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Fix multi-model dispatcher indexing and column order #3

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Fix multi-model dispatcher indexing and column order #3

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34 changes: 18 additions & 16 deletions R/predictRTConfModels.R
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Original file line number Diff line number Diff line change
Expand Up @@ -168,9 +168,9 @@ predictConfModels <- function(paramDf,
### Determine number of jobs, i.e. model-participant-combinations
nJobs <- nrow(paramDf)

call_predfct <- function(X) {
cur_model <- models[X[1]]
cur_sbj <- X[2]
call_predfct <- function(job) {
cur_model <- models[as.integer(job$model)]
cur_sbj <- job$sbj
sbj <- NULL # to omit a note because of an unbound variable
model <- NULL # to omit a note because of an unbound variable
params <- subset(paramDf, sbj==cur_sbj & model==cur_model)
Expand All @@ -184,13 +184,13 @@ predictConfModels <- function(paramDf,
return(res)
}

jobs <- expand.grid(model=1:length(models), sbj=subjects)
jobs <- expand.grid(model=seq_along(models), sbj=subjects, stringsAsFactors = FALSE)
if (nrow(jobs) < nJobs) stop("model and participant don't produce distinct rows!\nThere should be only one row per participant and model combination")

if (parallel) {
listjobs <- list()
for (i in 1:nrow(jobs)) {
listjobs[[i]] <- c(model = jobs[["model"]][i], sbj = jobs[["sbj"]][i])
for (i in seq_len(nrow(jobs))) {
listjobs[[i]] <- list(model = jobs[["model"]][i], sbj = jobs[["sbj"]][i])
}
if (is.null(n.cores)) {
n.cores <- detectCores()-1
Expand All @@ -205,12 +205,13 @@ predictConfModels <- function(paramDf,
res <- clusterApplyLB(cl, listjobs, fun=call_predfct)
stopCluster(cl)
} else {
res <- apply(jobs, 1, call_predfct)
res <- lapply(seq_len(nrow(jobs)), function(i) call_predfct(list(model = jobs$model[i], sbj = jobs$sbj[i])))
}
res <- do.call(rbind, res)

# Put sbj and model column to the front
res[,c(ncol(res),(ncol(res)-1), 1:(ncol(res)-2))]
leading_cols <- c(sbjcol, "model")
res <- res[, c(leading_cols, setdiff(names(res), leading_cols))]
return(res)
}

Expand Down Expand Up @@ -290,9 +291,9 @@ predictRTModels <- function(paramDf,
minrt <- min(minrt, pars_diffmodels$t0+pars_diffmodels$tau)
}
}
call_predfct <- function(X) {
cur_model <- models[X[1]]
cur_sbj <- X[2]
call_predfct <- function(job) {
cur_model <- models[as.integer(job$model)]
cur_sbj <- job$sbj
sbj <- NULL # to omit a note because of an unbound variable
model <- NULL # to omit a note because of an unbound variable
params <- subset(paramDf, sbj==cur_sbj & model==cur_model)
Expand All @@ -310,13 +311,13 @@ predictRTModels <- function(paramDf,
res[[sbjcol]] <- cur_sbj
return(res)
}
jobs <- expand.grid(model=1:length(models), sbj=subjects)
jobs <- expand.grid(model=seq_along(models), sbj=subjects, stringsAsFactors = FALSE)
if (nrow(jobs) < nJobs) stop("model and participant don't produce distinct rows!\nThere should be only one row per participant and model combination")

if (parallel) {
listjobs <- list()
for (i in 1:nrow(jobs)) {
listjobs[[i]] <- c(model = jobs[["model"]][i], sbj = jobs[["sbj"]][i])
for (i in seq_len(nrow(jobs))) {
listjobs[[i]] <- list(model = jobs[["model"]][i], sbj = jobs[["sbj"]][i])
}
cl <- makeCluster(type="SOCK", n.cores)
clusterExport(cl, c("paramDf", "sbjcol", "models",
Expand All @@ -327,11 +328,12 @@ predictRTModels <- function(paramDf,
res <- clusterApplyLB(cl, listjobs, fun=call_predfct)
stopCluster(cl)
} else {
res <- apply(jobs, 1, call_predfct)
res <- lapply(seq_len(nrow(jobs)), function(i) call_predfct(list(model = jobs$model[i], sbj = jobs$sbj[i])))
}
res <- do.call(rbind, res)

# Put sbj and model column to the front
res[,c(ncol(res),(ncol(res)-1), 1:(ncol(res)-2))]
leading_cols <- c(sbjcol, "model")
res <- res[, c(leading_cols, setdiff(names(res), leading_cols))]
return(res)
}