## Not run:
# #############################################################################
# # EXAMPLE 1: Plot item response functions from a unidimensional model
# #############################################################################
# data(data.Students)
# dat <- data.Students
#
# resp <- dat[ , paste0("sc",1:4) ]
# resp[ paste(resp[,1]) == 3 ,1] <- 2
# psych::describe(resp)
#
# #--- Model 1: PCM in gdm
# theta.k <- seq( -5 , 5 , len=21 )
# mod1 <- gdm( dat = resp , irtmodel="1PL" , theta.k=theta.k , skillspace="normal" ,
# centered.latent=TRUE)
# summary(mod1)
#
# # plot
# IRT.irfprobPlot( mod1 )
# # plot in graphics package (which comes with R base version)
# IRT.irfprobPlot( mod1 , package="graphics")
# # plot first and third item and do not smooth discretized item response
# # functions in IRT.irfprob
# IRT.irfprobPlot( mod1 , items = c(1,3) , smooth=FALSE )
# # cumulated IRF
# IRT.irfprobPlot( mod1 , cumul=TRUE )
#
# #############################################################################
# # EXAMPLE 2: Fitted mutidimensional model with gdm
# #############################################################################
#
# data( data.fraction2 )
# dat <- data.fraction2$data
# Qmatrix <- data.fraction2$q.matrix3
#
# # Model 1: 3-dimensional Rasch Model (normal distribution)
# theta.k <- seq( -4 , 4 , len=11 ) # discretized ability
# mod1 <- gdm( dat , irtmodel="1PL" , theta.k=theta.k , Qmatrix=Qmatrix ,
# centered.latent=TRUE , maxiter=10 )
# summary(mod1)
#
# # unsmoothed curves
# IRT.irfprobPlot(mod1 , smooth=FALSE)
# # smoothed curves
# IRT.irfprobPlot(mod1)
# ## End(Not run)
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