varImp (version 0.1)

varImp: varImp

Description

Computes the variable importance for arbitrary measures from the 'measures' package.

Usage

varImp(object, mincriterion = 0, conditional = FALSE, threshold = 0.2,
  nperm = 1, OOB = TRUE, pre1.0_0 = conditional,
  measure = "multiclass.Brier")

Arguments

object

an object as returned by cforest.

mincriterion

the value of the test statistic or 1 - p-value that must be exceeded in order to include a split in the computation of the importance. The default mincriterion = 0 guarantees that all splits are included.

conditional

the value of the test statistic or 1 - p-value that must be exceeded in order to include a split in the computation of the importance. The default mincriterion = 0 guarantees that all splits are included.

threshold

the threshold value for (1 - p-value) of the association between the variable of interest and a covariate, which must be exceeded inorder to include the covariate in the conditioning scheme for the variable of interest (only relevant if conditional = TRUE). A threshold value of zero includes all covariates.

nperm

the number of permutations performed.

OOB

a logical determining whether the importance is computed from the out-of-bag sample or the learning sample (not suggested).

pre1.0_0

Prior to party version 1.0-0, the actual data values were permuted according to the original permutation importance suggested by Breiman (2001). Now the assignments to child nodes of splits in the variable of interest are permuted as described by Hapfelmeier et al. (2012), which allows for missing values in the explanatory variables and is more efficient wrt memory consumption and computing time. This method does not apply to conditional variable importances.

measure

the name of the measure of the 'measures' package that should be used for the variable importance calculation.

Value

vector with computed permutation importance for each variable

Examples

# NOT RUN {
# multiclass case
data(iris)
iris.cf <- cforest(Species ~ ., data = iris, control = cforest_unbiased(mtry = 2, ntree = 50))
set.seed(123)
a = varImp(object = iris.cf, measure = "multiclass.Brier")
# }