plot.gg_vimp: Plot a 'gg_vimp' object, extracted variable importance of a...

In ehrlinger/ggRandomForests: Visually Exploring Random Forests

Plot a gg_vimp object, extracted variable importance of a rfsrc object

Description

Plot a gg_vimp object, extracted variable importance of a rfsrc object

Usage

## S3 method for class 'gg_vimp'
plot(x, relative, lbls, ...)

Arguments

x	gg_vimp object created from a rfsrc object
relative	should we plot vimp or relative vimp. Defaults to vimp.
lbls	A vector of alternative variable labels. Item names should be the same as the variable names.
...	optional arguments passed to gg_vimp if necessary

Value

ggplot object

References

Breiman L. (2001). Random forests, Machine Learning, 45:5-32.

Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R, Rnews, 7(2):25-31.

Ishwaran H. and Kogalur U.B. (2013). Random Forests for Survival, Regression and Classification (RF-SRC), R package version 1.4.

See Also

gg_vimp

Examples

## Not run: 
## ------------------------------------------------------------
## 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
## ------------------------------------------------------------
## -------- 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)

## -------- mtcars data
data(rfsrc_mtcars, package="ggRandomForests")
gg_dta <- gg_vimp(rfsrc_mtcars)
plot(gg_dta)

## ------------------------------------------------------------
## survival example
## ------------------------------------------------------------
## -------- 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)


## End(Not run)

ehrlinger/ggRandomForests documentation built on Sept. 9, 2022, 6:55 p.m.