49 lines
1.5 KiB
Text
49 lines
1.5 KiB
Text
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/vimp.R
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\name{vimp}
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\alias{vimp}
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\title{Variable Importance}
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\usage{
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vimp(forest, newData = NULL, randomSeed = NULL, type = c("mean", "z",
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"raw"), events = NULL, time = NULL, censoringDistribution = NULL,
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eventWeights = NULL)
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}
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\arguments{
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\item{forest}{The forest that was trained.}
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\item{newData}{A test set of the data if available. If not, then out of bag
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errors will be attempted on the training set.}
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\item{randomSeed}{The source of randomness used to permute the values. Can be
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left blank.}
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\item{events}{If using competing risks forest, the events that the error
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measure used for VIMP should be calculated on.}
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\item{time}{If using competing risks forest, the upper bound of the
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integrated Brier score.}
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\item{censoringDistribution}{(Optional) If using competing risks forest, the
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censoring distribution. See \code{\link{integratedBrierScore} for details.}}
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\item{eventWeights}{(Optional) If using competing risks forest, weights to be
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applied to the error for each of the \code{events}.}
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}
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\value{
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A named numeric vector of importance values.
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}
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\description{
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Calculate variable importance by recording the increase in error when a given
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predictor is randomly permuted. Regression forests uses mean squared error;
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competing risks uses integrated Brier score.
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}
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\examples{
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data(wihs)
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forest <- train(CR_Response(status, time) ~ ., wihs,
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ntree = 100, numberOfSplits = 0, mtry=3, nodeSize = 5)
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vimp(forest, events = 1:2, time = 8.0)
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}
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