Nothing
tune.nodesize.rfsrc <- function(formula, data,
nodesizeTry = c(1:9, seq(10, 150, by = 5)),
sampsize = function(x){min(x * .632, max(150, x ^ (4/5)))},
nsplit = 1, trace = FALSE, ...)
{
## restrict nodesize to values less than or equal to sampsize / 2
if (is.function(sampsize)) {
n <- sampsize(nrow(data)) / 2
}
else {
n <- sampsize / 2
}
n <- max(n, 10)
nodesizeTry <- nodesizeTry[nodesizeTry <= n]
## loop over nodesize acquiring the error rate
err <- sapply(nodesizeTry, function(nsz) {
## pull the error rate for each candidate nodesize value
err.nsz <- tryCatch({mean(get.mv.error(rfsrc.fast(formula, data, nodesize = nsz,
sampsize = sampsize, nsplit = nsplit, ...), TRUE), na.rm = TRUE)},
error=function(ex){NA})
if (trace) {
cat("nodesize = ", nsz,
" OOB 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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