vimp.rfsrc: VIMP for Single or Grouped Variables

In randomForestSRC: Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)

VIMP for Single or Grouped Variables

Description

Calculate variable importance (VIMP) for a single variable or group of variables for training or test data.

Usage

## S3 method for class 'rfsrc'
vimp(object, xvar.names, m.target = NULL, 
  importance = c("anti", "permute", "random"), block.size = 10,
  joint = FALSE, seed = NULL, do.trace = FALSE, ...)

Arguments

object	An object of class (rfsrc, grow) or (rfsrc, forest). Requires forest=TRUE in the original rfsrc call.
xvar.names	Names of the x-variables to be used. If not specified all variables are used.
m.target	Character value for multivariate families specifying the target outcome to be used. If left unspecified, the algorithm will choose a default target.
importance	Type of VIMP.
block.size	Specifies number of trees in a block when calculating VIMP.
joint	Individual or joint VIMP?
seed	Negative integer specifying seed for the random number generator.
do.trace	Number of seconds between updates to the user on approximate time to completion.
...	Further arguments passed to or from other methods.

Details

Using a previously trained forest, calculate the VIMP for variables xvar.names. By default, VIMP is calculated for the original data, but the user can specify a new test data for the VIMP calculation using newdata. See rfsrc for more details about how VIMP is calculated.

joint=TRUE returns joint VIMP, defined as importance for a group of variables when the group is perturbed simultaneously.

csv=TRUE return case specific VIMP. Applies to all families except survival families. See example below.

Value

An object of class (rfsrc, predict) containing importance values.

Author(s)

Hemant Ishwaran and Udaya B. Kogalur

References

Ishwaran H. (2007). Variable importance in binary regression trees and forests, Electronic J. Statist., 1:519-537.

See Also

holdout.vimp.rfsrc, rfsrc

Examples

## ------------------------------------------------------------
## classification example
## showcase different vimp
## ------------------------------------------------------------

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

## anti vimp (default)
print(vimp(iris.obj)$importance)

## anti vimp using brier prediction error
print(vimp(iris.obj, perf.type = "brier")$importance)

## permutation vimp
print(vimp(iris.obj, importance = "permute")$importance)

## random daughter vimp
print(vimp(iris.obj, importance = "random")$importance)

## joint anti vimp 
print(vimp(iris.obj, joint = TRUE)$importance)

## paired anti vimp
print(vimp(iris.obj, c("Petal.Length", "Petal.Width"), joint = TRUE)$importance)
print(vimp(iris.obj, c("Sepal.Length", "Petal.Width"), joint = TRUE)$importance)

## ------------------------------------------------------------
## survival example
## anti versus permute VIMP with different block sizes
## ------------------------------------------------------------

data(pbc, package = "randomForestSRC")
pbc.obj <- rfsrc(Surv(days, status) ~ ., pbc)

print(vimp(pbc.obj)$importance)
print(vimp(pbc.obj, block.size=1)$importance)
print(vimp(pbc.obj, importance="permute")$importance)
print(vimp(pbc.obj, importance="permute", block.size=1)$importance)

## ------------------------------------------------------------
## imbalanced classification example
## see the imbalanced function for more details
## ------------------------------------------------------------

data(breast, package = "randomForestSRC")
breast <- na.omit(breast)
f <- as.formula(status ~ .)
o <- rfsrc(f, breast, ntree = 2000)

## permutation vimp
print(100 * vimp(o, importance = "permute")$importance)

## anti vimp using gmean performance
print(100 * vimp(o, perf.type = "gmean")$importance[, 1])

## ------------------------------------------------------------
## regression example
## ------------------------------------------------------------

airq.obj <- rfsrc(Ozone ~ ., airquality)
print(vimp(airq.obj))

## ------------------------------------------------------------
## regression example where vimp is calculated on test data
## ------------------------------------------------------------

set.seed(100080)
train <- sample(1:nrow(airquality), size = 80)
airq.obj <- rfsrc(Ozone~., airquality[train, ])

## training data vimp
print(airq.obj$importance)
print(vimp(airq.obj)$importance)

## test data vimp
print(vimp(airq.obj, newdata = airquality[-train, ])$importance)

## ------------------------------------------------------------
## case-specific vimp
## returns VIMP for each case
## ------------------------------------------------------------

o <- rfsrc(mpg~., mtcars)
v <- vimp(o, csv = TRUE)
csvimp <- get.mv.csvimp(v, standardize=TRUE)
print(csvimp)

## ------------------------------------------------------------
## case-specific joint vimp
## returns joint VIMP for each case
## ------------------------------------------------------------

o <- rfsrc(mpg~., mtcars)
v <- vimp(o, joint = TRUE, csv = TRUE)
csvimp <- get.mv.csvimp(v, standardize=TRUE)
print(csvimp)

## ------------------------------------------------------------
## case-specific joint vimp for multivariate regression
## returns joint VIMP for each case, for each outcome
## ------------------------------------------------------------

o <- rfsrc(Multivar(mpg, cyl) ~., data = mtcars)
v <- vimp(o, joint = TRUE, csv = TRUE)
csvimp <- get.mv.csvimp(v, standardize=TRUE)
print(csvimp)

randomForestSRC documentation built on Sept. 11, 2024, 7:50 p.m.