CDM (version 4.8-0)

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 class 'din':
IRT.data(object, \dots)

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

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

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

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

Arguments

object

Object of classes din, gdina, mcdina, gdm or

...

More arguments to be passed.

Value

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

Examples

#############################################################################
# EXAMPLE 1: Several models for sim.dina data
#############################################################################

data(sim.dina)
data(sim.qmatrix)

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

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

#--- Model 3: Rasch model with gdm function
mod3 <- gdm( data = sim.dina , irtmodel="1PL" , theta.k=seq(-4,4,length=11) ,
                centered.latent=TRUE )
summary(mod3)
dmod3 <- 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 <- slca( dat , Xdes=Xdes , maxiter=30 )            
summary(mod4)
dmod4 <- IRT.data(mod4)