calcprob: calculates item response probabilities

In lordif: Logistic Ordinal Regression Differential Item Functioning using IRT

calculates item response probabilities

Description

Calculates item response probabilities over a theta grid according to either the GRM or the GPCM.

Usage

  calcprob(ipar, theta, model = "GRM")

Arguments

ipar	a data frame containing the following columns: a, cb1, cb2,..., cb(maxCat-1)
theta	a grid of theta values, e.g., theta <- seq(-4,4,.1)
model	IRT model, either "GRM" or "GPCM"

Details

Calculates an array of item response probabilities according to either the Graded Response Model (GRM: Samejima, 1969) or the Generalized Partial Credit Model (GPCM: Muraki, 1992) over a grid of theta values. The two required input objects are ipar and theta. ipar is a data frame containing item parameters in the following order: a, cb1, cb2,..., cb(maxCat-1). Items may have different numbers of categories. The variable maxCAT is the maximum number of response categories across all items. theta is a vector containing a grid of theta values. The IRT model can be either "GRM" or "GPCM".

Value

Returns an array of item response probabilities of dimension, c(nq, ni, maxCAT-1), where nq is the length of the theta grid, ni is the number of items in ipar, i.e., nrow(ipar), and maxCAT is the maximum number of response categories across all items.

Author(s)

Seung W. Choi <[e-mail]>

References

Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph, 17.

Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16, 159-176.

See Also

probgrm, probgpcm

Examples

##item.par<-read.csv(fn,head=F,col.names=c("a","cb1","cb2","cb3","cb4"))
##theta <- seq(-4,4,.1)
## Not run: calcprob(item.par,theta,model="GPCM")

lordif documentation built on April 3, 2025, 10:37 p.m.