Nothing
`predict.homals` <-
function(object, ...)
{
#computes classification table and misclassification rate
cl.table <- NULL
nvec <- colnames(object$dframe)
for (i in 1:length(object$loadings)) #runs over variables
{
ax <- object$loadings[[i]]
ay <- object$catscores[[i]]
ag <- object$dframe[,i] #observed response categories
bx <- lsfit(t(ax),t(object$objscores),intercept=FALSE)$coef #regress loadings on each of the object scores --> beta
ux <- crossprod(bx,ax) #multiply beta with loading (for each dimension)
d <- outer(rowSums(ux^2),rowSums(ay^2),"+")-2*tcrossprod(ux,ay) #residuals for each category
h <- levels(ag)[apply(d,1,which.min)] #pick minimum distance --> predicted response categories
cl.table[[i]] <- table(ag,h,dnn=list("obs","pre")) #cross-classify observed vs. predicted
names(cl.table)[[i]] <- nvec[i]
}
cr.vec <- sapply(cl.table, function(x) (sum(diag(x)))/sum(x)) #
result <- list(cl.table = cl.table, cr.vec = cr.vec)
class(result) <- "predict.homals"
result
}
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