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Infnrm <- function(ESTlist, fromto=c(-5,5), gran=200)
{
# ESTlist --> internal ESTlist created by nrm. must contain the estimated values which stem from the EM procedure as well as the centered category parameters
#relstv <- relist(opp,SKEL)
relstv <- ESTlist$ZLpar
thetas <- seq(fromto[1], fromto[2], length.out=gran)
catinfG <- mapply(function(levs,stvl)
{ # loops all groups
# pitem = stvl[[1]]
catinfI <- mapply(function(pitem)
{ # loops all items
Km <- matrix(c(rep(1,length(thetas)), thetas),ncol=2)
LAM <- matrix(pitem,nrow=2,byrow=T)
Z <- Km %*% LAM
ez <- exp(Z)
ezrs <- rowSums(ez)
ZQstern <- ez / ezrs
LAMs <- pitem[(length(pitem)/2 + 1):length(pitem)]
W_g <- sapply(1:nrow(ZQstern),function(zei) # geht die nodes durch
{
Zqrow <- ZQstern[zei,]
z <- thetas[zei]
Pqrep <- matrix(-Zqrow, length(Zqrow), length(Zqrow))
diag(Pqrep) <- 1-Zqrow
Pdi <- diag(Zqrow)
Wi <- Pqrep %*% Pdi
as.vector(LAMs %*% Wi %*% LAMs) * ZQstern[zei,]
})
t(W_g) # Category Information
},pitem=stvl, SIMPLIFY = F) ### was T before
catinfI
},levs=levels(ESTlist$reshOBJ$gr), stvl=relstv ,SIMPLIFY = FALSE)
#browser()
# GRs <- catinfG[[1]]
# TIFall <- lapply(catinfG,function(GRs)
# {
# apply(simplify2array(GRs, higher=TRUE),1,sum)
# })
TIFall <- lapply(catinfG,function(GRs)
{
rowSums(do.call("cbind",GRs))
})
# category informations - for different thetas for each group
class(catinfG) <- "infnrm"
return(list(catinfG=catinfG, thetas=thetas, TestInfGROUPS=TIFall))
}
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