#steps towards an IRT program #we find the difficulties using ir.item.diff.rasch #now estimate the thetas #Then, to find the person parameters, use optimize "irt.person.rasch" <- function(diff,items) { # #the basic one parameter model irt <- function(x,diff,scores) { fit <- -1*(log(scores/(1+exp(diff-x)) + (1-scores)/(1+exp(x-diff)))) mean(fit,na.rm=TRUE) } # diff<- diff items <-items num <- dim(items)[1] fit <- matrix(NA,num,2) total <- rowMeans(items,na.rm=TRUE) count <- rowSums(!is.na(items)) for (i in 1:num) { if (count[i]>0) {myfit <- optimize(irt,c(-4,4),diff=diff,scores=items[i,]) #how to do an apply? fit[i,1] <- myfit$minimum fit[i,2] <- myfit$objective #fit of optimizing program } else { fit[i,1] <- NA fit[i,2] <- NA } #end if else } #end loop irt.person.rasch <-data.frame(total,theta=fit[,1],fit=fit[,2],count)}