cacIRT (version 1.3)

Useful IRT Functions: A collection of useful IRT functions.

Description

Most are modified from the package irtoys.

Usage

iif(ip, x, D = 1.7)
irf(ip, x, D = 1.7)
MLE(resp, ip, D = 1.7)
normal.qu(n = 15, lower = -4, upper = 4, mu = 0, sigma = 1)
SEM(ip, x, D = 1.7)
sim(ip, x, D = 1.7)
tif(ip, x, D = 1.7)

Arguments

ip

A Jx3 matrix of item parameters. Columns are discrimination, difficulty, and guessing

x

Vector of theta points

resp

Response data matrix, subjects by items

n

Number of quadrature points wanted

lower, upper

Range of points wanted

mu, sigma

The normal distribution from which points and weights are taken

D

The scaling constant for the IRT parameters, defaults to 1.7, alternatively often set to 1.

Details

iif give item information, irf gives item response function, MLE returns maximum likelihood estimates of theta (perfect scores get +-4), normal.qu returns a list length 2 of quadrature points and weights, SEM gives the inverse of the test information, sim returns simulated response matrix, tif gives the test information function.

References

see R package irtoys for further details

Examples

params<-matrix(c(1,1,1,1,-2,1,0,1,0,0,0,0),4,3)
rdm<-sim(params, rnorm(100))

theta.hat <- MLE(rdm, params)
theta.se  <- SEM(rdm, params)

## transform a cut score of theta = 0 to the expected true score scale

	t.cut <- 0
	x.cut <- sum(irf(params, t.cut)$f)