Useful IRT Functions {cacIRT} | R Documentation |
Modified from the package irtoys
.
iif(ip, x, D = 1.7) irf(ip, x, D = 1.7) MLE(resp, ip, D = 1.7, min= -4, max = 4) 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)
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 |
min, max |
MLE is undefined for perfect scores. These parameters define the range in which to search for the MLE, if the score is perfect, the min or max will be returned. |
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. |
iif
gives 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 normal quadrature points and weights, SEM
gives the standard error of measurement at the given ability points, sim
returns simulated response matrix, tif
gives the test information function.
Quinn N. Lathrop
Partchev, I. (2014) irtoys: Simple interface to the estimation and plotting of IRT models. R package version 0.1.7.
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)