varImpPlot: Variable Importance Plot

In randomForest: Breiman and Cutler's random forests for classification and regression

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

x	An object of class randomForest.
sort	Should the variables be sorted in decreasing order of importance?
n.var	How many variables to show? (Ignored if sort=FALSE.)
type, class, scale	arguments to be passed on to importance
main	plot title.
...	Other graphical parameters to be passed on to dotchart.

Value

Invisibly, the importance of the variables that were plotted.

Author(s)

Andy Liaw andy\[e-mail]

See Also

randomForest, importance

Examples

set.seed(4543)
data(mtcars)
mtcars.rf <- randomForest(mpg ~ ., data=mtcars, ntree=1000, keep.forest=FALSE,
                          importance=TRUE)
varImpPlot(mtcars.rf)

Example output

randomForest 4.6-12
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randomForest documentation built on May 2, 2019, 5:54 p.m.