R/Rud.P.R

In cacIRT: Classification Accuracy and Consistency under Item Response Theory

Rud.P <-
function(cutscore, theta, sem) 
	 { 
  os<-theta
	 	nn<-length(os)
	 	nc <- length(cutscore)
	 	
	 	if(nn != length(sem)) stop("Ability and se of different length")
	
		esacc<-matrix(NA,length(cutscore), nn, dimnames = list(paste("cut at",round(cutscore,3)), round(os,3)))
		escon <-esacc
		
		for(j in 1:length(cutscore)){
			cuts<-c(-Inf, cutscore[j], Inf)		 	
	 		categ<-cut(os,cuts,labels=FALSE,right=FALSE)
			
		for(i in 1:nn) {
			esacc[j,i]<-(pnorm(cuts[categ[i]+1],os[i],sem[i])-pnorm(cuts[categ[i]],os[i],sem[i]))
			escon[j,i]<-((pnorm(cuts[2], os[i],sem[i]) - pnorm(cuts[1],os[i],sem[i]))^2	+ (pnorm(cuts[3], os[i],sem[i]) - pnorm(cuts[2],os[i],sem[i]))^2	 )
			}}
			
	if(nc ==1)	{
		ans<- (list("Marginal" = cbind("Accuracy" = rowMeans(esacc), "Consistency" = rowMeans(escon)), "Conditional" = list("Accuracy" =t(esacc), "Consistency" = t(escon))))
				
			return(ans)
		
		} else {
				
				simul <- matrix(NA,nn, 2, dimnames = list(round(os,3), c("Accuracy", "Consistency")))
				cuts <- c(-Inf, cutscore, Inf)
				categ<-cut(os,cuts,labels=FALSE,right=FALSE)
				
				for(i in 1:nn){
					simul[i,1] <- (pnorm(cuts[categ[i]+1],os[i],sem[i])-pnorm(cuts[categ[i]],os[i],sem[i]))
					
					sha <- 0
			for(j in 1:(nc+1)){
			  sha <- sha + (pnorm(cuts[j+1],os[i],sem[i])-pnorm(cuts[j],os[i],sem[i]))^2}
			simul[i,2]<-sha}
			
			ans<- (list("Marginal" = rbind(cbind("Accuracy" = rowMeans(esacc), "Consistency" = rowMeans(escon)), "Simultaneous" = colMeans(simul)), "Conditional" = list("Accuracy" =cbind(t(esacc), "Simultaneous" =simul[,1]), "Consistency" = cbind(t(escon),"Simultaneous" =simul[,2]))))
				ans}
				}