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\name{importance}
\alias{importance}
\alias{importance.default}
\alias{importance.randomForest}
\title{Extract variable importance measure}
\description{
This is the extractor function for variable importance measures as
produced by \code{\link{randomForest}}.
}
\usage{
\method{importance}{randomForest}(x, type=NULL, class=NULL, scale=TRUE, ...)
}
\arguments{
\item{x}{an object of class \code{\link{randomForest}}}.
\item{type}{either 1 or 2, specifying the type of importance measure
(1=mean decrease in accuracy, 2=mean decrease in node impurity).}
\item{class}{for classification problem, which class-specific measure
to return.}
\item{scale}{For permutation based measures, should the measures be
divided their ``standard errors''?}
\item{...}{not used.}
}
\value{
A matrix of importance measure, one row for each predictor variable.
The column(s) are different importance measures.
}
\details{
Here are the definitions of the variable importance measures. The
first measure is computed from permuting OOB data: For
each tree, the prediction error on the out-of-bag portion of the
data is recorded (error rate for classification, MSE for regression).
Then the same is done after permuting each predictor variable. The
difference between the two are then averaged over all trees, and
normalized by the standard deviation of the differences. If the
standard deviation of the differences is equal to 0 for a variable,
the division is not done (but the average is almost always equal to 0
in that case).
The second measure is the total decrease in node impurities from
splitting on the variable, averaged over all trees. For
classification, the node impurity is measured by the Gini index.
For regression, it is measured by residual sum of squares.
}
%\references{
%}
\seealso{
\code{\link{randomForest}}, \code{\link{varImpPlot}}
}
\examples{
set.seed(4543)
data(mtcars)
mtcars.rf <- randomForest(mpg ~ ., data=mtcars, ntree=1000,
keep.forest=FALSE, importance=TRUE)
importance(mtcars.rf)
importance(mtcars.rf, type=1)
}
%\author{}
\keyword{regression}
\keyword{classif}
\keyword{tree}
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