66 lines
2.2 KiB
Text
66 lines
2.2 KiB
Text
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/predict.R
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\name{predict.JRandomForest}
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\alias{predict.JRandomForest}
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\title{Predict}
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\usage{
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\method{predict}{JRandomForest}(forest, newData = NULL,
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parallel = TRUE, out.of.bag = FALSE)
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}
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\arguments{
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\item{forest}{A forest that was previously \code{\link{train}}ed}
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\item{newData}{The new data containing all of the previous predictor
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covariates. Note that even if predictions are being made on the training
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set, the dataset must be specified. \code{largeRCRF} doesn't keep track of
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the dataset after the forest is trained.}
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\item{parallel}{A logical indicating whether multiple cores should be
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utilized when making the predictions. Available as an option because it's
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been observed by this author that using Java's \code{parallelStream} can be
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unstable on some systems. Default value is \code{TRUE}.}
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\item{out.of.bag}{A logical indicating whether predictions should be based on
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'out of bag' trees; set only to \code{TRUE} if you're running predictions
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on data that was used in the training. Default value is \code{FALSE}.}
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}
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\value{
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A list of responses corresponding with each row of \code{newData} if
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it's a non-regression random forest; otherwise it returns a numeric vector.
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}
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\description{
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Predict on the random forest.
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}
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\examples{
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# Regression Example
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x1 <- rnorm(1000)
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x2 <- rnorm(1000)
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y <- 1 + x1 + x2 + rnorm(1000)
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data <- data.frame(x1, x2, y)
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forest <- train(y ~ x1 + x2, data, WeightedVarianceSplitFinder(), MeanResponseCombiner(), MeanResponseCombiner(), ntree=100, numberOfSplits = 5, mtry = 1, nodeSize = 5)
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# Fix x2 to be 0
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newData <- data.frame(x1 = seq(from=-2, to=2, by=0.5), x2 = 0)
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ypred <- predict(forest, newData)
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plot(ypred ~ newData$x1, type="l")
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# Competing Risk Example
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x1 <- abs(rnorm(1000))
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x2 <- abs(rnorm(1000))
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T1 <- rexp(1000, rate=x1)
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T2 <- rweibull(1000, shape=x1, scale=x2)
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C <- rexp(1000)
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u <- pmin(T1, T2, C)
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delta <- ifelse(u==T1, 1, ifelse(u==T2, 2, 0))
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data <- data.frame(x1, x2)
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forest <- train(CR_Response(delta, u) ~ x1 + x2, data,
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LogRankSplitFinder(1:2), CompetingRiskResponseCombiner(1:2), CompetingRiskFunctionCombiner(1:2), ntree=100, numberOfSplits=5, mtry=1, nodeSize=10)
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newData <- data.frame(x1 = c(-1, 0, 1), x2 = 0)
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ypred <- predict(forest, newData)
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}
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