CDM (version 4.991-1)

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, ...)
"IRT.data"(object, ...)
"IRT.data"(object, ...)
"IRT.data"(object, ...)
"IRT.data"(object, ...)
"IRT.data"(object, ...)

Arguments

object

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

...

More arguments to be passed.

Value

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

Examples

## Not run: 
# #############################################################################
# # 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)
# ## End(Not run)