62 lines
2.5 KiB
R
62 lines
2.5 KiB
R
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#' WeightedVarianceSplitFinder
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#'
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#' This split finder is used in regression random forests. When a split is made,
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#' this finder computes the sample variance in each group (divided by n, not
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#' n-1); it then minimizes the sum of these variances, each of them weighted by
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#' their sample size divided by the total sample size of that node.
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#'
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#' @note There are other split finders that are used in regression random
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#' forests that are not included in this package. This package is oriented
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#' toward the competing risks side of survival analysis; the regression
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#' options are provided as an example of how extensible the back-end Java
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#' package is. If you are interested in using this package for regression (or
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#' other uses), feel free to write your own components. It's not too hard to
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#' write these components; the WeightedVarianceSplitFinder Java class is quite
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#' short; most of the code is to reuse calculations from previous considered
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#' splits. I (the author) am also willing to assist if you have any questions.
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#' @export
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#' @return A split finder object to be used in \code{\link{train}}; not useful
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#' on its own.
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#' @examples
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#' splitFinder <- WeightedVarianceSplitFinder()
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#' # You would then use it in train()
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#'
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#' @references https://kogalur.github.io/randomForestSRC/theory.html#section8.3
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WeightedVarianceSplitFinder <- function(){
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javaObject <- .jnew(.class_WeightedVarianceSplitFinder)
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javaObject <- .jcast(javaObject, .class_SplitFinder)
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splitFinder <- list(javaObject=javaObject, call=match.call())
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class(splitFinder) <- "SplitFinder"
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return(splitFinder)
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}
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#' MeanResponseCombiner
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#'
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#' This response combiner is used in regression random forests, where the
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#' response in the data is a single number that needs to be averaged in each
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#' terminal node, and then averaged across trees. This response combiner is
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#' appropriate as an argument for both the \code{nodeResponseCombiner} and
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#' \code{forestResponseCombiner} parameters in \code{\link{train}} when doing
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#' regression.
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#' @export
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#' @examples
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#' responseCombiner <- MeanResponseCombiner()
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#' # You would then use it in train()
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#'
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MeanResponseCombiner <- function(){
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javaObject <- .jnew(.class_MeanResponseCombiner)
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javaObject <- .jcast(javaObject, .class_ResponseCombiner)
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combiner <- list(javaObject=javaObject, call=match.call(), outputClass="numeric")
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combiner$convertToRFunction <- function(javaObject, ...){
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return(.jcall(javaObject, "D", "doubleValue"))
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}
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class(combiner) <- "ResponseCombiner"
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return(combiner)
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}
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