Expected Values and Predicted Probabilities from Item Response Response Models

This function computes expected values for each person and each item based on the individual posterior distribution. The output of this function can be the basis of creating item and person fit statistics.

IRT.predict(object, dat, group=1)

# S3 method for din
predict(object, group=1, ...)

# S3 method for gdina
predict(object, group=1, ...)

# S3 method for mcdina
predict(object, group=1, ...)

# S3 method for gdm
predict(object, group=1, ...)

# S3 method for slca
predict(object, group=1, ...)

Arguments

object	Object for the S3 methods IRT.irfprob and IRT.posterior are defined. In the CDM packages, these are the objects of class din, gdina, mcdina, slca or gdm.
dat	Dataset with item responses
group	Group index for use
...	Further arguments to be passed.

Value

A list with following entries

expected

Array with expected values (persons \(\times\) classes \(\times\) items)

probs.categ

Array with expected probabilities for each category (persons \(\times\) categories \(\times\) classes \(\times\) items)

variance

Array with variance in predicted values for each person and each item.

residuals

Array with residuals for each person and each item

stand.resid

Array with standardized residuals for each person and each item

Examples

if (FALSE) {
#############################################################################
# EXAMPLE 1: Fitted Rasch model in TAM package
#############################################################################

#--- Model 1: Rasch model
library(TAM)
mod1 <- TAM::tam.mml(resp=TAM::sim.rasch)
# apply IRT.predict function
prmod1 <- CDM::IRT.predict(mod1, mod1$resp )
str(prmod1)
}

#############################################################################
# EXAMPLE 2: Predict function for din
#############################################################################

# DINA Model
mod1 <- CDM::din( CDM::sim.dina, q.matr=CDM::sim.qmatrix, rule="DINA" )
summary(mod1)
# apply predict method
prmod1 <- CDM::IRT.predict( mod1, sim.dina )
str(prmod1)