Nothing
calctheta <-
function(ipar,resp.data,theta,prior.mean=0.0,prior.sd=1.0,model="GRM") {
if (!(model %in% c("GRM","GPCM"))) {
warning("model must be either \"GRM\", or \"GPCM\"; will be reset to default")
model<-"GRM"
}
prior<-dnorm((theta-prior.mean)/prior.sd)
pp<-calcprob(ipar,theta,model=model)
nExaminees<-nrow(resp.data)
nq<-length(theta)
posterior<-matrix(rep(prior,nExaminees),nExaminees,nq,byrow=T)
for (i in 1:nrow(ipar)) {
resp<-matrix(resp.data[,i],nExaminees,1)
prob<-t(pp[,i,resp])
prob[is.na(prob)]<-1.0
posterior<-posterior*prob
}
EAP<-posterior%*%theta/rowSums(posterior)
SEM<-sqrt(rowSums(posterior*(matrix(theta,nExaminees,nq,byrow=T)-matrix(EAP,nExaminees,nq))^2)/rowSums(posterior))
return(list(theta=as.vector(EAP),SE=SEM))
}
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