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.

plot(x, = NULL, = TRUE, sorted = TRUE, verbose = TRUE,  ...)



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

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

Should plots be placed on one page?


Should variables be sorted by importance values?


Should VIMP be printed?


Further arguments passed to or from other methods.


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.


Hemant Ishwaran and Udaya B. Kogalur


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.


# \donttest{
## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------

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

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

# }