vimp {randomSurvivalForest}R Documentation

VIMP for Single or Grouped Variables

Description

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

Usage

    vimp(object,
         predictorNames = NULL,
         subset = NULL,
         joint = TRUE,
         rough = FALSE,
         importance = c("randomsplit", "permute", "none")[1],
         seed = NULL,
         do.trace = FALSE,
         ...)

Arguments

object An object of class (rsf, grow) or (rsf, forest). Requires forest=TRUE in the original rsf call.
predictorNames Character vector of x-variable names to be considered. If NULL (the default) all variables are used. Only x-variables listed in the object predictor matrix will be used.
subset Indices indicating which rows of the predictor matrix to be used (note: this applies to the object predictor matrix, predictors). Default is to use all rows.
joint Should joint-VIMP or individual VIMP be calculated? See details below.
rough Logical value indicating whether fast approximation should be used. Default is FALSE.
importance Type of VIMP.
seed Seed for random number generator. Must be a negative integer (the R wrapper handles incorrectly set seed values).
do.trace Logical. Should trace output be enabled? Default is FALSE. Integer values can also be passed. A positive value causes output to be printed each do.trace iteration.
... Further arguments passed to or from other methods.

Details

Using a previously grown forest, and restricting the data to that indicated by subset, calculate the VIMP for variables listed in predictorNames. If joint=TRUE, a joint-VIMP is calculated. The joint-VIMP is the importance for a group of variables, when the group is perturbed simultaneously. If joint=FALSE, the VIMP for each variable considered separately is calculated.

Depending upon the option importance, VIMP is calculated either by random daugther assignment, by random permutation of the variable(s), or without perturbation (none).

For competing risk data, VIMP and error rates are given for the ensemble CHF and the conditional CHF (CCHF) for each event type.

Value

A list with the following components:

err.rate OOB error rate for the (unperturbed) ensemble restricted to the subsetted data.
err.perturb.rate OOB error rate for the perturbed ensemble restricted to the subsetted data. Dimension depends upon the option joint.
importance Variable importance (VIMP). Dimension depends upon the option joint.

Author(s)

Hemant Ishwaran hemant.ishwaran@gmail.com and Udaya B. Kogalur ubk2101@columbia.edu

References

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

See Also

vimp.

Examples

#------------------------------------------------------------------------
# Example of paired-VIMP. 
# Veteran data.

data(veteran, package = "randomSurvivalForest") 
v.out <- rsf(Survrsf(time,status)~., veteran, ntree = 1000, forest = TRUE)
vimp(v.out, c("karno","celltype"))$importance

## Not run: 
#------------------------------------------------------------------------
# Individual VIMP for data restricted to events only.
# PBC data.

data(pbc, package = "randomSurvivalForest") 
rsf.out <- rsf(Survrsf(days,status)~., pbc, ntree = 1000,
               forest = TRUE, nsplit = 3)
o.r <- rev(order(rsf.out$importance))
imp <- rsf.out$importance[o.r]
imp.events <- rep(0, length(imp))
events <- which(rsf.out$cens == 1)
imp.events <-
 vimp(rsf.out, names(imp), events, joint = FALSE)$importance
imp.all <- as.data.frame(cbind(imp.events = imp.events, imp = imp))
print(round(imp.all, 3))

#------------------------------------------------------------------------
# Estimate variability of VIMP in two ways (PBC data):
# (i)  Monte Carlo:  Estimates variability of the procedure
# (ii) Bootstrap:    Estimates statistical variability

data(pbc, package = "randomSurvivalForest") 
rsf.out <- rsf(Survrsf(days,status)~., pbc, ntree = 1000,
               nsplit = 3, forest = TRUE)
o.r <- rev(order(rsf.out$importance))
imp.names <- names(rsf.out$importance[o.r])
subset.index <- 1:nrow(rsf.out$predictors)
imp.mc <- imp.boot <- NULL
for (k in 1:100) {
  cat("iteration:", k , "\n")
  imp.mc <-
    cbind(imp.mc, vimp(rsf.out, imp.names, joint = FALSE)$importance)
  imp.boot <-
    cbind(imp.boot, vimp(rsf.out, imp.names,
    subset = sample(subset.index, replace = TRUE), joint = FALSE)$importance)
}
imp.mc <- as.data.frame(cbind(imp.mean = apply(imp.mc, 1, mean),
                    imp.sd = apply(imp.mc, 1, sd)))
imp.boot <- as.data.frame(cbind(imp.mean = apply(imp.boot, 1, mean),
                    imp.sd = apply(imp.boot, 1, sd)))
print(round(imp.mc, 3))
print(round(imp.boot, 3))
## End(Not run)

[Package randomSurvivalForest version 3.6.1 Index]