% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vimp.R
\name{vimp}
\alias{vimp}
\title{Variable Importance}
\usage{
vimp(forest, newData = NULL, randomSeed = NULL, type = c("mean", "z",
  "raw"), events = NULL, time = NULL, censoringDistribution = NULL,
  eventWeights = NULL)
}
\arguments{
\item{forest}{The forest that was trained.}

\item{newData}{A test set of the data if available. If not, then out of bag
errors will be attempted on the training set.}

\item{randomSeed}{The source of randomness used to permute the values. Can be
left blank.}

\item{events}{If using competing risks forest, the events that the error
measure used for VIMP should be calculated on.}

\item{time}{If using competing risks forest, the upper bound of the
integrated Brier score.}

\item{censoringDistribution}{(Optional) If using competing risks forest, the
censoring distribution. See \code{\link{integratedBrierScore} for details.}}

\item{eventWeights}{(Optional) If using competing risks forest, weights to be
applied to the error for each of the \code{events}.}
}
\value{
A named numeric vector of importance values.
}
\description{
Calculate variable importance by recording the increase in error when a given
predictor is randomly permuted. Regression forests uses mean squared error;
competing risks uses integrated Brier score.
}
\examples{
data(wihs)

forest <- train(CR_Response(status, time) ~ ., wihs,
 ntree = 100, numberOfSplits = 0, mtry=3, nodeSize = 5)

vimp(forest, events = 1:2, time = 8.0)

}