Create a model-type vector template

This is a small helper function which creates a vector template quick and easily for the PPall() function. Modify this template as you like.

findmodel(thres)

Arguments

thres

A numeric matrix which contains the threshold parameter for each item. NA is allowed - in fact expected!

Details

This function tries to guess the model which was applied to each item by using the matrix of threshold parameters. It only discriminates between GPCM and 4-PL model, and returns a character vector of length equal to the number of items, that contains "GPCM" or "4PL" entries depending on the structure of the thres matrix.

See also

PPall

Author

Manuel Reif

Examples

################# GPCM  and 4PL mixed #########################################

# some threshold parameters
THRES  <- matrix(c(-2,-1.23,1.11,3.48,1
                   ,2,-1,-0.2,0.5,1.3,-0.8,1.5),nrow=2)
# slopes
sl     <- c(0.5,1,1.5,1.1,1,0.98)

THRESx <- THRES
THRESx[2,1:3] <- NA

# for the 4PL item the estimated parameters are submitted, 
# for the GPCM items the lower asymptote = 0 
# and the upper asymptote = 1.
la     <- c(0.02,0.1,0,0,0,0)
ua     <- c(0.97,0.91,1,1,1,1)

awmatrix <- matrix(c(1,0,1,0,1,1,1,0,0,1
                     ,2,0,0,0,0,0,0,0,0,1
                     ,1,2,2,1,1,1,1,0,0,1),byrow=TRUE,nrow=5)

# create model2est
# this function tries to help finding the appropriate 
# model by inspecting the THRESx.
model2est <- findmodel(THRESx)


# MLE
respmixed_mle <- PPall(respm = awmatrix,thres = THRESx, 
                     slopes = sl,lowerA = la, upperA=ua,type = "mle",
                     model2est=model2est)
#> Estimating: mixed  4PL, GPCM ... 
#> type = mle 
#> Estimation finished!
# WLE
respmixed_wle <- PPall(respm = awmatrix,thres = THRESx, 
                    slopes = sl,lowerA = la, upperA=ua,type = "wle",
                    model2est=model2est)
#> Estimating: mixed  4PL, GPCM ... 
#> type = wle 
#> Estimation finished!
# MAP estimation
respmixed_map <- PPall(respm = awmatrix,thres = THRESx, 
                    slopes = sl,lowerA = la, upperA=ua, type = "map",
                    model2est=model2est)
#> Warning: all mu's are set to 0! 
#> Warning: all sigma2's are set to 1! 
#> Estimating: mixed  4PL, GPCM ... 
#> type = map 
#> Estimation finished!

# EAP estimation
respmixed_eap <- PPall(respm = awmatrix,thres = THRESx, 
                    slopes = sl,lowerA = la, upperA=ua, type = "eap",
                    model2est=model2est)
#> Estimating: mixed  4PL, GPCM ... 
#> type = eap 
#> Estimation finished!

# Robust estimation
respmixed_rob <- PPall(respm = awmatrix,thres = THRESx, 
                    slopes = sl,lowerA = la, upperA=ua, type = "robust",
                    model2est=model2est)
#> Estimating: mixed  4PL, GPCM ... 
#> type = robust 
#> Warning: Robust estimation for GPCM is still very experimental! 
#> Estimation finished!


# summary to summarize the results
summary(respmixed_mle)
#> PP Version:  0.6.4.0 
#> 
#>  Call: PPall(respm = awmatrix, thres = THRESx, slopes = sl, lowerA = la,      upperA = ua, type = "mle", model2est = model2est) 
#> - job started @ Tue Apr 21 13:42:09 2026 
#> 
#> Estimation type: mle 
#> 
#> Number of iterations: 4 
#> -------------------------------------
#>      estimate     SE
#> [1,]   0.1298 0.7195
#> [2,]  -0.0653 0.7333
#> [3,]     -Inf     NA
#> [4,]   2.1216 0.9344
#> [5,]  -0.0155 0.7295
summary(respmixed_wle)
#> PP Version:  0.6.4.0 
#> 
#>  Call: PPall(respm = awmatrix, thres = THRESx, slopes = sl, lowerA = la,      upperA = ua, type = "wle", model2est = model2est) 
#> - job started @ Tue Apr 21 13:42:09 2026 
#> 
#> Estimation type: wle 
#> 
#> Number of iterations: 7 
#> -------------------------------------
#>      estimate     SE
#> [1,]   0.1684 0.7171
#> [2,]  -0.0146 0.7294
#> [3,]  -3.1369 1.8953
#> [4,]   1.8696 0.8552
#> [5,]   0.0359 0.7257
summary(respmixed_map)
#> PP Version:  0.6.4.0 
#> 
#>  Call: PPall(respm = awmatrix, thres = THRESx, slopes = sl, lowerA = la,      upperA = ua, type = "map", model2est = model2est) 
#> - job started @ Tue Apr 21 13:42:09 2026 
#> 
#> Estimation type: map 
#> 
#> Number of iterations: 3 
#> -------------------------------------
#>      estimate     SE
#> [1,]   0.0862 0.7223
#> [2,]  -0.0429 0.7316
#> [3,]  -1.4487 0.9789
#> [4,]   1.2758 0.7372
#> [5,]  -0.0099 0.7291
summary(respmixed_eap)
#> PP Version:  0.6.4.0 
#> 
#>  Call: PPall(respm = awmatrix, thres = THRESx, slopes = sl, lowerA = la,      upperA = ua, type = "eap", model2est = model2est) 
#> - job started @ Tue Apr 21 13:42:09 2026 
#> 
#> Estimation type: eap 
#> 
#> Number of iterations: 0 
#> -------------------------------------
#>      estimate     SE
#> [1,]   0.0682 0.5905
#> [2,]  -0.0653 0.5950
#> [3,]  -1.5237 0.7046
#> [4,]   1.3112 0.6142
#> [5,]  -0.0417 0.6119
summary(respmixed_rob)
#> PP Version:  0.6.4.0 
#> 
#>  Call: PPall(respm = awmatrix, thres = THRESx, slopes = sl, lowerA = la,      upperA = ua, type = "robust", model2est = model2est) 
#> - job started @ Tue Apr 21 13:42:09 2026 
#> 
#> Estimation type: robust 
#> 
#> Number of iterations: 8 
#> -------------------------------------
#>      estimate     SE
#> [1,]  -0.1231 0.7382
#> [2,]  -0.0060 0.7288
#> [3,]     -Inf     NA
#> [4,]   2.3417 1.0189
#> [5,]  -0.5460 0.7835