cacIRT (version 1.0)

class.Rud: Computes classification accuracy and consistency with Rudner's approach.

Description

Computes classification accuracy and consistency with Rudner's approach.

Usage

class.Rud(cutscore, ip, irt.model = "dich", ability = NULL, se = NULL, rdm = NULL, quadrature = NULL)

Arguments

cutscore

A scalar or vector of cut scores on the theta scale

ip

Matrix of item parameters, for 3PL columns assumed to be discrimination, difficultly, guessing.

irt.model

Currently must by dich for dichotomous

ability

Ability estimates for each subject.

se

Standard errors of ability estimates

rdm

The response data matrix with rows as subjects and columns as items

quadrature

A list containing 1) The quadrature points and 2) Their corresponding weigths

Value

• Marginal
  A matrix with two columns of marginal accuracy and consistency per cut score
• Conditional
  A list of conditional accuracy and conditional consistency returned in the order of subjects if ability or rdm is given

Details

Must give only ability and se, rdm, or quadrature. If ability and se are given, those scores are used for the P method. If rdm is given, ability and se are estimated with MLE (perfect response patterns given a -4 or 4) and used for the P method. If quadrature, the D method is used.

Examples

##from rdm, item parameters denote 4 item 1PL test, cut score at theta=.5
##only return marginal indices

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

class.Rud(2, params, rdm = rdm)$Marginal

##or from 40 quadrature points and weights

quad <- normal.qu(40)

class.Rud(2, params, quadrature = quad)$Marginal