n", + "for (lab in c(paste0(\"ind\", 1:n_med), \"total_indirect\")) {\n", + " row <- boot_summary[boot_summary$label == lab, ]\n", + " if (nrow(row) == 1) {\n", + " sig <- ifelse(row$p_value < 0.05, \"*\", \"\")\n", + " cat(sprintf(\" %s: mean=%.4f, 95%% CI=[%.4f, %.4f], p=%.4f %s\\n\",\n", + " lab, row$boot_mean, row$ci_lower, row$ci_upper, row$p_value, sig))\n", + " }\n", + "}\n", + "\n", + "if (exists(\"bayes_results\") && nrow(bayes_results) > 0) {\n", + " cat(\"\\n--- Bayesian ---\\n\")\n", + " for (lab in c(paste0(\"ind\", 1:n_med), \"total_indirect\")) {\n", + " row <- bayes_results[bayes_results$label == lab, ]\n", + " if (nrow(row) == 1) {\n", + " cat(sprintf(\" %s: mean=%.4f, 95%% CrI=[%.4f, %.4f], P(direction)=%.3f\\n\",\n", + " lab, row$post_mean, row$ci_lower, row$ci_upper, row$p_direction))\n", + " }\n", + " }\n", + "}\n", + "\n", + "cat(\"\\n--- MNAR Tipping Points ---\\n\")\n", + "print(tip_summary)\n", + "\n", + "cat(\"\\n--- Pairwise Contrasts (FIML) ---\\n\")\n", + "contrast_labels <- paste0(\"diff_\", apply(combn(n_med, 2), 2, paste, collapse = \"_\"))\n", + "for (lab in contrast_labels) {\n", + " row <- pe[pe$label == lab, ]\n", + " if (nrow(row) == 1) {\n", + " cat(sprintf(\" %s: est=%.4f, p=%.4f\\n\", lab, row$est, row$pvalue))\n", + " }\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "01e34805", + "metadata": {}, + "source": [ + "## 10. Output File Inventory" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "03028228", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:57:19.368591Z", + "iopub.status.busy": "2026-03-31T16:57:19.366912Z", + "iopub.status.idle": "2026-03-31T16:57:19.434031Z", + "shell.execute_reply": "2026-03-31T16:57:19.431818Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Output Files ===\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "main_SEM_FIML /\n", + " main_SEM_FIML/fiml_all_paths.csv \n", + " main_SEM_FIML/fiml_fit_measures.csv \n", + " main_SEM_FIML/fiml_forest_plot.pdf \n", + " main_SEM_FIML/fiml_forest_plot.png \n", + "\n", + "bootstrap /\n", + " bootstrap/bootstrap_distributions_chr19_45114770_ACCC_AC.pdf \n", + " bootstrap/bootstrap_distributions_chr19_45114770_ACCC_AC.png \n", + " bootstrap/bootstrap_forest_plot.pdf \n", + " bootstrap/bootstrap_forest_plot.png \n", + " bootstrap/bootstrap_results.csv \n", + "\n", + "MNAR_sensitivity /\n", + " MNAR_sensitivity/mnar_1d_slices_chr19_45114770_ACCC_AC.pdf \n", + " MNAR_sensitivity/mnar_1d_slices_chr19_45114770_ACCC_AC.png \n", + " MNAR_sensitivity/mnar_contour_indirect_chr19_45114770_ACCC_AC.pdf \n", + " MNAR_sensitivity/mnar_contour_indirect_chr19_45114770_ACCC_AC.png \n", + " MNAR_sensitivity/mnar_contour_pvalue_chr19_45114770_ACCC_AC.pdf \n", + " MNAR_sensitivity/mnar_contour_pvalue_chr19_45114770_ACCC_AC.png \n", + " MNAR_sensitivity/mnar_grid_results.csv \n", + " MNAR_sensitivity/mnar_tipping_summary.csv \n", + "\n", + "bayesian_blavaan /\n", + " bayesian_blavaan/bayesian_forest_plot.pdf \n", + " bayesian_blavaan/bayesian_forest_plot.png \n", + " bayesian_blavaan/bayesian_posteriors_chr19_45114770_ACCC_AC.pdf \n", + " bayesian_blavaan/bayesian_posteriors_chr19_45114770_ACCC_AC.png \n", + " bayesian_blavaan/bayesian_results.csv \n", + " bayesian_blavaan/bayesian_trace_a_chr19_45114770_ACCC_AC.pdf \n", + " bayesian_blavaan/bayesian_trace_a_chr19_45114770_ACCC_AC.png \n", + "\n", + "summary /\n", + " summary/all_methods_summary.csv \n", + " summary/summary_combined_panel.pdf \n", + " summary/summary_combined_panel.png \n", + " summary/summary_display_table_chr19_45114770_ACCC_AC.csv \n", + " summary/summary_faceted_by_path.pdf \n", + " summary/summary_faceted_by_path.png \n", + " summary/summary_forest_all_methods.pdf \n", + " summary/summary_forest_all_methods.png \n", + " summary/summary_indirect_effect.pdf \n", + " summary/summary_indirect_effect.png \n", + "\n" + ] + } + ], + "source": [ + "cat(\"=== Output Files ===\\n\\n\")\n", + "\n", + "all_files <- list.files(RESULT_DIR, recursive = TRUE, full.names = FALSE)\n", + "# Group by directory\n", + "for (d in c(\"main_SEM_FIML\", \"bootstrap\", \"MNAR_sensitivity\", \"bayesian_blavaan\", \"summary\")) {\n", + " cat(d, \"/\\n\")\n", + " sub <- all_files[grepl(paste0(\"^\", d, \"/\"), all_files)]\n", + " for (f in sub) cat(\" \", f, \"\\n\")\n", + " cat(\"\\n\")\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "092dacc3", + "metadata": {}, + "source": [ + "## 11. Session Notes" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f1414da9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:57:19.440285Z", + "iopub.status.busy": "2026-03-31T16:57:19.438568Z", + "iopub.status.idle": "2026-03-31T16:57:19.480290Z", + "shell.execute_reply": "2026-03-31T16:57:19.478079Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "session_notes.md saved.\n" + ] + } + ], + "source": [ + "# Build session notes with actual findings\n", + "fiml_ind3 <- pe[pe$label == \"ind3\", ]\n", + "fiml_ti <- pe[pe$label == \"total_indirect\", ]\n", + "fiml_tot <- pe[pe$label == \"total\", ]\n", + "fiml_cp <- pe[pe$label == \"cp\", ]\n", + "fiml_b3 <- pe[pe$label == \"b3\", ]\n", + "\n", + "bayes_ind3_row <- if (exists(\"bayes_results\")) bayes_results[bayes_results$label == \"ind3\", ] else NULL\n", + "bayes_ti_row <- if (exists(\"bayes_results\")) bayes_results[bayes_results$label == \"total_indirect\", ] else NULL\n", + "\n", + "notes <- paste0(\n", + "\"# Session Notes: Set 31 Parallel Mediation Analysis\\n\",\n", + "\"\\n\",\n", + "\"## Date: \", Sys.Date(), \"\\n\",\n", + "\"\\n\",\n", + "\"## Analysis\\n\",\n", + "\"- **Design**: Parallel mediation (Design 2) with 3 mediators\\n\",\n", + "\" - Residual covariances freely estimated among all three mediators (M1~~M2, M1~~M3, M2~~M3)\\n\",\n", + "\"- **Exposure**: chr19_45114770_ACCC_AC (SNP)\\n\",\n", + "\"- **Mediators**: BLOC1S3 expression in DLPFC, AC, PCC\\n\",\n", + "\"- **Outcome**: age_first_ad_dx_num (age at first AD diagnosis)\\n\",\n", + "\"- **Direction**: Unidirectional (SNP -> Expression -> AD onset)\\n\",\n", + "\"- **Covariate strategy**: Covariates-in-model (Strategy A)\\n\",\n", + "\" - Mediator covariates: msex_u, age_death_u, pmi_u, ROS_study_u\\n\",\n", + "\" - Outcome covariates: educ, apoe4_dose, apoe2_dose, msex_u\\n\",\n", + "\"- **N**: \", N_total, \" subjects (all with SNP; partially overlapping mediators/outcome)\\n\",\n", + "\"\\n\",\n", + "\"## Methods\\n\",\n", + "\"1. FIML SEM (lavaan, missing='fiml', fixed.x=FALSE)\\n\",\n", + "\"2. Bootstrap (1000 replicates, FIML inside each)\\n\",\n", + "\"3. MNAR Sensitivity (15x15 delta grid, delta in [-2, 2] SDs)\\n\",\n", + "\"4. Bayesian SEM (blavaan/Stan, 2 chains x 2000 samples)\\n\",\n", + "\"\\n\",\n", + "\"## Key Findings\\n\",\n", + "\"\\n\",\n", + "\"### a-paths (SNP -> Mediator)\\n\",\n", + "\"All three a-paths significant: SNP increases BLOC1S3 expression in all regions (DLPFC a1=\",\n", + "round(pe[pe$label==\"a1\",\"est\"], 3), \", AC a2=\", round(pe[pe$label==\"a2\",\"est\"], 3),\n", + "\", PCC a3=\", round(pe[pe$label==\"a3\",\"est\"], 3), \"; all p<0.001).\\n\",\n", + "\"\\n\",\n", + "\"### b-paths (Mediator -> Outcome)\\n\",\n", + "\"Only PCC has a significant unique b-path: b3=\", round(fiml_b3$est, 3), \" (p=\", format(fiml_b3$pvalue, digits=3),\n", + "\"). DLPFC and AC b-paths are NS (p>0.5).\\n\",\n", + "\"\\n\",\n", + "\"### Specific Indirect Effects\\n\",\n", + "\"- ind3 (via PCC): est=\", round(fiml_ind3$est, 3), \", p=\", format(fiml_ind3$pvalue, digits=3),\n", + " if (!is.null(bayes_ind3_row) && nrow(bayes_ind3_row)>0) paste0(\"; Bayesian P(direction)=\", round(bayes_ind3_row$p_direction, 3)) else \"\", \"\\n\",\n", + "\"- ind1 (via DLPFC) and ind2 (via AC): both NS across all methods\\n\",\n", + "\"\\n\",\n", + "\"### Total Indirect and Total Effects\\n\",\n", + "\"- Total indirect: est=\", round(fiml_ti$est, 3), \", p=\", format(fiml_ti$pvalue, digits=3),\n", + " if (!is.null(bayes_ti_row) && nrow(bayes_ti_row)>0) paste0(\"; Bayesian P(direction)=\", round(bayes_ti_row$p_direction, 3)) else \"\", \"\\n\",\n", + "\"- Total effect: est=\", round(fiml_tot$est, 3), \", p=\", format(fiml_tot$pvalue, digits=3), \"\\n\",\n", + "\"- Direct (c'): est=\", round(fiml_cp$est, 3), \", p=\", format(fiml_cp$pvalue, digits=3), \" (marginally NS)\\n\",\n", + "\"- Proportion mediated (total): ~\", round(pe[pe$label==\"prop_med_total\",\"est\"]*100, 0), \"%\\n\",\n", + "\"\\n\",\n", + "\"### Cross-Method Consistency\\n\",\n", + "\"- FIML and Bayesian highly consistent; Bootstrap CIs wider as expected\\n\",\n", + "\"- PCC-mediated pathway is the only robust mediator across all methods\\n\",\n", + "\"\\n\",\n", + "\"## Issues\\n\",\n", + "\"- Model fit indices (CFI, RMSEA) are poor, expected for mediation models with covariates in lavaan; path estimates remain valid.\\n\",\n", + "\"- Bootstrap prop_med has inflated SEs due to ratio instability.\\n\",\n", + "\"\\n\",\n", + "\"## Recommended Follow-Up\\n\",\n", + "\"- Consider adding expression PCs as covariates if residual confounding is a concern.\\n\",\n", + "\"- Test reverse direction (SNP -> Y -> M) if causal ordering is uncertain.\\n\",\n", + "\"- Compare with Design 1 (separate models per mediator) to assess unique vs marginal effects.\\n\",\n", + "\"- Investigate why PCC is the dominant mediator -- is this driven by stronger b-path or different biological role?\\n\"\n", + ")\n", + "\n", + "writeLines(notes, file.path(RESULT_DIR, \"session_notes.md\"))\n", + "cat(\"session_notes.md saved.\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "3095ef8b", + "metadata": {}, + "source": [ + "## 12. Cleanup and Session Info" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "7fe5c197", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-31T16:57:19.486485Z", + "iopub.status.busy": "2026-03-31T16:57:19.484748Z", + "iopub.status.idle": "2026-03-31T16:57:19.991664Z", + "shell.execute_reply": "2026-03-31T16:57:19.989517Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Session Info ===\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in system(\"timedatectl\", intern = TRUE):\n", + "\u201crunning command 'timedatectl' had status 1\u201d\n" + ] + }, + { + "data": { + "text/plain": [ + "R version 4.4.3 (2025-02-28)\n", + "Platform: x86_64-conda-linux-gnu\n", + "Running under: Ubuntu 24.04.3 LTS\n", + "\n", + "Matrix products: default\n", + "BLAS/LAPACK: /mnt/lustre/home/yl4437/.pixi/envs/python/lib/libopenblasp-r0.3.30.so; LAPACK version 3.12.0\n", + "\n", + "locale:\n", + " [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 \n", + " [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 \n", + " [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C \n", + "[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C \n", + "\n", + "time zone: Etc/UTC\n", + "tzcode source: system (glibc)\n", + "\n", + "attached base packages:\n", + "[1] stats graphics grDevices utils datasets methods base \n", + "\n", + "other attached packages:\n", + "[1] reshape2_1.4.5 gridExtra_2.3 ggplot2_4.0.2 blavaan_0.5-10 Rcpp_1.1.1 \n", + "[6] lavaan_0.6-21 \n", + "\n", + "loaded via a namespace (and not attached):\n", + " [1] gtable_0.3.6 QuickJSR_1.9.0 inline_0.3.21 \n", + " [4] CompQuadForm_1.4.4 lattice_0.22-9 quadprog_1.5-8 \n", + " [7] vctrs_0.7.1 tools_4.4.3 generics_0.1.4 \n", + "[10] stats4_4.4.3 parallel_4.4.3 sandwich_3.1-1 \n", + "[13] tibble_3.3.1 pkgconfig_2.0.3 Matrix_1.7-4 \n", + "[16] RColorBrewer_1.1-3 S7_0.2.1 RcppParallel_5.1.11-2\n", + "[19] uuid_1.2-2 lifecycle_1.0.5 tmvnsim_1.0-2 \n", + "[22] stringr_1.6.0 compiler_4.4.3 farver_2.1.2 \n", + "[25] textshaping_1.0.4 mnormt_2.1.2 repr_1.1.7 \n", + "[28] codetools_0.2-20 htmltools_0.5.9 bayesplot_1.15.0 \n", + "[31] pillar_1.11.1 crayon_1.5.3 MASS_7.3-65 \n", + "[34] StanHeaders_2.32.10 parallelly_1.46.1 rstan_2.32.7 \n", + "[37] tidyselect_1.2.1 digest_0.6.39 stringi_1.8.7 \n", + "[40] mvtnorm_1.3-3 future_1.69.0 nonnest2_0.5-8 \n", + "[43] dplyr_1.2.0 listenv_0.10.0 labeling_0.4.3 \n", + "[46] fastmap_1.2.0 grid_4.4.3 cli_3.6.5 \n", + "[49] magrittr_2.0.4 loo_2.9.0 base64enc_0.1-6 \n", + "[52] pkgbuild_1.4.8 IRdisplay_1.1 future.apply_1.20.1 \n", + "[55] pbivnorm_0.6.0 withr_3.0.2 scales_1.4.0 \n", + "[58] IRkernel_1.3.2 matrixStats_1.5.0 globals_0.19.0 \n", + "[61] igraph_2.2.2 pbdZMQ_0.3-14 ragg_1.5.0 \n", + "[64] zoo_1.8-15 coda_0.19-4.1 evaluate_1.0.5 \n", + "[67] viridisLite_0.4.3 rstantools_2.6.0 rlang_1.1.7 \n", + "[70] isoband_0.3.0 glue_1.8.0 jsonlite_2.0.0 \n", + "[73] plyr_1.8.9 R6_2.6.1 systemfonts_1.3.1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Clean up temporary files\n", + "tmp_patterns <- c(\"lavExport*\", \"*.stan\", \"tmp_*\", \"*.bak\")\n", + "for (pat in tmp_patterns) {\n", + " files <- Sys.glob(file.path(RESULT_DIR, pat))\n", + " if (length(files) > 0) {\n", + " file.remove(files)\n", + " cat(\"Removed:\", length(files), \"files matching\", pat, \"\\n\")\n", + " }\n", + "}\n", + "# Also clean from working directory\n", + "for (pat in tmp_patterns) {\n", + " files <- Sys.glob(pat)\n", + " if (length(files) > 0) {\n", + " file.remove(files)\n", + " cat(\"Removed from cwd:\", length(files), \"files matching\", pat, \"\\n\")\n", + " }\n", + "}\n", + "\n", + "cat(\"\\n=== Session Info ===\\n\")\n", + "sessionInfo()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_57_SEM_mediation.ipynb b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_57_SEM_mediation.ipynb new file mode 100644 index 0000000..ec7812d --- /dev/null +++ b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/APOE_ind_set_57_SEM_mediation.ipynb @@ -0,0 +1,17124 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e946c853", + "metadata": {}, + "source": [ + "# APOE-ind Set 57 → ARHGAP35 Expression → Tangles (sqrt) Mediation Analysis\n", + "\n", + "## Overview\n", + "This notebook performs mediation analysis with two variants in a APOE-independent eQTL set of ARHGAP35 in Ast.10 as the exposure:\n", + "1. **chr19_44827033_TGATAGATAGATAGATA_TGATA** → ARHGAP35_expression → tangles_sqrt\n", + "2. **chr19_44840322_G_A** → ARHGAP35_expression → tangles_sqrt\n", + "\n", + "## Mediation Model\n", + "- **Path a** (X → M): variant → ARHGAP35_expression, covariates: msex_u, age_death_u, pmi_u\n", + "- **Path b** (M → Y): ARHGAP35_expression → tangles_sqrt, covariates: msex_u, age_death_u, educ, apoe4_dose, apoe2_dose\n", + "- **Path c'** (X → Y direct): variant → tangles_sqrt, covariates: msex_u, age_death_u, educ, apoe4_dose, apoe2_dose\n", + "\n", + "## Methods\n", + "1. **SEM with FIML** (primary) — handles partial overlap / missing data under MAR\n", + "2. **Bootstrap** — resamples entire pipeline for robust CI\n", + "3. **MNAR Sensitivity** — tipping-point analysis for departures from MAR\n", + "4. **Bayesian (blavaan)** — MCMC-based posterior inference\n", + "\n", + "## Data\n", + "- N = 1153 total samples\n", + "- 113 with expression data, 1067 with tangles_sqrt, 108 with both\n", + "- Partial overlap handled by FIML\n", + "\n", + "## Conclusion\n", + " - All four analytical approaches used the full dataset (N=1,153) with missing-data handling (FIML or Bayesian data augmentation) to ensure maximum statistical power.\n", + " - Across all four analytical approaches, the indirect (mediation) effects were consistently small and non-significant for both variants (FIML indirect: 0.044, p=0.205 for the indel; 0.048, p=0.195 for the SNP; all 95% CIs spanning zero). \n", + " - The direct effects of both variants on tangles were highly significant (c' ≈ −0.33, p<0.001), indicating that the variant–tangle association operates through pathways other than ARHGAP35 expression in Ast.10 (consistent with bootstrap with FIML and Bayesian blavaan sensitivity analyses on the full dataset).\n", + " - The MNAR sensitivity analysis showed that even moderate departures from the missing-at-random assumption would not change this conclusion.\n", + " - Together, these results provide evidence that ARHGAP35 expression in Ast.10 does not mediate the set 57 genetic effects on tangle burden." + ] + }, + { + "cell_type": "markdown", + "id": "cdf1b425", + "metadata": {}, + "source": [ + "## 0. Setup and Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "88b6d8b0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T14:37:51.922575Z", + "iopub.status.busy": "2026-03-27T14:37:51.918045Z", + "iopub.status.idle": "2026-03-27T14:37:54.739575Z", + "shell.execute_reply": "2026-03-27T14:37:54.736973Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total samples: 1153 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Non-NA expression: 113 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Non-NA tangles_sqrt: 1067 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Non-NA both (expression & tangles): 108 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Exposures: chr19_44827033_TGATAGATAGATAGATA_TGATA chr19_44840322_G_A \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mediator: ARHGAP35_expression \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Outcome: tangles_sqrt \n" + ] + } + ], + "source": [ + "# ── Libraries ──────────────────────────────────────────────────────────────\n", + "suppressPackageStartupMessages({\n", + " library(lavaan)\n", + " library(ggplot2)\n", + " library(dplyr)\n", + " library(tidyr)\n", + " library(gridExtra)\n", + " library(boot)\n", + "})\n", + "\n", + "# ── Paths ──────────────────────────────────────────────────────────────────\n", + "base_dir <- \"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set57\"\n", + "input_file <- file.path(base_dir, \"APOE_ind_set_57_mediation_all_input.txt\")\n", + "fiml_dir <- file.path(base_dir, \"main_SEM_FIML\")\n", + "boot_dir <- file.path(base_dir, \"bootstrap\")\n", + "mnar_dir <- file.path(base_dir, \"MNAR_sensitivity\")\n", + "bayes_dir <- file.path(base_dir, \"bayesian_blavaan\")\n", + "summ_dir <- file.path(base_dir, \"summary\")\n", + "\n", + "# ── Read data ─────────────────────────────────────────────────────────────\n", + "dat <- read.delim(input_file, stringsAsFactors = FALSE)\n", + "cat(\"Total samples:\", nrow(dat), \"\\n\")\n", + "cat(\"Non-NA expression:\", sum(!is.na(dat$ARHGAP35_expression)), \"\\n\")\n", + "cat(\"Non-NA tangles_sqrt:\", sum(!is.na(dat$tangles_sqrt)), \"\\n\")\n", + "cat(\"Non-NA both (expression & tangles):\", sum(!is.na(dat$ARHGAP35_expression) & !is.na(dat$tangles_sqrt)), \"\\n\")\n", + "\n", + "# ── Define exposure variants ──────────────────────────────────────────────\n", + "exposures <- c(\"chr19_44827033_TGATAGATAGATAGATA_TGATA\", \"chr19_44840322_G_A\")\n", + "mediator <- \"ARHGAP35_expression\"\n", + "outcome <- \"tangles_sqrt\"\n", + "\n", + "# Covariates for each path\n", + "cov_xm <- c(\"msex_u\", \"age_death_u\", \"pmi_u\") # X → M\n", + "cov_my <- c(\"msex_u\", \"age_death_u\", \"educ\", \"apoe4_dose\", \"apoe2_dose\") # M → Y and X → Y\n", + "\n", + "cat(\"\\nExposures:\", exposures, \"\\n\")\n", + "cat(\"Mediator:\", mediator, \"\\n\")\n", + "cat(\"Outcome:\", outcome, \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "32693884", + "metadata": {}, + "source": [ + "## 1. Primary Analysis: SEM with FIML" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ba89180d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T14:37:54.823892Z", + "iopub.status.busy": "2026-03-27T14:37:54.775260Z", + "iopub.status.idle": "2026-03-27T14:37:54.851671Z", + "shell.execute_reply": "2026-03-27T14:37:54.849112Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model syntax for exposure 1:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ARHGAP35_expression ~ a*chr19_44827033_TGATAGATAGATAGATA_TGATA + msex_u + age_death_u + pmi_u\n", + "tangles_sqrt ~ b*ARHGAP35_expression + c_prime*chr19_44827033_TGATAGATAGATAGATA_TGATA + msex_u + age_death_u + educ + apoe4_dose + apoe2_dose\n", + "indirect := a*b\n", + "total := c_prime + a*b\n", + "prop_med := (a*b) / (c_prime + a*b) \n" + ] + } + ], + "source": [ + "# ── Function to build lavaan model syntax for one exposure ─────────────────\n", + "build_mediation_syntax <- function(exposure) {\n", + " # Path a: M ~ X + covariates_xm\n", + " path_a <- paste0(mediator, \" ~ a*\", exposure, \" + \",\n", + " paste(cov_xm, collapse = \" + \"))\n", + " # Path b + c': Y ~ M + X + covariates_my\n", + " path_bc <- paste0(outcome, \" ~ b*\", mediator, \" + c_prime*\", exposure, \" + \",\n", + " paste(cov_my, collapse = \" + \"))\n", + " # Defined parameters\n", + " defs <- c(\n", + " \"indirect := a*b\",\n", + " \"total := c_prime + a*b\",\n", + " \"prop_med := (a*b) / (c_prime + a*b)\"\n", + " )\n", + " paste(c(path_a, path_bc, defs), collapse = \"\\n\")\n", + "}\n", + "\n", + "cat(\"Model syntax for exposure 1:\\n\")\n", + "cat(build_mediation_syntax(exposures[1]), \"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7431a91a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T14:37:54.858574Z", + "iopub.status.busy": "2026-03-27T14:37:54.856470Z", + "iopub.status.idle": "2026-03-27T14:38:01.928943Z", + "shell.execute_reply": "2026-03-27T14:38:01.926804Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "========================================================\n", + "FIML: Exposure = chr19_44827033_TGATAGATAGATAGATA_TGATA \n", + "========================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_data_full(): \n", + " due to missing values, some pairwise combinations have less than 10% \n", + " coverage; use lavInspect(fit, \"coverage\") to investigate.”\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model fit summary:\n", + "lavaan 0.6-21 ended normally after 110 iterations\n", + "\n", + " Estimator ML\n", + " Optimization method NLMINB\n", + " Number of model parameters 50\n", + "\n", + " Number of observations 1153\n", + " Number of missing patterns 8\n", + "\n", + "Model Test User Model:\n", + " \n", + " Test statistic 7.413\n", + " Degrees of freedom 4\n", + " P-value (Chi-square) 0.116\n", + "\n", + "Model Test Baseline Model:\n", + "\n", + " Test statistic 214.255\n", + " Degrees of freedom 15\n", + " P-value 0.000\n", + "\n", + "User Model versus Baseline Model:\n", + "\n", + " Comparative Fit Index (CFI) 0.983\n", + " Tucker-Lewis Index (TLI) 0.936\n", + " \n", + " Robust Comparative Fit Index (CFI) 0.649\n", + " Robust Tucker-Lewis Index (TLI) -0.315\n", + "\n", + "Loglikelihood and Information Criteria:\n", + "\n", + " Loglikelihood user model (H0) -15085.536\n", + " Loglikelihood unrestricted model (H1) -15081.830\n", + " \n", + " Akaike (AIC) 30271.073\n", + " Bayesian (BIC) 30523.579\n", + " Sample-size adjusted Bayesian (SABIC) 30364.763\n", + "\n", + "Root Mean Square Error of Approximation:\n", + "\n", + " RMSEA 0.027\n", + " 90 Percent confidence interval - lower 0.000\n", + " 90 Percent confidence interval - upper 0.057\n", + " P-value H_0: RMSEA <= 0.050 0.880\n", + " P-value H_0: RMSEA >= 0.080 0.001\n", + " \n", + " Robust RMSEA 0.123\n", + " 90 Percent confidence interval - lower 0.000\n", + " 90 Percent confidence interval - upper 0.233\n", + " P-value H_0: Robust RMSEA <= 0.050 0.112\n", + " P-value H_0: Robust RMSEA >= 0.080 0.795\n", + "\n", + "Standardized Root Mean Square Residual:\n", + "\n", + " SRMR 0.046\n", + "\n", + "Parameter Estimates:\n", + "\n", + " Standard errors Standard\n", + " Information Observed\n", + " Observed information based on Hessian\n", + "\n", + "Regressions:\n", + " Estimate Std.Err z-value P(>|z|) ci.lower ci.upper\n", + " ARHGAP35_expression ~ \n", + " c19_448 (a) -0.328 0.200 -1.639 0.101 -0.720 0.064\n", + " msex_u 0.082 0.185 0.445 0.657 -0.281 0.445\n", + " ag_dth_ -0.006 0.014 -0.420 0.674 -0.034 0.022\n", + " pmi_u 0.046 0.021 2.200 0.028 0.005 0.086\n", + " tangles_sqrt ~ \n", + " ARHGAP3 (b) -0.134 0.073 -1.836 0.066 -0.277 0.009\n", + " c19_448 (c_pr) -0.330 0.087 -3.808 0.000 -0.500 -0.160\n", + " msex_u -0.375 0.084 -4.485 0.000 -0.539 -0.211\n", + " ag_dth_ 0.039 0.006 6.397 0.000 0.027 0.051\n", + " educ -0.419 0.099 -4.215 0.000 -0.614 -0.224\n", + " apo4_ds -0.030 0.011 -2.827 0.005 -0.051 -0.009\n", + " apo2_ds 0.769 0.079 9.779 0.000 0.615 0.923\n", + " Std.lv Std.all\n", + " \n", + " -0.328 -0.149\n", + " 0.082 0.038\n", + " -0.006 -0.038\n", + " 0.046 0.328\n", + " \n", + " -0.134 -0.104\n", + " -0.330 -0.117\n", + " -0.375 -0.134\n", + " 0.039 0.194\n", + " -0.419 -0.121\n", + " -0.030 -0.081\n", + " 0.769 0.278\n", + "\n", + "Covariances:\n", + " Estimate Std.Err z-value P(>|z|)\n", + " chr19_44827033_TGATAGATAGATAGATA_TGATA ~~ \n", + " msex_u 0.004 0.007 0.528 0.597\n", + " age_death_u 0.092 0.093 0.993 0.321\n", + " pmi_u 0.008 0.105 0.075 0.940\n", + " educ -0.020 0.005 -3.691 0.000\n", + " apoe4_dose 0.035 0.051 0.681 0.496\n", + " apoe2_dose -0.005 0.007 -0.722 0.470\n", + " msex_u ~~ \n", + " age_death_u -0.599 0.095 -6.276 0.000\n", + " pmi_u 0.100 0.106 0.945 0.345\n", + " educ -0.011 0.005 -1.979 0.048\n", + " apoe4_dose 0.348 0.052 6.633 0.000\n", + " apoe2_dose 0.000 0.007 0.001 0.999\n", + " age_death_u ~~ \n", + " pmi_u 0.723 1.464 0.494 0.621\n", + " educ 0.219 0.076 2.877 0.004\n", + " apoe4_dose -3.210 0.718 -4.468 0.000\n", + " apoe2_dose -0.350 0.096 -3.665 0.000\n", + " pmi_u ~~ \n", + " educ -0.006 0.086 -0.075 0.940\n", + " apoe4_dose 0.124 0.802 0.155 0.877\n", + " apoe2_dose -0.071 0.107 -0.664 0.507\n", + " educ ~~ \n", + " apoe4_dose -0.092 0.042 -2.224 0.026\n", + " apoe2_dose -0.025 0.006 -4.526 0.000\n", + " apoe4_dose ~~ \n", + " apoe2_dose 0.132 0.052 2.541 0.011\n", + " ci.lower ci.upper Std.lv Std.all\n", + " \n", + " -0.010 0.017 0.004 0.016\n", + " -0.090 0.274 0.092 0.030\n", + " -0.197 0.213 0.008 0.002\n", + " -0.031 -0.009 -0.020 -0.112\n", + " -0.065 0.134 0.035 0.020\n", + " -0.018 0.008 -0.005 -0.022\n", + " \n", + " -0.786 -0.412 -0.599 -0.191\n", + " -0.107 0.307 0.100 0.028\n", + " -0.022 -0.000 -0.011 -0.060\n", + " 0.245 0.451 0.348 0.203\n", + " -0.013 0.013 0.000 0.000\n", + " \n", + " -2.146 3.592 0.723 0.015\n", + " 0.070 0.368 0.219 0.087\n", + " -4.618 -1.802 -3.210 -0.135\n", + " -0.538 -0.163 -0.350 -0.111\n", + " \n", + " -0.174 0.161 -0.006 -0.002\n", + " -1.448 1.696 0.124 0.005\n", + " -0.282 0.139 -0.071 -0.020\n", + " \n", + " -0.174 -0.011 -0.092 -0.067\n", + " -0.036 -0.014 -0.025 -0.137\n", + " \n", + " 0.030 0.235 0.132 0.077\n", + "\n", + "Intercepts:\n", + " Estimate Std.Err z-value P(>|z|) ci.lower ci.upper\n", + " .ARHGAP35_xprss 0.276 1.248 0.221 0.825 -2.170 2.723\n", + " .tangles_sqrt -0.684 0.593 -1.154 0.248 -1.847 0.478\n", + " c19_44827033_T 0.268 0.014 19.381 0.000 0.241 0.295\n", + " msex_u 0.346 0.014 24.293 0.000 0.318 0.374\n", + " age_death_u 88.935 0.197 451.410 0.000 88.549 89.321\n", + " pmi_u 8.757 0.222 39.382 0.000 8.321 9.193\n", + " educ 0.159 0.012 13.836 0.000 0.137 0.182\n", + " apoe4_dose 16.371 0.108 151.572 0.000 16.160 16.583\n", + " apoe2_dose 0.272 0.014 18.838 0.000 0.244 0.300\n", + " Std.lv Std.all\n", + " 0.276 0.268\n", + " -0.684 -0.515\n", + " 0.268 0.571\n", + " 0.346 0.727\n", + " 88.935 13.512\n", + " 8.757 1.179\n", + " 0.159 0.416\n", + " 16.371 4.543\n", + " 0.272 0.566\n", + "\n", + "Variances:\n", + " Estimate Std.Err z-value P(>|z|) ci.lower ci.upper\n", + " .ARHGAP35_xprss 0.921 0.123 7.511 0.000 0.681 1.161\n", + " .tangles_sqrt 1.446 0.065 22.210 0.000 1.319 1.574\n", + " c19_44827033_T 0.220 0.009 24.010 0.000 0.202 0.238\n", + " msex_u 0.226 0.010 23.622 0.000 0.207 0.245\n", + " age_death_u 43.319 1.834 23.622 0.000 39.725 46.914\n", + " pmi_u 55.179 2.336 23.622 0.000 50.601 59.758\n", + " educ 0.146 0.006 23.512 0.000 0.134 0.159\n", + " apoe4_dose 12.986 0.551 23.586 0.000 11.907 14.065\n", + " apoe2_dose 0.231 0.010 23.516 0.000 0.211 0.250\n", + " Std.lv Std.all\n", + " 0.921 0.866\n", + " 1.446 0.819\n", + " 0.220 1.000\n", + " 0.226 1.000\n", + " 43.319 1.000\n", + " 55.179 1.000\n", + " 0.146 1.000\n", + " 12.986 1.000\n", + " 0.231 1.000\n", + "\n", + "Defined Parameters:\n", + " Estimate Std.Err z-value P(>|z|) ci.lower ci.upper\n", + " indirect 0.044 0.035 1.267 0.205 -0.024 0.112\n", + " total -0.286 0.080 -3.572 0.000 -0.443 -0.129\n", + " prop_med -0.153 0.129 -1.187 0.235 -0.407 0.100\n", + " Std.lv Std.all\n", + " 0.044 0.015\n", + " -0.286 -0.101\n", + " -0.153 -0.153\n", + "\n", + "\n", + "Key path estimates:\n", + " label est se z pvalue ci.lower ci.upper CI_95 sig\n", + "1 a -0.328 0.200 -1.639 0.101 -0.720 0.064 [-0.7202, 0.0643] \n", + "5 b -0.134 0.073 -1.836 0.066 -0.277 0.009 [-0.2766, 0.0090] .\n", + "6 c_prime -0.330 0.087 -3.808 0.000 -0.500 -0.160 [-0.4997, -0.1601] ***\n", + "51 indirect 0.044 0.035 1.267 0.205 -0.024 0.112 [-0.0240, 0.1118] \n", + "52 total -0.286 0.080 -3.572 0.000 -0.443 -0.129 [-0.4429, -0.1291] ***\n", + "53 prop_med -0.153 0.129 -1.187 0.235 -0.407 0.100 [-0.4068, 0.1000] \n", + "\n", + "========================================================\n", + "FIML: Exposure = chr19_44840322_G_A \n", + "========================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_data_full(): \n", + " due to missing values, some pairwise combinations have less than 10% \n", + " coverage; use lavInspect(fit, \"coverage\") to investigate.”\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model fit summary:\n", + "lavaan 0.6-21 ended normally after 110 iterations\n", + "\n", + " Estimator ML\n", + " Optimization method NLMINB\n", + " Number of model parameters 50\n", + "\n", + " Number of observations 1153\n", + " Number of missing patterns 8\n", + "\n", + "Model Test User Model:\n", + " \n", + " Test statistic 7.697\n", + " Degrees of freedom 4\n", + " P-value (Chi-square) 0.103\n", + "\n", + "Model Test Baseline Model:\n", + "\n", + " Test statistic 215.626\n", + " Degrees of freedom 15\n", + " P-value 0.000\n", + "\n", + "User Model versus Baseline Model:\n", + "\n", + " Comparative Fit Index (CFI) 0.982\n", + " Tucker-Lewis Index (TLI) 0.931\n", + " \n", + " Robust Comparative Fit Index (CFI) 0.642\n", + " Robust Tucker-Lewis Index (TLI) -0.344\n", + "\n", + "Loglikelihood and Information Criteria:\n", + "\n", + " Loglikelihood user model (H0) -15113.587\n", + " Loglikelihood unrestricted model (H1) -15109.739\n", + " \n", + " Akaike (AIC) 30327.175\n", + " Bayesian (BIC) 30579.681\n", + " Sample-size adjusted Bayesian (SABIC) 30420.865\n", + "\n", + "Root Mean Square Error of Approximation:\n", + "\n", + " RMSEA 0.028\n", + " 90 Percent confidence interval - lower 0.000\n", + " 90 Percent confidence interval - upper 0.058\n", + " P-value H_0: RMSEA <= 0.050 0.868\n", + " P-value H_0: RMSEA >= 0.080 0.001\n", + " \n", + " Robust RMSEA 0.127\n", + " 90 Percent confidence interval - lower 0.000\n", + " 90 Percent confidence interval - upper 0.235\n", + " P-value H_0: Robust RMSEA <= 0.050 0.098\n", + " P-value H_0: Robust RMSEA >= 0.080 0.814\n", + "\n", + "Standardized Root Mean Square Residual:\n", + "\n", + " SRMR 0.045\n", + "\n", + "Parameter Estimates:\n", + "\n", + " Standard errors Standard\n", + " Information Observed\n", + " Observed information based on Hessian\n", + "\n", + "Regressions:\n", + " Estimate Std.Err z-value P(>|z|) ci.lower ci.upper\n", + " ARHGAP35_expression ~ \n", + " c19_448 (a) -0.343 0.202 -1.700 0.089 -0.739 0.052\n", + " msex_u 0.087 0.185 0.469 0.639 -0.276 0.450\n", + " ag_dth_ -0.006 0.014 -0.459 0.646 -0.034 0.021\n", + " pmi_u 0.046 0.020 2.224 0.026 0.005 0.086\n", + " tangles_sqrt ~ \n", + " ARHGAP3 (b) -0.139 0.073 -1.904 0.057 -0.281 0.004\n", + " c19_448 (c_pr) -0.336 0.086 -3.928 0.000 -0.504 -0.168\n", + " msex_u -0.369 0.084 -4.396 0.000 -0.533 -0.204\n", + " ag_dth_ 0.039 0.006 6.328 0.000 0.027 0.051\n", + " educ -0.420 0.099 -4.225 0.000 -0.614 -0.225\n", + " apo4_ds -0.030 0.011 -2.860 0.004 -0.051 -0.010\n", + " apo2_ds 0.753 0.079 9.538 0.000 0.598 0.907\n", + " Std.lv Std.all\n", + " \n", + " -0.343 -0.161\n", + " 0.087 0.040\n", + " -0.006 -0.041\n", + " 0.046 0.327\n", + " \n", + " -0.139 -0.108\n", + " -0.336 -0.122\n", + " -0.369 -0.132\n", + " 0.039 0.192\n", + " -0.420 -0.121\n", + " -0.030 -0.082\n", + " 0.753 0.272\n", + "\n", + "Covariances:\n", + " Estimate Std.Err z-value P(>|z|) ci.lower ci.upper\n", + " chr19_44840322_G_A ~~ \n", + " msex_u 0.008 0.007 1.102 0.270 -0.006 0.021\n", + " age_death_u 0.071 0.096 0.743 0.457 -0.116 0.258\n", + " pmi_u -0.026 0.108 -0.244 0.807 -0.238 0.185\n", + " educ -0.020 0.006 -3.639 0.000 -0.031 -0.009\n", + " apoe4_dose 0.022 0.052 0.424 0.672 -0.080 0.125\n", + " apoe2_dose -0.018 0.007 -2.598 0.009 -0.032 -0.004\n", + " msex_u ~~ \n", + " age_death_u -0.599 0.095 -6.276 0.000 -0.786 -0.412\n", + " pmi_u 0.100 0.106 0.945 0.345 -0.107 0.307\n", + " educ -0.011 0.005 -1.986 0.047 -0.022 -0.000\n", + " apoe4_dose 0.348 0.052 6.633 0.000 0.245 0.451\n", + " apoe2_dose 0.000 0.007 0.002 0.998 -0.013 0.013\n", + " age_death_u ~~ \n", + " pmi_u 0.723 1.464 0.494 0.621 -2.146 3.592\n", + " educ 0.219 0.076 2.882 0.004 0.070 0.368\n", + " apoe4_dose -3.210 0.718 -4.469 0.000 -4.618 -1.802\n", + " apoe2_dose -0.350 0.096 -3.665 0.000 -0.538 -0.163\n", + " pmi_u ~~ \n", + " educ -0.007 0.086 -0.077 0.938 -0.175 0.161\n", + " apoe4_dose 0.124 0.802 0.154 0.877 -1.448 1.695\n", + " apoe2_dose -0.071 0.107 -0.660 0.509 -0.282 0.140\n", + " educ ~~ \n", + " apoe4_dose -0.093 0.042 -2.234 0.025 -0.174 -0.011\n", + " apoe2_dose -0.025 0.006 -4.526 0.000 -0.036 -0.014\n", + " apoe4_dose ~~ \n", + " apoe2_dose 0.132 0.052 2.537 0.011 0.030 0.235\n", + " Std.lv Std.all\n", + " \n", + " 0.008 0.033\n", + " 0.071 0.022\n", + " -0.026 -0.007\n", + " -0.020 -0.110\n", + " 0.022 0.013\n", + " -0.018 -0.078\n", + " \n", + " -0.599 -0.191\n", + " 0.100 0.028\n", + " -0.011 -0.060\n", + " 0.348 0.203\n", + " 0.000 0.000\n", + " \n", + " 0.723 0.015\n", + " 0.219 0.087\n", + " -3.210 -0.135\n", + " -0.350 -0.111\n", + " \n", + " -0.007 -0.002\n", + " 0.124 0.005\n", + " -0.071 -0.020\n", + " \n", + " -0.093 -0.067\n", + " -0.025 -0.137\n", + " \n", + " 0.132 0.076\n", + "\n", + "Intercepts:\n", + " Estimate Std.Err z-value P(>|z|) ci.lower ci.upper\n", + " .ARHGAP35_xprss 0.324 1.247 0.259 0.795 -2.120 2.767\n", + " .tangles_sqrt -0.643 0.595 -1.081 0.280 -1.808 0.522\n", + " c19_44840322_G 0.281 0.014 19.756 0.000 0.253 0.309\n", + " msex_u 0.346 0.014 24.297 0.000 0.318 0.374\n", + " age_death_u 88.936 0.197 451.405 0.000 88.549 89.322\n", + " pmi_u 8.757 0.222 39.380 0.000 8.321 9.192\n", + " educ 0.159 0.012 13.834 0.000 0.137 0.182\n", + " apoe4_dose 16.371 0.108 151.571 0.000 16.160 16.583\n", + " apoe2_dose 0.272 0.014 18.835 0.000 0.244 0.300\n", + " Std.lv Std.all\n", + " 0.324 0.313\n", + " -0.643 -0.484\n", + " 0.281 0.582\n", + " 0.346 0.727\n", + " 88.936 13.512\n", + " 8.757 1.179\n", + " 0.159 0.416\n", + " 16.371 4.543\n", + " 0.272 0.566\n", + "\n", + "Variances:\n", + " Estimate Std.Err z-value P(>|z|) ci.lower ci.upper\n", + " .ARHGAP35_xprss 0.920 0.122 7.512 0.000 0.680 1.160\n", + " .tangles_sqrt 1.444 0.065 22.154 0.000 1.316 1.572\n", + " c19_44840322_G 0.233 0.010 24.010 0.000 0.214 0.252\n", + " msex_u 0.226 0.010 23.622 0.000 0.207 0.245\n", + " age_death_u 43.319 1.834 23.622 0.000 39.725 46.913\n", + " pmi_u 55.179 2.336 23.622 0.000 50.601 59.758\n", + " educ 0.147 0.006 23.510 0.000 0.134 0.159\n", + " apoe4_dose 12.986 0.551 23.586 0.000 11.907 14.066\n", + " apoe2_dose 0.231 0.010 23.518 0.000 0.211 0.250\n", + " Std.lv Std.all\n", + " 0.920 0.862\n", + " 1.444 0.817\n", + " 0.233 1.000\n", + " 0.226 1.000\n", + " 43.319 1.000\n", + " 55.179 1.000\n", + " 0.147 1.000\n", + " 12.986 1.000\n", + " 0.231 1.000\n", + "\n", + "Defined Parameters:\n", + " Estimate Std.Err z-value P(>|z|) ci.lower ci.upper\n", + " indirect 0.048 0.037 1.297 0.195 -0.024 0.120\n", + " total -0.289 0.078 -3.704 0.000 -0.441 -0.136\n", + " prop_med -0.165 0.135 -1.218 0.223 -0.430 0.101\n", + " Std.lv Std.all\n", + " 0.048 0.017\n", + " -0.289 -0.105\n", + " -0.165 -0.165\n", + "\n", + "\n", + "Key path estimates:\n", + " label est se z pvalue ci.lower ci.upper CI_95 sig\n", + "1 a -0.343 0.202 -1.700 0.089 -0.739 0.052 [-0.7393, 0.0525] .\n", + "5 b -0.139 0.073 -1.904 0.057 -0.281 0.004 [-0.2813, 0.0041] .\n", + "6 c_prime -0.336 0.086 -3.928 0.000 -0.504 -0.168 [-0.5040, -0.1685] ***\n", + "51 indirect 0.048 0.037 1.297 0.195 -0.024 0.120 [-0.0244, 0.1195] \n", + "52 total -0.289 0.078 -3.704 0.000 -0.441 -0.136 [-0.4414, -0.1359] ***\n", + "53 prop_med -0.165 0.135 -1.218 0.223 -0.430 0.101 [-0.4303, 0.1005] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "FIML analysis complete for both exposures.\n" + ] + } + ], + "source": [ + "# ── Run FIML for both exposures ─────────────────────────────────────────────\n", + "fiml_results_list <- list()\n", + "\n", + "for (exp_var in exposures) {\n", + " cat(\"\\n========================================================\\n\")\n", + " cat(\"FIML: Exposure =\", exp_var, \"\\n\")\n", + " cat(\"========================================================\\n\")\n", + " \n", + " mod_syntax <- build_mediation_syntax(exp_var)\n", + " \n", + " fit <- sem(mod_syntax, data = dat, missing = \"fiml.x\",\n", + " fixed.x = FALSE, estimator = \"ML\")\n", + " \n", + " cat(\"\\nModel fit summary:\\n\")\n", + " print(summary(fit, fit.measures = TRUE, ci = TRUE, standardized = TRUE))\n", + " \n", + " # Extract all parameters\n", + " params <- parameterEstimates(fit, ci = TRUE, standardized = TRUE)\n", + " \n", + " # Fit measures\n", + " fm <- fitMeasures(fit, c(\"chisq\", \"df\", \"pvalue\", \"cfi\", \"rmsea\", \"srmr\",\n", + " \"aic\", \"bic\", \"logl\", \"npar\"))\n", + " \n", + " # Key paths\n", + " key_labels <- c(\"a\", \"b\", \"c_prime\", \"indirect\", \"total\", \"prop_med\")\n", + " key <- params[params$label %in% key_labels,\n", + " c(\"label\", \"est\", \"se\", \"z\", \"pvalue\", \"ci.lower\", \"ci.upper\")]\n", + " key$CI_95 <- sprintf(\"[%.4f, %.4f]\", key$ci.lower, key$ci.upper)\n", + " key$sig <- ifelse(key$pvalue < 0.001, \"***\",\n", + " ifelse(key$pvalue < 0.01, \"**\",\n", + " ifelse(key$pvalue < 0.05, \"*\",\n", + " ifelse(key$pvalue < 0.1, \".\", \"\"))))\n", + " \n", + " cat(\"\\nKey path estimates:\\n\")\n", + " print(key)\n", + " \n", + " fiml_results_list[[exp_var]] <- list(\n", + " fit = fit, params = params, fit_measures = fm, key = key, exposure = exp_var\n", + " )\n", + "}\n", + "\n", + "cat(\"\\nFIML analysis complete for both exposures.\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0e788159", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T14:38:01.934512Z", + "iopub.status.busy": "2026-03-27T14:38:01.932853Z", + "iopub.status.idle": "2026-03-27T14:38:02.019039Z", + "shell.execute_reply": "2026-03-27T14:38:02.016960Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FIML results saved to: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set57/main_SEM_FIML \n" + ] + } + ], + "source": [ + "# ── Save FIML results ───────────────────────────────────────────────────────\n", + "for (exp_var in exposures) {\n", + " res <- fiml_results_list[[exp_var]]\n", + " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", + " \n", + " # All parameters\n", + " write.csv(res$params, file.path(fiml_dir, paste0(\"fiml_all_params_\", prefix, \".csv\")),\n", + " row.names = FALSE)\n", + " # Key paths\n", + " write.csv(res$key, file.path(fiml_dir, paste0(\"fiml_key_paths_\", prefix, \".csv\")),\n", + " row.names = FALSE)\n", + " # Fit measures — convert named vector to single-row data.frame safely\n", + " fm_df <- data.frame(as.list(res$fit_measures))\n", + " write.csv(fm_df, file.path(fiml_dir, paste0(\"fiml_fit_measures_\", prefix, \".csv\")),\n", + " row.names = FALSE)\n", + " \n", + " # Summary table\n", + " summ_tbl <- res$key[, c(\"label\", \"est\", \"se\", \"pvalue\", \"CI_95\", \"sig\")]\n", + " write.csv(summ_tbl, file.path(fiml_dir, paste0(\"fiml_summary_table_\", prefix, \".csv\")),\n", + " row.names = FALSE)\n", + "}\n", + "\n", + "cat(\"FIML results saved to:\", fiml_dir, \"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e676f1f2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T14:38:02.024642Z", + "iopub.status.busy": "2026-03-27T14:38:02.022966Z", + "iopub.status.idle": "2026-03-27T14:38:04.697728Z", + "shell.execute_reply": "2026-03-27T14:38:04.688446Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n", + "“for 'ARHGAP35 expression mediating variant → tangles_sqrt' in 'mbcsToSbcs': -> substituted for → (U+2192)”\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FIML forest plot saved.\n" + ] + }, + { + "data": { + "image/png": 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IFgBwAAAMARCHYAAAAAHIFgBwAAAMAR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7sIAEAj4/V6CwoKioq0GdYPZ2ocPXbLli377LPPJEly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\nQEJAYgcAAAAAICEgsQMAAAAAkBD/D+kEHWXYtxhPAAAAAElFTkSuQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ── FIML Forest Plot ────────────────────────────────────────────────────────\n", + "fiml_plot_data <- do.call(rbind, lapply(exposures, function(exp_var) {\n", + " res <- fiml_results_list[[exp_var]]\n", + " key <- res$key\n", + " key$exposure <- exp_var\n", + " key\n", + "}))\n", + "\n", + "fiml_plot_data$label <- factor(fiml_plot_data$label,\n", + " levels = c(\"a\", \"b\", \"c_prime\", \"indirect\", \"total\", \"prop_med\"))\n", + "fiml_plot_data$exposure_short <- ifelse(\n", + " grepl(\"44827033\", fiml_plot_data$exposure), \"44827033_indel\", \"44840322_G_A\")\n", + "\n", + "p_fiml <- ggplot(fiml_plot_data, aes(x = est, y = label, color = exposure_short)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci.lower, xmax = ci.upper),\n", + " position = position_dodge(width = 0.5), size = 0.6) +\n", + " labs(title = \"FIML SEM: Path Estimates\",\n", + " subtitle = \"ARHGAP35 expression mediating variant → tangles_sqrt\",\n", + " x = \"Estimate (95% CI)\", y = \"Path\", color = \"Exposure\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + "ggsave(file.path(fiml_dir, \"fiml_forest_plot.png\"), p_fiml, width = 8, height = 5, dpi = 150)\n", + "ggsave(file.path(fiml_dir, \"fiml_forest_plot.pdf\"), p_fiml, width = 8, height = 5)\n", + "print(p_fiml)\n", + "cat(\"FIML forest plot saved.\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "9b413415", + "metadata": {}, + "source": [ + "## 2. Sensitivity Analysis: Bootstrap" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "442faf59", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T14:38:04.704428Z", + "iopub.status.busy": "2026-03-27T14:38:04.702642Z", + "iopub.status.idle": "2026-03-27T15:34:35.089886Z", + "shell.execute_reply": "2026-03-27T15:34:35.087603Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bootstrap: Exposure = chr19_44827033_TGATAGATAGATAGATA_TGATA \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_data_full(): \n", + " due to missing values, some pairwise combinations have less than 10% \n", + " coverage; use lavInspect(fit, \"coverage\") to investigate.”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " 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Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Key bootstrap results:\n", + " label est se z pvalue ci.lower ci.upper CI_95 sig\n", + "1 a -0.328 0.219 -1.494 0.135 -0.739 0.116 [-0.7389, 0.1157] \n", + "5 b -0.134 0.127 -1.054 0.292 -0.288 0.209 [-0.2879, 0.2090] \n", + "6 c_prime -0.330 0.091 -3.619 0.000 -0.525 -0.166 [-0.5250, -0.1664] ***\n", + "51 indirect 0.044 0.055 0.795 0.427 -0.040 0.191 [-0.0399, 0.1914] \n", + "52 total -0.286 0.074 -3.885 0.000 -0.432 -0.145 [-0.4319, -0.1447] ***\n", + "53 prop_med -0.153 0.224 -0.684 0.494 -0.890 0.143 [-0.8904, 0.1427] \n", + "\n", + "Bootstrap: Exposure = chr19_44840322_G_A \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_data_full(): \n", + " due to missing values, some pairwise combinations have less than 10% \n", + " coverage; use lavInspect(fit, \"coverage\") to investigate.”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_mvnorm_missing_h1_estimate_moments(): \n", + " Maximum number of iterations reached when computing the sample moments \n", + " using EM; use the em.h1.iter.max= argument to increase the number of \n", + " iterations”\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Key bootstrap results:\n", + " label est se z pvalue ci.lower ci.upper CI_95 sig\n", + "1 a -0.343 0.221 -1.554 0.120 -0.763 0.094 [-0.7626, 0.0939] \n", + "5 b -0.139 0.125 -1.108 0.268 -0.294 0.201 [-0.2937, 0.2009] \n", + "6 c_prime -0.336 0.090 -3.719 0.000 -0.536 -0.174 [-0.5356, -0.1738] ***\n", + "51 indirect 0.048 0.056 0.844 0.399 -0.037 0.198 [-0.0368, 0.1978] \n", + "52 total -0.289 0.070 -4.113 0.000 -0.432 -0.159 [-0.4323, -0.1586] ***\n", + "53 prop_med -0.165 0.224 -0.737 0.461 -0.819 0.148 [-0.8188, 0.1476] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bootstrap analysis complete (N = 1153 with FIML).\n" + ] + } + ], + "source": [ + "# ── Bootstrap mediation (FIML + bootstrap) ───────────────────────────────────\n", + "# lavaan supports bootstrap with missing=\"fiml.x\", so we use the full dataset\n", + "# (N=1153) rather than restricting to complete cases (N=107).\n", + "\n", + "set.seed(12345)\n", + "n_boot <- 2000\n", + "\n", + "boot_results_list <- list()\n", + "\n", + "for (exp_var in exposures) {\n", + " cat(\"\\nBootstrap: Exposure =\", exp_var, \"\\n\")\n", + " \n", + " mod_syntax <- build_mediation_syntax(exp_var)\n", + " \n", + " fit_boot <- sem(mod_syntax, data = dat, missing = \"fiml.x\",\n", + " fixed.x = FALSE, se = \"bootstrap\",\n", + " bootstrap = n_boot, iseed = 12345)\n", + " \n", + " # BCa bootstrap CIs\n", + " params_boot <- parameterEstimates(fit_boot, boot.ci.type = \"bca.simple\",\n", + " level = 0.95, ci = TRUE)\n", + " \n", + " key_labels <- c(\"a\", \"b\", \"c_prime\", \"indirect\", \"total\", \"prop_med\")\n", + " key_boot <- params_boot[params_boot$label %in% key_labels,\n", + " c(\"label\", \"est\", \"se\", \"z\", \"pvalue\", \"ci.lower\", \"ci.upper\")]\n", + " key_boot$CI_95 <- sprintf(\"[%.4f, %.4f]\", key_boot$ci.lower, key_boot$ci.upper)\n", + " key_boot$sig <- ifelse(key_boot$pvalue < 0.001, \"***\",\n", + " ifelse(key_boot$pvalue < 0.01, \"**\",\n", + " ifelse(key_boot$pvalue < 0.05, \"*\",\n", + " ifelse(key_boot$pvalue < 0.1, \".\", \"\"))))\n", + " \n", + " cat(\"Key bootstrap results:\\n\")\n", + " print(key_boot)\n", + " \n", + " # Extract bootstrap distribution of indirect effect\n", + " boot_samples <- lavInspect(fit_boot, what = \"boot\")\n", + " \n", + " boot_results_list[[exp_var]] <- list(\n", + " fit = fit_boot, key = key_boot, params = params_boot,\n", + " boot_samples = boot_samples, exposure = exp_var\n", + " )\n", + "}\n", + "\n", + "cat(\"\\nBootstrap analysis complete (N =\", nrow(dat), \"with FIML).\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f517d080", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T15:34:35.448045Z", + "iopub.status.busy": "2026-03-27T15:34:35.446231Z", + "iopub.status.idle": "2026-03-27T15:34:38.968251Z", + "shell.execute_reply": "2026-03-27T15:34:38.966155Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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870Z7r+dEJt8tVWKqSfQeBmJvM5dv6wWq1Dhw6tXr36pEmTVqxY8emnn3o0OH/+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QkJ/j8YgvbXluvAgQOffvppXFxccXGxOViT1Wq12Wz/+te/pk+fHhsbW9E6AQAw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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrap results saved to: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set57/bootstrap \n" + ] + }, + { + "data": { + "image/png": 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8/kyZN/+OEHscMpGc/zzZs3b9iw4aJFi5544gkiOn78+LBhw6ZOnRodHd2lSxdT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HZmjUOMeuQUtNTT179uzkyZOVyY4dO/bo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\npT/PyyIv9u7du2fPHs/jv/76S/k1t27dmpeXV76ly+Xas2fPz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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ── Save bootstrap results ───────────────────────────────────────────────────\n", + "for (exp_var in exposures) {\n", + " res <- boot_results_list[[exp_var]]\n", + " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", + " \n", + " write.csv(res$key, file.path(boot_dir, paste0(\"bootstrap_results_\", prefix, \".csv\")),\n", + " row.names = FALSE)\n", + " write.csv(as.data.frame(res$boot_samples),\n", + " file.path(boot_dir, paste0(\"bootstrap_distributions_\", prefix, \".csv\")),\n", + " row.names = FALSE)\n", + "}\n", + "\n", + "# ── Bootstrap distribution plots ─────────────────────────────────────────────\n", + "for (exp_var in exposures) {\n", + " res <- boot_results_list[[exp_var]]\n", + " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", + " \n", + " # Get the indirect effect column from bootstrap samples\n", + " boot_mat <- as.data.frame(res$boot_samples)\n", + " # The defined parameters are the last columns\n", + " params_est <- parameterEstimates(res$fit)\n", + " # Find the column index for indirect effect\n", + " free_params <- params_est[params_est$op != \":=\", ]\n", + " def_params <- params_est[params_est$op == \":=\", ]\n", + " \n", + " # Bootstrap samples: free params first, then defined params appended\n", + " n_free <- nrow(free_params)\n", + " if (ncol(boot_mat) > n_free) {\n", + " indirect_col <- n_free + which(def_params$label == \"indirect\")\n", + " indirect_boot <- boot_mat[, indirect_col]\n", + " } else {\n", + " # Compute from a and b columns\n", + " a_idx <- which(free_params$label == \"a\")\n", + " b_idx <- which(free_params$label == \"b\")\n", + " indirect_boot <- boot_mat[, a_idx] * boot_mat[, b_idx]\n", + " }\n", + " \n", + " p_boot <- ggplot(data.frame(indirect = indirect_boot), aes(x = indirect)) +\n", + " geom_histogram(bins = 50, fill = \"steelblue\", alpha = 0.7, color = \"white\") +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"red\", linewidth = 0.8) +\n", + " geom_vline(xintercept = median(indirect_boot), linetype = \"solid\", color = \"darkblue\") +\n", + " labs(title = paste(\"Bootstrap Distribution of Indirect Effect\"),\n", + " subtitle = paste(\"Exposure:\", prefix),\n", + " x = \"Indirect Effect (a x b)\", y = \"Count\") +\n", + " theme_minimal(base_size = 12)\n", + " \n", + " ggsave(file.path(boot_dir, paste0(\"bootstrap_distributions_\", prefix, \".png\")),\n", + " p_boot, width = 7, height = 5, dpi = 150)\n", + " ggsave(file.path(boot_dir, paste0(\"bootstrap_distributions_\", prefix, \".pdf\")),\n", + " p_boot, width = 7, height = 5)\n", + " print(p_boot)\n", + "}\n", + "\n", + "# ── Bootstrap forest plot (both exposures) ─────────────────────────────────\n", + "boot_plot_data <- do.call(rbind, lapply(exposures, function(exp_var) {\n", + " res <- boot_results_list[[exp_var]]\n", + " key <- res$key\n", + " key$exposure <- exp_var\n", + " key\n", + "}))\n", + "boot_plot_data$label <- factor(boot_plot_data$label,\n", + " levels = c(\"a\", \"b\", \"c_prime\", \"indirect\", \"total\", \"prop_med\"))\n", + "boot_plot_data$exposure_short <- ifelse(\n", + " grepl(\"44827033\", boot_plot_data$exposure), \"44827033_indel\", \"44840322_G_A\")\n", + "\n", + "p_boot_forest <- ggplot(boot_plot_data, aes(x = est, y = label, color = exposure_short)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci.lower, xmax = ci.upper),\n", + " position = position_dodge(width = 0.5), size = 0.6) +\n", + " labs(title = \"Bootstrap (BCa + FIML): Path Estimates\",\n", + " subtitle = paste0(\"N = \", nrow(dat), \" (FIML), \", n_boot, \" resamples\"),\n", + " x = \"Estimate (95% BCa CI)\", y = \"Path\", color = \"Exposure\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + "ggsave(file.path(boot_dir, \"bootstrap_forest_plot.png\"), p_boot_forest, width = 8, height = 5, dpi = 150)\n", + "ggsave(file.path(boot_dir, \"bootstrap_forest_plot.pdf\"), p_boot_forest, width = 8, height = 5)\n", + "print(p_boot_forest)\n", + "cat(\"Bootstrap results saved to:\", boot_dir, \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "560bb320", + "metadata": {}, + "source": [ + "## 3. Sensitivity Analysis: MNAR Tipping-Point" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c5a1a3de", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T15:34:38.979477Z", + "iopub.status.busy": "2026-03-27T15:34:38.977773Z", + "iopub.status.idle": "2026-03-27T15:35:00.553529Z", + "shell.execute_reply": "2026-03-27T15:35:00.551373Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mediator mean = -0.0163 SD = 0.9826 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Outcome mean = 2.2144 SD = 1.3302 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "MNAR sensitivity: Exposure = chr19_44827033_TGATAGATAGATAGATA_TGATA \n", + " Grid complete: 81 / 81 converged\n", + "\n", + "MNAR sensitivity: Exposure = chr19_44840322_G_A \n", + " Grid complete: 81 / 81 converged\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "MNAR sensitivity analysis complete.\n" + ] + } + ], + "source": [ + "# ── MNAR Sensitivity Analysis ────────────────────────────────────────────────\n", + "# Under MNAR, missing expression/outcome values could be systematically different\n", + "# from observed values. We impute the missing values as mean + delta * SD, then\n", + "# re-run SEM on the completed dataset to see if conclusions change.\n", + "#\n", + "# delta = 0 means imputing at the observed mean (MAR assumption).\n", + "# delta > 0 means the missing values are systematically higher than observed.\n", + "# delta < 0 means the missing values are systematically lower than observed.\n", + "\n", + "delta_m_grid <- seq(-1.0, 1.0, by = 0.25)\n", + "delta_y_grid <- seq(-1.0, 1.0, by = 0.25)\n", + "\n", + "# Pre-compute observed means and SDs for imputation\n", + "med_mean <- mean(dat[[mediator]], na.rm = TRUE)\n", + "med_sd <- sd(dat[[mediator]], na.rm = TRUE)\n", + "out_mean <- mean(dat[[outcome]], na.rm = TRUE)\n", + "out_sd <- sd(dat[[outcome]], na.rm = TRUE)\n", + "\n", + "cat(\"Mediator mean =\", round(med_mean, 4), \" SD =\", round(med_sd, 4), \"\\n\")\n", + "cat(\"Outcome mean =\", round(out_mean, 4), \" SD =\", round(out_sd, 4), \"\\n\")\n", + "\n", + "mnar_results_all <- list()\n", + "\n", + "for (exp_var in exposures) {\n", + " cat(\"\\nMNAR sensitivity: Exposure =\", exp_var, \"\\n\")\n", + " mod_syntax <- build_mediation_syntax(exp_var)\n", + " \n", + " grid_results <- expand.grid(delta_m = delta_m_grid, delta_y = delta_y_grid,\n", + " a = NA_real_, b = NA_real_, indirect = NA_real_,\n", + " indirect_p = NA_real_, c_prime = NA_real_,\n", + " total = NA_real_, total_p = NA_real_,\n", + " stringsAsFactors = FALSE)\n", + " \n", + " for (i in seq_len(nrow(grid_results))) {\n", + " dm <- grid_results$delta_m[i]\n", + " dy <- grid_results$delta_y[i]\n", + " \n", + " # Impute missing values only (leave observed values untouched)\n", + " dat_imp <- dat\n", + " dat_imp[[mediator]][is.na(dat_imp[[mediator]])] <- med_mean + dm * med_sd\n", + " dat_imp[[outcome]][is.na(dat_imp[[outcome]])] <- out_mean + dy * out_sd\n", + " \n", + " # Fit on completed data (no missing= needed since all values are now present)\n", + " fit_s <- tryCatch(\n", + " sem(mod_syntax, data = dat_imp, fixed.x = FALSE, estimator = \"ML\"),\n", + " error = function(e) NULL\n", + " )\n", + " \n", + " if (!is.null(fit_s) && lavInspect(fit_s, \"converged\")) {\n", + " pe <- parameterEstimates(fit_s)\n", + " get_val <- function(lbl, col) {\n", + " row <- pe[pe$label == lbl, ]\n", + " if (nrow(row) > 0) row[[col]][1] else NA\n", + " }\n", + " grid_results$a[i] <- get_val(\"a\", \"est\")\n", + " grid_results$b[i] <- get_val(\"b\", \"est\")\n", + " grid_results$indirect[i] <- get_val(\"indirect\", \"est\")\n", + " grid_results$indirect_p[i] <- get_val(\"indirect\", \"pvalue\")\n", + " grid_results$c_prime[i] <- get_val(\"c_prime\", \"est\")\n", + " grid_results$total[i] <- get_val(\"total\", \"est\")\n", + " grid_results$total_p[i] <- get_val(\"total\", \"pvalue\")\n", + " }\n", + " }\n", + " \n", + " grid_results$exposure <- exp_var\n", + " mnar_results_all[[exp_var]] <- grid_results\n", + " \n", + " cat(\" Grid complete:\", sum(!is.na(grid_results$indirect)), \"/\",\n", + " nrow(grid_results), \"converged\\n\")\n", + "}\n", + "\n", + "mnar_all <- do.call(rbind, mnar_results_all)\n", + "cat(\"\\nMNAR sensitivity analysis complete.\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "81949068", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T15:35:00.560539Z", + "iopub.status.busy": "2026-03-27T15:35:00.558848Z", + "iopub.status.idle": "2026-03-27T15:35:00.635342Z", + "shell.execute_reply": "2026-03-27T15:35:00.633295Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tipping points:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " exposure\n", + "chr19_44827033_TGATAGATAGATAGATA_TGATA chr19_44827033_TGATAGATAGATAGATA_TGATA\n", + "chr19_44840322_G_A chr19_44840322_G_A\n", + " ref_indirect sign_flip_delta_m\n", + "chr19_44827033_TGATAGATAGATAGATA_TGATA 0.0007993011 -0.25\n", + "chr19_44840322_G_A 0.0008035587 -0.25\n", + " sign_flip_delta_y n_nonsig_of_total\n", + "chr19_44827033_TGATAGATAGATAGATA_TGATA 0 81/81\n", + "chr19_44840322_G_A 0 81/81\n" + ] + } + ], + "source": [ + "# ── Save MNAR results ────────────────────────────────────────────────────────\n", + "write.csv(mnar_all, file.path(mnar_dir, \"mnar_sensitivity_grid.csv\"), row.names = FALSE)\n", + "\n", + "# ── Find tipping points (where indirect effect crosses 0 or becomes non-significant)\n", + "# Reference: FIML indirect at delta_m=0, delta_y=0\n", + "tipping_points <- list()\n", + "for (exp_var in exposures) {\n", + " gr <- mnar_results_all[[exp_var]]\n", + " ref_indirect <- gr$indirect[gr$delta_m == 0 & gr$delta_y == 0]\n", + " ref_sign <- sign(ref_indirect)\n", + " \n", + " # Points where sign flips\n", + " sign_flips <- gr[sign(gr$indirect) != ref_sign & !is.na(gr$indirect), ]\n", + " # Points where p > 0.05 (becomes non-significant)\n", + " nonsig <- gr[gr$indirect_p > 0.05 & !is.na(gr$indirect_p), ]\n", + " \n", + " if (nrow(sign_flips) > 0) {\n", + " closest_flip <- sign_flips[which.min(abs(sign_flips$delta_m) + abs(sign_flips$delta_y)), ]\n", + " } else {\n", + " closest_flip <- data.frame(delta_m = NA, delta_y = NA, indirect = NA)\n", + " }\n", + " \n", + " tipping_points[[exp_var]] <- data.frame(\n", + " exposure = exp_var,\n", + " ref_indirect = ref_indirect,\n", + " sign_flip_delta_m = closest_flip$delta_m[1],\n", + " sign_flip_delta_y = closest_flip$delta_y[1],\n", + " n_nonsig_of_total = paste0(nrow(nonsig), \"/\", nrow(gr))\n", + " )\n", + "}\n", + "\n", + "tipping_df <- do.call(rbind, tipping_points)\n", + "write.csv(tipping_df, file.path(mnar_dir, \"mnar_tipping_points.csv\"), row.names = FALSE)\n", + "cat(\"Tipping points:\\n\")\n", + "print(tipping_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7140a04e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T15:35:00.641047Z", + "iopub.status.busy": "2026-03-27T15:35:00.639409Z", + "iopub.status.idle": "2026-03-27T15:35:08.047489Z", + "shell.execute_reply": "2026-03-27T15:35:08.045421Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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ly5UmkxbFCganQdw87YRUZGajRmZ+zU7yORy+XPnj27detWZGRkhw4dTE1NNS4H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\nWYb/CtTpdCUlJR4eHuYoUgaqHOd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NkaNWoQ0V9//cWKzVVz9epViUQybdo0VsKPP/5IRB999FFycjLP8+fOnfPy8rK1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qajzTQRBC3KurjB8/XtrddQghAudJs0Bt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J/6unTpMm/evGvXri1atIiIajCOXhGb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\ntmyZgYBlKfqjyMrKGjdu3O+//x4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aE1a9YkoosXL7KASZMmEVFAQMCrV6/y8/OvXbtWq1YtIvr77781DNBEUFAQEbm7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h5qSE/QXjMtbd60tNhItX8TgMHLW0kH15PTRkRU+CxNWEaE8htguENhBxBChuLa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6LABAYAiLoQk/V2pLLR39F9M00BM0dqCq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\npgQAAABCSs/5/Q1jP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2Z+APC9D71dfXZ2dnS6VS7lstFouzs7MbGhq4v65du/bx\nxx8/d+5cdHQ0TdO//PKLVqtlWVatVpeVlQmaOADAjTh16lRGRob9p1ylUq1YsYKmacc2f/vb\n3wgh69atEypJEAReKQaBQKPR5Ofnt7S0lJWVMQwzbNiwsLAwpzbJycmDBg367rvvDh8+vGDB\nAkHyBADwCu6MxOXLl6uqqhQKRVJSkuujnbKyshYvXpyVlSVIhiAUFHYQONRqNXczbGcWLly4\nbdu2goICp2tTAAB6o+joaNwSAU5w8wT0IQ899NDOnTvj4uLi4uKEzgUAAMD70GMHgS8mJoZl\nWUJIeHj4E088Yb9VNjMzMygoSNDUAAB8RSqVTp48WS6XC50I8Ap3xQIAAAAECJyKBQAAAAgQ\nKOwAAAAAAgQKOwAAAIAAgcIOAAAAIECgsAMAAAAIECjsAAAAAAIECjsAAACAAIHCDgAAACBA\noLADAAAACBAo7AAAAAACBAo7AAAAgACBwg4AAAAgQKCwAwAAAAgQ/w/S1GrMgBAO1wAAAABJ\nRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MNAR sensitivity results saved to: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set57/MNAR_sensitivity \n" + ] + }, + { + "data": { + "image/png": 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x2YiN2AO378eKX/y7oS1nTyz0pHxepPSsKVpy8hISFskXUa038UR1lZGWuTMD4q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HqA1z7EANl0/K6Dr44ynD1E8CABAWUqdR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\nm8126aUD/wcYABuckfIW07Z7iYi8DtrzLM1+nHUm7cEcO+hQW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tjrkh2/MzSbaKKwSxKqpm3Zd8IXn1eQ0y871dh8AACIa+NulIwCf7zj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\nn1deeWX9+vXZ2dmBlvvuu6+oqEj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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ── MNAR contour plots ───────────────────────────────────────────────────────\n", + "for (exp_var in exposures) {\n", + " gr <- mnar_results_all[[exp_var]]\n", + " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", + " \n", + " # Contour: indirect effect\n", + " p_contour_ind <- ggplot(gr, aes(x = delta_m, y = delta_y, z = indirect)) +\n", + " geom_contour_filled(bins = 12) +\n", + " geom_point(data = gr[gr$delta_m == 0 & gr$delta_y == 0, ],\n", + " aes(x = delta_m, y = delta_y), color = \"red\", size = 3, shape = 4, stroke = 2) +\n", + " labs(title = paste(\"MNAR Sensitivity: Indirect Effect\"),\n", + " subtitle = paste(\"Exposure:\", prefix),\n", + " x = expression(delta[M]~\"(mediator shift)\"),\n", + " y = expression(delta[Y]~\"(outcome shift)\"),\n", + " fill = \"Indirect\\nEffect\") +\n", + " theme_minimal(base_size = 12)\n", + " \n", + " ggsave(file.path(mnar_dir, paste0(\"mnar_contour_indirect_\", prefix, \".png\")),\n", + " p_contour_ind, width = 7, height = 6, dpi = 150)\n", + " ggsave(file.path(mnar_dir, paste0(\"mnar_contour_indirect_\", prefix, \".pdf\")),\n", + " p_contour_ind, width = 7, height = 6)\n", + " print(p_contour_ind)\n", + " \n", + " # Contour: p-value of indirect effect\n", + " gr$log10p <- -log10(pmax(gr$indirect_p, 1e-20))\n", + " p_contour_p <- ggplot(gr, aes(x = delta_m, y = delta_y, z = log10p)) +\n", + " geom_contour_filled(bins = 12) +\n", + " geom_point(data = gr[gr$delta_m == 0 & gr$delta_y == 0, ],\n", + " aes(x = delta_m, y = delta_y), color = \"red\", size = 3, shape = 4, stroke = 2) +\n", + " labs(title = paste(\"MNAR Sensitivity: -log10(p) of Indirect Effect\"),\n", + " subtitle = paste(\"Exposure:\", prefix),\n", + " x = expression(delta[M]~\"(mediator shift)\"),\n", + " y = expression(delta[Y]~\"(outcome shift)\"),\n", + " fill = \"-log10(p)\") +\n", + " theme_minimal(base_size = 12)\n", + " \n", + " ggsave(file.path(mnar_dir, paste0(\"mnar_contour_pvalue_\", prefix, \".png\")),\n", + " p_contour_p, width = 7, height = 6, dpi = 150)\n", + " ggsave(file.path(mnar_dir, paste0(\"mnar_contour_pvalue_\", prefix, \".pdf\")),\n", + " p_contour_p, width = 7, height = 6)\n", + " print(p_contour_p)\n", + "}\n", + "\n", + "# ── 1D slices at delta_y=0 and delta_m=0 ─────────────────────────────────\n", + "slice_plots <- list()\n", + "for (exp_var in exposures) {\n", + " gr <- mnar_results_all[[exp_var]]\n", + " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", + " \n", + " slice_m <- gr[gr$delta_y == 0, ]\n", + " slice_y <- gr[gr$delta_m == 0, ]\n", + " \n", + " p1 <- ggplot(slice_m, aes(x = delta_m, y = indirect)) +\n", + " geom_line(color = \"steelblue\", linewidth = 1) +\n", + " geom_point(color = \"steelblue\") +\n", + " geom_hline(yintercept = 0, linetype = \"dashed\", color = \"red\") +\n", + " labs(title = paste0(prefix, \": Indirect vs delta_M\"),\n", + " x = expression(delta[M]), y = \"Indirect Effect\") +\n", + " theme_minimal(base_size = 11)\n", + " \n", + " p2 <- ggplot(slice_y, aes(x = delta_y, y = indirect)) +\n", + " geom_line(color = \"darkorange\", linewidth = 1) +\n", + " geom_point(color = \"darkorange\") +\n", + " geom_hline(yintercept = 0, linetype = \"dashed\", color = \"red\") +\n", + " labs(title = paste0(prefix, \": Indirect vs delta_Y\"),\n", + " x = expression(delta[Y]), y = \"Indirect Effect\") +\n", + " theme_minimal(base_size = 11)\n", + " \n", + " p_combined <- grid.arrange(p1, p2, ncol = 2)\n", + " \n", + " ggsave(file.path(mnar_dir, paste0(\"mnar_1d_slices_\", prefix, \".png\")),\n", + " p_combined, width = 10, height = 4, dpi = 150)\n", + " ggsave(file.path(mnar_dir, paste0(\"mnar_1d_slices_\", prefix, \".pdf\")),\n", + " p_combined, width = 10, height = 4)\n", + "}\n", + "\n", + "cat(\"MNAR sensitivity results saved to:\", mnar_dir, \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "3b514df1", + "metadata": {}, + "source": [ + "## 4. Sensitivity Analysis: Bayesian (blavaan)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8acbeb36", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T15:35:08.053188Z", + "iopub.status.busy": "2026-03-27T15:35:08.051534Z", + "iopub.status.idle": "2026-03-27T18:36:17.764728Z", + "shell.execute_reply": "2026-03-27T18:36:17.762328Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "========================================================\n", + "Bayesian: Exposure = chr19_44827033_TGATAGATAGATAGATA_TGATA \n", + "========================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_data_full(): \n", + " due to missing values, some pairwise combinations have less than 10% \n", + " coverage; use lavInspect(fit, \"coverage\") to investigate.”\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 1).\n", + "Chain 1: \n", + "Chain 1: Gradient evaluation took 0.003476 seconds\n", + "Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 34.76 seconds.\n", + "Chain 1: Adjust your expectations accordingly!\n", + "Chain 1: \n", + "Chain 1: \n", + "Chain 1: Iteration: 1 / 7000 [ 0%] (Warmup)\n", + "Chain 1: Iteration: 700 / 7000 [ 10%] (Warmup)\n", + "Chain 1: Iteration: 1400 / 7000 [ 20%] (Warmup)\n", + "Chain 1: Iteration: 2001 / 7000 [ 28%] (Sampling)\n", + "Chain 1: Iteration: 2700 / 7000 [ 38%] (Sampling)\n", + "Chain 1: Iteration: 3400 / 7000 [ 48%] (Sampling)\n", + "Chain 1: Iteration: 4100 / 7000 [ 58%] (Sampling)\n", + "Chain 1: Iteration: 4800 / 7000 [ 68%] (Sampling)\n", + "Chain 1: Iteration: 5500 / 7000 [ 78%] (Sampling)\n", + "Chain 1: Iteration: 6200 / 7000 [ 88%] (Sampling)\n", + "Chain 1: Iteration: 6900 / 7000 [ 98%] (Sampling)\n", + "Chain 1: Iteration: 7000 / 7000 [100%] (Sampling)\n", + "Chain 1: \n", + "Chain 1: Elapsed Time: 367.954 seconds (Warm-up)\n", + "Chain 1: 949.588 seconds (Sampling)\n", + "Chain 1: 1317.54 seconds (Total)\n", + "Chain 1: \n", + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 2).\n", + "Chain 2: \n", + "Chain 2: Gradient evaluation took 0.001459 seconds\n", + "Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 14.59 seconds.\n", + "Chain 2: Adjust your expectations accordingly!\n", + "Chain 2: \n", + "Chain 2: \n", + "Chain 2: Iteration: 1 / 7000 [ 0%] (Warmup)\n", + "Chain 2: Iteration: 700 / 7000 [ 10%] (Warmup)\n", + "Chain 2: Iteration: 1400 / 7000 [ 20%] (Warmup)\n", + "Chain 2: Iteration: 2001 / 7000 [ 28%] (Sampling)\n", + "Chain 2: Iteration: 2700 / 7000 [ 38%] (Sampling)\n", + "Chain 2: Iteration: 3400 / 7000 [ 48%] (Sampling)\n", + "Chain 2: Iteration: 4100 / 7000 [ 58%] (Sampling)\n", + "Chain 2: Iteration: 4800 / 7000 [ 68%] (Sampling)\n", + 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"Chain 3: Iteration: 3400 / 7000 [ 48%] (Sampling)\n", + "Chain 3: Iteration: 4100 / 7000 [ 58%] (Sampling)\n", + "Chain 3: Iteration: 4800 / 7000 [ 68%] (Sampling)\n", + "Chain 3: Iteration: 5500 / 7000 [ 78%] (Sampling)\n", + "Chain 3: Iteration: 6200 / 7000 [ 88%] (Sampling)\n", + "Chain 3: Iteration: 6900 / 7000 [ 98%] (Sampling)\n", + "Chain 3: Iteration: 7000 / 7000 [100%] (Sampling)\n", + "Chain 3: \n", + "Chain 3: Elapsed Time: 381.176 seconds (Warm-up)\n", + "Chain 3: 972.877 seconds (Sampling)\n", + "Chain 3: 1354.05 seconds (Total)\n", + "Chain 3: \n", + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 4).\n", + "Chain 4: \n", + "Chain 4: Gradient evaluation took 0.0014 seconds\n", + "Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 14 seconds.\n", + "Chain 4: Adjust your expectations accordingly!\n", + "Chain 4: \n", + "Chain 4: \n", + "Chain 4: Iteration: 1 / 7000 [ 0%] (Warmup)\n", + "Chain 4: Iteration: 700 / 7000 [ 10%] (Warmup)\n", + "Chain 4: Iteration: 1400 / 7000 [ 20%] (Warmup)\n", + "Chain 4: Iteration: 2001 / 7000 [ 28%] (Sampling)\n", + "Chain 4: Iteration: 2700 / 7000 [ 38%] (Sampling)\n", + "Chain 4: Iteration: 3400 / 7000 [ 48%] (Sampling)\n", + "Chain 4: Iteration: 4100 / 7000 [ 58%] (Sampling)\n", + "Chain 4: Iteration: 4800 / 7000 [ 68%] (Sampling)\n", + "Chain 4: Iteration: 5500 / 7000 [ 78%] (Sampling)\n", + "Chain 4: Iteration: 6200 / 7000 [ 88%] (Sampling)\n", + "Chain 4: Iteration: 6900 / 7000 [ 98%] (Sampling)\n", + "Chain 4: Iteration: 7000 / 7000 [100%] (Sampling)\n", + "Chain 4: \n", + "Chain 4: Elapsed Time: 371.604 seconds (Warm-up)\n", + "Chain 4: 978.783 seconds (Sampling)\n", + "Chain 4: 1350.39 seconds (Total)\n", + "Chain 4: \n", + "Computing post-estimation metrics (including lvs if requested)...\n", + "\n", + "Bayesian model summary:\n", + "blavaan 0.5.10 ended normally after 5000 iterations\n", + "\n", + " Estimator BAYES\n", + " Optimization method MCMC\n", + " Number of model parameters 50\n", + "\n", + " Number of observations 1153\n", + " Number of missing patterns 8\n", + "\n", + " Statistic MargLogLik PPP\n", + " Value -15356.102 0.391\n", + "\n", + "Parameter Estimates:\n", + "\n", + "\n", + "Regressions:\n", + " Estimate Post.SD pi.lower pi.upper Rhat\n", + " ARHGAP35_expression ~ \n", + " c19_448 (a) -0.337 0.207 -0.747 0.071 1.000\n", + " msex_u 0.089 0.194 -0.290 0.471 1.000\n", + " ag_dth_ -0.005 0.015 -0.034 0.024 1.000\n", + " pmi_u 0.036 0.027 -0.026 0.084 1.000\n", + " tangles_sqrt ~ \n", + " ARHGAP3 (b) -0.095 0.097 -0.259 0.138 1.000\n", + " c19_448 (c_pr) -0.315 0.091 -0.494 -0.135 1.000\n", + " msex_u -0.380 0.085 -0.544 -0.214 1.000\n", + " ag_dth_ 0.039 0.006 0.027 0.051 1.000\n", + " educ -0.416 0.102 -0.615 -0.217 1.000\n", + " apo4_ds -0.030 0.011 -0.051 -0.009 1.000\n", + " apo2_ds 0.770 0.078 0.616 0.922 1.000\n", + " Prior \n", + " \n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " \n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + "\n", + "Covariances:\n", + " Estimate Post.SD pi.lower pi.upper\n", + " chr19_44827033_TGATAGATAGATAGATA_TGATA ~~ \n", + " msex_u 0.004 0.007 -0.010 0.017\n", + " age_death_u 0.092 0.094 -0.093 0.276\n", + " pmi_u 0.008 0.106 -0.202 0.218\n", + " educ -0.020 0.005 -0.031 -0.009\n", + " apoe4_dose 0.035 0.052 -0.066 0.135\n", + " apoe2_dose -0.005 0.007 -0.018 0.008\n", + " msex_u ~~ \n", + " age_death_u -0.597 0.096 -0.790 -0.411\n", + " pmi_u 0.099 0.106 -0.109 0.310\n", + " educ -0.011 0.006 -0.022 0.000\n", + " apoe4_dose 0.348 0.053 0.246 0.455\n", + " apoe2_dose -0.000 0.007 -0.014 0.013\n", + " age_death_u ~~ \n", + " pmi_u 0.728 1.460 -2.125 3.549\n", + " educ 0.218 0.076 0.069 0.367\n", + " apoe4_dose -3.202 0.715 -4.605 -1.802\n", + " apoe2_dose -0.350 0.095 -0.536 -0.167\n", + " pmi_u ~~ \n", + " educ -0.007 0.085 -0.178 0.159\n", + " apoe4_dose 0.120 0.802 -1.467 1.681\n", + " apoe2_dose -0.072 0.108 -0.283 0.139\n", + " educ ~~ \n", + " apoe4_dose -0.092 0.042 -0.175 -0.010\n", + " apoe2_dose -0.025 0.006 -0.036 -0.014\n", + " apoe4_dose ~~ \n", + " apoe2_dose 0.132 0.052 0.031 0.235\n", + " Rhat Prior \n", + " \n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " \n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " \n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " \n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " \n", + " 1.000 lkj_corr(1)\n", + " 1.000 lkj_corr(1)\n", + " \n", + " 1.000 lkj_corr(1)\n", + "\n", + "Intercepts:\n", + " Estimate Post.SD pi.lower pi.upper Rhat Prior \n", + " .ARHGAP35_xprss 0.216 1.286 -2.298 2.750 1.000 normal(0,10)\n", + " .tangles_sqrt -0.694 0.600 -1.871 0.494 1.000 normal(0,10)\n", + " c19_44827033_T 0.268 0.014 0.241 0.295 1.000 normal(0,10)\n", + " msex_u 0.346 0.014 0.318 0.374 1.000 normal(0,10)\n", + " age_death_u 88.900 0.199 88.513 89.291 1.000 normal(0,10)\n", + " pmi_u 8.751 0.223 8.311 9.192 1.000 normal(0,10)\n", + " educ 0.159 0.012 0.136 0.182 1.000 normal(0,10)\n", + " apoe4_dose 16.372 0.110 16.155 16.587 1.000 normal(0,10)\n", + " apoe2_dose 0.272 0.014 0.244 0.301 1.000 normal(0,10)\n", + "\n", + "Variances:\n", + " Estimate Post.SD pi.lower pi.upper Rhat Prior \n", + " .ARHGAP35_xprss 0.982 0.137 0.749 1.285 1.000 gamma(1,.5)[sd]\n", + " .tangles_sqrt 1.461 0.065 1.339 1.594 1.000 gamma(1,.5)[sd]\n", + " c19_44827033_T 0.222 0.009 0.205 0.241 1.000 gamma(1,.5)[sd]\n", + " msex_u 0.228 0.010 0.210 0.248 1.000 gamma(1,.5)[sd]\n", + " age_death_u 43.561 1.861 40.099 47.339 1.000 gamma(1,.5)[sd]\n", + " pmi_u 55.462 2.341 51.054 60.230 1.000 gamma(1,.5)[sd]\n", + " educ 0.148 0.006 0.136 0.161 1.000 gamma(1,.5)[sd]\n", + " apoe4_dose 13.076 0.550 12.038 14.187 1.000 gamma(1,.5)[sd]\n", + " apoe2_dose 0.233 0.010 0.214 0.254 1.000 gamma(1,.5)[sd]\n", + "\n", + "Defined Parameters:\n", + " Estimate Post.SD pi.lower pi.upper Rhat Prior \n", + " indirect 0.032 0.044 -0.053 0.117 \n", + " total -0.283 0.082 -0.444 -0.123 \n", + " prop_med -0.113 0.229 -0.562 0.335 \n", + "\n", + " Estimate Post.SD pi.lower pi.upper Rhat \n", + " \" c19_448 (a)\" \" -0.337\" \" 0.207\" \" -0.747\" \" 0.071\" \" 1.000\"\n", + " \" msex_u \" \" 0.089\" \" 0.194\" \" -0.290\" \" 0.471\" \" 1.000\"\n", + " \" ag_dth_ \" \" -0.005\" \" 0.015\" \" -0.034\" \" 0.024\" \" 1.000\"\n", + " \" pmi_u \" \" 0.036\" \" 0.027\" \" -0.026\" \" 0.084\" \" 1.000\"\n", + " \" ARHGAP3 (b)\" \" -0.095\" \" 0.097\" \" -0.259\" \" 0.138\" \" 1.000\"\n", + " \" c19_448 (c_pr)\" \" -0.315\" \" 0.091\" \" -0.494\" \" -0.135\" \" 1.000\"\n", + " \" msex_u \" \" -0.380\" \" 0.085\" \" -0.544\" \" -0.214\" \" 1.000\"\n", + " \" ag_dth_ \" \" 0.039\" \" 0.006\" \" 0.027\" \" 0.051\" \" 1.000\"\n", + " \" educ \" \" -0.416\" \" 0.102\" \" -0.615\" \" -0.217\" \" 1.000\"\n", + " \" apo4_ds \" \" -0.030\" \" 0.011\" \" -0.051\" \" -0.009\" \" 1.000\"\n", + " \" apo2_ds \" \" 0.770\" \" 0.078\" \" 0.616\" \" 0.922\" \" 1.000\"\n", + " \" .ARHGAP35_xprss\" \" 0.982\" \" 0.137\" \" 0.749\" \" 1.285\" \" 1.000\"\n", + " \" .tangles_sqrt \" \" 1.461\" \" 0.065\" \" 1.339\" \" 1.594\" \" 1.000\"\n", + " \" c19_44827033_T\" \" 0.222\" \" 0.009\" \" 0.205\" \" 0.241\" \" 1.000\"\n", + " \" msex_u \" \" 0.004\" \" 0.007\" \" -0.010\" \" 0.017\" \" 1.000\"\n", + " \" age_death_u \" \" 0.092\" \" 0.094\" \" -0.093\" \" 0.276\" \" 1.000\"\n", + " \" pmi_u \" \" 0.008\" \" 0.106\" \" -0.202\" \" 0.218\" \" 1.000\"\n", + " \" educ \" \" -0.020\" \" 0.005\" \" -0.031\" \" -0.009\" \" 1.000\"\n", + " \" apoe4_dose \" \" 0.035\" \" 0.052\" \" -0.066\" \" 0.135\" \" 1.000\"\n", + " \" apoe2_dose \" \" -0.005\" \" 0.007\" \" -0.018\" \" 0.008\" \" 1.000\"\n", + " \" msex_u \" \" 0.228\" \" 0.010\" \" 0.210\" \" 0.248\" \" 1.000\"\n", + " \" age_death_u \" \" -0.597\" \" 0.096\" \" -0.790\" \" -0.411\" \" 1.000\"\n", + " \" pmi_u \" \" 0.099\" \" 0.106\" \" -0.109\" \" 0.310\" \" 1.000\"\n", + " \" educ \" \" -0.011\" \" 0.006\" \" -0.022\" \" 0.000\" \" 1.000\"\n", + " \" apoe4_dose \" \" 0.348\" \" 0.053\" \" 0.246\" \" 0.455\" \" 1.000\"\n", + " \" apoe2_dose \" \" -0.000\" \" 0.007\" \" -0.014\" \" 0.013\" \" 1.000\"\n", + " \" age_death_u \" \" 43.561\" \" 1.861\" \" 40.099\" \" 47.339\" \" 1.000\"\n", + " \" pmi_u \" \" 0.728\" \" 1.460\" \" -2.125\" \" 3.549\" \" 1.000\"\n", + " \" educ \" \" 0.218\" \" 0.076\" \" 0.069\" \" 0.367\" \" 1.000\"\n", + " \" apoe4_dose \" \" -3.202\" \" 0.715\" \" -4.605\" \" -1.802\" \" 1.000\"\n", + " \" apoe2_dose \" \" -0.350\" \" 0.095\" \" -0.536\" \" -0.167\" \" 1.000\"\n", + " \" pmi_u \" \" 55.462\" \" 2.341\" \" 51.054\" \" 60.230\" \" 1.000\"\n", + " \" educ \" \" -0.007\" \" 0.085\" \" -0.178\" \" 0.159\" \" 1.000\"\n", + " \" apoe4_dose \" \" 0.120\" \" 0.802\" \" -1.467\" \" 1.681\" \" 1.000\"\n", + " \" apoe2_dose \" \" -0.072\" \" 0.108\" \" -0.283\" \" 0.139\" \" 1.000\"\n", + " \" educ \" \" 0.148\" \" 0.006\" \" 0.136\" \" 0.161\" \" 1.000\"\n", + " \" apoe4_dose \" \" -0.092\" \" 0.042\" \" -0.175\" \" -0.010\" \" 1.000\"\n", + " \" apoe2_dose \" \" -0.025\" \" 0.006\" \" -0.036\" \" -0.014\" \" 1.000\"\n", + " \" apoe4_dose \" \" 13.076\" \" 0.550\" \" 12.038\" \" 14.187\" \" 1.000\"\n", + " \" apoe2_dose \" \" 0.132\" \" 0.052\" \" 0.031\" \" 0.235\" \" 1.000\"\n", + " \" apoe2_dose \" \" 0.233\" \" 0.010\" \" 0.214\" \" 0.254\" \" 1.000\"\n", + " \" .ARHGAP35_xprss\" \" 0.216\" \" 1.286\" \" -2.298\" \" 2.750\" \" 1.000\"\n", + " \" .tangles_sqrt \" \" -0.694\" \" 0.600\" \" -1.871\" \" 0.494\" \" 1.000\"\n", + " \" c19_44827033_T\" \" 0.268\" \" 0.014\" \" 0.241\" \" 0.295\" \" 1.000\"\n", + " \" msex_u \" \" 0.346\" \" 0.014\" \" 0.318\" \" 0.374\" \" 1.000\"\n", + " \" age_death_u \" \" 88.900\" \" 0.199\" \" 88.513\" \" 89.291\" \" 1.000\"\n", + " \" pmi_u \" \" 8.751\" \" 0.223\" \" 8.311\" \" 9.192\" \" 1.000\"\n", + " \" educ \" \" 0.159\" \" 0.012\" \" 0.136\" \" 0.182\" \" 1.000\"\n", + " \" apoe4_dose \" \" 16.372\" \" 0.110\" \" 16.155\" \" 16.587\" \" 1.000\"\n", + " \" apoe2_dose \" \" 0.272\" \" 0.014\" \" 0.244\" \" 0.301\" \" 1.000\"\n", + " \" indirect \" \" 0.032\" \" 0.044\" \" -0.053\" \" 0.117\" \" \"\n", + " \" total \" \" -0.283\" \" 0.082\" \" -0.444\" \" -0.123\" \" \"\n", + " \" prop_med \" \" -0.113\" \" 0.229\" \" -0.562\" \" 0.335\" \" \"\n", + " Prior \n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \" lkj_corr(1)\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" \"\n", + " \" \"\n", + " \" \"\n", + "\n", + "Key Bayesian results:\n", + " label est se ci.lower ci.upper\n", + "a a -0.33681603 0.20741446 -0.7471849 0.07100589\n", + "b b -0.09536300 0.09742921 -0.2585524 0.13771733\n", + "c_prime c_prime -0.31518642 0.09139647 -0.4942394 -0.13518656\n", + "indirect indirect 0.02974504 0.04351184 -0.0593363 0.12177711\n", + "total total -0.28544139 0.08187537 -0.4457079 -0.12558443\n", + "prop_med prop_med -0.11990361 0.22876997 -0.5499443 0.22254081\n", + " CI_95\n", + "a [-0.7472, 0.0710]\n", + "b [-0.2586, 0.1377]\n", + "c_prime [-0.4942, -0.1352]\n", + "indirect [-0.0593, 0.1218]\n", + "total [-0.4457, -0.1256]\n", + "prop_med [-0.5499, 0.2225]\n", + "\n", + "========================================================\n", + "Bayesian: Exposure = chr19_44840322_G_A \n", + "========================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "“lavaan->lav_data_full(): \n", + " due to missing values, some pairwise combinations have less than 10% \n", + " coverage; use lavInspect(fit, \"coverage\") to investigate.”\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "SAMPLING FOR MODEL 'stanmarg' NOW (CHAIN 1).\n", + "Chain 1: \n", + "Chain 1: Gradient evaluation took 0.004209 seconds\n", + "Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 42.09 seconds.\n", + "Chain 1: Adjust your expectations accordingly!\n", + "Chain 1: \n", + "Chain 1: \n", + "Chain 1: Iteration: 1 / 7000 [ 0%] (Warmup)\n", + "Chain 1: Iteration: 700 / 7000 [ 10%] (Warmup)\n", + "Chain 1: Iteration: 1400 / 7000 [ 20%] (Warmup)\n", + "Chain 1: Iteration: 2001 / 7000 [ 28%] (Sampling)\n", + "Chain 1: Iteration: 2700 / 7000 [ 38%] (Sampling)\n", + "Chain 1: Iteration: 3400 / 7000 [ 48%] (Sampling)\n", + "Chain 1: Iteration: 4100 / 7000 [ 58%] (Sampling)\n", + 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would take 14.46 seconds.\n", + "Chain 4: Adjust your expectations accordingly!\n", + "Chain 4: \n", + "Chain 4: \n", + "Chain 4: Iteration: 1 / 7000 [ 0%] (Warmup)\n", + "Chain 4: Iteration: 700 / 7000 [ 10%] (Warmup)\n", + "Chain 4: Iteration: 1400 / 7000 [ 20%] (Warmup)\n", + "Chain 4: Iteration: 2001 / 7000 [ 28%] (Sampling)\n", + "Chain 4: Iteration: 2700 / 7000 [ 38%] (Sampling)\n", + "Chain 4: Iteration: 3400 / 7000 [ 48%] (Sampling)\n", + "Chain 4: Iteration: 4100 / 7000 [ 58%] (Sampling)\n", + "Chain 4: Iteration: 4800 / 7000 [ 68%] (Sampling)\n", + "Chain 4: Iteration: 5500 / 7000 [ 78%] (Sampling)\n", + "Chain 4: Iteration: 6200 / 7000 [ 88%] (Sampling)\n", + "Chain 4: Iteration: 6900 / 7000 [ 98%] (Sampling)\n", + "Chain 4: Iteration: 7000 / 7000 [100%] (Sampling)\n", + "Chain 4: \n", + "Chain 4: Elapsed Time: 375.425 seconds (Warm-up)\n", + "Chain 4: 978.697 seconds (Sampling)\n", + "Chain 4: 1354.12 seconds (Total)\n", + "Chain 4: \n", + "Computing post-estimation metrics (including lvs if requested)...\n", + "\n", + "Bayesian model summary:\n", + "blavaan 0.5.10 ended normally after 5000 iterations\n", + "\n", + " Estimator BAYES\n", + " Optimization method MCMC\n", + " Number of model parameters 50\n", + "\n", + " Number of observations 1153\n", + " Number of missing patterns 8\n", + "\n", + " Statistic MargLogLik PPP\n", + " Value -15383.993 0.388\n", + "\n", + "Parameter Estimates:\n", + "\n", + "\n", + "Regressions:\n", + " Estimate Post.SD pi.lower pi.upper Rhat\n", + " ARHGAP35_expression ~ \n", + " c19_448 (a) -0.351 0.207 -0.755 0.050 1.000\n", + " msex_u 0.094 0.191 -0.283 0.472 1.000\n", + " ag_dth_ -0.005 0.014 -0.033 0.024 1.000\n", + " pmi_u 0.037 0.027 -0.025 0.084 1.000\n", + " tangles_sqrt ~ \n", + " ARHGAP3 (b) -0.102 0.096 -0.265 0.132 1.000\n", + " c19_448 (c_pr) -0.322 0.090 -0.499 -0.145 1.000\n", + " msex_u -0.374 0.085 -0.542 -0.207 1.000\n", + " ag_dth_ 0.039 0.006 0.027 0.051 1.000\n", + " educ -0.417 0.101 -0.617 -0.217 1.000\n", + " apo4_ds -0.030 0.011 -0.051 -0.009 1.000\n", + " apo2_ds 0.753 0.080 0.597 0.909 1.000\n", + " Prior \n", + " \n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " \n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + " normal(0,10)\n", + "\n", + "Covariances:\n", + " Estimate Post.SD pi.lower pi.upper Rhat\n", + " chr19_44840322_G_A ~~ \n", + " msex_u 0.008 0.007 -0.006 0.021 1.000\n", + " age_death_u 0.071 0.097 -0.119 0.263 1.000\n", + " pmi_u -0.026 0.108 -0.239 0.185 1.000\n", + " educ -0.020 0.006 -0.032 -0.009 1.000\n", + " apoe4_dose 0.022 0.053 -0.080 0.125 1.000\n", + " apoe2_dose -0.018 0.007 -0.032 -0.004 1.000\n", + " msex_u ~~ \n", + " age_death_u -0.597 0.095 -0.786 -0.412 1.000\n", + " pmi_u 0.100 0.106 -0.106 0.308 1.000\n", + " educ -0.011 0.006 -0.022 0.000 1.000\n", + " apoe4_dose 0.347 0.052 0.245 0.453 1.000\n", + " apoe2_dose -0.000 0.007 -0.014 0.014 1.000\n", + " age_death_u ~~ \n", + " pmi_u 0.722 1.475 -2.165 3.618 1.000\n", + " educ 0.219 0.078 0.068 0.372 1.000\n", + " apoe4_dose -3.192 0.712 -4.601 -1.835 1.000\n", + " apoe2_dose -0.349 0.097 -0.541 -0.160 1.000\n", + " pmi_u ~~ \n", + " educ -0.007 0.086 -0.178 0.161 1.000\n", + " apoe4_dose 0.126 0.802 -1.468 1.701 1.000\n", + " apoe2_dose -0.070 0.107 -0.280 0.141 1.000\n", + " educ ~~ \n", + " apoe4_dose -0.093 0.042 -0.175 -0.011 1.000\n", + " apoe2_dose -0.025 0.006 -0.037 -0.014 1.000\n", + " apoe4_dose ~~ \n", + " apoe2_dose 0.132 0.053 0.029 0.237 1.000\n", + " Prior \n", + " \n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " \n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " \n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " \n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " \n", + " lkj_corr(1)\n", + " lkj_corr(1)\n", + " \n", + " lkj_corr(1)\n", + "\n", + "Intercepts:\n", + " Estimate Post.SD pi.lower pi.upper Rhat Prior \n", + " .ARHGAP35_xprss 0.245 1.274 -2.279 2.754 1.000 normal(0,10)\n", + " .tangles_sqrt -0.659 0.599 -1.831 0.527 1.000 normal(0,10)\n", + " c19_44840322_G 0.281 0.014 0.253 0.309 1.000 normal(0,10)\n", + " msex_u 0.346 0.014 0.318 0.374 1.000 normal(0,10)\n", + " age_death_u 88.901 0.198 88.509 89.289 1.000 normal(0,10)\n", + " pmi_u 8.752 0.223 8.316 9.189 1.000 normal(0,10)\n", + " educ 0.159 0.012 0.136 0.181 1.000 normal(0,10)\n", + " apoe4_dose 16.372 0.108 16.159 16.584 1.000 normal(0,10)\n", + " apoe2_dose 0.272 0.014 0.244 0.300 1.000 normal(0,10)\n", + "\n", + "Variances:\n", + " Estimate Post.SD pi.lower pi.upper Rhat Prior \n", + " .ARHGAP35_xprss 0.980 0.136 0.746 1.279 1.000 gamma(1,.5)[sd]\n", + " .tangles_sqrt 1.458 0.065 1.335 1.592 1.000 gamma(1,.5)[sd]\n", + " c19_44840322_G 0.235 0.010 0.217 0.256 1.000 gamma(1,.5)[sd]\n", + " msex_u 0.228 0.010 0.210 0.248 1.000 gamma(1,.5)[sd]\n", + " age_death_u 43.555 1.851 40.079 47.312 1.000 gamma(1,.5)[sd]\n", + " pmi_u 55.484 2.334 51.029 60.227 1.000 gamma(1,.5)[sd]\n", + " educ 0.148 0.006 0.136 0.160 1.000 gamma(1,.5)[sd]\n", + " apoe4_dose 13.074 0.552 12.034 14.175 1.000 gamma(1,.5)[sd]\n", + " apoe2_dose 0.233 0.010 0.214 0.253 1.000 gamma(1,.5)[sd]\n", + "\n", + "Defined Parameters:\n", + " Estimate Post.SD pi.lower pi.upper Rhat Prior \n", + " indirect 0.036 0.044 -0.051 0.123 \n", + " total -0.286 0.079 -0.441 -0.132 \n", + " prop_med -0.125 0.219 -0.553 0.304 \n", + "\n", + " Estimate Post.SD pi.lower pi.upper Rhat \n", + " \" c19_448 (a)\" \" -0.351\" \" 0.207\" \" -0.755\" \" 0.050\" \" 1.000\"\n", + " \" msex_u \" \" 0.094\" \" 0.191\" \" -0.283\" \" 0.472\" \" 1.000\"\n", + " \" ag_dth_ \" \" -0.005\" \" 0.014\" \" -0.033\" \" 0.024\" \" 1.000\"\n", + " \" pmi_u \" \" 0.037\" \" 0.027\" \" -0.025\" \" 0.084\" \" 1.000\"\n", + " \" ARHGAP3 (b)\" \" -0.102\" \" 0.096\" \" -0.265\" \" 0.132\" \" 1.000\"\n", + " \" c19_448 (c_pr)\" \" -0.322\" \" 0.090\" \" -0.499\" \" -0.145\" \" 1.000\"\n", + " \" msex_u \" \" -0.374\" \" 0.085\" \" -0.542\" \" -0.207\" \" 1.000\"\n", + " \" ag_dth_ \" \" 0.039\" \" 0.006\" \" 0.027\" \" 0.051\" \" 1.000\"\n", + " \" educ \" \" -0.417\" \" 0.101\" \" -0.617\" \" -0.217\" \" 1.000\"\n", + " \" apo4_ds \" \" -0.030\" \" 0.011\" \" -0.051\" \" -0.009\" \" 1.000\"\n", + " \" apo2_ds \" \" 0.753\" \" 0.080\" \" 0.597\" \" 0.909\" \" 1.000\"\n", + " \" .ARHGAP35_xprss\" \" 0.980\" \" 0.136\" \" 0.746\" \" 1.279\" \" 1.000\"\n", + " \" .tangles_sqrt \" \" 1.458\" \" 0.065\" \" 1.335\" \" 1.592\" \" 1.000\"\n", + " \" c19_44840322_G\" \" 0.235\" \" 0.010\" \" 0.217\" \" 0.256\" \" 1.000\"\n", + " \" msex_u \" \" 0.008\" \" 0.007\" \" -0.006\" \" 0.021\" \" 1.000\"\n", + " \" age_death_u \" \" 0.071\" \" 0.097\" \" -0.119\" \" 0.263\" \" 1.000\"\n", + " \" pmi_u \" \" -0.026\" \" 0.108\" \" -0.239\" \" 0.185\" \" 1.000\"\n", + " \" educ \" \" -0.020\" \" 0.006\" \" -0.032\" \" -0.009\" \" 1.000\"\n", + " \" apoe4_dose \" \" 0.022\" \" 0.053\" \" -0.080\" \" 0.125\" \" 1.000\"\n", + " \" apoe2_dose \" \" -0.018\" \" 0.007\" \" -0.032\" \" -0.004\" \" 1.000\"\n", + " \" msex_u \" \" 0.228\" \" 0.010\" \" 0.210\" \" 0.248\" \" 1.000\"\n", + " \" age_death_u \" \" -0.597\" \" 0.095\" \" -0.786\" \" -0.412\" \" 1.000\"\n", + " \" pmi_u \" \" 0.100\" \" 0.106\" \" -0.106\" \" 0.308\" \" 1.000\"\n", + " \" educ \" \" -0.011\" \" 0.006\" \" -0.022\" \" 0.000\" \" 1.000\"\n", + " \" apoe4_dose \" \" 0.347\" \" 0.052\" \" 0.245\" \" 0.453\" \" 1.000\"\n", + " \" apoe2_dose \" \" -0.000\" \" 0.007\" \" -0.014\" \" 0.014\" \" 1.000\"\n", + " \" age_death_u \" \" 43.555\" \" 1.851\" \" 40.079\" \" 47.312\" \" 1.000\"\n", + " \" pmi_u \" \" 0.722\" \" 1.475\" \" -2.165\" \" 3.618\" \" 1.000\"\n", + " \" educ \" \" 0.219\" \" 0.078\" \" 0.068\" \" 0.372\" \" 1.000\"\n", + " \" apoe4_dose \" \" -3.192\" \" 0.712\" \" -4.601\" \" -1.835\" \" 1.000\"\n", + " \" apoe2_dose \" \" -0.349\" \" 0.097\" \" -0.541\" \" -0.160\" \" 1.000\"\n", + " \" pmi_u \" \" 55.484\" \" 2.334\" \" 51.029\" \" 60.227\" \" 1.000\"\n", + " \" educ \" \" -0.007\" \" 0.086\" \" -0.178\" \" 0.161\" \" 1.000\"\n", + " \" apoe4_dose \" \" 0.126\" \" 0.802\" \" -1.468\" \" 1.701\" \" 1.000\"\n", + " \" apoe2_dose \" \" -0.070\" \" 0.107\" \" -0.280\" \" 0.141\" \" 1.000\"\n", + " \" educ \" \" 0.148\" \" 0.006\" \" 0.136\" \" 0.160\" \" 1.000\"\n", + " \" apoe4_dose \" \" -0.093\" \" 0.042\" \" -0.175\" \" -0.011\" \" 1.000\"\n", + " \" apoe2_dose \" \" -0.025\" \" 0.006\" \" -0.037\" \" -0.014\" \" 1.000\"\n", + " \" apoe4_dose \" \" 13.074\" \" 0.552\" \" 12.034\" \" 14.175\" \" 1.000\"\n", + " \" apoe2_dose \" \" 0.132\" \" 0.053\" \" 0.029\" \" 0.237\" \" 1.000\"\n", + " \" apoe2_dose \" \" 0.233\" \" 0.010\" \" 0.214\" \" 0.253\" \" 1.000\"\n", + " \" .ARHGAP35_xprss\" \" 0.245\" \" 1.274\" \" -2.279\" \" 2.754\" \" 1.000\"\n", + " \" .tangles_sqrt \" \" -0.659\" \" 0.599\" \" -1.831\" \" 0.527\" \" 1.000\"\n", + " \" c19_44840322_G\" \" 0.281\" \" 0.014\" \" 0.253\" \" 0.309\" \" 1.000\"\n", + " \" msex_u \" \" 0.346\" \" 0.014\" \" 0.318\" \" 0.374\" \" 1.000\"\n", + " \" age_death_u \" \" 88.901\" \" 0.198\" \" 88.509\" \" 89.289\" \" 1.000\"\n", + " \" pmi_u \" \" 8.752\" \" 0.223\" \" 8.316\" \" 9.189\" \" 1.000\"\n", + " \" educ \" \" 0.159\" \" 0.012\" \" 0.136\" \" 0.181\" \" 1.000\"\n", + " \" apoe4_dose \" \" 16.372\" \" 0.108\" \" 16.159\" \" 16.584\" \" 1.000\"\n", + " \" apoe2_dose \" \" 0.272\" \" 0.014\" \" 0.244\" \" 0.300\" \" 1.000\"\n", + " \" indirect \" \" 0.036\" \" 0.044\" \" -0.051\" \" 0.123\" \" \"\n", + " \" total \" \" -0.286\" \" 0.079\" \" -0.441\" \" -0.132\" \" \"\n", + " \" prop_med \" \" -0.125\" \" 0.219\" \" -0.553\" \" 0.304\" \" \"\n", + " Prior \n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \" lkj_corr(1)\"\n", + " \" lkj_corr(1)\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \" lkj_corr(1)\"\n", + " \"gamma(1,.5)[sd]\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" normal(0,10)\"\n", + " \" \"\n", + " \" \"\n", + " \" \"\n", + "\n", + "Key Bayesian results:\n", + " label est se ci.lower ci.upper\n", + "a a -0.35088163 0.20690919 -0.75503414 0.04999733\n", + "b b -0.10174307 0.09644491 -0.26535413 0.13208840\n", + "c_prime c_prime -0.32203084 0.09031615 -0.49874731 -0.14486463\n", + "indirect indirect 0.03370831 0.04441762 -0.05473312 0.13078120\n", + "total total -0.28832252 0.07884741 -0.44195747 -0.13306433\n", + "prop_med prop_med -0.12952722 0.21876399 -0.55259349 0.20561102\n", + " CI_95\n", + "a [-0.7550, 0.0500]\n", + "b [-0.2654, 0.1321]\n", + "c_prime [-0.4987, -0.1449]\n", + "indirect [-0.0547, 0.1308]\n", + "total [-0.4420, -0.1331]\n", + "prop_med [-0.5526, 0.2056]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bayesian analysis complete (N = 1153 with fixed.x=FALSE for full missing data handling).\n" + ] + } + ], + "source": [ + "# ── Bayesian mediation via blavaan ───────────────────────────────────────────\n", + "# blavaan handles missing data natively via Bayesian data augmentation,\n", + "# so we use the full dataset (N=1153). fixed.x=FALSE is required to avoid\n", + "# listwise deletion of rows with missing exogenous variables.\n", + "suppressPackageStartupMessages(library(blavaan))\n", + "\n", + "bayes_results_list <- list()\n", + "\n", + "for (exp_var in exposures) {\n", + " cat(\"\\n========================================================\\n\")\n", + " cat(\"Bayesian: Exposure =\", exp_var, \"\\n\")\n", + " cat(\"========================================================\\n\")\n", + " \n", + " mod_syntax <- build_mediation_syntax(exp_var)\n", + " \n", + " fit_bayes <- bsem(mod_syntax, data = dat,\n", + " fixed.x = FALSE,\n", + " n.chains = 4, burnin = 2000, sample = 5000,\n", + " seed = 12345)\n", + " \n", + " cat(\"\\nBayesian model summary:\\n\")\n", + " print(summary(fit_bayes))\n", + " \n", + " # Extract MCMC draws\n", + " mcmc_draws <- blavInspect(fit_bayes, what = \"mcmc\")\n", + " mcmc_mat <- do.call(rbind, mcmc_draws)\n", + " \n", + " # blavaan parameterEstimates has limited columns; compute summaries from MCMC\n", + " pe <- parameterEstimates(fit_bayes)\n", + " \n", + " # Map key labels to MCMC column names\n", + " key_labels <- c(\"a\", \"b\", \"c_prime\", \"indirect\", \"total\", \"prop_med\")\n", + " mcmc_cols <- colnames(mcmc_mat)\n", + " \n", + " key_rows <- list()\n", + " for (lbl in key_labels) {\n", + " if (lbl %in% mcmc_cols) {\n", + " samps <- mcmc_mat[, lbl]\n", + " } else if (lbl == \"indirect\" && \"a\" %in% mcmc_cols && \"b\" %in% mcmc_cols) {\n", + " samps <- mcmc_mat[, \"a\"] * mcmc_mat[, \"b\"]\n", + " } else if (lbl == \"total\" && \"a\" %in% mcmc_cols && \"b\" %in% mcmc_cols && \"c_prime\" %in% mcmc_cols) {\n", + " samps <- mcmc_mat[, \"c_prime\"] + mcmc_mat[, \"a\"] * mcmc_mat[, \"b\"]\n", + " } else if (lbl == \"prop_med\" && \"a\" %in% mcmc_cols && \"b\" %in% mcmc_cols && \"c_prime\" %in% mcmc_cols) {\n", + " ab <- mcmc_mat[, \"a\"] * mcmc_mat[, \"b\"]\n", + " tot <- mcmc_mat[, \"c_prime\"] + ab\n", + " samps <- ifelse(abs(tot) > 1e-10, ab / tot, NA)\n", + " samps <- samps[!is.na(samps)]\n", + " } else {\n", + " next\n", + " }\n", + " key_rows[[lbl]] <- data.frame(\n", + " label = lbl,\n", + " est = mean(samps),\n", + " se = sd(samps),\n", + " ci.lower = quantile(samps, 0.025, names = FALSE),\n", + " ci.upper = quantile(samps, 0.975, names = FALSE),\n", + " stringsAsFactors = FALSE\n", + " )\n", + " }\n", + " key_bayes <- do.call(rbind, key_rows)\n", + " key_bayes$CI_95 <- sprintf(\"[%.4f, %.4f]\", key_bayes$ci.lower, key_bayes$ci.upper)\n", + " \n", + " cat(\"\\nKey Bayesian results:\\n\")\n", + " print(key_bayes)\n", + " \n", + " bayes_results_list[[exp_var]] <- list(\n", + " fit = fit_bayes, key = key_bayes, params = pe,\n", + " mcmc_draws = mcmc_draws, mcmc_mat = mcmc_mat, exposure = exp_var\n", + " )\n", + "}\n", + "\n", + "cat(\"\\nBayesian analysis complete (N =\", nrow(dat), \"with fixed.x=FALSE for full missing data handling).\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "41f28e3f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T18:36:18.928809Z", + "iopub.status.busy": "2026-03-27T18:36:18.926457Z", + "iopub.status.idle": "2026-03-27T18:36:19.713372Z", + "shell.execute_reply": "2026-03-27T18:36:19.711183Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian results saved to: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set57/bayesian_blavaan \n" + ] + } + ], + "source": [ + "# ── Save Bayesian results ────────────────────────────────────────────────────\n", + "for (exp_var in exposures) {\n", + " res <- bayes_results_list[[exp_var]]\n", + " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", + " mcmc_mat <- res$mcmc_mat\n", + " \n", + " write.csv(res$key, file.path(bayes_dir, paste0(\"bayesian_results_\", prefix, \".csv\")),\n", + " row.names = FALSE)\n", + " write.csv(res$params, file.path(bayes_dir, paste0(\"bayesian_all_params_\", prefix, \".csv\")),\n", + " row.names = FALSE)\n", + " \n", + " # Diagnostics: mean, sd, quantiles, and Rhat per MCMC column\n", + " mcmc_draws_list <- res$mcmc_draws\n", + " rhat_vals <- sapply(colnames(mcmc_mat), function(p) {\n", + " chains <- lapply(mcmc_draws_list, function(ch) ch[, p])\n", + " chain_means <- sapply(chains, mean)\n", + " chain_vars <- sapply(chains, var)\n", + " n <- length(chains[[1]])\n", + " m <- length(chains)\n", + " W <- mean(chain_vars)\n", + " B <- n * var(chain_means)\n", + " var_hat <- ((n - 1) / n) * W + (1 / n) * B\n", + " sqrt(var_hat / W)\n", + " })\n", + " \n", + " diag_df <- data.frame(\n", + " parameter = colnames(mcmc_mat),\n", + " mean = colMeans(mcmc_mat),\n", + " sd = apply(mcmc_mat, 2, sd),\n", + " q2.5 = apply(mcmc_mat, 2, quantile, 0.025),\n", + " q97.5 = apply(mcmc_mat, 2, quantile, 0.975),\n", + " rhat = rhat_vals,\n", + " row.names = NULL\n", + " )\n", + " write.csv(diag_df, file.path(bayes_dir, paste0(\"bayesian_diagnostics_\", prefix, \".csv\")),\n", + " row.names = FALSE)\n", + " \n", + " # Save MCMC draws for derived parameters\n", + " mcmc_derived <- data.frame(\n", + " a = mcmc_mat[, \"a\"],\n", + " b = mcmc_mat[, \"b\"],\n", + " c_prime = mcmc_mat[, \"c_prime\"],\n", + " indirect = mcmc_mat[, \"a\"] * mcmc_mat[, \"b\"],\n", + " total = mcmc_mat[, \"c_prime\"] + mcmc_mat[, \"a\"] * mcmc_mat[, \"b\"]\n", + " )\n", + " write.csv(mcmc_derived, file.path(bayes_dir, paste0(\"bayesian_mcmc_derived_\", prefix, \".csv\")),\n", + " row.names = FALSE)\n", + "}\n", + "\n", + "cat(\"Bayesian results saved to:\", bayes_dir, \"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "54a7fb0e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T18:36:19.748492Z", + "iopub.status.busy": "2026-03-27T18:36:19.746692Z", + "iopub.status.idle": "2026-03-27T18:36:31.448709Z", + "shell.execute_reply": "2026-03-27T18:36:31.446647Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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ltv2rRJscDBgwfh5fp5ryzQLCpu9CPLN0RFRSmN8hO7d+8GhVGtlJQUAOjVq1fD\nkr/++isAfPzxx69sV/1KMDb9i0197fSgRQhpkL4lduVNIKs3VVRUODs7e3l51dbW0jRdVVXl\n4uLi4uJC1vsliZ2Jicno0aNJgYKCAjc3Nw6Hc/v2bZqmJRKJn5+fgYGBPBe8cOGCtbW1jY0N\nmUw2atQoHo+3Y8cOsnCdRCIha7N98cUXNE2LxWITExMHBwf5UlIrV640MzNTndiR5U4AYNWq\nVVKplKbp/Pz8gIAAAwMD+QQ4NZc7UdTUOnZNFbhx4waHw3FyciI5Fk3Td+7c6dKlC7xcPbig\noIDL5Q4YMKDh4yXk8/aInj17AkCjzwIhEwr/7//+r6qqqra2dvXq1RRFydejyc/PNzQ0NDc3\n371794sXL6qrq48dO+bk5AQAp06dUqdAs6j4jJTJZG+//TYAeHt7//bbb4WFheXl5YWFhXv3\n7h02bBhFUXZ2dvJHdHzwwQcAsGfPnob1CIVCS0tLIyMj+UTMptpVvxKMTf9iU187PWgRQhqk\nb4ldU9hsNk3T5M6s5ORk+buOHDki/+JIEru4uLiBAwdyuVxXV1eKolgs1ooVK+Tlb968SSaZ\nubi4uLm5AYCjo2NGRgZ5taioiFyBEwgEjo6O5I7RQYMGyTO5FStWsFgsW1vbwYMHd+7cOSoq\naunSpfByVadGEztaYYFiS0tLJycniqLMzMyOHj0qL6D+AsVyzU3saJrevXs3GeK0srJycHCg\nKMrAwOC///0veXXBggVN7Xylq2XOzs4CgaDRRquqquQ3EZOR64iIiOfPn8sLJCYmkgFxOT6f\nrzgL+5UF1Kd6aS6pVLpo0SIrKyulzhoaGk6ZMkV+C8jz5895PJ6Li0ujq7/SNP3VV18BwPr1\n61W029xKMDb9i01N7fegRQhpiv7MsZs+fXpZWVlTr7JYrBcvXri5uW3cuLFv377y7W+++eaa\nNWueP39OFsIFAA6Hk5SUlJycfOXKFS6XO3DgQH9/f3l5Hx+f3NzcU6dO5ebm0jTt6+s7aNAg\ngUBAXrWzs7t48eK5c+dycnIqKipsbW3DwsLIZS1i5syZ/fv3T09Pr6ysDAgIGDRoUE5OTnx8\nPJmsZmpqGh8f33BtDnd39ytXrqSkpFy9elUmk3Xq1OmNN94wMjKSFxgxYkRgYKB86ow6Bg8e\nbG5urjgX7ZUFRowY8frrr588ebKgoEAmk7m5uQ0cOJDMPgSAqKgo8oyHhhT3AJxo+IAAACAA\nSURBVABMmzatqYX0jI2NT548efbs2ezsbJqmQ0NDX3vtNcU7V4YOHXr37t1Tp04VFhbW1NS4\nuroOHDhQcem7VxZQn7+/f3x8vHz5QCUsFmvOnDlffPFFampqYWFhaWmphYWFt7d3VFSU4hIw\nT58+nTNnTo8ePciqhA2Rh7Ir3sHTsN3mVoKx6V9samq/By1CSFMo+n/n8ndk+fn5fn5+H3/8\nMZn/gRBCCCHUvujbkycQQgghhDos/RmKRUi15cuXP3jwQEUBOzs78mxfhHQEHrQIoebCxO5f\n1tbW8fHxXbt2ZToQ1Cbu3Llz8+ZNFQUqKyu1FgxC6sCDFiHUXDjHDiGEEEJIT+AcO4QQQggh\nPYGJHUIIIYSQnsDEDiGEEEJIT2BihxBCCCGkJzCxQwghhBDSE5jYIYQQQgjpCUzsEEIIIYT0\nBCZ2CCGEEEJ6AhM7hBBCCCE9gYkdQgghhJCewMQOIYQQQkhPYGKHEEIIIaQnMLFDCCGEENIT\nmNghhBBCCOkJTOwQQgghhPQEJnYIIYQQQnoCEzuEEEIIIT2BiR1CCCGEkJ7AxA4hhBBCSE9g\nYofaJalUGhsb++WXX/79998uLi5//PGH4qtXr151dXX97bffmltt586dnZycrl69qrQ9KSnJ\nycnpnXfeqa+vf+edd+bMmdOq6BFC7V9+fr6Tk1NWVpY6heWnLFDjPNOsML7++mtfX99nz54p\nbqyvr4+JiXn77bclEgmesjoaTOxQu7Rs2bKSkpKFCxf26NHj448/Xrhw4dOnT8lL9fX1//nP\nf8LCwsaNG9eCmgUCwcGDB5U2Hj582MDAAAA4HM4vv/xy6NChhmUQQqgp8lMW+VH1eaZZ5syZ\nw+PxFi1apLhx8+bNhYWFixcv5nK5eMrqaDCxQ+3PkydP1q1b95///IecBL/++mtra+vZs2eT\nVzds2HDnzp2EhAQWq5HDOzk5+dixY1KptKnKu3fvfuTIEZlMJt8iFApPnz7dtWtX8qODg8OE\nCRN++OEHsVisyV4hhPSU0ikL1DjPKFFx4jIzM/vmm2/27dt3+fJlsqWkpCQhIWHcuHH+/v6A\np6yOBxM7pEOuXLnywQcf+Pv7e3t7Dxky5OzZs40W27hxo6Oj4xtvvEF+NDAwWLFixV9//XXg\nwIH79+8nJCR89dVXHh4ejb6Xy+V+++23kZGR69atq6ysbFggOjq6pKQkMzNTvuXEiRMmJiad\nO3eWb/nkk09KS0v379/f8q4ihPRCSUnJ2LFjvby8AgMDFy5cqJirySmdskC984wi1SeuDz74\nIDQ09Ntvv6VpGgB+/PFHgUBAhn0JPGV1KJjYIV0hEolGjRplbGy8Z8+eY8eOde/effz48UVF\nRQ1Lnj59um/fvhRFybd069btk08+iY+PnzVrlp+f34QJE5pqpXfv3llZWV988cXevXvDw8O/\n/fbbe/fuKRYwMzPr1auX4rDF4cOHhw4dqni+NjU1DQ8PP336dGv6ixDSA4sWLerTp8+hQ4c+\n/fTTdevW/f777w3LNDxlqXOeUaT6xEVR1OLFi69du7Znz55r167t3r173rx5JiYm8gJ4yupQ\nMLFDuoLD4Zw6dWrlypWBgYE+Pj5ffvmlUCjMzs5WKlZeXl5YWNijRw+l7bNnzzYwMPj777+X\nLFnS6CCsHJfLHTFiRHJy8q+//nr37t3o6OixY8c+fPhQXmDYsGHHjh2TSCQAUFlZeebMmYbT\nmbt3794wNoRQRxMTEzN+/PjAwMDp06dHREQcOHBAqUBTpyx1zjOKVJ+4AgMDx4wZs3jx4jlz\n5vTo0ePdd99VejuesjoOTOyQrmCz2deuXRs+fLinp6eTk5OPjw8AVFRUKBUjN3/Z2toqbc/P\nzy8uLjYwMDh69KjidpFIVPkSOYfK9e7de/v27WvXrk1LS7t+/bp8++uvvy4SiVJSUgDg2LFj\n9vb2YWFhSs3Z2dmVlZXV19e3qs8IoXauZ8+e8v8HBgYWFBQoFWjqlKX6PNOCE9fs2bNpms7N\nzf3hhx8axomnrI4DEzukK27evDlx4sSwsLDMzMxHjx7dv3+/0WJkfompqaniRrFYPHPmzCFD\nhixYsGDt2rU3btyQv5SQkOD3ktJ9YWfPnv3oo48mT54cHR1NZhkTJiYm/fv3J4UPHz789ttv\nNwzD1NSUpunq6upW9Bgh1O5ZWFjI/29oaCgUCpUKNHrKgledZ1pw4jI1NY2MjPT09PT19W0Y\nJ56yOg4O0wEg9I/k5GQ+nx8fH8/hcADg+fPnjRYj50el6cNLlix5/vz5vn37rKys9u3b9/nn\nnx87dozNZgPAmDFj+vfvT4qROyokEsmhQ4fWr1//4MGD4cOHp6Wlubu7K7UybNiwqVOnPnr0\nKCMjIz4+vmEYlZWVFEUZGxu3ttsIofZM8VxUWVlpZGSkVKDRUxah4jzTshOX6jjxlNVBYGKH\ndEV1dTWfzydZHQDs3bsXAMhNXorIiIZi2nfx4sUNGzYsX77c2toaAJYuXdq/f/9169ZNmTIF\nAJydnZ2dneWF09LSZsyYweVyx40bN2rUqIZfo4l+/frxeLwff/zR09PTz8+vYYHi4mJLS0t5\ntAihjunSpUuDBw8m/79+/TqZQ6Ko4SlLTsV5pmUnLhXwlNVx4FAs0hXh4eFlZWU7d+4sLi7e\nsmVLXl6era3t9evXq6qqFItZWFh4enrKV3uvq6ubOXNmVFTU8OHDyRYvL6+pU6cmJCQUFhY2\nbEUikSxYsCAzM3PSpEkqTo48Hm/IkCFHjhxpajpzVlZW9+7dW9hVhJC+SEpKOnz48IMHDzZv\n3pydnR0bG6tUQOmUpeiV5xk5NU9cKuApq+PAxA7pir59+06dOnXx4sV9+vS5fPnyTz/9NGbM\nmAMHDixbtkypZP/+/VNSUuQrNj19+nTJkiWKBaZPn+7k5PTll182vODXr1+/oUOHklFa1d59\n912pVNroBLvKysoLFy4MGDCgeT1ECOkRck/DggUL9u3b99prry1fvnzatGkffvhhw5KKpywl\nKs4zitQ/cTUKT1kdCtXooYaQLnv69GlkZOSaNWsUF/zUpuXLl+/cufPcuXM8Ho+RABBC7Qie\nspA24RU71P44ODhMmjRp6dKldXV12m/96dOnGzdunDt3Lp4iEULqwFMW0ia8YofaJalU+v77\n73fq1KnhQG2bqq+vj42N7dy5848//qjNdhFC7RqespDWYGKHEEIIIaQncCgWIYQQQkhPYGKH\nEEIIIaQntLpW4cOHD7ds2ZKfnw8APj4+cXFxbm5uDYsVFRVt3LgxJyeHxWKFhYVNmDDB3Nxc\nm3EihBBCCLVH2ptjV15ePn36dGdn5+HDh0ul0h07dpSXl69du9bQ0FCxmFgsnjx5srW19Ycf\nfiiVSrdt28bhcH766SeKorQTJ0IIIYRQO6W9K3YpKSm1tbXz5s0jmZy9vf2UKVNyc3OV1sI+\nc+ZMeXl5QkKCmZkZANja2k6ePPny5ctdu3bVWqgIIaSmgoKCzz//XGnj1q1bFZ8NjxBCWqO9\nxG7QoEERERHy63PksZ4Nn4t89epVX19fktUBgLOzs729/ZUrV3Q/sautrQUAgUDAdCD/qqur\n4/P5unOxUywWS6VSPp/PYunK5E6JREJRlO48P7G+vl4ikXA4HC6Xy3Qs/5BKpVKpVHdWwKJp\nuq6ujsVi8fl8pmMBePmHP2fOHMXHq5uYmLRRczKZTCQSMdh9sVjMZrNb/AiE1mD8V8943ymK\nMjAw0H7r0OH7zmKxGPmYaFnftReosbGx4onvwoULFEU1fLz606dPO3XqpLjF3t7+yZMnWoiw\nlYRCIU3TupbYcblcRv4UGyUSiUQiEYfD0Z3EjpytdCqxq6mpMTQ01KnETiQS6U5iJ5PJampq\nOByOTiV2Xbp00c7fPk3TNTU1XC6Xqe6LRCI+n8/IWYXxXz35Q2AquSF9ZzC5YerThPSdzWYz\n2HcOh8PUx0QL+s5MoM+ePVu/fn3//v2dnJyUXiKfaopbDA0NX7x4oaI2oVBIzq3MIrMVS0tL\nmQ7kXzRNV1RUMB2FsoaXaRlEfms1NTVMB/I/amtrdeGQlqNpWiwWMx3F/6ivr9fg35q5uXmL\nP7GEQiEAMPWRgxBCShhI7B4/fjxv3rxOnTpNnDhRU3XqzjLLuhMJoWvxAIakBl2LBzCkptXW\n1vJ4PN2Z8IAQ6uC0ndgVFBR8//33/v7+X3zxRaODO8bGxuQbsFxNTY2RkZGKOg0NDZUu8jGi\ntLSUpmkyd1BHVFRUmJiY6M5QbFVVlUgkMjMz051xRmav8DckunOn7soVno+PICiI6Vj+IRaL\nRSJR200aay6pVFpeXs7hcHRkFaTa2loOh7Nu3brMzEyRSOTu7j527NjOnTureAv5Q2hNoxKJ\npKSkpDU1tEYrg2+l+vp6ZvteVVWl/XYpkYh7/jxtZFQSHq791om6ujpG+k5IpVIGf+8AUF1d\nzVTTDftuZWWl4sukVhO7x48fx8fHR0RETJ06tamYnJycHj9+rPSuPn36aCVAhBjGOnrUbOZM\nyX/+A0uXMh0LUotMJmOxWFwu98svvxSLxQcPHpw7d+7y5csbXaSToCiqNVf4aJpm8AIh460D\nAFMBMNh3VkmJWWysNCCgIjWVkQA6+O+d2dab27T2EjupVLpw4cLg4GAVWR0AdO3adeXKleXl\n5WSxgNu3b5eUlIQz9x0FIYRUiI2NjY2Nlf8YGBj4ySefHD9+XMVUE6U7yZpFfsFSvnSAllVV\nVfH5fEZupmH8Ym1VVRWPx2Pk1g3Zy0tlVlZW2m8dAKqrq5m6ZUcmk5WVlbHZbKaWEKqurmbq\nthWapktLS1ksVrP6rr2bE5OSkoqKimJiYnJzc3NeIhfn6uvrv/zyy+TkZACIiopycHBYvHjx\n1atXL1y4sHz58pCQkICAAK3FiRBCLcbn8+3t7YuLi5kOBCHUQWnvit3Vq1elUukPP/yguHHw\n4MGTJ0+WyWS3bt3q1q0bAHA4nPnz569fv/6HH35gsVgRERGffPKJ1oJECKFm2bdvX21t7ejR\no8mPQqHw4cOHffv2ZTYqhFCHpb3Ebu7cuU29xOPxjhw5Iv/R2tpaRWGEENIdZmZm27Ztk0gk\nkZGR1dXV+/fvl8lkQ4YMYTouhFAHpSvrsiKEUHs0YMAAADh69GhSUpKhoaGvr29CQkLDFToR\nQkg7MLFDCKFWGTBgAEnvEEKIcZjYIaRD6IiImnnz2FFRurLQH0JIF5ib18ybR9naMr9kK9J5\nmNihdkwoFObk5Ny+fbuoqKiiokIgEHh7e/ft21enloluFllISK2npy4suI1Qh5Kenn7u3Dln\nZ+fY2FgdeQzx/zA1rZ0+ncPh4KkBvRImdqj9kclke/bs+fXXX9PS0iQSidKrfD5/4sSJixYt\nUv3AEoQQAgCpVDpp0qSNGzeSHxcuXJiYmOjl5cVsVAi1GCZ2qJ3Jy8sbPXr0xYsXAcDIwKCn\nX2dfF2cnaytzI+OqWmFO4b0jmedXrVp19uzZEydO2NjYMB0vQkinffXVVxs3bjQxE7wzstuF\nc3fyruUPGDAgOzu7/V74Rx0cJnaoPTl79uzQoUMrKyv93dzmjR75Zs8e/AaPnb1XVPz+gkWX\nLl8eMmRIamqqQCBgJFSEkO47c+bMihUrBIa8hC1jPX3tPprU59vJO7LT73z66acHDhxgOjqE\nWkJ7T55AqJWuXr1KsrpPh76R/cuq2N5RDbM6AOhkb3dy6aIgD/fs7OwvvvhC+3EihNoFmUw2\nY8YMmqYnfN7f09cOADgc1pwl71paGx88eDAxMZHpABFqCUzsUPtQWVn57rvvVlZWTnnnzTXT\np/A4qi42mxkZ7Y//1tTQcN26deRRdQghpGT//v3Xrl3r5GXz5vv/Po7czMJw4n8GAMDs2bOl\nUilz0SHUQpjYofbh888/Lyws7BvaJeGzT9Up7+5gv+TTj2manjJlilgsbuvwEELtzk8//QQA\noyf3YbEpxe39hgR5+dnn5eXt2rWLodAQajlM7FA7kJaWtmnTJjMjo03/+ZzNUvegHf/6oHBf\nn5s3b/7yyy9tGp4GsY4dM4uN5WzbxnQgCOm5v//+Ozs7297JvPcAf6WXKBY1ZnIfAFi8eDFN\n00xE18CzZ2axsYazZjEdB2oHMLFDuk4+D2bBuDFO1lbqv5FFUQmfTQCAhQsXVlZWtlmAmkQ9\nesRNTaUKC5kOBCE9t2HDBgB48/1wpct1RGRfXzdPm+vXryclJWk9tMbU1XFTU9nZ2UzHgdoB\nTOyQrtu5c+eVK1f83dwmDHm9ue+NDPB/KzKipKTk559/bovYEELtUU1Nzd69e9ls1sB3Qhot\nQFFU7NgIAFi1apV2Q0OotTCxQzpNKpXOnz8fAOaPG81hs1tQw/djR7MoasWKFeXl5ZqODiHU\nLh0+fLiqqqpblKeltXFTZfoNDTazMDx58uTNmze1GRtCrYSJHdJp+/btu3XrVhcvz7d6RrSs\nhkD3TrG9oysqKhISEjQbG0KonSJ3RfQdEqSiDN+AM/jdUJqm165dq624ENIATOyQTiO3rc3+\nYARFNTIPRk3zRo9ks1irVq16/vy55kJDCLVLlZWVJ0+e5Btwer7mq7rkW++HUyxq27ZtQqFQ\nO7Eh1HqY2CHddfbs2YsXL7o72A+LimxNPZ1dXUb2e62qqurHH3/UVGwIoXbq6NGjIpEovJeX\noRFPdUl7Z/PuUV4VFRW47glqRzCxQ7rrv//9LwBMeftN9Zc4acp3o0fxOJw1a9bcvXtXE6Eh\nhNqrw4cPA0D0AD91CpO1i9evX9+2MSGkOZjYIR1VXFx86NAhQz5/7MABra+tk73dZ28NEYlE\nX331VetrazvSiRNLnj+X/N//MR0IQvpJLBYfP36czWZF9PFWp3yP3t429qZZWVlXrlxp69hU\ncXUtef68Kj2dyRhQO4GJHdJR27ZtE4vFw2N6mxsbaaTCuaM+tDI12bdv36lTpzRSIUKo3UlN\nTa2srAzs6mJiJlCnPItNvfFeV8CLdqj9wMQO6ajNmzcDwLhBGrhcR1iamPwwPg4AJk2ahFOh\nEeqYjh49CgA9+vio/5bX3wtls1nbt2+vqalps7gQ0hhM7JAuunDhQl5enqejY2SA8tN+WmP8\n64OigwLv3Lmj4wOyCKE2cuzYMQDoGdOMxM7G3rRHb++qqqq9e/e2WVwIaQwmdkgX7dixAwBG\n9otpzSonDVEU9duXs0wEgrVr1x45ckSDNSOEdN+tW7fu3Llj72zu6mHdrDcOGd4VALZs2dIm\nYSGkURymA0BImUwm27NnDwB8+FqMxit3d7BfOXXS+J+Wx8XFXbp0ycbGRuNNIKRaXV2dRCJp\n2XvJM+mlUmlVVZVGg1JXfX29TCYTiUTab7r1fT906BAAhEd6iMXiZr2xS4Sbjb3ppUuX/v77\n79DQ0Ja13noymYzB37tUKm3uftMI8ntntu/19fUt/pttvYZ9NzExUVEeEzukczIyMh4/ftzF\ny9Pb2akt6h89oF/qtZytJ069//77x48fFwjUmkONkKZwuVx2i56PBwAymUwsFrNYLAMDA81G\npSahUMjj8TgcBj47Wt/3v/76CwB69PZuQfyD3+3y+9q07du39+zZs2WttwZN0yKRiKIopn7v\ntbW1HA6Hy+Vqv2nye2e272w2m8d7xaqHbaFlv3dM7JDOOXjwIAC8F92r7ZpYNXXSxVu3s7Ky\nFixYsGjRorZrqLmox4+5V65QPj4QpOphR6hdY7PZLU7spFIpAFAUxchHLACwWCw2m81I663s\ne11dXVpaGofLDo3wYDV/acwhsWHb153ds2fPzz//rP1vgzKhkJuayjYz4/bvr+WmCZFIxNTv\nXSaTAaPHPIN9J1crm9t3nGOHdA5J7N7p1aqnTahmyOfv+Ga2gM9buXJlZmZm2zXUXKyjR81i\nYzm//850IAjpm/T0dKFQGNjVRWDYkksvNvamXSM9KioqmLmF4tkzs9hYwcyZDDSN2htM7JBu\nycnJuXv3ro+zc2dXlzZtyM/NdeG4sTKZbMqUKQxOnkAIacfJkycBoFsvrxbXMOjtYADYtGmT\nxmJCqA3ow1AsTdPkUq0uIIMFOoKmaV2LBwBkMpmKqMjs5qER3UnhNjXlnbe2J/91KS9v1apV\nM3Xjq7C817rzi5PJZDp1IJFINBtSiwdGUTtCEruwSI8W1xAR421mYZiWllZQUODl1fIEEaE2\npQ+JnUgkqqurYzqKfz6Smbptp1EymaympkazK4a0Bsm/hUKhipDI8qH9Q7to57a7pZ+MH/D1\n3EWLFo0YMUL1fUbawauv5wHU19eLdOZAommapmmdOrBB07eFmpiYYG6n34qLi69du2ZmYejV\n2b7FlXC47H5DAw/8nrVly5aFCxdqMDyENEgfEjsDAwOmbpZRVFpaStO0ubk504H8q6KiQqc+\nsaqqqkQikbGxcVPzQMvKyi5evGhmZBQTGsLVym13kYH+QyN6JGae/+OPP+bMmaOFFlWTcLkA\nwOFwBDpzIInFYpFIpAtZLyGVSsvLyzkcjk79rSEdl5ycTNN0aIQ7xWrVF92B73Q58HvW77//\nPn/+/BbcgYGQFuBxiXTI6dOnpVJpv65dtJPVEXM+HAEAq1atYmRpLoSQFiQnJwNAeKRnK+vx\n8LH18rN/8OBBamqqJuJCSPMwsUM6hEyCGRjWVZuNhnp5vtYlpKioaPfu3dpsFyGkNSkpKQAQ\nGuHe+qoGvh0CANu2bWt9VQi1BUzskA45deoUAPTXbmIHAJPffhMA1q9fr+V2G6JDQ2unT5dF\nRTEdCEL6o7Cw8N69ew4uFvZOGhi+7zskiM1m7d+/XygUtr42dZma1k6fLh41SnstonYLEzuk\nK27duvXgwQMvJ0c3O1stNz00oruDpWVGRkZ+fr6Wm1Yi6969Zt48KUNrkCKkl/65XNdDA5fr\nAMDCyqhrT4+qqiqtPm/a3Lxm3jzRpEnaaxG1W5jYIV1BJsH0De2i/aY5bPaH/WIAh1cQ0kea\nTewAYMBbwQCwfft2TVWIkAZhYod0BXmMY0xIMCOtf9SvLwDs2rVLC+vnIYS0idzoENLNTVMV\nRvb1NRBwT5w4UVpaqqk6EdIUTOyQTqBpOjU1laKo3sHMPCM1yMPdz8317t272dnZjASAEGoL\nt27devLkiXMnKytbjS3ZIzDk9erXWSKRMPN4MYRUwsQO6YS8vLxnz575ubraWTC2ONnw3tEA\nsH//fqYCQAhpXFpaGmj0ch3R941AANi5c6dmq0Wo9TCxQzqBnHx7BwcyGMOwqEgAOHjwIIMx\nIIQ06+zZswAQHK7hxC48ysvUXHD27Nn79+9rtmaEWgkTO6QTyCSYaEYTu0D3Th6ODrdv387L\ny2MwDISQBpEvjRpP7DgcVu+B/jRN42gs0jWY2CGdQL5VRwUymdgBwJsRPQBAq6sY/C/WsWNm\nsbEcvDkXIU149OjRvXv3bB3MbB3MNF45GY3dtWuXxmtuxLNnZrGxhrNmaaMt1M5hYoeYV1hY\n+PjxYw9HB0crS2YjGRLRHQD+/PNPpgKgHj3ipqZShYVMBYCQPklPTweAoDDXtqg8ONzNxs70\n4sWLN27caIv6/0ddHTc1lY23diE1YGKHmEdOvlEB/kwHAlGBAaaGhhkZGeXl5UzHghBqrYyM\nDAAI6OLSFpVTLCrmjQAA2LNnT1vUj1DLYGKHmHfu3DkA6BUYwHQgwOVw+nXtUl9ff/r0aaZj\nQQi1VmZmJgD4d3Fuo/r7vhEEeG8s0jGY2CHmkSt2upDYAcCAsK4AcOLECaYDQQi1Sm1t7dWr\nVw0EXA9fuzZqwifAwbmT1a1bty5cuNBGTSDUXJjYIYZVVFTk5+dbmpj4ODsxHQsAwIDwrgBw\n8uRJpgNB7U9dXV1OTg6O4+uIS5cuSSQSnwBHNrsNP+n6DgkEgB07drRdEwg1CyZ2iGEZGRky\nmSwywJ+iKKZjAQDoZGfn5eT48OHD/Px8pmNB7czvv/8+d+5cfHiJjsjKygIAv5C2Gocl+g0J\nAoBdu3ZJpdI2bQghNWFihxhGJsFE+HdmOpB/kdHYU6dOMR0Iak8KCgpOnTrF5XKZDgT9gyR2\nnYPadijAuZOVb6Dj06dPU1JS2rQhhNSEiR1iGLltrae/H9OB/Ktf1y4AkJycrP2mpXFxpbdv\nS2bP1n7TqDVkMtmaNWveeecdTOx0xz9X7ILbfI5H/zeDAWD79u1t2Iazc+nt29VJSW3YBNIX\nmNghJkml0qysLC6HE+7rw3Qs/+oTHMxmsVJTUxkYW+HzaXNzEAi03S5qnSNHjtTW1sbGxjId\nCPpHWVnZ3bt3La2NbexN27qtvkMC2WzWgQMHhEJhW7XBYtHm5rSJSVvVj/QIh+kAUId27dq1\n6urqMB9vQz6f6Vj+ZW5s1MXL8+Kt2xcvXuzevTvT4SBdV1JSsmPHjrlz5/J4PHXK19fXt/g7\ng0wmI/+KRKKW1dBKUqlUIpHQNK39pknfaZpWp++ZmZk0TXsHOGjw6xkJoCETM4OuPd2z0+/s\n379/xIgRmmpOEdnhava9LTA4g1BH+s5I6031na/yExMTO8QkHZxgR7zWJeTirdt//fUXJnbo\nldatW9ezZ8+QkBA1y9fW1rbyQ0IqlVZVVbWmhtaor69nqmlQu+/k3OLpa6vZz2OZTNZo9/sM\n9stOv7Nt27bXX39dg80pYfb3LpFI6urqmGpdJpNh3+V4PJ6K2w0xsUNMejnBjvlnTiiJCQle\ntmffX3/9NRunuyGVMjIy8vLy1q5dq/5buFxui+8BJ9/dWSyWmlcHNU4ikbDZbBaLgWk8zep7\nbm4uAPgEOHA4GvuYk8lkFEU1+ruL6t95zeKTZ86cqaystLW11VSLcrrwe2exWGw2W/tNk75T\nFKX6MlXbYbDvAFBXV9ew76pPIJjYISaRb9U6decE0SvQn8vhnDt3TiKRxgRbAAAAIABJREFU\n4HR41BSRSLRx48Y+ffo8fvz48ePHACCTyZ4+fXrz5k1fX9+m3mVgYNDiFqVSqUgkYrPZxsbG\nLa6kNaqqqvh8PiPpBek7i8VSp+/Xrl0DAL9gVw2GKhaL2Wx2ox/wPB6v90D/EwevJCYmzpgx\nQ1MtypHBdzX73haqq6u5XC4jqZUu9J3D4bTmz7bFaJquq6trbt/x5gnEmKKiosLCQidrK1db\nG6ZjUWYsEIT5eFVXV+OC8kiFqqoqsVicmpr6w0sikejYsWMJCQlMh9ahlZeX37t3z9zSyNpO\ne3cbkHtjf//9d621iFCj8IodYowOLnSiqE9w8Pkb+WfOnOnZs6fWGqVKSjg3b1JubuDlpbVG\nUYtZW1srLXLxwQcfjB8/fuDAgUyFhADgypUrNE17+9trs9HQ7p1s7E0vXrx4/fr1gABNPyBR\nLOZcvco2NoYePTRcM9I7eMUOMUbHE7vewUEAkJaWps1GWfv3m/fvz9mwQZuNIqRnrly5AgBe\nfg7abJRiUW140a6oyLx/f8OPP9Z8zUjvYGKHGPNPYhegc3dOEJEBfhw2+9y5c8zeA4jaF39/\nf0tLS6aj6Oj+Sew6a/WKHbwcjd2xY0dTC6MgpAUMDMUWFxeXlpb6N3EjZF1d3e3bt5U2+vn5\nafDOJqQL6urqLl26JODzunh6MB1L44wFglAvz+ybty5fvtytWzemw0Htw3fffcd0COifxM5T\n64ldJy8bLz/7gryHaWlpMTExWm4dIULb2dKpU6fWr1/P4XB27drVaIEHDx7MnTtXaePWrVst\nLCzaPjqkPRcvXhSJRNFBgVwdTtmjgwOzb95KS0vDxA6h9kIsFufl5RkIuM5uDFw67Tc0qCCv\naOfOnZjYIaZodSh25cqV27Zt69Kli4oytbW1APD7778fUYBZnf5JT08HgEhdHYclooMCQevT\n7BBCrZGXlyeRSNy9bSlWCxcLbI3XXg+kWNS+ffvEYrH2W0cItJzYPX/+fMWKFZ07q3rMAHnW\nHiMLxiBtIoldr0CdTux6BQawKCo9PR1nzCDUXly9ehWYGIclbOxNg8Ncy8rKTp48yUgACGk1\nsfv++++tra1Vl6mtrWVwgWmkHTRNZ2RksCgqQldviSUsjI0D3DuVlZXduHGD6VgQQmohSxN7\n+NgxFUDM64EAsGfPHqYCQB2cVqc3qfNEjtraWj6fT9P0kydPRCKRk5PTK5M8qVSqO/ctMvWU\n4kbJZDKxWMzIw38aRa57SSSSnJycsrKywE5upgIBg8+WhpfPCFIRQ68A/5zCuykpKd7e3tqI\nJzi4fvp0iIyU6cyBRJ5YrzsHNjmKyGL0mqpT9YMXUfvyT2Lny1hi13ug3+of/kxMTBSLxRp7\n7oWpae306WBvL9BMdUif6dy89draWplMNn369EePHslkMi6XO2rUqGHDhql4i0gkIgO4uoDB\npxQ3qqamhukQlAmFwuTkZACI8OusI+mCii8G3Xy81gGkpaWNHDlSG6EEBUFQEACAjh1IunZg\na/aJ4BYWFkw9CBJp3LVr1ygK3H00/8BWNZlbGgV1db2afT8lJWXw4MEaqtS8Zt48DoeDiR16\nJZ1L7GxsbMLCwvr16xcWFiaRSPbs2bN582YnJ6fu3bs39RamHuKmpK6uDnRsdqBYLG7N48Y1\nTiKRSKVSHo9HntPVKzCA8VVsVDzVm4gOCgKAv//+Wzu/WalUKpFIOBwO43tGTiaTSaVS3Xlg\nbls8EVx3/kZQKxUXFxcXF9s5mhmbMHkqjhrgdzX7/qFDhzSW2CGkNl358JDr06dPnz59yP/Z\nbPaYMWPS09MzMzNVJHY8Ho+RJ1IrEYlENE0z9ZTiRlVUVBgaGurOpYiqqiqpVCoQCMjSxK+F\ndmH8FyeRSCiKUpFFuTs6uNnZ3X/0qLS01M3Nra3jqaurk0gkPB7P0NCwrdtSk1gsFolEunNg\nk3FhNputOyEh3ZGTkwOMjsMSka/5rll0PDEx8ZdffsGvDUjLdGX2lQrGxsaVlZVMR4E05u7d\nu48ePfJwdHCytmI6FrVEBfrDy9t4EUK6jCR27t4MJ3b2TubuPrZPnjy5dOkSs5GgDkhXEjux\nWEzmRK9cuXL27NnyyexPnjy5d++ej48Po9EhTUpNTQWA3mQmWXvQKzAAAM6dO8d0IAihVyB3\nTngyfcUOAHr09gaAP//8k+lAUIejvcTu6dOnO3fu3Llz59WrVyUSCfn/+fPnAUAsFsfGxu7d\nuxcA+vfvX1BQMHv27L17927ZsuWrr75ycHAYOnSo1uJEbe3MmTMA0CekHSV2eMUOofaBJHad\nvBm7c0Kue7Q3AJw4cYLpQFCHo705dlVVVeQiOQD4+vqS/5MZ2SwWKzAw0NbWFgACAgJWrFhx\n4sSJvLw8gUDw/vvvDx48WHcmbqPWI1fs2lFi5+fqamlicv369fLycnwICkI6SyqV5uXlcXls\n507MT/MI7OpiaMz/+++/KysrTU1NmQ4HdSDaS+x8fHwWLVrUeBAcjuJLrq6uEyZM0FZcSKsK\nCwsfP37s5eToYmPDdCzqoiiqp7/fsb+zMjMz33jjjTZti3X6tMnGjTBsGMTFtWlDCOmf27dv\n19bWevjacTjMzzJis1nBYa7nU2+npqa++eabra2upMRkwgRwc4NVqzQRHdJnzB/9qEM5e/Ys\nALzWJYTpQJqHPNNWC9PsqDt3+EeOsPBBFwg138s7J5gfhyVCIzwA4K+//tJAXUIh/8gRTkqK\nBqpC+g4TO6RVaWlp0B4Tu0AtJXYIoRa7fv066MadE0SX7p3g5axihLQGEzukPTKZLD09nUVR\n7S6xC/P25nE42dnZEomE6VgQQo0jd064M/eUWCWevnYmpgZXr16tqKhgOhbUgWBih7Tn2rVr\nZWVlwR7u1mbtbCqxgM/r6uMlFApxVSqEdJburHVCUCwqoKurTCYjS7IjpB2Y2CHtSUlJAYC+\noV2YDqQltDbNDiHUAjU1NXfv3jUxE1jZmjAdy78CQ10AzxtIuzCxQ9pDJhH37xrKdCAtQRI7\n/OaNkG7Kzc2VyWSMP0xMSVCYK+B5A2kXJnZIS2pqas6fPy/g8ch6v+1OT38/iqLwmzdCuunK\nlSugS+OwhLe/I4fLzs7Orq+vZzoW1FFgYoe0JDU1VSQSRQb4G/B4TMfSErbm5l5OjkVFRXfu\n3Gm7VqRxcaW3b0tmz267JhDSS2StEw+duXOC4BtwPH3tampqcnNzW1WRs3Pp7dvVSUkaigvp\nM+0tUIw6OPJonX6h7ex+WEWRAf63Hz1OT0/39PRsqzb4fNrcHASCtqof6YCqqiqRSNSaGiQS\nSUlJiabiaa5WBt9K9fX1jfad3Njk4GoqFArbtPXmvsXb3/5m7pPk5GRnZ+dWtW1uLgFg8Pde\nV1dXVVXFVOtSqZTBvgNAdXU1U0037LuVlRVFUU2Vx8QOacnx48cBoF/7vHOC6BXgv/XEqXPn\nzo0dO5bpWFA7ZmJiYmLSwgn+Uqm0vLycy+WamZlpNio1VVVV8fl8HhPX3UnfORyOubm50ks0\nTefl5bHYlF+QK9+grR5BKRaL2Ww2m81u1rsCQl2P7rmUl5dnbW3d4qZlMllZWVmjfdeO6upq\nLpfL5/O13zTpO5vNZuqJjtXV1RwOx8DAQPtN0zRdWlra3L7jUCzShnv37t26dcvZ2rqzS+u+\nszKK3D+Rnp7OdCAIof9x//79iooKZzertsvqWqxzkBMAZGdnMx0I6igwsUPakJSUBAADwtrl\n/bByPs5Otubm+fn5z58/ZzoWhNC/yAp2ujbBjnDpZGVoxLtx40abjhEjJIeJHdKGkydPQrtd\n6ESOoqie/n40TePiBQjplKtXrwKArq11QlAsyrOzfX19Pck+EWprmNihNieRSFJSUjhs9mtd\ngpmOpbXIWi04GouQTrl8+TIAePnZMx1I43wCHAHg4sWLTAeCOgRM7FCby8zMrKys7N7Z18zI\niOlYWqtXYAC0aWJXWcm+f58qL2+r+hHSR/9csdPJoVh4mXGS7LOF6uvZ9++znjzRWExIf2Fi\nh9ocGYcdGN6V6UA0INTL05DPv3TpUhtNl2Hv3GkRHs5ZubItKkdIL7148eLu3btmFoY29jr6\nEGpvfwd4uYRyCz15YhEebjRihMZiQvoLEzvU5sgKdgPC9CGx43I4Pfw6i8XirKwspmNBCAEA\nXLlyhaZpnR2HBQBXD2sen5ObmyuRSJiOBek/TOxQ2yopKbl06ZKliUmYjzfTsWhGVFAAAJw9\ne5bpQBBCAC/HYb06625ix2az3L1tRSJRfn4+07Eg/YeJHWpbycnJMpnstS4hbJaeHGxRgZjY\nIaRDyNw1Tx1O7OBleCQHRahN6clnLdJZp0+fBoD+7XwFO0U9/DpzOZzMzEx8qjdCuoA8TIzM\nY9NZZCkWXPEEaQEmdqhtJScnQzt/kpgSIwODrt5e1dXV5OMEIcSgurq6vLw8gSHPpZMV07Go\n4ulrB3jFDmkFJnaoDd29e/fu3btudnbuDjo9StJc0UGBAJCWlsZ0IAh1dDk5ORKJxMPXjmI1\n+Ux0XeCBiR3SFkzsUBtKSUkBgNe6hDAdiIa13f0TtL9/3ZgxsrAwjdeMkF5qF+OwAP/P3n0H\nNlH+fwB/7i6zTbr33ntBS0uBQtkbQZEhQ2SoX1FAFHCLA1HRLwoqXxFEkCHIFhRkD5kFCm2h\nQIEuCt0rbbPu7vfHQX9YSkea5Lkkn9dftFxy70uul0+eewaSySXObjbFxcUlJSU6PV6mnDxZ\nM2KEvnMBMyTAHQCYsyNHjiCEUk1/wYkmekRFUiR54sQJhmFIvQ4KYVJSFJ06WVlZ6fE5ATBj\naWlpCKHQSA/cQVoXEOJaer8mIyOjb9++7X6wg4Pi668FAoHEAMGAmYEWO2BAR48eRQj1io3G\nHUTPbK2to/z9KisrMzMzcWcBwKJx63Txv8UOIeQf4oIQysjIwB0EmDko7ICh5OTk3L17N8jT\nw8vJCXcW/eO62R07dgx3EAAsl1qtzsrKEksEPoEmcJHxD3ZBCMG3QWBoUNgBQ+HGFnAFkPmB\n8RMAYJeRkaFWqwND3SjKBD7L/ENcEbTYAcMzgT8GYKK4sQVmW9jFRBEEceLECZZlcWcBwEI9\nuA8baQL3YRFCPv5OFEVmZWUxDIM7CzBn5jB4QqPRqNVq3CkQ9wFfV1eHO8j/YximoaGBIPDM\nAsDdpuwaHtq4PCJ3OdNqtbgiPY6maYIgdCjO7KysQr08swsK09PTQ0JC9JgHIaTRaPhzItE0\nTdM0f/JwbxbDMHqMJJVK9TsIBhiHqQyJ5QhFlJefY96t0jt37gQGBuKOA8yWORR2BEFQFIU7\nxQP8SYIQIgiCJEksn1j379+/c+eOu6ODv1vTGewIguBPYUc8pMNje0RHZhcUnjp1Kjw8XF95\nuNqXV6c0y7IMw/AnT2Nrhx4j8eeEBO3CtdiZxJBYjn+IS96t0szMTCjsgOGYQ2EnEAgEAvwH\nUldXx7KsRMKj0ehKpVIsFmP5SObmIOgRFfnoW8MwDFci8Kd1hGVZgiB0O396xsSs+nP/6dOn\nZ86cqa885MGDop9+QqNGiadM0ddzdpBarebViU3TdH19PUmS/IkEsFCr1RkZGWKJ0DfIGXeW\ntvILdEYIZWZmPvXUU+17ZFmZfMYM5OuLli0zSDJgRvDXQ8AsnTp1CiHULTICdxADSonR//gJ\n4tYt0e7dmtBQPT4nMLTa2to9e/bcvn1bIBAEBwcPGTIEik4jyMzMVKlUEXFeJjFygsONn9Bl\nYGx9vXj3bjoyUv+ZgNkxmb8HYFq4wi45Qm/3KHnIy8kpwMO9oKDgzp07uLMAbCorK2fPnn38\n+HE/Pz9nZ+dt27a98847Wq0Wdy7zx92HDTGd+7AIZjwBRgEtdkD/lErlpUuXrMTimAB/3FkM\nKyU66nbRvePHj/v7m/mRgifZu3evSqVavny5tbU1Qig+Pv7999/PysqKjTW3lfT4xoTWnGjk\n4W0vlgivX7+uVqtFIhHuOMA8QYsd0L+LFy+q1erOwUFCHvR9NKiUKEMtGgtMxaBBgxYvXsxV\ndQghb29vhJBCocAayiKY1pBYDkESvoFOGo3mxo0buLMAswWFHdC/M2fOIISSwsNwBzG4HtGR\nCKYptmxOTk4+Pj7cv2ma3rlzp5WVVVSUeU7fyB8PR06YxpoTj9K9mx0AbWPmDSoAi7NnzyKE\nEsPMfwRAoIeHm4P9zZs379+/7/bYxC7AcuTk5Hz33XclJSVBQUFffPGFra1tCxsrlUqdO+Fx\n0/jRNI2rUVCr1bIsi2Xq0MYpDBUKxeXLl1UqVWiUB01radpIARiGYVmW7tj+vP0cEEIXL14c\nNmxY2x9FNDRwbcK43neNRkPTdOOkpMb06Ptu/L2jh8eOsePs48cuk8la2B4KO6B/586dQwgl\nhpt/YYcQ6hEVufX4yZMnT44ePRp3FoCNjY1NUlJSSUnJ6dOnd+3aNXPmzBamGdJoNCqVqiO7\nYxhGqVR25Bk6ooOVTQdxx85dZALDXU1unIqXvwNCKDMzs13vIKlSWSPEsize9x1LYceBY3/0\nN9bW1i3MvgmFHdCz4uLi3NxcD0cHLycTu0Wim26REVuPn/znn3/0UtjR48crevSQuLkJO/5c\nwIhcXFzGjx+PEBo1atScOXMCAwOHDh36pI2lUqnOHee5JTcoirKystIxa8c0NDQIhUIsU4c+\neuzXr19HCIVEeIjFYqMF0Gg0JEl2cGbQkEhPhFB2drZcLm/7o9igoMq0NFIiadej9EipVFIU\nJRRiuDKxLKtQKEiSbOzJamQ8PPaW51SHwg7o2fnz55Fl3Ifl9IiOQnocP2FjQ/v6spg+s4EO\nFAqFVqu1s7PjfvTx8fH29r569WoLhV1H5lTnlncjSdKYBc2j1Gq1UCjEMqKTO3aCIMRi8eXL\nlxFCYdGexpyAnaZpiqI6uEdXdzu5rTQ3N1etVre9SmOEQoWvLyEQ4HrfNRqNUCjEsvfG9Xgw\nHrsA0yvPFXbtPXYYPAH0jCvsEkL1tnwqz8UE+Mul0suXL8NASMu0ePHiJUuWNP7IsmxVVVXL\nfexAB9E0nZGRIRCQfkEuuLPowj/YhWXZq1ev4g4CzBMUdkDPuL4vXSymsKNIMik8TKvVckNG\ngKXp3bt3RkbGH3/8odFolErlunXrKisrk5KScOcyZzdu3Kirq/MNdBaK+LKEcbv4h7gghDIy\nMnAHAeYJbsUCPbtw4QJBEJ2Dg3EHMZ5ukREHL146efJk3759cWcBxtavX7/S0tK1a9euWrUK\nISQWi2fMmAGzExvUpUuXkKnNYPcoWH8CGBQUdkCf8vPzS0tLAz087GR4erli0T0qAj1cRQ1Y\noPHjx48aNerevXskSbq7u8OKAobGdbALDDPVCYb8g10RtNgBg4HCDugT18GuS6gFNdchhJLC\nwwQUdebMGa5vNe44AAOJRALLyhlNeno6Qigw1BV3EB0FhLgQBBR2wFCgjx3QJ25Z7k5BQbiD\nGJW1RBLp51tTU6OH3tA1NVReHlFZqY9cAJiny5cvEwQKDDfVFjsrmdjVw660tPT+/fttfYxW\nS+XlkUVFhswFzAQUdkCfHhR2wYG4gxhbckQ40sfdWGrTJvuEBMG33+ojFABmqKSkpLi42NXD\nTiaX4M6iu4AQV4TQlStX2vqAoiL7hATrMWMMmAmYCyjsgD5dvHiRIAhLa7FDCCVHhiOETp8+\njTsIAGaOG3PAFUamKyC0nYUdAG0GhR3Qm7y8vLKysgB3d4saOcFJDofCDgBj4Aq7wDAo7ABo\nHhR2QG+4+7DxwRbXXIcQ8nd3c3Owv3nzZnl5Oe4sAJizrKwshFBAqKl2sONwLY7c8F4A9AsK\nO6A3Fy9eRBbZwY6TFBbGsuyZM2dwBwHAnHFDlLip4EyXp4+DWCLMzs5Wq9W4swBzA4Ud0JsH\nhZ3ldbDjJIWHIbgbC4AhaTSaGzduiCVCTx8H3Fk6hKQI/2AXtVp97do13FmAuYHCDugNV9jF\nBVloi11iWChCCFrsADCcnJwctVrtF+RMUgTuLB3FdROEu7FA72CCYqAfhYWFxcXFfm6ujjZy\n3FnwSAgNFlDUuXPnOjJNMRsYqBoxAkVE6DcbAObBPO7DcgJD3VDbCzsrK9WIEcjXFyZAB62C\nwg7oB7d6YydLba5DD6cpvnzr9tWrV6Ojo3V7EqZfP0XXrlZWVvrNBoB5yM7ORgj5BZlFYdeu\nFjsnp9rVqwUCgdiwoYA5gFuxQD+4IbGdLXJIbCOumx3cjQXAQLgeaf4h5lDYBYS4EiTBLY8G\ngB5BYQf0w8JHTnCSwkIRQmfPnsUdBADz9KCwM4tbsVYysYeXfXl5eX5+Pu4swKxAYQf0g7sV\na7EjJzjQYgeA4SgUioKCArmtxNHFTDryBoa5IYSg0Q7oFxR2QA9KSkoKCwu9nJxc7e1wZ8Ep\nxMvTXia7du1adXU17iwAmJtr166xLOsb6Iw7iN4ER7ihh9+KAdAXYw+eUCqVP//8c1lZ2Qcf\nfPCkbTQazd69ezMyMgiCiI+PHzRoEEGY/Mh285aWloYQ6hxi0fdhEUIEQXQJC/k77eL58+f7\n9euHOw4AZoVbTMw3yAl3EL0JCndHUNgBfTNqi11eXt7cuXOPHj3KDVlvFsuyH3/88bZt2wIC\nAnx8fNatW7d8+XJjhgQ6eHAfNtCi78NyEqGbHQCGwX1w+AaYT4tdUDi02AH9M2qL3TvvvDNg\nwACpVLp9+/YnbZOWlnblypVvvvnG398fIRQVFbVw4cLhw4dzPwJ+svDFxB7VwWmKyRMnZOvX\nE0OGoLFj9ZoLAJNnfi12Dk4yR2dZfn5+WVmZk1OLx1VRIXvjDeTpiT77zFjpgKkyaovd3Llz\nn3/+eZJsaafnzp3z9/dvLOM6depka2sL7R88B0NiGyWGhREEofMZS1y9Klm3jrxwQb+pADAD\nXIudT4D5FHYIoeAId/TwEtoShUKybp1w925jZAImzqiFXXx8fKvb5Ofn+/j4NP5IEIS3t3de\nXp4hc4EOKS8vz83NdXOw93RyxJ0FP0cbeZCnR2lp6a1bt3BnAcB8VFVV3b17197R2sZOijuL\nPrW1sAOgzXi38kRNTU3Qvxt+5HJ5TU1NCw9RKpUqlcrAuVrHsixCiFfDIWmarq2tNfTQkxMn\nTiCEYgP8W30XGIZBCKnVav6MhmEYhiAImqb1+JxdQoJvFt49fPhwK/dWmiPUaIQI0TRdz5sT\niWEYlmX5c2Jzf2g0Tesxklwub/lOAsAuKysLIeQTaFbNdQih4EgP9HCCdwD0gneF3ePrbJIk\nqdVqW3gIwzAajcbAudqKP0k4Lb90esFdkuICA9pYHnHlHX9whYIeJYQEbzx89Ny5c6NGjWrv\nYymGQTw7pTk8fNf0+BLp/RxoGU3TOr+e3AP1e/jtDUDTtPH3fuXKFYSQT4Ajwnc2sizLfRXU\n43NyM55cuHChlZeUprlvHpb2viMLPufRw0vT48cuFApbeBTvCjupVKpUKh/9jVKplEpbanuX\nSCQikcjAuVpXXV3NsqydHY8mcqutrbW2tjZ0UwQ3F3xieJhEIml5S41GQ9O0SCTiT+uIVqsl\nCKLJd4kO6h4dhRC6dOmSDicDIxIhhAQCAX9OJI1Go9Fo+LN8LcMwNTU1FEXJ5XqbpdbIJyT3\nkur2WO5CzzBMk+uk0TAMo1arjfCNsYmMjAyEkE+AE8uyxt87h2u91u/XADsHqYOTLDc3t6io\nyMHB4UmbkWo190mO632naZplWf3e3GijxuIG47EzDIPxy+3jx25ihZ2rq2tJScmjvykpKWl5\nSXWSJPlTKAgEPHpJuZJFv1XL47hJ7LqEhbb6LnDfdHn1fhEEQRCEfvPEBgbIpNIrV66o1er2\n1kMaguBS8edEYhhGq9XyJw/30cKrl6i9JBJJq9+CnoSmabVard+6tl1qa2vFYrHxv0vn5OQg\nhHwCHAmCwPVNnnvl9X5FDYl0P3Ps5o0bN/r37/+kbZiHrRu43neFQiEUCsVisfF3zTBMRUUF\nSZIYj10gEOj8N9sRLMuqVKr2HjtfPl8bRUREZGdnq9Vq7sfy8vLCwsKWCzuAUXl5eV5enruD\ng/uTv2taGook40OCNBoNdIgGQF+4Pnbe5jUklhMS5YEefkMGoON4UdjRNL127Vqupb1v374I\nodWrV6vV6vr6+hUrVri4uCQlJeHOCJrHXYziQ4JxB+GXruHhCKHTp0+394H0+PGVaWna2bMN\nEAoAU1VZWVlUVOTgLJPbYGg1MbSQiDYUdh4elWlpdVu2GCkTMGXGu5dx8eLFhQsXNv44YsQI\nhFCfPn3mzJlD0/S2bdskEkl0dLRcLn/77be/+uqrv//+m2VZFxeXd955x3TvuZi98+fPIyjs\nHtM1IgwhdOrUqXY/0saG9vVledOhDQA+4Jrr/IJccAcxiNDoNhR2AgHt60vARyFoA+OdJcHB\nwYsWLWryS3t7e4SQUChctGiRq6sr98vY2Ng1a9bk5uaSJOnv78+fqTHA47ghsZ2DYWrif+ka\nHk4QhC6FHQDgMQ8LO/NZTOxRDk4yZ1eb/Pz84uLixs9BAHRmvMJOLpc/qascQRBN/ksgEATB\nMgamgGuxSwiFFrt/cbK1Cfb0vFFYmJOTA2cyAB1k3i12CKHQaI/S4ppz584NHz4cdxZg8njR\nxw6YqLt37969e9fX1dWFN3Nz8EdyZDhC6J9//sEdBACTxxV25rRKbBNh0Z7o4fdkADoICjug\nuwfNddDBrjndIyMQFHYA6ENWVhZBIL9AM26x80QInTt3DncQYA6gsAO64y5DXcJCcAfho26R\nEQihkydP4g4CgGkrLS0tLi52crGxlmOYRM04QiM9CJI4d+6ckRdBAWYJCjugu7NnzyKEEsNC\ncQfhoxAvTxc7u+zs7NLS0nY8TKUiqqpQQ4PBcgFgYh50sAs22+argu5oAAAgAElEQVQ6hJC1\nXOzj71RZWXnz5s3mt2AYoqqKqK01bi5gkqCwAzpiGObChQsUSXYKCsSdhY8IgugWGc6ybLvu\nxlK//OIYHCz84gvDBQPAtGRmZiKE/ILNc0hso7AYT/Tw23IzCgsdg4NlgwcbNRMwTVDYAR1d\nu3aturo6ws9X1uJKvpasR3QUQujEiRO4gwBgwrgWO3/zHRLLiYj1QgidOXMGdxBg8qCwAzri\nllXoGh6GOwh/pURHIYSOHz+OOwgAJuxhi52ZF3attNgB0GZQ2AEdcSMnEmHkxJPFBgbYWltf\nunSpuroadxYATFVmZiZBEuY6O3Ej/2AXqZXoypUr9fX1uLMA0waFHdAR12KXHBGBOwh/USTZ\nPSqCpmkYGwuAbgoKCqqqqjy87MUSIe4shkVRZFi0p0ajaWVtMQBaA4Ud0EVNTc3Vq1ftZbIQ\nL0/cWXitV0wMQujIkSO4gwBgkjIyMhBC/iFmfh+WExHnhR5+ZwZAZ1DYAV2cPXuWYZjE8FBY\nybdlqXFQ2AGgu4eFnUWsoBoR541gVnPQYcZbKxaYE+7SkxwRjjsI38UFBTrZ2qSnp5eVlTk5\ntb4gEhsYqBoxAsENbgAQQg8LuwDLaLGL7ORNkMSpU6dYlm36ndnKSjViBPL1pTBlAyYEWuyA\nLrgx+V3DobBrBUkQPWOiGYZpY6Md069f7erV9Jgxhg4GgEl4MCTW3Oc64chtJD7+TuXl5dev\nX2/6f05OtatXN3z8MY5cwMRAYQfajabpM2fOUCSZGA5rTrSub6c4hNCBAwdwBwHAxGg0muzs\nbLFE4OXriDuLkUR19kYw+SXoGCjsQLtlZGRUV1dHB/jLYWriNujXuROCwg6A9svOzlapVH5B\nLiRlKX15o+N9EawxDToGCjvQblwHu+5R0A+sTQI83AM83HNzc2/cuIE7CwCm5GEHO4sYOcGJ\njvdBMKs56BgYPAHajfs22S0SCru2GhDf+X9Fe/fv3x8SAvM5m6GCgoJffvklOzsbIRQSEjJl\nyhRfX1/coczBlStXEEIBoRZU2Ll52rl62Obm5ubl5cFZBHQDLXag3bj+H90jI3EHMRkDEuIR\nQn/99RfuIED/Kisr33nnnfr6+jfeeGPOnDlVVVUffvghLB6gF5cvX0YW1mKHEIpN9EMIHTt2\nDHcQYKqgsAPtc/v27bt37/q7u3k6WUp35o7rHRcjFgqPHj0Kn/fm5/Dhww0NDe+//37nzp27\ndOkyd+7ciooKbiwn6CCusAsMs6zCLq6LH4LJL0EHQGEH2oe7D8stbw/aSCaVpkRHNTQ0tHqx\nJs+ds/7kE+rgQeMEAx03cODAb7/91srKivuRm62wpqYGayhzUFJScu/ePVcPW7mtZQ3S6tTV\nHyF0+PDhf/22qsr6k0/EK1bgyQRMChR2oH24Xr09obBrpyFJXRBCf/75Z8ubEZcuSZctI2FM\nnOmQyWSenv+/sF5aWhpBEOEwxWOHpaenIwvrYMdxcbf19HHIz8//13CrmhrpsmWiDRvw5QIm\nAwZPgPbhen5Ai117DUnqMnfFyj179nz//fe4swBDKSkp+fHHH/v16/doqfc4hUKhUqk6siOt\nVlteXt6RZ9AZy7JqtdoIO+JG3/sFOTU0NDz6e4ZhmvzGaFiWpWnaCDuK6eJzN79i586d06ZN\n435DVlfbI4QQwvi+q1QqhUKBZe8IIZqmMR47Qqiurg7L3hFCDMM0OXYHB4cW1vOEwg60Q1FR\nUU5OjoejQ4CHO+4sJibQwyPU2+t6fn56enpcXBzuOED/7t69+/777/v5+b300kstb8myLPdR\nobMOPryDjLP3B2tOBDs/vjuMh2+cXccl+f61Lf3IkSNTp059dL8dP3M6Au9Zhz2ACb3yUNiB\nduC6iKXGxuIOYpKGdU26XlD4xx9/QGFnfnJycj766KOIiIg33nhDJBK1vLFcLpfL5brtiKbp\nyspKoVBoa2ur2zN0UG1trVgsbvUYO+7atWsIoYhY38b+iyzLNjQ0kCQpkUgMvfdmqdVqiqIo\nyuDrtSb3ChcI/jh58qRcLheLxQghRqFACBEE0ZYlpw1BoVAIhUIujJExDFNRUUFRlL29vfH3\njhBSKBQCgQDLWceybHl5eXuPHfrYgXY4evQoQqhnDNyH1cWIbl0RQrt378YdBOjZ3bt3P/zw\nw8TExLfeessIFY8lqK+vv3HjhrVc7O5lhzsLBtZycWQnb4VCATMVAx1AYQfagSvsesXG4A5i\nkrpGhLvY2V24cKGwsBB3FqA3NE1/+umnMTExr776agu9XkC7XL58mabpoDA3i31Jk3oGI4T2\n7t2LOwgwPVDYgbYqLCzMycnxcnIK8vTAncUkkQQxtGsiy7K7du3CnQXozV9//XX//v3U1NTM\nzMyMh+7evYs7l2m7ePEiQig4wnL78ib3DkUI7dy5E3cQYHqgjx1oK25epdQ4aK7T3YhuXdfs\n+3vXrl0zZ85sdgPmmWeqoqLEvr5CIycDuuLalhYtWvToLwcNGvTKK6/gimQGuLlOAsPccAfB\nxifAydPXIS8v7/Lly7GxscjNrergQUom07FvJrAk5lDY1dfX4xr9/ihu3Aqu8dhPUlVVpa+n\n4lbE6hEZ0cFXu4MTPRiCRqMxzo66hYdZScRHjx69deuWnV1znYekUjY2liaIep6dSHw7sfU7\n34ednZ3OPeLfffddfcUAjS5cuIAsu8UOIdRzQMSmn05u3749NjYWiUTa2FgkMIePbGBo5nCW\nWFlZNQ6bwqi8vJxlWUdHHi20VVVVJZfL9TWGi5tWqn+XBKlUx4ng1Wq1VqsVi8UkyZc+ABqN\nhiAIgbEul1KpdFCXhO0n/jl9+vTEiRMf30CpVCoUCqlUyodTmqNWq1Uqlc6jOPWOGxYqEAia\nr4yB6VOr1VlZWWKJwCcAzwhQnkjpH77pp5Nbtmz56KOPcGcBpoQvn6+A565fv15QUBDk6eHj\n4ow7i2kb0S0ZIbRjxw7cQQDgqczMTLVaHRDiSlEW/QkVEunu4W2fnZ3N3ZgGoI0s+s8GtN2h\nQ4cQQn06wQRsHTU0KVEoEOzfv58P/QcA4CHuPmxIpKUP0iIIoveQKITQ+vXrcWcBpgQKO9Am\nBw8eRAj1hcKuw+xk1qmxMXV1dX///TfuLADwEQyJbdR/RCxCaOPGjVqtFncWYDKgsAOto2n6\n6NGjFEnCkFi9GNkd7sYC8EQPRk5EQmGHvP0dI+K87t27x41dA6AtoLADrTt//nxlZWVcUKAD\nb3rQm7Th3bqSBPHHH3808y1cpSKqqhDcpQWWSqPRZGRkiMQCvyAX3Fl4Ycjozgihn378kaiq\nImprcccBJgAKO9C6AwcOIIT6x3fGHcRMuDs4JIaHVlRUHDt2rMl/Ub/84hgcLPziCyzBAMAu\nIyNDqVQGhbkJBPDxhBBCfYZEyeSSrP37HYODZYMH444DTAD85YDWcYVdv86dcAcxHyO7d0Mw\nrTwAj4H7sE2IJcKBo+IYhsEdBJgMKOxAK2pqas6cOSOXSpMjw3FnMR9cN7udO3dy81oDADjn\nz59HCIVa/JDYR42akEhSBEKIpmncWYAJgMIOtOLw4cMajaZXbIwIJj3Xn0APj0g/38LCQu5j\nDADASUtLQwiFREFh9//cve0TewQhhCoqKnBnASYACjvQiv379yOEBiRABzs94+7GwthYABop\nlcrMzEyplcg3ACZC/5cR47sghMrLyxUKBe4sgO+gsAOt2LdvH4LCzgBg0hMAmkhPT9doNEHh\nbtydR9DIL8gZIUTT9PLly3FnAXwHhR1oSXZ2dm5ubqCHR6AH3BnRs7igQD831+vXr1+9ehV3\nFgB44UEHu2hP3EH466uvvqqqqsKdAvAaFHagJdysmAO7xOMOYp6eemzdWNbLS9OrFxsQgC8U\nANicO3cOIRQGHewew4gFZYm+uZ52FRUVX375Je44gNegsAMt4Qq7wYkJuIOYp5E9mk56wgwd\nWr11q3byZHyhAMDmQWEHLXaPUTtYn/lhzL1loymK/OabbwoLC3EnAvwFhR14IoVCcfz4cSux\nuFcMrCRmEMkR4S52dhcuXMjPz8edBQDMKisrb968aWtv5eZlhzsLT3n7Ow4Z3bmhoeHdd9/F\nnQXwFxR24IkOHjyoUqlS42KkYhHuLOaJIsnh3bqyLAtDKAA4f/48y7Jh0Z4EASMnnmjKa6lW\nMvH69ethpiTwJFDYgSfas2cPQmhIYhfcQcwZjI0FgAP3YdvCzsF64kspDMPMnj0bpjcHzYLC\nDjSPYZi9e/cSBDGkayLuLOasd1ysrbX1yZMnS0pKcGcBAKezZ88ihMJjobBrxTOTu3r6Opw+\nfXrdunW4swA+gsIONO/8+fP379+PDQzwdoaZQg1ILBQOSepC0/Tu3btxZwEAp3PnzhEEtNi1\nTiCkZr49CCH01ltvVVdX444DeAcKO9C8Xbt2IYSGJyfhDmL+noK7scDi3b59u6SkxMPHQW4r\nxZ3FBCT1DO7WO/T+/fsffPAB7iyAd2D1T9C8h4VdV9xBzN/AhHipWHTo0KGamhpJerr1tm1k\n375oxAjcuYChKJVKrVar22O5blU0TeNaWkqr1bIsq1ar9fu0R48eRQiFRXu0+syG2HsbMQzD\nsixN08bftbBWGb7qlMrJOn/Sgy/bM97sk3bq1g8//DBu3Ljo6GhDB9BoNDRNazQaQ+/ocdw5\nzzAMrnOeO3ad/2Y77vFjl8lkLWwPhR1oxo0bN65everj4hwXCDPlGpxMKh0QH7/r1Ok///zz\nmeJi6bJlGrEYCjszJhAIdB74yTCMSqUiCEIoFOo3VRvRNE1RlECg58+OCxcuIIQiYr0oinrS\nNtwHPEEQLWxjUAzD4Nq7sF4buPZsbbDz3SnduN94+jiOn9F97XfH5s6de+TIEUMPJebedyxn\nHcuyfDjncR07Qqi9xw6FHWjG9u3bEUJPde8G8w4Yx8ge3XadOr19+/ZnunfHnQUYnEAg0Lkw\nomm6rq6OJEmxWKzfVG2kVquFQqFIpOcpkLjJOyI7+bRa2CGEcBV23Ac8lr1TFPnwH/+/9/HT\nUw7uzjhz5symTZteeOEFgwbQaDRCoRDLWccwDEKIIAhc57xGoxEIBFj2zrKsQqFo77FDHzvQ\nDK6wG9WjG+4glmJo10ShQPDXX39hudMBAF4NDQ3p6ekSqTAg1BV3FlMiFFGvvTcYIbRgwYLK\nykrccQBfQGEHmrpz505aWpqrvV23yAjcWSyFvUyWGhujUCiys7NxZwHA2NLS0jQaTViMZ2O7\nFGijLj2Ceg4ILy0tff/993FnAXwBf0Wgqa1bt7IsO7JHd4qE08N4uJmKr1y5gjsIAMZ26tQp\nhFBErDfuICbpPwsGiiXC//3vf+np6bizAF6AT27Q1O+//44QGt2zB+4glmVEt2SSIDIzM3EH\nAcDYuMIushMUdrpwcbd97sUeNE3PmjUL1qIACAo70MTt27fT0tLcHOxToqNwZ7Esbg723SIj\nGhoacAcBwKhYlj19+jRBoMg4L9xZTNWYF7q5e9ufOHGC+1oOLJyxR8Vqtdrc3FySJH19fZsd\nW6RUKm/evNnkl+Hh4XofXQ+atWnTJpZlR/dMgfuwxjeqR7elmVm+/fu/NmkSnmH9ABjd9evX\nS0tLfQKcYGriFqgdrM/8MIaxbn5opEgseOnN/gtnb1mwYMGIESMkEomR4wFeMWq1dP78+W++\n+aa+vp5lWXt7+wULFoSFhTXZJj8//913323yy7Vr19rb2xsrpkXbuHEjQmhsai/cQSzRiO7J\nb/646qv09Ff8/XFnAcBITp48iRCKjvfBHYTXGLGgLNGXJMknlWwp/cNjEnyvpOUuX7583rx5\nRg0HeMZ4rTIVFRVffvllr169Nm/e/Ntvv0VFRS1evFilUjXZjLsV9euvv+5+BFR1xnHp0qWr\nV6/6u7slhYfizmKJ/FxdYwMDSktLz5w5gzsLAEbyzz//IISiOkNh11EvzetPEGjx4sUw9YmF\nM15hd/jwYZFINHXqVJFIJJFIXnzxxZqamtOnTzfZrL6+HiEELclYrF+/HiH0XJ/eMC8xLqO6\nJyOEdu/ejTsIAEZy4sQJhFBMvC/uICYvLNozpX9EZWXlkiVLcGcBOBmvsMvOzg4NDW3sKieX\ny729va9evdpks4aGBowTTFsyrVbL3Yed2K8P7iyW66luDwo7GN0GLEFRUdGtW7ecXW3cvOxw\nZzEHL8zqTVLE8uXLS0pKcGcB2Bivj11JSUmTHnVOTk6Pn3wNDQ1isZhl2aKiIpVK5enp2WqR\nx7Ist+QIH2BZH/pJ2rVe9b59++7fv981PCzQw91AVQX3tCzL8qdqaYyEO8gDIV6eYd5e2Xl5\naWlpnTt3xh0HIawLnzeLS6LfSLiWqALHjx9HCEUnwH1Y/fAJcOo3LObvXZeXLFkC7XYWy3iF\nnVKpbFKiicXiioqKJps1NDQwDDNr1qzCwkKGYYRC4YQJE0aNGtXCMzc0NHA3cPmAb50bampq\n2rjlTz/9hBAal9rT0DNuPN6xEjteLeQ1PDkpu6Bw48aN/nwaQqFWq3FH+BeapvX4t2Zvbw+1\nHRZHjx5FCMV28cOcw4xM/E/PQ3syVqxYMX/+fGdnZ9xxAAbGK+woimrSrsYwzOOTmDg7O8fH\nx/ft2zc+Pl6j0WzZsmXNmjWenp6JiYlPemaSJPkwGYpWq0UI8SFJI5qmSZJsS4e5ioqK/fv3\nS0Wi0T17kAab6IRrqzPc8+uAa6vjVZ/CYUmJS7Zs+/PPPz/88EPcWRB62CLOq7qHh39rQDfH\njh1DCMUkQAc7vfH0ceg3Imb/jvSvv/76888/xx0HYGC8K6OtrW2T1qOamprHh7v26tWrV68H\nc21QFDV58uSTJ0+ePn26hcJOIpHwYbBFeXk5y7J2djzqKVJVVSWXy9vykbxu3TqVSjW+T6qr\no6Ph8qjVaq1WKxKJ+FPbaTQagiD4UyKwO3f3/OHH5dZWr924cf/+/cfnAzI+tVqtUqnkcjnu\nIA9wbXUCgYBXf2tAB/fu3bt+/bqjs8zb34CXHfMguVfdt9+3tcHO53e90urGE15MObD78g8/\n/DBv3jxHQ17SAT8Z7/PVz88vNze38UeGYfLy8gICAlp9oEwma/v9RKCb1atXI4SmDOyPOwhA\nCKEQT0+E0I4dO3AHAcCAjhw5wrJsXJI/r5rMzYCnr0PvwVG1tbXffvst7iwAA+MVdsnJyXl5\neY2rSpw6daquri45OZn7Ua1Wczdqv/322wULFjR2iy4qKsrNzQ0JCTFaTgt09uzZK1eu+Lu7\n9Y6LxZ0FIIRQsJcnQmj79u24gwBgQIcOHUIIdUriUV9SszF+Rg+CJL777jtoFrFAxivsYmNj\nu3bt+vHHH2/YsGHNmjXLli0bPny4p6cnQkitVo8ePZpb5K5fv345OTkLFiz4/ffff/nll/nz\n57u7uw8bNsxoOS3QypUrEULTBg+E78084eXk5OZgf+HChby8PNxZADCUgwcPIoQ6dYXCTv/8\ng1169A2rrKz87rvvcGcBxmbUrk4LFix49tlnb926VVRU9NJLL02bNu1BCJKMiopycXFBCEVG\nRi5dujQ0NPTatWulpaVjx4795ptvrK2tjZnTolRXV2/evFkoEDw/AO7D8gVBoOHJXVmWhbux\nwFzdvHkzPz/fw9vezRP6ShrEhJdSCAItXbpUoVDgzgKMyqh9ximKGjFixIgRI5qGEAg+++yz\nxh99fHxmzJhhzGCW7Ndff62rq3smpYebA6zbxiOjenT7ae9fO3bsmDNnDu4sAOjfgQMHEELx\n3QJxBzFbwRHuSb1Czhy98cMPP8yfPx93HGA8fBmcCLBgWXbFihUIoRlDB+POAv4lNTbGXib7\n559/iouLcWcBQP/279+PEEroDoWdAU1+pRdBoK+++goa7SwKFHYW7ejRo1evXg3x8urTCYZN\n8ALr7KyNjWHc3IQCwdCuiTRN79y5E3co0CbFxcWPr5EImqVSqQ4fPkxRJIycaCNGLChL9K2M\n9mzXo0KjPLqmhpSWli5btsxAwQAPQWFn0b7//nuE0H9GDIVhEzzBJHWp/+h9ekA/hNCoHt0R\nQtu2bcMdCrTuwIEDM2fO/Pjjj3EHMQ0nT55UKBSRnbyt5bAseJuoHazP/DAm872B7X3glFd7\nEwRasmTJ4+s8AXMFhZ3lKigo2LVrl1wqnTygH+4soBn94zvLpNKjR4+Wl5fjzgJa8u23365b\nty4uLg53EJPx559/IoQSU4JwBzF/QeFuvQZFVlVVPdqRHZg3KOws1/fff6/VaicN6GdjZYU7\nC2iGVCwakthFo9Hs2rULdxbQktLS0qVLl/JhmRBTsXfvXoRQ114wQakxTJ3dRyCkvvvuuzt3\n7uDOAowBCjsLVVdXt3LlSpIgXn1qOO4s4Ime7tkdIcRN8Qh466OPPnJycsKdwmTk5ORcv37d\n1cPWP8QFdxaL4OnjMGJcgkqlgrGxFoIvS2QCI1uzZk1lZeXQpERukQPAT4MTu1hLJIcOHaqo\nqHBwcMAdBzSvLcsxP4qmaW6hHR1wD2RZVqPR6PYMHcQwDE3THdk7t6RK19SQ9r4ILMs2ZtB5\n7x3BsizDMFh6JHfw2Ce+nHLwjytbt249ePBg42rs7dLx911nZnDO64x73x8/dqFQ2MKjoLCz\nRDRNL126FCE0Z/Qo3FlAS6zE4qFJiVuOHd+xY0fjhN7A1NXX16tUqo48g1arra6u1lee9urg\nJxw37XZCD3+lUqnDwxmG0e2BeoHrA56j87ELxcSEl3us+PzAa6+9dvjw4ZbLgidRq9U6PEpf\nGIbBeM4jhBoaGnDt+vFjd3R0bOELBhR2luj333+/fft25+Cg1NgY3FlAK0b3Stly7PjmzZuh\nsDMbun2scliWVavVJEl25Ek6QqvVkiRJkjp24ykpKTl//ry1XBKX6C8QtO9JWJalaZogiPY2\nkeoL11yHaw4BrVbbkWMfNib+wK6M7GvZP//886xZs3TYe0fe947gznmCIEQikfH3jjr8yneQ\nSqV6/NhbPgmhsLM4LMt+8cUXCKE3x4zGnQU0Rd66LT6fhiIjUKcHQywHJybYWFkdPny4uLjY\n1dUVbzygFxKJRCKR6PZYmqbVajVFUXK5XL+p2qi2tlYsFuv8Ebtp0yaappNTI62s2v0KsCzb\n0NCA8QOee+WxfMBTtUqX9Wc1DtZlYxN1fpI5Hw577bnVn3/++aRJk3x8fNr1WIVCIRQKxWIM\n09MwDFNRUUGSJK5zXqFQCAQCnf9mO4JlWZVK1d5jh8ETFmffvn3p6enBXp5Pp3THnQU0RVzL\nFq/bQF281PgbiUj0VPdkmqZhCAUwA1u3bkUIpfQPxx3ExAhqleHLj/tvTOvIk4THeA4fE69Q\nKF555RV9BQM8BIWdxfn0008RQvPHjqZwNKoDHYzrnYoQWr9+Pe4gAHRIWVnZkSNHrKxFXXrA\nDHZ4TH+9n6OzbO/evZs2bcKdBRgKfLRblkOHDp06dcrX1WVC3z64s4C26ts5zs3B/uzZszdu\n3MCdBTR17969TZs2bdq06fLlyxqNhvv3mTNncOfio61bt2q12uTeoWIJ9ALCw1ounvX+UITQ\nrFmzSkpKcMcBBgGFnWXhljxaMG6MUAAXVpNBkSTXaLdu3TrcWUBTtbW1GRkZGRkZNE2HhoZy\n/y4sLMSdi49+++03hFDvwVG4g1i0Hv3Ceg+JKisre+mll3BnAQYBn+4W5PDhw8ePH/d2dn5+\nYH/cWUD7TO7f95ttO9atW/fRRx/hGpwFmhUSEgKLNbVFQUHBiRMn5LbSLj0CcWexdLPeG3Ll\nfO7OnTtXrlz54osv4o4D9Axa7CzIwoULEUILxj0rguY6UxMd4B8fElxQUPD333/jzgKALjZt\n2sQwTK+BEQIhfDPBzMZOOm/RSIJAc+bMSU9Pxx0H6BkUdpbi4MGDJ06c8HFxnjJoAO4sQBfT\nBg9ECK1cuRJ3EAB0wXUk6D8C5s7khS49AsfP6NHQ0DBq1CjobGdmoLCzFB9++CFC6K3xY8WY\n5jUFbcF0Tar/6H1tv76P/9e4PqlyqXTPnj0FBQXGDwZAR6SlpWVlZXl420d28sadxSSpHazP\n/DAm471BenzOF17r06VHYG5u7rBhwxQKhR6fGeAFhZ1F2Ldv36lTp3xdXaF3Hc+xTo7a2BjW\nw/3x/5JLpRP69dFqtStWrDB+MAA6Ys2aNQihASPjcC3bYOoYsaAs0bcq2kOPz0lSxAf/fTYw\n1PX8+fODBw/Gu2AX0CMo7CwC11z37oRx0LvOpM18ajhBECtXrqyvr8edBYC2amho2LhxI0ES\nA0fG4s4C/sVKJv5i1SSfAKeTJ0/27NkT7gaYByjszN+ePXvOnTsX6OExqX8zN/iACQnz8R7U\nJaG8vHz16tW4swDQVlu3bq2qqkroFujibos7C2jK3tF66boXQqM8rly5kpCQcPToUdyJQEdB\nYWfmWJb96KOPEELvTBgngGkyTN+bY55BCH399ddqtRp3FgDahBvxM2R0Z9xBQPPsHKyWrpvS\ne0hUSUlJ//79ly5dijsR6BAo7MzcH3/8kZaWFuzlOaFvb9xZgB70jInuFhmRl5e3du1a3FkA\naF1mZubJkycdnWXd+oTizgKeSCwRvvfVM/+ZP4Blmblz506dOlWj0eAOBXQEhZ05a2yue3fC\neFgZ1my8P+k5hNAnn3yiVCpxZwGgFT/88ANCaMjozgIBXIL4bvSU5C9WTZTbStesWTNixAjo\ny2ui4C/NnO3Zs+fy5cuh3l7jevfCnQXoTb/OnVJjYwoKCpYtW4Y7CwAtqaqq+vXXXymKHPps\nPO4soE06Jfl/t2mam5fdvn37hgwZUldXhzsRaDco7MwWwzBffPEFQui9ic9Bc52poPb8aTPy\nWeG69S1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6fP559/PnfuXBiIbZlKSkoW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\npsunUyefTp0K7t+/vmvn3aNHE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yZNWrhwYXl5edLCHDx4cM2aNVdffbXL1eZuHDVqFBGpfRajIpHI9u3bx4wZoxZp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\nBow5VnB4xqzhfDPWa/InkZQ878QwcEu64EBP8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MtPFrIhk6j2vTx1pk8JBoV880WqpmJ9qKQtaWcFfpM/xW4cjXD+kdhkX58DabtepxkX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\ne9/73p6NHXfv1772tR2vyfn5+auuuurPf/5ztVrdtGlTGty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ADwV199pfoUSekoiho3btzGjRuXLFnS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\nwYHCzuR8+umnKpWq29T3RA4OuLMALWs/cqS1u/ulS5eOHTuGOwsAhmH58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/zww6NHj/7xj3/829/+duHCheEEgUBg06ZN4RGmhw4dampquuSSS4howoQJBQUF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3nn6Cd/XFd7j90HZaviQsMD/4Ul2/uLT/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\njAgQ7AAAAAAARgQIdgAAAAAAIwIEuwCwtBsAAAAAAOBBDILd7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ll2jsSrCI99ZW/rlsJaWHFFRVlORCRDkzD2ZH1KBVkgysrUdzVoZj/L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\nmSl2iOjjcnfw4MGRLcWw0hEEkR4C4ktXlsKelbnpnIz3EWA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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bayesian plots saved to: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set57/bayesian_blavaan \n" + ] + }, + { + "data": { + "image/png": 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K72BBwSEjJkyJCioqK///3vOp1u7Nix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\naZObm9vUqVO/+uqr169f796928XFhd4AYuXKlUZGRjdv3mROSCKETJw4kcl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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ── Bayesian posterior plots ──────────────────────────────────────────────────\n", + "for (exp_var in exposures) {\n", + " res <- bayes_results_list[[exp_var]]\n", + " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", + " mcmc_mat <- res$mcmc_mat\n", + " \n", + " a_post <- mcmc_mat[, \"a\"]\n", + " b_post <- mcmc_mat[, \"b\"]\n", + " c_post <- mcmc_mat[, \"c_prime\"]\n", + " indirect_post <- a_post * b_post\n", + " \n", + " post_df <- data.frame(\n", + " value = c(a_post, b_post, c_post, indirect_post),\n", + " path = rep(c(\"a (X->M)\", \"b (M->Y)\", \"c' (X->Y direct)\", \"indirect (a*b)\"),\n", + " each = length(a_post))\n", + " )\n", + " \n", + " p_post <- ggplot(post_df, aes(x = value, fill = path)) +\n", + " geom_density(alpha = 0.5) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"red\") +\n", + " facet_wrap(~path, scales = \"free\") +\n", + " labs(title = paste(\"Bayesian Posterior Distributions\"),\n", + " subtitle = paste(\"Exposure:\", prefix),\n", + " x = \"Parameter Value\", y = \"Density\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"none\")\n", + " \n", + " ggsave(file.path(bayes_dir, paste0(\"bayesian_posteriors_\", prefix, \".png\")),\n", + " p_post, width = 10, height = 7, dpi = 150)\n", + " ggsave(file.path(bayes_dir, paste0(\"bayesian_posteriors_\", prefix, \".pdf\")),\n", + " p_post, width = 10, height = 7)\n", + " print(p_post)\n", + " \n", + " # Trace plot for path a\n", + " n_per_chain <- length(a_post) / 4\n", + " trace_df <- data.frame(\n", + " iteration = seq_along(a_post),\n", + " a = a_post,\n", + " chain = rep(1:4, each = n_per_chain)\n", + " )\n", + " p_trace <- ggplot(trace_df, aes(x = iteration, y = a, color = factor(chain))) +\n", + " geom_line(alpha = 0.5, linewidth = 0.3) +\n", + " labs(title = paste(\"MCMC Trace: Path a (X -> M)\"),\n", + " subtitle = paste(\"Exposure:\", prefix),\n", + " x = \"Iteration\", y = \"a\", color = \"Chain\") +\n", + " theme_minimal(base_size = 12)\n", + " \n", + " ggsave(file.path(bayes_dir, paste0(\"bayesian_trace_a_\", prefix, \".png\")),\n", + " p_trace, width = 8, height = 4, dpi = 150)\n", + " ggsave(file.path(bayes_dir, paste0(\"bayesian_trace_a_\", prefix, \".pdf\")),\n", + " p_trace, width = 8, height = 4)\n", + " print(p_trace)\n", + "}\n", + "\n", + "# ── Bayesian forest plot ──────────────────────────────────────────────────\n", + "bayes_plot_data <- do.call(rbind, lapply(exposures, function(exp_var) {\n", + " res <- bayes_results_list[[exp_var]]\n", + " key <- res$key\n", + " key$exposure <- exp_var\n", + " key\n", + "}))\n", + "bayes_plot_data$label <- factor(bayes_plot_data$label,\n", + " levels = c(\"a\", \"b\", \"c_prime\", \"indirect\", \"total\", \"prop_med\"))\n", + "bayes_plot_data$exposure_short <- ifelse(\n", + " grepl(\"44827033\", bayes_plot_data$exposure), \"44827033_indel\", \"44840322_G_A\")\n", + "\n", + "p_bayes_forest <- ggplot(bayes_plot_data, aes(x = est, y = label, color = exposure_short)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci.lower, xmax = ci.upper),\n", + " position = position_dodge(width = 0.5), size = 0.6) +\n", + " labs(title = \"Bayesian (blavaan): Path Estimates\",\n", + " subtitle = paste0(\"N = \", nrow(dat), \" (missing data handled), 4 chains x 5000 samples\"),\n", + " x = \"Posterior Mean (95% Credible Interval)\", y = \"Path\", color = \"Exposure\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + "ggsave(file.path(bayes_dir, \"bayesian_forest_plot.png\"), p_bayes_forest, width = 8, height = 5, dpi = 150)\n", + "ggsave(file.path(bayes_dir, \"bayesian_forest_plot.pdf\"), p_bayes_forest, width = 8, height = 5)\n", + "print(p_bayes_forest)\n", + "cat(\"Bayesian plots saved to:\", bayes_dir, \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "84850dd6", + "metadata": {}, + "source": [ + "## 5. Combined Summary: All Methods" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "28b3e2dc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T18:36:31.454680Z", + "iopub.status.busy": "2026-03-27T18:36:31.453004Z", + "iopub.status.idle": "2026-03-27T18:36:31.518783Z", + "shell.execute_reply": "2026-03-27T18:36:31.516661Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Combined summary saved. Rows: 36 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " exposure method label\n", + "1 chr19_44827033_TGATAGATAGATAGATA_TGATA FIML a\n", + "2 chr19_44827033_TGATAGATAGATAGATA_TGATA FIML b\n", + "3 chr19_44827033_TGATAGATAGATAGATA_TGATA FIML c_prime\n", + "4 chr19_44827033_TGATAGATAGATAGATA_TGATA FIML indirect\n", + "5 chr19_44827033_TGATAGATAGATAGATA_TGATA FIML total\n", + "6 chr19_44827033_TGATAGATAGATAGATA_TGATA FIML prop_med\n", + "7 chr19_44840322_G_A FIML a\n", + "8 chr19_44840322_G_A FIML b\n", + "9 chr19_44840322_G_A FIML c_prime\n", + "10 chr19_44840322_G_A FIML indirect\n", + "11 chr19_44840322_G_A FIML total\n", + "12 chr19_44840322_G_A FIML prop_med\n", + "13 chr19_44827033_TGATAGATAGATAGATA_TGATA Bootstrap (BCa) a\n", + "14 chr19_44827033_TGATAGATAGATAGATA_TGATA Bootstrap (BCa) b\n", + "15 chr19_44827033_TGATAGATAGATAGATA_TGATA Bootstrap (BCa) c_prime\n", + "16 chr19_44827033_TGATAGATAGATAGATA_TGATA Bootstrap (BCa) indirect\n", + "17 chr19_44827033_TGATAGATAGATAGATA_TGATA Bootstrap (BCa) total\n", + "18 chr19_44827033_TGATAGATAGATAGATA_TGATA Bootstrap (BCa) prop_med\n", + "19 chr19_44840322_G_A Bootstrap (BCa) a\n", + "20 chr19_44840322_G_A Bootstrap (BCa) b\n", + "21 chr19_44840322_G_A Bootstrap (BCa) c_prime\n", + "22 chr19_44840322_G_A Bootstrap (BCa) indirect\n", + "23 chr19_44840322_G_A Bootstrap (BCa) total\n", + "24 chr19_44840322_G_A Bootstrap (BCa) prop_med\n", + "25 chr19_44827033_TGATAGATAGATAGATA_TGATA Bayesian (blavaan) a\n", + "26 chr19_44827033_TGATAGATAGATAGATA_TGATA Bayesian (blavaan) b\n", + "27 chr19_44827033_TGATAGATAGATAGATA_TGATA Bayesian (blavaan) c_prime\n", + "28 chr19_44827033_TGATAGATAGATAGATA_TGATA Bayesian (blavaan) indirect\n", + "29 chr19_44827033_TGATAGATAGATAGATA_TGATA Bayesian (blavaan) total\n", + "30 chr19_44827033_TGATAGATAGATAGATA_TGATA Bayesian (blavaan) prop_med\n", + "31 chr19_44840322_G_A Bayesian (blavaan) a\n", + "32 chr19_44840322_G_A Bayesian (blavaan) b\n", + "33 chr19_44840322_G_A Bayesian (blavaan) c_prime\n", + "34 chr19_44840322_G_A Bayesian (blavaan) indirect\n", + "35 chr19_44840322_G_A Bayesian (blavaan) total\n", + "36 chr19_44840322_G_A Bayesian (blavaan) prop_med\n", + " est se ci_lower ci_upper p_value\n", + "1 -0.32797860 0.20014057 -0.72024691 0.064289704 1.012672e-01\n", + "2 -0.13376766 0.07284994 -0.27655092 0.009015594 6.632686e-02\n", + "3 -0.32987871 0.08662469 -0.49965998 -0.160097443 1.400175e-04\n", + "4 0.04387293 0.03463612 -0.02401261 0.111758475 2.052692e-01\n", + "5 -0.28600578 0.08007268 -0.44294535 -0.129066220 3.544990e-04\n", + "6 -0.15339876 0.12927432 -0.40677177 0.099974261 2.353798e-01\n", + "7 -0.34340449 0.20198706 -0.73929185 0.052482869 8.910626e-02\n", + "8 -0.13858606 0.07279253 -0.28125679 0.004084666 5.692970e-02\n", + "9 -0.33625436 0.08561110 -0.50404903 -0.168459695 8.576354e-05\n", + "10 0.04759108 0.03670718 -0.02435368 0.119535835 1.948013e-01\n", + "11 -0.28866329 0.07793526 -0.44141358 -0.135912991 2.123219e-04\n", + "12 -0.16486709 0.13540235 -0.43025082 0.100516641 2.233727e-01\n", + "13 -0.32797860 0.21947150 -0.73888374 0.115653587 1.350706e-01\n", + "14 -0.13376766 0.12692465 -0.28786545 0.208961184 2.919223e-01\n", + "15 -0.32987871 0.09115803 -0.52501924 -0.166436543 2.960219e-04\n", + "16 0.04387293 0.05518981 -0.03989360 0.191389161 4.266448e-01\n", + "17 -0.28600578 0.07362732 -0.43186060 -0.144749457 1.025382e-04\n", + "18 -0.15339876 0.22430723 -0.89039139 0.142715118 4.940523e-01\n", + "19 -0.34340449 0.22093852 -0.76255229 0.093902787 1.201131e-01\n", + "20 -0.13858606 0.12506830 -0.29372836 0.200925485 2.678260e-01\n", + "21 -0.33625436 0.09040947 -0.53562462 -0.173825939 1.998245e-04\n", + "22 0.04759108 0.05641407 -0.03682666 0.197823453 3.988913e-01\n", + "23 -0.28866329 0.07017654 -0.43230530 -0.158608443 3.898952e-05\n", + "24 -0.16486709 0.22365207 -0.81878946 0.147626582 4.610258e-01\n", + "25 -0.33681603 0.20741446 -0.74718487 0.071005886 NA\n", + "26 -0.09536300 0.09742921 -0.25855238 0.137717332 NA\n", + "27 -0.31518642 0.09139647 -0.49423945 -0.135186556 NA\n", + "28 0.02974504 0.04351184 -0.05933630 0.121777112 NA\n", + "29 -0.28544139 0.08187537 -0.44570788 -0.125584434 NA\n", + "30 -0.11990361 0.22876997 -0.54994434 0.222540809 NA\n", + "31 -0.35088163 0.20690919 -0.75503414 0.049997332 NA\n", + "32 -0.10174307 0.09644491 -0.26535413 0.132088398 NA\n", + "33 -0.32203084 0.09031615 -0.49874731 -0.144864625 NA\n", + "34 0.03370831 0.04441762 -0.05473312 0.130781196 NA\n", + "35 -0.28832252 0.07884741 -0.44195747 -0.133064334 NA\n", + "36 -0.12952722 0.21876399 -0.55259349 0.205611024 NA\n", + " ci_type n_eff\n", + "1 Delta-method 95% CI N=1153 (FIML)\n", + "2 Delta-method 95% CI N=1153 (FIML)\n", + "3 Delta-method 95% CI N=1153 (FIML)\n", + "4 Delta-method 95% CI N=1153 (FIML)\n", + "5 Delta-method 95% CI N=1153 (FIML)\n", + "6 Delta-method 95% CI N=1153 (FIML)\n", + "7 Delta-method 95% CI N=1153 (FIML)\n", + "8 Delta-method 95% CI N=1153 (FIML)\n", + "9 Delta-method 95% CI N=1153 (FIML)\n", + "10 Delta-method 95% CI N=1153 (FIML)\n", + "11 Delta-method 95% CI N=1153 (FIML)\n", + "12 Delta-method 95% CI N=1153 (FIML)\n", + "13 BCa bootstrap 95% CI N=1153 (Bootstrap FIML)\n", + "14 BCa bootstrap 95% CI N=1153 (Bootstrap FIML)\n", + "15 BCa bootstrap 95% CI N=1153 (Bootstrap FIML)\n", + "16 BCa bootstrap 95% CI N=1153 (Bootstrap FIML)\n", + "17 BCa bootstrap 95% CI N=1153 (Bootstrap FIML)\n", + "18 BCa bootstrap 95% CI N=1153 (Bootstrap FIML)\n", + "19 BCa bootstrap 95% CI N=1153 (Bootstrap FIML)\n", + "20 BCa bootstrap 95% CI N=1153 (Bootstrap FIML)\n", + "21 BCa bootstrap 95% CI N=1153 (Bootstrap FIML)\n", + "22 BCa bootstrap 95% CI N=1153 (Bootstrap FIML)\n", + "23 BCa bootstrap 95% CI N=1153 (Bootstrap FIML)\n", + "24 BCa bootstrap 95% CI N=1153 (Bootstrap FIML)\n", + "25 95% Credible Interval N=1153 (Bayesian, fixed.x=FALSE)\n", + "26 95% Credible Interval N=1153 (Bayesian, fixed.x=FALSE)\n", + "27 95% Credible Interval N=1153 (Bayesian, fixed.x=FALSE)\n", + "28 95% Credible Interval N=1153 (Bayesian, fixed.x=FALSE)\n", + "29 95% Credible Interval N=1153 (Bayesian, fixed.x=FALSE)\n", + "30 95% Credible Interval N=1153 (Bayesian, fixed.x=FALSE)\n", + "31 95% Credible Interval N=1153 (Bayesian, fixed.x=FALSE)\n", + "32 95% Credible Interval N=1153 (Bayesian, fixed.x=FALSE)\n", + "33 95% Credible Interval N=1153 (Bayesian, fixed.x=FALSE)\n", + "34 95% Credible Interval N=1153 (Bayesian, fixed.x=FALSE)\n", + "35 95% Credible Interval N=1153 (Bayesian, fixed.x=FALSE)\n", + "36 95% Credible Interval N=1153 (Bayesian, fixed.x=FALSE)\n" + ] + } + ], + "source": [ + "# ── Combine all results into one summary table ────────────────────────────────\n", + "combine_key <- function(method_name, results_list, ci_type, n_note) {\n", + " do.call(rbind, lapply(exposures, function(exp_var) {\n", + " res <- results_list[[exp_var]]\n", + " key <- res$key\n", + " data.frame(\n", + " exposure = exp_var,\n", + " method = method_name,\n", + " label = key$label,\n", + " est = key$est,\n", + " se = key$se,\n", + " ci_lower = key$ci.lower,\n", + " ci_upper = key$ci.upper,\n", + " p_value = if (\"pvalue\" %in% names(key)) key$pvalue else NA_real_,\n", + " ci_type = ci_type,\n", + " n_eff = n_note,\n", + " stringsAsFactors = FALSE\n", + " )\n", + " }))\n", + "}\n", + "\n", + "n_fiml <- paste0(\"N=\", nrow(dat), \" (FIML)\")\n", + "n_boot <- paste0(\"N=\", nrow(dat), \" (Bootstrap FIML)\")\n", + "n_bayes <- paste0(\"N=\", nrow(dat), \" (Bayesian, fixed.x=FALSE)\")\n", + "\n", + "summary_all <- rbind(\n", + " combine_key(\"FIML\", fiml_results_list, \"Delta-method 95% CI\", n_fiml),\n", + " combine_key(\"Bootstrap (BCa)\", boot_results_list, \"BCa bootstrap 95% CI\", n_boot),\n", + " combine_key(\"Bayesian (blavaan)\", bayes_results_list, \"95% Credible Interval\", n_bayes)\n", + ")\n", + "\n", + "write.csv(summary_all, file.path(summ_dir, \"all_methods_summary.csv\"), row.names = FALSE)\n", + "cat(\"Combined summary saved. Rows:\", nrow(summary_all), \"\\n\")\n", + "print(summary_all)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f0b54fa1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T18:36:31.524545Z", + "iopub.status.busy": "2026-03-27T18:36:31.522843Z", + "iopub.status.idle": "2026-03-27T18:36:33.278415Z", + "shell.execute_reply": "2026-03-27T18:36:33.276347Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n", + "“for 'ARHGAP35 expression mediating variant → tangles_sqrt' in 'mbcsToSbcs': -> substituted for → (U+2192)”\n" + ] + }, + { + "data": { + "image/png": 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vhDeJcuOTnZyclp0KBBHMf16NFDqVTev3+frWJfJO7u7oWFhfzmv/32GxHNnj2b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1whw/GDT/U4HK8Sq9Xs8wTGRkJE7FBjKK4zhBNhDxQ3MAACAASURBVPzkk09u3Lix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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ── Summary forest plot: all methods, by exposure ─────────────────────────────\n", + "summary_all$label <- factor(summary_all$label,\n", + " levels = c(\"a\", \"b\", \"c_prime\", \"indirect\", \"total\", \"prop_med\"))\n", + "summary_all$exposure_short <- ifelse(\n", + " grepl(\"44827033\", summary_all$exposure), \"44827033_indel\", \"44840322_G_A\")\n", + "\n", + "# ── Plot 1: Forest plot faceted by exposure, showing all methods per path ──\n", + "p_forest_all <- ggplot(summary_all, aes(x = est, y = label, color = method, shape = method)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper),\n", + " position = position_dodge(width = 0.6), size = 0.5) +\n", + " facet_wrap(~exposure_short, ncol = 1, scales = \"free_x\") +\n", + " labs(title = \"All Methods Comparison: Path Estimates\",\n", + " subtitle = \"ARHGAP35 expression mediating variant → tangles_sqrt\",\n", + " x = \"Estimate (95% CI)\", y = \"Path\",\n", + " color = \"Method\", shape = \"Method\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + "ggsave(file.path(summ_dir, \"summary_forest_all_methods.png\"), p_forest_all,\n", + " width = 10, height = 8, dpi = 150)\n", + "ggsave(file.path(summ_dir, \"summary_forest_all_methods.pdf\"), p_forest_all,\n", + " width = 10, height = 8)\n", + "print(p_forest_all)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "47319f76", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T18:36:33.284280Z", + "iopub.status.busy": "2026-03-27T18:36:33.282539Z", + "iopub.status.idle": "2026-03-27T18:36:36.591958Z", + "shell.execute_reply": "2026-03-27T18:36:36.589834Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n", + "“for 'ARHGAP35 expression mediating variant → tangles_sqrt' in 'mbcsToSbcs': -> substituted for → (U+2192)”\n" + ] + }, + { + "data": { + "image/png": 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REE0blz5z179nTq1CklJeXJkyc8bUij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XLpTL5YSQmpqadevWpaenx8TEmC7C1quB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\n/crKSh0dnYULF9rZ2UlOX7p06ciRI4UZ5s+fP2HCBAzDwsPDx4wZI0yfO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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ── Plot 2: Faceted by path (each effect type gets its own panel) ─────────\n", + "p_facet_path <- ggplot(summary_all, aes(x = est, y = method, color = exposure_short)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper),\n", + " position = position_dodge(width = 0.5), size = 0.5) +\n", + " facet_wrap(~label, scales = \"free_x\", ncol = 2) +\n", + " labs(title = \"Estimates by Effect Type Across Methods\",\n", + " subtitle = \"ARHGAP35 expression mediating variant → tangles_sqrt\",\n", + " x = \"Estimate (95% CI)\", y = \"Method\", color = \"Exposure\") +\n", + " theme_minimal(base_size = 11) +\n", + " theme(legend.position = \"bottom\",\n", + " strip.text = element_text(face = \"bold\"))\n", + "\n", + "ggsave(file.path(summ_dir, \"summary_faceted_by_path.png\"), p_facet_path,\n", + " width = 10, height = 8, dpi = 150)\n", + "ggsave(file.path(summ_dir, \"summary_faceted_by_path.pdf\"), p_facet_path,\n", + " width = 10, height = 8)\n", + "print(p_facet_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ac7c8440", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T18:36:36.597791Z", + "iopub.status.busy": "2026-03-27T18:36:36.596115Z", + "iopub.status.idle": "2026-03-27T18:36:37.666298Z", + "shell.execute_reply": "2026-03-27T18:36:37.664138Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n", + "“for 'Mediation: variant → ARHGAP35 expression → tangles_sqrt' in 'mbcsToSbcs': -> substituted for → (U+2192)”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n", + "“for 'Mediation: variant → ARHGAP35 expression → tangles_sqrt' in 'mbcsToSbcs': -> substituted for → (U+2192)”\n" + ] + }, + { + "data": { + "image/png": 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FdX1y5durzxxhunT5+OiopKTU1dtmxZ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\nSUVORK5CYII=", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ── Plot 3: Indirect effect comparison (focused) ─────────────────────────────\n", + "indirect_data <- summary_all[summary_all$label == \"indirect\", ]\n", + "\n", + "p_indirect <- ggplot(indirect_data, aes(x = est, y = method, color = exposure_short)) +\n", + " geom_vline(xintercept = 0, linetype = \"dashed\", color = \"grey50\") +\n", + " geom_pointrange(aes(xmin = ci_lower, xmax = ci_upper),\n", + " position = position_dodge(width = 0.4), size = 0.8) +\n", + " labs(title = \"Indirect Effect (a x b) Across Methods\",\n", + " subtitle = \"Mediation: variant → ARHGAP35 expression → tangles_sqrt\",\n", + " x = \"Indirect Effect (95% CI)\", y = \"Method\", color = \"Exposure\") +\n", + " theme_minimal(base_size = 12) +\n", + " theme(legend.position = \"bottom\")\n", + "\n", + "ggsave(file.path(summ_dir, \"summary_indirect_effect.png\"), p_indirect,\n", + " width = 8, height = 4, dpi = 150)\n", + "ggsave(file.path(summ_dir, \"summary_indirect_effect.pdf\"), p_indirect,\n", + " width = 8, height = 4)\n", + "print(p_indirect)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f8e3c236", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T18:36:37.672159Z", + "iopub.status.busy": "2026-03-27T18:36:37.670459Z", + "iopub.status.idle": "2026-03-27T18:36:39.483875Z", + "shell.execute_reply": "2026-03-27T18:36:39.481807Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n", + "“for 'ARHGAP35 expression mediating variant → tangles_sqrt' in 'mbcsToSbcs': -> substituted for → (U+2192)”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n", + "“for 'Mediation: variant → ARHGAP35 expression → tangles_sqrt' in 'mbcsToSbcs': -> substituted for → (U+2192)”\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :\n", + "“for 'Mediation: variant → ARHGAP35 expression → tangles_sqrt' in 'mbcsToSbcs': -> substituted for → (U+2192)”\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All summary plots saved to: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set57/summary \n" + ] + }, + { + "data": { + "image/png": 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nbl5eXM5gcOHACAH3/8kf7vu8rU1LSoqEjzgy5ZsgQAzp8/z6yVSqXnz58nqYP6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Cm5vb7t27ly9fvnPnzhEjRpiYmFy+fLlf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\nTNUXmi0VFRVpFyYlJRFC2rVrp53VEUIMf0Lr1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gMDuVwu/dD79OlDCLlw4YJ2BT3pGk2mNX+9DEzsjhw5QggZMWJErYVKT8L269evzGbz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\nAACgJ/4Pg3AO3jOlA+IAAAAASUVORK5CYII=", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# ── Plot 4: Combined panel ───────────────────────────────────────────────────\n", + "# Top: forest by exposure; Bottom: indirect effect focused\n", + "p_combined_panel <- grid.arrange(\n", + " p_forest_all + theme(legend.position = \"right\"),\n", + " p_indirect + theme(legend.position = \"right\"),\n", + " ncol = 1, heights = c(2, 1)\n", + ")\n", + "\n", + "ggsave(file.path(summ_dir, \"summary_combined_panel.png\"), p_combined_panel,\n", + " width = 11, height = 10, dpi = 150)\n", + "ggsave(file.path(summ_dir, \"summary_combined_panel.pdf\"), p_combined_panel,\n", + " width = 11, height = 10)\n", + "\n", + "cat(\"All summary plots saved to:\", summ_dir, \"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "481f304d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T18:36:39.489564Z", + "iopub.status.busy": "2026-03-27T18:36:39.487913Z", + "iopub.status.idle": "2026-03-27T18:36:39.950387Z", + "shell.execute_reply": "2026-03-27T18:36:39.948264Z" + }, + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Display Table: Exposure = 44827033_TGATAGATAGATAGATA_TGATA ===\n", + "\u001b[90m# A tibble: 6 × 10\u001b[39m\n", + " label estimate_ci_FIML estimate_ci_Bootstra…¹ estimate_ci_Bayesian…²\n", + " \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \n", + "\u001b[90m1\u001b[39m a -0.3280 [-0.7202, 0.06… -0.3280 [-0.7389, 0.1… -0.3368 [-0.7472, 0.0…\n", + "\u001b[90m2\u001b[39m b -0.1338 [-0.2766, 0.00… -0.1338 [-0.2879, 0.2… -0.0954 [-0.2586, 0.1…\n", + "\u001b[90m3\u001b[39m c_prime -0.3299 [-0.4997, -0.1… -0.3299 [-0.5250, -0.… -0.3152 [-0.4942, -0.…\n", + "\u001b[90m4\u001b[39m indirect 0.0439 [-0.0240, 0.111… 0.0439 [-0.0399, 0.19… 0.0297 [-0.0593, 0.12…\n", + "\u001b[90m5\u001b[39m total -0.2860 [-0.4429, -0.1… -0.2860 [-0.4319, -0.… -0.2854 [-0.4457, -0.…\n", + "\u001b[90m6\u001b[39m prop_med -0.1534 [-0.4068, 0.10… -0.1534 [-0.8904, 0.1… -0.1199 [-0.5499, 0.2…\n", + "\u001b[90m# ℹ abbreviated names: ¹​`estimate_ci_Bootstrap (BCa)`,\u001b[39m\n", + "\u001b[90m# ²​`estimate_ci_Bayesian (blavaan)`\u001b[39m\n", + "\u001b[90m# ℹ 6 more variables: p_value_FIML , `p_value_Bootstrap (BCa)` ,\u001b[39m\n", + "\u001b[90m# `p_value_Bayesian (blavaan)` , ci_type_FIML ,\u001b[39m\n", + "\u001b[90m# `ci_type_Bootstrap (BCa)` , `ci_type_Bayesian (blavaan)` \u001b[39m\n", + "\n", + "=== Display Table: Exposure = 44840322_G_A ===\n", + "\u001b[90m# A tibble: 6 × 10\u001b[39m\n", + " label estimate_ci_FIML estimate_ci_Bootstra…¹ estimate_ci_Bayesian…²\n", + " \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \u001b[3m\u001b[90m\u001b[39m\u001b[23m \n", + "\u001b[90m1\u001b[39m a -0.3434 [-0.7393, 0.05… -0.3434 [-0.7626, 0.0… -0.3509 [-0.7550, 0.0…\n", + "\u001b[90m2\u001b[39m b -0.1386 [-0.2813, 0.00… -0.1386 [-0.2937, 0.2… -0.1017 [-0.2654, 0.1…\n", + "\u001b[90m3\u001b[39m c_prime -0.3363 [-0.5040, -0.1… -0.3363 [-0.5356, -0.… -0.3220 [-0.4987, -0.…\n", + "\u001b[90m4\u001b[39m indirect 0.0476 [-0.0244, 0.119… 0.0476 [-0.0368, 0.19… 0.0337 [-0.0547, 0.13…\n", + "\u001b[90m5\u001b[39m total -0.2887 [-0.4414, -0.1… -0.2887 [-0.4323, -0.… -0.2883 [-0.4420, -0.…\n", + "\u001b[90m6\u001b[39m prop_med -0.1649 [-0.4303, 0.10… -0.1649 [-0.8188, 0.1… -0.1295 [-0.5526, 0.2…\n", + "\u001b[90m# ℹ abbreviated names: ¹​`estimate_ci_Bootstrap (BCa)`,\u001b[39m\n", + "\u001b[90m# ²​`estimate_ci_Bayesian (blavaan)`\u001b[39m\n", + "\u001b[90m# ℹ 6 more variables: p_value_FIML , `p_value_Bootstrap (BCa)` ,\u001b[39m\n", + "\u001b[90m# `p_value_Bayesian (blavaan)` , ci_type_FIML ,\u001b[39m\n", + "\u001b[90m# `ci_type_Bootstrap (BCa)` , `ci_type_Bayesian (blavaan)` \u001b[39m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Display tables saved to: /mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set57/summary \n" + ] + } + ], + "source": [ + "# ── Display table for each exposure ──────────────────────────────────────────\n", + "for (exp_var in exposures) {\n", + " prefix <- gsub(\"chr19_\", \"\", exp_var)\n", + " sub_df <- summary_all[summary_all$exposure == exp_var, ]\n", + " \n", + " display_tbl <- sub_df %>%\n", + " mutate(estimate_ci = sprintf(\"%.4f [%.4f, %.4f]\", est, ci_lower, ci_upper)) %>%\n", + " select(method, label, estimate_ci, p_value, ci_type) %>%\n", + " pivot_wider(names_from = method, values_from = c(estimate_ci, p_value, ci_type))\n", + " \n", + " write.csv(display_tbl, file.path(summ_dir, paste0(\"summary_display_table_\", prefix, \".csv\")),\n", + " row.names = FALSE)\n", + " \n", + " cat(\"\\n=== Display Table: Exposure =\", prefix, \"===\\n\")\n", + " print(display_tbl)\n", + "}\n", + "\n", + "cat(\"\\nDisplay tables saved to:\", summ_dir, \"\\n\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/set26_27_31_SEM_mediation_input.ipynb b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/set26_27_31_SEM_mediation_input.ipynb new file mode 100644 index 0000000..8ef1565 --- /dev/null +++ b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/set26_27_31_SEM_mediation_input.ipynb @@ -0,0 +1,7343 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "df4420e4", + "metadata": {}, + "source": [ + "# Prepare input for APOE-independent set 26, 27, and 31 for SEM mediation analysis" + ] + }, + { + "cell_type": "markdown", + "id": "9ac3aa94", + "metadata": {}, + "source": [ + "## Summary of associations\n", + "\n", + "1. Adjusted for APOE4 and APOE2:\n", + "\n", + "| set id | outcome | variant | molecular_outcome |\n", + "|:---------|:---------|:---------|:---------|\n", + "| set_27 | caa_4gp | chr19:44954310:T:C | AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208 |\n", + "| set_27 | caa_neo4 | chr19:44938650:G:C | AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208 |\n", + "| set_27 | tdp_st4 | chr19:45001091:C:G | AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208 |\n", + "| set_31 | age_first_ad_dx_num | chr19:45114770:ACCC:AC | AC_DeJager_eQTL_ENSG00000189114; DLPFC_DeJager_eQTL_ENSG00000189114; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000189114; PCC_DeJager_eQTL_ENSG00000189114; ROSMAP_AC_ENSG00000189114; ROSMAP_DLPFC_ENSG00000189114; ROSMAP_PCC_ENSG00000189114 |\n", + "\n", + "2. Adjusted for APOE4 only:\n", + "\n", + "| set id | outcome | variant | molecular_outcome |\n", + "|:---------|:---------|:---------|:---------|\n", + "| set_26 | age_first_ad_dx_num | chr19:44918487:G:T | AC_DeJager_eQTL_ENSG00000130208; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130208; ROSMAP_AC_ENSG00000130208; ROSMAP_DLPFC_ENSG00000130208 |\n", + "| set_27 | caa_4gp | chr19:44954310:T:C | AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208 |\n", + "| set_27 | caa_neo4 | chr19:44954310:T:C | AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208 |\n", + "| set_27 | tdp_st4 | chr19:44999110:TAAAA:TAA | AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208 |\n", + "| set_31 | age_first_ad_dx_num | chr19:45114770:ACCC:AC | AC_DeJager_eQTL_ENSG00000189114; DLPFC_DeJager_eQTL_ENSG00000189114; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000189114; PCC_DeJager_eQTL_ENSG00000189114; ROSMAP_AC_ENSG00000189114; ROSMAP_DLPFC_ENSG00000189114; ROSMAP_PCC_ENSG00000189114 |\n", + "\n", + "## Summary for mediation (ROSMAP):\n", + "\n", + "| set id | outcome | variant | covariate | mediator |\n", + "|:---------|:---------|:---------|:---------|:---------|\n", + "| set_26 | age_first_ad_dx_num | chr19:44918487:G:T | APOE4 | ENSG00000130208 expression in DLPFC & AC |\n", + "| set_27 | caa_4gp | chr19:44954310:T:C | APOE4 & APOE2 | ENSG00000224916 expression in AC, ENSG00000234906 expression in AC, ENSG00000130208 expression in DeJager_Mic; ENSG00000130203 expression in Mega_Mic |\n", + "| set_27 | caa_neo4 | chr19:44938650:G:C | APOE4 & APOE2 | ENSG00000224916 expression in AC, ENSG00000234906 expression in AC, ENSG00000130208 expression in DeJager_Mic; ENSG00000130203 expression in Mega_Mic |\n", + "| set_27 | caa_neo4 | chr19:44954310:T:C| APOE4 | ENSG00000224916 expression in AC, ENSG00000234906 expression in AC, ENSG00000130208 expression in DeJager_Mic; ENSG00000130203 expression in Mega_Mic |\n", + "| set_27 | tdp_st4 | chr19:45001091:C:G | APOE4 & APOE2 | ENSG00000224916 expression in AC, ENSG00000234906 expression in AC, ENSG00000130208 expression in DeJager_Mic; ENSG00000130203 expression in Mega_Mic |\n", + "| set_27 | tdp_st4 | chr19:44999110:TAAAA:TAA | APOE4 | ENSG00000224916 expression in AC, ENSG00000234906 expression in AC, ENSG00000130208 expression in DeJager_Mic; ENSG00000130203 expression in Mega_Mic |\n", + "| set_31 | age_first_ad_dx_num | chr19:45114770:ACCC:AC | APOE4 & APOE2 | ENSG00000189114 expression in DLPFC, AC, and PCC |\n", + "\n", + "**Note:** Pick DeJager for ENSG00000130208 expression in Mic (DeJager & Kellis); pick Mega for ENSG00000130203 expression in Mic (DeJager, Kellis, and Mega) \n", + "\n", + "## Genotype data:\n", + " - original: `/mnt/lustre/lab/gwang/ftp_fgc_xqtl/ROSMAP/genotype/analysis_ready/geno_by_chrom/ROSMAP_NIA_WGS.leftnorm.bcftools_qc.plink_qc.19`\n", + " - extracted: `/mnt/lustre/home/yl4437/xqtl_flagship/APOE/ROSMAP_geno_apoe4_indep_snps.vcf`\n", + "\n", + "## Outcome data:\n", + " - phenotype: `/mnt/lustre/home/yl4437/xqtl_flagship/APOE/ROSMAP_geno_apoecs_adpathology.csv.gz`\n", + " - covariates: `msex`, `educ`, `age_death`, `apoe4_dose`, `apoe2_dose` (`age_first_ad_dx_num` should not adjust for `age_death`)\n", + "\n", + " **Questions:** Check covariate adjustment" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fba7c1f6", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "library(data.table)\n", + "library(dplyr)\n", + "library(stringr)\n", + "library(tibble)\n", + "library(purrr)\n", + "library(mediation)\n", + "\n", + "setwd(\"/mnt/lustre/home/yl4437/xqtl_flagship/APOE\")" + ] + }, + { + "cell_type": "markdown", + "id": "0972adf7", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "source": [ + "## 0. Check association results" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5b1bedae", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "apoecs_clin_sig_42 <- fread(\"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/res_lm_apoecs_adpathology_apoe4_2_adj_sig.txt\")\n", + "apoecs_clin_sig_4 <- fread(\"/mnt/lustre/home/yl4437/xqtl_flagship/APOE/res_lm_apoecs_adpathology_apoe4_adj_sig.txt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "804cff82", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "126" + ], + "text/latex": [ + "126" + ], + "text/markdown": [ + "126" + ], + "text/plain": [ + "[1] 126" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "140" + ], + "text/latex": [ + "140" + ], + "text/markdown": [ + "140" + ], + "text/plain": [ + "[1] 140" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "apoecs_clin_sig_42_sub <- apoecs_clin_sig_42 %>% filter(cs_id %in% c('set_27', 'set_31')) %>%\n", + " dplyr::select(cs_id, outcome, variant_id, molecular_outcome, padj)\n", + "apoecs_clin_sig_4_sub <- apoecs_clin_sig_4 %>% filter(cs_id %in% c('set_26', 'set_27', 'set_31')) %>%\n", + " dplyr::select(cs_id, outcome, variant_id, molecular_outcome, padj)\n", + "nrow(apoecs_clin_sig_42_sub); nrow(apoecs_clin_sig_4_sub)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "db4c99c3", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Merge with PIP \n", + "all_sets <- readRDS(\"/mnt/lustre/home/yl4437/xqtl_flagship/xqtl-paper/main_text/5_AD_xQTL_genes_cis_trans/figure_data/xqtl_only_APOE_all_cohorts_merged_cos_cs_after_between_purity.rds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "ae90401f", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "List of 3\n", + " $ final_set :List of 415\n", + " ..$ ind_set_after_between_purity_1 : chr [1:2] \"chr19:42360920:C:T\" \"chr19:42392812:T:C\"\n", + " ..$ ind_set_after_between_purity_2 : chr [1:13] \"chr19:43462777:G:A\" \"chr19:43463540:T:C\" \"chr19:43464160:G:A\" \"chr19:43466818:CA:C\" ...\n", + " ..$ ind_set_after_between_purity_3 : chr [1:2] \"chr19:43526358:G:C\" \"chr19:43532778:TG:T\"\n", + " ..$ ind_set_after_between_purity_4 : chr [1:26] \"chr19:43532187:CCTGA:C\" \"chr19:43532265:G:C\" \"chr19:43537273:G:T\" \"chr19:43537776:TTTTTGTTTTG:TTTTTG\" ...\n", + " ..$ ind_set_after_between_purity_5 : chr [1:22] \"chr19:43534795:G:A\" \"chr19:43536101:A:G\" \"chr19:43538047:A:G\" \"chr19:43542576:T:C\" ...\n", + " ..$ ind_set_after_between_purity_6 : chr [1:29] \"chr19:43577584:T:G\" \"chr19:43578398:C:A\" \"chr19:43579641:G:A\" \"chr19:43580251:G:A\" ...\n", + " ..$ ind_set_after_between_purity_7 : chr [1:11] \"chr19:43678402:T:A\" \"chr19:43678653:T:C\" \"chr19:43679546:G:C\" \"chr19:43680810:A:T\" ...\n", + " ..$ ind_set_after_between_purity_8 : chr [1:7] \"chr19:43779937:G:T\" \"chr19:43782321:G:A\" \"chr19:43783321:C:T\" \"chr19:43792734:A:G\" ...\n", + " ..$ ind_set_after_between_purity_9 : chr [1:56] \"chr19:43786083:C:T\" \"chr19:43787249:T:C\" \"chr19:43787834:T:C\" \"chr19:43790716:C:T\" ...\n", + " ..$ ind_set_after_between_purity_10 : chr [1:42] \"chr19:43805635:G:A\" \"chr19:43807066:C:T\" \"chr19:43807280:TCTTTTTC:TTTTTTC\" \"chr19:43807283:TTTTC:T\" ...\n", + " ..$ ind_set_after_between_purity_11 : chr [1:2] \"chr19:43827658:G:A\" \"chr19:43833651:T:C\"\n", + " ..$ ind_set_after_between_purity_12 : chr [1:120] \"chr19:43853890:C:T\" \"chr19:43855412:C:G\" \"chr19:43860084:T:TACA\" \"chr19:43863186:A:G\" ...\n", + " ..$ ind_set_after_between_purity_13 : chr [1:58] \"chr19:43931487:T:C\" \"chr19:43931488:C:T\" \"chr19:43932903:C:T\" \"chr19:43933283:A:G\" ...\n", + " ..$ ind_set_after_between_purity_14 : chr [1:19] \"chr19:43943420:T:C\" \"chr19:43954083:CAA:CAAA\" \"chr19:43981556:A:G\" \"chr19:44103699:T:C\" ...\n", + " ..$ ind_set_after_between_purity_15 : chr \"chr19:43984200:G:A\"\n", + " ..$ ind_set_after_between_purity_16 : chr [1:28] \"chr19:43987775:G:A\" \"chr19:43987828:C:T\" \"chr19:43988084:T:G\" \"chr19:43988335:C:T\" ...\n", + " ..$ ind_set_after_between_purity_17 : chr [1:65] \"chr19:43998016:GCTCCTGATTGGTCCCAGGCCAAGCTGAATAGCCCTTAGGAATAATCACTTCAA:ACTCCTGATTGGTCCCAGGCCAAGCTGAATAGCCCTTAGGAATAATCACTTCAA\" \"chr19:43998032:AGGCCAAGCTGAATAGCCCTTAGGAATAATCACTTCAACTCCTGATTGGTCCCG:GGGCCAAGCTGAATAGCCCTTAGGAATAATCACTTCAACTCCTGATTGGTCCCG\" \"chr19:43999953:AAC:A\" \"chr19:44002969:G:C\" ...\n", + " ..$ ind_set_after_between_purity_18 : chr \"chr19:44165808:G:T\"\n", + " ..$ ind_set_after_between_purity_19 : chr [1:88] \"chr19:44168024:A:G\" \"chr19:44168247:GTT:GT\" \"chr19:44171995:G:C\" \"chr19:44181291:G:A\" ...\n", + " ..$ ind_set_after_between_purity_20 : chr [1:62] \"chr19:44397585:A:G\" \"chr19:44397841:G:A\" \"chr19:44397865:C:G\" \"chr19:44397867:G:C\" ...\n", + " ..$ ind_set_after_between_purity_21 : chr [1:50] \"chr19:44419111:A:G\" \"chr19:44421289:T:C\" \"chr19:44428797:C:T\" \"chr19:44429772:G:A\" ...\n", + " ..$ ind_set_after_between_purity_22 : chr [1:21] \"chr19:44453603:A:C\" \"chr19:44455420:AG:A\" \"chr19:44470397:A:G\" \"chr19:44471492:T:C\" ...\n", + " ..$ ind_set_after_between_purity_23 : chr \"chr19:44613609:T:TA\"\n", + " ..$ ind_set_after_between_purity_24 : chr [1:9] \"chr19:44643329:A:G\" \"chr19:44644040:G:T\" \"chr19:44631092:C:CAA\" \"chr19:44639551:G:T\" ...\n", + " ..$ ind_set_after_between_purity_25 : chr [1:3] \"chr19:44646342:T:C\" \"chr19:44662645:A:C\" \"chr19:44683693:G:C\"\n", + " ..$ ind_set_after_between_purity_26 : chr [1:5] \"chr19:44918487:G:T\" \"chr19:44918715:AG:A\" \"chr19:44920379:G:A\" \"chr19:44925202:C:T\" ...\n", + " ..$ ind_set_after_between_purity_27 : chr [1:90] \"chr19:44980181:A:C\" \"chr19:44986934:A:G\" \"chr19:44987312:A:G\" \"chr19:44989803:T:C\" ...\n", + " ..$ ind_set_after_between_purity_28 : chr [1:12] \"chr19:45086281:A:C\" \"chr19:45091941:A:T\" \"chr19:45092214:A:G\" \"chr19:45092518:G:C\" ...\n", + " ..$ ind_set_after_between_purity_29 : chr \"chr19:45090912:A:G\"\n", + " ..$ ind_set_after_between_purity_30 : chr [1:114] \"chr19:45102550:A:T\" \"chr19:45117255:G:T\" \"chr19:45118925:T:C\" \"chr19:45120525:A:G\" ...\n", + " ..$ ind_set_after_between_purity_31 : chr [1:3] \"chr19:45114770:ACCC:AC\" \"chr19:45137043:GCC:CCC\" \"chr19:45179662:C:T\"\n", + " ..$ ind_set_after_between_purity_32 : chr [1:22] \"chr19:45283207:C:T\" \"chr19:45289506:C:T\" \"chr19:45293188:T:C\" \"chr19:45293973:T:G\" ...\n", + " ..$ ind_set_after_between_purity_33 : chr [1:16] \"chr19:45337140:C:T\" \"chr19:45340057:G:T\" \"chr19:45326124:G:A\" \"chr19:45326536:G:C\" ...\n", + " ..$ ind_set_after_between_purity_34 : chr [1:3] \"chr19:45340147:T:C\" \"chr19:45341392:C:T\" \"chr19:45358087:C:T\"\n", + " ..$ ind_set_after_between_purity_35 : chr [1:9] \"chr19:45344298:C:T\" \"chr19:45345352:T:A\" \"chr19:45346432:G:C\" \"chr19:45346493:C:T\" ...\n", + " ..$ ind_set_after_between_purity_36 : chr [1:6] \"chr19:45379339:G:C\" \"chr19:45379500:C:G\" \"chr19:45380296:C:T\" \"chr19:45380558:C:G\" ...\n", + " ..$ ind_set_after_between_purity_37 : chr [1:17] \"chr19:45524423:CA:CAAA\" \"chr19:45529477:CAG:C\" \"chr19:45530229:C:T\" \"chr19:45530515:GT:GTT\" ...\n", + " ..$ ind_set_after_between_purity_38 : chr [1:29] \"chr19:45549196:A:G\" \"chr19:45549362:TCAGGG:T\" \"chr19:45550127:C:T\" \"chr19:45550397:G:GGCT\" ...\n", + " ..$ ind_set_after_between_purity_39 : chr [1:5] \"chr19:45570316:A:G\" \"chr19:45571034:A:T\" \"chr19:45579878:T:C\" \"chr19:45584839:C:T\" ...\n", + " ..$ ind_set_after_between_purity_40 : chr [1:8] \"chr19:45576979:G:A\" \"chr19:45576981:G:T\" \"chr19:45577017:T:C\" \"chr19:45577118:G:A\" ...\n", + " ..$ ind_set_after_between_purity_41 : chr [1:11] \"chr19:45671202:AAGCACTTGGCCCACTGCGCAGT:A\" \"chr19:45680417:C:T\" \"chr19:45718429:C:T\" \"chr19:45745920:C:T\" ...\n", + " ..$ ind_set_after_between_purity_42 : chr [1:17] \"chr19:45683393:CT:C\" \"chr19:45684742:G:T\" \"chr19:45685099:C:T\" \"chr19:45685222:G:A\" ...\n", + " ..$ ind_set_after_between_purity_43 : chr [1:44] \"chr19:45811394:T:TC\" \"chr19:45811721:C:A\" \"chr19:45814451:G:C\" \"chr19:45823214:G:C\" ...\n", + " ..$ ind_set_after_between_purity_44 : chr [1:20] \"chr19:46029850:A:T\" \"chr19:46029891:C:T\" \"chr19:46030629:C:T\" \"chr19:46030656:G:A\" ...\n", + " ..$ ind_set_after_between_purity_45 : chr [1:15] \"chr19:46130510:T:A\" \"chr19:46131687:G:C\" \"chr19:46134925:G:A\" \"chr19:46137294:C:A\" ...\n", + " ..$ ind_set_after_between_purity_46 : chr \"chr19:46404293:C:T\"\n", + " ..$ ind_set_after_between_purity_47 : chr [1:15] \"chr19:46423410:C:T\" \"chr19:46423527:A:G\" \"chr19:46423696:C:T\" \"chr19:46424078:T:C\" ...\n", + " ..$ ind_set_after_between_purity_48 : chr [1:11] \"chr19:46424263:C:T\" \"chr19:46426268:C:T\" \"chr19:46426768:C:A\" \"chr19:46429192:TAA:TA\" ...\n", + " ..$ ind_set_after_between_purity_49 : chr [1:25] \"chr19:46467482:C:T\" \"chr19:46471050:C:T\" \"chr19:46471471:C:T\" \"chr19:46471524:G:A\" ...\n", + " ..$ ind_set_after_between_purity_50 : chr [1:7] \"chr19:46493671:C:A\" \"chr19:46494979:C:T\" \"chr19:46500649:C:T\" \"chr19:46501038:C:T\" ...\n", + " ..$ ind_set_after_between_purity_51 : chr [1:8] \"chr19:46496720:A:G\" \"chr19:46497431:G:T\" \"chr19:46497587:G:T\" \"chr19:46497668:G:A\" ...\n", + " ..$ ind_set_after_between_purity_52 : chr [1:5] \"chr19:46685553:C:T\" \"chr19:46685046:G:A\" \"chr19:46671636:A:C\" \"chr19:46673623:G:A\" ...\n", + " ..$ ind_set_after_between_purity_53 : chr [1:27] \"chr19:46706892:CT:C\" \"chr19:46709086:C:T\" \"chr19:46709870:A:AT\" \"chr19:46717081:T:TCGG\" ...\n", + " ..$ ind_set_after_between_purity_54 : chr [1:90] \"chr19:46707602:C:A\" \"chr19:46714657:A:C\" \"chr19:46717670:G:C\" \"chr19:46717872:G:A\" ...\n", + " ..$ ind_set_after_between_purity_55 : chr [1:42] \"chr19:46713856:C:T\" \"chr19:46724249:CAAAAAAAAAAAAAAAAAAAA:CAAAAAAAAA\" \"chr19:46727136:C:CT\" \"chr19:46734294:T:C\" ...\n", + " ..$ ind_set_after_between_purity_56 : chr [1:8] \"chr19:46748673:C:G\" \"chr19:46759685:C:A\" \"chr19:46762046:A:AT\" \"chr19:46762047:A:ATATAT\" ...\n", + " ..$ ind_set_after_between_purity_57 : chr [1:2] \"chr19:44827033:TGATAGATAGATAGATA:TGATA\" \"chr19:44840322:G:A\"\n", + " ..$ ind_set_after_between_purity_58 : chr [1:15] \"chr19:43586043:A:C\" \"chr19:43591855:GAA:GAAAA\" \"chr19:43594811:C:G\" \"chr19:43595611:C:T\" ...\n", + " ..$ ind_set_after_between_purity_59 : chr [1:25] \"chr19:44137900:C:A\" \"chr19:44138028:G:T\" \"chr19:44138411:CA:C\" \"chr19:44138491:T:C\" ...\n", + " ..$ ind_set_after_between_purity_60 : chr [1:3] \"chr19:44844654:C:A\" \"chr19:44846145:T:C\" \"chr19:44849230:A:T\"\n", + " ..$ ind_set_after_between_purity_61 : chr [1:30] \"chr19:46044892:CCTGA:C\" \"chr19:46045398:G:A\" \"chr19:46046063:C:A\" \"chr19:46046406:C:T\" ...\n", + " ..$ ind_set_after_between_purity_62 : chr [1:6] \"chr19:46048607:T:G\" \"chr19:46057247:T:A\" \"chr19:46060081:T:C\" \"chr19:46067482:G:A\" ...\n", + " ..$ ind_set_after_between_purity_63 : chr [1:18] \"chr19:46346219:A:G\" \"chr19:46351675:A:G\" \"chr19:46351721:A:G\" \"chr19:46363525:T:G\" ...\n", + " ..$ ind_set_after_between_purity_64 : chr [1:47] \"chr19:45716700:CAA:CA\" \"chr19:45720898:A:G\" \"chr19:45722694:G:A\" \"chr19:45724622:T:C\" ...\n", + " ..$ ind_set_after_between_purity_65 : chr [1:7] \"chr19:42394184:T:G\" \"chr19:42394813:T:C\" \"chr19:42395973:A:C\" \"chr19:42397253:A:T\" ...\n", + " ..$ ind_set_after_between_purity_66 : chr \"chr19:42742159:T:C\"\n", + " ..$ ind_set_after_between_purity_67 : chr [1:273] \"chr19:42780248:C:T\" \"chr19:42814105:G:A\" \"chr19:42814474:C:A\" \"chr19:42814746:G:GTATT\" ...\n", + " ..$ ind_set_after_between_purity_68 : chr \"chr19:42816631:G:A\"\n", + " ..$ ind_set_after_between_purity_69 : chr [1:2] \"chr19:43090637:G:T\" \"chr19:42994877:C:G\"\n", + " ..$ ind_set_after_between_purity_70 : chr [1:25] \"chr19:43188949:C:A\" \"chr19:43189306:C:G\" \"chr19:43189921:G:T\" \"chr19:43190949:A:T\" ...\n", + " ..$ ind_set_after_between_purity_71 : chr [1:3] \"chr19:43322051:C:T\" \"chr19:43324554:A:G\" \"chr19:43325764:T:C\"\n", + " ..$ ind_set_after_between_purity_72 : chr [1:10] \"chr19:43457467:AG:A\" \"chr19:43459876:A:G\" \"chr19:43462302:A:G\" \"chr19:43470695:C:CTT\" ...\n", + " ..$ ind_set_after_between_purity_73 : chr [1:2] \"chr19:43522968:T:C\" \"chr19:43632995:C:T\"\n", + " ..$ ind_set_after_between_purity_74 : chr [1:88] \"chr19:43854445:G:C\" \"chr19:43854609:G:A\" \"chr19:43854984:T:C\" \"chr19:43855419:T:G\" ...\n", + " ..$ ind_set_after_between_purity_75 : chr [1:55] \"chr19:44248702:C:T\" \"chr19:44248790:T:C\" \"chr19:44253749:T:C\" \"chr19:44258018:T:C\" ...\n", + " ..$ ind_set_after_between_purity_76 : chr [1:17] \"chr19:44432646:C:G\" \"chr19:44432694:G:C\" \"chr19:44436209:C:T\" \"chr19:44447777:A:G\" ...\n", + " ..$ ind_set_after_between_purity_77 : chr [1:5] \"chr19:45021867:G:A\" \"chr19:45022774:G:C\" \"chr19:45027466:C:T\" \"chr19:45029367:A:G\" ...\n", + " ..$ ind_set_after_between_purity_78 : chr [1:20] \"chr19:45581383:G:T\" \"chr19:45584249:A:G\" \"chr19:45585666:T:G\" \"chr19:45587140:T:A\" ...\n", + " ..$ ind_set_after_between_purity_79 : chr [1:12] \"chr19:46020609:G:A\" \"chr19:46020959:C:T\" \"chr19:46026662:C:T\" \"chr19:46026992:A:T\" ...\n", + " ..$ ind_set_after_between_purity_80 : chr [1:4] \"chr19:46413718:G:C\" \"chr19:46433267:A:G\" \"chr19:46411290:C:G\" \"chr19:46411670:C:T\"\n", + " ..$ ind_set_after_between_purity_81 : chr [1:2] \"chr19:46654954:T:C\" \"chr19:46675930:C:T\"\n", + " ..$ ind_set_after_between_purity_82 : chr [1:13] \"chr19:42789378:G:A\" \"chr19:42794670:T:C\" \"chr19:42796543:G:A\" \"chr19:42798742:G:A\" ...\n", + " ..$ ind_set_after_between_purity_83 : chr \"chr19:42983777:A:C\"\n", + " ..$ ind_set_after_between_purity_84 : chr [1:2] \"chr19:43363506:G:A\" \"chr19:43365815:G:A\"\n", + " ..$ ind_set_after_between_purity_85 : chr [1:17] \"chr19:43437604:T:G\" \"chr19:43440017:A:C\" \"chr19:43441287:T:G\" \"chr19:43441543:C:T\" ...\n", + " ..$ ind_set_after_between_purity_86 : chr [1:3] \"chr19:43709823:A:T\" \"chr19:43709902:C:A\" \"chr19:43713684:C:T\"\n", + " ..$ ind_set_after_between_purity_87 : chr \"chr19:44370726:T:C\"\n", + " ..$ ind_set_after_between_purity_88 : chr [1:3] \"chr19:44389828:G:A\" \"chr19:44390634:G:A\" \"chr19:44391156:A:AT\"\n", + " ..$ ind_set_after_between_purity_89 : chr [1:3] \"chr19:44705412:T:G\" \"chr19:44706173:C:G\" \"chr19:44706263:T:C\"\n", + " ..$ ind_set_after_between_purity_90 : chr \"chr19:45320960:G:A\"\n", + " ..$ ind_set_after_between_purity_91 : chr [1:65] \"chr19:45717571:G:A\" \"chr19:45722221:C:T\" \"chr19:45728537:G:T\" \"chr19:45731441:T:C\" ...\n", + " ..$ ind_set_after_between_purity_92 : chr [1:2] \"chr19:46020426:T:C\" \"chr19:46085553:C:T\"\n", + " ..$ ind_set_after_between_purity_93 : chr [1:2] \"chr19:46428369:C:T\" \"chr19:46432515:A:G\"\n", + " ..$ ind_set_after_between_purity_94 : chr [1:10] \"chr19:46786674:A:C\" \"chr19:46789526:T:C\" \"chr19:46791024:T:G\" \"chr19:46791533:C:T\" ...\n", + " ..$ ind_set_after_between_purity_95 : chr \"chr19:42496449:A:G\"\n", + " ..$ ind_set_after_between_purity_96 : chr [1:25] \"chr19:43807118:G:A\" \"chr19:43810900:G:T\" \"chr19:43810912:G:A\" \"chr19:43811456:C:T\" ...\n", + " ..$ ind_set_after_between_purity_97 : chr \"chr19:44698757:A:G\"\n", + " ..$ ind_set_after_between_purity_98 : chr [1:3] \"chr19:44791769:A:C\" \"chr19:44793637:G:A\" \"chr19:44800726:A:G\"\n", + " ..$ ind_set_after_between_purity_99 : chr [1:4] \"chr19:45660914:C:T\" \"chr19:45662292:G:C\" \"chr19:45662346:C:A\" \"chr19:45658546:A:G\"\n", + " .. [list output truncated]\n", + " $ final_pip_vcp:List of 415\n", + " ..$ ind_set_after_between_purity_1 : num [1:2] 0.497 0.497\n", + " ..$ ind_set_after_between_purity_2 : num [1:13] 0.1446 0.9431 0.1446 0.0875 0.1446 ...\n", + " ..$ ind_set_after_between_purity_3 : num [1:2] 0.5 0.5\n", + " ..$ ind_set_after_between_purity_4 : num [1:26] 0.1005 0.05 0.0849 0.0309 0.1069 ...\n", + " ..$ ind_set_after_between_purity_5 : num [1:22] 0.0538 0.0538 0.0538 0.0538 0.0538 ...\n", + " ..$ ind_set_after_between_purity_6 : num [1:29] 0.216 0.216 0.383 0.105 0.301 ...\n", + " ..$ ind_set_after_between_purity_7 : num [1:11] 0.337 0.1815 0.054 0.0314 0.054 ...\n", + " ..$ ind_set_after_between_purity_8 : num [1:7] 0.147 0.334 0.105 0.107 0.107 ...\n", + " ..$ ind_set_after_between_purity_9 : num [1:56] 0.1216 0.0729 0.0729 0.1216 0.9433 ...\n", + " ..$ ind_set_after_between_purity_10 : num [1:42] 1 0.1055 0.1621 0.0802 0.3893 ...\n", + " ..$ ind_set_after_between_purity_11 : num [1:2] 0.817 0.549\n", + " ..$ ind_set_after_between_purity_12 : num [1:120] 0.0518 0.0306 0.0155 0.0563 0.1318 ...\n", + " ..$ ind_set_after_between_purity_13 : num [1:58] 0.1686 0.1816 0.0591 0.0591 0.1929 ...\n", + " ..$ ind_set_after_between_purity_14 : num [1:19] 0.02677 0.00816 0.02475 0.01016 0.00861 ...\n", + " ..$ ind_set_after_between_purity_15 : num 1\n", + " ..$ ind_set_after_between_purity_16 : num [1:28] 0.0441 0.0948 0.051 0.0973 0.0764 ...\n", + " ..$ ind_set_after_between_purity_17 : num [1:65] 0.294 0.1087 0.0423 0.0423 0.0394 ...\n", + " ..$ ind_set_after_between_purity_18 : num 1\n", + " ..$ ind_set_after_between_purity_19 : num [1:88] 0.1628 0.0644 0.0884 0.0884 0.0162 ...\n", + " ..$ ind_set_after_between_purity_20 : num [1:62] 0.0299 0.0299 0.0299 0.0299 0.0299 ...\n", + " ..$ ind_set_after_between_purity_21 : num [1:50] 0.0304 0.0304 0.0857 0.0525 0.157 ...\n", + " ..$ ind_set_after_between_purity_22 : num [1:21] 0.0895 0.0853 0.1568 0.0465 0.1568 ...\n", + " ..$ ind_set_after_between_purity_23 : num 0.986\n", + " ..$ ind_set_after_between_purity_24 : num [1:9] 0.5297 0.9999 0.3288 0.0513 0.068 ...\n", + " ..$ ind_set_after_between_purity_25 : num [1:3] 0.844 1 0.213\n", + " ..$ ind_set_after_between_purity_26 : num [1:5] 0.15 0.308 0.122 0.604 1\n", + " ..$ ind_set_after_between_purity_27 : num [1:90] 0.235 0.235 0.235 0.123 0.101 ...\n", + " ..$ ind_set_after_between_purity_28 : num [1:12] 0.0952 0.1131 0.0967 0.1217 0.1131 ...\n", + " ..$ ind_set_after_between_purity_29 : num 1\n", + " ..$ ind_set_after_between_purity_30 : num [1:114] 0.1253 0.0474 0.0474 0.0474 0.0474 ...\n", + " ..$ ind_set_after_between_purity_31 : num [1:3] 0.1194 0.0595 0.9988\n", + " ..$ ind_set_after_between_purity_32 : num [1:22] 0.0175 0.0579 0.0256 0.0146 0.0911 ...\n", + " ..$ ind_set_after_between_purity_33 : num [1:16] 0.5193 0.6215 0.052 0.0673 0.0565 ...\n", + " ..$ ind_set_after_between_purity_34 : num [1:3] 0.239 1 0.563\n", + " ..$ ind_set_after_between_purity_35 : num [1:9] 0.504 0.108 0.108 0.259 0.357 ...\n", + " ..$ ind_set_after_between_purity_36 : num [1:6] 0.0712 0.1151 0.025 0.0541 0.019 ...\n", + " ..$ ind_set_after_between_purity_37 : num [1:17] 0.106 0.41 0.41 0.107 0.145 ...\n", + " ..$ ind_set_after_between_purity_38 : num [1:29] 0.1012 0.0246 0.1012 0.0819 0.1012 ...\n", + " ..$ ind_set_after_between_purity_39 : num [1:5] 0.2052 0.2052 0.4927 0.4927 0.0314\n", + " ..$ ind_set_after_between_purity_40 : num [1:8] 0.2025 0.2025 0.2025 0.0812 0.0975 ...\n", + " ..$ ind_set_after_between_purity_41 : num [1:11] 0.125 0.166 0.166 0.166 0.249 ...\n", + " ..$ ind_set_after_between_purity_42 : num [1:17] 0.122 0.146 0.106 0.138 0.138 ...\n", + " ..$ ind_set_after_between_purity_43 : num [1:44] 0.1942 0.1942 0.1942 0.0557 0.0557 ...\n", + " ..$ ind_set_after_between_purity_44 : num [1:20] 0.153 0.153 0.133 0.133 0.133 ...\n", + " ..$ ind_set_after_between_purity_45 : num [1:15] 0.0675 0.1594 0.1128 0.0819 0.0667 ...\n", + " ..$ ind_set_after_between_purity_46 : num 0.99\n", + " ..$ ind_set_after_between_purity_47 : num [1:15] 0.1264 0.0986 0.109 0.1745 0.1476 ...\n", + " ..$ ind_set_after_between_purity_48 : num [1:11] 0.395 0.488 0.488 0.23 0.327 ...\n", + " ..$ ind_set_after_between_purity_49 : num [1:25] 0.522 0.33 0.49 0.473 0.49 ...\n", + " ..$ ind_set_after_between_purity_50 : num [1:7] 0.344 0.387 0.563 0.267 0.407 ...\n", + " ..$ ind_set_after_between_purity_51 : num [1:8] 0.1398 0.1398 0.0701 0.0398 0.1398 ...\n", + " ..$ ind_set_after_between_purity_52 : num [1:5] 0.997 0.851 1 0.381 0.128\n", + " ..$ ind_set_after_between_purity_53 : num [1:27] 0.1675 0.714 0.0422 0.1692 0.0446 ...\n", + " ..$ ind_set_after_between_purity_54 : num [1:90] 0.0707 0.1604 0.1098 0.2666 0.1823 ...\n", + " ..$ ind_set_after_between_purity_55 : num [1:42] 0.0877 0.018 0.0877 0.0877 0.0877 ...\n", + " ..$ ind_set_after_between_purity_56 : num [1:8] 0.741 0.134 0.105 0.014 0.134 ...\n", + " ..$ ind_set_after_between_purity_57 : num [1:2] 0.53 0.422\n", + " ..$ ind_set_after_between_purity_58 : num [1:15] 0.75 0.045 0.881 0.121 0.15 ...\n", + " ..$ ind_set_after_between_purity_59 : num [1:25] 0.0273 0.0273 0.0385 0.0915 0.0385 ...\n", + " ..$ ind_set_after_between_purity_60 : num [1:3] 0.0678 0.1124 0.9015\n", + " ..$ ind_set_after_between_purity_61 : num [1:30] 0.00335 0.00202 0.01366 0.01366 0.09082 ...\n", + " ..$ ind_set_after_between_purity_62 : num [1:6] 0.491 0.663 0.491 0.577 0.106 ...\n", + " ..$ ind_set_after_between_purity_63 : num [1:18] 0.0686 0.0629 0.0629 0.084 0.084 ...\n", + " ..$ ind_set_after_between_purity_64 : num [1:47] 0.1294 0.0659 0.1851 0.0645 0.1036 ...\n", + " ..$ ind_set_after_between_purity_65 : num [1:7] 0.162 0.162 0.162 0.162 0.162 ...\n", + " ..$ ind_set_after_between_purity_66 : num 0.0179\n", + " ..$ ind_set_after_between_purity_67 : num [1:273] 0.00129 0.01331 0.01331 0.02178 0.02178 ...\n", + " ..$ ind_set_after_between_purity_68 : num 0.998\n", + " ..$ ind_set_after_between_purity_69 : num [1:2] 0.969 0.196\n", + " ..$ ind_set_after_between_purity_70 : num [1:25] 0.1082 0.0698 0.0698 0.1082 0.1082 ...\n", + " ..$ ind_set_after_between_purity_71 : num [1:3] 0.0681 0.0712 0.8151\n", + " ..$ ind_set_after_between_purity_72 : num [1:10] 0.7196 0.2424 0.033 0.7181 0.0469 ...\n", + " ..$ ind_set_after_between_purity_73 : num [1:2] 0.497 0.497\n", + " ..$ ind_set_after_between_purity_74 : num [1:88] 0.0128 0.0128 0.0128 0.0128 0.0128 ...\n", + " ..$ ind_set_after_between_purity_75 : num [1:55] 0.0545 0.9705 0.0876 0.0423 0.0545 ...\n", + " ..$ ind_set_after_between_purity_76 : num [1:17] 0.314 0.314 0.314 0.103 0.89 ...\n", + " ..$ ind_set_after_between_purity_77 : num [1:5] 0.0996 0.0996 0.0996 0.5962 0.0996\n", + " ..$ ind_set_after_between_purity_78 : num [1:20] 0.0529 0.0529 0.0529 0.0833 0.115 ...\n", + " ..$ ind_set_after_between_purity_79 : num [1:12] 0.0527 0.3679 0.0885 0.0615 0.0615 ...\n", + " ..$ ind_set_after_between_purity_80 : num [1:4] 0.9999 0.0692 0.7401 0.3335\n", + " ..$ ind_set_after_between_purity_81 : num [1:2] 0.489 0.489\n", + " ..$ ind_set_after_between_purity_82 : num [1:13] 0.0511 0.0511 0.0511 0.0293 0.0511 ...\n", + " ..$ ind_set_after_between_purity_83 : num 0.953\n", + " ..$ ind_set_after_between_purity_84 : num [1:2] 0.987 0.276\n", + " ..$ ind_set_after_between_purity_85 : num [1:17] 0.1127 0.0808 0.0808 0.0808 0.0808 ...\n", + " ..$ ind_set_after_between_purity_86 : num [1:3] 0.0558 0.0558 0.8399\n", + " ..$ ind_set_after_between_purity_87 : num 0.996\n", + " ..$ ind_set_after_between_purity_88 : num [1:3] 0.258 0.258 0.443\n", + " ..$ ind_set_after_between_purity_89 : num [1:3] 0.265 0.265 0.456\n", + " ..$ ind_set_after_between_purity_90 : num 0.974\n", + " ..$ ind_set_after_between_purity_91 : num [1:65] 0.01327 0.01327 0.01327 0.01327 0.00744 ...\n", + " ..$ ind_set_after_between_purity_92 : num [1:2] 0.032 0.919\n", + " ..$ ind_set_after_between_purity_93 : num [1:2] 0.485 0.485\n", + " ..$ ind_set_after_between_purity_94 : num [1:10] 0.158 0.183 0.158 0.158 0.158 ...\n", + " ..$ ind_set_after_between_purity_95 : num 0.977\n", + " ..$ ind_set_after_between_purity_96 : num [1:25] 0.0876 0.0308 0.0433 0.0475 0.0626 ...\n", + " ..$ ind_set_after_between_purity_97 : num 0.993\n", + " ..$ ind_set_after_between_purity_98 : num [1:3] 0.329 0.329 0.329\n", + " ..$ ind_set_after_between_purity_99 : num [1:4] 0.3335 0.3335 0.3335 0.0704\n", + " .. [list output truncated]\n", + " $ final_Outcome: Named chr [1:415] \"AC_DeJager_eQTL_ENSG00000105323\" \"AC_DeJager_eQTL_ENSG00000124466; BM_36_MSBB_eQTL_ENSG00000124466; DLPFC_DeJager_eQTL_ENSG00000124466; ROSMAP_AC\"| __truncated__ \"AC_DeJager_eQTL_ENSG00000105755; ROSMAP_AC_ENSG00000105755\" \"AC_DeJager_eQTL_ENSG00000268361; AC_DeJager_eQTL_ENSG00000073050; BM_10_MSBB_eQTL_ENSG00000073050; BM_22_MSBB_e\"| __truncated__ ...\n", + " ..- attr(*, \"names\")= chr [1:415] \"ind_set_after_between_purity_1\" \"ind_set_after_between_purity_2\" \"ind_set_after_between_purity_3\" \"ind_set_after_between_purity_4\" ...\n" + ] + } + ], + "source": [ + "str(all_sets)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "fcdc16c3", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 3
setvariant_idpip
<chr><chr><dbl>
set_26chr19:44918487:G:T 0.1495240
set_26chr19:44918715:AG:A0.3076244
set_26chr19:44920379:G:A 0.1220275
set_26chr19:44925202:C:T 0.6035547
set_26chr19:44913034:C:T 0.9997039
set_27chr19:44980181:A:C 0.2349525
\n" + ], + "text/latex": [ + "A tibble: 6 × 3\n", + "\\begin{tabular}{lll}\n", + " set & variant\\_id & pip\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t set\\_26 & chr19:44918487:G:T & 0.1495240\\\\\n", + "\t set\\_26 & chr19:44918715:AG:A & 0.3076244\\\\\n", + "\t set\\_26 & chr19:44920379:G:A & 0.1220275\\\\\n", + "\t set\\_26 & chr19:44925202:C:T & 0.6035547\\\\\n", + "\t set\\_26 & chr19:44913034:C:T & 0.9997039\\\\\n", + "\t set\\_27 & chr19:44980181:A:C & 0.2349525\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 3\n", + "\n", + "| set <chr> | variant_id <chr> | pip <dbl> |\n", + "|---|---|---|\n", + "| set_26 | chr19:44918487:G:T | 0.1495240 |\n", + "| set_26 | chr19:44918715:AG:A | 0.3076244 |\n", + "| set_26 | chr19:44920379:G:A | 0.1220275 |\n", + "| set_26 | chr19:44925202:C:T | 0.6035547 |\n", + "| set_26 | chr19:44913034:C:T | 0.9997039 |\n", + "| set_27 | chr19:44980181:A:C | 0.2349525 |\n", + "\n" + ], + "text/plain": [ + " set variant_id pip \n", + "1 set_26 chr19:44918487:G:T 0.1495240\n", + "2 set_26 chr19:44918715:AG:A 0.3076244\n", + "3 set_26 chr19:44920379:G:A 0.1220275\n", + "4 set_26 chr19:44925202:C:T 0.6035547\n", + "5 set_26 chr19:44913034:C:T 0.9997039\n", + "6 set_27 chr19:44980181:A:C 0.2349525" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "98" + ], + "text/latex": [ + "98" + ], + "text/markdown": [ + "98" + ], + "text/plain": [ + "[1] 98" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_sets <- c('ind_set_after_between_purity_26', 'ind_set_after_between_purity_27', 'ind_set_after_between_purity_31')\n", + "\n", + "selected_sets_pip <- map_df(selected_sets, ~ tibble(\n", + " set = .x,\n", + " variant_id = all_sets$final_set[[.x]],\n", + " pip = all_sets$final_pip_vcp[[.x]]\n", + "))\n", + "\n", + "selected_sets_pip$set[selected_sets_pip$set == 'ind_set_after_between_purity_26'] <- 'set_26'\n", + "selected_sets_pip$set[selected_sets_pip$set == 'ind_set_after_between_purity_27'] <- 'set_27'\n", + "selected_sets_pip$set[selected_sets_pip$set == 'ind_set_after_between_purity_31'] <- 'set_31'\n", + "\n", + "head(selected_sets_pip); nrow(selected_sets_pip)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "a774145f", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "126" + ], + "text/latex": [ + "126" + ], + "text/markdown": [ + "126" + ], + "text/plain": [ + "[1] 126" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "140" + ], + "text/latex": [ + "140" + ], + "text/markdown": [ + "140" + ], + "text/plain": [ + "[1] 140" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_sets_pip_sub <- selected_sets_pip[,c(2,3)]\n", + "apoecs_clin_sig_42_pip <- merge(apoecs_clin_sig_42_sub, selected_sets_pip_sub, by = 'variant_id')\n", + "apoecs_clin_sig_4_pip <- merge(apoecs_clin_sig_4_sub, selected_sets_pip_sub, by = 'variant_id')\n", + "nrow(apoecs_clin_sig_42_pip); nrow(apoecs_clin_sig_4_pip)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "9ec07cbb", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A data.table: 1 × 6
variant_idcs_idoutcomemolecular_outcomepadjpip
<chr><chr><chr><chr><dbl><dbl>
chr19:44918487:G:Tset_26age_first_ad_dx_numAC_DeJager_eQTL_ENSG00000130208; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130208; ROSMAP_AC_ENSG00000130208; ROSMAP_DLPFC_ENSG000001302080.041552420.149524
\n" + ], + "text/latex": [ + "A data.table: 1 × 6\n", + "\\begin{tabular}{llllll}\n", + " variant\\_id & cs\\_id & outcome & molecular\\_outcome & padj & pip\\\\\n", + " & & & & & \\\\\n", + "\\hline\n", + "\t chr19:44918487:G:T & set\\_26 & age\\_first\\_ad\\_dx\\_num & AC\\_DeJager\\_eQTL\\_ENSG00000130208; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000130208; STARNET\\_eQTL\\_Mac\\_ENSG00000130208; ROSMAP\\_AC\\_ENSG00000130208; ROSMAP\\_DLPFC\\_ENSG00000130208 & 0.04155242 & 0.149524\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 1 × 6\n", + "\n", + "| variant_id <chr> | cs_id <chr> | outcome <chr> | molecular_outcome <chr> | padj <dbl> | pip <dbl> |\n", + "|---|---|---|---|---|---|\n", + "| chr19:44918487:G:T | set_26 | age_first_ad_dx_num | AC_DeJager_eQTL_ENSG00000130208; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130208; ROSMAP_AC_ENSG00000130208; ROSMAP_DLPFC_ENSG00000130208 | 0.04155242 | 0.149524 |\n", + "\n" + ], + "text/plain": [ + " variant_id cs_id outcome \n", + "1 chr19:44918487:G:T set_26 age_first_ad_dx_num\n", + " molecular_outcome \n", + "1 AC_DeJager_eQTL_ENSG00000130208; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130208; ROSMAP_AC_ENSG00000130208; ROSMAP_DLPFC_ENSG00000130208\n", + " padj pip \n", + "1 0.04155242 0.149524" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Select exposure for set_26\n", + "apoecs_clin_sig_4_pip %>% filter(cs_id == 'set_26')" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "b9fde4e5", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'caa_4gp'
  2. 'caa_neo4'
  3. 'tdp_st4'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'caa\\_4gp'\n", + "\\item 'caa\\_neo4'\n", + "\\item 'tdp\\_st4'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'caa_4gp'\n", + "2. 'caa_neo4'\n", + "3. 'tdp_st4'\n", + "\n", + "\n" + ], + "text/plain": [ + "[1] \"caa_4gp\" \"caa_neo4\" \"tdp_st4\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Select exposure for set_27\n", + "apoecs_clin_sig_42_pip_set27 <- apoecs_clin_sig_42_pip %>% filter(cs_id == 'set_27') %>% \n", + " group_by(outcome) %>%\n", + " arrange(padj, .by_group = TRUE)\n", + "unique(apoecs_clin_sig_42_pip_set27$outcome) " + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "d38e1c3f", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A grouped_df: 1 × 6
variant_idcs_idoutcomemolecular_outcomepadjpip
<chr><chr><chr><chr><dbl><dbl>
chr19:44954310:T:Cset_27caa_4gpAC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG000001302080.0061125830.0280672
\n" + ], + "text/latex": [ + "A grouped\\_df: 1 × 6\n", + "\\begin{tabular}{llllll}\n", + " variant\\_id & cs\\_id & outcome & molecular\\_outcome & padj & pip\\\\\n", + " & & & & & \\\\\n", + "\\hline\n", + "\t chr19:44954310:T:C & set\\_27 & caa\\_4gp & AC\\_DeJager\\_eQTL\\_ENSG00000224916; AC\\_DeJager\\_eQTL\\_ENSG00000234906; BM\\_10\\_MSBB\\_eQTL\\_ENSG00000104853; Metabrain\\_Basalganglia\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Metabrain\\_Cerebellum\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000104853; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Mic\\_DeJager\\_eQTL\\_ENSG00000130208; Mic\\_DeJager\\_eQTL\\_ENSG00000130203; Mic\\_mega\\_eQTL\\_ENSG00000130203; STARNET\\_eQTL\\_Mac\\_ENSG00000130208; STARNET\\_eQTL\\_Mac\\_ENSG00000130203; STARNET\\_eQTL\\_Mac\\_ENSG00000267467; STARNET\\_eQTL\\_Mac\\_ENSG00000224916; STARNET\\_eQTL\\_Mac\\_ENSG00000234906; DeJager\\_Mic\\_ENSG00000130203; Kellis\\_Mic\\_ENSG00000130203; DeJager\\_Mic\\_ENSG00000130208; Kellis\\_Mic\\_ENSG00000130208 & 0.006112583 & 0.0280672\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A grouped_df: 1 × 6\n", + "\n", + "| variant_id <chr> | cs_id <chr> | outcome <chr> | molecular_outcome <chr> | padj <dbl> | pip <dbl> |\n", + "|---|---|---|---|---|---|\n", + "| chr19:44954310:T:C | set_27 | caa_4gp | AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208 | 0.006112583 | 0.0280672 |\n", + "\n" + ], + "text/plain": [ + " variant_id cs_id outcome\n", + "1 chr19:44954310:T:C set_27 caa_4gp\n", + " molecular_outcome \n", + "1 AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208\n", + " padj pip \n", + "1 0.006112583 0.0280672" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "apoecs_clin_sig_42_pip_set27 %>% filter(outcome == 'caa_4gp') %>% head(n = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "b33f37cd", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A grouped_df: 1 × 6
variant_idcs_idoutcomemolecular_outcomepadjpip
<chr><chr><chr><chr><dbl><dbl>
chr19:44938650:G:Cset_27caa_neo4AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG000001302080.010772080.02187051
\n" + ], + "text/latex": [ + "A grouped\\_df: 1 × 6\n", + "\\begin{tabular}{llllll}\n", + " variant\\_id & cs\\_id & outcome & molecular\\_outcome & padj & pip\\\\\n", + " & & & & & \\\\\n", + "\\hline\n", + "\t chr19:44938650:G:C & set\\_27 & caa\\_neo4 & AC\\_DeJager\\_eQTL\\_ENSG00000224916; AC\\_DeJager\\_eQTL\\_ENSG00000234906; BM\\_10\\_MSBB\\_eQTL\\_ENSG00000104853; Metabrain\\_Basalganglia\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Metabrain\\_Cerebellum\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000104853; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Mic\\_DeJager\\_eQTL\\_ENSG00000130208; Mic\\_DeJager\\_eQTL\\_ENSG00000130203; Mic\\_mega\\_eQTL\\_ENSG00000130203; STARNET\\_eQTL\\_Mac\\_ENSG00000130208; STARNET\\_eQTL\\_Mac\\_ENSG00000130203; STARNET\\_eQTL\\_Mac\\_ENSG00000267467; STARNET\\_eQTL\\_Mac\\_ENSG00000224916; STARNET\\_eQTL\\_Mac\\_ENSG00000234906; DeJager\\_Mic\\_ENSG00000130203; Kellis\\_Mic\\_ENSG00000130203; DeJager\\_Mic\\_ENSG00000130208; Kellis\\_Mic\\_ENSG00000130208 & 0.01077208 & 0.02187051\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A grouped_df: 1 × 6\n", + "\n", + "| variant_id <chr> | cs_id <chr> | outcome <chr> | molecular_outcome <chr> | padj <dbl> | pip <dbl> |\n", + "|---|---|---|---|---|---|\n", + "| chr19:44938650:G:C | set_27 | caa_neo4 | AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208 | 0.01077208 | 0.02187051 |\n", + "\n" + ], + "text/plain": [ + " variant_id cs_id outcome \n", + "1 chr19:44938650:G:C set_27 caa_neo4\n", + " molecular_outcome \n", + "1 AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208\n", + " padj pip \n", + "1 0.01077208 0.02187051" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "apoecs_clin_sig_42_pip_set27 %>% filter(outcome == 'caa_neo4') %>% head(n = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "80bd5737", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A grouped_df: 1 × 6
variant_idcs_idoutcomemolecular_outcomepadjpip
<chr><chr><chr><chr><dbl><dbl>
chr19:45001091:C:Gset_27tdp_st4AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG000001302080.039307210.148894
\n" + ], + "text/latex": [ + "A grouped\\_df: 1 × 6\n", + "\\begin{tabular}{llllll}\n", + " variant\\_id & cs\\_id & outcome & molecular\\_outcome & padj & pip\\\\\n", + " & & & & & \\\\\n", + "\\hline\n", + "\t chr19:45001091:C:G & set\\_27 & tdp\\_st4 & AC\\_DeJager\\_eQTL\\_ENSG00000224916; AC\\_DeJager\\_eQTL\\_ENSG00000234906; BM\\_10\\_MSBB\\_eQTL\\_ENSG00000104853; Metabrain\\_Basalganglia\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Metabrain\\_Cerebellum\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000104853; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Mic\\_DeJager\\_eQTL\\_ENSG00000130208; Mic\\_DeJager\\_eQTL\\_ENSG00000130203; Mic\\_mega\\_eQTL\\_ENSG00000130203; STARNET\\_eQTL\\_Mac\\_ENSG00000130208; STARNET\\_eQTL\\_Mac\\_ENSG00000130203; STARNET\\_eQTL\\_Mac\\_ENSG00000267467; STARNET\\_eQTL\\_Mac\\_ENSG00000224916; STARNET\\_eQTL\\_Mac\\_ENSG00000234906; DeJager\\_Mic\\_ENSG00000130203; Kellis\\_Mic\\_ENSG00000130203; DeJager\\_Mic\\_ENSG00000130208; Kellis\\_Mic\\_ENSG00000130208 & 0.03930721 & 0.148894\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A grouped_df: 1 × 6\n", + "\n", + "| variant_id <chr> | cs_id <chr> | outcome <chr> | molecular_outcome <chr> | padj <dbl> | pip <dbl> |\n", + "|---|---|---|---|---|---|\n", + "| chr19:45001091:C:G | set_27 | tdp_st4 | AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208 | 0.03930721 | 0.148894 |\n", + "\n" + ], + "text/plain": [ + " variant_id cs_id outcome\n", + "1 chr19:45001091:C:G set_27 tdp_st4\n", + " molecular_outcome \n", + "1 AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208\n", + " padj pip \n", + "1 0.03930721 0.148894" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "apoecs_clin_sig_42_pip_set27 %>% filter(outcome == 'tdp_st4') %>% head(n = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "afc57758", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'caa_4gp'
  2. 'caa_neo4'
  3. 'tdp_st4'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'caa\\_4gp'\n", + "\\item 'caa\\_neo4'\n", + "\\item 'tdp\\_st4'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'caa_4gp'\n", + "2. 'caa_neo4'\n", + "3. 'tdp_st4'\n", + "\n", + "\n" + ], + "text/plain": [ + "[1] \"caa_4gp\" \"caa_neo4\" \"tdp_st4\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "apoecs_clin_sig_4_pip_set27 <- apoecs_clin_sig_4_pip %>% filter(cs_id == 'set_27') %>% \n", + " group_by(outcome) %>%\n", + " arrange(padj, .by_group = TRUE)\n", + "unique(apoecs_clin_sig_4_pip_set27$outcome) " + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "d618cb11", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A grouped_df: 1 × 6
variant_idcs_idoutcomemolecular_outcomepadjpip
<chr><chr><chr><chr><dbl><dbl>
chr19:44954310:T:Cset_27caa_4gpAC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG000001302080.0036460530.0280672
\n" + ], + "text/latex": [ + "A grouped\\_df: 1 × 6\n", + "\\begin{tabular}{llllll}\n", + " variant\\_id & cs\\_id & outcome & molecular\\_outcome & padj & pip\\\\\n", + " & & & & & \\\\\n", + "\\hline\n", + "\t chr19:44954310:T:C & set\\_27 & caa\\_4gp & AC\\_DeJager\\_eQTL\\_ENSG00000224916; AC\\_DeJager\\_eQTL\\_ENSG00000234906; BM\\_10\\_MSBB\\_eQTL\\_ENSG00000104853; Metabrain\\_Basalganglia\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Metabrain\\_Cerebellum\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000104853; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Mic\\_DeJager\\_eQTL\\_ENSG00000130208; Mic\\_DeJager\\_eQTL\\_ENSG00000130203; Mic\\_mega\\_eQTL\\_ENSG00000130203; STARNET\\_eQTL\\_Mac\\_ENSG00000130208; STARNET\\_eQTL\\_Mac\\_ENSG00000130203; STARNET\\_eQTL\\_Mac\\_ENSG00000267467; STARNET\\_eQTL\\_Mac\\_ENSG00000224916; STARNET\\_eQTL\\_Mac\\_ENSG00000234906; DeJager\\_Mic\\_ENSG00000130203; Kellis\\_Mic\\_ENSG00000130203; DeJager\\_Mic\\_ENSG00000130208; Kellis\\_Mic\\_ENSG00000130208 & 0.003646053 & 0.0280672\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A grouped_df: 1 × 6\n", + "\n", + "| variant_id <chr> | cs_id <chr> | outcome <chr> | molecular_outcome <chr> | padj <dbl> | pip <dbl> |\n", + "|---|---|---|---|---|---|\n", + "| chr19:44954310:T:C | set_27 | caa_4gp | AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208 | 0.003646053 | 0.0280672 |\n", + "\n" + ], + "text/plain": [ + " variant_id cs_id outcome\n", + "1 chr19:44954310:T:C set_27 caa_4gp\n", + " molecular_outcome \n", + "1 AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208\n", + " padj pip \n", + "1 0.003646053 0.0280672" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "apoecs_clin_sig_4_pip_set27 %>% filter(outcome == 'caa_4gp') %>% head(n = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "5544491c", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A grouped_df: 1 × 6
variant_idcs_idoutcomemolecular_outcomepadjpip
<chr><chr><chr><chr><dbl><dbl>
chr19:44954310:T:Cset_27caa_neo4AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG000001302080.0070361520.0280672
\n" + ], + "text/latex": [ + "A grouped\\_df: 1 × 6\n", + "\\begin{tabular}{llllll}\n", + " variant\\_id & cs\\_id & outcome & molecular\\_outcome & padj & pip\\\\\n", + " & & & & & \\\\\n", + "\\hline\n", + "\t chr19:44954310:T:C & set\\_27 & caa\\_neo4 & AC\\_DeJager\\_eQTL\\_ENSG00000224916; AC\\_DeJager\\_eQTL\\_ENSG00000234906; BM\\_10\\_MSBB\\_eQTL\\_ENSG00000104853; Metabrain\\_Basalganglia\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Metabrain\\_Cerebellum\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000104853; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Mic\\_DeJager\\_eQTL\\_ENSG00000130208; Mic\\_DeJager\\_eQTL\\_ENSG00000130203; Mic\\_mega\\_eQTL\\_ENSG00000130203; STARNET\\_eQTL\\_Mac\\_ENSG00000130208; STARNET\\_eQTL\\_Mac\\_ENSG00000130203; STARNET\\_eQTL\\_Mac\\_ENSG00000267467; STARNET\\_eQTL\\_Mac\\_ENSG00000224916; STARNET\\_eQTL\\_Mac\\_ENSG00000234906; DeJager\\_Mic\\_ENSG00000130203; Kellis\\_Mic\\_ENSG00000130203; DeJager\\_Mic\\_ENSG00000130208; Kellis\\_Mic\\_ENSG00000130208 & 0.007036152 & 0.0280672\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A grouped_df: 1 × 6\n", + "\n", + "| variant_id <chr> | cs_id <chr> | outcome <chr> | molecular_outcome <chr> | padj <dbl> | pip <dbl> |\n", + "|---|---|---|---|---|---|\n", + "| chr19:44954310:T:C | set_27 | caa_neo4 | AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208 | 0.007036152 | 0.0280672 |\n", + "\n" + ], + "text/plain": [ + " variant_id cs_id outcome \n", + "1 chr19:44954310:T:C set_27 caa_neo4\n", + " molecular_outcome \n", + "1 AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208\n", + " padj pip \n", + "1 0.007036152 0.0280672" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "apoecs_clin_sig_4_pip_set27 %>% filter(outcome == 'caa_neo4') %>% head(n = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "eb897ae7", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A grouped_df: 1 × 6
variant_idcs_idoutcomemolecular_outcomepadjpip
<chr><chr><chr><chr><dbl><dbl>
chr19:44999110:TAAAA:TAAset_27tdp_st4AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG000001302080.0393050.03982054
\n" + ], + "text/latex": [ + "A grouped\\_df: 1 × 6\n", + "\\begin{tabular}{llllll}\n", + " variant\\_id & cs\\_id & outcome & molecular\\_outcome & padj & pip\\\\\n", + " & & & & & \\\\\n", + "\\hline\n", + "\t chr19:44999110:TAAAA:TAA & set\\_27 & tdp\\_st4 & AC\\_DeJager\\_eQTL\\_ENSG00000224916; AC\\_DeJager\\_eQTL\\_ENSG00000234906; BM\\_10\\_MSBB\\_eQTL\\_ENSG00000104853; Metabrain\\_Basalganglia\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Metabrain\\_Cerebellum\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000104853; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000234906; Mic\\_DeJager\\_eQTL\\_ENSG00000130208; Mic\\_DeJager\\_eQTL\\_ENSG00000130203; Mic\\_mega\\_eQTL\\_ENSG00000130203; STARNET\\_eQTL\\_Mac\\_ENSG00000130208; STARNET\\_eQTL\\_Mac\\_ENSG00000130203; STARNET\\_eQTL\\_Mac\\_ENSG00000267467; STARNET\\_eQTL\\_Mac\\_ENSG00000224916; STARNET\\_eQTL\\_Mac\\_ENSG00000234906; DeJager\\_Mic\\_ENSG00000130203; Kellis\\_Mic\\_ENSG00000130203; DeJager\\_Mic\\_ENSG00000130208; Kellis\\_Mic\\_ENSG00000130208 & 0.039305 & 0.03982054\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A grouped_df: 1 × 6\n", + "\n", + "| variant_id <chr> | cs_id <chr> | outcome <chr> | molecular_outcome <chr> | padj <dbl> | pip <dbl> |\n", + "|---|---|---|---|---|---|\n", + "| chr19:44999110:TAAAA:TAA | set_27 | tdp_st4 | AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208 | 0.039305 | 0.03982054 |\n", + "\n" + ], + "text/plain": [ + " variant_id cs_id outcome\n", + "1 chr19:44999110:TAAAA:TAA set_27 tdp_st4\n", + " molecular_outcome \n", + "1 AC_DeJager_eQTL_ENSG00000224916; AC_DeJager_eQTL_ENSG00000234906; BM_10_MSBB_eQTL_ENSG00000104853; Metabrain_Basalganglia_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cerebellum_chr19_41840000_47960000_ENSG00000234906; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000104853; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000234906; Mic_DeJager_eQTL_ENSG00000130208; Mic_DeJager_eQTL_ENSG00000130203; Mic_mega_eQTL_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000130208; STARNET_eQTL_Mac_ENSG00000130203; STARNET_eQTL_Mac_ENSG00000267467; STARNET_eQTL_Mac_ENSG00000224916; STARNET_eQTL_Mac_ENSG00000234906; DeJager_Mic_ENSG00000130203; Kellis_Mic_ENSG00000130203; DeJager_Mic_ENSG00000130208; Kellis_Mic_ENSG00000130208\n", + " padj pip \n", + "1 0.039305 0.03982054" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "apoecs_clin_sig_4_pip_set27 %>% filter(outcome == 'tdp_st4') %>% head(n = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "acbff4f5", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A data.table: 1 × 6
variant_idcs_idoutcomemolecular_outcomepadjpip
<chr><chr><chr><chr><dbl><dbl>
chr19:45114770:ACCC:ACset_31age_first_ad_dx_numAC_DeJager_eQTL_ENSG00000189114; DLPFC_DeJager_eQTL_ENSG00000189114; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000189114; PCC_DeJager_eQTL_ENSG00000189114; ROSMAP_AC_ENSG00000189114; ROSMAP_DLPFC_ENSG00000189114; ROSMAP_PCC_ENSG000001891140.021580520.1194284
\n" + ], + "text/latex": [ + "A data.table: 1 × 6\n", + "\\begin{tabular}{llllll}\n", + " variant\\_id & cs\\_id & outcome & molecular\\_outcome & padj & pip\\\\\n", + " & & & & & \\\\\n", + "\\hline\n", + "\t chr19:45114770:ACCC:AC & set\\_31 & age\\_first\\_ad\\_dx\\_num & AC\\_DeJager\\_eQTL\\_ENSG00000189114; DLPFC\\_DeJager\\_eQTL\\_ENSG00000189114; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000189114; PCC\\_DeJager\\_eQTL\\_ENSG00000189114; ROSMAP\\_AC\\_ENSG00000189114; ROSMAP\\_DLPFC\\_ENSG00000189114; ROSMAP\\_PCC\\_ENSG00000189114 & 0.02158052 & 0.1194284\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 1 × 6\n", + "\n", + "| variant_id <chr> | cs_id <chr> | outcome <chr> | molecular_outcome <chr> | padj <dbl> | pip <dbl> |\n", + "|---|---|---|---|---|---|\n", + "| chr19:45114770:ACCC:AC | set_31 | age_first_ad_dx_num | AC_DeJager_eQTL_ENSG00000189114; DLPFC_DeJager_eQTL_ENSG00000189114; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000189114; PCC_DeJager_eQTL_ENSG00000189114; ROSMAP_AC_ENSG00000189114; ROSMAP_DLPFC_ENSG00000189114; ROSMAP_PCC_ENSG00000189114 | 0.02158052 | 0.1194284 |\n", + "\n" + ], + "text/plain": [ + " variant_id cs_id outcome \n", + "1 chr19:45114770:ACCC:AC set_31 age_first_ad_dx_num\n", + " molecular_outcome \n", + "1 AC_DeJager_eQTL_ENSG00000189114; DLPFC_DeJager_eQTL_ENSG00000189114; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000189114; PCC_DeJager_eQTL_ENSG00000189114; ROSMAP_AC_ENSG00000189114; ROSMAP_DLPFC_ENSG00000189114; ROSMAP_PCC_ENSG00000189114\n", + " padj pip \n", + "1 0.02158052 0.1194284" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Select exposure for set_31\n", + "apoecs_clin_sig_42_pip %>% filter(cs_id == 'set_31')" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "1685ba04", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A data.table: 1 × 6
variant_idcs_idoutcomemolecular_outcomepadjpip
<chr><chr><chr><chr><dbl><dbl>
chr19:45114770:ACCC:ACset_31age_first_ad_dx_numAC_DeJager_eQTL_ENSG00000189114; DLPFC_DeJager_eQTL_ENSG00000189114; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000189114; PCC_DeJager_eQTL_ENSG00000189114; ROSMAP_AC_ENSG00000189114; ROSMAP_DLPFC_ENSG00000189114; ROSMAP_PCC_ENSG000001891140.021032080.1194284
\n" + ], + "text/latex": [ + "A data.table: 1 × 6\n", + "\\begin{tabular}{llllll}\n", + " variant\\_id & cs\\_id & outcome & molecular\\_outcome & padj & pip\\\\\n", + " & & & & & \\\\\n", + "\\hline\n", + "\t chr19:45114770:ACCC:AC & set\\_31 & age\\_first\\_ad\\_dx\\_num & AC\\_DeJager\\_eQTL\\_ENSG00000189114; DLPFC\\_DeJager\\_eQTL\\_ENSG00000189114; Metabrain\\_Cortex\\_chr19\\_41840000\\_47960000\\_ENSG00000189114; PCC\\_DeJager\\_eQTL\\_ENSG00000189114; ROSMAP\\_AC\\_ENSG00000189114; ROSMAP\\_DLPFC\\_ENSG00000189114; ROSMAP\\_PCC\\_ENSG00000189114 & 0.02103208 & 0.1194284\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 1 × 6\n", + "\n", + "| variant_id <chr> | cs_id <chr> | outcome <chr> | molecular_outcome <chr> | padj <dbl> | pip <dbl> |\n", + "|---|---|---|---|---|---|\n", + "| chr19:45114770:ACCC:AC | set_31 | age_first_ad_dx_num | AC_DeJager_eQTL_ENSG00000189114; DLPFC_DeJager_eQTL_ENSG00000189114; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000189114; PCC_DeJager_eQTL_ENSG00000189114; ROSMAP_AC_ENSG00000189114; ROSMAP_DLPFC_ENSG00000189114; ROSMAP_PCC_ENSG00000189114 | 0.02103208 | 0.1194284 |\n", + "\n" + ], + "text/plain": [ + " variant_id cs_id outcome \n", + "1 chr19:45114770:ACCC:AC set_31 age_first_ad_dx_num\n", + " molecular_outcome \n", + "1 AC_DeJager_eQTL_ENSG00000189114; DLPFC_DeJager_eQTL_ENSG00000189114; Metabrain_Cortex_chr19_41840000_47960000_ENSG00000189114; PCC_DeJager_eQTL_ENSG00000189114; ROSMAP_AC_ENSG00000189114; ROSMAP_DLPFC_ENSG00000189114; ROSMAP_PCC_ENSG00000189114\n", + " padj pip \n", + "1 0.02103208 0.1194284" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "apoecs_clin_sig_4_pip %>% filter(cs_id == 'set_31')" + ] + }, + { + "cell_type": "markdown", + "id": "4ba26411", + "metadata": {}, + "source": [ + "## 1. Prepare input for set 26 \n", + "### Genotype data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "eb01bd21", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.table: 6 × 8
#CHROMPOSIDREFALTSIDgenodose
<int><int><chr><chr><chr><fct><chr><int>
1942994877chr19:42994877_C_GCG0_MAP153874210/00
1943090637chr19:43090637_G_TGT0_MAP153874210/00
1943337238chr19:43337238_G_AGA0_MAP153874210/00
1943522968chr19:43522968_T_CTC0_MAP153874210/00
1943632995chr19:43632995_C_TCT0_MAP153874210/00
1943656997chr19:43656997_G_AGA0_MAP153874210/00
\n" + ], + "text/latex": [ + "A data.table: 6 × 8\n", + "\\begin{tabular}{llllllll}\n", + " \\#CHROM & POS & ID & REF & ALT & SID & geno & dose\\\\\n", + " & & & & & & & \\\\\n", + "\\hline\n", + "\t 19 & 42994877 & chr19:42994877\\_C\\_G & C & G & 0\\_MAP15387421 & 0/0 & 0\\\\\n", + "\t 19 & 43090637 & chr19:43090637\\_G\\_T & G & T & 0\\_MAP15387421 & 0/0 & 0\\\\\n", + "\t 19 & 43337238 & chr19:43337238\\_G\\_A & G & A & 0\\_MAP15387421 & 0/0 & 0\\\\\n", + "\t 19 & 43522968 & chr19:43522968\\_T\\_C & T & C & 0\\_MAP15387421 & 0/0 & 0\\\\\n", + "\t 19 & 43632995 & chr19:43632995\\_C\\_T & C & T & 0\\_MAP15387421 & 0/0 & 0\\\\\n", + "\t 19 & 43656997 & chr19:43656997\\_G\\_A & G & A & 0\\_MAP15387421 & 0/0 & 0\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 6 × 8\n", + "\n", + "| #CHROM <int> | POS <int> | ID <chr> | REF <chr> | ALT <chr> | SID <fct> | geno <chr> | dose <int> |\n", + "|---|---|---|---|---|---|---|---|\n", + "| 19 | 42994877 | chr19:42994877_C_G | C | G | 0_MAP15387421 | 0/0 | 0 |\n", + "| 19 | 43090637 | chr19:43090637_G_T | G | T | 0_MAP15387421 | 0/0 | 0 |\n", + "| 19 | 43337238 | chr19:43337238_G_A | G | A | 0_MAP15387421 | 0/0 | 0 |\n", + "| 19 | 43522968 | chr19:43522968_T_C | T | C | 0_MAP15387421 | 0/0 | 0 |\n", + "| 19 | 43632995 | chr19:43632995_C_T | C | T | 0_MAP15387421 | 0/0 | 0 |\n", + "| 19 | 43656997 | chr19:43656997_G_A | G | A | 0_MAP15387421 | 0/0 | 0 |\n", + "\n" + ], + "text/plain": [ + " #CHROM POS ID REF ALT SID geno dose\n", + "1 19 42994877 chr19:42994877_C_G C G 0_MAP15387421 0/0 0 \n", + "2 19 43090637 chr19:43090637_G_T G T 0_MAP15387421 0/0 0 \n", + "3 19 43337238 chr19:43337238_G_A G A 0_MAP15387421 0/0 0 \n", + "4 19 43522968 chr19:43522968_T_C T C 0_MAP15387421 0/0 0 \n", + "5 19 43632995 chr19:43632995_C_T C T 0_MAP15387421 0/0 0 \n", + "6 19 43656997 chr19:43656997_G_A G A 0_MAP15387421 0/0 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geno <- fread('/mnt/lustre/home/yl4437/xqtl_flagship/APOE/ROSMAP_geno_apoe4_indep_snps.vcf')\n", + "geno <- melt(geno[,-c(6:9)],\n", + " id.vars = c('#CHROM','POS','ID','REF','ALT'),\n", + " variable.name = 'SID', value.name = 'geno')\n", + "geno[,dose:=str_count(geno, '1')]\n", + "head(geno)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "249744c6", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.table: 6 × 2
SIDchr19_44918487_G_T
<chr><int>
MAP153874210
MAP266378670
MAP296298490
MAP333326461
MAP347260401
MAP462466041
\n" + ], + "text/latex": [ + "A data.table: 6 × 2\n", + "\\begin{tabular}{ll}\n", + " SID & chr19\\_44918487\\_G\\_T\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t MAP15387421 & 0\\\\\n", + "\t MAP26637867 & 0\\\\\n", + "\t MAP29629849 & 0\\\\\n", + "\t MAP33332646 & 1\\\\\n", + "\t MAP34726040 & 1\\\\\n", + "\t MAP46246604 & 1\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 6 × 2\n", + "\n", + "| SID <chr> | chr19_44918487_G_T <int> |\n", + "|---|---|\n", + "| MAP15387421 | 0 |\n", + "| MAP26637867 | 0 |\n", + "| MAP29629849 | 0 |\n", + "| MAP33332646 | 1 |\n", + "| MAP34726040 | 1 |\n", + "| MAP46246604 | 1 |\n", + "\n" + ], + "text/plain": [ + " SID chr19_44918487_G_T\n", + "1 MAP15387421 0 \n", + "2 MAP26637867 0 \n", + "3 MAP29629849 0 \n", + "4 MAP33332646 1 \n", + "5 MAP34726040 1 \n", + "6 MAP46246604 1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geno_44918487 <- geno %>% filter(ID == 'chr19:44918487_G_T') %>% dplyr::select(SID, dose)\n", + "colnames(geno_44918487)[2] <- 'chr19_44918487_G_T'\n", + "geno_44918487$SID <- str_remove(geno_44918487$SID,'0_')\n", + "head(geno_44918487); nrow(geno_44918487)" + ] + }, + { + "cell_type": "markdown", + "id": "47c1ac1b", + "metadata": {}, + "source": [ + "### Mediator data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1cedd98d", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "DLPFC_exp_pheno <- fread('/mnt/lustre/lab/gwang/ftp_fgc_xqtl/eQTL/ROSMAP/DLPFC/analysis_ready/phenotype_preprocessing/dlpfc_batch_all.rnaseqc.low_expression_filtered.outlier_removed.tmm.expression.bed.chr19.mol_phe.bed.gz')\n", + "DLPFC_exp_covar <- fread('/mnt/lustre/lab/gwang/ftp_fgc_xqtl/eQTL/ROSMAP/DLPFC/analysis_ready/covariate_preprocessing/DLPFC_samples_list.rnaseqc.gene_tpm.low_expression_filtered.outlier_removed.tmm.expression.ROSMAP_xqtl_complete_samples_covariates_sex_death_pmi_study_transpose.ROSMAP_NIA_WGS.leftnorm.bcftools_qc.plink_qc.unrelated.plink_qc.prune.pca.Marchenko_PC.gz')\n", + "AC_exp_pheno <- fread('/mnt/lustre/lab/gwang/ftp_fgc_xqtl/eQTL/ROSMAP/AC/analysis_ready/phenotype_preprocessing/phenotype_by_chrom/AC_samples_list.rnaseqc.gene_tpm.low_expression_filtered.outlier_removed.tmm.expression.bed.chr19.bed.gz')\n", + "AC_exp_covar <- fread('/mnt/lustre/lab/gwang/ftp_fgc_xqtl/eQTL/ROSMAP/AC/analysis_ready/covariate_preprocessing/AC_samples_list.rnaseqc.gene_tpm.low_expression_filtered.outlier_removed.tmm.expression.ROSMAP_xqtl_complete_samples_covariates_sex_death_pmi_study_transpose.ROSMAP_NIA_WGS.leftnorm.bcftools_qc.plink_qc.unrelated.plink_qc.prune.pca.Marchenko_PC.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "089d5a9e", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 2
SIDAPOC1_DLPFC_exp
<chr><dbl>
1SM-CTEFX 1.968271759
2SM-CJIWX 0.468776468
303_120405-0.772903176
404_120405 0.277904311
5SM-CJIXW 0.340074599
607_120410-0.004389906
\n" + ], + "text/latex": [ + "A data.frame: 6 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & SID & APOC1\\_DLPFC\\_exp\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & SM-CTEFX & 1.968271759\\\\\n", + "\t2 & SM-CJIWX & 0.468776468\\\\\n", + "\t3 & 03\\_120405 & -0.772903176\\\\\n", + "\t4 & 04\\_120405 & 0.277904311\\\\\n", + "\t5 & SM-CJIXW & 0.340074599\\\\\n", + "\t6 & 07\\_120410 & -0.004389906\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 2\n", + "\n", + "| | SID <chr> | APOC1_DLPFC_exp <dbl> |\n", + "|---|---|---|\n", + "| 1 | SM-CTEFX | 1.968271759 |\n", + "| 2 | SM-CJIWX | 0.468776468 |\n", + "| 3 | 03_120405 | -0.772903176 |\n", + "| 4 | 04_120405 | 0.277904311 |\n", + "| 5 | SM-CJIXW | 0.340074599 |\n", + "| 6 | 07_120410 | -0.004389906 |\n", + "\n" + ], + "text/plain": [ + " SID APOC1_DLPFC_exp\n", + "1 SM-CTEFX 1.968271759 \n", + "2 SM-CJIWX 0.468776468 \n", + "3 03_120405 -0.772903176 \n", + "4 04_120405 0.277904311 \n", + "5 SM-CJIXW 0.340074599 \n", + "6 07_120410 -0.004389906 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "DLPFC_exp_pheno_sub <- DLPFC_exp_pheno %>% filter(gene_id == 'ENSG00000130208') %>% \n", + " dplyr::select(-`#chr`, -start, -end, -gene_id) %>%\n", + " t() %>%\n", + " as.data.frame(stringsAsFactors = FALSE) %>%\n", + " rownames_to_column(\"SID\") %>% \n", + " mutate(across(-SID, as.numeric))\n", + "\n", + "colnames(DLPFC_exp_pheno_sub)[2] <- 'APOC1_DLPFC_exp'\n", + "head(DLPFC_exp_pheno_sub)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "871c4bee", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 17
SIDmsex_DLPFC_expage_death_DLPFC_exppmi_DLPFC_expROS_study_DLPFC_expPC1_DLPFC_expPC2_DLPFC_expPC3_DLPFC_expPC4_DLPFC_expPC5_DLPFC_expPC6_DLPFC_expPC7_DLPFC_expPC8_DLPFC_expPC9_DLPFC_expPC10_DLPFC_expPC11_DLPFC_expPC12_DLPFC_exp
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
2SM-CJFK80100.873377.5000000-0.007971932 0.01336898-0.032869362 0.05378547-0.0028054140 0.073599931-0.005276867-0.0147743290 0.000984040-0.019207604 0.04349062-0.002447494
3SM-CTDUU0 80.657085.5000000-0.001664299-0.01360660 0.004228869 0.02498046-0.0013251170 0.003963579 0.016989900 0.0001775362 0.067203700 0.025429690-0.01176594-0.001185043
4SM-CTDSC0 83.690623.9333330-0.006954012-0.02511610-0.002598248 0.01682041-0.0086091390 0.002888398 0.001716816-0.0034459020-0.007470021 0.027241552 0.06561792 0.007193531
5SM-CTEMS0 90.384673.7833330-0.009058868-0.05901013-0.007955308-0.04685020-0.0129203200 0.009988655 0.001555237-0.0002037048-0.010267840 0.070831600-0.02484252-0.004629301
6SM-CJFLR0 94.313485.0000000-0.008728673 0.01084817-0.026902400 0.06359475-0.0007060546 0.060597070 0.022354150 0.0001102251 0.014913270 0.004142071-0.02022443 0.002024246
7SM-CJGMZ0101.190974.8333330-0.002032936 0.04536795 0.002328515 0.00435687 0.0015663000-0.036754003-0.002489963-0.0225318700-0.002075444 0.023443907 0.01031206-0.022296774
\n" + ], + "text/latex": [ + "A data.frame: 6 × 17\n", + "\\begin{tabular}{r|lllllllllllllllll}\n", + " & SID & msex\\_DLPFC\\_exp & age\\_death\\_DLPFC\\_exp & pmi\\_DLPFC\\_exp & ROS\\_study\\_DLPFC\\_exp & PC1\\_DLPFC\\_exp & PC2\\_DLPFC\\_exp & PC3\\_DLPFC\\_exp & PC4\\_DLPFC\\_exp & PC5\\_DLPFC\\_exp & PC6\\_DLPFC\\_exp & PC7\\_DLPFC\\_exp & PC8\\_DLPFC\\_exp & PC9\\_DLPFC\\_exp & PC10\\_DLPFC\\_exp & PC11\\_DLPFC\\_exp & PC12\\_DLPFC\\_exp\\\\\n", + " & & & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t2 & SM-CJFK8 & 0 & 100.87337 & 7.500000 & 0 & -0.007971932 & 0.01336898 & -0.032869362 & 0.05378547 & -0.0028054140 & 0.073599931 & -0.005276867 & -0.0147743290 & 0.000984040 & -0.019207604 & 0.04349062 & -0.002447494\\\\\n", + "\t3 & SM-CTDUU & 0 & 80.65708 & 5.500000 & 0 & -0.001664299 & -0.01360660 & 0.004228869 & 0.02498046 & -0.0013251170 & 0.003963579 & 0.016989900 & 0.0001775362 & 0.067203700 & 0.025429690 & -0.01176594 & -0.001185043\\\\\n", + "\t4 & SM-CTDSC & 0 & 83.69062 & 3.933333 & 0 & -0.006954012 & -0.02511610 & -0.002598248 & 0.01682041 & -0.0086091390 & 0.002888398 & 0.001716816 & -0.0034459020 & -0.007470021 & 0.027241552 & 0.06561792 & 0.007193531\\\\\n", + "\t5 & SM-CTEMS & 0 & 90.38467 & 3.783333 & 0 & -0.009058868 & -0.05901013 & -0.007955308 & -0.04685020 & -0.0129203200 & 0.009988655 & 0.001555237 & -0.0002037048 & -0.010267840 & 0.070831600 & -0.02484252 & -0.004629301\\\\\n", + "\t6 & SM-CJFLR & 0 & 94.31348 & 5.000000 & 0 & -0.008728673 & 0.01084817 & -0.026902400 & 0.06359475 & -0.0007060546 & 0.060597070 & 0.022354150 & 0.0001102251 & 0.014913270 & 0.004142071 & -0.02022443 & 0.002024246\\\\\n", + "\t7 & SM-CJGMZ & 0 & 101.19097 & 4.833333 & 0 & -0.002032936 & 0.04536795 & 0.002328515 & 0.00435687 & 0.0015663000 & -0.036754003 & -0.002489963 & -0.0225318700 & -0.002075444 & 0.023443907 & 0.01031206 & -0.022296774\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 17\n", + "\n", + "| | SID <chr> | msex_DLPFC_exp <dbl> | age_death_DLPFC_exp <dbl> | pmi_DLPFC_exp <dbl> | ROS_study_DLPFC_exp <dbl> | PC1_DLPFC_exp <dbl> | PC2_DLPFC_exp <dbl> | PC3_DLPFC_exp <dbl> | PC4_DLPFC_exp <dbl> | PC5_DLPFC_exp <dbl> | PC6_DLPFC_exp <dbl> | PC7_DLPFC_exp <dbl> | PC8_DLPFC_exp <dbl> | PC9_DLPFC_exp <dbl> | PC10_DLPFC_exp <dbl> | PC11_DLPFC_exp <dbl> | PC12_DLPFC_exp <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 2 | SM-CJFK8 | 0 | 100.87337 | 7.500000 | 0 | -0.007971932 | 0.01336898 | -0.032869362 | 0.05378547 | -0.0028054140 | 0.073599931 | -0.005276867 | -0.0147743290 | 0.000984040 | -0.019207604 | 0.04349062 | -0.002447494 |\n", + "| 3 | SM-CTDUU | 0 | 80.65708 | 5.500000 | 0 | -0.001664299 | -0.01360660 | 0.004228869 | 0.02498046 | -0.0013251170 | 0.003963579 | 0.016989900 | 0.0001775362 | 0.067203700 | 0.025429690 | -0.01176594 | -0.001185043 |\n", + "| 4 | SM-CTDSC | 0 | 83.69062 | 3.933333 | 0 | -0.006954012 | -0.02511610 | -0.002598248 | 0.01682041 | -0.0086091390 | 0.002888398 | 0.001716816 | -0.0034459020 | -0.007470021 | 0.027241552 | 0.06561792 | 0.007193531 |\n", + "| 5 | SM-CTEMS | 0 | 90.38467 | 3.783333 | 0 | -0.009058868 | -0.05901013 | -0.007955308 | -0.04685020 | -0.0129203200 | 0.009988655 | 0.001555237 | -0.0002037048 | -0.010267840 | 0.070831600 | -0.02484252 | -0.004629301 |\n", + "| 6 | SM-CJFLR | 0 | 94.31348 | 5.000000 | 0 | -0.008728673 | 0.01084817 | -0.026902400 | 0.06359475 | -0.0007060546 | 0.060597070 | 0.022354150 | 0.0001102251 | 0.014913270 | 0.004142071 | -0.02022443 | 0.002024246 |\n", + "| 7 | SM-CJGMZ | 0 | 101.19097 | 4.833333 | 0 | -0.002032936 | 0.04536795 | 0.002328515 | 0.00435687 | 0.0015663000 | -0.036754003 | -0.002489963 | -0.0225318700 | -0.002075444 | 0.023443907 | 0.01031206 | -0.022296774 |\n", + "\n" + ], + "text/plain": [ + " SID msex_DLPFC_exp age_death_DLPFC_exp pmi_DLPFC_exp ROS_study_DLPFC_exp\n", + "2 SM-CJFK8 0 100.87337 7.500000 0 \n", + "3 SM-CTDUU 0 80.65708 5.500000 0 \n", + "4 SM-CTDSC 0 83.69062 3.933333 0 \n", + "5 SM-CTEMS 0 90.38467 3.783333 0 \n", + "6 SM-CJFLR 0 94.31348 5.000000 0 \n", + "7 SM-CJGMZ 0 101.19097 4.833333 0 \n", + " PC1_DLPFC_exp PC2_DLPFC_exp PC3_DLPFC_exp PC4_DLPFC_exp PC5_DLPFC_exp\n", + "2 -0.007971932 0.01336898 -0.032869362 0.05378547 -0.0028054140\n", + "3 -0.001664299 -0.01360660 0.004228869 0.02498046 -0.0013251170\n", + "4 -0.006954012 -0.02511610 -0.002598248 0.01682041 -0.0086091390\n", + "5 -0.009058868 -0.05901013 -0.007955308 -0.04685020 -0.0129203200\n", + "6 -0.008728673 0.01084817 -0.026902400 0.06359475 -0.0007060546\n", + "7 -0.002032936 0.04536795 0.002328515 0.00435687 0.0015663000\n", + " PC6_DLPFC_exp PC7_DLPFC_exp PC8_DLPFC_exp PC9_DLPFC_exp PC10_DLPFC_exp\n", + "2 0.073599931 -0.005276867 -0.0147743290 0.000984040 -0.019207604 \n", + "3 0.003963579 0.016989900 0.0001775362 0.067203700 0.025429690 \n", + "4 0.002888398 0.001716816 -0.0034459020 -0.007470021 0.027241552 \n", + "5 0.009988655 0.001555237 -0.0002037048 -0.010267840 0.070831600 \n", + "6 0.060597070 0.022354150 0.0001102251 0.014913270 0.004142071 \n", + "7 -0.036754003 -0.002489963 -0.0225318700 -0.002075444 0.023443907 \n", + " PC11_DLPFC_exp PC12_DLPFC_exp\n", + "2 0.04349062 -0.002447494 \n", + "3 -0.01176594 -0.001185043 \n", + "4 0.06561792 0.007193531 \n", + "5 -0.02484252 -0.004629301 \n", + "6 -0.02022443 0.002024246 \n", + "7 0.01031206 -0.022296774 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "DLPFC_exp_covar_t <- DLPFC_exp_covar %>% \n", + " t() %>% \n", + " as.data.frame() %>% \n", + " rownames_to_column(\"SID\")\n", + "\n", + "colnames(DLPFC_exp_covar_t) <- DLPFC_exp_covar_t[1, ]\n", + "DLPFC_exp_covar_t <- DLPFC_exp_covar_t[-1, ]\n", + "colnames(DLPFC_exp_covar_t)[1] <- 'SID'\n", + "DLPFC_exp_covar_t <- DLPFC_exp_covar_t %>% mutate(across(-SID, as.numeric)) %>% dplyr::select(-contains(\"Hidden_Factor\"))\n", + "DLPFC_exp_covar_t <- DLPFC_exp_covar_t |>\n", + " setNames(ifelse(names(DLPFC_exp_covar_t) == \"SID\", \"SID\", paste0(names(DLPFC_exp_covar_t), \"_DLPFC_exp\")))\n", + "\n", + "head(DLPFC_exp_covar_t)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "id": "8672e1de", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 18
SIDAPOC1_DLPFC_expmsex_DLPFC_expage_death_DLPFC_exppmi_DLPFC_expROS_study_DLPFC_expPC1_DLPFC_expPC2_DLPFC_expPC3_DLPFC_expPC4_DLPFC_expPC5_DLPFC_expPC6_DLPFC_expPC7_DLPFC_expPC8_DLPFC_expPC9_DLPFC_expPC10_DLPFC_expPC11_DLPFC_expPC12_DLPFC_exp
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1SM-CJEFQ 0.35172601 85.826153.2500000-0.004302403-0.01222881-0.002536536 0.01595026-0.01160160-0.01889253900.042032741-0.007465418 0.029123271 0.029619128 0.021662642-0.020592877
2SM-CJEFR 2.12874990 95.184125.0000001-0.007027830-0.06427368-0.006788190-0.04463996-0.02534136-0.00474419900.008329738-0.012461613-0.033234464-0.028106255-0.001766579 0.020196593
3SM-CJEFS 0.87139660101.943874.5000001-0.009688911-0.07511777-0.013614288-0.07057625-0.01217562 0.03593179200.004934510-0.007277167 0.023746249 0.044585788-0.006362661 0.045385102
4SM-CJEFU 0.11217711 81.938408.8333330-0.008678988 0.01030657-0.036951274 0.08630428 0.01070526 0.11178369000.047131621 0.046753381-0.065000877-0.007433710-0.064559876 0.006876538
5SM-CJEFX 0.50336020 87.846684.2166671-0.002660709-0.02199336 0.008628434-0.02679754-0.01556061-0.02522736300.012658205 0.003777747-0.006815708-0.006335293-0.020766429 0.025645201
6SM-CJEG1-0.67311260 81.325122.0000001-0.005346334 0.02597680-0.030165860-0.03856278-0.02216014-0.00017726370.044046290 0.037845550-0.007722891-0.011441090-0.042836990 0.026212520
\n" + ], + "text/latex": [ + "A data.frame: 6 × 18\n", + "\\begin{tabular}{r|llllllllllllllllll}\n", + " & SID & APOC1\\_DLPFC\\_exp & msex\\_DLPFC\\_exp & age\\_death\\_DLPFC\\_exp & pmi\\_DLPFC\\_exp & ROS\\_study\\_DLPFC\\_exp & PC1\\_DLPFC\\_exp & PC2\\_DLPFC\\_exp & PC3\\_DLPFC\\_exp & PC4\\_DLPFC\\_exp & PC5\\_DLPFC\\_exp & PC6\\_DLPFC\\_exp & PC7\\_DLPFC\\_exp & PC8\\_DLPFC\\_exp & PC9\\_DLPFC\\_exp & PC10\\_DLPFC\\_exp & PC11\\_DLPFC\\_exp & PC12\\_DLPFC\\_exp\\\\\n", + " & & & & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & SM-CJEFQ & 0.3517260 & 1 & 85.82615 & 3.250000 & 0 & -0.004302403 & -0.01222881 & -0.002536536 & 0.01595026 & -0.01160160 & -0.0188925390 & 0.042032741 & -0.007465418 & 0.029123271 & 0.029619128 & 0.021662642 & -0.020592877\\\\\n", + "\t2 & SM-CJEFR & 2.1287499 & 0 & 95.18412 & 5.000000 & 1 & -0.007027830 & -0.06427368 & -0.006788190 & -0.04463996 & -0.02534136 & -0.0047441990 & 0.008329738 & -0.012461613 & -0.033234464 & -0.028106255 & -0.001766579 & 0.020196593\\\\\n", + "\t3 & SM-CJEFS & 0.8713966 & 0 & 101.94387 & 4.500000 & 1 & -0.009688911 & -0.07511777 & -0.013614288 & -0.07057625 & -0.01217562 & 0.0359317920 & 0.004934510 & -0.007277167 & 0.023746249 & 0.044585788 & -0.006362661 & 0.045385102\\\\\n", + "\t4 & SM-CJEFU & 0.1121771 & 1 & 81.93840 & 8.833333 & 0 & -0.008678988 & 0.01030657 & -0.036951274 & 0.08630428 & 0.01070526 & 0.1117836900 & 0.047131621 & 0.046753381 & -0.065000877 & -0.007433710 & -0.064559876 & 0.006876538\\\\\n", + "\t5 & SM-CJEFX & 0.5033602 & 0 & 87.84668 & 4.216667 & 1 & -0.002660709 & -0.02199336 & 0.008628434 & -0.02679754 & -0.01556061 & -0.0252273630 & 0.012658205 & 0.003777747 & -0.006815708 & -0.006335293 & -0.020766429 & 0.025645201\\\\\n", + "\t6 & SM-CJEG1 & -0.6731126 & 0 & 81.32512 & 2.000000 & 1 & -0.005346334 & 0.02597680 & -0.030165860 & -0.03856278 & -0.02216014 & -0.0001772637 & 0.044046290 & 0.037845550 & -0.007722891 & -0.011441090 & -0.042836990 & 0.026212520\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 18\n", + "\n", + "| | SID <chr> | APOC1_DLPFC_exp <dbl> | msex_DLPFC_exp <dbl> | age_death_DLPFC_exp <dbl> | pmi_DLPFC_exp <dbl> | ROS_study_DLPFC_exp <dbl> | PC1_DLPFC_exp <dbl> | PC2_DLPFC_exp <dbl> | PC3_DLPFC_exp <dbl> | PC4_DLPFC_exp <dbl> | PC5_DLPFC_exp <dbl> | PC6_DLPFC_exp <dbl> | PC7_DLPFC_exp <dbl> | PC8_DLPFC_exp <dbl> | PC9_DLPFC_exp <dbl> | PC10_DLPFC_exp <dbl> | PC11_DLPFC_exp <dbl> | PC12_DLPFC_exp <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | SM-CJEFQ | 0.3517260 | 1 | 85.82615 | 3.250000 | 0 | -0.004302403 | -0.01222881 | -0.002536536 | 0.01595026 | -0.01160160 | -0.0188925390 | 0.042032741 | -0.007465418 | 0.029123271 | 0.029619128 | 0.021662642 | -0.020592877 |\n", + "| 2 | SM-CJEFR | 2.1287499 | 0 | 95.18412 | 5.000000 | 1 | -0.007027830 | -0.06427368 | -0.006788190 | -0.04463996 | -0.02534136 | -0.0047441990 | 0.008329738 | -0.012461613 | -0.033234464 | -0.028106255 | -0.001766579 | 0.020196593 |\n", + "| 3 | SM-CJEFS | 0.8713966 | 0 | 101.94387 | 4.500000 | 1 | -0.009688911 | -0.07511777 | -0.013614288 | -0.07057625 | -0.01217562 | 0.0359317920 | 0.004934510 | -0.007277167 | 0.023746249 | 0.044585788 | -0.006362661 | 0.045385102 |\n", + "| 4 | SM-CJEFU | 0.1121771 | 1 | 81.93840 | 8.833333 | 0 | -0.008678988 | 0.01030657 | -0.036951274 | 0.08630428 | 0.01070526 | 0.1117836900 | 0.047131621 | 0.046753381 | -0.065000877 | -0.007433710 | -0.064559876 | 0.006876538 |\n", + "| 5 | SM-CJEFX | 0.5033602 | 0 | 87.84668 | 4.216667 | 1 | -0.002660709 | -0.02199336 | 0.008628434 | -0.02679754 | -0.01556061 | -0.0252273630 | 0.012658205 | 0.003777747 | -0.006815708 | -0.006335293 | -0.020766429 | 0.025645201 |\n", + "| 6 | SM-CJEG1 | -0.6731126 | 0 | 81.32512 | 2.000000 | 1 | -0.005346334 | 0.02597680 | -0.030165860 | -0.03856278 | -0.02216014 | -0.0001772637 | 0.044046290 | 0.037845550 | -0.007722891 | -0.011441090 | -0.042836990 | 0.026212520 |\n", + "\n" + ], + "text/plain": [ + " SID APOC1_DLPFC_exp msex_DLPFC_exp age_death_DLPFC_exp pmi_DLPFC_exp\n", + "1 SM-CJEFQ 0.3517260 1 85.82615 3.250000 \n", + "2 SM-CJEFR 2.1287499 0 95.18412 5.000000 \n", + "3 SM-CJEFS 0.8713966 0 101.94387 4.500000 \n", + "4 SM-CJEFU 0.1121771 1 81.93840 8.833333 \n", + "5 SM-CJEFX 0.5033602 0 87.84668 4.216667 \n", + "6 SM-CJEG1 -0.6731126 0 81.32512 2.000000 \n", + " ROS_study_DLPFC_exp PC1_DLPFC_exp PC2_DLPFC_exp PC3_DLPFC_exp PC4_DLPFC_exp\n", + "1 0 -0.004302403 -0.01222881 -0.002536536 0.01595026 \n", + "2 1 -0.007027830 -0.06427368 -0.006788190 -0.04463996 \n", + "3 1 -0.009688911 -0.07511777 -0.013614288 -0.07057625 \n", + "4 0 -0.008678988 0.01030657 -0.036951274 0.08630428 \n", + "5 1 -0.002660709 -0.02199336 0.008628434 -0.02679754 \n", + "6 1 -0.005346334 0.02597680 -0.030165860 -0.03856278 \n", + " PC5_DLPFC_exp PC6_DLPFC_exp PC7_DLPFC_exp PC8_DLPFC_exp PC9_DLPFC_exp\n", + "1 -0.01160160 -0.0188925390 0.042032741 -0.007465418 0.029123271 \n", + "2 -0.02534136 -0.0047441990 0.008329738 -0.012461613 -0.033234464 \n", + "3 -0.01217562 0.0359317920 0.004934510 -0.007277167 0.023746249 \n", + "4 0.01070526 0.1117836900 0.047131621 0.046753381 -0.065000877 \n", + "5 -0.01556061 -0.0252273630 0.012658205 0.003777747 -0.006815708 \n", + "6 -0.02216014 -0.0001772637 0.044046290 0.037845550 -0.007722891 \n", + " PC10_DLPFC_exp PC11_DLPFC_exp PC12_DLPFC_exp\n", + "1 0.029619128 0.021662642 -0.020592877 \n", + "2 -0.028106255 -0.001766579 0.020196593 \n", + "3 0.044585788 -0.006362661 0.045385102 \n", + "4 -0.007433710 -0.064559876 0.006876538 \n", + "5 -0.006335293 -0.020766429 0.025645201 \n", + "6 -0.011441090 -0.042836990 0.026212520 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "777" + ], + "text/latex": [ + "777" + ], + "text/markdown": [ + "777" + ], + "text/plain": [ + "[1] 777" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1141" + ], + "text/latex": [ + "1141" + ], + "text/markdown": [ + "1141" + ], + "text/plain": [ + "[1] 1141" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "784" + ], + "text/latex": [ + "784" + ], + "text/markdown": [ + "784" + ], + "text/plain": [ + "[1] 784" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "DLPFC_exp <- merge(DLPFC_exp_pheno_sub, DLPFC_exp_covar_t, by = 'SID')\n", + "head(DLPFC_exp); nrow(DLPFC_exp); nrow(DLPFC_exp_pheno_sub); nrow(DLPFC_exp_covar_t) " + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "b31340b0", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 2
SIDAPOC1_AC_exp
<chr><dbl>
1SM-CTDUU 0.5671604
2SM-CTDSC-1.4820442
3SM-CJFLR 1.2685238
4SM-CJGMZ-2.4468965
5SM-CJFM3 0.3044213
6SM-CJIXK-0.6320143
\n" + ], + "text/latex": [ + "A data.frame: 6 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & SID & APOC1\\_AC\\_exp\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & SM-CTDUU & 0.5671604\\\\\n", + "\t2 & SM-CTDSC & -1.4820442\\\\\n", + "\t3 & SM-CJFLR & 1.2685238\\\\\n", + "\t4 & SM-CJGMZ & -2.4468965\\\\\n", + "\t5 & SM-CJFM3 & 0.3044213\\\\\n", + "\t6 & SM-CJIXK & -0.6320143\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 2\n", + "\n", + "| | SID <chr> | APOC1_AC_exp <dbl> |\n", + "|---|---|---|\n", + "| 1 | SM-CTDUU | 0.5671604 |\n", + "| 2 | SM-CTDSC | -1.4820442 |\n", + "| 3 | SM-CJFLR | 1.2685238 |\n", + "| 4 | SM-CJGMZ | -2.4468965 |\n", + "| 5 | SM-CJFM3 | 0.3044213 |\n", + "| 6 | SM-CJIXK | -0.6320143 |\n", + "\n" + ], + "text/plain": [ + " SID APOC1_AC_exp\n", + "1 SM-CTDUU 0.5671604 \n", + "2 SM-CTDSC -1.4820442 \n", + "3 SM-CJFLR 1.2685238 \n", + "4 SM-CJGMZ -2.4468965 \n", + "5 SM-CJFM3 0.3044213 \n", + "6 SM-CJIXK -0.6320143 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "AC_exp_pheno_sub <- AC_exp_pheno %>% filter(ID == 'ENSG00000130208') %>% \n", + " dplyr::select(-`#chr`, -start, -end, -ID) %>%\n", + " t() %>%\n", + " as.data.frame(stringsAsFactors = FALSE) %>%\n", + " rownames_to_column(\"SID\") %>% \n", + " mutate(across(-SID, as.numeric))\n", + "\n", + "colnames(AC_exp_pheno_sub)[2] <- 'APOC1_AC_exp'\n", + "head(AC_exp_pheno_sub)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e6d6788b", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 18
SIDmsex_AC_expage_death_AC_exppmi_AC_expROS_study_AC_expPC1_AC_expPC2_AC_expPC3_AC_expPC4_AC_expPC5_AC_expPC6_AC_expPC7_AC_expPC8_AC_expPC9_AC_expPC10_AC_expPC11_AC_expPC12_AC_expPC13_AC_exp
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
2SM-CTDUU0 80.657085.5000000-0.002028330 0.01267274-0.0073589890 0.028408290 0.002776726-0.0015122870 0.0240048400-0.051004110-0.0137526000 0.0351421800-0.028160540-0.06511871 0.05669249
3SM-CTDSC0 83.690623.9333330-0.009245062 0.02713577-0.0014786020 0.022028150-0.002251011-0.0008904241-0.0108843400 0.022480490-0.0005260747 0.0193375400 0.032302710-0.01923247-0.02049597
4SM-CJFLR0 94.313485.0000000-0.009777243-0.01158281 0.0348950500 0.074533490-0.055542410 0.0015615090-0.0004858981-0.028568420-0.0207736900 0.0014505450-0.013776590-0.03926469 0.00649618
5SM-CJGMZ0101.191004.8333330-0.001211438-0.05402400 0.0002197553 0.004262954 0.050953410-0.0073102460 0.0206991500-0.014335920 0.0565040100-0.0128607900-0.006149298-0.02222290-0.03553244
6SM-CJFM31 93.691997.2500000-0.006770115 0.03819788 0.0059271570-0.009119545 0.024700760-0.0030082470 0.0204426000 0.009838271-0.0152109700-0.0003636517 0.001628015 0.04323453 0.05926590
7SM-CJIXK1 74.450387.0166670-0.004168081 0.01368265 0.0048596830 0.030104940-0.009242118-0.0008272919-0.0344562800-0.011519680 0.0689378800 0.0081541650-0.010508220-0.03979766 0.07294265
\n" + ], + "text/latex": [ + "A data.frame: 6 × 18\n", + "\\begin{tabular}{r|llllllllllllllllll}\n", + " & SID & msex\\_AC\\_exp & age\\_death\\_AC\\_exp & pmi\\_AC\\_exp & ROS\\_study\\_AC\\_exp & PC1\\_AC\\_exp & PC2\\_AC\\_exp & PC3\\_AC\\_exp & PC4\\_AC\\_exp & PC5\\_AC\\_exp & PC6\\_AC\\_exp & PC7\\_AC\\_exp & PC8\\_AC\\_exp & PC9\\_AC\\_exp & PC10\\_AC\\_exp & PC11\\_AC\\_exp & PC12\\_AC\\_exp & PC13\\_AC\\_exp\\\\\n", + " & & & & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t2 & SM-CTDUU & 0 & 80.65708 & 5.500000 & 0 & -0.002028330 & 0.01267274 & -0.0073589890 & 0.028408290 & 0.002776726 & -0.0015122870 & 0.0240048400 & -0.051004110 & -0.0137526000 & 0.0351421800 & -0.028160540 & -0.06511871 & 0.05669249\\\\\n", + "\t3 & SM-CTDSC & 0 & 83.69062 & 3.933333 & 0 & -0.009245062 & 0.02713577 & -0.0014786020 & 0.022028150 & -0.002251011 & -0.0008904241 & -0.0108843400 & 0.022480490 & -0.0005260747 & 0.0193375400 & 0.032302710 & -0.01923247 & -0.02049597\\\\\n", + "\t4 & SM-CJFLR & 0 & 94.31348 & 5.000000 & 0 & -0.009777243 & -0.01158281 & 0.0348950500 & 0.074533490 & -0.055542410 & 0.0015615090 & -0.0004858981 & -0.028568420 & -0.0207736900 & 0.0014505450 & -0.013776590 & -0.03926469 & 0.00649618\\\\\n", + "\t5 & SM-CJGMZ & 0 & 101.19100 & 4.833333 & 0 & -0.001211438 & -0.05402400 & 0.0002197553 & 0.004262954 & 0.050953410 & -0.0073102460 & 0.0206991500 & -0.014335920 & 0.0565040100 & -0.0128607900 & -0.006149298 & -0.02222290 & -0.03553244\\\\\n", + "\t6 & SM-CJFM3 & 1 & 93.69199 & 7.250000 & 0 & -0.006770115 & 0.03819788 & 0.0059271570 & -0.009119545 & 0.024700760 & -0.0030082470 & 0.0204426000 & 0.009838271 & -0.0152109700 & -0.0003636517 & 0.001628015 & 0.04323453 & 0.05926590\\\\\n", + "\t7 & SM-CJIXK & 1 & 74.45038 & 7.016667 & 0 & -0.004168081 & 0.01368265 & 0.0048596830 & 0.030104940 & -0.009242118 & -0.0008272919 & -0.0344562800 & -0.011519680 & 0.0689378800 & 0.0081541650 & -0.010508220 & -0.03979766 & 0.07294265\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 18\n", + "\n", + "| | SID <chr> | msex_AC_exp <dbl> | age_death_AC_exp <dbl> | pmi_AC_exp <dbl> | ROS_study_AC_exp <dbl> | PC1_AC_exp <dbl> | PC2_AC_exp <dbl> | PC3_AC_exp <dbl> | PC4_AC_exp <dbl> | PC5_AC_exp <dbl> | PC6_AC_exp <dbl> | PC7_AC_exp <dbl> | PC8_AC_exp <dbl> | PC9_AC_exp <dbl> | PC10_AC_exp <dbl> | PC11_AC_exp <dbl> | PC12_AC_exp <dbl> | PC13_AC_exp <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 2 | SM-CTDUU | 0 | 80.65708 | 5.500000 | 0 | -0.002028330 | 0.01267274 | -0.0073589890 | 0.028408290 | 0.002776726 | -0.0015122870 | 0.0240048400 | -0.051004110 | -0.0137526000 | 0.0351421800 | -0.028160540 | -0.06511871 | 0.05669249 |\n", + "| 3 | SM-CTDSC | 0 | 83.69062 | 3.933333 | 0 | -0.009245062 | 0.02713577 | -0.0014786020 | 0.022028150 | -0.002251011 | -0.0008904241 | -0.0108843400 | 0.022480490 | -0.0005260747 | 0.0193375400 | 0.032302710 | -0.01923247 | -0.02049597 |\n", + "| 4 | SM-CJFLR | 0 | 94.31348 | 5.000000 | 0 | -0.009777243 | -0.01158281 | 0.0348950500 | 0.074533490 | -0.055542410 | 0.0015615090 | -0.0004858981 | -0.028568420 | -0.0207736900 | 0.0014505450 | -0.013776590 | -0.03926469 | 0.00649618 |\n", + "| 5 | SM-CJGMZ | 0 | 101.19100 | 4.833333 | 0 | -0.001211438 | -0.05402400 | 0.0002197553 | 0.004262954 | 0.050953410 | -0.0073102460 | 0.0206991500 | -0.014335920 | 0.0565040100 | -0.0128607900 | -0.006149298 | -0.02222290 | -0.03553244 |\n", + "| 6 | SM-CJFM3 | 1 | 93.69199 | 7.250000 | 0 | -0.006770115 | 0.03819788 | 0.0059271570 | -0.009119545 | 0.024700760 | -0.0030082470 | 0.0204426000 | 0.009838271 | -0.0152109700 | -0.0003636517 | 0.001628015 | 0.04323453 | 0.05926590 |\n", + "| 7 | SM-CJIXK | 1 | 74.45038 | 7.016667 | 0 | -0.004168081 | 0.01368265 | 0.0048596830 | 0.030104940 | -0.009242118 | -0.0008272919 | -0.0344562800 | -0.011519680 | 0.0689378800 | 0.0081541650 | -0.010508220 | -0.03979766 | 0.07294265 |\n", + "\n" + ], + "text/plain": [ + " SID msex_AC_exp age_death_AC_exp pmi_AC_exp ROS_study_AC_exp\n", + "2 SM-CTDUU 0 80.65708 5.500000 0 \n", + "3 SM-CTDSC 0 83.69062 3.933333 0 \n", + "4 SM-CJFLR 0 94.31348 5.000000 0 \n", + "5 SM-CJGMZ 0 101.19100 4.833333 0 \n", + "6 SM-CJFM3 1 93.69199 7.250000 0 \n", + "7 SM-CJIXK 1 74.45038 7.016667 0 \n", + " PC1_AC_exp PC2_AC_exp PC3_AC_exp PC4_AC_exp PC5_AC_exp \n", + "2 -0.002028330 0.01267274 -0.0073589890 0.028408290 0.002776726\n", + "3 -0.009245062 0.02713577 -0.0014786020 0.022028150 -0.002251011\n", + "4 -0.009777243 -0.01158281 0.0348950500 0.074533490 -0.055542410\n", + "5 -0.001211438 -0.05402400 0.0002197553 0.004262954 0.050953410\n", + "6 -0.006770115 0.03819788 0.0059271570 -0.009119545 0.024700760\n", + "7 -0.004168081 0.01368265 0.0048596830 0.030104940 -0.009242118\n", + " PC6_AC_exp PC7_AC_exp PC8_AC_exp PC9_AC_exp PC10_AC_exp \n", + "2 -0.0015122870 0.0240048400 -0.051004110 -0.0137526000 0.0351421800\n", + "3 -0.0008904241 -0.0108843400 0.022480490 -0.0005260747 0.0193375400\n", + "4 0.0015615090 -0.0004858981 -0.028568420 -0.0207736900 0.0014505450\n", + "5 -0.0073102460 0.0206991500 -0.014335920 0.0565040100 -0.0128607900\n", + "6 -0.0030082470 0.0204426000 0.009838271 -0.0152109700 -0.0003636517\n", + "7 -0.0008272919 -0.0344562800 -0.011519680 0.0689378800 0.0081541650\n", + " PC11_AC_exp PC12_AC_exp PC13_AC_exp\n", + "2 -0.028160540 -0.06511871 0.05669249\n", + "3 0.032302710 -0.01923247 -0.02049597\n", + "4 -0.013776590 -0.03926469 0.00649618\n", + "5 -0.006149298 -0.02222290 -0.03553244\n", + "6 0.001628015 0.04323453 0.05926590\n", + "7 -0.010508220 -0.03979766 0.07294265" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "AC_exp_covar_t <- AC_exp_covar %>% \n", + " t() %>% \n", + " as.data.frame() %>% \n", + " rownames_to_column(\"SID\")\n", + "\n", + "colnames(AC_exp_covar_t) <- AC_exp_covar_t[1, ]\n", + "AC_exp_covar_t <- AC_exp_covar_t[-1, ]\n", + "colnames(AC_exp_covar_t)[1] <- 'SID'\n", + "AC_exp_covar_t <- AC_exp_covar_t %>% mutate(across(-SID, as.numeric)) %>% dplyr::select(-contains(\"Hidden_Factor\"))\n", + "AC_exp_covar_t <- AC_exp_covar_t |>\n", + " setNames(ifelse(names(AC_exp_covar_t) == \"SID\", \"SID\", paste0(names(AC_exp_covar_t), \"_AC_exp\")))\n", + "\n", + "head(AC_exp_covar_t)" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "a9585e04", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 19
SIDAPOC1_AC_expmsex_AC_expage_death_AC_exppmi_AC_expROS_study_AC_expPC1_AC_expPC2_AC_expPC3_AC_expPC4_AC_expPC5_AC_expPC6_AC_expPC7_AC_expPC8_AC_expPC9_AC_expPC10_AC_expPC11_AC_expPC12_AC_expPC13_AC_exp
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1SM-CJEFR-0.36555470 95.184125.0000001-0.009288654 0.07222711 0.002891650-0.049175427-0.009331941 0.02367040 0.012981030-0.0012170090-0.07232593 0.010685649 0.022902699 0.053007585-0.032927999
2SM-CJEFS 0.54607500101.943904.5000001-0.011878860 0.07627014 0.012650560-0.073416920-0.023965680-0.01003319 0.030615930-0.0002483342-0.04340587 0.042140530-0.023707780-0.041120410 0.047723010
3SM-CJEFU-0.82526371 81.938408.8333330-0.009894409-0.01305638 0.049875899 0.108320959-0.116313249 0.01894686-0.072541359 0.0430160410-0.05709961 0.026557430-0.022803826 0.005868232-0.028957413
4SM-CJEG1-1.30998260 81.325122.0000001-0.006808607-0.02436349 0.036853414-0.038215084-0.002585761 0.03118097-0.063388884 0.0175124050 0.02736089-0.005039676 0.011211747-0.031767847-0.011675806
5SM-CJEG3 0.22213670 91.553736.2500000 0.012294605 0.02106597-0.005452404 0.024195028 0.004051295 0.03228929 0.050129614-0.0156169510 0.08987513-0.061211599-0.002553259-0.258960234-0.009280432
6SM-CJEG7 0.33483150 88.369618.1666671-0.006441862 0.03414639-0.006961758 0.005733466 0.028457510 0.01953690-0.007155527 0.0405081860 0.04029579-0.087856525-0.077338595 0.095284697-0.131160245
\n" + ], + "text/latex": [ + "A data.frame: 6 × 19\n", + "\\begin{tabular}{r|lllllllllllllllllll}\n", + " & SID & APOC1\\_AC\\_exp & msex\\_AC\\_exp & age\\_death\\_AC\\_exp & pmi\\_AC\\_exp & ROS\\_study\\_AC\\_exp & PC1\\_AC\\_exp & PC2\\_AC\\_exp & PC3\\_AC\\_exp & PC4\\_AC\\_exp & PC5\\_AC\\_exp & PC6\\_AC\\_exp & PC7\\_AC\\_exp & PC8\\_AC\\_exp & PC9\\_AC\\_exp & PC10\\_AC\\_exp & PC11\\_AC\\_exp & PC12\\_AC\\_exp & PC13\\_AC\\_exp\\\\\n", + " & & & & & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & SM-CJEFR & -0.3655547 & 0 & 95.18412 & 5.000000 & 1 & -0.009288654 & 0.07222711 & 0.002891650 & -0.049175427 & -0.009331941 & 0.02367040 & 0.012981030 & -0.0012170090 & -0.07232593 & 0.010685649 & 0.022902699 & 0.053007585 & -0.032927999\\\\\n", + "\t2 & SM-CJEFS & 0.5460750 & 0 & 101.94390 & 4.500000 & 1 & -0.011878860 & 0.07627014 & 0.012650560 & -0.073416920 & -0.023965680 & -0.01003319 & 0.030615930 & -0.0002483342 & -0.04340587 & 0.042140530 & -0.023707780 & -0.041120410 & 0.047723010\\\\\n", + "\t3 & SM-CJEFU & -0.8252637 & 1 & 81.93840 & 8.833333 & 0 & -0.009894409 & -0.01305638 & 0.049875899 & 0.108320959 & -0.116313249 & 0.01894686 & -0.072541359 & 0.0430160410 & -0.05709961 & 0.026557430 & -0.022803826 & 0.005868232 & -0.028957413\\\\\n", + "\t4 & SM-CJEG1 & -1.3099826 & 0 & 81.32512 & 2.000000 & 1 & -0.006808607 & -0.02436349 & 0.036853414 & -0.038215084 & -0.002585761 & 0.03118097 & -0.063388884 & 0.0175124050 & 0.02736089 & -0.005039676 & 0.011211747 & -0.031767847 & -0.011675806\\\\\n", + "\t5 & SM-CJEG3 & 0.2221367 & 0 & 91.55373 & 6.250000 & 0 & 0.012294605 & 0.02106597 & -0.005452404 & 0.024195028 & 0.004051295 & 0.03228929 & 0.050129614 & -0.0156169510 & 0.08987513 & -0.061211599 & -0.002553259 & -0.258960234 & -0.009280432\\\\\n", + "\t6 & SM-CJEG7 & 0.3348315 & 0 & 88.36961 & 8.166667 & 1 & -0.006441862 & 0.03414639 & -0.006961758 & 0.005733466 & 0.028457510 & 0.01953690 & -0.007155527 & 0.0405081860 & 0.04029579 & -0.087856525 & -0.077338595 & 0.095284697 & -0.131160245\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 19\n", + "\n", + "| | SID <chr> | APOC1_AC_exp <dbl> | msex_AC_exp <dbl> | age_death_AC_exp <dbl> | pmi_AC_exp <dbl> | ROS_study_AC_exp <dbl> | PC1_AC_exp <dbl> | PC2_AC_exp <dbl> | PC3_AC_exp <dbl> | PC4_AC_exp <dbl> | PC5_AC_exp <dbl> | PC6_AC_exp <dbl> | PC7_AC_exp <dbl> | PC8_AC_exp <dbl> | PC9_AC_exp <dbl> | PC10_AC_exp <dbl> | PC11_AC_exp <dbl> | PC12_AC_exp <dbl> | PC13_AC_exp <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | SM-CJEFR | -0.3655547 | 0 | 95.18412 | 5.000000 | 1 | -0.009288654 | 0.07222711 | 0.002891650 | -0.049175427 | -0.009331941 | 0.02367040 | 0.012981030 | -0.0012170090 | -0.07232593 | 0.010685649 | 0.022902699 | 0.053007585 | -0.032927999 |\n", + "| 2 | SM-CJEFS | 0.5460750 | 0 | 101.94390 | 4.500000 | 1 | -0.011878860 | 0.07627014 | 0.012650560 | -0.073416920 | -0.023965680 | -0.01003319 | 0.030615930 | -0.0002483342 | -0.04340587 | 0.042140530 | -0.023707780 | -0.041120410 | 0.047723010 |\n", + "| 3 | SM-CJEFU | -0.8252637 | 1 | 81.93840 | 8.833333 | 0 | -0.009894409 | -0.01305638 | 0.049875899 | 0.108320959 | -0.116313249 | 0.01894686 | -0.072541359 | 0.0430160410 | -0.05709961 | 0.026557430 | -0.022803826 | 0.005868232 | -0.028957413 |\n", + "| 4 | SM-CJEG1 | -1.3099826 | 0 | 81.32512 | 2.000000 | 1 | -0.006808607 | -0.02436349 | 0.036853414 | -0.038215084 | -0.002585761 | 0.03118097 | -0.063388884 | 0.0175124050 | 0.02736089 | -0.005039676 | 0.011211747 | -0.031767847 | -0.011675806 |\n", + "| 5 | SM-CJEG3 | 0.2221367 | 0 | 91.55373 | 6.250000 | 0 | 0.012294605 | 0.02106597 | -0.005452404 | 0.024195028 | 0.004051295 | 0.03228929 | 0.050129614 | -0.0156169510 | 0.08987513 | -0.061211599 | -0.002553259 | -0.258960234 | -0.009280432 |\n", + "| 6 | SM-CJEG7 | 0.3348315 | 0 | 88.36961 | 8.166667 | 1 | -0.006441862 | 0.03414639 | -0.006961758 | 0.005733466 | 0.028457510 | 0.01953690 | -0.007155527 | 0.0405081860 | 0.04029579 | -0.087856525 | -0.077338595 | 0.095284697 | -0.131160245 |\n", + "\n" + ], + "text/plain": [ + " SID APOC1_AC_exp msex_AC_exp age_death_AC_exp pmi_AC_exp\n", + "1 SM-CJEFR -0.3655547 0 95.18412 5.000000 \n", + "2 SM-CJEFS 0.5460750 0 101.94390 4.500000 \n", + "3 SM-CJEFU -0.8252637 1 81.93840 8.833333 \n", + "4 SM-CJEG1 -1.3099826 0 81.32512 2.000000 \n", + "5 SM-CJEG3 0.2221367 0 91.55373 6.250000 \n", + "6 SM-CJEG7 0.3348315 0 88.36961 8.166667 \n", + " ROS_study_AC_exp PC1_AC_exp PC2_AC_exp PC3_AC_exp PC4_AC_exp \n", + "1 1 -0.009288654 0.07222711 0.002891650 -0.049175427\n", + "2 1 -0.011878860 0.07627014 0.012650560 -0.073416920\n", + "3 0 -0.009894409 -0.01305638 0.049875899 0.108320959\n", + "4 1 -0.006808607 -0.02436349 0.036853414 -0.038215084\n", + "5 0 0.012294605 0.02106597 -0.005452404 0.024195028\n", + "6 1 -0.006441862 0.03414639 -0.006961758 0.005733466\n", + " PC5_AC_exp PC6_AC_exp PC7_AC_exp PC8_AC_exp PC9_AC_exp PC10_AC_exp \n", + "1 -0.009331941 0.02367040 0.012981030 -0.0012170090 -0.07232593 0.010685649\n", + "2 -0.023965680 -0.01003319 0.030615930 -0.0002483342 -0.04340587 0.042140530\n", + "3 -0.116313249 0.01894686 -0.072541359 0.0430160410 -0.05709961 0.026557430\n", + "4 -0.002585761 0.03118097 -0.063388884 0.0175124050 0.02736089 -0.005039676\n", + "5 0.004051295 0.03228929 0.050129614 -0.0156169510 0.08987513 -0.061211599\n", + "6 0.028457510 0.01953690 -0.007155527 0.0405081860 0.04029579 -0.087856525\n", + " PC11_AC_exp PC12_AC_exp PC13_AC_exp \n", + "1 0.022902699 0.053007585 -0.032927999\n", + "2 -0.023707780 -0.041120410 0.047723010\n", + "3 -0.022803826 0.005868232 -0.028957413\n", + "4 0.011211747 -0.031767847 -0.011675806\n", + "5 -0.002553259 -0.258960234 -0.009280432\n", + "6 -0.077338595 0.095284697 -0.131160245" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "593" + ], + "text/latex": [ + "593" + ], + "text/markdown": [ + "593" + ], + "text/plain": [ + "[1] 593" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "693" + ], + "text/latex": [ + "693" + ], + "text/markdown": [ + "693" + ], + "text/plain": [ + "[1] 693" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "593" + ], + "text/latex": [ + "593" + ], + "text/markdown": [ + "593" + ], + "text/plain": [ + "[1] 593" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "AC_exp <- merge(AC_exp_pheno_sub, AC_exp_covar_t, by = 'SID')\n", + "head(AC_exp); nrow(AC_exp); nrow(AC_exp_pheno_sub); nrow(AC_exp_covar_t) " + ] + }, + { + "cell_type": "markdown", + "id": "d70b027d", + "metadata": {}, + "source": [ + "### Outcome data" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "449f6eb4", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "patho <- fread('/mnt/lustre/home/yl4437/xqtl_flagship/APOE/ROSMAP_geno_apoecs_adpathology.csv.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "id": "5d01cdf3", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "patho_sub <- patho %>% filter(cs_id == 'set_26') %>% dplyr::select(specimenID, age_first_ad_dx_num, educ, apoe4_dose, msex, age_death, pmi, ROS_study)\n", + "patho_sub <- unique(patho_sub)\n", + "colnames(patho_sub) <- c('SID', \"age_first_ad_dx_num\", \"educ\", \"apoe4_dose\", \"msex_p\", \"age_death_p\", \"pmi_p\", \"ROS_study_p\")" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "id": "ecf40f53", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.table: 6 × 8
SIDage_first_ad_dx_numeducapoe4_dosemsex_page_death_ppmi_pROS_study_p
<chr><dbl><int><int><int><dbl><dbl><int>
SM-CJFK8 NA1600100.873407.5000000
SM-CTDUU NA1610 80.657085.5000000
SM-CTDSC NA1200 83.690623.9333330
SM-CJIYB88.635181501 92.618755.0833330
SM-CTEMS89.722111200 90.384673.7833330
SM-CJFLR86.387411500 94.313485.0000000
\n" + ], + "text/latex": [ + "A data.table: 6 × 8\n", + "\\begin{tabular}{llllllll}\n", + " SID & age\\_first\\_ad\\_dx\\_num & educ & apoe4\\_dose & msex\\_p & age\\_death\\_p & pmi\\_p & ROS\\_study\\_p\\\\\n", + " & & & & & & & \\\\\n", + "\\hline\n", + "\t SM-CJFK8 & NA & 16 & 0 & 0 & 100.87340 & 7.500000 & 0\\\\\n", + "\t SM-CTDUU & NA & 16 & 1 & 0 & 80.65708 & 5.500000 & 0\\\\\n", + "\t SM-CTDSC & NA & 12 & 0 & 0 & 83.69062 & 3.933333 & 0\\\\\n", + "\t SM-CJIYB & 88.63518 & 15 & 0 & 1 & 92.61875 & 5.083333 & 0\\\\\n", + "\t SM-CTEMS & 89.72211 & 12 & 0 & 0 & 90.38467 & 3.783333 & 0\\\\\n", + "\t SM-CJFLR & 86.38741 & 15 & 0 & 0 & 94.31348 & 5.000000 & 0\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 6 × 8\n", + "\n", + "| SID <chr> | age_first_ad_dx_num <dbl> | educ <int> | apoe4_dose <int> | msex_p <int> | age_death_p <dbl> | pmi_p <dbl> | ROS_study_p <int> |\n", + "|---|---|---|---|---|---|---|---|\n", + "| SM-CJFK8 | NA | 16 | 0 | 0 | 100.87340 | 7.500000 | 0 |\n", + "| SM-CTDUU | NA | 16 | 1 | 0 | 80.65708 | 5.500000 | 0 |\n", + "| SM-CTDSC | NA | 12 | 0 | 0 | 83.69062 | 3.933333 | 0 |\n", + "| SM-CJIYB | 88.63518 | 15 | 0 | 1 | 92.61875 | 5.083333 | 0 |\n", + "| SM-CTEMS | 89.72211 | 12 | 0 | 0 | 90.38467 | 3.783333 | 0 |\n", + "| SM-CJFLR | 86.38741 | 15 | 0 | 0 | 94.31348 | 5.000000 | 0 |\n", + "\n" + ], + "text/plain": [ + " SID age_first_ad_dx_num educ apoe4_dose msex_p age_death_p pmi_p \n", + "1 SM-CJFK8 NA 16 0 0 100.87340 7.500000\n", + "2 SM-CTDUU NA 16 1 0 80.65708 5.500000\n", + "3 SM-CTDSC NA 12 0 0 83.69062 3.933333\n", + "4 SM-CJIYB 88.63518 15 0 1 92.61875 5.083333\n", + "5 SM-CTEMS 89.72211 12 0 0 90.38467 3.783333\n", + "6 SM-CJFLR 86.38741 15 0 0 94.31348 5.000000\n", + " ROS_study_p\n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "5 0 \n", + "6 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1113" + ], + "text/latex": [ + "1113" + ], + "text/markdown": [ + "1113" + ], + "text/plain": [ + "[1] 1113" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(patho_sub); nrow(patho_sub)" + ] + }, + { + "cell_type": "markdown", + "id": "521d8d7b", + "metadata": {}, + "source": [ + "### Merge the input for SEM" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "951fa630", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'SID'
  2. 'chr19_44918487_G_T'
  3. 'APOC1_DLPFC_exp'
  4. 'msex_DLPFC_exp'
  5. 'age_death_DLPFC_exp'
  6. 'pmi_DLPFC_exp'
  7. 'ROS_study_DLPFC_exp'
  8. 'PC1_DLPFC_exp'
  9. 'PC2_DLPFC_exp'
  10. 'PC3_DLPFC_exp'
  11. 'PC4_DLPFC_exp'
  12. 'PC5_DLPFC_exp'
  13. 'PC6_DLPFC_exp'
  14. 'PC7_DLPFC_exp'
  15. 'PC8_DLPFC_exp'
  16. 'PC9_DLPFC_exp'
  17. 'PC10_DLPFC_exp'
  18. 'PC11_DLPFC_exp'
  19. 'PC12_DLPFC_exp'
  20. 'APOC1_AC_exp'
  21. 'msex_AC_exp'
  22. 'age_death_AC_exp'
  23. 'pmi_AC_exp'
  24. 'ROS_study_AC_exp'
  25. 'PC1_AC_exp'
  26. 'PC2_AC_exp'
  27. 'PC3_AC_exp'
  28. 'PC4_AC_exp'
  29. 'PC5_AC_exp'
  30. 'PC6_AC_exp'
  31. 'PC7_AC_exp'
  32. 'PC8_AC_exp'
  33. 'PC9_AC_exp'
  34. 'PC10_AC_exp'
  35. 'PC11_AC_exp'
  36. 'PC12_AC_exp'
  37. 'PC13_AC_exp'
  38. 'age_first_ad_dx_num'
  39. 'educ'
  40. 'apoe4_dose'
  41. 'msex_p'
  42. 'age_death_p'
  43. 'pmi_p'
  44. 'ROS_study_p'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'SID'\n", + "\\item 'chr19\\_44918487\\_G\\_T'\n", + "\\item 'APOC1\\_DLPFC\\_exp'\n", + "\\item 'msex\\_DLPFC\\_exp'\n", + "\\item 'age\\_death\\_DLPFC\\_exp'\n", + "\\item 'pmi\\_DLPFC\\_exp'\n", + "\\item 'ROS\\_study\\_DLPFC\\_exp'\n", + "\\item 'PC1\\_DLPFC\\_exp'\n", + "\\item 'PC2\\_DLPFC\\_exp'\n", + "\\item 'PC3\\_DLPFC\\_exp'\n", + "\\item 'PC4\\_DLPFC\\_exp'\n", + "\\item 'PC5\\_DLPFC\\_exp'\n", + "\\item 'PC6\\_DLPFC\\_exp'\n", + "\\item 'PC7\\_DLPFC\\_exp'\n", + "\\item 'PC8\\_DLPFC\\_exp'\n", + "\\item 'PC9\\_DLPFC\\_exp'\n", + "\\item 'PC10\\_DLPFC\\_exp'\n", + "\\item 'PC11\\_DLPFC\\_exp'\n", + "\\item 'PC12\\_DLPFC\\_exp'\n", + "\\item 'APOC1\\_AC\\_exp'\n", + "\\item 'msex\\_AC\\_exp'\n", + "\\item 'age\\_death\\_AC\\_exp'\n", + "\\item 'pmi\\_AC\\_exp'\n", + "\\item 'ROS\\_study\\_AC\\_exp'\n", + "\\item 'PC1\\_AC\\_exp'\n", + "\\item 'PC2\\_AC\\_exp'\n", + "\\item 'PC3\\_AC\\_exp'\n", + "\\item 'PC4\\_AC\\_exp'\n", + "\\item 'PC5\\_AC\\_exp'\n", + "\\item 'PC6\\_AC\\_exp'\n", + "\\item 'PC7\\_AC\\_exp'\n", + "\\item 'PC8\\_AC\\_exp'\n", + "\\item 'PC9\\_AC\\_exp'\n", + "\\item 'PC10\\_AC\\_exp'\n", + "\\item 'PC11\\_AC\\_exp'\n", + "\\item 'PC12\\_AC\\_exp'\n", + "\\item 'PC13\\_AC\\_exp'\n", + "\\item 'age\\_first\\_ad\\_dx\\_num'\n", + "\\item 'educ'\n", + "\\item 'apoe4\\_dose'\n", + "\\item 'msex\\_p'\n", + "\\item 'age\\_death\\_p'\n", + "\\item 'pmi\\_p'\n", + "\\item 'ROS\\_study\\_p'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'SID'\n", + "2. 'chr19_44918487_G_T'\n", + "3. 'APOC1_DLPFC_exp'\n", + "4. 'msex_DLPFC_exp'\n", + "5. 'age_death_DLPFC_exp'\n", + "6. 'pmi_DLPFC_exp'\n", + "7. 'ROS_study_DLPFC_exp'\n", + "8. 'PC1_DLPFC_exp'\n", + "9. 'PC2_DLPFC_exp'\n", + "10. 'PC3_DLPFC_exp'\n", + "11. 'PC4_DLPFC_exp'\n", + "12. 'PC5_DLPFC_exp'\n", + "13. 'PC6_DLPFC_exp'\n", + "14. 'PC7_DLPFC_exp'\n", + "15. 'PC8_DLPFC_exp'\n", + "16. 'PC9_DLPFC_exp'\n", + "17. 'PC10_DLPFC_exp'\n", + "18. 'PC11_DLPFC_exp'\n", + "19. 'PC12_DLPFC_exp'\n", + "20. 'APOC1_AC_exp'\n", + "21. 'msex_AC_exp'\n", + "22. 'age_death_AC_exp'\n", + "23. 'pmi_AC_exp'\n", + "24. 'ROS_study_AC_exp'\n", + "25. 'PC1_AC_exp'\n", + "26. 'PC2_AC_exp'\n", + "27. 'PC3_AC_exp'\n", + "28. 'PC4_AC_exp'\n", + "29. 'PC5_AC_exp'\n", + "30. 'PC6_AC_exp'\n", + "31. 'PC7_AC_exp'\n", + "32. 'PC8_AC_exp'\n", + "33. 'PC9_AC_exp'\n", + "34. 'PC10_AC_exp'\n", + "35. 'PC11_AC_exp'\n", + "36. 'PC12_AC_exp'\n", + "37. 'PC13_AC_exp'\n", + "38. 'age_first_ad_dx_num'\n", + "39. 'educ'\n", + "40. 'apoe4_dose'\n", + "41. 'msex_p'\n", + "42. 'age_death_p'\n", + "43. 'pmi_p'\n", + "44. 'ROS_study_p'\n", + "\n", + "\n" + ], + "text/plain": [ + " [1] \"SID\" \"chr19_44918487_G_T\" \"APOC1_DLPFC_exp\" \n", + " [4] \"msex_DLPFC_exp\" \"age_death_DLPFC_exp\" \"pmi_DLPFC_exp\" \n", + " [7] \"ROS_study_DLPFC_exp\" \"PC1_DLPFC_exp\" \"PC2_DLPFC_exp\" \n", + "[10] \"PC3_DLPFC_exp\" \"PC4_DLPFC_exp\" \"PC5_DLPFC_exp\" \n", + "[13] \"PC6_DLPFC_exp\" \"PC7_DLPFC_exp\" \"PC8_DLPFC_exp\" \n", + "[16] \"PC9_DLPFC_exp\" \"PC10_DLPFC_exp\" \"PC11_DLPFC_exp\" \n", + "[19] \"PC12_DLPFC_exp\" \"APOC1_AC_exp\" \"msex_AC_exp\" \n", + "[22] \"age_death_AC_exp\" \"pmi_AC_exp\" \"ROS_study_AC_exp\" \n", + "[25] \"PC1_AC_exp\" \"PC2_AC_exp\" \"PC3_AC_exp\" \n", + "[28] \"PC4_AC_exp\" \"PC5_AC_exp\" \"PC6_AC_exp\" \n", + "[31] \"PC7_AC_exp\" \"PC8_AC_exp\" \"PC9_AC_exp\" \n", + "[34] \"PC10_AC_exp\" \"PC11_AC_exp\" \"PC12_AC_exp\" \n", + "[37] \"PC13_AC_exp\" \"age_first_ad_dx_num\" \"educ\" \n", + "[40] \"apoe4_dose\" \"msex_p\" \"age_death_p\" \n", + "[43] \"pmi_p\" \"ROS_study_p\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Partially overlapping samples for FIML-SEM mediation analysis\n", + "input_all <- merge(geno_44918487, DLPFC_exp, by = \"SID\", all = TRUE)\n", + "input_all <- merge(input_all, AC_exp, by = \"SID\", all = TRUE)\n", + "input_all <- merge(input_all, patho_sub, by = \"SID\", all = TRUE)\n", + "colnames(input_all); nrow(input_all)" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "id": "f78aeda1", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'SID'
  2. 'chr19_44918487_G_T'
  3. 'APOC1_DLPFC_exp'
  4. 'PC1_DLPFC_exp'
  5. 'PC2_DLPFC_exp'
  6. 'PC3_DLPFC_exp'
  7. 'PC4_DLPFC_exp'
  8. 'PC5_DLPFC_exp'
  9. 'PC6_DLPFC_exp'
  10. 'PC7_DLPFC_exp'
  11. 'PC8_DLPFC_exp'
  12. 'PC9_DLPFC_exp'
  13. 'PC10_DLPFC_exp'
  14. 'PC11_DLPFC_exp'
  15. 'PC12_DLPFC_exp'
  16. 'APOC1_AC_exp'
  17. 'PC1_AC_exp'
  18. 'PC2_AC_exp'
  19. 'PC3_AC_exp'
  20. 'PC4_AC_exp'
  21. 'PC5_AC_exp'
  22. 'PC6_AC_exp'
  23. 'PC7_AC_exp'
  24. 'PC8_AC_exp'
  25. 'PC9_AC_exp'
  26. 'PC10_AC_exp'
  27. 'PC11_AC_exp'
  28. 'PC12_AC_exp'
  29. 'PC13_AC_exp'
  30. 'age_first_ad_dx_num'
  31. 'educ'
  32. 'apoe4_dose'
  33. 'msex_u'
  34. 'age_death_u'
  35. 'pmi_u'
  36. 'ROS_study_u'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'SID'\n", + "\\item 'chr19\\_44918487\\_G\\_T'\n", + "\\item 'APOC1\\_DLPFC\\_exp'\n", + "\\item 'PC1\\_DLPFC\\_exp'\n", + "\\item 'PC2\\_DLPFC\\_exp'\n", + "\\item 'PC3\\_DLPFC\\_exp'\n", + "\\item 'PC4\\_DLPFC\\_exp'\n", + "\\item 'PC5\\_DLPFC\\_exp'\n", + "\\item 'PC6\\_DLPFC\\_exp'\n", + "\\item 'PC7\\_DLPFC\\_exp'\n", + "\\item 'PC8\\_DLPFC\\_exp'\n", + "\\item 'PC9\\_DLPFC\\_exp'\n", + "\\item 'PC10\\_DLPFC\\_exp'\n", + "\\item 'PC11\\_DLPFC\\_exp'\n", + "\\item 'PC12\\_DLPFC\\_exp'\n", + "\\item 'APOC1\\_AC\\_exp'\n", + "\\item 'PC1\\_AC\\_exp'\n", + "\\item 'PC2\\_AC\\_exp'\n", + "\\item 'PC3\\_AC\\_exp'\n", + "\\item 'PC4\\_AC\\_exp'\n", + "\\item 'PC5\\_AC\\_exp'\n", + "\\item 'PC6\\_AC\\_exp'\n", + "\\item 'PC7\\_AC\\_exp'\n", + "\\item 'PC8\\_AC\\_exp'\n", + "\\item 'PC9\\_AC\\_exp'\n", + "\\item 'PC10\\_AC\\_exp'\n", + "\\item 'PC11\\_AC\\_exp'\n", + "\\item 'PC12\\_AC\\_exp'\n", + "\\item 'PC13\\_AC\\_exp'\n", + "\\item 'age\\_first\\_ad\\_dx\\_num'\n", + "\\item 'educ'\n", + "\\item 'apoe4\\_dose'\n", + "\\item 'msex\\_u'\n", + "\\item 'age\\_death\\_u'\n", + "\\item 'pmi\\_u'\n", + "\\item 'ROS\\_study\\_u'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'SID'\n", + "2. 'chr19_44918487_G_T'\n", + "3. 'APOC1_DLPFC_exp'\n", + "4. 'PC1_DLPFC_exp'\n", + "5. 'PC2_DLPFC_exp'\n", + "6. 'PC3_DLPFC_exp'\n", + "7. 'PC4_DLPFC_exp'\n", + "8. 'PC5_DLPFC_exp'\n", + "9. 'PC6_DLPFC_exp'\n", + "10. 'PC7_DLPFC_exp'\n", + "11. 'PC8_DLPFC_exp'\n", + "12. 'PC9_DLPFC_exp'\n", + "13. 'PC10_DLPFC_exp'\n", + "14. 'PC11_DLPFC_exp'\n", + "15. 'PC12_DLPFC_exp'\n", + "16. 'APOC1_AC_exp'\n", + "17. 'PC1_AC_exp'\n", + "18. 'PC2_AC_exp'\n", + "19. 'PC3_AC_exp'\n", + "20. 'PC4_AC_exp'\n", + "21. 'PC5_AC_exp'\n", + "22. 'PC6_AC_exp'\n", + "23. 'PC7_AC_exp'\n", + "24. 'PC8_AC_exp'\n", + "25. 'PC9_AC_exp'\n", + "26. 'PC10_AC_exp'\n", + "27. 'PC11_AC_exp'\n", + "28. 'PC12_AC_exp'\n", + "29. 'PC13_AC_exp'\n", + "30. 'age_first_ad_dx_num'\n", + "31. 'educ'\n", + "32. 'apoe4_dose'\n", + "33. 'msex_u'\n", + "34. 'age_death_u'\n", + "35. 'pmi_u'\n", + "36. 'ROS_study_u'\n", + "\n", + "\n" + ], + "text/plain": [ + " [1] \"SID\" \"chr19_44918487_G_T\" \"APOC1_DLPFC_exp\" \n", + " [4] \"PC1_DLPFC_exp\" \"PC2_DLPFC_exp\" \"PC3_DLPFC_exp\" \n", + " [7] \"PC4_DLPFC_exp\" \"PC5_DLPFC_exp\" \"PC6_DLPFC_exp\" \n", + "[10] \"PC7_DLPFC_exp\" \"PC8_DLPFC_exp\" \"PC9_DLPFC_exp\" \n", + "[13] \"PC10_DLPFC_exp\" \"PC11_DLPFC_exp\" \"PC12_DLPFC_exp\" \n", + "[16] \"APOC1_AC_exp\" \"PC1_AC_exp\" \"PC2_AC_exp\" \n", + "[19] \"PC3_AC_exp\" \"PC4_AC_exp\" \"PC5_AC_exp\" \n", + "[22] \"PC6_AC_exp\" \"PC7_AC_exp\" \"PC8_AC_exp\" \n", + "[25] \"PC9_AC_exp\" \"PC10_AC_exp\" \"PC11_AC_exp\" \n", + "[28] \"PC12_AC_exp\" \"PC13_AC_exp\" \"age_first_ad_dx_num\"\n", + "[31] \"educ\" \"apoe4_dose\" \"msex_u\" \n", + "[34] \"age_death_u\" \"pmi_u\" \"ROS_study_u\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create unified covariates — msex/age_death/pmi/ROS_study\n", + "input_all <- input_all %>%\n", + " mutate(msex_u = coalesce(msex_DLPFC_exp, msex_AC_exp, msex_p)) %>%\n", + " mutate(age_death_u = coalesce(age_death_DLPFC_exp, age_death_AC_exp, age_death_p)) %>%\n", + " mutate(pmi_u = coalesce(pmi_DLPFC_exp, pmi_AC_exp, pmi_p)) %>%\n", + " mutate(ROS_study_u = coalesce(ROS_study_DLPFC_exp, ROS_study_AC_exp, ROS_study_p))\n", + "\n", + "input_all_out <- input_all %>% dplyr::select(-c(msex_DLPFC_exp, msex_AC_exp, msex_p, age_death_DLPFC_exp, age_death_AC_exp, age_death_p,\n", + " pmi_DLPFC_exp, pmi_AC_exp, pmi_p, ROS_study_DLPFC_exp, ROS_study_AC_exp, ROS_study_p))\n", + "colnames(input_all_out); nrow(input_all_out)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "id": "a3e8155c", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "write.table(input_all_out, file = '/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set26/APOE_ind_set_26_mediation_all_input.txt',\n", + " quote = FALSE, sep = '\\t', col.names = TRUE, row.names = FALSE)" + ] + }, + { + "cell_type": "markdown", + "id": "f8b04d06", + "metadata": {}, + "source": [ + "## 2. Prepare input for set 31" + ] + }, + { + "cell_type": "markdown", + "id": "759cb210", + "metadata": {}, + "source": [ + "### Genotype data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ab5ff9fa", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.table: 6 × 2
SIDchr19_45114770_ACCC_AC
<chr><int>
MAP153874210
MAP266378670
MAP296298490
MAP333326460
MAP347260400
MAP462466040
\n" + ], + "text/latex": [ + "A data.table: 6 × 2\n", + "\\begin{tabular}{ll}\n", + " SID & chr19\\_45114770\\_ACCC\\_AC\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t MAP15387421 & 0\\\\\n", + "\t MAP26637867 & 0\\\\\n", + "\t MAP29629849 & 0\\\\\n", + "\t MAP33332646 & 0\\\\\n", + "\t MAP34726040 & 0\\\\\n", + "\t MAP46246604 & 0\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 6 × 2\n", + "\n", + "| SID <chr> | chr19_45114770_ACCC_AC <int> |\n", + "|---|---|\n", + "| MAP15387421 | 0 |\n", + "| MAP26637867 | 0 |\n", + "| MAP29629849 | 0 |\n", + "| MAP33332646 | 0 |\n", + "| MAP34726040 | 0 |\n", + "| MAP46246604 | 0 |\n", + "\n" + ], + "text/plain": [ + " SID chr19_45114770_ACCC_AC\n", + "1 MAP15387421 0 \n", + "2 MAP26637867 0 \n", + "3 MAP29629849 0 \n", + "4 MAP33332646 0 \n", + "5 MAP34726040 0 \n", + "6 MAP46246604 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geno_45114770 <- geno %>% filter(ID == 'chr19:45114770_ACCC_AC') %>% dplyr::select(SID, dose)\n", + "colnames(geno_45114770)[2] <- 'chr19_45114770_ACCC_AC'\n", + "geno_45114770$SID <- str_remove(geno_45114770$SID,'0_')\n", + "head(geno_45114770); nrow(geno_45114770)" + ] + }, + { + "cell_type": "markdown", + "id": "13080423", + "metadata": {}, + "source": [ + "### Mediator data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92c625ef", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# DLPFC_exp_pheno <- fread('/mnt/lustre/lab/gwang/ftp_fgc_xqtl/eQTL/ROSMAP/DLPFC/analysis_ready/phenotype_preprocessing/dlpfc_batch_all.rnaseqc.low_expression_filtered.outlier_removed.tmm.expression.bed.chr19.mol_phe.bed.gz')\n", + "# DLPFC_exp_covar <- fread('/mnt/lustre/lab/gwang/ftp_fgc_xqtl/eQTL/ROSMAP/DLPFC/analysis_ready/covariate_preprocessing/DLPFC_samples_list.rnaseqc.gene_tpm.low_expression_filtered.outlier_removed.tmm.expression.ROSMAP_xqtl_complete_samples_covariates_sex_death_pmi_study_transpose.ROSMAP_NIA_WGS.leftnorm.bcftools_qc.plink_qc.unrelated.plink_qc.prune.pca.Marchenko_PC.gz')\n", + "# AC_exp_pheno <- fread('/mnt/lustre/lab/gwang/ftp_fgc_xqtl/eQTL/ROSMAP/AC/analysis_ready/phenotype_preprocessing/phenotype_by_chrom/AC_samples_list.rnaseqc.gene_tpm.low_expression_filtered.outlier_removed.tmm.expression.bed.chr19.bed.gz')\n", + "# AC_exp_covar <- fread('/mnt/lustre/lab/gwang/ftp_fgc_xqtl/eQTL/ROSMAP/AC/analysis_ready/covariate_preprocessing/AC_samples_list.rnaseqc.gene_tpm.low_expression_filtered.outlier_removed.tmm.expression.ROSMAP_xqtl_complete_samples_covariates_sex_death_pmi_study_transpose.ROSMAP_NIA_WGS.leftnorm.bcftools_qc.plink_qc.unrelated.plink_qc.prune.pca.Marchenko_PC.gz')\n", + "PCC_exp_pheno <- fread('/mnt/lustre/lab/gwang/ftp_fgc_xqtl/eQTL/ROSMAP/PCC/analysis_ready/phenotype_preprocessing/phenotype_by_chrom/PCC_samples_list.rnaseqc.gene_tpm.low_expression_filtered.outlier_removed.tmm.expression.bed.chr19.bed.gz')\n", + "PCC_exp_covar <- fread('/mnt/lustre/lab/gwang/ftp_fgc_xqtl/eQTL/ROSMAP/PCC/analysis_ready/covariate_preprocessing/PCC_samples_list.rnaseqc.gene_tpm.low_expression_filtered.outlier_removed.tmm.expression.ROSMAP_xqtl_complete_samples_covariates_sex_death_pmi_study_transpose.ROSMAP_NIA_WGS.leftnorm.bcftools_qc.plink_qc.unrelated.plink_qc.prune.pca.Marchenko_PC.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b033214f", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 2
SIDBLOC1S3_DLPFC_exp
<chr><dbl>
1SM-CTEFX 0.53348879
2SM-CJIWX 0.08570771
303_120405 0.47368185
404_120405-0.22353732
5SM-CJIXW 0.68972399
607_120410 1.52968559
\n" + ], + "text/latex": [ + "A data.frame: 6 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & SID & BLOC1S3\\_DLPFC\\_exp\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & SM-CTEFX & 0.53348879\\\\\n", + "\t2 & SM-CJIWX & 0.08570771\\\\\n", + "\t3 & 03\\_120405 & 0.47368185\\\\\n", + "\t4 & 04\\_120405 & -0.22353732\\\\\n", + "\t5 & SM-CJIXW & 0.68972399\\\\\n", + "\t6 & 07\\_120410 & 1.52968559\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 2\n", + "\n", + "| | SID <chr> | BLOC1S3_DLPFC_exp <dbl> |\n", + "|---|---|---|\n", + "| 1 | SM-CTEFX | 0.53348879 |\n", + "| 2 | SM-CJIWX | 0.08570771 |\n", + "| 3 | 03_120405 | 0.47368185 |\n", + "| 4 | 04_120405 | -0.22353732 |\n", + "| 5 | SM-CJIXW | 0.68972399 |\n", + "| 6 | 07_120410 | 1.52968559 |\n", + "\n" + ], + "text/plain": [ + " SID BLOC1S3_DLPFC_exp\n", + "1 SM-CTEFX 0.53348879 \n", + "2 SM-CJIWX 0.08570771 \n", + "3 03_120405 0.47368185 \n", + "4 04_120405 -0.22353732 \n", + "5 SM-CJIXW 0.68972399 \n", + "6 07_120410 1.52968559 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "DLPFC_exp_pheno_sub <- DLPFC_exp_pheno %>% filter(gene_id == 'ENSG00000189114') %>% \n", + " dplyr::select(-`#chr`, -start, -end, -gene_id) %>%\n", + " t() %>%\n", + " as.data.frame(stringsAsFactors = FALSE) %>%\n", + " rownames_to_column(\"SID\") %>% \n", + " mutate(across(-SID, as.numeric))\n", + "\n", + "colnames(DLPFC_exp_pheno_sub)[2] <- 'BLOC1S3_DLPFC_exp'\n", + "head(DLPFC_exp_pheno_sub)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "75eb08dd", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 18
SIDBLOC1S3_DLPFC_expmsex_DLPFC_expage_death_DLPFC_exppmi_DLPFC_expROS_study_DLPFC_expPC1_DLPFC_expPC2_DLPFC_expPC3_DLPFC_expPC4_DLPFC_expPC5_DLPFC_expPC6_DLPFC_expPC7_DLPFC_expPC8_DLPFC_expPC9_DLPFC_expPC10_DLPFC_expPC11_DLPFC_expPC12_DLPFC_exp
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1SM-CJEFQ-0.054901221 85.826153.2500000-0.004302403-0.01222881-0.002536536 0.01595026-0.01160160-0.01889253900.042032741-0.007465418 0.029123271 0.029619128 0.021662642-0.020592877
2SM-CJEFR 0.761120770 95.184125.0000001-0.007027830-0.06427368-0.006788190-0.04463996-0.02534136-0.00474419900.008329738-0.012461613-0.033234464-0.028106255-0.001766579 0.020196593
3SM-CJEFS 0.954649820101.943874.5000001-0.009688911-0.07511777-0.013614288-0.07057625-0.01217562 0.03593179200.004934510-0.007277167 0.023746249 0.044585788-0.006362661 0.045385102
4SM-CJEFU 0.483527071 81.938408.8333330-0.008678988 0.01030657-0.036951274 0.08630428 0.01070526 0.11178369000.047131621 0.046753381-0.065000877-0.007433710-0.064559876 0.006876538
5SM-CJEFX 0.561538010 87.846684.2166671-0.002660709-0.02199336 0.008628434-0.02679754-0.01556061-0.02522736300.012658205 0.003777747-0.006815708-0.006335293-0.020766429 0.025645201
6SM-CJEG1 1.282550110 81.325122.0000001-0.005346334 0.02597680-0.030165860-0.03856278-0.02216014-0.00017726370.044046290 0.037845550-0.007722891-0.011441090-0.042836990 0.026212520
\n" + ], + "text/latex": [ + "A data.frame: 6 × 18\n", + "\\begin{tabular}{r|llllllllllllllllll}\n", + " & SID & BLOC1S3\\_DLPFC\\_exp & msex\\_DLPFC\\_exp & age\\_death\\_DLPFC\\_exp & pmi\\_DLPFC\\_exp & ROS\\_study\\_DLPFC\\_exp & PC1\\_DLPFC\\_exp & PC2\\_DLPFC\\_exp & PC3\\_DLPFC\\_exp & PC4\\_DLPFC\\_exp & PC5\\_DLPFC\\_exp & PC6\\_DLPFC\\_exp & PC7\\_DLPFC\\_exp & PC8\\_DLPFC\\_exp & PC9\\_DLPFC\\_exp & PC10\\_DLPFC\\_exp & PC11\\_DLPFC\\_exp & PC12\\_DLPFC\\_exp\\\\\n", + " & & & & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & SM-CJEFQ & -0.05490122 & 1 & 85.82615 & 3.250000 & 0 & -0.004302403 & -0.01222881 & -0.002536536 & 0.01595026 & -0.01160160 & -0.0188925390 & 0.042032741 & -0.007465418 & 0.029123271 & 0.029619128 & 0.021662642 & -0.020592877\\\\\n", + "\t2 & SM-CJEFR & 0.76112077 & 0 & 95.18412 & 5.000000 & 1 & -0.007027830 & -0.06427368 & -0.006788190 & -0.04463996 & -0.02534136 & -0.0047441990 & 0.008329738 & -0.012461613 & -0.033234464 & -0.028106255 & -0.001766579 & 0.020196593\\\\\n", + "\t3 & SM-CJEFS & 0.95464982 & 0 & 101.94387 & 4.500000 & 1 & -0.009688911 & -0.07511777 & -0.013614288 & -0.07057625 & -0.01217562 & 0.0359317920 & 0.004934510 & -0.007277167 & 0.023746249 & 0.044585788 & -0.006362661 & 0.045385102\\\\\n", + "\t4 & SM-CJEFU & 0.48352707 & 1 & 81.93840 & 8.833333 & 0 & -0.008678988 & 0.01030657 & -0.036951274 & 0.08630428 & 0.01070526 & 0.1117836900 & 0.047131621 & 0.046753381 & -0.065000877 & -0.007433710 & -0.064559876 & 0.006876538\\\\\n", + "\t5 & SM-CJEFX & 0.56153801 & 0 & 87.84668 & 4.216667 & 1 & -0.002660709 & -0.02199336 & 0.008628434 & -0.02679754 & -0.01556061 & -0.0252273630 & 0.012658205 & 0.003777747 & -0.006815708 & -0.006335293 & -0.020766429 & 0.025645201\\\\\n", + "\t6 & SM-CJEG1 & 1.28255011 & 0 & 81.32512 & 2.000000 & 1 & -0.005346334 & 0.02597680 & -0.030165860 & -0.03856278 & -0.02216014 & -0.0001772637 & 0.044046290 & 0.037845550 & -0.007722891 & -0.011441090 & -0.042836990 & 0.026212520\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 18\n", + "\n", + "| | SID <chr> | BLOC1S3_DLPFC_exp <dbl> | msex_DLPFC_exp <dbl> | age_death_DLPFC_exp <dbl> | pmi_DLPFC_exp <dbl> | ROS_study_DLPFC_exp <dbl> | PC1_DLPFC_exp <dbl> | PC2_DLPFC_exp <dbl> | PC3_DLPFC_exp <dbl> | PC4_DLPFC_exp <dbl> | PC5_DLPFC_exp <dbl> | PC6_DLPFC_exp <dbl> | PC7_DLPFC_exp <dbl> | PC8_DLPFC_exp <dbl> | PC9_DLPFC_exp <dbl> | PC10_DLPFC_exp <dbl> | PC11_DLPFC_exp <dbl> | PC12_DLPFC_exp <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | SM-CJEFQ | -0.05490122 | 1 | 85.82615 | 3.250000 | 0 | -0.004302403 | -0.01222881 | -0.002536536 | 0.01595026 | -0.01160160 | -0.0188925390 | 0.042032741 | -0.007465418 | 0.029123271 | 0.029619128 | 0.021662642 | -0.020592877 |\n", + "| 2 | SM-CJEFR | 0.76112077 | 0 | 95.18412 | 5.000000 | 1 | -0.007027830 | -0.06427368 | -0.006788190 | -0.04463996 | -0.02534136 | -0.0047441990 | 0.008329738 | -0.012461613 | -0.033234464 | -0.028106255 | -0.001766579 | 0.020196593 |\n", + "| 3 | SM-CJEFS | 0.95464982 | 0 | 101.94387 | 4.500000 | 1 | -0.009688911 | -0.07511777 | -0.013614288 | -0.07057625 | -0.01217562 | 0.0359317920 | 0.004934510 | -0.007277167 | 0.023746249 | 0.044585788 | -0.006362661 | 0.045385102 |\n", + "| 4 | SM-CJEFU | 0.48352707 | 1 | 81.93840 | 8.833333 | 0 | -0.008678988 | 0.01030657 | -0.036951274 | 0.08630428 | 0.01070526 | 0.1117836900 | 0.047131621 | 0.046753381 | -0.065000877 | -0.007433710 | -0.064559876 | 0.006876538 |\n", + "| 5 | SM-CJEFX | 0.56153801 | 0 | 87.84668 | 4.216667 | 1 | -0.002660709 | -0.02199336 | 0.008628434 | -0.02679754 | -0.01556061 | -0.0252273630 | 0.012658205 | 0.003777747 | -0.006815708 | -0.006335293 | -0.020766429 | 0.025645201 |\n", + "| 6 | SM-CJEG1 | 1.28255011 | 0 | 81.32512 | 2.000000 | 1 | -0.005346334 | 0.02597680 | -0.030165860 | -0.03856278 | -0.02216014 | -0.0001772637 | 0.044046290 | 0.037845550 | -0.007722891 | -0.011441090 | -0.042836990 | 0.026212520 |\n", + "\n" + ], + "text/plain": [ + " SID BLOC1S3_DLPFC_exp msex_DLPFC_exp age_death_DLPFC_exp pmi_DLPFC_exp\n", + "1 SM-CJEFQ -0.05490122 1 85.82615 3.250000 \n", + "2 SM-CJEFR 0.76112077 0 95.18412 5.000000 \n", + "3 SM-CJEFS 0.95464982 0 101.94387 4.500000 \n", + "4 SM-CJEFU 0.48352707 1 81.93840 8.833333 \n", + "5 SM-CJEFX 0.56153801 0 87.84668 4.216667 \n", + "6 SM-CJEG1 1.28255011 0 81.32512 2.000000 \n", + " ROS_study_DLPFC_exp PC1_DLPFC_exp PC2_DLPFC_exp PC3_DLPFC_exp PC4_DLPFC_exp\n", + "1 0 -0.004302403 -0.01222881 -0.002536536 0.01595026 \n", + "2 1 -0.007027830 -0.06427368 -0.006788190 -0.04463996 \n", + "3 1 -0.009688911 -0.07511777 -0.013614288 -0.07057625 \n", + "4 0 -0.008678988 0.01030657 -0.036951274 0.08630428 \n", + "5 1 -0.002660709 -0.02199336 0.008628434 -0.02679754 \n", + "6 1 -0.005346334 0.02597680 -0.030165860 -0.03856278 \n", + " PC5_DLPFC_exp PC6_DLPFC_exp PC7_DLPFC_exp PC8_DLPFC_exp PC9_DLPFC_exp\n", + "1 -0.01160160 -0.0188925390 0.042032741 -0.007465418 0.029123271 \n", + "2 -0.02534136 -0.0047441990 0.008329738 -0.012461613 -0.033234464 \n", + "3 -0.01217562 0.0359317920 0.004934510 -0.007277167 0.023746249 \n", + "4 0.01070526 0.1117836900 0.047131621 0.046753381 -0.065000877 \n", + "5 -0.01556061 -0.0252273630 0.012658205 0.003777747 -0.006815708 \n", + "6 -0.02216014 -0.0001772637 0.044046290 0.037845550 -0.007722891 \n", + " PC10_DLPFC_exp PC11_DLPFC_exp PC12_DLPFC_exp\n", + "1 0.029619128 0.021662642 -0.020592877 \n", + "2 -0.028106255 -0.001766579 0.020196593 \n", + "3 0.044585788 -0.006362661 0.045385102 \n", + "4 -0.007433710 -0.064559876 0.006876538 \n", + "5 -0.006335293 -0.020766429 0.025645201 \n", + "6 -0.011441090 -0.042836990 0.026212520 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "777" + ], + "text/latex": [ + "777" + ], + "text/markdown": [ + "777" + ], + "text/plain": [ + "[1] 777" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1141" + ], + "text/latex": [ + "1141" + ], + "text/markdown": [ + "1141" + ], + "text/plain": [ + "[1] 1141" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "784" + ], + "text/latex": [ + "784" + ], + "text/markdown": [ + "784" + ], + "text/plain": [ + "[1] 784" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "DLPFC_exp <- merge(DLPFC_exp_pheno_sub, DLPFC_exp_covar_t, by = 'SID')\n", + "head(DLPFC_exp); nrow(DLPFC_exp); nrow(DLPFC_exp_pheno_sub); nrow(DLPFC_exp_covar_t) " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5d02cf14", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 2
SIDBLOC1S3_AC_exp
<chr><dbl>
1SM-CTDUU-1.66908334
2SM-CTDSC-0.24440596
3SM-CJFLR 0.63201432
4SM-CJGMZ-0.79517284
5SM-CJFM3 0.06144019
6SM-CJIXK-0.03974088
\n" + ], + "text/latex": [ + "A data.frame: 6 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & SID & BLOC1S3\\_AC\\_exp\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & SM-CTDUU & -1.66908334\\\\\n", + "\t2 & SM-CTDSC & -0.24440596\\\\\n", + "\t3 & SM-CJFLR & 0.63201432\\\\\n", + "\t4 & SM-CJGMZ & -0.79517284\\\\\n", + "\t5 & SM-CJFM3 & 0.06144019\\\\\n", + "\t6 & SM-CJIXK & -0.03974088\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 2\n", + "\n", + "| | SID <chr> | BLOC1S3_AC_exp <dbl> |\n", + "|---|---|---|\n", + "| 1 | SM-CTDUU | -1.66908334 |\n", + "| 2 | SM-CTDSC | -0.24440596 |\n", + "| 3 | SM-CJFLR | 0.63201432 |\n", + "| 4 | SM-CJGMZ | -0.79517284 |\n", + "| 5 | SM-CJFM3 | 0.06144019 |\n", + "| 6 | SM-CJIXK | -0.03974088 |\n", + "\n" + ], + "text/plain": [ + " SID BLOC1S3_AC_exp\n", + "1 SM-CTDUU -1.66908334 \n", + "2 SM-CTDSC -0.24440596 \n", + "3 SM-CJFLR 0.63201432 \n", + "4 SM-CJGMZ -0.79517284 \n", + "5 SM-CJFM3 0.06144019 \n", + "6 SM-CJIXK -0.03974088 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "AC_exp_pheno_sub <- AC_exp_pheno %>% filter(ID == 'ENSG00000189114') %>% \n", + " dplyr::select(-`#chr`, -start, -end, -ID) %>%\n", + " t() %>%\n", + " as.data.frame(stringsAsFactors = FALSE) %>%\n", + " rownames_to_column(\"SID\") %>% \n", + " mutate(across(-SID, as.numeric))\n", + "\n", + "colnames(AC_exp_pheno_sub)[2] <- 'BLOC1S3_AC_exp'\n", + "head(AC_exp_pheno_sub)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0777c833", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 19
SIDBLOC1S3_AC_expmsex_AC_expage_death_AC_exppmi_AC_expROS_study_AC_expPC1_AC_expPC2_AC_expPC3_AC_expPC4_AC_expPC5_AC_expPC6_AC_expPC7_AC_expPC8_AC_expPC9_AC_expPC10_AC_expPC11_AC_expPC12_AC_expPC13_AC_exp
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1SM-CJEFR-1.03088730 95.184125.0000001-0.009288654 0.07222711 0.002891650-0.049175427-0.009331941 0.02367040 0.012981030-0.0012170090-0.07232593 0.010685649 0.022902699 0.053007585-0.032927999
2SM-CJEFS-1.19894960101.943904.5000001-0.011878860 0.07627014 0.012650560-0.073416920-0.023965680-0.01003319 0.030615930-0.0002483342-0.04340587 0.042140530-0.023707780-0.041120410 0.047723010
3SM-CJEFU 0.63643081 81.938408.8333330-0.009894409-0.01305638 0.049875899 0.108320959-0.116313249 0.01894686-0.072541359 0.0430160410-0.05709961 0.026557430-0.022803826 0.005868232-0.028957413
4SM-CJEG1 0.58421180 81.325122.0000001-0.006808607-0.02436349 0.036853414-0.038215084-0.002585761 0.03118097-0.063388884 0.0175124050 0.02736089-0.005039676 0.011211747-0.031767847-0.011675806
5SM-CJEG3-0.30442130 91.553736.2500000 0.012294605 0.02106597-0.005452404 0.024195028 0.004051295 0.03228929 0.050129614-0.0156169510 0.08987513-0.061211599-0.002553259-0.258960234-0.009280432
6SM-CJEG7-1.89844960 88.369618.1666671-0.006441862 0.03414639-0.006961758 0.005733466 0.028457510 0.01953690-0.007155527 0.0405081860 0.04029579-0.087856525-0.077338595 0.095284697-0.131160245
\n" + ], + "text/latex": [ + "A data.frame: 6 × 19\n", + "\\begin{tabular}{r|lllllllllllllllllll}\n", + " & SID & BLOC1S3\\_AC\\_exp & msex\\_AC\\_exp & age\\_death\\_AC\\_exp & pmi\\_AC\\_exp & ROS\\_study\\_AC\\_exp & PC1\\_AC\\_exp & PC2\\_AC\\_exp & PC3\\_AC\\_exp & PC4\\_AC\\_exp & PC5\\_AC\\_exp & PC6\\_AC\\_exp & PC7\\_AC\\_exp & PC8\\_AC\\_exp & PC9\\_AC\\_exp & PC10\\_AC\\_exp & PC11\\_AC\\_exp & PC12\\_AC\\_exp & PC13\\_AC\\_exp\\\\\n", + " & & & & & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & SM-CJEFR & -1.0308873 & 0 & 95.18412 & 5.000000 & 1 & -0.009288654 & 0.07222711 & 0.002891650 & -0.049175427 & -0.009331941 & 0.02367040 & 0.012981030 & -0.0012170090 & -0.07232593 & 0.010685649 & 0.022902699 & 0.053007585 & -0.032927999\\\\\n", + "\t2 & SM-CJEFS & -1.1989496 & 0 & 101.94390 & 4.500000 & 1 & -0.011878860 & 0.07627014 & 0.012650560 & -0.073416920 & -0.023965680 & -0.01003319 & 0.030615930 & -0.0002483342 & -0.04340587 & 0.042140530 & -0.023707780 & -0.041120410 & 0.047723010\\\\\n", + "\t3 & SM-CJEFU & 0.6364308 & 1 & 81.93840 & 8.833333 & 0 & -0.009894409 & -0.01305638 & 0.049875899 & 0.108320959 & -0.116313249 & 0.01894686 & -0.072541359 & 0.0430160410 & -0.05709961 & 0.026557430 & -0.022803826 & 0.005868232 & -0.028957413\\\\\n", + "\t4 & SM-CJEG1 & 0.5842118 & 0 & 81.32512 & 2.000000 & 1 & -0.006808607 & -0.02436349 & 0.036853414 & -0.038215084 & -0.002585761 & 0.03118097 & -0.063388884 & 0.0175124050 & 0.02736089 & -0.005039676 & 0.011211747 & -0.031767847 & -0.011675806\\\\\n", + "\t5 & SM-CJEG3 & -0.3044213 & 0 & 91.55373 & 6.250000 & 0 & 0.012294605 & 0.02106597 & -0.005452404 & 0.024195028 & 0.004051295 & 0.03228929 & 0.050129614 & -0.0156169510 & 0.08987513 & -0.061211599 & -0.002553259 & -0.258960234 & -0.009280432\\\\\n", + "\t6 & SM-CJEG7 & -1.8984496 & 0 & 88.36961 & 8.166667 & 1 & -0.006441862 & 0.03414639 & -0.006961758 & 0.005733466 & 0.028457510 & 0.01953690 & -0.007155527 & 0.0405081860 & 0.04029579 & -0.087856525 & -0.077338595 & 0.095284697 & -0.131160245\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 19\n", + "\n", + "| | SID <chr> | BLOC1S3_AC_exp <dbl> | msex_AC_exp <dbl> | age_death_AC_exp <dbl> | pmi_AC_exp <dbl> | ROS_study_AC_exp <dbl> | PC1_AC_exp <dbl> | PC2_AC_exp <dbl> | PC3_AC_exp <dbl> | PC4_AC_exp <dbl> | PC5_AC_exp <dbl> | PC6_AC_exp <dbl> | PC7_AC_exp <dbl> | PC8_AC_exp <dbl> | PC9_AC_exp <dbl> | PC10_AC_exp <dbl> | PC11_AC_exp <dbl> | PC12_AC_exp <dbl> | PC13_AC_exp <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | SM-CJEFR | -1.0308873 | 0 | 95.18412 | 5.000000 | 1 | -0.009288654 | 0.07222711 | 0.002891650 | -0.049175427 | -0.009331941 | 0.02367040 | 0.012981030 | -0.0012170090 | -0.07232593 | 0.010685649 | 0.022902699 | 0.053007585 | -0.032927999 |\n", + "| 2 | SM-CJEFS | -1.1989496 | 0 | 101.94390 | 4.500000 | 1 | -0.011878860 | 0.07627014 | 0.012650560 | -0.073416920 | -0.023965680 | -0.01003319 | 0.030615930 | -0.0002483342 | -0.04340587 | 0.042140530 | -0.023707780 | -0.041120410 | 0.047723010 |\n", + "| 3 | SM-CJEFU | 0.6364308 | 1 | 81.93840 | 8.833333 | 0 | -0.009894409 | -0.01305638 | 0.049875899 | 0.108320959 | -0.116313249 | 0.01894686 | -0.072541359 | 0.0430160410 | -0.05709961 | 0.026557430 | -0.022803826 | 0.005868232 | -0.028957413 |\n", + "| 4 | SM-CJEG1 | 0.5842118 | 0 | 81.32512 | 2.000000 | 1 | -0.006808607 | -0.02436349 | 0.036853414 | -0.038215084 | -0.002585761 | 0.03118097 | -0.063388884 | 0.0175124050 | 0.02736089 | -0.005039676 | 0.011211747 | -0.031767847 | -0.011675806 |\n", + "| 5 | SM-CJEG3 | -0.3044213 | 0 | 91.55373 | 6.250000 | 0 | 0.012294605 | 0.02106597 | -0.005452404 | 0.024195028 | 0.004051295 | 0.03228929 | 0.050129614 | -0.0156169510 | 0.08987513 | -0.061211599 | -0.002553259 | -0.258960234 | -0.009280432 |\n", + "| 6 | SM-CJEG7 | -1.8984496 | 0 | 88.36961 | 8.166667 | 1 | -0.006441862 | 0.03414639 | -0.006961758 | 0.005733466 | 0.028457510 | 0.01953690 | -0.007155527 | 0.0405081860 | 0.04029579 | -0.087856525 | -0.077338595 | 0.095284697 | -0.131160245 |\n", + "\n" + ], + "text/plain": [ + " SID BLOC1S3_AC_exp msex_AC_exp age_death_AC_exp pmi_AC_exp\n", + "1 SM-CJEFR -1.0308873 0 95.18412 5.000000 \n", + "2 SM-CJEFS -1.1989496 0 101.94390 4.500000 \n", + "3 SM-CJEFU 0.6364308 1 81.93840 8.833333 \n", + "4 SM-CJEG1 0.5842118 0 81.32512 2.000000 \n", + "5 SM-CJEG3 -0.3044213 0 91.55373 6.250000 \n", + "6 SM-CJEG7 -1.8984496 0 88.36961 8.166667 \n", + " ROS_study_AC_exp PC1_AC_exp PC2_AC_exp PC3_AC_exp PC4_AC_exp \n", + "1 1 -0.009288654 0.07222711 0.002891650 -0.049175427\n", + "2 1 -0.011878860 0.07627014 0.012650560 -0.073416920\n", + "3 0 -0.009894409 -0.01305638 0.049875899 0.108320959\n", + "4 1 -0.006808607 -0.02436349 0.036853414 -0.038215084\n", + "5 0 0.012294605 0.02106597 -0.005452404 0.024195028\n", + "6 1 -0.006441862 0.03414639 -0.006961758 0.005733466\n", + " PC5_AC_exp PC6_AC_exp PC7_AC_exp PC8_AC_exp PC9_AC_exp PC10_AC_exp \n", + "1 -0.009331941 0.02367040 0.012981030 -0.0012170090 -0.07232593 0.010685649\n", + "2 -0.023965680 -0.01003319 0.030615930 -0.0002483342 -0.04340587 0.042140530\n", + "3 -0.116313249 0.01894686 -0.072541359 0.0430160410 -0.05709961 0.026557430\n", + "4 -0.002585761 0.03118097 -0.063388884 0.0175124050 0.02736089 -0.005039676\n", + "5 0.004051295 0.03228929 0.050129614 -0.0156169510 0.08987513 -0.061211599\n", + "6 0.028457510 0.01953690 -0.007155527 0.0405081860 0.04029579 -0.087856525\n", + " PC11_AC_exp PC12_AC_exp PC13_AC_exp \n", + "1 0.022902699 0.053007585 -0.032927999\n", + "2 -0.023707780 -0.041120410 0.047723010\n", + "3 -0.022803826 0.005868232 -0.028957413\n", + "4 0.011211747 -0.031767847 -0.011675806\n", + "5 -0.002553259 -0.258960234 -0.009280432\n", + "6 -0.077338595 0.095284697 -0.131160245" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "593" + ], + "text/latex": [ + "593" + ], + "text/markdown": [ + "593" + ], + "text/plain": [ + "[1] 593" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "693" + ], + "text/latex": [ + "693" + ], + "text/markdown": [ + "693" + ], + "text/plain": [ + "[1] 693" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "593" + ], + "text/latex": [ + "593" + ], + "text/markdown": [ + "593" + ], + "text/plain": [ + "[1] 593" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "AC_exp <- merge(AC_exp_pheno_sub, AC_exp_covar_t, by = 'SID')\n", + "head(AC_exp); nrow(AC_exp); nrow(AC_exp_pheno_sub); nrow(AC_exp_covar_t) " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8bfefd27", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + 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A data.frame: 6 × 2
SIDBLOC1S3_PCC_exp
<chr><dbl>
1SM-CJFK8 -1.0711274
2SM-CTDUU -0.4427537
3SM-CTDSC -1.6774325
42040-PCC.final.bam 0.6554943
5SM-CTEMS 0.1103276
6SM-CJFLR 0.7330115
\n" + ], + "text/latex": [ + "A data.frame: 6 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & SID & BLOC1S3\\_PCC\\_exp\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & SM-CJFK8 & -1.0711274\\\\\n", + "\t2 & SM-CTDUU & -0.4427537\\\\\n", + "\t3 & SM-CTDSC & -1.6774325\\\\\n", + "\t4 & 2040-PCC.final.bam & 0.6554943\\\\\n", + "\t5 & SM-CTEMS & 0.1103276\\\\\n", + "\t6 & SM-CJFLR & 0.7330115\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 2\n", + "\n", + "| | SID <chr> | BLOC1S3_PCC_exp <dbl> |\n", + "|---|---|---|\n", + "| 1 | SM-CJFK8 | -1.0711274 |\n", + "| 2 | SM-CTDUU | -0.4427537 |\n", + "| 3 | SM-CTDSC | -1.6774325 |\n", + "| 4 | 2040-PCC.final.bam | 0.6554943 |\n", + "| 5 | SM-CTEMS | 0.1103276 |\n", + "| 6 | SM-CJFLR | 0.7330115 |\n", + "\n" + ], + "text/plain": [ + " SID BLOC1S3_PCC_exp\n", + "1 SM-CJFK8 -1.0711274 \n", + "2 SM-CTDUU -0.4427537 \n", + "3 SM-CTDSC -1.6774325 \n", + "4 2040-PCC.final.bam 0.6554943 \n", + "5 SM-CTEMS 0.1103276 \n", + "6 SM-CJFLR 0.7330115 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PCC_exp_pheno_sub <- PCC_exp_pheno %>% filter(ID == 'ENSG00000189114') %>% \n", + " dplyr::select(-`#chr`, -start, -end, -ID) %>%\n", + " t() %>%\n", + " as.data.frame(stringsAsFactors = FALSE) %>%\n", + " rownames_to_column(\"SID\") %>% \n", + " mutate(across(-SID, as.numeric))\n", + "\n", + "colnames(PCC_exp_pheno_sub)[2] <- 'BLOC1S3_PCC_exp'\n", + "head(PCC_exp_pheno_sub)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "479580d5", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 18
SIDmsex_PCC_expage_death_PCC_exppmi_PCC_expROS_study_PCC_expPC1_PCC_expPC2_PCC_expPC3_PCC_expPC4_PCC_expPC5_PCC_expPC6_PCC_expPC7_PCC_expPC8_PCC_expPC9_PCC_expPC10_PCC_expPC11_PCC_expPC12_PCC_expPC13_PCC_exp
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
2SM-CJFK80100.873407.5000000 0.025736070-0.077820970 0.06445574-0.010922670 0.051561970 0.035614680 0.03134955 0.004765899 0.021554780 0.01395718-0.0002373548 0.079519880 0.0112285400
3SM-CTDUU0 80.657085.5000000-0.008149845-0.003498844 0.02607122 0.014385420-0.028152860 0.011368700-0.04503941-0.075419510-0.023187780-0.01837065 0.0364916300-0.037675790-0.0001032716
4SM-CTDSC0 83.690623.9333330-0.024581708-0.009278022 0.02370770 0.030673622-0.009559659 0.000136435 0.05453378-0.024948988-0.012749012 0.01053091-0.0619746110-0.047584352 0.0071536880
5SM-CTEMS0 90.384673.7833330-0.075616694-0.002934595-0.04678128-0.035651749 0.017229725-0.040869099 0.01152465-0.057153036-0.002165177 0.04408152 0.0255486400-0.042933618-0.0669162910
6SM-CJFLR0 94.313485.0000000 0.023101710-0.054116170 0.07212816-0.004799705 0.023663910 0.005184623-0.02989073 0.034927560-0.018051180 0.02740401-0.0225251100-0.003823874 0.0043125510
7SM-CJFM31 93.691997.2500000-0.039227310-0.008822888-0.01511602-0.002626929-0.008618900 0.042066303 0.01766018 0.062097787 0.002608101 0.01941650-0.0178076580-0.041133350 0.0860434940
\n" + ], + "text/latex": [ + "A data.frame: 6 × 18\n", + "\\begin{tabular}{r|llllllllllllllllll}\n", + " & SID & msex\\_PCC\\_exp & age\\_death\\_PCC\\_exp & pmi\\_PCC\\_exp & ROS\\_study\\_PCC\\_exp & PC1\\_PCC\\_exp & PC2\\_PCC\\_exp & PC3\\_PCC\\_exp & PC4\\_PCC\\_exp & PC5\\_PCC\\_exp & PC6\\_PCC\\_exp & PC7\\_PCC\\_exp & PC8\\_PCC\\_exp & PC9\\_PCC\\_exp & PC10\\_PCC\\_exp & PC11\\_PCC\\_exp & PC12\\_PCC\\_exp & PC13\\_PCC\\_exp\\\\\n", + " & & & & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t2 & SM-CJFK8 & 0 & 100.87340 & 7.500000 & 0 & 0.025736070 & -0.077820970 & 0.06445574 & -0.010922670 & 0.051561970 & 0.035614680 & 0.03134955 & 0.004765899 & 0.021554780 & 0.01395718 & -0.0002373548 & 0.079519880 & 0.0112285400\\\\\n", + "\t3 & SM-CTDUU & 0 & 80.65708 & 5.500000 & 0 & -0.008149845 & -0.003498844 & 0.02607122 & 0.014385420 & -0.028152860 & 0.011368700 & -0.04503941 & -0.075419510 & -0.023187780 & -0.01837065 & 0.0364916300 & -0.037675790 & -0.0001032716\\\\\n", + "\t4 & SM-CTDSC & 0 & 83.69062 & 3.933333 & 0 & -0.024581708 & -0.009278022 & 0.02370770 & 0.030673622 & -0.009559659 & 0.000136435 & 0.05453378 & -0.024948988 & -0.012749012 & 0.01053091 & -0.0619746110 & -0.047584352 & 0.0071536880\\\\\n", + "\t5 & SM-CTEMS & 0 & 90.38467 & 3.783333 & 0 & -0.075616694 & -0.002934595 & -0.04678128 & -0.035651749 & 0.017229725 & -0.040869099 & 0.01152465 & -0.057153036 & -0.002165177 & 0.04408152 & 0.0255486400 & -0.042933618 & -0.0669162910\\\\\n", + "\t6 & SM-CJFLR & 0 & 94.31348 & 5.000000 & 0 & 0.023101710 & -0.054116170 & 0.07212816 & -0.004799705 & 0.023663910 & 0.005184623 & -0.02989073 & 0.034927560 & -0.018051180 & 0.02740401 & -0.0225251100 & -0.003823874 & 0.0043125510\\\\\n", + "\t7 & SM-CJFM3 & 1 & 93.69199 & 7.250000 & 0 & -0.039227310 & -0.008822888 & -0.01511602 & -0.002626929 & -0.008618900 & 0.042066303 & 0.01766018 & 0.062097787 & 0.002608101 & 0.01941650 & -0.0178076580 & -0.041133350 & 0.0860434940\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 18\n", + "\n", + "| | SID <chr> | msex_PCC_exp <dbl> | age_death_PCC_exp <dbl> | pmi_PCC_exp <dbl> | ROS_study_PCC_exp <dbl> | PC1_PCC_exp <dbl> | PC2_PCC_exp <dbl> | PC3_PCC_exp <dbl> | PC4_PCC_exp <dbl> | PC5_PCC_exp <dbl> | PC6_PCC_exp <dbl> | PC7_PCC_exp <dbl> | PC8_PCC_exp <dbl> | PC9_PCC_exp <dbl> | PC10_PCC_exp <dbl> | PC11_PCC_exp <dbl> | PC12_PCC_exp <dbl> | PC13_PCC_exp <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 2 | SM-CJFK8 | 0 | 100.87340 | 7.500000 | 0 | 0.025736070 | -0.077820970 | 0.06445574 | -0.010922670 | 0.051561970 | 0.035614680 | 0.03134955 | 0.004765899 | 0.021554780 | 0.01395718 | -0.0002373548 | 0.079519880 | 0.0112285400 |\n", + "| 3 | SM-CTDUU | 0 | 80.65708 | 5.500000 | 0 | -0.008149845 | -0.003498844 | 0.02607122 | 0.014385420 | -0.028152860 | 0.011368700 | -0.04503941 | -0.075419510 | -0.023187780 | -0.01837065 | 0.0364916300 | -0.037675790 | -0.0001032716 |\n", + "| 4 | SM-CTDSC | 0 | 83.69062 | 3.933333 | 0 | -0.024581708 | -0.009278022 | 0.02370770 | 0.030673622 | -0.009559659 | 0.000136435 | 0.05453378 | -0.024948988 | -0.012749012 | 0.01053091 | -0.0619746110 | -0.047584352 | 0.0071536880 |\n", + "| 5 | SM-CTEMS | 0 | 90.38467 | 3.783333 | 0 | -0.075616694 | -0.002934595 | -0.04678128 | -0.035651749 | 0.017229725 | -0.040869099 | 0.01152465 | -0.057153036 | -0.002165177 | 0.04408152 | 0.0255486400 | -0.042933618 | -0.0669162910 |\n", + "| 6 | SM-CJFLR | 0 | 94.31348 | 5.000000 | 0 | 0.023101710 | -0.054116170 | 0.07212816 | -0.004799705 | 0.023663910 | 0.005184623 | -0.02989073 | 0.034927560 | -0.018051180 | 0.02740401 | -0.0225251100 | -0.003823874 | 0.0043125510 |\n", + "| 7 | SM-CJFM3 | 1 | 93.69199 | 7.250000 | 0 | -0.039227310 | -0.008822888 | -0.01511602 | -0.002626929 | -0.008618900 | 0.042066303 | 0.01766018 | 0.062097787 | 0.002608101 | 0.01941650 | -0.0178076580 | -0.041133350 | 0.0860434940 |\n", + "\n" + ], + "text/plain": [ + " SID msex_PCC_exp age_death_PCC_exp pmi_PCC_exp ROS_study_PCC_exp\n", + "2 SM-CJFK8 0 100.87340 7.500000 0 \n", + "3 SM-CTDUU 0 80.65708 5.500000 0 \n", + "4 SM-CTDSC 0 83.69062 3.933333 0 \n", + "5 SM-CTEMS 0 90.38467 3.783333 0 \n", + "6 SM-CJFLR 0 94.31348 5.000000 0 \n", + "7 SM-CJFM3 1 93.69199 7.250000 0 \n", + " PC1_PCC_exp PC2_PCC_exp PC3_PCC_exp PC4_PCC_exp PC5_PCC_exp PC6_PCC_exp \n", + "2 0.025736070 -0.077820970 0.06445574 -0.010922670 0.051561970 0.035614680\n", + "3 -0.008149845 -0.003498844 0.02607122 0.014385420 -0.028152860 0.011368700\n", + "4 -0.024581708 -0.009278022 0.02370770 0.030673622 -0.009559659 0.000136435\n", + "5 -0.075616694 -0.002934595 -0.04678128 -0.035651749 0.017229725 -0.040869099\n", + "6 0.023101710 -0.054116170 0.07212816 -0.004799705 0.023663910 0.005184623\n", + "7 -0.039227310 -0.008822888 -0.01511602 -0.002626929 -0.008618900 0.042066303\n", + " PC7_PCC_exp PC8_PCC_exp PC9_PCC_exp PC10_PCC_exp PC11_PCC_exp PC12_PCC_exp\n", + "2 0.03134955 0.004765899 0.021554780 0.01395718 -0.0002373548 0.079519880\n", + "3 -0.04503941 -0.075419510 -0.023187780 -0.01837065 0.0364916300 -0.037675790\n", + "4 0.05453378 -0.024948988 -0.012749012 0.01053091 -0.0619746110 -0.047584352\n", + "5 0.01152465 -0.057153036 -0.002165177 0.04408152 0.0255486400 -0.042933618\n", + "6 -0.02989073 0.034927560 -0.018051180 0.02740401 -0.0225251100 -0.003823874\n", + "7 0.01766018 0.062097787 0.002608101 0.01941650 -0.0178076580 -0.041133350\n", + " PC13_PCC_exp \n", + "2 0.0112285400\n", + "3 -0.0001032716\n", + "4 0.0071536880\n", + "5 -0.0669162910\n", + "6 0.0043125510\n", + "7 0.0860434940" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PCC_exp_covar_t <- PCC_exp_covar %>% \n", + " t() %>% \n", + " as.data.frame() %>% \n", + " rownames_to_column(\"SID\")\n", + "\n", + "colnames(PCC_exp_covar_t) <- PCC_exp_covar_t[1, ]\n", + "PCC_exp_covar_t <- PCC_exp_covar_t[-1, ]\n", + "colnames(PCC_exp_covar_t)[1] <- 'SID'\n", + "PCC_exp_covar_t <- PCC_exp_covar_t %>% mutate(across(-SID, as.numeric)) %>% dplyr::select(-contains(\"Hidden_Factor\"))\n", + "PCC_exp_covar_t <- PCC_exp_covar_t |>\n", + " setNames(ifelse(names(PCC_exp_covar_t) == \"SID\", \"SID\", paste0(names(PCC_exp_covar_t), \"_PCC_exp\")))\n", + "\n", + "head(PCC_exp_covar_t)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0527857d", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 19
SIDBLOC1S3_PCC_expmsex_PCC_expage_death_PCC_exppmi_PCC_expROS_study_PCC_expPC1_PCC_expPC2_PCC_expPC3_PCC_expPC4_PCC_expPC5_PCC_expPC6_PCC_expPC7_PCC_expPC8_PCC_expPC9_PCC_expPC10_PCC_expPC11_PCC_expPC12_PCC_expPC13_PCC_exp
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1SM-CJEFQ-1.88216892185.826153.2500000-0.01823600-0.008187452 0.025870265-0.015861189-0.07516598-0.035041250-0.08326391-0.01201968-0.029241261-0.003537228 0.0126817890-0.03012732 0.009504051
2SM-CJEFR-0.02108535095.184125.0000001-0.07567655-0.002418030-0.044114120-0.039811758 0.02042177 0.002480059 0.04734363 0.03029872-0.059420706-0.021755758-0.0177169280-0.01733458 0.053627127
3SM-CJEFU-0.61535886181.938408.8333330 0.03159592-0.086668669 0.108249891-0.001591115 0.12096388-0.115177804 0.03197835-0.03277327-0.026961797-0.084943663-0.0044049080 0.07095587 0.073482400
4SM-CJEFX 1.35411110087.846684.2166671-0.02476271 0.010424350-0.028217190-0.024551190-0.03249882-0.018128840 0.01486032 0.03716051 0.055949210 0.002537551-0.0008729918 0.02597951 0.046166980
5SM-CJEG3 1.26573639091.553736.2500000-0.01916532 0.014625380 0.026514000-0.038163500-0.01530379 0.062428800-0.07350877-0.06956588-0.009466472-0.015602270 0.0389509300-0.06534122-0.192902700
6SM-CJEG7-0.47398021088.369618.1666671-0.04210138 0.002147556 0.006206564-0.048405429-0.03275954-0.014660494 0.12555148 0.11999185 0.004017858-0.189189447 0.0231738310 0.17742896-0.092014052
\n" + ], + "text/latex": [ + "A data.frame: 6 × 19\n", + "\\begin{tabular}{r|lllllllllllllllllll}\n", + " & SID & BLOC1S3\\_PCC\\_exp & msex\\_PCC\\_exp & age\\_death\\_PCC\\_exp & pmi\\_PCC\\_exp & ROS\\_study\\_PCC\\_exp & PC1\\_PCC\\_exp & PC2\\_PCC\\_exp & PC3\\_PCC\\_exp & PC4\\_PCC\\_exp & PC5\\_PCC\\_exp & PC6\\_PCC\\_exp & PC7\\_PCC\\_exp & PC8\\_PCC\\_exp & PC9\\_PCC\\_exp & PC10\\_PCC\\_exp & PC11\\_PCC\\_exp & PC12\\_PCC\\_exp & PC13\\_PCC\\_exp\\\\\n", + " & & & & & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & SM-CJEFQ & -1.88216892 & 1 & 85.82615 & 3.250000 & 0 & -0.01823600 & -0.008187452 & 0.025870265 & -0.015861189 & -0.07516598 & -0.035041250 & -0.08326391 & -0.01201968 & -0.029241261 & -0.003537228 & 0.0126817890 & -0.03012732 & 0.009504051\\\\\n", + "\t2 & SM-CJEFR & -0.02108535 & 0 & 95.18412 & 5.000000 & 1 & -0.07567655 & -0.002418030 & -0.044114120 & -0.039811758 & 0.02042177 & 0.002480059 & 0.04734363 & 0.03029872 & -0.059420706 & -0.021755758 & -0.0177169280 & -0.01733458 & 0.053627127\\\\\n", + "\t3 & SM-CJEFU & -0.61535886 & 1 & 81.93840 & 8.833333 & 0 & 0.03159592 & -0.086668669 & 0.108249891 & -0.001591115 & 0.12096388 & -0.115177804 & 0.03197835 & -0.03277327 & -0.026961797 & -0.084943663 & -0.0044049080 & 0.07095587 & 0.073482400\\\\\n", + "\t4 & SM-CJEFX & 1.35411110 & 0 & 87.84668 & 4.216667 & 1 & -0.02476271 & 0.010424350 & -0.028217190 & -0.024551190 & -0.03249882 & -0.018128840 & 0.01486032 & 0.03716051 & 0.055949210 & 0.002537551 & -0.0008729918 & 0.02597951 & 0.046166980\\\\\n", + "\t5 & SM-CJEG3 & 1.26573639 & 0 & 91.55373 & 6.250000 & 0 & -0.01916532 & 0.014625380 & 0.026514000 & -0.038163500 & -0.01530379 & 0.062428800 & -0.07350877 & -0.06956588 & -0.009466472 & -0.015602270 & 0.0389509300 & -0.06534122 & -0.192902700\\\\\n", + "\t6 & SM-CJEG7 & -0.47398021 & 0 & 88.36961 & 8.166667 & 1 & -0.04210138 & 0.002147556 & 0.006206564 & -0.048405429 & -0.03275954 & -0.014660494 & 0.12555148 & 0.11999185 & 0.004017858 & -0.189189447 & 0.0231738310 & 0.17742896 & -0.092014052\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 19\n", + "\n", + "| | SID <chr> | BLOC1S3_PCC_exp <dbl> | msex_PCC_exp <dbl> | age_death_PCC_exp <dbl> | pmi_PCC_exp <dbl> | ROS_study_PCC_exp <dbl> | PC1_PCC_exp <dbl> | PC2_PCC_exp <dbl> | PC3_PCC_exp <dbl> | PC4_PCC_exp <dbl> | PC5_PCC_exp <dbl> | PC6_PCC_exp <dbl> | PC7_PCC_exp <dbl> | PC8_PCC_exp <dbl> | PC9_PCC_exp <dbl> | PC10_PCC_exp <dbl> | PC11_PCC_exp <dbl> | PC12_PCC_exp <dbl> | PC13_PCC_exp <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | SM-CJEFQ | -1.88216892 | 1 | 85.82615 | 3.250000 | 0 | -0.01823600 | -0.008187452 | 0.025870265 | -0.015861189 | -0.07516598 | -0.035041250 | -0.08326391 | -0.01201968 | -0.029241261 | -0.003537228 | 0.0126817890 | -0.03012732 | 0.009504051 |\n", + "| 2 | SM-CJEFR | -0.02108535 | 0 | 95.18412 | 5.000000 | 1 | -0.07567655 | -0.002418030 | -0.044114120 | -0.039811758 | 0.02042177 | 0.002480059 | 0.04734363 | 0.03029872 | -0.059420706 | -0.021755758 | -0.0177169280 | -0.01733458 | 0.053627127 |\n", + "| 3 | SM-CJEFU | -0.61535886 | 1 | 81.93840 | 8.833333 | 0 | 0.03159592 | -0.086668669 | 0.108249891 | -0.001591115 | 0.12096388 | -0.115177804 | 0.03197835 | -0.03277327 | -0.026961797 | -0.084943663 | -0.0044049080 | 0.07095587 | 0.073482400 |\n", + "| 4 | SM-CJEFX | 1.35411110 | 0 | 87.84668 | 4.216667 | 1 | -0.02476271 | 0.010424350 | -0.028217190 | -0.024551190 | -0.03249882 | -0.018128840 | 0.01486032 | 0.03716051 | 0.055949210 | 0.002537551 | -0.0008729918 | 0.02597951 | 0.046166980 |\n", + "| 5 | SM-CJEG3 | 1.26573639 | 0 | 91.55373 | 6.250000 | 0 | -0.01916532 | 0.014625380 | 0.026514000 | -0.038163500 | -0.01530379 | 0.062428800 | -0.07350877 | -0.06956588 | -0.009466472 | -0.015602270 | 0.0389509300 | -0.06534122 | -0.192902700 |\n", + "| 6 | SM-CJEG7 | -0.47398021 | 0 | 88.36961 | 8.166667 | 1 | -0.04210138 | 0.002147556 | 0.006206564 | -0.048405429 | -0.03275954 | -0.014660494 | 0.12555148 | 0.11999185 | 0.004017858 | -0.189189447 | 0.0231738310 | 0.17742896 | -0.092014052 |\n", + "\n" + ], + "text/plain": [ + " SID BLOC1S3_PCC_exp msex_PCC_exp age_death_PCC_exp pmi_PCC_exp\n", + "1 SM-CJEFQ -1.88216892 1 85.82615 3.250000 \n", + "2 SM-CJEFR -0.02108535 0 95.18412 5.000000 \n", + "3 SM-CJEFU -0.61535886 1 81.93840 8.833333 \n", + "4 SM-CJEFX 1.35411110 0 87.84668 4.216667 \n", + "5 SM-CJEG3 1.26573639 0 91.55373 6.250000 \n", + "6 SM-CJEG7 -0.47398021 0 88.36961 8.166667 \n", + " ROS_study_PCC_exp PC1_PCC_exp PC2_PCC_exp PC3_PCC_exp PC4_PCC_exp \n", + "1 0 -0.01823600 -0.008187452 0.025870265 -0.015861189\n", + "2 1 -0.07567655 -0.002418030 -0.044114120 -0.039811758\n", + "3 0 0.03159592 -0.086668669 0.108249891 -0.001591115\n", + "4 1 -0.02476271 0.010424350 -0.028217190 -0.024551190\n", + "5 0 -0.01916532 0.014625380 0.026514000 -0.038163500\n", + "6 1 -0.04210138 0.002147556 0.006206564 -0.048405429\n", + " PC5_PCC_exp PC6_PCC_exp PC7_PCC_exp PC8_PCC_exp PC9_PCC_exp PC10_PCC_exp\n", + "1 -0.07516598 -0.035041250 -0.08326391 -0.01201968 -0.029241261 -0.003537228\n", + "2 0.02042177 0.002480059 0.04734363 0.03029872 -0.059420706 -0.021755758\n", + "3 0.12096388 -0.115177804 0.03197835 -0.03277327 -0.026961797 -0.084943663\n", + "4 -0.03249882 -0.018128840 0.01486032 0.03716051 0.055949210 0.002537551\n", + "5 -0.01530379 0.062428800 -0.07350877 -0.06956588 -0.009466472 -0.015602270\n", + "6 -0.03275954 -0.014660494 0.12555148 0.11999185 0.004017858 -0.189189447\n", + " PC11_PCC_exp PC12_PCC_exp PC13_PCC_exp\n", + "1 0.0126817890 -0.03012732 0.009504051\n", + "2 -0.0177169280 -0.01733458 0.053627127\n", + "3 -0.0044049080 0.07095587 0.073482400\n", + "4 -0.0008729918 0.02597951 0.046166980\n", + "5 0.0389509300 -0.06534122 -0.192902700\n", + "6 0.0231738310 0.17742896 -0.092014052" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "441" + ], + "text/latex": [ + "441" + ], + "text/markdown": [ + "441" + ], + "text/plain": [ + "[1] 441" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "534" + ], + "text/latex": [ + "534" + ], + "text/markdown": [ + "534" + ], + "text/plain": [ + "[1] 534" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "441" + ], + "text/latex": [ + "441" + ], + "text/markdown": [ + "441" + ], + "text/plain": [ + "[1] 441" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PCC_exp <- merge(PCC_exp_pheno_sub, PCC_exp_covar_t, by = 'SID')\n", + "head(PCC_exp); nrow(PCC_exp); nrow(PCC_exp_pheno_sub); nrow(PCC_exp_covar_t) " + ] + }, + { + "cell_type": "markdown", + "id": "5e53037a", + "metadata": {}, + "source": [ + "### Outcome data" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "05063c3a", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "patho_sub <- patho %>% filter(cs_id == 'set_31') %>% dplyr::select(specimenID, age_first_ad_dx_num, educ, apoe4_dose, apoe2_dose, msex, age_death, pmi, ROS_study)\n", + "patho_sub <- unique(patho_sub)\n", + "colnames(patho_sub) <- c('SID', \"age_first_ad_dx_num\", \"educ\", \"apoe4_dose\", \"apoe2_dose\", \"msex_p\", \"age_death_p\", \"pmi_p\", \"ROS_study_p\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "e8b8d6f8", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.table: 6 × 9
SIDage_first_ad_dx_numeducapoe4_doseapoe2_dosemsex_page_death_ppmi_pROS_study_p
<chr><dbl><int><int><int><int><dbl><dbl><int>
SM-CJFK8 NA16000100.873407.5000000
SM-CTDUU NA16100 80.657085.5000000
SM-CTDSC NA12010 83.690623.9333330
SM-CJIYB88.6351815001 92.618755.0833330
SM-CTEMS89.7221112000 90.384673.7833330
SM-CJFLR86.3874115000 94.313485.0000000
\n" + ], + "text/latex": [ + "A data.table: 6 × 9\n", + "\\begin{tabular}{lllllllll}\n", + " SID & age\\_first\\_ad\\_dx\\_num & educ & apoe4\\_dose & apoe2\\_dose & msex\\_p & age\\_death\\_p & pmi\\_p & ROS\\_study\\_p\\\\\n", + " & & & & & & & & \\\\\n", + "\\hline\n", + "\t SM-CJFK8 & NA & 16 & 0 & 0 & 0 & 100.87340 & 7.500000 & 0\\\\\n", + "\t SM-CTDUU & NA & 16 & 1 & 0 & 0 & 80.65708 & 5.500000 & 0\\\\\n", + "\t SM-CTDSC & NA & 12 & 0 & 1 & 0 & 83.69062 & 3.933333 & 0\\\\\n", + "\t SM-CJIYB & 88.63518 & 15 & 0 & 0 & 1 & 92.61875 & 5.083333 & 0\\\\\n", + "\t SM-CTEMS & 89.72211 & 12 & 0 & 0 & 0 & 90.38467 & 3.783333 & 0\\\\\n", + "\t SM-CJFLR & 86.38741 & 15 & 0 & 0 & 0 & 94.31348 & 5.000000 & 0\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 6 × 9\n", + "\n", + "| SID <chr> | age_first_ad_dx_num <dbl> | educ <int> | apoe4_dose <int> | apoe2_dose <int> | msex_p <int> | age_death_p <dbl> | pmi_p <dbl> | ROS_study_p <int> |\n", + "|---|---|---|---|---|---|---|---|---|\n", + "| SM-CJFK8 | NA | 16 | 0 | 0 | 0 | 100.87340 | 7.500000 | 0 |\n", + "| SM-CTDUU | NA | 16 | 1 | 0 | 0 | 80.65708 | 5.500000 | 0 |\n", + "| SM-CTDSC | NA | 12 | 0 | 1 | 0 | 83.69062 | 3.933333 | 0 |\n", + "| SM-CJIYB | 88.63518 | 15 | 0 | 0 | 1 | 92.61875 | 5.083333 | 0 |\n", + "| SM-CTEMS | 89.72211 | 12 | 0 | 0 | 0 | 90.38467 | 3.783333 | 0 |\n", + "| SM-CJFLR | 86.38741 | 15 | 0 | 0 | 0 | 94.31348 | 5.000000 | 0 |\n", + "\n" + ], + "text/plain": [ + " SID age_first_ad_dx_num educ apoe4_dose apoe2_dose msex_p age_death_p\n", + "1 SM-CJFK8 NA 16 0 0 0 100.87340 \n", + "2 SM-CTDUU NA 16 1 0 0 80.65708 \n", + "3 SM-CTDSC NA 12 0 1 0 83.69062 \n", + "4 SM-CJIYB 88.63518 15 0 0 1 92.61875 \n", + "5 SM-CTEMS 89.72211 12 0 0 0 90.38467 \n", + "6 SM-CJFLR 86.38741 15 0 0 0 94.31348 \n", + " pmi_p ROS_study_p\n", + "1 7.500000 0 \n", + "2 5.500000 0 \n", + "3 3.933333 0 \n", + "4 5.083333 0 \n", + "5 3.783333 0 \n", + "6 5.000000 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1113" + ], + "text/latex": [ + "1113" + ], + "text/markdown": [ + "1113" + ], + "text/plain": [ + "[1] 1113" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(patho_sub); nrow(patho_sub)" + ] + }, + { + "cell_type": "markdown", + "id": "7685b7d2", + "metadata": {}, + "source": [ + "### Merge the input for SEM" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "646f3063", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'SID'
  2. 'chr19_45114770_ACCC_AC'
  3. 'BLOC1S3_DLPFC_exp'
  4. 'msex_DLPFC_exp'
  5. 'age_death_DLPFC_exp'
  6. 'pmi_DLPFC_exp'
  7. 'ROS_study_DLPFC_exp'
  8. 'PC1_DLPFC_exp'
  9. 'PC2_DLPFC_exp'
  10. 'PC3_DLPFC_exp'
  11. 'PC4_DLPFC_exp'
  12. 'PC5_DLPFC_exp'
  13. 'PC6_DLPFC_exp'
  14. 'PC7_DLPFC_exp'
  15. 'PC8_DLPFC_exp'
  16. 'PC9_DLPFC_exp'
  17. 'PC10_DLPFC_exp'
  18. 'PC11_DLPFC_exp'
  19. 'PC12_DLPFC_exp'
  20. 'BLOC1S3_AC_exp'
  21. 'msex_AC_exp'
  22. 'age_death_AC_exp'
  23. 'pmi_AC_exp'
  24. 'ROS_study_AC_exp'
  25. 'PC1_AC_exp'
  26. 'PC2_AC_exp'
  27. 'PC3_AC_exp'
  28. 'PC4_AC_exp'
  29. 'PC5_AC_exp'
  30. 'PC6_AC_exp'
  31. 'PC7_AC_exp'
  32. 'PC8_AC_exp'
  33. 'PC9_AC_exp'
  34. 'PC10_AC_exp'
  35. 'PC11_AC_exp'
  36. 'PC12_AC_exp'
  37. 'PC13_AC_exp'
  38. 'BLOC1S3_PCC_exp'
  39. 'msex_PCC_exp'
  40. 'age_death_PCC_exp'
  41. 'pmi_PCC_exp'
  42. 'ROS_study_PCC_exp'
  43. 'PC1_PCC_exp'
  44. 'PC2_PCC_exp'
  45. 'PC3_PCC_exp'
  46. 'PC4_PCC_exp'
  47. 'PC5_PCC_exp'
  48. 'PC6_PCC_exp'
  49. 'PC7_PCC_exp'
  50. 'PC8_PCC_exp'
  51. 'PC9_PCC_exp'
  52. 'PC10_PCC_exp'
  53. 'PC11_PCC_exp'
  54. 'PC12_PCC_exp'
  55. 'PC13_PCC_exp'
  56. 'age_first_ad_dx_num'
  57. 'educ'
  58. 'apoe4_dose'
  59. 'apoe2_dose'
  60. 'msex_p'
  61. 'age_death_p'
  62. 'pmi_p'
  63. 'ROS_study_p'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'SID'\n", + "\\item 'chr19\\_45114770\\_ACCC\\_AC'\n", + "\\item 'BLOC1S3\\_DLPFC\\_exp'\n", + "\\item 'msex\\_DLPFC\\_exp'\n", + "\\item 'age\\_death\\_DLPFC\\_exp'\n", + "\\item 'pmi\\_DLPFC\\_exp'\n", + "\\item 'ROS\\_study\\_DLPFC\\_exp'\n", + "\\item 'PC1\\_DLPFC\\_exp'\n", + "\\item 'PC2\\_DLPFC\\_exp'\n", + "\\item 'PC3\\_DLPFC\\_exp'\n", + "\\item 'PC4\\_DLPFC\\_exp'\n", + "\\item 'PC5\\_DLPFC\\_exp'\n", + "\\item 'PC6\\_DLPFC\\_exp'\n", + "\\item 'PC7\\_DLPFC\\_exp'\n", + "\\item 'PC8\\_DLPFC\\_exp'\n", + "\\item 'PC9\\_DLPFC\\_exp'\n", + "\\item 'PC10\\_DLPFC\\_exp'\n", + "\\item 'PC11\\_DLPFC\\_exp'\n", + "\\item 'PC12\\_DLPFC\\_exp'\n", + "\\item 'BLOC1S3\\_AC\\_exp'\n", + "\\item 'msex\\_AC\\_exp'\n", + "\\item 'age\\_death\\_AC\\_exp'\n", + "\\item 'pmi\\_AC\\_exp'\n", + "\\item 'ROS\\_study\\_AC\\_exp'\n", + "\\item 'PC1\\_AC\\_exp'\n", + "\\item 'PC2\\_AC\\_exp'\n", + "\\item 'PC3\\_AC\\_exp'\n", + "\\item 'PC4\\_AC\\_exp'\n", + "\\item 'PC5\\_AC\\_exp'\n", + "\\item 'PC6\\_AC\\_exp'\n", + "\\item 'PC7\\_AC\\_exp'\n", + "\\item 'PC8\\_AC\\_exp'\n", + "\\item 'PC9\\_AC\\_exp'\n", + "\\item 'PC10\\_AC\\_exp'\n", + "\\item 'PC11\\_AC\\_exp'\n", + "\\item 'PC12\\_AC\\_exp'\n", + "\\item 'PC13\\_AC\\_exp'\n", + "\\item 'BLOC1S3\\_PCC\\_exp'\n", + "\\item 'msex\\_PCC\\_exp'\n", + "\\item 'age\\_death\\_PCC\\_exp'\n", + "\\item 'pmi\\_PCC\\_exp'\n", + "\\item 'ROS\\_study\\_PCC\\_exp'\n", + "\\item 'PC1\\_PCC\\_exp'\n", + "\\item 'PC2\\_PCC\\_exp'\n", + "\\item 'PC3\\_PCC\\_exp'\n", + "\\item 'PC4\\_PCC\\_exp'\n", + "\\item 'PC5\\_PCC\\_exp'\n", + "\\item 'PC6\\_PCC\\_exp'\n", + "\\item 'PC7\\_PCC\\_exp'\n", + "\\item 'PC8\\_PCC\\_exp'\n", + "\\item 'PC9\\_PCC\\_exp'\n", + "\\item 'PC10\\_PCC\\_exp'\n", + "\\item 'PC11\\_PCC\\_exp'\n", + "\\item 'PC12\\_PCC\\_exp'\n", + "\\item 'PC13\\_PCC\\_exp'\n", + "\\item 'age\\_first\\_ad\\_dx\\_num'\n", + "\\item 'educ'\n", + "\\item 'apoe4\\_dose'\n", + "\\item 'apoe2\\_dose'\n", + "\\item 'msex\\_p'\n", + "\\item 'age\\_death\\_p'\n", + "\\item 'pmi\\_p'\n", + "\\item 'ROS\\_study\\_p'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'SID'\n", + "2. 'chr19_45114770_ACCC_AC'\n", + "3. 'BLOC1S3_DLPFC_exp'\n", + "4. 'msex_DLPFC_exp'\n", + "5. 'age_death_DLPFC_exp'\n", + "6. 'pmi_DLPFC_exp'\n", + "7. 'ROS_study_DLPFC_exp'\n", + "8. 'PC1_DLPFC_exp'\n", + "9. 'PC2_DLPFC_exp'\n", + "10. 'PC3_DLPFC_exp'\n", + "11. 'PC4_DLPFC_exp'\n", + "12. 'PC5_DLPFC_exp'\n", + "13. 'PC6_DLPFC_exp'\n", + "14. 'PC7_DLPFC_exp'\n", + "15. 'PC8_DLPFC_exp'\n", + "16. 'PC9_DLPFC_exp'\n", + "17. 'PC10_DLPFC_exp'\n", + "18. 'PC11_DLPFC_exp'\n", + "19. 'PC12_DLPFC_exp'\n", + "20. 'BLOC1S3_AC_exp'\n", + "21. 'msex_AC_exp'\n", + "22. 'age_death_AC_exp'\n", + "23. 'pmi_AC_exp'\n", + "24. 'ROS_study_AC_exp'\n", + "25. 'PC1_AC_exp'\n", + "26. 'PC2_AC_exp'\n", + "27. 'PC3_AC_exp'\n", + "28. 'PC4_AC_exp'\n", + "29. 'PC5_AC_exp'\n", + "30. 'PC6_AC_exp'\n", + "31. 'PC7_AC_exp'\n", + "32. 'PC8_AC_exp'\n", + "33. 'PC9_AC_exp'\n", + "34. 'PC10_AC_exp'\n", + "35. 'PC11_AC_exp'\n", + "36. 'PC12_AC_exp'\n", + "37. 'PC13_AC_exp'\n", + "38. 'BLOC1S3_PCC_exp'\n", + "39. 'msex_PCC_exp'\n", + "40. 'age_death_PCC_exp'\n", + "41. 'pmi_PCC_exp'\n", + "42. 'ROS_study_PCC_exp'\n", + "43. 'PC1_PCC_exp'\n", + "44. 'PC2_PCC_exp'\n", + "45. 'PC3_PCC_exp'\n", + "46. 'PC4_PCC_exp'\n", + "47. 'PC5_PCC_exp'\n", + "48. 'PC6_PCC_exp'\n", + "49. 'PC7_PCC_exp'\n", + "50. 'PC8_PCC_exp'\n", + "51. 'PC9_PCC_exp'\n", + "52. 'PC10_PCC_exp'\n", + "53. 'PC11_PCC_exp'\n", + "54. 'PC12_PCC_exp'\n", + "55. 'PC13_PCC_exp'\n", + "56. 'age_first_ad_dx_num'\n", + "57. 'educ'\n", + "58. 'apoe4_dose'\n", + "59. 'apoe2_dose'\n", + "60. 'msex_p'\n", + "61. 'age_death_p'\n", + "62. 'pmi_p'\n", + "63. 'ROS_study_p'\n", + "\n", + "\n" + ], + "text/plain": [ + " [1] \"SID\" \"chr19_45114770_ACCC_AC\" \"BLOC1S3_DLPFC_exp\" \n", + " [4] \"msex_DLPFC_exp\" \"age_death_DLPFC_exp\" \"pmi_DLPFC_exp\" \n", + " [7] \"ROS_study_DLPFC_exp\" \"PC1_DLPFC_exp\" \"PC2_DLPFC_exp\" \n", + "[10] \"PC3_DLPFC_exp\" \"PC4_DLPFC_exp\" \"PC5_DLPFC_exp\" \n", + "[13] \"PC6_DLPFC_exp\" \"PC7_DLPFC_exp\" \"PC8_DLPFC_exp\" \n", + "[16] \"PC9_DLPFC_exp\" \"PC10_DLPFC_exp\" \"PC11_DLPFC_exp\" \n", + "[19] \"PC12_DLPFC_exp\" \"BLOC1S3_AC_exp\" \"msex_AC_exp\" \n", + "[22] \"age_death_AC_exp\" \"pmi_AC_exp\" \"ROS_study_AC_exp\" \n", + "[25] \"PC1_AC_exp\" \"PC2_AC_exp\" \"PC3_AC_exp\" \n", + "[28] \"PC4_AC_exp\" \"PC5_AC_exp\" \"PC6_AC_exp\" \n", + "[31] \"PC7_AC_exp\" \"PC8_AC_exp\" \"PC9_AC_exp\" \n", + "[34] \"PC10_AC_exp\" \"PC11_AC_exp\" \"PC12_AC_exp\" \n", + "[37] \"PC13_AC_exp\" \"BLOC1S3_PCC_exp\" \"msex_PCC_exp\" \n", + "[40] \"age_death_PCC_exp\" \"pmi_PCC_exp\" \"ROS_study_PCC_exp\" \n", + "[43] \"PC1_PCC_exp\" \"PC2_PCC_exp\" \"PC3_PCC_exp\" \n", + "[46] \"PC4_PCC_exp\" \"PC5_PCC_exp\" \"PC6_PCC_exp\" \n", + "[49] \"PC7_PCC_exp\" \"PC8_PCC_exp\" \"PC9_PCC_exp\" \n", + "[52] \"PC10_PCC_exp\" \"PC11_PCC_exp\" \"PC12_PCC_exp\" \n", + "[55] \"PC13_PCC_exp\" \"age_first_ad_dx_num\" \"educ\" \n", + "[58] \"apoe4_dose\" \"apoe2_dose\" \"msex_p\" \n", + "[61] \"age_death_p\" \"pmi_p\" \"ROS_study_p\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Partially overlapping samples for FIML-SEM mediation analysis\n", + "input_all <- merge(geno_45114770, DLPFC_exp, by = \"SID\", all = TRUE)\n", + "input_all <- merge(input_all, AC_exp, by = \"SID\", all = TRUE)\n", + "input_all <- merge(input_all, PCC_exp, by = \"SID\", all = TRUE)\n", + "input_all <- merge(input_all, patho_sub, by = \"SID\", all = TRUE)\n", + "colnames(input_all); nrow(input_all)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "29b8099a", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'SID'
  2. 'chr19_45114770_ACCC_AC'
  3. 'BLOC1S3_DLPFC_exp'
  4. 'PC1_DLPFC_exp'
  5. 'PC2_DLPFC_exp'
  6. 'PC3_DLPFC_exp'
  7. 'PC4_DLPFC_exp'
  8. 'PC5_DLPFC_exp'
  9. 'PC6_DLPFC_exp'
  10. 'PC7_DLPFC_exp'
  11. 'PC8_DLPFC_exp'
  12. 'PC9_DLPFC_exp'
  13. 'PC10_DLPFC_exp'
  14. 'PC11_DLPFC_exp'
  15. 'PC12_DLPFC_exp'
  16. 'BLOC1S3_AC_exp'
  17. 'PC1_AC_exp'
  18. 'PC2_AC_exp'
  19. 'PC3_AC_exp'
  20. 'PC4_AC_exp'
  21. 'PC5_AC_exp'
  22. 'PC6_AC_exp'
  23. 'PC7_AC_exp'
  24. 'PC8_AC_exp'
  25. 'PC9_AC_exp'
  26. 'PC10_AC_exp'
  27. 'PC11_AC_exp'
  28. 'PC12_AC_exp'
  29. 'PC13_AC_exp'
  30. 'BLOC1S3_PCC_exp'
  31. 'PC1_PCC_exp'
  32. 'PC2_PCC_exp'
  33. 'PC3_PCC_exp'
  34. 'PC4_PCC_exp'
  35. 'PC5_PCC_exp'
  36. 'PC6_PCC_exp'
  37. 'PC7_PCC_exp'
  38. 'PC8_PCC_exp'
  39. 'PC9_PCC_exp'
  40. 'PC10_PCC_exp'
  41. 'PC11_PCC_exp'
  42. 'PC12_PCC_exp'
  43. 'PC13_PCC_exp'
  44. 'age_first_ad_dx_num'
  45. 'educ'
  46. 'apoe4_dose'
  47. 'apoe2_dose'
  48. 'msex_u'
  49. 'age_death_u'
  50. 'pmi_u'
  51. 'ROS_study_u'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'SID'\n", + "\\item 'chr19\\_45114770\\_ACCC\\_AC'\n", + "\\item 'BLOC1S3\\_DLPFC\\_exp'\n", + "\\item 'PC1\\_DLPFC\\_exp'\n", + "\\item 'PC2\\_DLPFC\\_exp'\n", + "\\item 'PC3\\_DLPFC\\_exp'\n", + "\\item 'PC4\\_DLPFC\\_exp'\n", + "\\item 'PC5\\_DLPFC\\_exp'\n", + "\\item 'PC6\\_DLPFC\\_exp'\n", + "\\item 'PC7\\_DLPFC\\_exp'\n", + "\\item 'PC8\\_DLPFC\\_exp'\n", + "\\item 'PC9\\_DLPFC\\_exp'\n", + "\\item 'PC10\\_DLPFC\\_exp'\n", + "\\item 'PC11\\_DLPFC\\_exp'\n", + "\\item 'PC12\\_DLPFC\\_exp'\n", + "\\item 'BLOC1S3\\_AC\\_exp'\n", + "\\item 'PC1\\_AC\\_exp'\n", + "\\item 'PC2\\_AC\\_exp'\n", + "\\item 'PC3\\_AC\\_exp'\n", + "\\item 'PC4\\_AC\\_exp'\n", + "\\item 'PC5\\_AC\\_exp'\n", + "\\item 'PC6\\_AC\\_exp'\n", + "\\item 'PC7\\_AC\\_exp'\n", + "\\item 'PC8\\_AC\\_exp'\n", + "\\item 'PC9\\_AC\\_exp'\n", + "\\item 'PC10\\_AC\\_exp'\n", + "\\item 'PC11\\_AC\\_exp'\n", + "\\item 'PC12\\_AC\\_exp'\n", + "\\item 'PC13\\_AC\\_exp'\n", + "\\item 'BLOC1S3\\_PCC\\_exp'\n", + "\\item 'PC1\\_PCC\\_exp'\n", + "\\item 'PC2\\_PCC\\_exp'\n", + "\\item 'PC3\\_PCC\\_exp'\n", + "\\item 'PC4\\_PCC\\_exp'\n", + "\\item 'PC5\\_PCC\\_exp'\n", + "\\item 'PC6\\_PCC\\_exp'\n", + "\\item 'PC7\\_PCC\\_exp'\n", + "\\item 'PC8\\_PCC\\_exp'\n", + "\\item 'PC9\\_PCC\\_exp'\n", + "\\item 'PC10\\_PCC\\_exp'\n", + "\\item 'PC11\\_PCC\\_exp'\n", + "\\item 'PC12\\_PCC\\_exp'\n", + "\\item 'PC13\\_PCC\\_exp'\n", + "\\item 'age\\_first\\_ad\\_dx\\_num'\n", + "\\item 'educ'\n", + "\\item 'apoe4\\_dose'\n", + "\\item 'apoe2\\_dose'\n", + "\\item 'msex\\_u'\n", + "\\item 'age\\_death\\_u'\n", + "\\item 'pmi\\_u'\n", + "\\item 'ROS\\_study\\_u'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'SID'\n", + "2. 'chr19_45114770_ACCC_AC'\n", + "3. 'BLOC1S3_DLPFC_exp'\n", + "4. 'PC1_DLPFC_exp'\n", + "5. 'PC2_DLPFC_exp'\n", + "6. 'PC3_DLPFC_exp'\n", + "7. 'PC4_DLPFC_exp'\n", + "8. 'PC5_DLPFC_exp'\n", + "9. 'PC6_DLPFC_exp'\n", + "10. 'PC7_DLPFC_exp'\n", + "11. 'PC8_DLPFC_exp'\n", + "12. 'PC9_DLPFC_exp'\n", + "13. 'PC10_DLPFC_exp'\n", + "14. 'PC11_DLPFC_exp'\n", + "15. 'PC12_DLPFC_exp'\n", + "16. 'BLOC1S3_AC_exp'\n", + "17. 'PC1_AC_exp'\n", + "18. 'PC2_AC_exp'\n", + "19. 'PC3_AC_exp'\n", + "20. 'PC4_AC_exp'\n", + "21. 'PC5_AC_exp'\n", + "22. 'PC6_AC_exp'\n", + "23. 'PC7_AC_exp'\n", + "24. 'PC8_AC_exp'\n", + "25. 'PC9_AC_exp'\n", + "26. 'PC10_AC_exp'\n", + "27. 'PC11_AC_exp'\n", + "28. 'PC12_AC_exp'\n", + "29. 'PC13_AC_exp'\n", + "30. 'BLOC1S3_PCC_exp'\n", + "31. 'PC1_PCC_exp'\n", + "32. 'PC2_PCC_exp'\n", + "33. 'PC3_PCC_exp'\n", + "34. 'PC4_PCC_exp'\n", + "35. 'PC5_PCC_exp'\n", + "36. 'PC6_PCC_exp'\n", + "37. 'PC7_PCC_exp'\n", + "38. 'PC8_PCC_exp'\n", + "39. 'PC9_PCC_exp'\n", + "40. 'PC10_PCC_exp'\n", + "41. 'PC11_PCC_exp'\n", + "42. 'PC12_PCC_exp'\n", + "43. 'PC13_PCC_exp'\n", + "44. 'age_first_ad_dx_num'\n", + "45. 'educ'\n", + "46. 'apoe4_dose'\n", + "47. 'apoe2_dose'\n", + "48. 'msex_u'\n", + "49. 'age_death_u'\n", + "50. 'pmi_u'\n", + "51. 'ROS_study_u'\n", + "\n", + "\n" + ], + "text/plain": [ + " [1] \"SID\" \"chr19_45114770_ACCC_AC\" \"BLOC1S3_DLPFC_exp\" \n", + " [4] \"PC1_DLPFC_exp\" \"PC2_DLPFC_exp\" \"PC3_DLPFC_exp\" \n", + " [7] \"PC4_DLPFC_exp\" \"PC5_DLPFC_exp\" \"PC6_DLPFC_exp\" \n", + "[10] \"PC7_DLPFC_exp\" \"PC8_DLPFC_exp\" \"PC9_DLPFC_exp\" \n", + "[13] \"PC10_DLPFC_exp\" \"PC11_DLPFC_exp\" \"PC12_DLPFC_exp\" \n", + "[16] \"BLOC1S3_AC_exp\" \"PC1_AC_exp\" \"PC2_AC_exp\" \n", + "[19] \"PC3_AC_exp\" \"PC4_AC_exp\" \"PC5_AC_exp\" \n", + "[22] \"PC6_AC_exp\" \"PC7_AC_exp\" \"PC8_AC_exp\" \n", + "[25] \"PC9_AC_exp\" \"PC10_AC_exp\" \"PC11_AC_exp\" \n", + "[28] \"PC12_AC_exp\" \"PC13_AC_exp\" \"BLOC1S3_PCC_exp\" \n", + "[31] \"PC1_PCC_exp\" \"PC2_PCC_exp\" \"PC3_PCC_exp\" \n", + "[34] \"PC4_PCC_exp\" \"PC5_PCC_exp\" \"PC6_PCC_exp\" \n", + "[37] \"PC7_PCC_exp\" \"PC8_PCC_exp\" \"PC9_PCC_exp\" \n", + "[40] \"PC10_PCC_exp\" \"PC11_PCC_exp\" \"PC12_PCC_exp\" \n", + "[43] \"PC13_PCC_exp\" \"age_first_ad_dx_num\" \"educ\" \n", + "[46] \"apoe4_dose\" \"apoe2_dose\" \"msex_u\" \n", + "[49] \"age_death_u\" \"pmi_u\" \"ROS_study_u\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create unified covariates — msex/age_death/pmi/ROS_study\n", + "input_all <- input_all %>%\n", + " mutate(msex_u = coalesce(msex_DLPFC_exp, msex_AC_exp, msex_PCC_exp, msex_p)) %>%\n", + " mutate(age_death_u = coalesce(age_death_DLPFC_exp, age_death_AC_exp, age_death_PCC_exp, age_death_p)) %>%\n", + " mutate(pmi_u = coalesce(pmi_DLPFC_exp, pmi_AC_exp, pmi_PCC_exp, pmi_p)) %>%\n", + " mutate(ROS_study_u = coalesce(ROS_study_DLPFC_exp, ROS_study_AC_exp, ROS_study_PCC_exp, ROS_study_p))\n", + "\n", + "input_all_out <- input_all %>% dplyr::select(-c(msex_DLPFC_exp, msex_AC_exp, msex_PCC_exp, msex_p, \n", + " age_death_DLPFC_exp, age_death_AC_exp, age_death_PCC_exp, age_death_p,\n", + " pmi_DLPFC_exp, pmi_AC_exp, pmi_PCC_exp, pmi_p, \n", + " ROS_study_DLPFC_exp, ROS_study_AC_exp, ROS_study_PCC_exp, ROS_study_p))\n", + "colnames(input_all_out); nrow(input_all_out)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8bbb19bb", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "write.table(input_all_out, file = '/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set31/APOE_ind_set_31_mediation_all_input.txt',\n", + " quote = FALSE, sep = '\\t', col.names = TRUE, row.names = FALSE)" + ] + }, + { + "cell_type": "markdown", + "id": "615427b3", + "metadata": {}, + "source": [ + "## 3. Prepare input for set 27" + ] + }, + { + "cell_type": "markdown", + "id": "ea953d7f", + "metadata": {}, + "source": [ + "### 3.1 caa_4gp" + ] + }, + { + "cell_type": "markdown", + "id": "e2ddffee", + "metadata": {}, + "source": [ + "#### Genotype data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b9d71aed", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.table: 6 × 2
SIDchr19_44954310_T_C
<chr><int>
MAP153874211
MAP266378670
MAP296298490
MAP333326461
MAP347260400
MAP462466040
\n" + ], + "text/latex": [ + "A data.table: 6 × 2\n", + "\\begin{tabular}{ll}\n", + " SID & chr19\\_44954310\\_T\\_C\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t MAP15387421 & 1\\\\\n", + "\t MAP26637867 & 0\\\\\n", + "\t MAP29629849 & 0\\\\\n", + "\t MAP33332646 & 1\\\\\n", + "\t MAP34726040 & 0\\\\\n", + "\t MAP46246604 & 0\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 6 × 2\n", + "\n", + "| SID <chr> | chr19_44954310_T_C <int> |\n", + "|---|---|\n", + "| MAP15387421 | 1 |\n", + "| MAP26637867 | 0 |\n", + "| MAP29629849 | 0 |\n", + "| MAP33332646 | 1 |\n", + "| MAP34726040 | 0 |\n", + "| MAP46246604 | 0 |\n", + "\n" + ], + "text/plain": [ + " SID chr19_44954310_T_C\n", + "1 MAP15387421 1 \n", + "2 MAP26637867 0 \n", + "3 MAP29629849 0 \n", + "4 MAP33332646 1 \n", + "5 MAP34726040 0 \n", + "6 MAP46246604 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geno_44954310 <- geno %>% filter(ID == 'chr19:44954310_T_C') %>% dplyr::select(SID, dose)\n", + "colnames(geno_44954310)[2] <- 'chr19_44954310_T_C'\n", + "geno_44954310$SID <- str_remove(geno_44954310$SID,'0_')\n", + "head(geno_44954310); nrow(geno_44954310)" + ] + }, + { + "cell_type": "markdown", + "id": "d3a96b75", + "metadata": {}, + "source": [ + "#### Mediator data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "903ae965", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 3
SIDAPOC4_APOC2_AC_expAPOC2_AC_exp
<chr><dbl><dbl>
2SM-CTDUU-0.3694188-0.3310139
3SM-CTDSC-0.4720549-0.4801455
4SM-CJFLR 1.6135350 1.6003990
5SM-CJGMZ-1.1278910-1.1347400
6SM-CJFM3 0.5971125 0.6057684
7SM-CJIXK-2.1481700-2.0806720
\n" + ], + "text/latex": [ + "A data.frame: 6 × 3\n", + "\\begin{tabular}{r|lll}\n", + " & SID & APOC4\\_APOC2\\_AC\\_exp & APOC2\\_AC\\_exp\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\t2 & SM-CTDUU & -0.3694188 & -0.3310139\\\\\n", + "\t3 & SM-CTDSC & -0.4720549 & -0.4801455\\\\\n", + "\t4 & SM-CJFLR & 1.6135350 & 1.6003990\\\\\n", + "\t5 & SM-CJGMZ & -1.1278910 & -1.1347400\\\\\n", + "\t6 & SM-CJFM3 & 0.5971125 & 0.6057684\\\\\n", + "\t7 & SM-CJIXK & -2.1481700 & -2.0806720\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 3\n", + "\n", + "| | SID <chr> | APOC4_APOC2_AC_exp <dbl> | APOC2_AC_exp <dbl> |\n", + "|---|---|---|---|\n", + "| 2 | SM-CTDUU | -0.3694188 | -0.3310139 |\n", + "| 3 | SM-CTDSC | -0.4720549 | -0.4801455 |\n", + "| 4 | SM-CJFLR | 1.6135350 | 1.6003990 |\n", + "| 5 | SM-CJGMZ | -1.1278910 | -1.1347400 |\n", + "| 6 | SM-CJFM3 | 0.5971125 | 0.6057684 |\n", + "| 7 | SM-CJIXK | -2.1481700 | -2.0806720 |\n", + "\n" + ], + "text/plain": [ + " SID APOC4_APOC2_AC_exp APOC2_AC_exp\n", + "2 SM-CTDUU -0.3694188 -0.3310139 \n", + "3 SM-CTDSC -0.4720549 -0.4801455 \n", + "4 SM-CJFLR 1.6135350 1.6003990 \n", + "5 SM-CJGMZ -1.1278910 -1.1347400 \n", + "6 SM-CJFM3 0.5971125 0.6057684 \n", + "7 SM-CJIXK -2.1481700 -2.0806720 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "AC_exp_pheno_sub <- AC_exp_pheno %>% filter(ID %in% c('ENSG00000224916', 'ENSG00000234906')) %>% \n", + " dplyr::select(-`#chr`, -start, -end) %>%\n", + " t() %>%\n", + " as.data.frame(stringsAsFactors = FALSE) %>%\n", + " rownames_to_column(\"SID\")\n", + "\n", + "colnames(AC_exp_pheno_sub) <- AC_exp_pheno_sub[1, ]\n", + "AC_exp_pheno_sub <- AC_exp_pheno_sub[-1, ]\n", + "colnames(AC_exp_pheno_sub) <- c('SID', 'APOC4_APOC2_AC_exp', 'APOC2_AC_exp')\n", + "AC_exp_pheno_sub <- AC_exp_pheno_sub %>% mutate(across(-SID, as.numeric))\n", + "head(AC_exp_pheno_sub)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ca1e5fbb", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 20
SIDAPOC4_APOC2_AC_expAPOC2_AC_expmsex_AC_expage_death_AC_exppmi_AC_expROS_study_AC_expPC1_AC_expPC2_AC_expPC3_AC_expPC4_AC_expPC5_AC_expPC6_AC_expPC7_AC_expPC8_AC_expPC9_AC_expPC10_AC_expPC11_AC_expPC12_AC_expPC13_AC_exp
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1SM-CJEFR 0.8665892 0.82526370 95.184125.0000001-0.009288654 0.07222711 0.002891650-0.049175427-0.009331941 0.02367040 0.012981030-0.0012170090-0.07232593 0.010685649 0.022902699 0.053007585-0.032927999
2SM-CJEFS 1.0813560 1.09441000101.943904.5000001-0.011878860 0.07627014 0.012650560-0.073416920-0.023965680-0.01003319 0.030615930-0.0002483342-0.04340587 0.042140530-0.023707780-0.041120410 0.047723010
3SM-CJEFU-0.4241317-0.45596521 81.938408.8333330-0.009894409-0.01305638 0.049875899 0.108320959-0.116313249 0.01894686-0.072541359 0.0430160410-0.05709961 0.026557430-0.022803826 0.005868232-0.028957413
4SM-CJEG1-0.8509220-0.94272550 81.325122.0000001-0.006808607-0.02436349 0.036853414-0.038215084-0.002585761 0.03118097-0.063388884 0.0175124050 0.02736089-0.005039676 0.011211747-0.031767847-0.011675806
5SM-CJEG3-0.4842026-0.52938010 91.553736.2500000 0.012294605 0.02106597-0.005452404 0.024195028 0.004051295 0.03228929 0.050129614-0.0156169510 0.08987513-0.061211599-0.002553259-0.258960234-0.009280432
6SM-CJEG7 0.4479647 0.50460990 88.369618.1666671-0.006441862 0.03414639-0.006961758 0.005733466 0.028457510 0.01953690-0.007155527 0.0405081860 0.04029579-0.087856525-0.077338595 0.095284697-0.131160245
\n" + ], + "text/latex": [ + "A data.frame: 6 × 20\n", + "\\begin{tabular}{r|llllllllllllllllllll}\n", + " & SID & APOC4\\_APOC2\\_AC\\_exp & APOC2\\_AC\\_exp & msex\\_AC\\_exp & age\\_death\\_AC\\_exp & pmi\\_AC\\_exp & ROS\\_study\\_AC\\_exp & PC1\\_AC\\_exp & PC2\\_AC\\_exp & PC3\\_AC\\_exp & PC4\\_AC\\_exp & PC5\\_AC\\_exp & PC6\\_AC\\_exp & PC7\\_AC\\_exp & PC8\\_AC\\_exp & PC9\\_AC\\_exp & PC10\\_AC\\_exp & PC11\\_AC\\_exp & PC12\\_AC\\_exp & PC13\\_AC\\_exp\\\\\n", + " & & & & & & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & SM-CJEFR & 0.8665892 & 0.8252637 & 0 & 95.18412 & 5.000000 & 1 & -0.009288654 & 0.07222711 & 0.002891650 & -0.049175427 & -0.009331941 & 0.02367040 & 0.012981030 & -0.0012170090 & -0.07232593 & 0.010685649 & 0.022902699 & 0.053007585 & -0.032927999\\\\\n", + "\t2 & SM-CJEFS & 1.0813560 & 1.0944100 & 0 & 101.94390 & 4.500000 & 1 & -0.011878860 & 0.07627014 & 0.012650560 & -0.073416920 & -0.023965680 & -0.01003319 & 0.030615930 & -0.0002483342 & -0.04340587 & 0.042140530 & -0.023707780 & -0.041120410 & 0.047723010\\\\\n", + "\t3 & SM-CJEFU & -0.4241317 & -0.4559652 & 1 & 81.93840 & 8.833333 & 0 & -0.009894409 & -0.01305638 & 0.049875899 & 0.108320959 & -0.116313249 & 0.01894686 & -0.072541359 & 0.0430160410 & -0.05709961 & 0.026557430 & -0.022803826 & 0.005868232 & -0.028957413\\\\\n", + "\t4 & SM-CJEG1 & -0.8509220 & -0.9427255 & 0 & 81.32512 & 2.000000 & 1 & -0.006808607 & -0.02436349 & 0.036853414 & -0.038215084 & -0.002585761 & 0.03118097 & -0.063388884 & 0.0175124050 & 0.02736089 & -0.005039676 & 0.011211747 & -0.031767847 & -0.011675806\\\\\n", + "\t5 & SM-CJEG3 & -0.4842026 & -0.5293801 & 0 & 91.55373 & 6.250000 & 0 & 0.012294605 & 0.02106597 & -0.005452404 & 0.024195028 & 0.004051295 & 0.03228929 & 0.050129614 & -0.0156169510 & 0.08987513 & -0.061211599 & -0.002553259 & -0.258960234 & -0.009280432\\\\\n", + "\t6 & SM-CJEG7 & 0.4479647 & 0.5046099 & 0 & 88.36961 & 8.166667 & 1 & -0.006441862 & 0.03414639 & -0.006961758 & 0.005733466 & 0.028457510 & 0.01953690 & -0.007155527 & 0.0405081860 & 0.04029579 & -0.087856525 & -0.077338595 & 0.095284697 & -0.131160245\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 20\n", + "\n", + "| | SID <chr> | APOC4_APOC2_AC_exp <dbl> | APOC2_AC_exp <dbl> | msex_AC_exp <dbl> | age_death_AC_exp <dbl> | pmi_AC_exp <dbl> | ROS_study_AC_exp <dbl> | PC1_AC_exp <dbl> | PC2_AC_exp <dbl> | PC3_AC_exp <dbl> | PC4_AC_exp <dbl> | PC5_AC_exp <dbl> | PC6_AC_exp <dbl> | PC7_AC_exp <dbl> | PC8_AC_exp <dbl> | PC9_AC_exp <dbl> | PC10_AC_exp <dbl> | PC11_AC_exp <dbl> | PC12_AC_exp <dbl> | PC13_AC_exp <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | SM-CJEFR | 0.8665892 | 0.8252637 | 0 | 95.18412 | 5.000000 | 1 | -0.009288654 | 0.07222711 | 0.002891650 | -0.049175427 | -0.009331941 | 0.02367040 | 0.012981030 | -0.0012170090 | -0.07232593 | 0.010685649 | 0.022902699 | 0.053007585 | -0.032927999 |\n", + "| 2 | SM-CJEFS | 1.0813560 | 1.0944100 | 0 | 101.94390 | 4.500000 | 1 | -0.011878860 | 0.07627014 | 0.012650560 | -0.073416920 | -0.023965680 | -0.01003319 | 0.030615930 | -0.0002483342 | -0.04340587 | 0.042140530 | -0.023707780 | -0.041120410 | 0.047723010 |\n", + "| 3 | SM-CJEFU | -0.4241317 | -0.4559652 | 1 | 81.93840 | 8.833333 | 0 | -0.009894409 | -0.01305638 | 0.049875899 | 0.108320959 | -0.116313249 | 0.01894686 | -0.072541359 | 0.0430160410 | -0.05709961 | 0.026557430 | -0.022803826 | 0.005868232 | -0.028957413 |\n", + "| 4 | SM-CJEG1 | -0.8509220 | -0.9427255 | 0 | 81.32512 | 2.000000 | 1 | -0.006808607 | -0.02436349 | 0.036853414 | -0.038215084 | -0.002585761 | 0.03118097 | -0.063388884 | 0.0175124050 | 0.02736089 | -0.005039676 | 0.011211747 | -0.031767847 | -0.011675806 |\n", + "| 5 | SM-CJEG3 | -0.4842026 | -0.5293801 | 0 | 91.55373 | 6.250000 | 0 | 0.012294605 | 0.02106597 | -0.005452404 | 0.024195028 | 0.004051295 | 0.03228929 | 0.050129614 | -0.0156169510 | 0.08987513 | -0.061211599 | -0.002553259 | -0.258960234 | -0.009280432 |\n", + "| 6 | SM-CJEG7 | 0.4479647 | 0.5046099 | 0 | 88.36961 | 8.166667 | 1 | -0.006441862 | 0.03414639 | -0.006961758 | 0.005733466 | 0.028457510 | 0.01953690 | -0.007155527 | 0.0405081860 | 0.04029579 | -0.087856525 | -0.077338595 | 0.095284697 | -0.131160245 |\n", + "\n" + ], + "text/plain": [ + " SID APOC4_APOC2_AC_exp APOC2_AC_exp msex_AC_exp age_death_AC_exp\n", + "1 SM-CJEFR 0.8665892 0.8252637 0 95.18412 \n", + "2 SM-CJEFS 1.0813560 1.0944100 0 101.94390 \n", + "3 SM-CJEFU -0.4241317 -0.4559652 1 81.93840 \n", + "4 SM-CJEG1 -0.8509220 -0.9427255 0 81.32512 \n", + "5 SM-CJEG3 -0.4842026 -0.5293801 0 91.55373 \n", + "6 SM-CJEG7 0.4479647 0.5046099 0 88.36961 \n", + " pmi_AC_exp ROS_study_AC_exp PC1_AC_exp PC2_AC_exp PC3_AC_exp \n", + "1 5.000000 1 -0.009288654 0.07222711 0.002891650\n", + "2 4.500000 1 -0.011878860 0.07627014 0.012650560\n", + "3 8.833333 0 -0.009894409 -0.01305638 0.049875899\n", + "4 2.000000 1 -0.006808607 -0.02436349 0.036853414\n", + "5 6.250000 0 0.012294605 0.02106597 -0.005452404\n", + "6 8.166667 1 -0.006441862 0.03414639 -0.006961758\n", + " PC4_AC_exp PC5_AC_exp PC6_AC_exp PC7_AC_exp PC8_AC_exp PC9_AC_exp \n", + "1 -0.049175427 -0.009331941 0.02367040 0.012981030 -0.0012170090 -0.07232593\n", + "2 -0.073416920 -0.023965680 -0.01003319 0.030615930 -0.0002483342 -0.04340587\n", + "3 0.108320959 -0.116313249 0.01894686 -0.072541359 0.0430160410 -0.05709961\n", + "4 -0.038215084 -0.002585761 0.03118097 -0.063388884 0.0175124050 0.02736089\n", + "5 0.024195028 0.004051295 0.03228929 0.050129614 -0.0156169510 0.08987513\n", + "6 0.005733466 0.028457510 0.01953690 -0.007155527 0.0405081860 0.04029579\n", + " PC10_AC_exp PC11_AC_exp PC12_AC_exp PC13_AC_exp \n", + "1 0.010685649 0.022902699 0.053007585 -0.032927999\n", + "2 0.042140530 -0.023707780 -0.041120410 0.047723010\n", + "3 0.026557430 -0.022803826 0.005868232 -0.028957413\n", + "4 -0.005039676 0.011211747 -0.031767847 -0.011675806\n", + "5 -0.061211599 -0.002553259 -0.258960234 -0.009280432\n", + "6 -0.087856525 -0.077338595 0.095284697 -0.131160245" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "593" + ], + "text/latex": [ + "593" + ], + "text/markdown": [ + "593" + ], + "text/plain": [ + "[1] 593" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "693" + ], + "text/latex": [ + "693" + ], + "text/markdown": [ + "693" + ], + "text/plain": [ + "[1] 693" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "593" + ], + "text/latex": [ + "593" + ], + "text/markdown": [ + "593" + ], + "text/plain": [ + "[1] 593" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "AC_exp <- merge(AC_exp_pheno_sub, AC_exp_covar_t, by = 'SID')\n", + "head(AC_exp); nrow(AC_exp); nrow(AC_exp_pheno_sub); nrow(AC_exp_covar_t) " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "06a9dfab", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "DeJager_Mic_exp_pheno <- fread('/mnt/lustre/lab/gwang/ftp_fgc_xqtl/eQTL/ROSMAP/DLPFC_Mic/analysis_ready/phenotype_preprocessing/phenotype_by_chrom/Mic.log2cpm.bed.chr19.bed.gz')\n", + "DeJager_Mic_exp_covar <- fread('/mnt/lustre/lab/gwang/ftp_fgc_xqtl/eQTL/ROSMAP/DLPFC_Mic/analysis_ready/covariate_preprocessing/Mic.log2cpm.Mic.rosmap_cov.ROSMAP_NIA_WGS.leftnorm.bcftools_qc.plink_qc.snuc_pseudo_bulk.related.plink_qc.extracted.pca.projected.Marchenko_PC.gz')\n", + "Mega_Mic_exp_pheno <- fread('/mnt/lustre/lab/gwang/ftp_fgc_xqtl/eQTL/ROSMAP/mega/Mic/analysis_ready/phenotype_preprocessing/phenotype_by_chrom/snuc_pseudo_bulk.Mic.mega.normalized.log2cpm.bed.chr19.bed.gz')\n", + "Mega_Mic_exp_covar <- fread('/mnt/lustre/lab/gwang/ftp_fgc_xqtl/eQTL/ROSMAP/mega/Mic/analysis_ready/covariate_preprocessing/snuc_pseudo_bulk.Mic.mega.normalized.log2cpm.rosmap_cov.ROSMAP_NIA_WGS.leftnorm.bcftools_qc.plink_qc.snuc_pseudo_bulk_mega.related.plink_qc.extracted.pca.projected.Marchenko_PC.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b8027e6f", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 2
SIDAPOC1_DeJager_Mic_exp
<chr><dbl>
1MAP153874211.778668554
2MAP228680240.760327541
3MAP462466040.461129243
4MAP501041340.002948979
5MAP501048460.121204056
6MAP501064420.377391944
\n" + ], + "text/latex": [ + "A data.frame: 6 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & SID & APOC1\\_DeJager\\_Mic\\_exp\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & MAP15387421 & 1.778668554\\\\\n", + "\t2 & MAP22868024 & 0.760327541\\\\\n", + "\t3 & MAP46246604 & 0.461129243\\\\\n", + "\t4 & MAP50104134 & 0.002948979\\\\\n", + "\t5 & MAP50104846 & 0.121204056\\\\\n", + "\t6 & MAP50106442 & 0.377391944\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 2\n", + "\n", + "| | SID <chr> | APOC1_DeJager_Mic_exp <dbl> |\n", + "|---|---|---|\n", + "| 1 | MAP15387421 | 1.778668554 |\n", + "| 2 | MAP22868024 | 0.760327541 |\n", + "| 3 | MAP46246604 | 0.461129243 |\n", + "| 4 | MAP50104134 | 0.002948979 |\n", + "| 5 | MAP50104846 | 0.121204056 |\n", + "| 6 | MAP50106442 | 0.377391944 |\n", + "\n" + ], + "text/plain": [ + " SID APOC1_DeJager_Mic_exp\n", + "1 MAP15387421 1.778668554 \n", + "2 MAP22868024 0.760327541 \n", + "3 MAP46246604 0.461129243 \n", + "4 MAP50104134 0.002948979 \n", + "5 MAP50104846 0.121204056 \n", + "6 MAP50106442 0.377391944 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "DeJager_Mic_exp_pheno_sub <- DeJager_Mic_exp_pheno %>% filter(ID == 'ENSG00000130208') %>% \n", + " dplyr::select(-`#chr`, -start, -end, -ID) %>%\n", + " t() %>%\n", + " as.data.frame(stringsAsFactors = FALSE) %>%\n", + " rownames_to_column(\"SID\") %>% \n", + " mutate(across(-SID, as.numeric))\n", + "\n", + "colnames(DeJager_Mic_exp_pheno_sub)[2] <- 'APOC1_DeJager_Mic_exp'\n", + "head(DeJager_Mic_exp_pheno_sub)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "061b27bc", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 19
SIDmsex_DeJager_Mic_expage_death_DeJager_Mic_exppmi_DeJager_Mic_expPC1_DeJager_Mic_expPC2_DeJager_Mic_expPC3_DeJager_Mic_expPC4_DeJager_Mic_expPC5_DeJager_Mic_expPC6_DeJager_Mic_expPC7_DeJager_Mic_expPC8_DeJager_Mic_expPC9_DeJager_Mic_expPC10_DeJager_Mic_expPC11_DeJager_Mic_expPC12_DeJager_Mic_expPC13_DeJager_Mic_expPC14_DeJager_Mic_expPC15_DeJager_Mic_exp
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
2SM-CTDSC0 83.69062 3.933333-0.02616273 0.0110695540-0.017410185 0.003133548-0.049837162-0.020046619-0.01588715 0.00465551 0.042802380 0.065688505-0.01840336-0.01931986 0.006876464 0.007784447-0.04516301
3SM-CTEMS0 90.38467 3.783333-0.07539524 0.0248258980 0.051232352 0.020282634 0.008649787 0.002051615-0.01095519 0.06349183 0.080658682 0.032025375-0.01879726-0.10749333-0.024782011-0.014753078 0.00383289
4SM-CJGMZ0101.19097 4.833333 0.05292045-0.0231848920-0.007250396-0.005203040-0.022478260-0.016302938 0.02144873-0.03977448 0.020387242-0.013389412 0.01248108-0.01197203-0.023978025-0.047377517-0.03683771
5SM-CJIXK1 74.45038 7.016667-0.01999390 0.0008450253-0.036233760 0.019520310-0.023129300 0.031686500 0.06473197-0.07145390 0.013664140 0.033158550 0.03548186-0.02032258-0.146172400-0.033078090 0.04633378
6SM-CJEG60 84.9117014.750000 0.06525972 0.0139363070-0.016738464 0.002083605 0.023189047 0.045637974-0.05405078-0.10029237 0.002496504-0.019388523 0.02294865-0.11494169 0.027777744 0.060772735 0.01728713
7SM-CJGID0 94.43669 4.916667 0.01761293-0.0594433610-0.037008908 0.010302268-0.035599748-0.036403885-0.03397714-0.02504429-0.047595636 0.008985785-0.04055590 0.01710334 0.019268537 0.022928799-0.02046347
\n" + ], + "text/latex": [ + "A data.frame: 6 × 19\n", + "\\begin{tabular}{r|lllllllllllllllllll}\n", + " & SID & msex\\_DeJager\\_Mic\\_exp & age\\_death\\_DeJager\\_Mic\\_exp & pmi\\_DeJager\\_Mic\\_exp & PC1\\_DeJager\\_Mic\\_exp & PC2\\_DeJager\\_Mic\\_exp & PC3\\_DeJager\\_Mic\\_exp & PC4\\_DeJager\\_Mic\\_exp & PC5\\_DeJager\\_Mic\\_exp & PC6\\_DeJager\\_Mic\\_exp & PC7\\_DeJager\\_Mic\\_exp & PC8\\_DeJager\\_Mic\\_exp & PC9\\_DeJager\\_Mic\\_exp & PC10\\_DeJager\\_Mic\\_exp & PC11\\_DeJager\\_Mic\\_exp & PC12\\_DeJager\\_Mic\\_exp & PC13\\_DeJager\\_Mic\\_exp & PC14\\_DeJager\\_Mic\\_exp & PC15\\_DeJager\\_Mic\\_exp\\\\\n", + " & & & & & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t2 & SM-CTDSC & 0 & 83.69062 & 3.933333 & -0.02616273 & 0.0110695540 & -0.017410185 & 0.003133548 & -0.049837162 & -0.020046619 & -0.01588715 & 0.00465551 & 0.042802380 & 0.065688505 & -0.01840336 & -0.01931986 & 0.006876464 & 0.007784447 & -0.04516301\\\\\n", + "\t3 & SM-CTEMS & 0 & 90.38467 & 3.783333 & -0.07539524 & 0.0248258980 & 0.051232352 & 0.020282634 & 0.008649787 & 0.002051615 & -0.01095519 & 0.06349183 & 0.080658682 & 0.032025375 & -0.01879726 & -0.10749333 & -0.024782011 & -0.014753078 & 0.00383289\\\\\n", + "\t4 & SM-CJGMZ & 0 & 101.19097 & 4.833333 & 0.05292045 & -0.0231848920 & -0.007250396 & -0.005203040 & -0.022478260 & -0.016302938 & 0.02144873 & -0.03977448 & 0.020387242 & -0.013389412 & 0.01248108 & -0.01197203 & -0.023978025 & -0.047377517 & -0.03683771\\\\\n", + "\t5 & SM-CJIXK & 1 & 74.45038 & 7.016667 & -0.01999390 & 0.0008450253 & -0.036233760 & 0.019520310 & -0.023129300 & 0.031686500 & 0.06473197 & -0.07145390 & 0.013664140 & 0.033158550 & 0.03548186 & -0.02032258 & -0.146172400 & -0.033078090 & 0.04633378\\\\\n", + "\t6 & SM-CJEG6 & 0 & 84.91170 & 14.750000 & 0.06525972 & 0.0139363070 & -0.016738464 & 0.002083605 & 0.023189047 & 0.045637974 & -0.05405078 & -0.10029237 & 0.002496504 & -0.019388523 & 0.02294865 & -0.11494169 & 0.027777744 & 0.060772735 & 0.01728713\\\\\n", + "\t7 & SM-CJGID & 0 & 94.43669 & 4.916667 & 0.01761293 & -0.0594433610 & -0.037008908 & 0.010302268 & -0.035599748 & -0.036403885 & -0.03397714 & -0.02504429 & -0.047595636 & 0.008985785 & -0.04055590 & 0.01710334 & 0.019268537 & 0.022928799 & -0.02046347\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 19\n", + "\n", + "| | SID <chr> | msex_DeJager_Mic_exp <dbl> | age_death_DeJager_Mic_exp <dbl> | pmi_DeJager_Mic_exp <dbl> | PC1_DeJager_Mic_exp <dbl> | PC2_DeJager_Mic_exp <dbl> | PC3_DeJager_Mic_exp <dbl> | PC4_DeJager_Mic_exp <dbl> | PC5_DeJager_Mic_exp <dbl> | PC6_DeJager_Mic_exp <dbl> | PC7_DeJager_Mic_exp <dbl> | PC8_DeJager_Mic_exp <dbl> | PC9_DeJager_Mic_exp <dbl> | PC10_DeJager_Mic_exp <dbl> | PC11_DeJager_Mic_exp <dbl> | PC12_DeJager_Mic_exp <dbl> | PC13_DeJager_Mic_exp <dbl> | PC14_DeJager_Mic_exp <dbl> | PC15_DeJager_Mic_exp <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 2 | SM-CTDSC | 0 | 83.69062 | 3.933333 | -0.02616273 | 0.0110695540 | -0.017410185 | 0.003133548 | -0.049837162 | -0.020046619 | -0.01588715 | 0.00465551 | 0.042802380 | 0.065688505 | -0.01840336 | -0.01931986 | 0.006876464 | 0.007784447 | -0.04516301 |\n", + "| 3 | SM-CTEMS | 0 | 90.38467 | 3.783333 | -0.07539524 | 0.0248258980 | 0.051232352 | 0.020282634 | 0.008649787 | 0.002051615 | -0.01095519 | 0.06349183 | 0.080658682 | 0.032025375 | -0.01879726 | -0.10749333 | -0.024782011 | -0.014753078 | 0.00383289 |\n", + "| 4 | SM-CJGMZ | 0 | 101.19097 | 4.833333 | 0.05292045 | -0.0231848920 | -0.007250396 | -0.005203040 | -0.022478260 | -0.016302938 | 0.02144873 | -0.03977448 | 0.020387242 | -0.013389412 | 0.01248108 | -0.01197203 | -0.023978025 | -0.047377517 | -0.03683771 |\n", + "| 5 | SM-CJIXK | 1 | 74.45038 | 7.016667 | -0.01999390 | 0.0008450253 | -0.036233760 | 0.019520310 | -0.023129300 | 0.031686500 | 0.06473197 | -0.07145390 | 0.013664140 | 0.033158550 | 0.03548186 | -0.02032258 | -0.146172400 | -0.033078090 | 0.04633378 |\n", + "| 6 | SM-CJEG6 | 0 | 84.91170 | 14.750000 | 0.06525972 | 0.0139363070 | -0.016738464 | 0.002083605 | 0.023189047 | 0.045637974 | -0.05405078 | -0.10029237 | 0.002496504 | -0.019388523 | 0.02294865 | -0.11494169 | 0.027777744 | 0.060772735 | 0.01728713 |\n", + "| 7 | SM-CJGID | 0 | 94.43669 | 4.916667 | 0.01761293 | -0.0594433610 | -0.037008908 | 0.010302268 | -0.035599748 | -0.036403885 | -0.03397714 | -0.02504429 | -0.047595636 | 0.008985785 | -0.04055590 | 0.01710334 | 0.019268537 | 0.022928799 | -0.02046347 |\n", + "\n" + ], + "text/plain": [ + " SID msex_DeJager_Mic_exp age_death_DeJager_Mic_exp pmi_DeJager_Mic_exp\n", + "2 SM-CTDSC 0 83.69062 3.933333 \n", + "3 SM-CTEMS 0 90.38467 3.783333 \n", + "4 SM-CJGMZ 0 101.19097 4.833333 \n", + "5 SM-CJIXK 1 74.45038 7.016667 \n", + "6 SM-CJEG6 0 84.91170 14.750000 \n", + "7 SM-CJGID 0 94.43669 4.916667 \n", + " PC1_DeJager_Mic_exp PC2_DeJager_Mic_exp PC3_DeJager_Mic_exp\n", + "2 -0.02616273 0.0110695540 -0.017410185 \n", + "3 -0.07539524 0.0248258980 0.051232352 \n", + "4 0.05292045 -0.0231848920 -0.007250396 \n", + "5 -0.01999390 0.0008450253 -0.036233760 \n", + "6 0.06525972 0.0139363070 -0.016738464 \n", + "7 0.01761293 -0.0594433610 -0.037008908 \n", + " PC4_DeJager_Mic_exp PC5_DeJager_Mic_exp PC6_DeJager_Mic_exp\n", + "2 0.003133548 -0.049837162 -0.020046619 \n", + "3 0.020282634 0.008649787 0.002051615 \n", + "4 -0.005203040 -0.022478260 -0.016302938 \n", + "5 0.019520310 -0.023129300 0.031686500 \n", + "6 0.002083605 0.023189047 0.045637974 \n", + "7 0.010302268 -0.035599748 -0.036403885 \n", + " PC7_DeJager_Mic_exp PC8_DeJager_Mic_exp PC9_DeJager_Mic_exp\n", + "2 -0.01588715 0.00465551 0.042802380 \n", + "3 -0.01095519 0.06349183 0.080658682 \n", + "4 0.02144873 -0.03977448 0.020387242 \n", + "5 0.06473197 -0.07145390 0.013664140 \n", + "6 -0.05405078 -0.10029237 0.002496504 \n", + "7 -0.03397714 -0.02504429 -0.047595636 \n", + " PC10_DeJager_Mic_exp PC11_DeJager_Mic_exp PC12_DeJager_Mic_exp\n", + "2 0.065688505 -0.01840336 -0.01931986 \n", + "3 0.032025375 -0.01879726 -0.10749333 \n", + "4 -0.013389412 0.01248108 -0.01197203 \n", + "5 0.033158550 0.03548186 -0.02032258 \n", + "6 -0.019388523 0.02294865 -0.11494169 \n", + "7 0.008985785 -0.04055590 0.01710334 \n", + " PC13_DeJager_Mic_exp PC14_DeJager_Mic_exp PC15_DeJager_Mic_exp\n", + "2 0.006876464 0.007784447 -0.04516301 \n", + "3 -0.024782011 -0.014753078 0.00383289 \n", + "4 -0.023978025 -0.047377517 -0.03683771 \n", + "5 -0.146172400 -0.033078090 0.04633378 \n", + "6 0.027777744 0.060772735 0.01728713 \n", + "7 0.019268537 0.022928799 -0.02046347 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "DeJager_Mic_exp_covar_t <- DeJager_Mic_exp_covar %>% \n", + " t() %>% \n", + " as.data.frame() %>% \n", + " rownames_to_column(\"SID\")\n", + "\n", + "colnames(DeJager_Mic_exp_covar_t) <- DeJager_Mic_exp_covar_t[1, ]\n", + "DeJager_Mic_exp_covar_t <- DeJager_Mic_exp_covar_t[-1, ]\n", + "colnames(DeJager_Mic_exp_covar_t)[1] <- 'SID'\n", + "DeJager_Mic_exp_covar_t <- DeJager_Mic_exp_covar_t %>% mutate(across(-SID, as.numeric)) %>% dplyr::select(-contains(\"Hidden_Factor\"))\n", + "DeJager_Mic_exp_covar_t <- DeJager_Mic_exp_covar_t |>\n", + " setNames(ifelse(names(DeJager_Mic_exp_covar_t) == \"SID\", \"SID\", paste0(names(DeJager_Mic_exp_covar_t), \"_DeJager_Mic_exp\")))\n", + "\n", + "head(DeJager_Mic_exp_covar_t)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "af6bbe25", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 20
SIDAPOC1_DeJager_Mic_expmsex_DeJager_Mic_expage_death_DeJager_Mic_exppmi_DeJager_Mic_expPC1_DeJager_Mic_expPC2_DeJager_Mic_expPC3_DeJager_Mic_expPC4_DeJager_Mic_expPC5_DeJager_Mic_expPC6_DeJager_Mic_expPC7_DeJager_Mic_expPC8_DeJager_Mic_expPC9_DeJager_Mic_expPC10_DeJager_Mic_expPC11_DeJager_Mic_expPC12_DeJager_Mic_expPC13_DeJager_Mic_expPC14_DeJager_Mic_expPC15_DeJager_Mic_exp
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1MAP153874211.7786685540 92.402468.833333 0.030748651 0.008129914 0.04566422 0.01368809-0.03835653-0.005014206 0.069764313 0.049545475-0.0879638270 0.0322273730-0.06564929-0.037932959 0.034960757-0.124137524 0.020442481
2MAP462466040.4611292430 89.448323.750000-0.057939158 0.004366824 0.04860006-0.03017768-0.04517439 0.107212604 0.041790375 0.109996647-0.0645883570-0.0522399500-0.03540515 0.019338182 0.045959633 0.151518373 0.101876953
3MAP501041340.0029489791 90.036965.333333-0.005724776-0.012738140-0.03092883-0.01154606-0.02729140 0.023264206 0.061973706-0.042456196-0.0354291130-0.0810296050 0.03030571-0.162690054-0.057322086-0.024194658-0.002923682
4MAP501048460.1212040560 91.904184.000000 0.011015010 0.020813320-0.09980753 0.01653993 0.04113268 0.063429440-0.081638040 0.078240160 0.1643765900-0.0309312300 0.01750777 0.026648620-0.079458160-0.021245750-0.092409580
5MAP501064420.3773919440 87.947987.216667-0.007747675-0.008697100-0.02889843 0.01265543-0.06306697 0.004250624 0.008469213-0.006596004-0.0003796181-0.0002196695-0.06196570-0.006355808 0.008656075-0.001692986-0.073489640
6MAP501084620.8416212340101.415475.666667 0.049092348 0.006814402-0.02371451-0.02898146-0.04402057 0.023426141 0.051231654 0.003902817-0.0072046590-0.0712319770 0.03835327-0.031920865 0.063607226 0.040612079 0.041653115
\n" + ], + "text/latex": [ + "A data.frame: 6 × 20\n", + "\\begin{tabular}{r|llllllllllllllllllll}\n", + " & SID & APOC1\\_DeJager\\_Mic\\_exp & msex\\_DeJager\\_Mic\\_exp & age\\_death\\_DeJager\\_Mic\\_exp & pmi\\_DeJager\\_Mic\\_exp & PC1\\_DeJager\\_Mic\\_exp & PC2\\_DeJager\\_Mic\\_exp & PC3\\_DeJager\\_Mic\\_exp & PC4\\_DeJager\\_Mic\\_exp & PC5\\_DeJager\\_Mic\\_exp & PC6\\_DeJager\\_Mic\\_exp & PC7\\_DeJager\\_Mic\\_exp & PC8\\_DeJager\\_Mic\\_exp & PC9\\_DeJager\\_Mic\\_exp & PC10\\_DeJager\\_Mic\\_exp & PC11\\_DeJager\\_Mic\\_exp & PC12\\_DeJager\\_Mic\\_exp & PC13\\_DeJager\\_Mic\\_exp & PC14\\_DeJager\\_Mic\\_exp & PC15\\_DeJager\\_Mic\\_exp\\\\\n", + " & & & & & & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & MAP15387421 & 1.778668554 & 0 & 92.40246 & 8.833333 & 0.030748651 & 0.008129914 & 0.04566422 & 0.01368809 & -0.03835653 & -0.005014206 & 0.069764313 & 0.049545475 & -0.0879638270 & 0.0322273730 & -0.06564929 & -0.037932959 & 0.034960757 & -0.124137524 & 0.020442481\\\\\n", + "\t2 & MAP46246604 & 0.461129243 & 0 & 89.44832 & 3.750000 & -0.057939158 & 0.004366824 & 0.04860006 & -0.03017768 & -0.04517439 & 0.107212604 & 0.041790375 & 0.109996647 & -0.0645883570 & -0.0522399500 & -0.03540515 & 0.019338182 & 0.045959633 & 0.151518373 & 0.101876953\\\\\n", + "\t3 & MAP50104134 & 0.002948979 & 1 & 90.03696 & 5.333333 & -0.005724776 & -0.012738140 & -0.03092883 & -0.01154606 & -0.02729140 & 0.023264206 & 0.061973706 & -0.042456196 & -0.0354291130 & -0.0810296050 & 0.03030571 & -0.162690054 & -0.057322086 & -0.024194658 & -0.002923682\\\\\n", + "\t4 & MAP50104846 & 0.121204056 & 0 & 91.90418 & 4.000000 & 0.011015010 & 0.020813320 & -0.09980753 & 0.01653993 & 0.04113268 & 0.063429440 & -0.081638040 & 0.078240160 & 0.1643765900 & -0.0309312300 & 0.01750777 & 0.026648620 & -0.079458160 & -0.021245750 & -0.092409580\\\\\n", + "\t5 & MAP50106442 & 0.377391944 & 0 & 87.94798 & 7.216667 & -0.007747675 & -0.008697100 & -0.02889843 & 0.01265543 & -0.06306697 & 0.004250624 & 0.008469213 & -0.006596004 & -0.0003796181 & -0.0002196695 & -0.06196570 & -0.006355808 & 0.008656075 & -0.001692986 & -0.073489640\\\\\n", + "\t6 & MAP50108462 & 0.841621234 & 0 & 101.41547 & 5.666667 & 0.049092348 & 0.006814402 & -0.02371451 & -0.02898146 & -0.04402057 & 0.023426141 & 0.051231654 & 0.003902817 & -0.0072046590 & -0.0712319770 & 0.03835327 & -0.031920865 & 0.063607226 & 0.040612079 & 0.041653115\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 20\n", + "\n", + "| | SID <chr> | APOC1_DeJager_Mic_exp <dbl> | msex_DeJager_Mic_exp <dbl> | age_death_DeJager_Mic_exp <dbl> | pmi_DeJager_Mic_exp <dbl> | PC1_DeJager_Mic_exp <dbl> | PC2_DeJager_Mic_exp <dbl> | PC3_DeJager_Mic_exp <dbl> | PC4_DeJager_Mic_exp <dbl> | PC5_DeJager_Mic_exp <dbl> | PC6_DeJager_Mic_exp <dbl> | PC7_DeJager_Mic_exp <dbl> | PC8_DeJager_Mic_exp <dbl> | PC9_DeJager_Mic_exp <dbl> | PC10_DeJager_Mic_exp <dbl> | PC11_DeJager_Mic_exp <dbl> | PC12_DeJager_Mic_exp <dbl> | PC13_DeJager_Mic_exp <dbl> | PC14_DeJager_Mic_exp <dbl> | PC15_DeJager_Mic_exp <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | MAP15387421 | 1.778668554 | 0 | 92.40246 | 8.833333 | 0.030748651 | 0.008129914 | 0.04566422 | 0.01368809 | -0.03835653 | -0.005014206 | 0.069764313 | 0.049545475 | -0.0879638270 | 0.0322273730 | -0.06564929 | -0.037932959 | 0.034960757 | -0.124137524 | 0.020442481 |\n", + "| 2 | MAP46246604 | 0.461129243 | 0 | 89.44832 | 3.750000 | -0.057939158 | 0.004366824 | 0.04860006 | -0.03017768 | -0.04517439 | 0.107212604 | 0.041790375 | 0.109996647 | -0.0645883570 | -0.0522399500 | -0.03540515 | 0.019338182 | 0.045959633 | 0.151518373 | 0.101876953 |\n", + "| 3 | MAP50104134 | 0.002948979 | 1 | 90.03696 | 5.333333 | -0.005724776 | -0.012738140 | -0.03092883 | -0.01154606 | -0.02729140 | 0.023264206 | 0.061973706 | -0.042456196 | -0.0354291130 | -0.0810296050 | 0.03030571 | -0.162690054 | -0.057322086 | -0.024194658 | -0.002923682 |\n", + "| 4 | MAP50104846 | 0.121204056 | 0 | 91.90418 | 4.000000 | 0.011015010 | 0.020813320 | -0.09980753 | 0.01653993 | 0.04113268 | 0.063429440 | -0.081638040 | 0.078240160 | 0.1643765900 | -0.0309312300 | 0.01750777 | 0.026648620 | -0.079458160 | -0.021245750 | -0.092409580 |\n", + "| 5 | MAP50106442 | 0.377391944 | 0 | 87.94798 | 7.216667 | -0.007747675 | -0.008697100 | -0.02889843 | 0.01265543 | -0.06306697 | 0.004250624 | 0.008469213 | -0.006596004 | -0.0003796181 | -0.0002196695 | -0.06196570 | -0.006355808 | 0.008656075 | -0.001692986 | -0.073489640 |\n", + "| 6 | MAP50108462 | 0.841621234 | 0 | 101.41547 | 5.666667 | 0.049092348 | 0.006814402 | -0.02371451 | -0.02898146 | -0.04402057 | 0.023426141 | 0.051231654 | 0.003902817 | -0.0072046590 | -0.0712319770 | 0.03835327 | -0.031920865 | 0.063607226 | 0.040612079 | 0.041653115 |\n", + "\n" + ], + "text/plain": [ + " SID APOC1_DeJager_Mic_exp msex_DeJager_Mic_exp\n", + "1 MAP15387421 1.778668554 0 \n", + "2 MAP46246604 0.461129243 0 \n", + "3 MAP50104134 0.002948979 1 \n", + "4 MAP50104846 0.121204056 0 \n", + "5 MAP50106442 0.377391944 0 \n", + "6 MAP50108462 0.841621234 0 \n", + " age_death_DeJager_Mic_exp pmi_DeJager_Mic_exp PC1_DeJager_Mic_exp\n", + "1 92.40246 8.833333 0.030748651 \n", + "2 89.44832 3.750000 -0.057939158 \n", + "3 90.03696 5.333333 -0.005724776 \n", + "4 91.90418 4.000000 0.011015010 \n", + "5 87.94798 7.216667 -0.007747675 \n", + "6 101.41547 5.666667 0.049092348 \n", + " PC2_DeJager_Mic_exp PC3_DeJager_Mic_exp PC4_DeJager_Mic_exp\n", + "1 0.008129914 0.04566422 0.01368809 \n", + "2 0.004366824 0.04860006 -0.03017768 \n", + "3 -0.012738140 -0.03092883 -0.01154606 \n", + "4 0.020813320 -0.09980753 0.01653993 \n", + "5 -0.008697100 -0.02889843 0.01265543 \n", + "6 0.006814402 -0.02371451 -0.02898146 \n", + " PC5_DeJager_Mic_exp PC6_DeJager_Mic_exp PC7_DeJager_Mic_exp\n", + "1 -0.03835653 -0.005014206 0.069764313 \n", + "2 -0.04517439 0.107212604 0.041790375 \n", + "3 -0.02729140 0.023264206 0.061973706 \n", + "4 0.04113268 0.063429440 -0.081638040 \n", + "5 -0.06306697 0.004250624 0.008469213 \n", + "6 -0.04402057 0.023426141 0.051231654 \n", + " PC8_DeJager_Mic_exp PC9_DeJager_Mic_exp PC10_DeJager_Mic_exp\n", + "1 0.049545475 -0.0879638270 0.0322273730 \n", + "2 0.109996647 -0.0645883570 -0.0522399500 \n", + "3 -0.042456196 -0.0354291130 -0.0810296050 \n", + "4 0.078240160 0.1643765900 -0.0309312300 \n", + "5 -0.006596004 -0.0003796181 -0.0002196695 \n", + "6 0.003902817 -0.0072046590 -0.0712319770 \n", + " PC11_DeJager_Mic_exp PC12_DeJager_Mic_exp PC13_DeJager_Mic_exp\n", + "1 -0.06564929 -0.037932959 0.034960757 \n", + "2 -0.03540515 0.019338182 0.045959633 \n", + "3 0.03030571 -0.162690054 -0.057322086 \n", + "4 0.01750777 0.026648620 -0.079458160 \n", + "5 -0.06196570 -0.006355808 0.008656075 \n", + "6 0.03835327 -0.031920865 0.063607226 \n", + " PC14_DeJager_Mic_exp PC15_DeJager_Mic_exp\n", + "1 -0.124137524 0.020442481 \n", + "2 0.151518373 0.101876953 \n", + "3 -0.024194658 -0.002923682 \n", + "4 -0.021245750 -0.092409580 \n", + "5 -0.001692986 -0.073489640 \n", + "6 0.040612079 0.041653115 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "419" + ], + "text/latex": [ + "419" + ], + "text/markdown": [ + "419" + ], + "text/plain": [ + "[1] 419" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "424" + ], + "text/latex": [ + "424" + ], + "text/markdown": [ + "424" + ], + "text/plain": [ + "[1] 424" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "419" + ], + "text/latex": [ + "419" + ], + "text/markdown": [ + "419" + ], + "text/plain": [ + "[1] 419" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "DeJager_Mic_exp <- merge(DeJager_Mic_exp_pheno_sub, DeJager_Mic_exp_covar_t, by = 'SID')\n", + "head(DeJager_Mic_exp); nrow(DeJager_Mic_exp); nrow(DeJager_Mic_exp_pheno_sub); nrow(DeJager_Mic_exp_covar_t) " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "7b361ff9", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 2
SIDAPOE_Mega_Mic_exp
<chr><dbl>
1SM-CTDSC-0.17861814
2SM-CTEMS 0.88152597
3SM-CJGMZ 0.54248558
4SM-CJIXK-0.09136438
503380931-0.13324775
6SM-CJEG6 0.87212212
\n" + ], + "text/latex": [ + "A data.frame: 6 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & SID & APOE\\_Mega\\_Mic\\_exp\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & SM-CTDSC & -0.17861814\\\\\n", + "\t2 & SM-CTEMS & 0.88152597\\\\\n", + "\t3 & SM-CJGMZ & 0.54248558\\\\\n", + "\t4 & SM-CJIXK & -0.09136438\\\\\n", + "\t5 & 03380931 & -0.13324775\\\\\n", + "\t6 & SM-CJEG6 & 0.87212212\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 2\n", + "\n", + "| | SID <chr> | APOE_Mega_Mic_exp <dbl> |\n", + "|---|---|---|\n", + "| 1 | SM-CTDSC | -0.17861814 |\n", + "| 2 | SM-CTEMS | 0.88152597 |\n", + "| 3 | SM-CJGMZ | 0.54248558 |\n", + "| 4 | SM-CJIXK | -0.09136438 |\n", + "| 5 | 03380931 | -0.13324775 |\n", + "| 6 | SM-CJEG6 | 0.87212212 |\n", + "\n" + ], + "text/plain": [ + " SID APOE_Mega_Mic_exp\n", + "1 SM-CTDSC -0.17861814 \n", + "2 SM-CTEMS 0.88152597 \n", + "3 SM-CJGMZ 0.54248558 \n", + "4 SM-CJIXK -0.09136438 \n", + "5 03380931 -0.13324775 \n", + "6 SM-CJEG6 0.87212212 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Mega_Mic_exp_pheno_sub <- Mega_Mic_exp_pheno %>% filter(ID == 'ENSG00000130203') %>% \n", + " dplyr::select(-`#chr`, -start, -end, -ID) %>%\n", + " t() %>%\n", + " as.data.frame(stringsAsFactors = FALSE) %>%\n", + " rownames_to_column(\"SID\") %>% \n", + " mutate(across(-SID, as.numeric))\n", + "\n", + "colnames(Mega_Mic_exp_pheno_sub)[2] <- 'APOE_Mega_Mic_exp'\n", + "head(Mega_Mic_exp_pheno_sub)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "54bc406b", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 19
SIDmsex_Mega_Mic_expage_death_Mega_Mic_exppmi_Mega_Mic_expPC1_Mega_Mic_expPC2_Mega_Mic_expPC3_Mega_Mic_expPC4_Mega_Mic_expPC5_Mega_Mic_expPC6_Mega_Mic_expPC7_Mega_Mic_expPC8_Mega_Mic_expPC9_Mega_Mic_expPC10_Mega_Mic_expPC11_Mega_Mic_expPC12_Mega_Mic_expPC13_Mega_Mic_expPC14_Mega_Mic_expPC15_Mega_Mic_exp
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
2SM-CTDUU0 80.657085.500000 0.01332407-0.002264932-0.023936410 0.0032013950 0.0052091250-0.0349282800-0.054333630 0.006505401 0.05314363 0.029166280 0.015376690 0.0001069089 0.008891668 0.025362420 0.008777232
3SM-CTDSC0 83.690623.933333 0.02531316 0.002334625-0.017195610 0.0066890270-0.0004383718 0.0004711505-0.022497860 0.037493910-0.01958989-0.002238536-0.022085930-0.0286603900 0.004572501 0.002473230 0.051152140
4SM-CTEMS0 90.384673.783333 0.06258269 0.002590935 0.043945314 0.0140478130 0.0173019310-0.0124740400 0.011671641 0.072533987 0.01487947-0.069854441 0.031047110 0.0269667600-0.008535465 0.003668046 0.035318257
5SM-CJFLR0 94.313485.000000-0.01030023 0.030426478-0.062715261 0.0025109292 0.0627235655-0.0123856310-0.028477009-0.016171070 0.01468809 0.007737003 0.003523451 0.0006795184-0.011854529 0.019477847 0.022238080
6SM-CJGMZ0101.190974.833333-0.04586442 0.001154410-0.008476834-0.0022940190-0.0460027500 0.0163762310-0.015549829 0.040637895 0.04065787 0.003042223 0.006427504-0.0327943160 0.020914695-0.008726888 0.009124972
7SM-CJFM31 93.691997.250000 0.03692605 0.004040117 0.008026252 0.0003362893-0.0337351400 0.0053903170-0.001298781-0.010981250-0.04004311 0.028257960 0.006800764 0.0236422100 0.017116480 0.029315040-0.040484540
\n" + ], + "text/latex": [ + "A data.frame: 6 × 19\n", + "\\begin{tabular}{r|lllllllllllllllllll}\n", + " & SID & msex\\_Mega\\_Mic\\_exp & age\\_death\\_Mega\\_Mic\\_exp & pmi\\_Mega\\_Mic\\_exp & PC1\\_Mega\\_Mic\\_exp & PC2\\_Mega\\_Mic\\_exp & PC3\\_Mega\\_Mic\\_exp & PC4\\_Mega\\_Mic\\_exp & PC5\\_Mega\\_Mic\\_exp & PC6\\_Mega\\_Mic\\_exp & PC7\\_Mega\\_Mic\\_exp & PC8\\_Mega\\_Mic\\_exp & PC9\\_Mega\\_Mic\\_exp & PC10\\_Mega\\_Mic\\_exp & PC11\\_Mega\\_Mic\\_exp & PC12\\_Mega\\_Mic\\_exp & PC13\\_Mega\\_Mic\\_exp & PC14\\_Mega\\_Mic\\_exp & PC15\\_Mega\\_Mic\\_exp\\\\\n", + " & & & & & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t2 & SM-CTDUU & 0 & 80.65708 & 5.500000 & 0.01332407 & -0.002264932 & -0.023936410 & 0.0032013950 & 0.0052091250 & -0.0349282800 & -0.054333630 & 0.006505401 & 0.05314363 & 0.029166280 & 0.015376690 & 0.0001069089 & 0.008891668 & 0.025362420 & 0.008777232\\\\\n", + "\t3 & SM-CTDSC & 0 & 83.69062 & 3.933333 & 0.02531316 & 0.002334625 & -0.017195610 & 0.0066890270 & -0.0004383718 & 0.0004711505 & -0.022497860 & 0.037493910 & -0.01958989 & -0.002238536 & -0.022085930 & -0.0286603900 & 0.004572501 & 0.002473230 & 0.051152140\\\\\n", + "\t4 & SM-CTEMS & 0 & 90.38467 & 3.783333 & 0.06258269 & 0.002590935 & 0.043945314 & 0.0140478130 & 0.0173019310 & -0.0124740400 & 0.011671641 & 0.072533987 & 0.01487947 & -0.069854441 & 0.031047110 & 0.0269667600 & -0.008535465 & 0.003668046 & 0.035318257\\\\\n", + "\t5 & SM-CJFLR & 0 & 94.31348 & 5.000000 & -0.01030023 & 0.030426478 & -0.062715261 & 0.0025109292 & 0.0627235655 & -0.0123856310 & -0.028477009 & -0.016171070 & 0.01468809 & 0.007737003 & 0.003523451 & 0.0006795184 & -0.011854529 & 0.019477847 & 0.022238080\\\\\n", + "\t6 & SM-CJGMZ & 0 & 101.19097 & 4.833333 & -0.04586442 & 0.001154410 & -0.008476834 & -0.0022940190 & -0.0460027500 & 0.0163762310 & -0.015549829 & 0.040637895 & 0.04065787 & 0.003042223 & 0.006427504 & -0.0327943160 & 0.020914695 & -0.008726888 & 0.009124972\\\\\n", + "\t7 & SM-CJFM3 & 1 & 93.69199 & 7.250000 & 0.03692605 & 0.004040117 & 0.008026252 & 0.0003362893 & -0.0337351400 & 0.0053903170 & -0.001298781 & -0.010981250 & -0.04004311 & 0.028257960 & 0.006800764 & 0.0236422100 & 0.017116480 & 0.029315040 & -0.040484540\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 19\n", + "\n", + "| | SID <chr> | msex_Mega_Mic_exp <dbl> | age_death_Mega_Mic_exp <dbl> | pmi_Mega_Mic_exp <dbl> | PC1_Mega_Mic_exp <dbl> | PC2_Mega_Mic_exp <dbl> | PC3_Mega_Mic_exp <dbl> | PC4_Mega_Mic_exp <dbl> | PC5_Mega_Mic_exp <dbl> | PC6_Mega_Mic_exp <dbl> | PC7_Mega_Mic_exp <dbl> | PC8_Mega_Mic_exp <dbl> | PC9_Mega_Mic_exp <dbl> | PC10_Mega_Mic_exp <dbl> | PC11_Mega_Mic_exp <dbl> | PC12_Mega_Mic_exp <dbl> | PC13_Mega_Mic_exp <dbl> | PC14_Mega_Mic_exp <dbl> | PC15_Mega_Mic_exp <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 2 | SM-CTDUU | 0 | 80.65708 | 5.500000 | 0.01332407 | -0.002264932 | -0.023936410 | 0.0032013950 | 0.0052091250 | -0.0349282800 | -0.054333630 | 0.006505401 | 0.05314363 | 0.029166280 | 0.015376690 | 0.0001069089 | 0.008891668 | 0.025362420 | 0.008777232 |\n", + "| 3 | SM-CTDSC | 0 | 83.69062 | 3.933333 | 0.02531316 | 0.002334625 | -0.017195610 | 0.0066890270 | -0.0004383718 | 0.0004711505 | -0.022497860 | 0.037493910 | -0.01958989 | -0.002238536 | -0.022085930 | -0.0286603900 | 0.004572501 | 0.002473230 | 0.051152140 |\n", + "| 4 | SM-CTEMS | 0 | 90.38467 | 3.783333 | 0.06258269 | 0.002590935 | 0.043945314 | 0.0140478130 | 0.0173019310 | -0.0124740400 | 0.011671641 | 0.072533987 | 0.01487947 | -0.069854441 | 0.031047110 | 0.0269667600 | -0.008535465 | 0.003668046 | 0.035318257 |\n", + "| 5 | SM-CJFLR | 0 | 94.31348 | 5.000000 | -0.01030023 | 0.030426478 | -0.062715261 | 0.0025109292 | 0.0627235655 | -0.0123856310 | -0.028477009 | -0.016171070 | 0.01468809 | 0.007737003 | 0.003523451 | 0.0006795184 | -0.011854529 | 0.019477847 | 0.022238080 |\n", + "| 6 | SM-CJGMZ | 0 | 101.19097 | 4.833333 | -0.04586442 | 0.001154410 | -0.008476834 | -0.0022940190 | -0.0460027500 | 0.0163762310 | -0.015549829 | 0.040637895 | 0.04065787 | 0.003042223 | 0.006427504 | -0.0327943160 | 0.020914695 | -0.008726888 | 0.009124972 |\n", + "| 7 | SM-CJFM3 | 1 | 93.69199 | 7.250000 | 0.03692605 | 0.004040117 | 0.008026252 | 0.0003362893 | -0.0337351400 | 0.0053903170 | -0.001298781 | -0.010981250 | -0.04004311 | 0.028257960 | 0.006800764 | 0.0236422100 | 0.017116480 | 0.029315040 | -0.040484540 |\n", + "\n" + ], + "text/plain": [ + " SID msex_Mega_Mic_exp age_death_Mega_Mic_exp pmi_Mega_Mic_exp\n", + "2 SM-CTDUU 0 80.65708 5.500000 \n", + "3 SM-CTDSC 0 83.69062 3.933333 \n", + "4 SM-CTEMS 0 90.38467 3.783333 \n", + "5 SM-CJFLR 0 94.31348 5.000000 \n", + "6 SM-CJGMZ 0 101.19097 4.833333 \n", + "7 SM-CJFM3 1 93.69199 7.250000 \n", + " PC1_Mega_Mic_exp PC2_Mega_Mic_exp PC3_Mega_Mic_exp PC4_Mega_Mic_exp\n", + "2 0.01332407 -0.002264932 -0.023936410 0.0032013950 \n", + "3 0.02531316 0.002334625 -0.017195610 0.0066890270 \n", + "4 0.06258269 0.002590935 0.043945314 0.0140478130 \n", + "5 -0.01030023 0.030426478 -0.062715261 0.0025109292 \n", + "6 -0.04586442 0.001154410 -0.008476834 -0.0022940190 \n", + "7 0.03692605 0.004040117 0.008026252 0.0003362893 \n", + " PC5_Mega_Mic_exp PC6_Mega_Mic_exp PC7_Mega_Mic_exp PC8_Mega_Mic_exp\n", + "2 0.0052091250 -0.0349282800 -0.054333630 0.006505401 \n", + "3 -0.0004383718 0.0004711505 -0.022497860 0.037493910 \n", + "4 0.0173019310 -0.0124740400 0.011671641 0.072533987 \n", + "5 0.0627235655 -0.0123856310 -0.028477009 -0.016171070 \n", + "6 -0.0460027500 0.0163762310 -0.015549829 0.040637895 \n", + "7 -0.0337351400 0.0053903170 -0.001298781 -0.010981250 \n", + " PC9_Mega_Mic_exp PC10_Mega_Mic_exp PC11_Mega_Mic_exp PC12_Mega_Mic_exp\n", + "2 0.05314363 0.029166280 0.015376690 0.0001069089 \n", + "3 -0.01958989 -0.002238536 -0.022085930 -0.0286603900 \n", + "4 0.01487947 -0.069854441 0.031047110 0.0269667600 \n", + "5 0.01468809 0.007737003 0.003523451 0.0006795184 \n", + "6 0.04065787 0.003042223 0.006427504 -0.0327943160 \n", + "7 -0.04004311 0.028257960 0.006800764 0.0236422100 \n", + " PC13_Mega_Mic_exp PC14_Mega_Mic_exp PC15_Mega_Mic_exp\n", + "2 0.008891668 0.025362420 0.008777232 \n", + "3 0.004572501 0.002473230 0.051152140 \n", + "4 -0.008535465 0.003668046 0.035318257 \n", + "5 -0.011854529 0.019477847 0.022238080 \n", + "6 0.020914695 -0.008726888 0.009124972 \n", + "7 0.017116480 0.029315040 -0.040484540 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Mega_Mic_exp_covar_t <- Mega_Mic_exp_covar %>% \n", + " t() %>% \n", + " as.data.frame() %>% \n", + " rownames_to_column(\"SID\")\n", + "\n", + "colnames(Mega_Mic_exp_covar_t) <- Mega_Mic_exp_covar_t[1, ]\n", + "Mega_Mic_exp_covar_t <- Mega_Mic_exp_covar_t[-1, ]\n", + "colnames(Mega_Mic_exp_covar_t)[1] <- 'SID'\n", + "Mega_Mic_exp_covar_t <- Mega_Mic_exp_covar_t %>% mutate(across(-SID, as.numeric)) %>% dplyr::select(-contains(\"Hidden_Factor\"))\n", + "Mega_Mic_exp_covar_t <- Mega_Mic_exp_covar_t |>\n", + " setNames(ifelse(names(Mega_Mic_exp_covar_t) == \"SID\", \"SID\", paste0(names(Mega_Mic_exp_covar_t), \"_Mega_Mic_exp\")))\n", + "\n", + "head(Mega_Mic_exp_covar_t)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "2b040bac", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 20
SIDAPOE_Mega_Mic_expmsex_Mega_Mic_expage_death_Mega_Mic_exppmi_Mega_Mic_expPC1_Mega_Mic_expPC2_Mega_Mic_expPC3_Mega_Mic_expPC4_Mega_Mic_expPC5_Mega_Mic_expPC6_Mega_Mic_expPC7_Mega_Mic_expPC8_Mega_Mic_expPC9_Mega_Mic_expPC10_Mega_Mic_expPC11_Mega_Mic_expPC12_Mega_Mic_expPC13_Mega_Mic_expPC14_Mega_Mic_expPC15_Mega_Mic_exp
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1MAP15387421 1.0499480092.402468.833333-0.026413372 0.009446507 0.030957416 0.0072657930-0.0259818630-0.025986701-0.081959593-0.02513906-0.07688351-0.003960723-0.028433814 0.067776847 0.03245780-0.055378513-0.07403810
2MAP26637867 0.6535373091.244357.450000-0.102884772 0.058035238 0.068333174 0.0101228720 0.0201860050-0.023527459-0.003932029 0.02215219-0.01188208-0.051581400 0.038882531 0.030486067-0.09828694 0.010735132-0.01372170
3MAP34726040 1.3685606172.826834.666667 0.033241660 0.003200769 0.001793618 0.0008338955 0.0004187812 0.038869150 0.051368940 0.02805153-0.01767804 0.008619974-0.004202912 0.046887140-0.01163225 0.070096120-0.05209028
4MAP46246604-0.5313924089.448323.750000 0.043656845-0.006078659 0.033108009-0.0017823240-0.0067135330 0.004785349 0.026168681-0.06109080-0.02043681 0.064950351-0.046316314-0.007330002 0.03796938-0.036921573-0.08153443
5MAP46251007-0.6495789089.051336.116667 0.031367219 0.013899470 0.004656262 0.0161891770-0.0080772800 0.077552862 0.058958417-0.09098549 0.02870551-0.028566985 0.090725255 0.020988975-0.03499663 0.004960082-0.02248620
6MAP50104134-2.2736256190.036965.333333 0.001579799 0.001960506-0.028294932-0.0157547150-0.0228753400 0.048154134-0.036488175 0.01084915 0.03027681 0.046686530 0.044634965 0.006510071-0.01496493-0.075206897-0.01769568
\n" + ], + "text/latex": [ + "A data.frame: 6 × 20\n", + "\\begin{tabular}{r|llllllllllllllllllll}\n", + " & SID & APOE\\_Mega\\_Mic\\_exp & msex\\_Mega\\_Mic\\_exp & age\\_death\\_Mega\\_Mic\\_exp & pmi\\_Mega\\_Mic\\_exp & PC1\\_Mega\\_Mic\\_exp & PC2\\_Mega\\_Mic\\_exp & PC3\\_Mega\\_Mic\\_exp & PC4\\_Mega\\_Mic\\_exp & PC5\\_Mega\\_Mic\\_exp & PC6\\_Mega\\_Mic\\_exp & PC7\\_Mega\\_Mic\\_exp & PC8\\_Mega\\_Mic\\_exp & PC9\\_Mega\\_Mic\\_exp & PC10\\_Mega\\_Mic\\_exp & PC11\\_Mega\\_Mic\\_exp & PC12\\_Mega\\_Mic\\_exp & PC13\\_Mega\\_Mic\\_exp & PC14\\_Mega\\_Mic\\_exp & PC15\\_Mega\\_Mic\\_exp\\\\\n", + " & & & & & & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & MAP15387421 & 1.0499480 & 0 & 92.40246 & 8.833333 & -0.026413372 & 0.009446507 & 0.030957416 & 0.0072657930 & -0.0259818630 & -0.025986701 & -0.081959593 & -0.02513906 & -0.07688351 & -0.003960723 & -0.028433814 & 0.067776847 & 0.03245780 & -0.055378513 & -0.07403810\\\\\n", + "\t2 & MAP26637867 & 0.6535373 & 0 & 91.24435 & 7.450000 & -0.102884772 & 0.058035238 & 0.068333174 & 0.0101228720 & 0.0201860050 & -0.023527459 & -0.003932029 & 0.02215219 & -0.01188208 & -0.051581400 & 0.038882531 & 0.030486067 & -0.09828694 & 0.010735132 & -0.01372170\\\\\n", + "\t3 & MAP34726040 & 1.3685606 & 1 & 72.82683 & 4.666667 & 0.033241660 & 0.003200769 & 0.001793618 & 0.0008338955 & 0.0004187812 & 0.038869150 & 0.051368940 & 0.02805153 & -0.01767804 & 0.008619974 & -0.004202912 & 0.046887140 & -0.01163225 & 0.070096120 & -0.05209028\\\\\n", + "\t4 & MAP46246604 & -0.5313924 & 0 & 89.44832 & 3.750000 & 0.043656845 & -0.006078659 & 0.033108009 & -0.0017823240 & -0.0067135330 & 0.004785349 & 0.026168681 & -0.06109080 & -0.02043681 & 0.064950351 & -0.046316314 & -0.007330002 & 0.03796938 & -0.036921573 & -0.08153443\\\\\n", + "\t5 & MAP46251007 & -0.6495789 & 0 & 89.05133 & 6.116667 & 0.031367219 & 0.013899470 & 0.004656262 & 0.0161891770 & -0.0080772800 & 0.077552862 & 0.058958417 & -0.09098549 & 0.02870551 & -0.028566985 & 0.090725255 & 0.020988975 & -0.03499663 & 0.004960082 & -0.02248620\\\\\n", + "\t6 & MAP50104134 & -2.2736256 & 1 & 90.03696 & 5.333333 & 0.001579799 & 0.001960506 & -0.028294932 & -0.0157547150 & -0.0228753400 & 0.048154134 & -0.036488175 & 0.01084915 & 0.03027681 & 0.046686530 & 0.044634965 & 0.006510071 & -0.01496493 & -0.075206897 & -0.01769568\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 20\n", + "\n", + "| | SID <chr> | APOE_Mega_Mic_exp <dbl> | msex_Mega_Mic_exp <dbl> | age_death_Mega_Mic_exp <dbl> | pmi_Mega_Mic_exp <dbl> | PC1_Mega_Mic_exp <dbl> | PC2_Mega_Mic_exp <dbl> | PC3_Mega_Mic_exp <dbl> | PC4_Mega_Mic_exp <dbl> | PC5_Mega_Mic_exp <dbl> | PC6_Mega_Mic_exp <dbl> | PC7_Mega_Mic_exp <dbl> | PC8_Mega_Mic_exp <dbl> | PC9_Mega_Mic_exp <dbl> | PC10_Mega_Mic_exp <dbl> | PC11_Mega_Mic_exp <dbl> | PC12_Mega_Mic_exp <dbl> | PC13_Mega_Mic_exp <dbl> | PC14_Mega_Mic_exp <dbl> | PC15_Mega_Mic_exp <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | MAP15387421 | 1.0499480 | 0 | 92.40246 | 8.833333 | -0.026413372 | 0.009446507 | 0.030957416 | 0.0072657930 | -0.0259818630 | -0.025986701 | -0.081959593 | -0.02513906 | -0.07688351 | -0.003960723 | -0.028433814 | 0.067776847 | 0.03245780 | -0.055378513 | -0.07403810 |\n", + "| 2 | MAP26637867 | 0.6535373 | 0 | 91.24435 | 7.450000 | -0.102884772 | 0.058035238 | 0.068333174 | 0.0101228720 | 0.0201860050 | -0.023527459 | -0.003932029 | 0.02215219 | -0.01188208 | -0.051581400 | 0.038882531 | 0.030486067 | -0.09828694 | 0.010735132 | -0.01372170 |\n", + "| 3 | MAP34726040 | 1.3685606 | 1 | 72.82683 | 4.666667 | 0.033241660 | 0.003200769 | 0.001793618 | 0.0008338955 | 0.0004187812 | 0.038869150 | 0.051368940 | 0.02805153 | -0.01767804 | 0.008619974 | -0.004202912 | 0.046887140 | -0.01163225 | 0.070096120 | -0.05209028 |\n", + "| 4 | MAP46246604 | -0.5313924 | 0 | 89.44832 | 3.750000 | 0.043656845 | -0.006078659 | 0.033108009 | -0.0017823240 | -0.0067135330 | 0.004785349 | 0.026168681 | -0.06109080 | -0.02043681 | 0.064950351 | -0.046316314 | -0.007330002 | 0.03796938 | -0.036921573 | -0.08153443 |\n", + "| 5 | MAP46251007 | -0.6495789 | 0 | 89.05133 | 6.116667 | 0.031367219 | 0.013899470 | 0.004656262 | 0.0161891770 | -0.0080772800 | 0.077552862 | 0.058958417 | -0.09098549 | 0.02870551 | -0.028566985 | 0.090725255 | 0.020988975 | -0.03499663 | 0.004960082 | -0.02248620 |\n", + "| 6 | MAP50104134 | -2.2736256 | 1 | 90.03696 | 5.333333 | 0.001579799 | 0.001960506 | -0.028294932 | -0.0157547150 | -0.0228753400 | 0.048154134 | -0.036488175 | 0.01084915 | 0.03027681 | 0.046686530 | 0.044634965 | 0.006510071 | -0.01496493 | -0.075206897 | -0.01769568 |\n", + "\n" + ], + "text/plain": [ + " SID APOE_Mega_Mic_exp msex_Mega_Mic_exp age_death_Mega_Mic_exp\n", + "1 MAP15387421 1.0499480 0 92.40246 \n", + "2 MAP26637867 0.6535373 0 91.24435 \n", + "3 MAP34726040 1.3685606 1 72.82683 \n", + "4 MAP46246604 -0.5313924 0 89.44832 \n", + "5 MAP46251007 -0.6495789 0 89.05133 \n", + "6 MAP50104134 -2.2736256 1 90.03696 \n", + " pmi_Mega_Mic_exp PC1_Mega_Mic_exp PC2_Mega_Mic_exp PC3_Mega_Mic_exp\n", + "1 8.833333 -0.026413372 0.009446507 0.030957416 \n", + "2 7.450000 -0.102884772 0.058035238 0.068333174 \n", + "3 4.666667 0.033241660 0.003200769 0.001793618 \n", + "4 3.750000 0.043656845 -0.006078659 0.033108009 \n", + "5 6.116667 0.031367219 0.013899470 0.004656262 \n", + "6 5.333333 0.001579799 0.001960506 -0.028294932 \n", + " PC4_Mega_Mic_exp PC5_Mega_Mic_exp PC6_Mega_Mic_exp PC7_Mega_Mic_exp\n", + "1 0.0072657930 -0.0259818630 -0.025986701 -0.081959593 \n", + "2 0.0101228720 0.0201860050 -0.023527459 -0.003932029 \n", + "3 0.0008338955 0.0004187812 0.038869150 0.051368940 \n", + "4 -0.0017823240 -0.0067135330 0.004785349 0.026168681 \n", + "5 0.0161891770 -0.0080772800 0.077552862 0.058958417 \n", + "6 -0.0157547150 -0.0228753400 0.048154134 -0.036488175 \n", + " PC8_Mega_Mic_exp PC9_Mega_Mic_exp PC10_Mega_Mic_exp PC11_Mega_Mic_exp\n", + "1 -0.02513906 -0.07688351 -0.003960723 -0.028433814 \n", + "2 0.02215219 -0.01188208 -0.051581400 0.038882531 \n", + "3 0.02805153 -0.01767804 0.008619974 -0.004202912 \n", + "4 -0.06109080 -0.02043681 0.064950351 -0.046316314 \n", + "5 -0.09098549 0.02870551 -0.028566985 0.090725255 \n", + "6 0.01084915 0.03027681 0.046686530 0.044634965 \n", + " PC12_Mega_Mic_exp PC13_Mega_Mic_exp PC14_Mega_Mic_exp PC15_Mega_Mic_exp\n", + "1 0.067776847 0.03245780 -0.055378513 -0.07403810 \n", + "2 0.030486067 -0.09828694 0.010735132 -0.01372170 \n", + "3 0.046887140 -0.01163225 0.070096120 -0.05209028 \n", + "4 -0.007330002 0.03796938 -0.036921573 -0.08153443 \n", + "5 0.020988975 -0.03499663 0.004960082 -0.02248620 \n", + "6 0.006510071 -0.01496493 -0.075206897 -0.01769568 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "733" + ], + "text/latex": [ + "733" + ], + "text/markdown": [ + "733" + ], + "text/plain": [ + "[1] 733" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "782" + ], + "text/latex": [ + "782" + ], + "text/markdown": [ + "782" + ], + "text/plain": [ + "[1] 782" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "733" + ], + "text/latex": [ + "733" + ], + "text/markdown": [ + "733" + ], + "text/plain": [ + "[1] 733" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Mega_Mic_exp <- merge(Mega_Mic_exp_pheno_sub, Mega_Mic_exp_covar_t, by = 'SID')\n", + "head(Mega_Mic_exp); nrow(Mega_Mic_exp); nrow(Mega_Mic_exp_pheno_sub); nrow(Mega_Mic_exp_covar_t) " + ] + }, + { + "cell_type": "markdown", + "id": "d584f8fc", + "metadata": {}, + "source": [ + "#### Outcome data" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "438642c3", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "patho_sub <- patho %>% filter(cs_id == 'set_27') %>% dplyr::select(specimenID, caa_4gp, educ, apoe4_dose, apoe2_dose, msex, age_death, pmi, ROS_study)\n", + "patho_sub <- unique(patho_sub)\n", + "colnames(patho_sub) <- c('SID', \"caa_4gp\", \"educ\", \"apoe4_dose\", \"apoe2_dose\", \"msex_p\", \"age_death_p\", \"pmi_p\", \"ROS_study_p\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "8394d5e1", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.table: 6 × 9
SIDcaa_4gpeducapoe4_doseapoe2_dosemsex_page_death_ppmi_pROS_study_p
<chr><int><int><int><int><int><dbl><dbl><int>
SM-CJFK8216000100.873407.5000000
SM-CTDUU116100 80.657085.5000000
SM-CTDSC112010 83.690623.9333330
SM-CJIYB115001 92.618755.0833330
SM-CTEMS112000 90.384673.7833330
SM-CJFLR115000 94.313485.0000000
\n" + ], + "text/latex": [ + "A data.table: 6 × 9\n", + "\\begin{tabular}{lllllllll}\n", + " SID & caa\\_4gp & educ & apoe4\\_dose & apoe2\\_dose & msex\\_p & age\\_death\\_p & pmi\\_p & ROS\\_study\\_p\\\\\n", + " & & & & & & & & \\\\\n", + "\\hline\n", + "\t SM-CJFK8 & 2 & 16 & 0 & 0 & 0 & 100.87340 & 7.500000 & 0\\\\\n", + "\t SM-CTDUU & 1 & 16 & 1 & 0 & 0 & 80.65708 & 5.500000 & 0\\\\\n", + "\t SM-CTDSC & 1 & 12 & 0 & 1 & 0 & 83.69062 & 3.933333 & 0\\\\\n", + "\t SM-CJIYB & 1 & 15 & 0 & 0 & 1 & 92.61875 & 5.083333 & 0\\\\\n", + "\t SM-CTEMS & 1 & 12 & 0 & 0 & 0 & 90.38467 & 3.783333 & 0\\\\\n", + "\t SM-CJFLR & 1 & 15 & 0 & 0 & 0 & 94.31348 & 5.000000 & 0\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 6 × 9\n", + "\n", + "| SID <chr> | caa_4gp <int> | educ <int> | apoe4_dose <int> | apoe2_dose <int> | msex_p <int> | age_death_p <dbl> | pmi_p <dbl> | ROS_study_p <int> |\n", + "|---|---|---|---|---|---|---|---|---|\n", + "| SM-CJFK8 | 2 | 16 | 0 | 0 | 0 | 100.87340 | 7.500000 | 0 |\n", + "| SM-CTDUU | 1 | 16 | 1 | 0 | 0 | 80.65708 | 5.500000 | 0 |\n", + "| SM-CTDSC | 1 | 12 | 0 | 1 | 0 | 83.69062 | 3.933333 | 0 |\n", + "| SM-CJIYB | 1 | 15 | 0 | 0 | 1 | 92.61875 | 5.083333 | 0 |\n", + "| SM-CTEMS | 1 | 12 | 0 | 0 | 0 | 90.38467 | 3.783333 | 0 |\n", + "| SM-CJFLR | 1 | 15 | 0 | 0 | 0 | 94.31348 | 5.000000 | 0 |\n", + "\n" + ], + "text/plain": [ + " SID caa_4gp educ apoe4_dose apoe2_dose msex_p age_death_p pmi_p \n", + "1 SM-CJFK8 2 16 0 0 0 100.87340 7.500000\n", + "2 SM-CTDUU 1 16 1 0 0 80.65708 5.500000\n", + "3 SM-CTDSC 1 12 0 1 0 83.69062 3.933333\n", + "4 SM-CJIYB 1 15 0 0 1 92.61875 5.083333\n", + "5 SM-CTEMS 1 12 0 0 0 90.38467 3.783333\n", + "6 SM-CJFLR 1 15 0 0 0 94.31348 5.000000\n", + " ROS_study_p\n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "5 0 \n", + "6 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1113" + ], + "text/latex": [ + "1113" + ], + "text/markdown": [ + "1113" + ], + "text/plain": [ + "[1] 1113" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(patho_sub); nrow(patho_sub)" + ] + }, + { + "cell_type": "markdown", + "id": "4cdcce62", + "metadata": {}, + "source": [ + "#### Merge the input for SEM" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "c78c26aa", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'SID'
  2. 'chr19_44954310_T_C'
  3. 'APOC4_APOC2_AC_exp'
  4. 'APOC2_AC_exp'
  5. 'msex_AC_exp'
  6. 'age_death_AC_exp'
  7. 'pmi_AC_exp'
  8. 'ROS_study_AC_exp'
  9. 'PC1_AC_exp'
  10. 'PC2_AC_exp'
  11. 'PC3_AC_exp'
  12. 'PC4_AC_exp'
  13. 'PC5_AC_exp'
  14. 'PC6_AC_exp'
  15. 'PC7_AC_exp'
  16. 'PC8_AC_exp'
  17. 'PC9_AC_exp'
  18. 'PC10_AC_exp'
  19. 'PC11_AC_exp'
  20. 'PC12_AC_exp'
  21. 'PC13_AC_exp'
  22. 'APOC1_DeJager_Mic_exp'
  23. 'msex_DeJager_Mic_exp'
  24. 'age_death_DeJager_Mic_exp'
  25. 'pmi_DeJager_Mic_exp'
  26. 'PC1_DeJager_Mic_exp'
  27. 'PC2_DeJager_Mic_exp'
  28. 'PC3_DeJager_Mic_exp'
  29. 'PC4_DeJager_Mic_exp'
  30. 'PC5_DeJager_Mic_exp'
  31. 'PC6_DeJager_Mic_exp'
  32. 'PC7_DeJager_Mic_exp'
  33. 'PC8_DeJager_Mic_exp'
  34. 'PC9_DeJager_Mic_exp'
  35. 'PC10_DeJager_Mic_exp'
  36. 'PC11_DeJager_Mic_exp'
  37. 'PC12_DeJager_Mic_exp'
  38. 'PC13_DeJager_Mic_exp'
  39. 'PC14_DeJager_Mic_exp'
  40. 'PC15_DeJager_Mic_exp'
  41. 'APOE_Mega_Mic_exp'
  42. 'msex_Mega_Mic_exp'
  43. 'age_death_Mega_Mic_exp'
  44. 'pmi_Mega_Mic_exp'
  45. 'PC1_Mega_Mic_exp'
  46. 'PC2_Mega_Mic_exp'
  47. 'PC3_Mega_Mic_exp'
  48. 'PC4_Mega_Mic_exp'
  49. 'PC5_Mega_Mic_exp'
  50. 'PC6_Mega_Mic_exp'
  51. 'PC7_Mega_Mic_exp'
  52. 'PC8_Mega_Mic_exp'
  53. 'PC9_Mega_Mic_exp'
  54. 'PC10_Mega_Mic_exp'
  55. 'PC11_Mega_Mic_exp'
  56. 'PC12_Mega_Mic_exp'
  57. 'PC13_Mega_Mic_exp'
  58. 'PC14_Mega_Mic_exp'
  59. 'PC15_Mega_Mic_exp'
  60. 'caa_4gp'
  61. 'educ'
  62. 'apoe4_dose'
  63. 'apoe2_dose'
  64. 'msex_p'
  65. 'age_death_p'
  66. 'pmi_p'
  67. 'ROS_study_p'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'SID'\n", + "\\item 'chr19\\_44954310\\_T\\_C'\n", + "\\item 'APOC4\\_APOC2\\_AC\\_exp'\n", + "\\item 'APOC2\\_AC\\_exp'\n", + "\\item 'msex\\_AC\\_exp'\n", + "\\item 'age\\_death\\_AC\\_exp'\n", + "\\item 'pmi\\_AC\\_exp'\n", + "\\item 'ROS\\_study\\_AC\\_exp'\n", + "\\item 'PC1\\_AC\\_exp'\n", + "\\item 'PC2\\_AC\\_exp'\n", + "\\item 'PC3\\_AC\\_exp'\n", + "\\item 'PC4\\_AC\\_exp'\n", + "\\item 'PC5\\_AC\\_exp'\n", + "\\item 'PC6\\_AC\\_exp'\n", + "\\item 'PC7\\_AC\\_exp'\n", + "\\item 'PC8\\_AC\\_exp'\n", + "\\item 'PC9\\_AC\\_exp'\n", + "\\item 'PC10\\_AC\\_exp'\n", + "\\item 'PC11\\_AC\\_exp'\n", + "\\item 'PC12\\_AC\\_exp'\n", + "\\item 'PC13\\_AC\\_exp'\n", + "\\item 'APOC1\\_DeJager\\_Mic\\_exp'\n", + "\\item 'msex\\_DeJager\\_Mic\\_exp'\n", + "\\item 'age\\_death\\_DeJager\\_Mic\\_exp'\n", + "\\item 'pmi\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC1\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC2\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC3\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC4\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC5\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC6\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC7\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC8\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC9\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC10\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC11\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC12\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC13\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC14\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC15\\_DeJager\\_Mic\\_exp'\n", + "\\item 'APOE\\_Mega\\_Mic\\_exp'\n", + "\\item 'msex\\_Mega\\_Mic\\_exp'\n", + "\\item 'age\\_death\\_Mega\\_Mic\\_exp'\n", + "\\item 'pmi\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC1\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC2\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC3\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC4\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC5\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC6\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC7\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC8\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC9\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC10\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC11\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC12\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC13\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC14\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC15\\_Mega\\_Mic\\_exp'\n", + "\\item 'caa\\_4gp'\n", + "\\item 'educ'\n", + "\\item 'apoe4\\_dose'\n", + "\\item 'apoe2\\_dose'\n", + "\\item 'msex\\_p'\n", + "\\item 'age\\_death\\_p'\n", + "\\item 'pmi\\_p'\n", + "\\item 'ROS\\_study\\_p'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'SID'\n", + "2. 'chr19_44954310_T_C'\n", + "3. 'APOC4_APOC2_AC_exp'\n", + "4. 'APOC2_AC_exp'\n", + "5. 'msex_AC_exp'\n", + "6. 'age_death_AC_exp'\n", + "7. 'pmi_AC_exp'\n", + "8. 'ROS_study_AC_exp'\n", + "9. 'PC1_AC_exp'\n", + "10. 'PC2_AC_exp'\n", + "11. 'PC3_AC_exp'\n", + "12. 'PC4_AC_exp'\n", + "13. 'PC5_AC_exp'\n", + "14. 'PC6_AC_exp'\n", + "15. 'PC7_AC_exp'\n", + "16. 'PC8_AC_exp'\n", + "17. 'PC9_AC_exp'\n", + "18. 'PC10_AC_exp'\n", + "19. 'PC11_AC_exp'\n", + "20. 'PC12_AC_exp'\n", + "21. 'PC13_AC_exp'\n", + "22. 'APOC1_DeJager_Mic_exp'\n", + "23. 'msex_DeJager_Mic_exp'\n", + "24. 'age_death_DeJager_Mic_exp'\n", + "25. 'pmi_DeJager_Mic_exp'\n", + "26. 'PC1_DeJager_Mic_exp'\n", + "27. 'PC2_DeJager_Mic_exp'\n", + "28. 'PC3_DeJager_Mic_exp'\n", + "29. 'PC4_DeJager_Mic_exp'\n", + "30. 'PC5_DeJager_Mic_exp'\n", + "31. 'PC6_DeJager_Mic_exp'\n", + "32. 'PC7_DeJager_Mic_exp'\n", + "33. 'PC8_DeJager_Mic_exp'\n", + "34. 'PC9_DeJager_Mic_exp'\n", + "35. 'PC10_DeJager_Mic_exp'\n", + "36. 'PC11_DeJager_Mic_exp'\n", + "37. 'PC12_DeJager_Mic_exp'\n", + "38. 'PC13_DeJager_Mic_exp'\n", + "39. 'PC14_DeJager_Mic_exp'\n", + "40. 'PC15_DeJager_Mic_exp'\n", + "41. 'APOE_Mega_Mic_exp'\n", + "42. 'msex_Mega_Mic_exp'\n", + "43. 'age_death_Mega_Mic_exp'\n", + "44. 'pmi_Mega_Mic_exp'\n", + "45. 'PC1_Mega_Mic_exp'\n", + "46. 'PC2_Mega_Mic_exp'\n", + "47. 'PC3_Mega_Mic_exp'\n", + "48. 'PC4_Mega_Mic_exp'\n", + "49. 'PC5_Mega_Mic_exp'\n", + "50. 'PC6_Mega_Mic_exp'\n", + "51. 'PC7_Mega_Mic_exp'\n", + "52. 'PC8_Mega_Mic_exp'\n", + "53. 'PC9_Mega_Mic_exp'\n", + "54. 'PC10_Mega_Mic_exp'\n", + "55. 'PC11_Mega_Mic_exp'\n", + "56. 'PC12_Mega_Mic_exp'\n", + "57. 'PC13_Mega_Mic_exp'\n", + "58. 'PC14_Mega_Mic_exp'\n", + "59. 'PC15_Mega_Mic_exp'\n", + "60. 'caa_4gp'\n", + "61. 'educ'\n", + "62. 'apoe4_dose'\n", + "63. 'apoe2_dose'\n", + "64. 'msex_p'\n", + "65. 'age_death_p'\n", + "66. 'pmi_p'\n", + "67. 'ROS_study_p'\n", + "\n", + "\n" + ], + "text/plain": [ + " [1] \"SID\" \"chr19_44954310_T_C\" \n", + " [3] \"APOC4_APOC2_AC_exp\" \"APOC2_AC_exp\" \n", + " [5] \"msex_AC_exp\" \"age_death_AC_exp\" \n", + " [7] \"pmi_AC_exp\" \"ROS_study_AC_exp\" \n", + " [9] \"PC1_AC_exp\" \"PC2_AC_exp\" \n", + "[11] \"PC3_AC_exp\" \"PC4_AC_exp\" \n", + "[13] \"PC5_AC_exp\" \"PC6_AC_exp\" \n", + "[15] \"PC7_AC_exp\" \"PC8_AC_exp\" \n", + "[17] \"PC9_AC_exp\" \"PC10_AC_exp\" \n", + "[19] \"PC11_AC_exp\" \"PC12_AC_exp\" \n", + "[21] \"PC13_AC_exp\" \"APOC1_DeJager_Mic_exp\" \n", + "[23] \"msex_DeJager_Mic_exp\" \"age_death_DeJager_Mic_exp\"\n", + "[25] \"pmi_DeJager_Mic_exp\" \"PC1_DeJager_Mic_exp\" \n", + "[27] \"PC2_DeJager_Mic_exp\" \"PC3_DeJager_Mic_exp\" \n", + "[29] \"PC4_DeJager_Mic_exp\" \"PC5_DeJager_Mic_exp\" \n", + "[31] \"PC6_DeJager_Mic_exp\" \"PC7_DeJager_Mic_exp\" \n", + "[33] \"PC8_DeJager_Mic_exp\" \"PC9_DeJager_Mic_exp\" \n", + "[35] \"PC10_DeJager_Mic_exp\" \"PC11_DeJager_Mic_exp\" \n", + "[37] \"PC12_DeJager_Mic_exp\" \"PC13_DeJager_Mic_exp\" \n", + "[39] \"PC14_DeJager_Mic_exp\" \"PC15_DeJager_Mic_exp\" \n", + "[41] \"APOE_Mega_Mic_exp\" \"msex_Mega_Mic_exp\" \n", + "[43] \"age_death_Mega_Mic_exp\" \"pmi_Mega_Mic_exp\" \n", + "[45] \"PC1_Mega_Mic_exp\" \"PC2_Mega_Mic_exp\" \n", + "[47] \"PC3_Mega_Mic_exp\" \"PC4_Mega_Mic_exp\" \n", + "[49] \"PC5_Mega_Mic_exp\" \"PC6_Mega_Mic_exp\" \n", + "[51] \"PC7_Mega_Mic_exp\" \"PC8_Mega_Mic_exp\" \n", + "[53] \"PC9_Mega_Mic_exp\" \"PC10_Mega_Mic_exp\" \n", + "[55] \"PC11_Mega_Mic_exp\" \"PC12_Mega_Mic_exp\" \n", + "[57] \"PC13_Mega_Mic_exp\" \"PC14_Mega_Mic_exp\" \n", + "[59] \"PC15_Mega_Mic_exp\" \"caa_4gp\" \n", + "[61] \"educ\" \"apoe4_dose\" \n", + "[63] \"apoe2_dose\" \"msex_p\" \n", + "[65] \"age_death_p\" \"pmi_p\" \n", + "[67] \"ROS_study_p\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Partially overlapping samples for FIML-SEM mediation analysis\n", + "input_all <- merge(geno_44954310, AC_exp, by = \"SID\", all = TRUE)\n", + "input_all <- merge(input_all, DeJager_Mic_exp, by = \"SID\", all = TRUE)\n", + "input_all <- merge(input_all, Mega_Mic_exp, by = \"SID\", all = TRUE)\n", + "input_all <- merge(input_all, patho_sub, by = \"SID\", all = TRUE)\n", + "colnames(input_all); nrow(input_all)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "f22d1035", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'SID'
  2. 'chr19_44954310_T_C'
  3. 'APOC4_APOC2_AC_exp'
  4. 'APOC2_AC_exp'
  5. 'PC1_AC_exp'
  6. 'PC2_AC_exp'
  7. 'PC3_AC_exp'
  8. 'PC4_AC_exp'
  9. 'PC5_AC_exp'
  10. 'PC6_AC_exp'
  11. 'PC7_AC_exp'
  12. 'PC8_AC_exp'
  13. 'PC9_AC_exp'
  14. 'PC10_AC_exp'
  15. 'PC11_AC_exp'
  16. 'PC12_AC_exp'
  17. 'PC13_AC_exp'
  18. 'APOC1_DeJager_Mic_exp'
  19. 'PC1_DeJager_Mic_exp'
  20. 'PC2_DeJager_Mic_exp'
  21. 'PC3_DeJager_Mic_exp'
  22. 'PC4_DeJager_Mic_exp'
  23. 'PC5_DeJager_Mic_exp'
  24. 'PC6_DeJager_Mic_exp'
  25. 'PC7_DeJager_Mic_exp'
  26. 'PC8_DeJager_Mic_exp'
  27. 'PC9_DeJager_Mic_exp'
  28. 'PC10_DeJager_Mic_exp'
  29. 'PC11_DeJager_Mic_exp'
  30. 'PC12_DeJager_Mic_exp'
  31. 'PC13_DeJager_Mic_exp'
  32. 'PC14_DeJager_Mic_exp'
  33. 'PC15_DeJager_Mic_exp'
  34. 'APOE_Mega_Mic_exp'
  35. 'PC1_Mega_Mic_exp'
  36. 'PC2_Mega_Mic_exp'
  37. 'PC3_Mega_Mic_exp'
  38. 'PC4_Mega_Mic_exp'
  39. 'PC5_Mega_Mic_exp'
  40. 'PC6_Mega_Mic_exp'
  41. 'PC7_Mega_Mic_exp'
  42. 'PC8_Mega_Mic_exp'
  43. 'PC9_Mega_Mic_exp'
  44. 'PC10_Mega_Mic_exp'
  45. 'PC11_Mega_Mic_exp'
  46. 'PC12_Mega_Mic_exp'
  47. 'PC13_Mega_Mic_exp'
  48. 'PC14_Mega_Mic_exp'
  49. 'PC15_Mega_Mic_exp'
  50. 'caa_4gp'
  51. 'educ'
  52. 'apoe4_dose'
  53. 'apoe2_dose'
  54. 'msex_u'
  55. 'age_death_u'
  56. 'pmi_u'
  57. 'ROS_study_u'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'SID'\n", + "\\item 'chr19\\_44954310\\_T\\_C'\n", + "\\item 'APOC4\\_APOC2\\_AC\\_exp'\n", + "\\item 'APOC2\\_AC\\_exp'\n", + "\\item 'PC1\\_AC\\_exp'\n", + "\\item 'PC2\\_AC\\_exp'\n", + "\\item 'PC3\\_AC\\_exp'\n", + "\\item 'PC4\\_AC\\_exp'\n", + "\\item 'PC5\\_AC\\_exp'\n", + "\\item 'PC6\\_AC\\_exp'\n", + "\\item 'PC7\\_AC\\_exp'\n", + "\\item 'PC8\\_AC\\_exp'\n", + "\\item 'PC9\\_AC\\_exp'\n", + "\\item 'PC10\\_AC\\_exp'\n", + "\\item 'PC11\\_AC\\_exp'\n", + "\\item 'PC12\\_AC\\_exp'\n", + "\\item 'PC13\\_AC\\_exp'\n", + "\\item 'APOC1\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC1\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC2\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC3\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC4\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC5\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC6\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC7\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC8\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC9\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC10\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC11\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC12\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC13\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC14\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC15\\_DeJager\\_Mic\\_exp'\n", + "\\item 'APOE\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC1\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC2\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC3\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC4\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC5\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC6\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC7\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC8\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC9\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC10\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC11\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC12\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC13\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC14\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC15\\_Mega\\_Mic\\_exp'\n", + "\\item 'caa\\_4gp'\n", + "\\item 'educ'\n", + "\\item 'apoe4\\_dose'\n", + "\\item 'apoe2\\_dose'\n", + "\\item 'msex\\_u'\n", + "\\item 'age\\_death\\_u'\n", + "\\item 'pmi\\_u'\n", + "\\item 'ROS\\_study\\_u'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'SID'\n", + "2. 'chr19_44954310_T_C'\n", + "3. 'APOC4_APOC2_AC_exp'\n", + "4. 'APOC2_AC_exp'\n", + "5. 'PC1_AC_exp'\n", + "6. 'PC2_AC_exp'\n", + "7. 'PC3_AC_exp'\n", + "8. 'PC4_AC_exp'\n", + "9. 'PC5_AC_exp'\n", + "10. 'PC6_AC_exp'\n", + "11. 'PC7_AC_exp'\n", + "12. 'PC8_AC_exp'\n", + "13. 'PC9_AC_exp'\n", + "14. 'PC10_AC_exp'\n", + "15. 'PC11_AC_exp'\n", + "16. 'PC12_AC_exp'\n", + "17. 'PC13_AC_exp'\n", + "18. 'APOC1_DeJager_Mic_exp'\n", + "19. 'PC1_DeJager_Mic_exp'\n", + "20. 'PC2_DeJager_Mic_exp'\n", + "21. 'PC3_DeJager_Mic_exp'\n", + "22. 'PC4_DeJager_Mic_exp'\n", + "23. 'PC5_DeJager_Mic_exp'\n", + "24. 'PC6_DeJager_Mic_exp'\n", + "25. 'PC7_DeJager_Mic_exp'\n", + "26. 'PC8_DeJager_Mic_exp'\n", + "27. 'PC9_DeJager_Mic_exp'\n", + "28. 'PC10_DeJager_Mic_exp'\n", + "29. 'PC11_DeJager_Mic_exp'\n", + "30. 'PC12_DeJager_Mic_exp'\n", + "31. 'PC13_DeJager_Mic_exp'\n", + "32. 'PC14_DeJager_Mic_exp'\n", + "33. 'PC15_DeJager_Mic_exp'\n", + "34. 'APOE_Mega_Mic_exp'\n", + "35. 'PC1_Mega_Mic_exp'\n", + "36. 'PC2_Mega_Mic_exp'\n", + "37. 'PC3_Mega_Mic_exp'\n", + "38. 'PC4_Mega_Mic_exp'\n", + "39. 'PC5_Mega_Mic_exp'\n", + "40. 'PC6_Mega_Mic_exp'\n", + "41. 'PC7_Mega_Mic_exp'\n", + "42. 'PC8_Mega_Mic_exp'\n", + "43. 'PC9_Mega_Mic_exp'\n", + "44. 'PC10_Mega_Mic_exp'\n", + "45. 'PC11_Mega_Mic_exp'\n", + "46. 'PC12_Mega_Mic_exp'\n", + "47. 'PC13_Mega_Mic_exp'\n", + "48. 'PC14_Mega_Mic_exp'\n", + "49. 'PC15_Mega_Mic_exp'\n", + "50. 'caa_4gp'\n", + "51. 'educ'\n", + "52. 'apoe4_dose'\n", + "53. 'apoe2_dose'\n", + "54. 'msex_u'\n", + "55. 'age_death_u'\n", + "56. 'pmi_u'\n", + "57. 'ROS_study_u'\n", + "\n", + "\n" + ], + "text/plain": [ + " [1] \"SID\" \"chr19_44954310_T_C\" \"APOC4_APOC2_AC_exp\" \n", + " [4] \"APOC2_AC_exp\" \"PC1_AC_exp\" \"PC2_AC_exp\" \n", + " [7] \"PC3_AC_exp\" \"PC4_AC_exp\" \"PC5_AC_exp\" \n", + "[10] \"PC6_AC_exp\" \"PC7_AC_exp\" \"PC8_AC_exp\" \n", + "[13] \"PC9_AC_exp\" \"PC10_AC_exp\" \"PC11_AC_exp\" \n", + "[16] \"PC12_AC_exp\" \"PC13_AC_exp\" \"APOC1_DeJager_Mic_exp\"\n", + "[19] \"PC1_DeJager_Mic_exp\" \"PC2_DeJager_Mic_exp\" \"PC3_DeJager_Mic_exp\" \n", + "[22] \"PC4_DeJager_Mic_exp\" \"PC5_DeJager_Mic_exp\" \"PC6_DeJager_Mic_exp\" \n", + "[25] \"PC7_DeJager_Mic_exp\" \"PC8_DeJager_Mic_exp\" \"PC9_DeJager_Mic_exp\" \n", + "[28] \"PC10_DeJager_Mic_exp\" \"PC11_DeJager_Mic_exp\" \"PC12_DeJager_Mic_exp\" \n", + "[31] \"PC13_DeJager_Mic_exp\" \"PC14_DeJager_Mic_exp\" \"PC15_DeJager_Mic_exp\" \n", + "[34] \"APOE_Mega_Mic_exp\" \"PC1_Mega_Mic_exp\" \"PC2_Mega_Mic_exp\" \n", + "[37] \"PC3_Mega_Mic_exp\" \"PC4_Mega_Mic_exp\" \"PC5_Mega_Mic_exp\" \n", + "[40] \"PC6_Mega_Mic_exp\" \"PC7_Mega_Mic_exp\" \"PC8_Mega_Mic_exp\" \n", + "[43] \"PC9_Mega_Mic_exp\" \"PC10_Mega_Mic_exp\" \"PC11_Mega_Mic_exp\" \n", + "[46] \"PC12_Mega_Mic_exp\" \"PC13_Mega_Mic_exp\" \"PC14_Mega_Mic_exp\" \n", + "[49] \"PC15_Mega_Mic_exp\" \"caa_4gp\" \"educ\" \n", + "[52] \"apoe4_dose\" \"apoe2_dose\" \"msex_u\" \n", + "[55] \"age_death_u\" \"pmi_u\" \"ROS_study_u\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create unified covariates — msex/age_death/pmi/ROS_study\n", + "input_all <- input_all %>%\n", + " mutate(msex_u = coalesce(msex_AC_exp, msex_DeJager_Mic_exp, msex_Mega_Mic_exp, msex_p)) %>%\n", + " mutate(age_death_u = coalesce(age_death_AC_exp, age_death_DeJager_Mic_exp, age_death_Mega_Mic_exp, age_death_p)) %>%\n", + " mutate(pmi_u = coalesce(pmi_AC_exp, pmi_DeJager_Mic_exp, pmi_Mega_Mic_exp, pmi_p)) %>%\n", + " mutate(ROS_study_u = coalesce(ROS_study_AC_exp, ROS_study_p))\n", + "\n", + "input_all_out <- input_all %>% dplyr::select(-c(msex_AC_exp, msex_DeJager_Mic_exp, msex_Mega_Mic_exp, msex_p,\n", + " age_death_AC_exp, age_death_DeJager_Mic_exp, age_death_Mega_Mic_exp, age_death_p,\n", + " pmi_AC_exp, pmi_DeJager_Mic_exp, pmi_Mega_Mic_exp, pmi_p,\n", + " ROS_study_AC_exp, ROS_study_p))\n", + "colnames(input_all_out); nrow(input_all_out)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d57cf812", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "write.table(input_all_out, file = '/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_4gp/APOE_ind_set_27_caa_4gp_mediation_all_input.txt',\n", + " quote = FALSE, sep = '\\t', col.names = TRUE, row.names = FALSE)" + ] + }, + { + "cell_type": "markdown", + "id": "426b2cf3", + "metadata": {}, + "source": [ + "### 3.2 caa_neo4" + ] + }, + { + "cell_type": "markdown", + "id": "07593a6c", + "metadata": {}, + "source": [ + "#### Genotype data" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "41bf11b2", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "geno_sub1 <- geno %>% filter(ID == 'chr19:44954310_T_C') %>% dplyr::select(SID, dose)\n", + "colnames(geno_sub1)[2] <- 'chr19_44954310_T_C'\n", + "geno_sub1$SID <- str_remove(geno_sub1$SID,'0_')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "962347d2", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "geno_sub2 <- geno %>% filter(ID == 'chr19:44938650_G_C') %>% dplyr::select(SID, dose)\n", + "colnames(geno_sub2)[2] <- 'chr19_44938650_G_C'\n", + "geno_sub2$SID <- str_remove(geno_sub2$SID,'0_')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "bd30f732", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.table: 6 × 3
SIDchr19_44954310_T_Cchr19_44938650_G_C
<chr><int><int>
MAP1538742112
MAP2663786702
MAP2962984900
MAP3333264610
MAP3472604000
MAP4624660400
\n" + ], + "text/latex": [ + "A data.table: 6 × 3\n", + "\\begin{tabular}{lll}\n", + " SID & chr19\\_44954310\\_T\\_C & chr19\\_44938650\\_G\\_C\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t MAP15387421 & 1 & 2\\\\\n", + "\t MAP26637867 & 0 & 2\\\\\n", + "\t MAP29629849 & 0 & 0\\\\\n", + "\t MAP33332646 & 1 & 0\\\\\n", + "\t MAP34726040 & 0 & 0\\\\\n", + "\t MAP46246604 & 0 & 0\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 6 × 3\n", + "\n", + "| SID <chr> | chr19_44954310_T_C <int> | chr19_44938650_G_C <int> |\n", + "|---|---|---|\n", + "| MAP15387421 | 1 | 2 |\n", + "| MAP26637867 | 0 | 2 |\n", + "| MAP29629849 | 0 | 0 |\n", + "| MAP33332646 | 1 | 0 |\n", + "| MAP34726040 | 0 | 0 |\n", + "| MAP46246604 | 0 | 0 |\n", + "\n" + ], + "text/plain": [ + " SID chr19_44954310_T_C chr19_44938650_G_C\n", + "1 MAP15387421 1 2 \n", + "2 MAP26637867 0 2 \n", + "3 MAP29629849 0 0 \n", + "4 MAP33332646 1 0 \n", + "5 MAP34726040 0 0 \n", + "6 MAP46246604 0 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geno_sub <- merge(geno_sub1, geno_sub2, by = 'SID')\n", + "head(geno_sub); nrow(geno_sub); nrow(geno_sub1); nrow(geno_sub2)" + ] + }, + { + "cell_type": "markdown", + "id": "c1411be3", + "metadata": {}, + "source": [ + "#### Mediator data" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "39bc8943", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Same as part 3.1: AC_exp, DeJager_Mic_exp, Mega_Mic_exp" + ] + }, + { + "cell_type": "markdown", + "id": "164cba17", + "metadata": {}, + "source": [ + "#### Outcome data" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "67ffeaa6", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "patho_sub <- patho %>% filter(cs_id == 'set_27') %>% dplyr::select(specimenID, caa_neo4, educ, apoe4_dose, apoe2_dose, msex, age_death, pmi, ROS_study)\n", + "patho_sub <- unique(patho_sub)\n", + "colnames(patho_sub) <- c('SID', \"caa_neo4\", \"educ\", \"apoe4_dose\", \"apoe2_dose\", \"msex_p\", \"age_death_p\", \"pmi_p\", \"ROS_study_p\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "93323524", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.table: 6 × 9
SIDcaa_neo4educapoe4_doseapoe2_dosemsex_page_death_ppmi_pROS_study_p
<chr><dbl><int><int><int><int><dbl><dbl><int>
SM-CJFK82.2516000100.873407.5000000
SM-CTDUU1.0016100 80.657085.5000000
SM-CTDSC0.2512010 83.690623.9333330
SM-CJIYB0.5015001 92.618755.0833330
SM-CTEMS0.7512000 90.384673.7833330
SM-CJFLR0.2515000 94.313485.0000000
\n" + ], + "text/latex": [ + "A data.table: 6 × 9\n", + "\\begin{tabular}{lllllllll}\n", + " SID & caa\\_neo4 & educ & apoe4\\_dose & apoe2\\_dose & msex\\_p & age\\_death\\_p & pmi\\_p & ROS\\_study\\_p\\\\\n", + " & & & & & & & & \\\\\n", + "\\hline\n", + "\t SM-CJFK8 & 2.25 & 16 & 0 & 0 & 0 & 100.87340 & 7.500000 & 0\\\\\n", + "\t SM-CTDUU & 1.00 & 16 & 1 & 0 & 0 & 80.65708 & 5.500000 & 0\\\\\n", + "\t SM-CTDSC & 0.25 & 12 & 0 & 1 & 0 & 83.69062 & 3.933333 & 0\\\\\n", + "\t SM-CJIYB & 0.50 & 15 & 0 & 0 & 1 & 92.61875 & 5.083333 & 0\\\\\n", + "\t SM-CTEMS & 0.75 & 12 & 0 & 0 & 0 & 90.38467 & 3.783333 & 0\\\\\n", + "\t SM-CJFLR & 0.25 & 15 & 0 & 0 & 0 & 94.31348 & 5.000000 & 0\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 6 × 9\n", + "\n", + "| SID <chr> | caa_neo4 <dbl> | educ <int> | apoe4_dose <int> | apoe2_dose <int> | msex_p <int> | age_death_p <dbl> | pmi_p <dbl> | ROS_study_p <int> |\n", + "|---|---|---|---|---|---|---|---|---|\n", + "| SM-CJFK8 | 2.25 | 16 | 0 | 0 | 0 | 100.87340 | 7.500000 | 0 |\n", + "| SM-CTDUU | 1.00 | 16 | 1 | 0 | 0 | 80.65708 | 5.500000 | 0 |\n", + "| SM-CTDSC | 0.25 | 12 | 0 | 1 | 0 | 83.69062 | 3.933333 | 0 |\n", + "| SM-CJIYB | 0.50 | 15 | 0 | 0 | 1 | 92.61875 | 5.083333 | 0 |\n", + "| SM-CTEMS | 0.75 | 12 | 0 | 0 | 0 | 90.38467 | 3.783333 | 0 |\n", + "| SM-CJFLR | 0.25 | 15 | 0 | 0 | 0 | 94.31348 | 5.000000 | 0 |\n", + "\n" + ], + "text/plain": [ + " SID caa_neo4 educ apoe4_dose apoe2_dose msex_p age_death_p pmi_p \n", + "1 SM-CJFK8 2.25 16 0 0 0 100.87340 7.500000\n", + "2 SM-CTDUU 1.00 16 1 0 0 80.65708 5.500000\n", + "3 SM-CTDSC 0.25 12 0 1 0 83.69062 3.933333\n", + "4 SM-CJIYB 0.50 15 0 0 1 92.61875 5.083333\n", + "5 SM-CTEMS 0.75 12 0 0 0 90.38467 3.783333\n", + "6 SM-CJFLR 0.25 15 0 0 0 94.31348 5.000000\n", + " ROS_study_p\n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "5 0 \n", + "6 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1113" + ], + "text/latex": [ + "1113" + ], + "text/markdown": [ + "1113" + ], + "text/plain": [ + "[1] 1113" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(patho_sub); nrow(patho_sub)" + ] + }, + { + "cell_type": "markdown", + "id": "3e31d30c", + "metadata": {}, + "source": [ + "#### Merge the input for SEM" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "ca1973bf", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'SID'
  2. 'chr19_44954310_T_C'
  3. 'chr19_44938650_G_C'
  4. 'APOC4_APOC2_AC_exp'
  5. 'APOC2_AC_exp'
  6. 'msex_AC_exp'
  7. 'age_death_AC_exp'
  8. 'pmi_AC_exp'
  9. 'ROS_study_AC_exp'
  10. 'PC1_AC_exp'
  11. 'PC2_AC_exp'
  12. 'PC3_AC_exp'
  13. 'PC4_AC_exp'
  14. 'PC5_AC_exp'
  15. 'PC6_AC_exp'
  16. 'PC7_AC_exp'
  17. 'PC8_AC_exp'
  18. 'PC9_AC_exp'
  19. 'PC10_AC_exp'
  20. 'PC11_AC_exp'
  21. 'PC12_AC_exp'
  22. 'PC13_AC_exp'
  23. 'APOC1_DeJager_Mic_exp'
  24. 'msex_DeJager_Mic_exp'
  25. 'age_death_DeJager_Mic_exp'
  26. 'pmi_DeJager_Mic_exp'
  27. 'PC1_DeJager_Mic_exp'
  28. 'PC2_DeJager_Mic_exp'
  29. 'PC3_DeJager_Mic_exp'
  30. 'PC4_DeJager_Mic_exp'
  31. 'PC5_DeJager_Mic_exp'
  32. 'PC6_DeJager_Mic_exp'
  33. 'PC7_DeJager_Mic_exp'
  34. 'PC8_DeJager_Mic_exp'
  35. 'PC9_DeJager_Mic_exp'
  36. 'PC10_DeJager_Mic_exp'
  37. 'PC11_DeJager_Mic_exp'
  38. 'PC12_DeJager_Mic_exp'
  39. 'PC13_DeJager_Mic_exp'
  40. 'PC14_DeJager_Mic_exp'
  41. 'PC15_DeJager_Mic_exp'
  42. 'APOE_Mega_Mic_exp'
  43. 'msex_Mega_Mic_exp'
  44. 'age_death_Mega_Mic_exp'
  45. 'pmi_Mega_Mic_exp'
  46. 'PC1_Mega_Mic_exp'
  47. 'PC2_Mega_Mic_exp'
  48. 'PC3_Mega_Mic_exp'
  49. 'PC4_Mega_Mic_exp'
  50. 'PC5_Mega_Mic_exp'
  51. 'PC6_Mega_Mic_exp'
  52. 'PC7_Mega_Mic_exp'
  53. 'PC8_Mega_Mic_exp'
  54. 'PC9_Mega_Mic_exp'
  55. 'PC10_Mega_Mic_exp'
  56. 'PC11_Mega_Mic_exp'
  57. 'PC12_Mega_Mic_exp'
  58. 'PC13_Mega_Mic_exp'
  59. 'PC14_Mega_Mic_exp'
  60. 'PC15_Mega_Mic_exp'
  61. 'caa_neo4'
  62. 'educ'
  63. 'apoe4_dose'
  64. 'apoe2_dose'
  65. 'msex_p'
  66. 'age_death_p'
  67. 'pmi_p'
  68. 'ROS_study_p'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'SID'\n", + "\\item 'chr19\\_44954310\\_T\\_C'\n", + "\\item 'chr19\\_44938650\\_G\\_C'\n", + "\\item 'APOC4\\_APOC2\\_AC\\_exp'\n", + "\\item 'APOC2\\_AC\\_exp'\n", + "\\item 'msex\\_AC\\_exp'\n", + "\\item 'age\\_death\\_AC\\_exp'\n", + "\\item 'pmi\\_AC\\_exp'\n", + "\\item 'ROS\\_study\\_AC\\_exp'\n", + "\\item 'PC1\\_AC\\_exp'\n", + "\\item 'PC2\\_AC\\_exp'\n", + "\\item 'PC3\\_AC\\_exp'\n", + "\\item 'PC4\\_AC\\_exp'\n", + "\\item 'PC5\\_AC\\_exp'\n", + "\\item 'PC6\\_AC\\_exp'\n", + "\\item 'PC7\\_AC\\_exp'\n", + "\\item 'PC8\\_AC\\_exp'\n", + "\\item 'PC9\\_AC\\_exp'\n", + "\\item 'PC10\\_AC\\_exp'\n", + "\\item 'PC11\\_AC\\_exp'\n", + "\\item 'PC12\\_AC\\_exp'\n", + "\\item 'PC13\\_AC\\_exp'\n", + "\\item 'APOC1\\_DeJager\\_Mic\\_exp'\n", + "\\item 'msex\\_DeJager\\_Mic\\_exp'\n", + "\\item 'age\\_death\\_DeJager\\_Mic\\_exp'\n", + "\\item 'pmi\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC1\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC2\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC3\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC4\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC5\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC6\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC7\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC8\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC9\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC10\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC11\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC12\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC13\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC14\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC15\\_DeJager\\_Mic\\_exp'\n", + "\\item 'APOE\\_Mega\\_Mic\\_exp'\n", + "\\item 'msex\\_Mega\\_Mic\\_exp'\n", + "\\item 'age\\_death\\_Mega\\_Mic\\_exp'\n", + "\\item 'pmi\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC1\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC2\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC3\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC4\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC5\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC6\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC7\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC8\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC9\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC10\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC11\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC12\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC13\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC14\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC15\\_Mega\\_Mic\\_exp'\n", + "\\item 'caa\\_neo4'\n", + "\\item 'educ'\n", + "\\item 'apoe4\\_dose'\n", + "\\item 'apoe2\\_dose'\n", + "\\item 'msex\\_p'\n", + "\\item 'age\\_death\\_p'\n", + "\\item 'pmi\\_p'\n", + "\\item 'ROS\\_study\\_p'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'SID'\n", + "2. 'chr19_44954310_T_C'\n", + "3. 'chr19_44938650_G_C'\n", + "4. 'APOC4_APOC2_AC_exp'\n", + "5. 'APOC2_AC_exp'\n", + "6. 'msex_AC_exp'\n", + "7. 'age_death_AC_exp'\n", + "8. 'pmi_AC_exp'\n", + "9. 'ROS_study_AC_exp'\n", + "10. 'PC1_AC_exp'\n", + "11. 'PC2_AC_exp'\n", + "12. 'PC3_AC_exp'\n", + "13. 'PC4_AC_exp'\n", + "14. 'PC5_AC_exp'\n", + "15. 'PC6_AC_exp'\n", + "16. 'PC7_AC_exp'\n", + "17. 'PC8_AC_exp'\n", + "18. 'PC9_AC_exp'\n", + "19. 'PC10_AC_exp'\n", + "20. 'PC11_AC_exp'\n", + "21. 'PC12_AC_exp'\n", + "22. 'PC13_AC_exp'\n", + "23. 'APOC1_DeJager_Mic_exp'\n", + "24. 'msex_DeJager_Mic_exp'\n", + "25. 'age_death_DeJager_Mic_exp'\n", + "26. 'pmi_DeJager_Mic_exp'\n", + "27. 'PC1_DeJager_Mic_exp'\n", + "28. 'PC2_DeJager_Mic_exp'\n", + "29. 'PC3_DeJager_Mic_exp'\n", + "30. 'PC4_DeJager_Mic_exp'\n", + "31. 'PC5_DeJager_Mic_exp'\n", + "32. 'PC6_DeJager_Mic_exp'\n", + "33. 'PC7_DeJager_Mic_exp'\n", + "34. 'PC8_DeJager_Mic_exp'\n", + "35. 'PC9_DeJager_Mic_exp'\n", + "36. 'PC10_DeJager_Mic_exp'\n", + "37. 'PC11_DeJager_Mic_exp'\n", + "38. 'PC12_DeJager_Mic_exp'\n", + "39. 'PC13_DeJager_Mic_exp'\n", + "40. 'PC14_DeJager_Mic_exp'\n", + "41. 'PC15_DeJager_Mic_exp'\n", + "42. 'APOE_Mega_Mic_exp'\n", + "43. 'msex_Mega_Mic_exp'\n", + "44. 'age_death_Mega_Mic_exp'\n", + "45. 'pmi_Mega_Mic_exp'\n", + "46. 'PC1_Mega_Mic_exp'\n", + "47. 'PC2_Mega_Mic_exp'\n", + "48. 'PC3_Mega_Mic_exp'\n", + "49. 'PC4_Mega_Mic_exp'\n", + "50. 'PC5_Mega_Mic_exp'\n", + "51. 'PC6_Mega_Mic_exp'\n", + "52. 'PC7_Mega_Mic_exp'\n", + "53. 'PC8_Mega_Mic_exp'\n", + "54. 'PC9_Mega_Mic_exp'\n", + "55. 'PC10_Mega_Mic_exp'\n", + "56. 'PC11_Mega_Mic_exp'\n", + "57. 'PC12_Mega_Mic_exp'\n", + "58. 'PC13_Mega_Mic_exp'\n", + "59. 'PC14_Mega_Mic_exp'\n", + "60. 'PC15_Mega_Mic_exp'\n", + "61. 'caa_neo4'\n", + "62. 'educ'\n", + "63. 'apoe4_dose'\n", + "64. 'apoe2_dose'\n", + "65. 'msex_p'\n", + "66. 'age_death_p'\n", + "67. 'pmi_p'\n", + "68. 'ROS_study_p'\n", + "\n", + "\n" + ], + "text/plain": [ + " [1] \"SID\" \"chr19_44954310_T_C\" \n", + " [3] \"chr19_44938650_G_C\" \"APOC4_APOC2_AC_exp\" \n", + " [5] \"APOC2_AC_exp\" \"msex_AC_exp\" \n", + " [7] \"age_death_AC_exp\" \"pmi_AC_exp\" \n", + " [9] \"ROS_study_AC_exp\" \"PC1_AC_exp\" \n", + "[11] \"PC2_AC_exp\" \"PC3_AC_exp\" \n", + "[13] \"PC4_AC_exp\" \"PC5_AC_exp\" \n", + "[15] \"PC6_AC_exp\" \"PC7_AC_exp\" \n", + "[17] \"PC8_AC_exp\" \"PC9_AC_exp\" \n", + "[19] \"PC10_AC_exp\" \"PC11_AC_exp\" \n", + "[21] \"PC12_AC_exp\" \"PC13_AC_exp\" \n", + "[23] \"APOC1_DeJager_Mic_exp\" \"msex_DeJager_Mic_exp\" \n", + "[25] \"age_death_DeJager_Mic_exp\" \"pmi_DeJager_Mic_exp\" \n", + "[27] \"PC1_DeJager_Mic_exp\" \"PC2_DeJager_Mic_exp\" \n", + "[29] \"PC3_DeJager_Mic_exp\" \"PC4_DeJager_Mic_exp\" \n", + "[31] \"PC5_DeJager_Mic_exp\" \"PC6_DeJager_Mic_exp\" \n", + "[33] \"PC7_DeJager_Mic_exp\" \"PC8_DeJager_Mic_exp\" \n", + "[35] \"PC9_DeJager_Mic_exp\" \"PC10_DeJager_Mic_exp\" \n", + "[37] \"PC11_DeJager_Mic_exp\" \"PC12_DeJager_Mic_exp\" \n", + "[39] \"PC13_DeJager_Mic_exp\" \"PC14_DeJager_Mic_exp\" \n", + "[41] \"PC15_DeJager_Mic_exp\" \"APOE_Mega_Mic_exp\" \n", + "[43] \"msex_Mega_Mic_exp\" \"age_death_Mega_Mic_exp\" \n", + "[45] \"pmi_Mega_Mic_exp\" \"PC1_Mega_Mic_exp\" \n", + "[47] \"PC2_Mega_Mic_exp\" \"PC3_Mega_Mic_exp\" \n", + "[49] \"PC4_Mega_Mic_exp\" \"PC5_Mega_Mic_exp\" \n", + "[51] \"PC6_Mega_Mic_exp\" \"PC7_Mega_Mic_exp\" \n", + "[53] \"PC8_Mega_Mic_exp\" \"PC9_Mega_Mic_exp\" \n", + "[55] \"PC10_Mega_Mic_exp\" \"PC11_Mega_Mic_exp\" \n", + "[57] \"PC12_Mega_Mic_exp\" \"PC13_Mega_Mic_exp\" \n", + "[59] \"PC14_Mega_Mic_exp\" \"PC15_Mega_Mic_exp\" \n", + "[61] \"caa_neo4\" \"educ\" \n", + "[63] \"apoe4_dose\" \"apoe2_dose\" \n", + "[65] \"msex_p\" \"age_death_p\" \n", + "[67] \"pmi_p\" \"ROS_study_p\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Partially overlapping samples for FIML-SEM mediation analysis\n", + "input_all <- merge(geno_sub, AC_exp, by = \"SID\", all = TRUE)\n", + "input_all <- merge(input_all, DeJager_Mic_exp, by = \"SID\", all = TRUE)\n", + "input_all <- merge(input_all, Mega_Mic_exp, by = \"SID\", all = TRUE)\n", + "input_all <- merge(input_all, patho_sub, by = \"SID\", all = TRUE)\n", + "colnames(input_all); nrow(input_all)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "39f90a4b", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'SID'
  2. 'chr19_44954310_T_C'
  3. 'chr19_44938650_G_C'
  4. 'APOC4_APOC2_AC_exp'
  5. 'APOC2_AC_exp'
  6. 'PC1_AC_exp'
  7. 'PC2_AC_exp'
  8. 'PC3_AC_exp'
  9. 'PC4_AC_exp'
  10. 'PC5_AC_exp'
  11. 'PC6_AC_exp'
  12. 'PC7_AC_exp'
  13. 'PC8_AC_exp'
  14. 'PC9_AC_exp'
  15. 'PC10_AC_exp'
  16. 'PC11_AC_exp'
  17. 'PC12_AC_exp'
  18. 'PC13_AC_exp'
  19. 'APOC1_DeJager_Mic_exp'
  20. 'PC1_DeJager_Mic_exp'
  21. 'PC2_DeJager_Mic_exp'
  22. 'PC3_DeJager_Mic_exp'
  23. 'PC4_DeJager_Mic_exp'
  24. 'PC5_DeJager_Mic_exp'
  25. 'PC6_DeJager_Mic_exp'
  26. 'PC7_DeJager_Mic_exp'
  27. 'PC8_DeJager_Mic_exp'
  28. 'PC9_DeJager_Mic_exp'
  29. 'PC10_DeJager_Mic_exp'
  30. 'PC11_DeJager_Mic_exp'
  31. 'PC12_DeJager_Mic_exp'
  32. 'PC13_DeJager_Mic_exp'
  33. 'PC14_DeJager_Mic_exp'
  34. 'PC15_DeJager_Mic_exp'
  35. 'APOE_Mega_Mic_exp'
  36. 'PC1_Mega_Mic_exp'
  37. 'PC2_Mega_Mic_exp'
  38. 'PC3_Mega_Mic_exp'
  39. 'PC4_Mega_Mic_exp'
  40. 'PC5_Mega_Mic_exp'
  41. 'PC6_Mega_Mic_exp'
  42. 'PC7_Mega_Mic_exp'
  43. 'PC8_Mega_Mic_exp'
  44. 'PC9_Mega_Mic_exp'
  45. 'PC10_Mega_Mic_exp'
  46. 'PC11_Mega_Mic_exp'
  47. 'PC12_Mega_Mic_exp'
  48. 'PC13_Mega_Mic_exp'
  49. 'PC14_Mega_Mic_exp'
  50. 'PC15_Mega_Mic_exp'
  51. 'caa_neo4'
  52. 'educ'
  53. 'apoe4_dose'
  54. 'apoe2_dose'
  55. 'msex_u'
  56. 'age_death_u'
  57. 'pmi_u'
  58. 'ROS_study_u'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'SID'\n", + "\\item 'chr19\\_44954310\\_T\\_C'\n", + "\\item 'chr19\\_44938650\\_G\\_C'\n", + "\\item 'APOC4\\_APOC2\\_AC\\_exp'\n", + "\\item 'APOC2\\_AC\\_exp'\n", + "\\item 'PC1\\_AC\\_exp'\n", + "\\item 'PC2\\_AC\\_exp'\n", + "\\item 'PC3\\_AC\\_exp'\n", + "\\item 'PC4\\_AC\\_exp'\n", + "\\item 'PC5\\_AC\\_exp'\n", + "\\item 'PC6\\_AC\\_exp'\n", + "\\item 'PC7\\_AC\\_exp'\n", + "\\item 'PC8\\_AC\\_exp'\n", + "\\item 'PC9\\_AC\\_exp'\n", + "\\item 'PC10\\_AC\\_exp'\n", + "\\item 'PC11\\_AC\\_exp'\n", + "\\item 'PC12\\_AC\\_exp'\n", + "\\item 'PC13\\_AC\\_exp'\n", + "\\item 'APOC1\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC1\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC2\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC3\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC4\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC5\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC6\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC7\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC8\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC9\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC10\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC11\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC12\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC13\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC14\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC15\\_DeJager\\_Mic\\_exp'\n", + "\\item 'APOE\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC1\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC2\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC3\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC4\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC5\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC6\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC7\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC8\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC9\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC10\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC11\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC12\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC13\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC14\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC15\\_Mega\\_Mic\\_exp'\n", + "\\item 'caa\\_neo4'\n", + "\\item 'educ'\n", + "\\item 'apoe4\\_dose'\n", + "\\item 'apoe2\\_dose'\n", + "\\item 'msex\\_u'\n", + "\\item 'age\\_death\\_u'\n", + "\\item 'pmi\\_u'\n", + "\\item 'ROS\\_study\\_u'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'SID'\n", + "2. 'chr19_44954310_T_C'\n", + "3. 'chr19_44938650_G_C'\n", + "4. 'APOC4_APOC2_AC_exp'\n", + "5. 'APOC2_AC_exp'\n", + "6. 'PC1_AC_exp'\n", + "7. 'PC2_AC_exp'\n", + "8. 'PC3_AC_exp'\n", + "9. 'PC4_AC_exp'\n", + "10. 'PC5_AC_exp'\n", + "11. 'PC6_AC_exp'\n", + "12. 'PC7_AC_exp'\n", + "13. 'PC8_AC_exp'\n", + "14. 'PC9_AC_exp'\n", + "15. 'PC10_AC_exp'\n", + "16. 'PC11_AC_exp'\n", + "17. 'PC12_AC_exp'\n", + "18. 'PC13_AC_exp'\n", + "19. 'APOC1_DeJager_Mic_exp'\n", + "20. 'PC1_DeJager_Mic_exp'\n", + "21. 'PC2_DeJager_Mic_exp'\n", + "22. 'PC3_DeJager_Mic_exp'\n", + "23. 'PC4_DeJager_Mic_exp'\n", + "24. 'PC5_DeJager_Mic_exp'\n", + "25. 'PC6_DeJager_Mic_exp'\n", + "26. 'PC7_DeJager_Mic_exp'\n", + "27. 'PC8_DeJager_Mic_exp'\n", + "28. 'PC9_DeJager_Mic_exp'\n", + "29. 'PC10_DeJager_Mic_exp'\n", + "30. 'PC11_DeJager_Mic_exp'\n", + "31. 'PC12_DeJager_Mic_exp'\n", + "32. 'PC13_DeJager_Mic_exp'\n", + "33. 'PC14_DeJager_Mic_exp'\n", + "34. 'PC15_DeJager_Mic_exp'\n", + "35. 'APOE_Mega_Mic_exp'\n", + "36. 'PC1_Mega_Mic_exp'\n", + "37. 'PC2_Mega_Mic_exp'\n", + "38. 'PC3_Mega_Mic_exp'\n", + "39. 'PC4_Mega_Mic_exp'\n", + "40. 'PC5_Mega_Mic_exp'\n", + "41. 'PC6_Mega_Mic_exp'\n", + "42. 'PC7_Mega_Mic_exp'\n", + "43. 'PC8_Mega_Mic_exp'\n", + "44. 'PC9_Mega_Mic_exp'\n", + "45. 'PC10_Mega_Mic_exp'\n", + "46. 'PC11_Mega_Mic_exp'\n", + "47. 'PC12_Mega_Mic_exp'\n", + "48. 'PC13_Mega_Mic_exp'\n", + "49. 'PC14_Mega_Mic_exp'\n", + "50. 'PC15_Mega_Mic_exp'\n", + "51. 'caa_neo4'\n", + "52. 'educ'\n", + "53. 'apoe4_dose'\n", + "54. 'apoe2_dose'\n", + "55. 'msex_u'\n", + "56. 'age_death_u'\n", + "57. 'pmi_u'\n", + "58. 'ROS_study_u'\n", + "\n", + "\n" + ], + "text/plain": [ + " [1] \"SID\" \"chr19_44954310_T_C\" \"chr19_44938650_G_C\" \n", + " [4] \"APOC4_APOC2_AC_exp\" \"APOC2_AC_exp\" \"PC1_AC_exp\" \n", + " [7] \"PC2_AC_exp\" \"PC3_AC_exp\" \"PC4_AC_exp\" \n", + "[10] \"PC5_AC_exp\" \"PC6_AC_exp\" \"PC7_AC_exp\" \n", + "[13] \"PC8_AC_exp\" \"PC9_AC_exp\" \"PC10_AC_exp\" \n", + "[16] \"PC11_AC_exp\" \"PC12_AC_exp\" \"PC13_AC_exp\" \n", + "[19] \"APOC1_DeJager_Mic_exp\" \"PC1_DeJager_Mic_exp\" \"PC2_DeJager_Mic_exp\" \n", + "[22] \"PC3_DeJager_Mic_exp\" \"PC4_DeJager_Mic_exp\" \"PC5_DeJager_Mic_exp\" \n", + "[25] \"PC6_DeJager_Mic_exp\" \"PC7_DeJager_Mic_exp\" \"PC8_DeJager_Mic_exp\" \n", + "[28] \"PC9_DeJager_Mic_exp\" \"PC10_DeJager_Mic_exp\" \"PC11_DeJager_Mic_exp\" \n", + "[31] \"PC12_DeJager_Mic_exp\" \"PC13_DeJager_Mic_exp\" \"PC14_DeJager_Mic_exp\" \n", + "[34] \"PC15_DeJager_Mic_exp\" \"APOE_Mega_Mic_exp\" \"PC1_Mega_Mic_exp\" \n", + "[37] \"PC2_Mega_Mic_exp\" \"PC3_Mega_Mic_exp\" \"PC4_Mega_Mic_exp\" \n", + "[40] \"PC5_Mega_Mic_exp\" \"PC6_Mega_Mic_exp\" \"PC7_Mega_Mic_exp\" \n", + "[43] \"PC8_Mega_Mic_exp\" \"PC9_Mega_Mic_exp\" \"PC10_Mega_Mic_exp\" \n", + "[46] \"PC11_Mega_Mic_exp\" \"PC12_Mega_Mic_exp\" \"PC13_Mega_Mic_exp\" \n", + "[49] \"PC14_Mega_Mic_exp\" \"PC15_Mega_Mic_exp\" \"caa_neo4\" \n", + "[52] \"educ\" \"apoe4_dose\" \"apoe2_dose\" \n", + "[55] \"msex_u\" \"age_death_u\" \"pmi_u\" \n", + "[58] \"ROS_study_u\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create unified covariates — msex/age_death/pmi/ROS_study\n", + "input_all <- input_all %>%\n", + " mutate(msex_u = coalesce(msex_AC_exp, msex_DeJager_Mic_exp, msex_Mega_Mic_exp, msex_p)) %>%\n", + " mutate(age_death_u = coalesce(age_death_AC_exp, age_death_DeJager_Mic_exp, age_death_Mega_Mic_exp, age_death_p)) %>%\n", + " mutate(pmi_u = coalesce(pmi_AC_exp, pmi_DeJager_Mic_exp, pmi_Mega_Mic_exp, pmi_p)) %>%\n", + " mutate(ROS_study_u = coalesce(ROS_study_AC_exp, ROS_study_p))\n", + "\n", + "input_all_out <- input_all %>% dplyr::select(-c(msex_AC_exp, msex_DeJager_Mic_exp, msex_Mega_Mic_exp, msex_p,\n", + " age_death_AC_exp, age_death_DeJager_Mic_exp, age_death_Mega_Mic_exp, age_death_p,\n", + " pmi_AC_exp, pmi_DeJager_Mic_exp, pmi_Mega_Mic_exp, pmi_p,\n", + " ROS_study_AC_exp, ROS_study_p))\n", + "colnames(input_all_out); nrow(input_all_out)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "d799f5c8", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "write.table(input_all_out, file = '/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/caa_neo4/APOE_ind_set_27_caa_neo4_mediation_all_input.txt',\n", + " quote = FALSE, sep = '\\t', col.names = TRUE, row.names = FALSE)" + ] + }, + { + "cell_type": "markdown", + "id": "f66011ff", + "metadata": {}, + "source": [ + "### 3.3 tdp_st4" + ] + }, + { + "cell_type": "markdown", + "id": "400104c5", + "metadata": {}, + "source": [ + "#### Genotype data" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "6f746004", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "geno_sub1 <- geno %>% filter(ID == 'chr19:45001091_C_G') %>% dplyr::select(SID, dose)\n", + "colnames(geno_sub1)[2] <- 'chr19_45001091_C_G'\n", + "geno_sub1$SID <- str_remove(geno_sub1$SID,'0_')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "14f303ab", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "geno_sub2 <- geno %>% filter(ID == 'chr19:44999110_TAAAA_TAA') %>% dplyr::select(SID, dose)\n", + "colnames(geno_sub2)[2] <- 'chr19_44999110_TAAAA_TAA'\n", + "geno_sub2$SID <- str_remove(geno_sub2$SID,'0_')" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "322db1aa", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.table: 6 × 3
SIDchr19_45001091_C_Gchr19_44999110_TAAAA_TAA
<chr><int><int>
MAP1538742111
MAP2663786722
MAP2962984900
MAP3333264611
MAP3472604000
MAP4624660400
\n" + ], + "text/latex": [ + "A data.table: 6 × 3\n", + "\\begin{tabular}{lll}\n", + " SID & chr19\\_45001091\\_C\\_G & chr19\\_44999110\\_TAAAA\\_TAA\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t MAP15387421 & 1 & 1\\\\\n", + "\t MAP26637867 & 2 & 2\\\\\n", + "\t MAP29629849 & 0 & 0\\\\\n", + "\t MAP33332646 & 1 & 1\\\\\n", + "\t MAP34726040 & 0 & 0\\\\\n", + "\t MAP46246604 & 0 & 0\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 6 × 3\n", + "\n", + "| SID <chr> | chr19_45001091_C_G <int> | chr19_44999110_TAAAA_TAA <int> |\n", + "|---|---|---|\n", + "| MAP15387421 | 1 | 1 |\n", + "| MAP26637867 | 2 | 2 |\n", + "| MAP29629849 | 0 | 0 |\n", + "| MAP33332646 | 1 | 1 |\n", + "| MAP34726040 | 0 | 0 |\n", + "| MAP46246604 | 0 | 0 |\n", + "\n" + ], + "text/plain": [ + " SID chr19_45001091_C_G chr19_44999110_TAAAA_TAA\n", + "1 MAP15387421 1 1 \n", + "2 MAP26637867 2 2 \n", + "3 MAP29629849 0 0 \n", + "4 MAP33332646 1 1 \n", + "5 MAP34726040 0 0 \n", + "6 MAP46246604 0 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geno_sub <- merge(geno_sub1, geno_sub2, by = 'SID')\n", + "head(geno_sub); nrow(geno_sub); nrow(geno_sub1); nrow(geno_sub2)" + ] + }, + { + "cell_type": "markdown", + "id": "e09e4561", + "metadata": {}, + "source": [ + "#### Mediator data" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "7e661616", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Same as part 3.1: AC_exp, DeJager_Mic_exp, Mega_Mic_exp" + ] + }, + { + "cell_type": "markdown", + "id": "44749c1c", + "metadata": {}, + "source": [ + "#### Outcome data" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "1e98492d", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "patho_sub <- patho %>% filter(cs_id == 'set_27') %>% dplyr::select(specimenID, tdp_st4, educ, apoe4_dose, apoe2_dose, msex, age_death, pmi, ROS_study)\n", + "patho_sub <- unique(patho_sub)\n", + "colnames(patho_sub) <- c('SID', \"tdp_st4\", \"educ\", \"apoe4_dose\", \"apoe2_dose\", \"msex_p\", \"age_death_p\", \"pmi_p\", \"ROS_study_p\")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "8cedd00c", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.table: 6 × 9
SIDtdp_st4educapoe4_doseapoe2_dosemsex_page_death_ppmi_pROS_study_p
<chr><int><int><int><int><int><dbl><dbl><int>
SM-CJFK8 316000100.873407.5000000
SM-CTDUU 016100 80.657085.5000000
SM-CTDSCNA12010 83.690623.9333330
SM-CJIYB 015001 92.618755.0833330
SM-CTEMS 112000 90.384673.7833330
SM-CJFLR 015000 94.313485.0000000
\n" + ], + "text/latex": [ + "A data.table: 6 × 9\n", + "\\begin{tabular}{lllllllll}\n", + " SID & tdp\\_st4 & educ & apoe4\\_dose & apoe2\\_dose & msex\\_p & age\\_death\\_p & pmi\\_p & ROS\\_study\\_p\\\\\n", + " & & & & & & & & \\\\\n", + "\\hline\n", + "\t SM-CJFK8 & 3 & 16 & 0 & 0 & 0 & 100.87340 & 7.500000 & 0\\\\\n", + "\t SM-CTDUU & 0 & 16 & 1 & 0 & 0 & 80.65708 & 5.500000 & 0\\\\\n", + "\t SM-CTDSC & NA & 12 & 0 & 1 & 0 & 83.69062 & 3.933333 & 0\\\\\n", + "\t SM-CJIYB & 0 & 15 & 0 & 0 & 1 & 92.61875 & 5.083333 & 0\\\\\n", + "\t SM-CTEMS & 1 & 12 & 0 & 0 & 0 & 90.38467 & 3.783333 & 0\\\\\n", + "\t SM-CJFLR & 0 & 15 & 0 & 0 & 0 & 94.31348 & 5.000000 & 0\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 6 × 9\n", + "\n", + "| SID <chr> | tdp_st4 <int> | educ <int> | apoe4_dose <int> | apoe2_dose <int> | msex_p <int> | age_death_p <dbl> | pmi_p <dbl> | ROS_study_p <int> |\n", + "|---|---|---|---|---|---|---|---|---|\n", + "| SM-CJFK8 | 3 | 16 | 0 | 0 | 0 | 100.87340 | 7.500000 | 0 |\n", + "| SM-CTDUU | 0 | 16 | 1 | 0 | 0 | 80.65708 | 5.500000 | 0 |\n", + "| SM-CTDSC | NA | 12 | 0 | 1 | 0 | 83.69062 | 3.933333 | 0 |\n", + "| SM-CJIYB | 0 | 15 | 0 | 0 | 1 | 92.61875 | 5.083333 | 0 |\n", + "| SM-CTEMS | 1 | 12 | 0 | 0 | 0 | 90.38467 | 3.783333 | 0 |\n", + "| SM-CJFLR | 0 | 15 | 0 | 0 | 0 | 94.31348 | 5.000000 | 0 |\n", + "\n" + ], + "text/plain": [ + " SID tdp_st4 educ apoe4_dose apoe2_dose msex_p age_death_p pmi_p \n", + "1 SM-CJFK8 3 16 0 0 0 100.87340 7.500000\n", + "2 SM-CTDUU 0 16 1 0 0 80.65708 5.500000\n", + "3 SM-CTDSC NA 12 0 1 0 83.69062 3.933333\n", + "4 SM-CJIYB 0 15 0 0 1 92.61875 5.083333\n", + "5 SM-CTEMS 1 12 0 0 0 90.38467 3.783333\n", + "6 SM-CJFLR 0 15 0 0 0 94.31348 5.000000\n", + " ROS_study_p\n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "5 0 \n", + "6 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1113" + ], + "text/latex": [ + "1113" + ], + "text/markdown": [ + "1113" + ], + "text/plain": [ + "[1] 1113" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(patho_sub); nrow(patho_sub)" + ] + }, + { + "cell_type": "markdown", + "id": "5a93743d", + "metadata": {}, + "source": [ + "#### Merge the input for SEM" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "43e5d956", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'SID'
  2. 'chr19_45001091_C_G'
  3. 'chr19_44999110_TAAAA_TAA'
  4. 'APOC4_APOC2_AC_exp'
  5. 'APOC2_AC_exp'
  6. 'msex_AC_exp'
  7. 'age_death_AC_exp'
  8. 'pmi_AC_exp'
  9. 'ROS_study_AC_exp'
  10. 'PC1_AC_exp'
  11. 'PC2_AC_exp'
  12. 'PC3_AC_exp'
  13. 'PC4_AC_exp'
  14. 'PC5_AC_exp'
  15. 'PC6_AC_exp'
  16. 'PC7_AC_exp'
  17. 'PC8_AC_exp'
  18. 'PC9_AC_exp'
  19. 'PC10_AC_exp'
  20. 'PC11_AC_exp'
  21. 'PC12_AC_exp'
  22. 'PC13_AC_exp'
  23. 'APOC1_DeJager_Mic_exp'
  24. 'msex_DeJager_Mic_exp'
  25. 'age_death_DeJager_Mic_exp'
  26. 'pmi_DeJager_Mic_exp'
  27. 'PC1_DeJager_Mic_exp'
  28. 'PC2_DeJager_Mic_exp'
  29. 'PC3_DeJager_Mic_exp'
  30. 'PC4_DeJager_Mic_exp'
  31. 'PC5_DeJager_Mic_exp'
  32. 'PC6_DeJager_Mic_exp'
  33. 'PC7_DeJager_Mic_exp'
  34. 'PC8_DeJager_Mic_exp'
  35. 'PC9_DeJager_Mic_exp'
  36. 'PC10_DeJager_Mic_exp'
  37. 'PC11_DeJager_Mic_exp'
  38. 'PC12_DeJager_Mic_exp'
  39. 'PC13_DeJager_Mic_exp'
  40. 'PC14_DeJager_Mic_exp'
  41. 'PC15_DeJager_Mic_exp'
  42. 'APOE_Mega_Mic_exp'
  43. 'msex_Mega_Mic_exp'
  44. 'age_death_Mega_Mic_exp'
  45. 'pmi_Mega_Mic_exp'
  46. 'PC1_Mega_Mic_exp'
  47. 'PC2_Mega_Mic_exp'
  48. 'PC3_Mega_Mic_exp'
  49. 'PC4_Mega_Mic_exp'
  50. 'PC5_Mega_Mic_exp'
  51. 'PC6_Mega_Mic_exp'
  52. 'PC7_Mega_Mic_exp'
  53. 'PC8_Mega_Mic_exp'
  54. 'PC9_Mega_Mic_exp'
  55. 'PC10_Mega_Mic_exp'
  56. 'PC11_Mega_Mic_exp'
  57. 'PC12_Mega_Mic_exp'
  58. 'PC13_Mega_Mic_exp'
  59. 'PC14_Mega_Mic_exp'
  60. 'PC15_Mega_Mic_exp'
  61. 'tdp_st4'
  62. 'educ'
  63. 'apoe4_dose'
  64. 'apoe2_dose'
  65. 'msex_p'
  66. 'age_death_p'
  67. 'pmi_p'
  68. 'ROS_study_p'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'SID'\n", + "\\item 'chr19\\_45001091\\_C\\_G'\n", + "\\item 'chr19\\_44999110\\_TAAAA\\_TAA'\n", + "\\item 'APOC4\\_APOC2\\_AC\\_exp'\n", + "\\item 'APOC2\\_AC\\_exp'\n", + "\\item 'msex\\_AC\\_exp'\n", + "\\item 'age\\_death\\_AC\\_exp'\n", + "\\item 'pmi\\_AC\\_exp'\n", + "\\item 'ROS\\_study\\_AC\\_exp'\n", + "\\item 'PC1\\_AC\\_exp'\n", + "\\item 'PC2\\_AC\\_exp'\n", + "\\item 'PC3\\_AC\\_exp'\n", + "\\item 'PC4\\_AC\\_exp'\n", + "\\item 'PC5\\_AC\\_exp'\n", + "\\item 'PC6\\_AC\\_exp'\n", + "\\item 'PC7\\_AC\\_exp'\n", + "\\item 'PC8\\_AC\\_exp'\n", + "\\item 'PC9\\_AC\\_exp'\n", + "\\item 'PC10\\_AC\\_exp'\n", + "\\item 'PC11\\_AC\\_exp'\n", + "\\item 'PC12\\_AC\\_exp'\n", + "\\item 'PC13\\_AC\\_exp'\n", + "\\item 'APOC1\\_DeJager\\_Mic\\_exp'\n", + "\\item 'msex\\_DeJager\\_Mic\\_exp'\n", + "\\item 'age\\_death\\_DeJager\\_Mic\\_exp'\n", + "\\item 'pmi\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC1\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC2\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC3\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC4\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC5\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC6\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC7\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC8\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC9\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC10\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC11\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC12\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC13\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC14\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC15\\_DeJager\\_Mic\\_exp'\n", + "\\item 'APOE\\_Mega\\_Mic\\_exp'\n", + "\\item 'msex\\_Mega\\_Mic\\_exp'\n", + "\\item 'age\\_death\\_Mega\\_Mic\\_exp'\n", + "\\item 'pmi\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC1\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC2\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC3\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC4\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC5\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC6\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC7\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC8\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC9\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC10\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC11\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC12\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC13\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC14\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC15\\_Mega\\_Mic\\_exp'\n", + "\\item 'tdp\\_st4'\n", + "\\item 'educ'\n", + "\\item 'apoe4\\_dose'\n", + "\\item 'apoe2\\_dose'\n", + "\\item 'msex\\_p'\n", + "\\item 'age\\_death\\_p'\n", + "\\item 'pmi\\_p'\n", + "\\item 'ROS\\_study\\_p'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'SID'\n", + "2. 'chr19_45001091_C_G'\n", + "3. 'chr19_44999110_TAAAA_TAA'\n", + "4. 'APOC4_APOC2_AC_exp'\n", + "5. 'APOC2_AC_exp'\n", + "6. 'msex_AC_exp'\n", + "7. 'age_death_AC_exp'\n", + "8. 'pmi_AC_exp'\n", + "9. 'ROS_study_AC_exp'\n", + "10. 'PC1_AC_exp'\n", + "11. 'PC2_AC_exp'\n", + "12. 'PC3_AC_exp'\n", + "13. 'PC4_AC_exp'\n", + "14. 'PC5_AC_exp'\n", + "15. 'PC6_AC_exp'\n", + "16. 'PC7_AC_exp'\n", + "17. 'PC8_AC_exp'\n", + "18. 'PC9_AC_exp'\n", + "19. 'PC10_AC_exp'\n", + "20. 'PC11_AC_exp'\n", + "21. 'PC12_AC_exp'\n", + "22. 'PC13_AC_exp'\n", + "23. 'APOC1_DeJager_Mic_exp'\n", + "24. 'msex_DeJager_Mic_exp'\n", + "25. 'age_death_DeJager_Mic_exp'\n", + "26. 'pmi_DeJager_Mic_exp'\n", + "27. 'PC1_DeJager_Mic_exp'\n", + "28. 'PC2_DeJager_Mic_exp'\n", + "29. 'PC3_DeJager_Mic_exp'\n", + "30. 'PC4_DeJager_Mic_exp'\n", + "31. 'PC5_DeJager_Mic_exp'\n", + "32. 'PC6_DeJager_Mic_exp'\n", + "33. 'PC7_DeJager_Mic_exp'\n", + "34. 'PC8_DeJager_Mic_exp'\n", + "35. 'PC9_DeJager_Mic_exp'\n", + "36. 'PC10_DeJager_Mic_exp'\n", + "37. 'PC11_DeJager_Mic_exp'\n", + "38. 'PC12_DeJager_Mic_exp'\n", + "39. 'PC13_DeJager_Mic_exp'\n", + "40. 'PC14_DeJager_Mic_exp'\n", + "41. 'PC15_DeJager_Mic_exp'\n", + "42. 'APOE_Mega_Mic_exp'\n", + "43. 'msex_Mega_Mic_exp'\n", + "44. 'age_death_Mega_Mic_exp'\n", + "45. 'pmi_Mega_Mic_exp'\n", + "46. 'PC1_Mega_Mic_exp'\n", + "47. 'PC2_Mega_Mic_exp'\n", + "48. 'PC3_Mega_Mic_exp'\n", + "49. 'PC4_Mega_Mic_exp'\n", + "50. 'PC5_Mega_Mic_exp'\n", + "51. 'PC6_Mega_Mic_exp'\n", + "52. 'PC7_Mega_Mic_exp'\n", + "53. 'PC8_Mega_Mic_exp'\n", + "54. 'PC9_Mega_Mic_exp'\n", + "55. 'PC10_Mega_Mic_exp'\n", + "56. 'PC11_Mega_Mic_exp'\n", + "57. 'PC12_Mega_Mic_exp'\n", + "58. 'PC13_Mega_Mic_exp'\n", + "59. 'PC14_Mega_Mic_exp'\n", + "60. 'PC15_Mega_Mic_exp'\n", + "61. 'tdp_st4'\n", + "62. 'educ'\n", + "63. 'apoe4_dose'\n", + "64. 'apoe2_dose'\n", + "65. 'msex_p'\n", + "66. 'age_death_p'\n", + "67. 'pmi_p'\n", + "68. 'ROS_study_p'\n", + "\n", + "\n" + ], + "text/plain": [ + " [1] \"SID\" \"chr19_45001091_C_G\" \n", + " [3] \"chr19_44999110_TAAAA_TAA\" \"APOC4_APOC2_AC_exp\" \n", + " [5] \"APOC2_AC_exp\" \"msex_AC_exp\" \n", + " [7] \"age_death_AC_exp\" \"pmi_AC_exp\" \n", + " [9] \"ROS_study_AC_exp\" \"PC1_AC_exp\" \n", + "[11] \"PC2_AC_exp\" \"PC3_AC_exp\" \n", + "[13] \"PC4_AC_exp\" \"PC5_AC_exp\" \n", + "[15] \"PC6_AC_exp\" \"PC7_AC_exp\" \n", + "[17] \"PC8_AC_exp\" \"PC9_AC_exp\" \n", + "[19] \"PC10_AC_exp\" \"PC11_AC_exp\" \n", + "[21] \"PC12_AC_exp\" \"PC13_AC_exp\" \n", + "[23] \"APOC1_DeJager_Mic_exp\" \"msex_DeJager_Mic_exp\" \n", + "[25] \"age_death_DeJager_Mic_exp\" \"pmi_DeJager_Mic_exp\" \n", + "[27] \"PC1_DeJager_Mic_exp\" \"PC2_DeJager_Mic_exp\" \n", + "[29] \"PC3_DeJager_Mic_exp\" \"PC4_DeJager_Mic_exp\" \n", + "[31] \"PC5_DeJager_Mic_exp\" \"PC6_DeJager_Mic_exp\" \n", + "[33] \"PC7_DeJager_Mic_exp\" \"PC8_DeJager_Mic_exp\" \n", + "[35] \"PC9_DeJager_Mic_exp\" \"PC10_DeJager_Mic_exp\" \n", + "[37] \"PC11_DeJager_Mic_exp\" \"PC12_DeJager_Mic_exp\" \n", + "[39] \"PC13_DeJager_Mic_exp\" \"PC14_DeJager_Mic_exp\" \n", + "[41] \"PC15_DeJager_Mic_exp\" \"APOE_Mega_Mic_exp\" \n", + "[43] \"msex_Mega_Mic_exp\" \"age_death_Mega_Mic_exp\" \n", + "[45] \"pmi_Mega_Mic_exp\" \"PC1_Mega_Mic_exp\" \n", + "[47] \"PC2_Mega_Mic_exp\" \"PC3_Mega_Mic_exp\" \n", + "[49] \"PC4_Mega_Mic_exp\" \"PC5_Mega_Mic_exp\" \n", + "[51] \"PC6_Mega_Mic_exp\" \"PC7_Mega_Mic_exp\" \n", + "[53] \"PC8_Mega_Mic_exp\" \"PC9_Mega_Mic_exp\" \n", + "[55] \"PC10_Mega_Mic_exp\" \"PC11_Mega_Mic_exp\" \n", + "[57] \"PC12_Mega_Mic_exp\" \"PC13_Mega_Mic_exp\" \n", + "[59] \"PC14_Mega_Mic_exp\" \"PC15_Mega_Mic_exp\" \n", + "[61] \"tdp_st4\" \"educ\" \n", + "[63] \"apoe4_dose\" \"apoe2_dose\" \n", + "[65] \"msex_p\" \"age_death_p\" \n", + "[67] \"pmi_p\" \"ROS_study_p\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Partially overlapping samples for FIML-SEM mediation analysis\n", + "input_all <- merge(geno_sub, AC_exp, by = \"SID\", all = TRUE)\n", + "input_all <- merge(input_all, DeJager_Mic_exp, by = \"SID\", all = TRUE)\n", + "input_all <- merge(input_all, Mega_Mic_exp, by = \"SID\", all = TRUE)\n", + "input_all <- merge(input_all, patho_sub, by = \"SID\", all = TRUE)\n", + "colnames(input_all); nrow(input_all)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "d195f64e", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'SID'
  2. 'chr19_45001091_C_G'
  3. 'chr19_44999110_TAAAA_TAA'
  4. 'APOC4_APOC2_AC_exp'
  5. 'APOC2_AC_exp'
  6. 'PC1_AC_exp'
  7. 'PC2_AC_exp'
  8. 'PC3_AC_exp'
  9. 'PC4_AC_exp'
  10. 'PC5_AC_exp'
  11. 'PC6_AC_exp'
  12. 'PC7_AC_exp'
  13. 'PC8_AC_exp'
  14. 'PC9_AC_exp'
  15. 'PC10_AC_exp'
  16. 'PC11_AC_exp'
  17. 'PC12_AC_exp'
  18. 'PC13_AC_exp'
  19. 'APOC1_DeJager_Mic_exp'
  20. 'PC1_DeJager_Mic_exp'
  21. 'PC2_DeJager_Mic_exp'
  22. 'PC3_DeJager_Mic_exp'
  23. 'PC4_DeJager_Mic_exp'
  24. 'PC5_DeJager_Mic_exp'
  25. 'PC6_DeJager_Mic_exp'
  26. 'PC7_DeJager_Mic_exp'
  27. 'PC8_DeJager_Mic_exp'
  28. 'PC9_DeJager_Mic_exp'
  29. 'PC10_DeJager_Mic_exp'
  30. 'PC11_DeJager_Mic_exp'
  31. 'PC12_DeJager_Mic_exp'
  32. 'PC13_DeJager_Mic_exp'
  33. 'PC14_DeJager_Mic_exp'
  34. 'PC15_DeJager_Mic_exp'
  35. 'APOE_Mega_Mic_exp'
  36. 'PC1_Mega_Mic_exp'
  37. 'PC2_Mega_Mic_exp'
  38. 'PC3_Mega_Mic_exp'
  39. 'PC4_Mega_Mic_exp'
  40. 'PC5_Mega_Mic_exp'
  41. 'PC6_Mega_Mic_exp'
  42. 'PC7_Mega_Mic_exp'
  43. 'PC8_Mega_Mic_exp'
  44. 'PC9_Mega_Mic_exp'
  45. 'PC10_Mega_Mic_exp'
  46. 'PC11_Mega_Mic_exp'
  47. 'PC12_Mega_Mic_exp'
  48. 'PC13_Mega_Mic_exp'
  49. 'PC14_Mega_Mic_exp'
  50. 'PC15_Mega_Mic_exp'
  51. 'tdp_st4'
  52. 'educ'
  53. 'apoe4_dose'
  54. 'apoe2_dose'
  55. 'msex_u'
  56. 'age_death_u'
  57. 'pmi_u'
  58. 'ROS_study_u'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'SID'\n", + "\\item 'chr19\\_45001091\\_C\\_G'\n", + "\\item 'chr19\\_44999110\\_TAAAA\\_TAA'\n", + "\\item 'APOC4\\_APOC2\\_AC\\_exp'\n", + "\\item 'APOC2\\_AC\\_exp'\n", + "\\item 'PC1\\_AC\\_exp'\n", + "\\item 'PC2\\_AC\\_exp'\n", + "\\item 'PC3\\_AC\\_exp'\n", + "\\item 'PC4\\_AC\\_exp'\n", + "\\item 'PC5\\_AC\\_exp'\n", + "\\item 'PC6\\_AC\\_exp'\n", + "\\item 'PC7\\_AC\\_exp'\n", + "\\item 'PC8\\_AC\\_exp'\n", + "\\item 'PC9\\_AC\\_exp'\n", + "\\item 'PC10\\_AC\\_exp'\n", + "\\item 'PC11\\_AC\\_exp'\n", + "\\item 'PC12\\_AC\\_exp'\n", + "\\item 'PC13\\_AC\\_exp'\n", + "\\item 'APOC1\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC1\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC2\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC3\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC4\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC5\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC6\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC7\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC8\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC9\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC10\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC11\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC12\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC13\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC14\\_DeJager\\_Mic\\_exp'\n", + "\\item 'PC15\\_DeJager\\_Mic\\_exp'\n", + "\\item 'APOE\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC1\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC2\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC3\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC4\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC5\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC6\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC7\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC8\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC9\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC10\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC11\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC12\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC13\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC14\\_Mega\\_Mic\\_exp'\n", + "\\item 'PC15\\_Mega\\_Mic\\_exp'\n", + "\\item 'tdp\\_st4'\n", + "\\item 'educ'\n", + "\\item 'apoe4\\_dose'\n", + "\\item 'apoe2\\_dose'\n", + "\\item 'msex\\_u'\n", + "\\item 'age\\_death\\_u'\n", + "\\item 'pmi\\_u'\n", + "\\item 'ROS\\_study\\_u'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'SID'\n", + "2. 'chr19_45001091_C_G'\n", + "3. 'chr19_44999110_TAAAA_TAA'\n", + "4. 'APOC4_APOC2_AC_exp'\n", + "5. 'APOC2_AC_exp'\n", + "6. 'PC1_AC_exp'\n", + "7. 'PC2_AC_exp'\n", + "8. 'PC3_AC_exp'\n", + "9. 'PC4_AC_exp'\n", + "10. 'PC5_AC_exp'\n", + "11. 'PC6_AC_exp'\n", + "12. 'PC7_AC_exp'\n", + "13. 'PC8_AC_exp'\n", + "14. 'PC9_AC_exp'\n", + "15. 'PC10_AC_exp'\n", + "16. 'PC11_AC_exp'\n", + "17. 'PC12_AC_exp'\n", + "18. 'PC13_AC_exp'\n", + "19. 'APOC1_DeJager_Mic_exp'\n", + "20. 'PC1_DeJager_Mic_exp'\n", + "21. 'PC2_DeJager_Mic_exp'\n", + "22. 'PC3_DeJager_Mic_exp'\n", + "23. 'PC4_DeJager_Mic_exp'\n", + "24. 'PC5_DeJager_Mic_exp'\n", + "25. 'PC6_DeJager_Mic_exp'\n", + "26. 'PC7_DeJager_Mic_exp'\n", + "27. 'PC8_DeJager_Mic_exp'\n", + "28. 'PC9_DeJager_Mic_exp'\n", + "29. 'PC10_DeJager_Mic_exp'\n", + "30. 'PC11_DeJager_Mic_exp'\n", + "31. 'PC12_DeJager_Mic_exp'\n", + "32. 'PC13_DeJager_Mic_exp'\n", + "33. 'PC14_DeJager_Mic_exp'\n", + "34. 'PC15_DeJager_Mic_exp'\n", + "35. 'APOE_Mega_Mic_exp'\n", + "36. 'PC1_Mega_Mic_exp'\n", + "37. 'PC2_Mega_Mic_exp'\n", + "38. 'PC3_Mega_Mic_exp'\n", + "39. 'PC4_Mega_Mic_exp'\n", + "40. 'PC5_Mega_Mic_exp'\n", + "41. 'PC6_Mega_Mic_exp'\n", + "42. 'PC7_Mega_Mic_exp'\n", + "43. 'PC8_Mega_Mic_exp'\n", + "44. 'PC9_Mega_Mic_exp'\n", + "45. 'PC10_Mega_Mic_exp'\n", + "46. 'PC11_Mega_Mic_exp'\n", + "47. 'PC12_Mega_Mic_exp'\n", + "48. 'PC13_Mega_Mic_exp'\n", + "49. 'PC14_Mega_Mic_exp'\n", + "50. 'PC15_Mega_Mic_exp'\n", + "51. 'tdp_st4'\n", + "52. 'educ'\n", + "53. 'apoe4_dose'\n", + "54. 'apoe2_dose'\n", + "55. 'msex_u'\n", + "56. 'age_death_u'\n", + "57. 'pmi_u'\n", + "58. 'ROS_study_u'\n", + "\n", + "\n" + ], + "text/plain": [ + " [1] \"SID\" \"chr19_45001091_C_G\" \n", + " [3] \"chr19_44999110_TAAAA_TAA\" \"APOC4_APOC2_AC_exp\" \n", + " [5] \"APOC2_AC_exp\" \"PC1_AC_exp\" \n", + " [7] \"PC2_AC_exp\" \"PC3_AC_exp\" \n", + " [9] \"PC4_AC_exp\" \"PC5_AC_exp\" \n", + "[11] \"PC6_AC_exp\" \"PC7_AC_exp\" \n", + "[13] \"PC8_AC_exp\" \"PC9_AC_exp\" \n", + "[15] \"PC10_AC_exp\" \"PC11_AC_exp\" \n", + "[17] \"PC12_AC_exp\" \"PC13_AC_exp\" \n", + "[19] \"APOC1_DeJager_Mic_exp\" \"PC1_DeJager_Mic_exp\" \n", + "[21] \"PC2_DeJager_Mic_exp\" \"PC3_DeJager_Mic_exp\" \n", + "[23] \"PC4_DeJager_Mic_exp\" \"PC5_DeJager_Mic_exp\" \n", + "[25] \"PC6_DeJager_Mic_exp\" \"PC7_DeJager_Mic_exp\" \n", + "[27] \"PC8_DeJager_Mic_exp\" \"PC9_DeJager_Mic_exp\" \n", + "[29] \"PC10_DeJager_Mic_exp\" \"PC11_DeJager_Mic_exp\" \n", + "[31] \"PC12_DeJager_Mic_exp\" \"PC13_DeJager_Mic_exp\" \n", + "[33] \"PC14_DeJager_Mic_exp\" \"PC15_DeJager_Mic_exp\" \n", + "[35] \"APOE_Mega_Mic_exp\" \"PC1_Mega_Mic_exp\" \n", + "[37] \"PC2_Mega_Mic_exp\" \"PC3_Mega_Mic_exp\" \n", + "[39] \"PC4_Mega_Mic_exp\" \"PC5_Mega_Mic_exp\" \n", + "[41] \"PC6_Mega_Mic_exp\" \"PC7_Mega_Mic_exp\" \n", + "[43] \"PC8_Mega_Mic_exp\" \"PC9_Mega_Mic_exp\" \n", + "[45] \"PC10_Mega_Mic_exp\" \"PC11_Mega_Mic_exp\" \n", + "[47] \"PC12_Mega_Mic_exp\" \"PC13_Mega_Mic_exp\" \n", + "[49] \"PC14_Mega_Mic_exp\" \"PC15_Mega_Mic_exp\" \n", + "[51] \"tdp_st4\" \"educ\" \n", + "[53] \"apoe4_dose\" \"apoe2_dose\" \n", + "[55] \"msex_u\" \"age_death_u\" \n", + "[57] \"pmi_u\" \"ROS_study_u\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "1153" + ], + "text/latex": [ + "1153" + ], + "text/markdown": [ + "1153" + ], + "text/plain": [ + "[1] 1153" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create unified covariates — msex/age_death/pmi/ROS_study\n", + "input_all <- input_all %>%\n", + " mutate(msex_u = coalesce(msex_AC_exp, msex_DeJager_Mic_exp, msex_Mega_Mic_exp, msex_p)) %>%\n", + " mutate(age_death_u = coalesce(age_death_AC_exp, age_death_DeJager_Mic_exp, age_death_Mega_Mic_exp, age_death_p)) %>%\n", + " mutate(pmi_u = coalesce(pmi_AC_exp, pmi_DeJager_Mic_exp, pmi_Mega_Mic_exp, pmi_p)) %>%\n", + " mutate(ROS_study_u = coalesce(ROS_study_AC_exp, ROS_study_p))\n", + "\n", + "input_all_out <- input_all %>% dplyr::select(-c(msex_AC_exp, msex_DeJager_Mic_exp, msex_Mega_Mic_exp, msex_p,\n", + " age_death_AC_exp, age_death_DeJager_Mic_exp, age_death_Mega_Mic_exp, age_death_p,\n", + " pmi_AC_exp, pmi_DeJager_Mic_exp, pmi_Mega_Mic_exp, pmi_p,\n", + " ROS_study_AC_exp, ROS_study_p))\n", + "colnames(input_all_out); nrow(input_all_out)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "0ad3f474", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "write.table(input_all_out, file = '/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set27/tdp_st4/APOE_ind_set_27_tdp_st4_mediation_all_input.txt',\n", + " quote = FALSE, sep = '\\t', col.names = TRUE, row.names = FALSE)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_ARHGAP35_mediation_analysis.ipynb b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/set57_APOE_ind_cs_ARHGAP35_tau_mediation.ipynb similarity index 98% rename from main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_ARHGAP35_mediation_analysis.ipynb rename to main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/set57_APOE_ind_cs_ARHGAP35_tau_mediation.ipynb index c9da407..22f84f5 100644 --- a/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_ARHGAP35_mediation_analysis.ipynb +++ b/main_text/5_AD_xQTL_genes_cis_trans/staging/APOE_mediation/set57_APOE_ind_cs_ARHGAP35_tau_mediation.ipynb @@ -35,14 +35,61 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 1, "id": "fba7c1f6", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Attaching package: ‘dplyr’\n", + "\n", + "\n", + "The following objects are masked from ‘package:data.table’:\n", + "\n", + " between, first, last\n", + "\n", + "\n", + "The following objects are masked from ‘package:stats’:\n", + "\n", + " filter, lag\n", + "\n", + "\n", + "The following objects are masked from ‘package:base’:\n", + "\n", + " intersect, setdiff, setequal, union\n", + "\n", + "\n", + "Loading required package: MASS\n", + "\n", + "\n", + "Attaching package: ‘MASS’\n", + "\n", + "\n", + "The following object is masked from ‘package:dplyr’:\n", + "\n", + " select\n", + "\n", + "\n", + "Loading required package: Matrix\n", + "\n", + "Loading required package: mvtnorm\n", + "\n", + "Loading required package: sandwich\n", + "\n", + "mediation: Causal Mediation Analysis\n", + "Version: 4.5.1\n", + "\n", + "\n" + ] + } + ], "source": [ "library(data.table)\n", "library(dplyr)\n", @@ -117,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 2, "id": "eb01bd21", "metadata": { "vscode": { @@ -197,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 3, "id": "249744c6", "metadata": { "vscode": { @@ -214,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 4, "id": "4dcef7cd", "metadata": { "vscode": { @@ -353,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 5, "id": "1cedd98d", "metadata": { "vscode": { @@ -368,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 6, "id": "089d5a9e", "metadata": { "vscode": { @@ -451,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 7, "id": "871c4bee", "metadata": { "vscode": { @@ -557,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 8, "id": "8672e1de", "metadata": { "vscode": { @@ -687,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 9, "id": "449f6eb4", "metadata": { "vscode": { @@ -701,7 +748,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 10, "id": "5d01cdf3", "metadata": { "vscode": { @@ -712,12 +759,12 @@ "source": [ "patho_sub <- patho %>% filter(cs_id == 'set_57') %>% dplyr::select(specimenID, tangles_sqrt, educ, apoe4_dose, apoe2_dose, msex, age_death, pmi, ROS_study)\n", "patho_sub <- unique(patho_sub)\n", - "colnames(patho_sub) <- c('SID', \"tangles_sqrt\", \"apoe4_dose\", \"apoe2_dose\", \"educ\", \"msex_p\", \"age_death_p\", \"pmi_p\", \"ROS_study\")" + "colnames(patho_sub) <- c('SID', \"tangles_sqrt\", \"educ\", \"apoe4_dose\", \"apoe2_dose\", \"msex_p\", \"age_death_p\", \"pmi_p\", \"ROS_study\")" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 11, "id": "ecf40f53", "metadata": { "vscode": { @@ -731,7 +778,7 @@ "\n", "\n", "\n", - "\t\n", + "\t\n", "\t\n", "\n", "\n", @@ -747,7 +794,7 @@ "text/latex": [ "A data.table: 6 × 9\n", "\\begin{tabular}{lllllllll}\n", - " SID & tangles\\_sqrt & apoe4\\_dose & apoe2\\_dose & educ & msex\\_p & age\\_death\\_p & pmi\\_p & ROS\\_study\\\\\n", + " SID & tangles\\_sqrt & educ & apoe4\\_dose & apoe2\\_dose & msex\\_p & age\\_death\\_p & pmi\\_p & ROS\\_study\\\\\n", " & & & & & & & & \\\\\n", "\\hline\n", "\t SM-CJFK8 & 2.218849 & 16 & 0 & 0 & 0 & 100.87340 & 7.500000 & 0\\\\\n", @@ -762,7 +809,7 @@ "\n", "A data.table: 6 × 9\n", "\n", - "| SID <chr> | tangles_sqrt <dbl> | apoe4_dose <int> | apoe2_dose <int> | educ <int> | msex_p <int> | age_death_p <dbl> | pmi_p <dbl> | ROS_study <int> |\n", + "| SID <chr> | tangles_sqrt <dbl> | educ <int> | apoe4_dose <int> | apoe2_dose <int> | msex_p <int> | age_death_p <dbl> | pmi_p <dbl> | ROS_study <int> |\n", "|---|---|---|---|---|---|---|---|---|\n", "| SM-CJFK8 | 2.218849 | 16 | 0 | 0 | 0 | 100.87340 | 7.500000 | 0 |\n", "| SM-CTDUU | 4.143426 | 16 | 1 | 0 | 0 | 80.65708 | 5.500000 | 0 |\n", @@ -773,13 +820,13 @@ "\n" ], "text/plain": [ - " SID tangles_sqrt apoe4_dose apoe2_dose educ msex_p age_death_p pmi_p \n", - "1 SM-CJFK8 2.218849 16 0 0 0 100.87340 7.500000\n", - "2 SM-CTDUU 4.143426 16 1 0 0 80.65708 5.500000\n", - "3 SM-CTDSC 2.516578 12 0 1 0 83.69062 3.933333\n", - "4 SM-CJIYB 3.815742 15 0 0 1 92.61875 5.083333\n", - "5 SM-CTEMS 2.196057 12 0 0 0 90.38467 3.783333\n", - "6 SM-CJFLR 5.088562 15 0 0 0 94.31348 5.000000\n", + " SID tangles_sqrt educ apoe4_dose apoe2_dose msex_p age_death_p pmi_p \n", + "1 SM-CJFK8 2.218849 16 0 0 0 100.87340 7.500000\n", + "2 SM-CTDUU 4.143426 16 1 0 0 80.65708 5.500000\n", + "3 SM-CTDSC 2.516578 12 0 1 0 83.69062 3.933333\n", + "4 SM-CJIYB 3.815742 15 0 0 1 92.61875 5.083333\n", + "5 SM-CTEMS 2.196057 12 0 0 0 90.38467 3.783333\n", + "6 SM-CJFLR 5.088562 15 0 0 0 94.31348 5.000000\n", " ROS_study\n", "1 0 \n", "2 0 \n", @@ -825,7 +872,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 12, "id": "7856abba", "metadata": { "vscode": { @@ -932,7 +979,7 @@ " 3rd Qu.: 2.34203 3rd Qu.: 2.1909 3rd Qu.: 2.3422 3rd Qu.: 2.78235 \n", " Max. : 16.48353 Max. : 24.0234 Max. : 17.0763 Max. : 22.11932 \n", " \n", - " Hidden_Factor_PC21 Hidden_Factor_PC22 tangles_sqrt apoe4_dose \n", + " Hidden_Factor_PC21 Hidden_Factor_PC22 tangles_sqrt educ \n", " Min. :-14.7906 Min. :-21.50346 Min. :0.0807 Min. : 8.00 \n", " 1st Qu.: -3.0078 1st Qu.: -1.87339 1st Qu.:1.3170 1st Qu.:15.00 \n", " Median : -0.1724 Median : 0.01799 Median :2.4721 Median :18.00 \n", @@ -940,7 +987,7 @@ " 3rd Qu.: 2.6886 3rd Qu.: 2.52123 3rd Qu.:3.7271 3rd Qu.:20.00 \n", " Max. : 17.7074 Max. : 22.48004 Max. :7.8112 Max. :24.00 \n", " NA's :2 \n", - " apoe2_dose educ msex_p age_death_p \n", + " apoe4_dose apoe2_dose msex_p age_death_p \n", " Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 73.79 \n", " 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 83.90 \n", " Median :0.0000 Median :0.0000 Median :0.0000 Median : 88.47 \n", @@ -971,7 +1018,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 13, "id": "30ec7803", "metadata": { "vscode": { @@ -987,7 +1034,7 @@ ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\n", - "
  1. 'SID'
  2. 'chr19_44827033_TGATAGATAGATAGATA_TGATA'
  3. 'chr19_44840322_G_A'
  4. 'ARHGAP35_expression'
  5. 'msex'
  6. 'age_death'
  7. 'pmi'
  8. 'PC1'
  9. 'PC2'
  10. 'PC3'
  11. 'PC4'
  12. 'PC5'
  13. 'PC6'
  14. 'PC7'
  15. 'PC8'
  16. 'PC9'
  17. 'PC10'
  18. 'PC11'
  19. 'PC12'
  20. 'PC13'
  21. 'PC14'
  22. 'PC15'
  23. 'Hidden_Factor_PC1'
  24. 'Hidden_Factor_PC2'
  25. 'Hidden_Factor_PC3'
  26. 'Hidden_Factor_PC4'
  27. 'Hidden_Factor_PC5'
  28. 'Hidden_Factor_PC6'
  29. 'Hidden_Factor_PC7'
  30. 'Hidden_Factor_PC8'
  31. 'Hidden_Factor_PC9'
  32. 'Hidden_Factor_PC10'
  33. 'Hidden_Factor_PC11'
  34. 'Hidden_Factor_PC12'
  35. 'Hidden_Factor_PC13'
  36. 'Hidden_Factor_PC14'
  37. 'Hidden_Factor_PC15'
  38. 'Hidden_Factor_PC16'
  39. 'Hidden_Factor_PC17'
  40. 'Hidden_Factor_PC18'
  41. 'Hidden_Factor_PC19'
  42. 'Hidden_Factor_PC20'
  43. 'Hidden_Factor_PC21'
  44. 'Hidden_Factor_PC22'
  45. 'tangles_sqrt'
  46. 'apoe4_dose'
  47. 'apoe2_dose'
  48. 'educ'
  49. 'msex_p'
  50. 'age_death_p'
  51. 'pmi_p'
  52. 'ROS_study'
\n" + "
  1. 'SID'
  2. 'chr19_44827033_TGATAGATAGATAGATA_TGATA'
  3. 'chr19_44840322_G_A'
  4. 'ARHGAP35_expression'
  5. 'msex'
  6. 'age_death'
  7. 'pmi'
  8. 'PC1'
  9. 'PC2'
  10. 'PC3'
  11. 'PC4'
  12. 'PC5'
  13. 'PC6'
  14. 'PC7'
  15. 'PC8'
  16. 'PC9'
  17. 'PC10'
  18. 'PC11'
  19. 'PC12'
  20. 'PC13'
  21. 'PC14'
  22. 'PC15'
  23. 'Hidden_Factor_PC1'
  24. 'Hidden_Factor_PC2'
  25. 'Hidden_Factor_PC3'
  26. 'Hidden_Factor_PC4'
  27. 'Hidden_Factor_PC5'
  28. 'Hidden_Factor_PC6'
  29. 'Hidden_Factor_PC7'
  30. 'Hidden_Factor_PC8'
  31. 'Hidden_Factor_PC9'
  32. 'Hidden_Factor_PC10'
  33. 'Hidden_Factor_PC11'
  34. 'Hidden_Factor_PC12'
  35. 'Hidden_Factor_PC13'
  36. 'Hidden_Factor_PC14'
  37. 'Hidden_Factor_PC15'
  38. 'Hidden_Factor_PC16'
  39. 'Hidden_Factor_PC17'
  40. 'Hidden_Factor_PC18'
  41. 'Hidden_Factor_PC19'
  42. 'Hidden_Factor_PC20'
  43. 'Hidden_Factor_PC21'
  44. 'Hidden_Factor_PC22'
  45. 'tangles_sqrt'
  46. 'educ'
  47. 'apoe4_dose'
  48. 'apoe2_dose'
  49. 'msex_p'
  50. 'age_death_p'
  51. 'pmi_p'
  52. 'ROS_study'
\n" ], "text/latex": [ "\\begin{enumerate*}\n", @@ -1036,9 +1083,9 @@ "\\item 'Hidden\\_Factor\\_PC21'\n", "\\item 'Hidden\\_Factor\\_PC22'\n", "\\item 'tangles\\_sqrt'\n", + "\\item 'educ'\n", "\\item 'apoe4\\_dose'\n", "\\item 'apoe2\\_dose'\n", - "\\item 'educ'\n", "\\item 'msex\\_p'\n", "\\item 'age\\_death\\_p'\n", "\\item 'pmi\\_p'\n", @@ -1091,9 +1138,9 @@ "43. 'Hidden_Factor_PC21'\n", "44. 'Hidden_Factor_PC22'\n", "45. 'tangles_sqrt'\n", - "46. 'apoe4_dose'\n", - "47. 'apoe2_dose'\n", - "48. 'educ'\n", + "46. 'educ'\n", + "47. 'apoe4_dose'\n", + "48. 'apoe2_dose'\n", "49. 'msex_p'\n", "50. 'age_death_p'\n", "51. 'pmi_p'\n", @@ -1147,9 +1194,9 @@ "[43] \"Hidden_Factor_PC21\" \n", "[44] \"Hidden_Factor_PC22\" \n", "[45] \"tangles_sqrt\" \n", - "[46] \"apoe4_dose\" \n", - "[47] \"apoe2_dose\" \n", - "[48] \"educ\" \n", + "[46] \"educ\" \n", + "[47] \"apoe4_dose\" \n", + "[48] \"apoe2_dose\" \n", "[49] \"msex_p\" \n", "[50] \"age_death_p\" \n", "[51] \"pmi_p\" \n", @@ -1239,7 +1286,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 14, "id": "951fa630", "metadata": { "vscode": { @@ -1255,7 +1302,7 @@ ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\n", - "
  1. 'SID'
  2. 'chr19_44827033_TGATAGATAGATAGATA_TGATA'
  3. 'chr19_44840322_G_A'
  4. 'ARHGAP35_expression'
  5. 'msex'
  6. 'age_death'
  7. 'pmi'
  8. 'PC1'
  9. 'PC2'
  10. 'PC3'
  11. 'PC4'
  12. 'PC5'
  13. 'PC6'
  14. 'PC7'
  15. 'PC8'
  16. 'PC9'
  17. 'PC10'
  18. 'PC11'
  19. 'PC12'
  20. 'PC13'
  21. 'PC14'
  22. 'PC15'
  23. 'Hidden_Factor_PC1'
  24. 'Hidden_Factor_PC2'
  25. 'Hidden_Factor_PC3'
  26. 'Hidden_Factor_PC4'
  27. 'Hidden_Factor_PC5'
  28. 'Hidden_Factor_PC6'
  29. 'Hidden_Factor_PC7'
  30. 'Hidden_Factor_PC8'
  31. 'Hidden_Factor_PC9'
  32. 'Hidden_Factor_PC10'
  33. 'Hidden_Factor_PC11'
  34. 'Hidden_Factor_PC12'
  35. 'Hidden_Factor_PC13'
  36. 'Hidden_Factor_PC14'
  37. 'Hidden_Factor_PC15'
  38. 'Hidden_Factor_PC16'
  39. 'Hidden_Factor_PC17'
  40. 'Hidden_Factor_PC18'
  41. 'Hidden_Factor_PC19'
  42. 'Hidden_Factor_PC20'
  43. 'Hidden_Factor_PC21'
  44. 'Hidden_Factor_PC22'
  45. 'tangles_sqrt'
  46. 'apoe4_dose'
  47. 'apoe2_dose'
  48. 'educ'
  49. 'msex_p'
  50. 'age_death_p'
  51. 'pmi_p'
  52. 'ROS_study'
\n" + "
  1. 'SID'
  2. 'chr19_44827033_TGATAGATAGATAGATA_TGATA'
  3. 'chr19_44840322_G_A'
  4. 'ARHGAP35_expression'
  5. 'msex'
  6. 'age_death'
  7. 'pmi'
  8. 'PC1'
  9. 'PC2'
  10. 'PC3'
  11. 'PC4'
  12. 'PC5'
  13. 'PC6'
  14. 'PC7'
  15. 'PC8'
  16. 'PC9'
  17. 'PC10'
  18. 'PC11'
  19. 'PC12'
  20. 'PC13'
  21. 'PC14'
  22. 'PC15'
  23. 'Hidden_Factor_PC1'
  24. 'Hidden_Factor_PC2'
  25. 'Hidden_Factor_PC3'
  26. 'Hidden_Factor_PC4'
  27. 'Hidden_Factor_PC5'
  28. 'Hidden_Factor_PC6'
  29. 'Hidden_Factor_PC7'
  30. 'Hidden_Factor_PC8'
  31. 'Hidden_Factor_PC9'
  32. 'Hidden_Factor_PC10'
  33. 'Hidden_Factor_PC11'
  34. 'Hidden_Factor_PC12'
  35. 'Hidden_Factor_PC13'
  36. 'Hidden_Factor_PC14'
  37. 'Hidden_Factor_PC15'
  38. 'Hidden_Factor_PC16'
  39. 'Hidden_Factor_PC17'
  40. 'Hidden_Factor_PC18'
  41. 'Hidden_Factor_PC19'
  42. 'Hidden_Factor_PC20'
  43. 'Hidden_Factor_PC21'
  44. 'Hidden_Factor_PC22'
  45. 'tangles_sqrt'
  46. 'educ'
  47. 'apoe4_dose'
  48. 'apoe2_dose'
  49. 'msex_p'
  50. 'age_death_p'
  51. 'pmi_p'
  52. 'ROS_study'
\n" ], "text/latex": [ "\\begin{enumerate*}\n", @@ -1304,9 +1351,9 @@ "\\item 'Hidden\\_Factor\\_PC21'\n", "\\item 'Hidden\\_Factor\\_PC22'\n", "\\item 'tangles\\_sqrt'\n", + "\\item 'educ'\n", "\\item 'apoe4\\_dose'\n", "\\item 'apoe2\\_dose'\n", - "\\item 'educ'\n", "\\item 'msex\\_p'\n", "\\item 'age\\_death\\_p'\n", "\\item 'pmi\\_p'\n", @@ -1359,9 +1406,9 @@ "43. 'Hidden_Factor_PC21'\n", "44. 'Hidden_Factor_PC22'\n", "45. 'tangles_sqrt'\n", - "46. 'apoe4_dose'\n", - "47. 'apoe2_dose'\n", - "48. 'educ'\n", + "46. 'educ'\n", + "47. 'apoe4_dose'\n", + "48. 'apoe2_dose'\n", "49. 'msex_p'\n", "50. 'age_death_p'\n", "51. 'pmi_p'\n", @@ -1415,9 +1462,9 @@ "[43] \"Hidden_Factor_PC21\" \n", "[44] \"Hidden_Factor_PC22\" \n", "[45] \"tangles_sqrt\" \n", - "[46] \"apoe4_dose\" \n", - "[47] \"apoe2_dose\" \n", - "[48] \"educ\" \n", + "[46] \"educ\" \n", + "[47] \"apoe4_dose\" \n", + "[48] \"apoe2_dose\" \n", "[49] \"msex_p\" \n", "[50] \"age_death_p\" \n", "[51] \"pmi_p\" \n", @@ -2264,7 +2311,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 15, "id": "72f46ebe", "metadata": { "vscode": { @@ -2280,7 +2327,7 @@ ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\n", - "
  1. 'SID'
  2. 'chr19_44827033_TGATAGATAGATAGATA_TGATA'
  3. 'chr19_44840322_G_A'
  4. 'ARHGAP35_expression'
  5. 'PC1'
  6. 'PC2'
  7. 'PC3'
  8. 'PC4'
  9. 'PC5'
  10. 'PC6'
  11. 'PC7'
  12. 'PC8'
  13. 'PC9'
  14. 'PC10'
  15. 'PC11'
  16. 'PC12'
  17. 'PC13'
  18. 'PC14'
  19. 'PC15'
  20. 'Hidden_Factor_PC1'
  21. 'Hidden_Factor_PC2'
  22. 'Hidden_Factor_PC3'
  23. 'Hidden_Factor_PC4'
  24. 'Hidden_Factor_PC5'
  25. 'Hidden_Factor_PC6'
  26. 'Hidden_Factor_PC7'
  27. 'Hidden_Factor_PC8'
  28. 'Hidden_Factor_PC9'
  29. 'Hidden_Factor_PC10'
  30. 'Hidden_Factor_PC11'
  31. 'Hidden_Factor_PC12'
  32. 'Hidden_Factor_PC13'
  33. 'Hidden_Factor_PC14'
  34. 'Hidden_Factor_PC15'
  35. 'Hidden_Factor_PC16'
  36. 'Hidden_Factor_PC17'
  37. 'Hidden_Factor_PC18'
  38. 'Hidden_Factor_PC19'
  39. 'Hidden_Factor_PC20'
  40. 'Hidden_Factor_PC21'
  41. 'Hidden_Factor_PC22'
  42. 'tangles_sqrt'
  43. 'apoe4_dose'
  44. 'apoe2_dose'
  45. 'educ'
  46. 'ROS_study'
  47. 'msex_u'
  48. 'age_death_u'
  49. 'pmi_u'
\n" + "
  1. 'SID'
  2. 'chr19_44827033_TGATAGATAGATAGATA_TGATA'
  3. 'chr19_44840322_G_A'
  4. 'ARHGAP35_expression'
  5. 'PC1'
  6. 'PC2'
  7. 'PC3'
  8. 'PC4'
  9. 'PC5'
  10. 'PC6'
  11. 'PC7'
  12. 'PC8'
  13. 'PC9'
  14. 'PC10'
  15. 'PC11'
  16. 'PC12'
  17. 'PC13'
  18. 'PC14'
  19. 'PC15'
  20. 'Hidden_Factor_PC1'
  21. 'Hidden_Factor_PC2'
  22. 'Hidden_Factor_PC3'
  23. 'Hidden_Factor_PC4'
  24. 'Hidden_Factor_PC5'
  25. 'Hidden_Factor_PC6'
  26. 'Hidden_Factor_PC7'
  27. 'Hidden_Factor_PC8'
  28. 'Hidden_Factor_PC9'
  29. 'Hidden_Factor_PC10'
  30. 'Hidden_Factor_PC11'
  31. 'Hidden_Factor_PC12'
  32. 'Hidden_Factor_PC13'
  33. 'Hidden_Factor_PC14'
  34. 'Hidden_Factor_PC15'
  35. 'Hidden_Factor_PC16'
  36. 'Hidden_Factor_PC17'
  37. 'Hidden_Factor_PC18'
  38. 'Hidden_Factor_PC19'
  39. 'Hidden_Factor_PC20'
  40. 'Hidden_Factor_PC21'
  41. 'Hidden_Factor_PC22'
  42. 'tangles_sqrt'
  43. 'educ'
  44. 'apoe4_dose'
  45. 'apoe2_dose'
  46. 'ROS_study'
  47. 'msex_u'
  48. 'age_death_u'
  49. 'pmi_u'
\n" ], "text/latex": [ "\\begin{enumerate*}\n", @@ -2326,9 +2373,9 @@ "\\item 'Hidden\\_Factor\\_PC21'\n", "\\item 'Hidden\\_Factor\\_PC22'\n", "\\item 'tangles\\_sqrt'\n", + "\\item 'educ'\n", "\\item 'apoe4\\_dose'\n", "\\item 'apoe2\\_dose'\n", - "\\item 'educ'\n", "\\item 'ROS\\_study'\n", "\\item 'msex\\_u'\n", "\\item 'age\\_death\\_u'\n", @@ -2378,9 +2425,9 @@ "40. 'Hidden_Factor_PC21'\n", "41. 'Hidden_Factor_PC22'\n", "42. 'tangles_sqrt'\n", - "43. 'apoe4_dose'\n", - "44. 'apoe2_dose'\n", - "45. 'educ'\n", + "43. 'educ'\n", + "44. 'apoe4_dose'\n", + "45. 'apoe2_dose'\n", "46. 'ROS_study'\n", "47. 'msex_u'\n", "48. 'age_death_u'\n", @@ -2431,9 +2478,9 @@ "[40] \"Hidden_Factor_PC21\" \n", "[41] \"Hidden_Factor_PC22\" \n", "[42] \"tangles_sqrt\" \n", - "[43] \"apoe4_dose\" \n", - "[44] \"apoe2_dose\" \n", - "[45] \"educ\" \n", + "[43] \"educ\" \n", + "[44] \"apoe4_dose\" \n", + "[45] \"apoe2_dose\" \n", "[46] \"ROS_study\" \n", "[47] \"msex_u\" \n", "[48] \"age_death_u\" \n", @@ -2473,7 +2520,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 17, "id": "a3e8155c", "metadata": { "vscode": { @@ -2482,7 +2529,7 @@ }, "outputs": [], "source": [ - "write.table(input_all_out, file = '/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/APOE_ind_set_57_mediation_all_input.txt',\n", + "write.table(input_all_out, file = '/mnt/lustre/home/yl4437/xqtl_flagship/APOE/mediation_partialoverlap_claude/set57/APOE_ind_set_57_mediation_all_input.txt',\n", " quote = FALSE, sep = '\\t', col.names = TRUE, row.names = FALSE)" ] }, @@ -2496,7 +2543,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 18, "id": "46c2bc02", "metadata": { "vscode": { @@ -2511,7 +2558,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 19, "id": "5c03df17", "metadata": { "vscode": { @@ -2536,7 +2583,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 20, "id": "91416747", "metadata": { "vscode": { @@ -2553,10 +2600,10 @@ "Nonparametric Bootstrap Confidence Intervals with the Percentile Method\n", "\n", " Estimate 95% CI Lower 95% CI Upper p-value\n", - "ACME 0.0111595 -0.0759331 0.1505477 0.820\n", - "ADE 0.0040875 -0.6774749 0.5794065 0.962\n", - "Total Effect 0.0152470 -0.6808906 0.5819206 0.992\n", - "Prop. Mediated 0.7319123 -2.0583794 1.7147752 0.948\n", + "ACME 0.0111595 -0.0899153 0.1685700 0.842\n", + "ADE 0.0040875 -0.7615924 0.6279823 0.976\n", + "Total Effect 0.0152470 -0.7621765 0.6052405 0.988\n", + "Prop. Mediated 0.7319123 -1.5633692 1.5705688 0.910\n", "\n", "Sample Size Used: 107 \n", "\n",
A data.table: 6 × 9
SIDtangles_sqrtapoe4_doseapoe2_doseeducmsex_page_death_ppmi_pROS_study
SIDtangles_sqrteducapoe4_doseapoe2_dosemsex_page_death_ppmi_pROS_study
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