R Under development (unstable) (2024-09-09 r87107 ucrt) -- "Unsuffered Consequences"
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> library("psychomix")
Loading required package: flexmix
Loading required package: lattice
Loading required package: psychotools
> suppressWarnings(RNGversion("3.5.0"))
> set.seed(1)
> 
> ### Rost
> r <- simRaschmix(design = "rost2", extremes = FALSE)
> re <- simRaschmix(design = "rost2", extremes = TRUE)
> 
> mr <- raschmix(r, k = 2, nrep = 1, scores = "saturated")
2 : *
> mrs <- raschmix(r, k = 1:2, nrep = 1, scores = "saturated")
1 : *
2 : *
> mre <- raschmix(data = re, k = 2, nrep = 1, scores = "saturated")
2 : *
> mres <- raschmix(data = re, k = 1:3, nrep = 1, scores = "saturated")
1 : *
2 : *
3 : *
> 
> mr

Call:
raschmix(formula = r, k = 2, scores = "saturated", nrep = 1)

Cluster sizes:
  1   2 
830 819 

convergence after 8 iterations
> mrs

Call:
raschmix(formula = r, k = 1:2, scores = "saturated", nrep = 1)

  iter converged k k0     logLik      AIC      BIC      ICL
1    2      TRUE 1  1 -10484.227 21002.45 21094.39 21094.39
2   10      TRUE 2  2  -8829.039 17728.08 17917.35 17987.33
> mre

Call:
raschmix(data = re, k = 2, scores = "saturated", nrep = 1)

Cluster sizes:
  1   2 
798 824 

convergence after 9 iterations
> mres

Call:
raschmix(data = re, k = 1:3, scores = "saturated", nrep = 1)

  iter converged k k0     logLik      AIC      BIC      ICL
1    2      TRUE 1  1 -11069.086 22176.17 22278.61 21208.96
2    9      TRUE 2  2  -9413.509 18901.02 19100.50 18101.17
3   67      TRUE 3  3  -9397.043 18904.09 19200.61 18314.56
> 
> options(digits = 4)
> parameters(mr)
               Comp.1    Comp.2
item.Item01        NA        NA
item.Item02 -0.502341  0.440684
item.Item03 -1.245896  0.874500
item.Item04 -1.773345  1.516747
item.Item05 -2.313591  2.322729
item.Item06 -2.920368  2.820232
item.Item07 -3.496550  3.502710
item.Item08 -4.072865  4.098091
item.Item09 -4.680364  4.614149
item.Item10 -5.262647  5.275194
score.1            NA        NA
score.2      0.230504  0.006782
score.3      0.049722  0.081736
score.4     -0.106396 -0.064024
score.5      0.086188 -0.197729
score.6      0.202391 -0.085467
score.7     -0.008855  0.050351
score.8      0.172875  0.036870
score.9      0.256703  0.141052
> 
> ## mrrefit <- refit(mr)
> ## summary(mrrefit)
> 
> ## -------------------------------------------------
> 
> ## ### DIFSim
> ## data("DIFSim", package = "psychotree")
> ## #data("DIFSim", package = "psychotools")
> ## DIFSim.na <- DIFSim
> ## DIFSim.na$resp[1,1] <- NA
> ## DIFSim.na$resp[2,] <- NA
> ## DIFSim.na$age[3] <- NA
> ## #m2 <- raschmix(DIFSim$resp, k = 2, nrep = 1, type = "rost")
> ## m1 <- raschmix(DIFSim$resp, k = 2, nrep = 1, scores = "saturated",
> ##                control = list(iter.max = 20))
> ## m2 <- raschmix(resp ~ 1, DIFSim.na, k = 1, nrep = 1, scores = "saturated")
> ## ## concomitant
> ## m3 <- raschmix(resp ~ age + gender, data = DIFSim, k = 2, nrep = 1,
> ##                scores = "saturated")
> ## m3mv <- raschmix(resp ~ age + gender, data = DIFSim, k = 2, nrep = 1,
> ##                scores = "meanvar")
> ## #m4 <- raschmix(resp ~ age + gender, data = DIFSim[-(1:10),], k = 2:3, nrep = 1,
> ## #               scores = "saturated")
> 
> ## ## print and summary
> ## m3
> ## summary(m3)
> ## ## m4
> ## ## m4.1 <- getModel(m4, which = 1)
> ## ## m4.1
> ## ## summary(m4.1)
> 
> ## ## logLik
> ## logLik(m3)
> 
> ## ## parameters
> ## parameters(m3, which = "concomitant", component = 2:1)
> ## parameters(m3, which = "score")
> ## score.probs(m3mv)
> 
> ## ## weights
> ## ## weights(m3)
> ## ## ## flexmix requires integer weights
> ## ## w <- sample(1:nrow(DIFSim), nrow(DIFSim))
> ## ## m3w <- raschmix(resp ~ age + gender, data = DIFSim, k = 2, nrep = 1,
> ## ##                scores = "saturated", weights = w)
> ## ## weights(m3w)
> 
> ## ## refit
> 
> ## ## plot
> ## ## plot(m3)
> ## ## histogram(m3)
> ## ## m.nident <- raschmix(data = cbind(0,DIFSim$resp[,1:5],1,1,DIFSim$resp[,-(1:5)]),
> ## ##                      scores = "saturated", k = 3, nrep = 1)
> ## ## plot(m.nident)
> ## ## plot(m.nident, pch = 19:21, cex = matrix(rep((1+1:23)/10, 3), ncol = 3))
> ## ## plot(m.nident, index = FALSE, component = 1:2)
> ## ## plot(m.nident, index = TRUE, component = 1:2)
> 
> 
> proc.time()
   user  system elapsed 
   3.87    0.62    4.48