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#Computing Miss Error and MiPP after LDA
get.mipp.lda <- function(x.train, y.train, x.test, y.test){
y <- y.train
dat.train <- cbind(x.train, y)
y <- y.test
dat.test <- cbind(x.test, y)
if(is.data.frame(dat.train)==FALSE) dat.train <- data.frame(dat.train)
if(is.data.frame(dat.test)==FALSE) dat.test <- data.frame(dat.test)
colnames(dat.train) <- c(1:ncol(x.train), "y")
colnames(dat.test) <- c(1:ncol(x.test), "y")
fit <- lda(y ~ ., dat.train)
out <- predict(fit, dat.test)
u.class <- unique(colnames(out$post))
n.class <- length(u.class)
True.class <- dat.test$y
Pred.class <- out$class
post.prob <-0
for(j in 1:n.class) {
i <- which(True.class == u.class[j])
post.prob <- post.prob + sum(out$post[i,j,drop=FALSE])
}
N <- length(True.class)
nMiss <- N- sum(True.class == Pred.class)
Er <- nMiss/nrow(dat.test)
MiPP <- post.prob - nMiss
sMiPP <- MiPP/N
return(list(N.Miss=nMiss, ErrorRate=Er, MiPP=MiPP, sMiPP=sMiPP))
}
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