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getTree <- function(rfobj, k=1, labelVar=FALSE) {
if (is.null(rfobj$forest)) {
stop("No forest component in ", deparse(substitute(rfobj)))
}
if (k > rfobj$ntree) {
stop("There are fewer than ", k, "trees in the forest")
}
if (rfobj$type == "regression") {
tree <- cbind(rfobj$forest$leftDaughter[,k],
rfobj$forest$rightDaughter[,k],
rfobj$forest$bestvar[,k],
rfobj$forest$xbestsplit[,k],
rfobj$forest$nodestatus[,k],
rfobj$forest$nodepred[,k])[1:rfobj$forest$ndbigtree[k],]
} else {
tree <- cbind(rfobj$forest$treemap[,,k],
rfobj$forest$bestvar[,k],
rfobj$forest$xbestsplit[,k],
rfobj$forest$nodestatus[,k],
rfobj$forest$nodepred[,k])[1:rfobj$forest$ndbigtree[k],]
}
dimnames(tree) <- list(1:nrow(tree), c("left daughter", "right daughter",
"split var", "split point",
"status", "prediction"))
if (labelVar) {
tree <- as.data.frame(tree)
v <- tree[[3]]
v[v == 0] <- NA
tree[[3]] <- factor(rownames(rfobj$importance)[v])
if (rfobj$type == "classification") {
v <- tree[[6]]
v[! v %in% 1:nlevels(rfobj$y)] <- NA
tree[[6]] <- levels(rfobj$y)[v]
}
}
tree
}
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