IRT.data(object, ...)

## S3 method for class 'din'
IRT.data(object, ...)

## S3 method for class 'gdina'
IRT.data(object, ...)

## S3 method for class 'gdm'
IRT.data(object, ...)

## S3 method for class 'mcdina'
IRT.data(object, ...)

## S3 method for class 'reglca'
IRT.data(object, ...)

## S3 method for class 'slca'
IRT.data(object, ...)

## Not run: 
#############################################################################
# EXAMPLE 1: Several models for sim.dina data
#############################################################################

data(sim.dina, package="CDM")
data(sim.qmatrix, package="CDM")

dat <- sim.dina
q.matrix <- sim.qmatrix

#--- Model 1: GDINA model
mod1 <- CDM::gdina( data=dat, q.matrix=q.matrix)
summary(mod1)
dmod1 <- CDM::IRT.data(mod1)
str(dmod1)

#--- Model 2: DINA model
mod2 <- CDM::din( data=dat, q.matrix=q.matrix)
summary(mod2)
dmod2 <- CDM::IRT.data(mod2)

#--- Model 3: Rasch model with gdm function
mod3 <- CDM::gdm( data=dat, irtmodel="1PL", theta.k=seq(-4,4,length=11),
                centered.latent=TRUE )
summary(mod3)
dmod3 <- CDM::IRT.data(mod3)

#--- Model 4: Latent class model with two classes

dat <- sim.dina
I <- ncol(dat)

# define design matrices
TP <- 2     # two classes
# The idea is that latent classes refer to two different "dimensions".
# Items load on latent class indicators 1 and 2, see below.
Xdes <- array(0, dim=c(I,2,2,2*I) )
items <- colnames(dat)
dimnames(Xdes)[[4]] <- c(paste0( colnames(dat), "Class", 1),
          paste0( colnames(dat), "Class", 2) )
    # items, categories, classes, parameters
# probabilities for correct solution
for (ii in 1:I){
    Xdes[ ii, 2, 1, ii ] <- 1    # probabilities class 1
    Xdes[ ii, 2, 2, ii+I ] <- 1  # probabilities class 2
}
# estimate model
mod4 <- CDM::slca( dat, Xdes=Xdes)
summary(mod4)
dmod4 <- CDM::IRT.data(mod4)

## End(Not run)