| interaction.rsf {randomSurvivalForest} | R Documentation |
Calculate variable importance (VIMP) for a single variable or group of variables.
interaction.rsf(object,
predictorNames = NULL,
subset = NULL,
joint = TRUE,
rough = FALSE,
importance = c("randomsplit", "permute", "none")[1],
seed = NULL,
do.trace = FALSE,
...)
object |
An object of class (rsf, grow) or (rsf,
forest). Note: forest=TRUE must be used in the
original rsf call. |
predictorNames |
Character vector of variable names to be considered. This must be specified. |
subset |
An index vector indicating which rows should be used. Default is to use all the data. |
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 |
Method used to compute variable importance (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. |
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 the 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 none (no perturbing).
A list with the following components:
err.rate |
Vector of length ntree containing OOB error
rates for the (unperturbed) ensemble restricted to the subsetted
data. |
importance |
Variable importance (VIMP). Either a vector
or a single number depending upon the option joint. |
Hemant Ishwaran hemant.ishwaran@gmail.com and Udaya B. Kogalur ubk2101@columbia.edu
H. Ishwaran (2007). Variable importance in binary regression trees and forests, Electronic J. Statist., 1:519-537.
find.interaction.
# Example of paired-VIMP.
# Veteran data.
data(veteran, package = "randomSurvivalForest")
v.out <- rsf(Survrsf(time,status)~., veteran, ntree = 1000, forest = TRUE)
interaction.rsf(v.out, c("karno","celltype"))$importance
## Not run:
# Individual VIMP for data restricted to events only.
# PBC data.
data(pbc, package = "randomSurvivalForest")
rsf.out <- rsf(Survrsf(days,status)~., pbc, ntree = 1000, forest = TRUE)
o.r <- rev(order(rsf.out$importance))
VIMP <- rsf.out$importance[o.r]
VIMP.events <- rep(0, length(VIMP))
names(VIMP.events) <- names(VIMP)
events <- which(rsf.out$cens == 1)
VIMP.events <-
interaction.rsf(rsf.out, names(VIMP), events, joint = FALSE)$importance
VIMP.all <- as.data.frame(cbind(VIMP.events = VIMP.events, VIMP = VIMP))
print(round(VIMP.all, 3))
# PBC data again.
# Monte Carlo estimates for VIMP.
# Bootstrap estimates for VIMP.
VIMP.MC <- VIMP.BOOT <- NULL
for (k in 1:100) {
VIMP.MC <-
cbind(VIMP.MC, interaction.rsf(rsf.out, names(VIMP), joint = FALSE)$importance)
VIMP.BOOT <-
cbind(VIMP.BOOT, interaction.rsf(rsf.out, names(VIMP),
subset = sample(1:dim(pbc)[1], replace = TRUE), joint = FALSE)$importance)
}
VIMP.MC <- as.data.frame(cbind(VIMP.mean = apply(VIMP.MC, 1, mean),
VIMP.sd = apply(VIMP.MC, 1, sd)))
VIMP.BOOT <- as.data.frame(cbind(VIMP.mean = apply(VIMP.BOOT, 1, mean),
VIMP.sd = apply(VIMP.BOOT, 1, sd)))
rownames(VIMP.MC) <- rownames(VIMP.BOOT) <- names(VIMP)
print(round(VIMP.MC, 3))
print(round(VIMP.BOOT, 3))
## End(Not run)