📄 varimpplot.rd
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\name{varImpPlot}\alias{varImpPlot}\title{Variable Importance Plot}\description{ Dotchart of variable importance as measured by a Random Forest}\usage{varImpPlot(x, sort=TRUE, n.var=min(30, nrow(x$importance)), type=NULL, class=NULL, scale=TRUE, main=deparse(substitute(x)), ...) }\arguments{ \item{x}{An object of class \code{randomForest}.} \item{sort}{Should the variables be sorted in decreasing order of importance?} \item{n.var}{How many variables to show? (Ignored if \code{sort=FALSE}.)} \item{type, class, scale}{arguments to be passed on to \code{\link{importance}}} \item{main}{plot title.} \item{...}{Other graphical parameters to be passed on to \code{\link{dotchart}}.}}\value{ Invisibly, the importance of the variables that were plotted.}\seealso{ \code{\link{randomForest}}, \code{\link{importance}}}\examples{set.seed(4543)data(mtcars)mtcars.rf <- randomForest(mpg ~ ., data=mtcars, ntree=1000, keep.forest=FALSE, importance=TRUE)varImpPlot(mtcars.rf)}\author{Andy Liaw \email{andy\_liaw@merck.com}}\keyword{regression}\keyword{classif}\keyword{tree}
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