randomForestSRC (version 1.6.1)

plot.rfsrc: Plot Error Rate and Variable Importance from a RF-SRC analysis

Description

Plot out-of-bag (OOB) error rates and variable importance (VIMP) from a RF-SRC analysis. This is the default plot method for the package.

Usage

## S3 method for class 'rfsrc':
plot(x, plots.one.page = TRUE, sorted = TRUE, verbose = TRUE,  ...)

Arguments

x

An object of class (rfsrc, grow), (rfsrc, synthetic), or (rfsrc, predict).

plots.one.page

Should plots be placed on one page?

sorted

Should variables be sorted by importance values?

verbose

Should VIMP be printed?

...

Further arguments passed to or from other methods.

Details

Plot cumulative OOB error rates as a function of number of trees. Plot variable importance (VIMP) if available.

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.

See Also

predict.rfsrc, rfsrc

Examples

## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------

iris.obj <- rfsrc(Species ~ ., data = iris)
plot(iris.obj)

## ------------------------------------------------------------
## competing risk example
## ------------------------------------------------------------

## use the pbc data from the survival package
## events are transplant (1) and death (2)
if (library("survival", logical.return = TRUE)) {
  data(pbc, package = "survival")
  pbc$id <- NULL
  plot(rfsrc(Surv(time, status) ~ ., pbc, nsplit = 10))
}