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tune.nodesize.rfsrc <- function(formula, data,
nodesizeTry = c(1:9, seq(10, 150, by = 5)), ntreeTry = 100,
sampsize = function(x){min(x * .632, max(150, x ^ (4/5)))},
nsplit = 1, trace = TRUE, ...)
{
## re-define the original data in case there are missing values
stump <- rfsrc(formula, data, nodedepth = 0, perf.type = "none", save.memory = TRUE, ntree = 1, splitrule = "random")
n <- stump$n
yvar.names <- stump$yvar.names
data <- data.frame(stump$yvar, stump$xvar)
colnames(data)[1:length(yvar.names)] <- yvar.names
rm(stump)
if (is.function(sampsize)) {##user has specified a function
ssize <- sampsize(n)
}
else {
ssize <- sampsize
}
## now hold out a test data set equal to the tree sample size (if possible)
if ((2 * ssize) < n) {
tst <- sample(1:n, size = ssize, replace = FALSE)
trn <- setdiff(1:n, tst)
newdata <- data[tst,, drop = FALSE]
}
else {
trn <- 1:n
newdata <- NULL
}
## restrict nodesize to values less than or equal to sampsize / 2
nodesizeTry <- nodesizeTry[nodesizeTry <= max(10, ssize / 2)]
## loop over nodesize acquiring the error rate
err <- sapply(nodesizeTry, function(nsz) {
## pull the error rate for each candidate nodesize value
if (is.null(newdata)) {
err.nsz <- tryCatch({mean(get.mv.error(rfsrc.fast(formula, data,
ntree = ntreeTry, nodesize = nsz,
sampsize = sampsize, nsplit = nsplit, ...), TRUE), na.rm = TRUE)},
error=function(ex){NA})
}
else {
err.nsz <- tryCatch({mean(get.mv.error(predict(rfsrc.fast(formula, data[trn,, drop = FALSE],
ntree = ntreeTry, nodesize = nsz,
sampsize = sampsize, nsplit = nsplit, forest = TRUE,
perf.type="none", save.memory = FALSE, ...), newdata, perf.type = "default"), TRUE), na.rm = TRUE)},
error=function(ex){NA})
}
## verbose output
if (trace) {
cat("nodesize = ", nsz,
" error =", paste(100 * round(err.nsz, 4), "%", sep = ""), "\n")
}
err.nsz
})
## is there OOB error?
if (all(is.na(err))) {
warning("OOB error is NA: check forest settings, especially sampsize")
return(data.frame(nodesize = nodesizeTry, err = err))
}
## identify the optimal nodesize
bestidx <- which.min(err)
if (length(bestidx) > 0) {
nsize.opt <- nodesizeTry[bestidx]
}
if (trace) {
cat("optimal nodesize:", nsize.opt, "\n")
}
return(list(nsize.opt = nsize.opt,
err = data.frame(nodesize = nodesizeTry,
err = err)))
}
tune.nodesize <- tune.nodesize.rfsrc
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