predict function

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 class 'din'
predict(object, group=1, ...)

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

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

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

## S3 method for class '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.

Returns

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

## Not run:

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

#############################################################################
# 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)