## File Name: gdm_person_parameters.R
## File Version: 0.08
###########################################
# person parameter estimates
gdm_person_parameters <- function( data, D, theta.k,
p.xi.aj, p.aj.xi, weights )
{
#**************************
person <- data.frame("case"=1:(nrow(data)), "M"=rowMeans( data, na.rm=T) )
EAP.rel <- rep(0,D)
names(EAP.rel) <- colnames(theta.k)
nstudl <- rep(1,nrow(data))
doeap <- TRUE
if ( is.list( p.aj.xi)){
p.aj.xi <- p.aj.xi[[1]]
nstudl <- rep(1,nrow(p.aj.xi ) )
weights <- weights[,1]
weights <- weights[ weights > 0 ]
doeap <- FALSE
}
if (doeap ){
for (dd in 1:D){ #dd <- 1
dd1 <- colnames(theta.k)[dd]
person$EAP <- rowSums( p.aj.xi * outer( nstudl, theta.k[,dd] ) )
person$SE.EAP <- sqrt(rowSums( p.aj.xi * outer( nstudl, theta.k[,dd]^2 ) ) - person$EAP^2)
EAP.variance <- stats::weighted.mean( person$EAP^2, weights ) -
( stats::weighted.mean( person$EAP, weights ) )^2
EAP.error <- stats::weighted.mean( person$SE.EAP^2, weights )
EAP.rel[dd] <- EAP.variance / ( EAP.variance + EAP.error )
colnames(person)[ which( colnames(person)=="EAP" ) ] <- paste("EAP.", dd1, sep="")
colnames(person)[ which( colnames(person)=="SE.EAP" ) ] <- paste("SE.EAP.", dd1, sep="")
}
# MLE
mle.est <- theta.k[ max.col( p.xi.aj ),, drop=FALSE]
colnames(mle.est) <- paste0( "MLE.", names(EAP.rel))
person <- cbind( person, mle.est )
# MAP
mle.est <- theta.k[ max.col( p.aj.xi ),, drop=FALSE]
colnames(mle.est) <- paste0( "MAP.", names(EAP.rel))
person <- cbind( person, mle.est )
}
#--- OUTPUT
res <- list( person=person, EAP.rel=EAP.rel )
return(res)
}
###########################################################################
.gdm.person.parameters <- gdm_person_parameters
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