gg_vimp: Variable Importance (VIMP) data object

Description

gg_vimp Extracts the variable importance (VIMP) information from a a rfsrc object.

Usage

gg_vimp(object, nvar, ...)

Value

gg_vimp object. A data.frame of VIMP measures, in rank order.

Arguments

object: A rfsrc object or output from vimp
nvar: argument to control the number of variables included in the output.
...: arguments passed to the vimp.rfsrc function if the rfsrc object does not contain importance information.

References

Ishwaran H. (2007). Variable importance in binary regression trees and forests, Electronic J. Statist., 1:519-537.

Examples

Run this code

## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## -------- iris data
# rfsrc_iris <- rfsrc(Species ~ ., data = iris)
data(rfsrc_iris, package="ggRandomForests")
gg_dta <- gg_vimp(rfsrc_iris)
plot(gg_dta)

## ------------------------------------------------------------
## regression example
## ------------------------------------------------------------
if (FALSE) {
## -------- air quality data
# rfsrc_airq <- rfsrc(Ozone ~ ., airquality)
data(rfsrc_airq, package="ggRandomForests")
gg_dta <- gg_vimp(rfsrc_airq)
plot(gg_dta)
}

## -------- Boston data
data(rfsrc_boston, package="ggRandomForests")
gg_dta <- gg_vimp(rfsrc_boston)
plot(gg_dta)

## -------- Boston data
data(Boston, package="MASS")
rf_boston <- randomForest::randomForest(medv~., Boston)
gg_dta <- gg_vimp(rf_boston)
plot(gg_dta)

if (FALSE) {
## -------- mtcars data
data(rfsrc_mtcars, package="ggRandomForests")
gg_dta <- gg_vimp(rfsrc_mtcars)
plot(gg_dta)
}
## ------------------------------------------------------------
## survival example
## ------------------------------------------------------------
if (FALSE) {
## -------- veteran data
data(rfsrc_veteran, package="ggRandomForests")
gg_dta <- gg_vimp(rfsrc_veteran)
plot(gg_dta)


## -------- pbc data
data(rfsrc_pbc, package="ggRandomForests")
gg_dta <- gg_vimp(rfsrc_pbc)
plot(gg_dta)

# Restrict to only the top 10.
gg_dta <- gg_vimp(rfsrc_pbc, nvar=10)
plot(gg_dta)
}