Nothing
sfs <-
function (data, method = c("lda", "knn", "rpart"), kvec = 5,
repet = 10)
{
# require("MASS")
# require("class")
# require("rpart")
if (sum(is.na(data))> 0)
stop("This dataset has missing values, impute them before running this function.\n",call.=FALSE)
if (!(method %in% c("lda", "knn", "rpart"))) {
cat("The classifier entered is not supported by this function.\n")
return(method)
}
n = dim(data)[1]
p = dim(data)[2]
numbersel = rep(0, repet)
fsel = rep(0, p - 1)
for (i in 1:repet) {
indic <- rep(0, p - 1)
output <- indic
varia <- p
for (k in 1:(p - 1)) {
correct <- rep(0, p - 1)
if (k > 1) {
varia <- c(where, varia)
}
for (m in 1:(p - 1)) {
if (indic[m] == 0) {
which <- c(m, varia)
if (method == "lda")
correct[m] <- cv10lda2(data[, which])
else if (method == "knn")
{correct[m] <- cv10knn2(data[, which], kvec)
} else correct[m] <- cv10rpart2(data[, which])
}
}
prov <- correct + runif(p - 1)
where <- sum((1:(p - 1)) * as.numeric(max(prov) ==
prov))
output[k] <- correct[where]/n
indic[where] <- 1
if (k > 1) {
if (output[k] <= output[k - 1]) {
indic <- rep(1, p - 1)
}
}
}
which <- rev(which)
which <- which[-1]
which1 <- which[1:(length(which) - 1)]
numbersel[i] = length(which1)
fsel[which1] = fsel[which1] + 1
}
bestsize = round(mean(numbersel))
rev(order(fsel))
bestsubset = rev(order(fsel))[1:bestsize]
cat("The best subset of features is:")
cat("\n")
bestsubset
}
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