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merge_mf_cluster <- function(rf1, rf2, ...){
## Compute importance
dots <- list(...)
predicted <- rf1$predictions
predicted[is.na(predicted)] <- rf2$predictions[!is.na(rf2$predictions)]
forest <- list(predictions = predicted,
num.trees = rf1$num.trees + rf2$num.trees,
num.independent.variables = rf1$num.independent.variables,
mtry = rf1$mtry,
min.node.size = rf1$min.node.size,
variable.importance = (rf1$variable.importance + rf2$variable.importance)/2,
prediction.error = mean(c(rf1$prediction.error, rf2$prediction.error)),
forest = list(dependent.varID = rf1$forest$dependent.varID,
num.trees = rf1$num.trees + rf2$num.trees,
child.nodeIDs = c(rbind(rf1$forest$child.nodeIDs, rf2$forest$child.nodeIDs)),
split.varIDs = c(rbind(rf1$forest$split.varIDs, rf2$forest$split.varIDs)),
split.values = c(rbind(rf1$forest$split.values, rf2$forest$split.values)),
is.ordered = rf1$forest$is.ordered,
independent.variable.names = rf1$forest$independent.variable.names,
treetype = rf1$forest$treetype),
#rf2 = rf2,
splitrule = rf1$splitrule,
treetype = rf1$treetype,
r.squared = 1 - mean(c(rf1$prediction.error, rf2$prediction.error)) / var(dots[["y"]]),
call = formula,
importance.mode = "permutation",
num.samples = rf1$num.samples,
cluster_forests = list(rf1, rf2)
)
if(!is.null(rf1[["inbag.counts"]])){
forest[["inbag.counts"]] <- c(rf1$inbag.counts, rf2$inbag.counts)
}
if(!is.null(rf1[["replace"]])){
forest[["replace"]] <- rf1$replace
}
class(forest) <- "ranger"
class(forest$forest) <- "ranger.forest"
forest
}
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