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 rfsrc
plot(x, m.target = NULL,
  plots.one.page = TRUE, sorted = TRUE, verbose = TRUE,  ...)

Arguments

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

m.target

Character value for multivariate families specifying the target outcome to be used. If left unspecified, the algorithm will choose a default target.

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 and variable importance (VIMP) if available. Note that the default settings are now such that the error rate is no longer calculated on every tree and VIMP is only calculated if requested. To get OOB error rates for ever tree, use the option block.size = 1 when growing or restoring the forest. Likewise, to view VIMP, use the option importance when growing or restoring the forest.

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.

Examples

Run this code

# NOT RUN {
## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------

iris.obj <- rfsrc(Species ~ ., data = iris,
     block.size = 1, importance = TRUE)
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, block.size = 1))
}

## ------------------------------------------------------------
## multivariate mixed forests
## ------------------------------------------------------------

mtcars.new <- mtcars
mtcars.new$cyl <- factor(mtcars.new$cyl)
mtcars.new$carb <- factor(mtcars.new$carb, ordered = TRUE)
mv.obj <- rfsrc(cbind(carb, mpg, cyl) ~., data = mtcars.new, block.size = 1)
plot(mv.obj, m.target = "carb")
plot(mv.obj, m.target = "mpg")
plot(mv.obj, m.target = "cyl")

# }