R: Plot Error Rate and Variable Importance from a RF-SRC...
plot.rfsrc
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, outcome.target = NULL,
plots.one.page = TRUE, sorted = TRUE, verbose = TRUE, ...)
Arguments
x
An object of class (rfsrc, grow), (rfsrc, synthetic),
or (rfsrc, predict).
outcome.target
Character value for multivariate families
specifying the target outcome to be used. The default is to use the
first coordinate.
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 tree.err = TRUE when
growing the forest or restore the model using the option
tree.err = TRUE. Likewise, to view VIMP, use the option
importance when growing the forest or restore the forest using
the option importance.
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
## Not run:
## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
iris.obj <- rfsrc(Species ~ ., data = iris,
tree.err = TRUE, 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$id <- NULL
plot(rfsrc(Surv(time, status) ~ ., pbc, nsplit = 10, tree.err = TRUE))
}
## ------------------------------------------------------------
## multivariate mixed forests
## ------------------------------------------------------------
mtcars.new <- mtcars
mtcars.new$cyl <- factor(mtcars.new$cyl)
mtcars.new$carb <- factor(mtcars.new$carb, ordered = TRUE)
mv.obj <- rfsrc(cbind(carb, mpg, cyl) ~., data = mtcars.new, tree.err = TRUE)
plot(mv.obj, outcome.target = "carb")
plot(mv.obj, outcome.target = "mpg")
plot(mv.obj, outcome.target = "cyl")
## End(Not run)