Nothing
vimp.rfsrc <- function(object,
xvar.names,
m.target = NULL,
importance = c("permute", "random", "anti"),
block.size = 10,
joint = FALSE,
subset,
seed = NULL,
do.trace = FALSE,
...)
{
## incoming parameter checks - all are fatal
if (missing(object)) {
stop("object is missing")
}
if (object$family == "unsupv") {
stop("vimp does not apply to unsupervised forests: consider using max.subtree and var.select")
}
if (sum(inherits(object, c("rfsrc", "grow"), TRUE) == c(1, 2)) != 2 &
sum(inherits(object, c("rfsrc", "forest"), TRUE) == c(1, 2)) != 2) {
stop("This function only works for objects of class `(rfsrc, grow)' or '(rfsrc, forest)'")
}
## process the importance specification
if (!is.logical(joint)) {
stop("joint must be a logical value")
}
importance <- importance[1]
if (joint & importance != "none") {
i.str <- unlist(strsplit(importance, "\\."))
if (length(i.str) == 1) {
importance <- paste(i.str[1], ".joint", sep = "")
}
else if (length(i.str) == 2) {
importance <- paste(i.str[1], ".joint.", i.str[2], sep = "")
}
}
importance <- match.arg(as.character(importance),
c("permute", "random", "anti",
"permute.joint", "random.joint", "anti.joint"))
## grow objects under non-standard bootstrapping are devoid of performance values
if (sum(inherits(object, c("rfsrc", "grow"), TRUE) == c(1, 2)) == 2) {
if (is.null(object$forest)) {
stop("The forest is empty. Re-run rfsrc (grow) call with forest=TRUE")
}
else {
bootstrap <- object$forest$bootstrap
}
}
else {
bootstrap <- object$bootstrap
}
if (bootstrap == "none" || bootstrap == "by.node") {
stop("grow objects under non-standard bootstrapping are devoid of performance values")
}
## process the subsetted index
## assumes the entire data set is to be used if not specified
if (missing(subset)) {
subset <- NULL
}
else {
## convert the user specified subset into a usable form
if (is.logical(subset)) {
subset <- which(subset)
}
subset <- unique(subset[subset >= 1 & subset <= nrow(object$xvar)])
if (length(subset) == 0) {
stop("'subset' not set properly")
}
}
## make the call to generic predict
result <- generic.predict.rfsrc(object,
m.target = m.target,
importance = importance,
block.size = block.size,
importance.xvar = xvar.names,
seed = seed,
do.trace = do.trace,
membership = FALSE,
subset = subset,
...)
return(result)
}
# vimp <- vimp.rfsrc
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