R: Plot Error Rate and Variable Importance from a RF-SRC...

plot.rfsrc {randomForestSRC} | R Documentation

## 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, m.target = NULL,
      plots.one.page = TRUE, sorted = TRUE, verbose = TRUE,  ...)

### Arguments

`x` | 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.

### Author(s)

Hemant Ishwaran and Udaya B. Kogalur

### 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,
         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")

[Package randomForestSRC version 2.8.0 Index]