varImpPlot: Variable Importance Plot

In randomForest: Breiman and Cutlers Random Forests for Classification and Regression

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

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 [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)

randomForest documentation built on Sept. 30, 2024, 9:19 a.m.