lordif (version 0.1-1)

calcprob: calculates item response probabilities

Description

Calculates item response probabilities over a theta grid.

Usage

calcprob(ipar, theta)

Arguments

ipar

a data frame containing the following columns: a, cb1, cb2,..., cb(maxCat)

theta

a grid of theta values, e.g., theta <- seq(-4,4,.1)

Value

• Returns an array of item response probabilities of dimension, c(nq, ni, maxCAT), 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.

Details

Calculates an array of item response probabilities according to the graded response model (GRM: Samejima, 1969) over a grid of theta values. 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). Items can have different numbers of categories. The variable maxCAT is determined by the function as the maximum number of category threshold parameters across all items plus 1. theta is a vector containing a grid of theta values.

References

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

See Also

probgrm

Examples

##item.par<-read.csv(fn,head=F,col.names=c("a","cb1","cb2","cb3"))
##theta <- seq(-4,4,.1)
calcprob(item.par,theta)