#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)}
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