CDM (version 8.2-6)

IRT.data: S3 Method for Extracting Used Item Response Dataset

Description

This S3 method extracts the used dataset with item responses.

Usage

IRT.data(object, ...)
# S3 method for din
IRT.data(object, ...)
# S3 method for gdina
IRT.data(object, ...)
# S3 method for gdm
IRT.data(object, ...)
# S3 method for mcdina
IRT.data(object, ...)
# S3 method for reglca
IRT.data(object, ...)
# S3 method for slca
IRT.data(object, ...)

Value

A matrix (or data frame) with item responses and group identifier and weights vector as attributes.

Arguments

object

Object of classes din, gdina, mcdina, gdm, slca, reglca.

...

More arguments to be passed.

Examples

if (FALSE) {
#############################################################################
# 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)
}