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e.g.
require(tergm)
set.seed(0)
nw <- network.initialize(100, dir = F)
nw <- simulate(nw ~ edges, coef = c(-4), dynamic = TRUE, time.slices = 10, output = "final", monitor = ~Change(~edges + triangle + mean.age + Form(~edges + triangle + mean.age)))
attributes(nw)$stats
produces
> attributes(nw)$stats
Markov Chain Monte Carlo (MCMC) output:
Start = 1
End = 10
Thinning interval = 1
Change~edges Change~triangle Change~mean.age Change~Form~edges Change~Form~triangle Change~Form~mean.age
[1,] 78 1 1.000000 78 1 1.000000
[2,] 182 5 2.417582 184 5 1.423913
[3,] 195 13 3.533333 197 26 1.554312
[4,] 176 11 4.522727 101 6 1.673013
[5,] 176 7 5.505682 -3 -22 1.690365
[6,] 186 9 6.505376 -85 -45 1.682063
[7,] 172 5 7.575581 -183 -66 1.772168
[8,] 162 3 8.549383 -290 -85 1.819206
[9,] 177 4 9.536723 -369 -105 1.691192
[10,] 189 16 10.597884 -442 -105 1.662247
I'm surprised to see negative ~Change(~Form(~edges)) and ~Change(~Form(~triangle)) statistics
also not totally clear to me that ~Change(~mean.age) is correct in any sense of the word (but maybe I just need to think about it more)
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