vimp.rfsrc: 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 rfsrc
vimp(object, xvar.names, m.target = NULL, 
  importance = c("permute", "random", "anti"), 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.

Value

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

Details

Using a previously grown 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 VIMP is requested using joint and equals importance for a group of variables when the group is perturbed simultaneously.

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

References

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

Examples

Run this code

# NOT RUN {
## ------------------------------------------------------------
## classification example
## showcase different vimp
## ------------------------------------------------------------

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

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

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

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

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

# Paired 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)


## ------------------------------------------------------------
## 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)

## Breiman importance
print(100 * vimp(o)$importance)

## G-mean importance
print(100 * vimp(o, perf.type = "g.mean")$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)

# }

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