fastvarImpAUC {moreparty}

Variable importance (with AUC performance measure) for conditional inference random forests

Description

Computes the variable importance regarding the AUC. Bindings are not taken into account in the AUC definition as they did not provide as good results as the version without bindings in the paper of Janitza et al. (2013).

Usage

  fastvarImpAUC(object, mincriterion = 0, conditional = FALSE,
            threshold = 0.2, nperm = 1, OOB = TRUE,
            pre1.0_0 = conditional,
            parallel = TRUE)

Arguments

object

An object as returned by cforest (or fastcforest).

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.

parallel

Logical indicating whether or not to run fastvarImpAUC in parallel using a backend provided by the foreach package. Default is FALSE.

Details

For using the original AUC definition and multiclass AUC you can use the fastvarImp function and specify the particular measure. The code is adapted from varImpAUC function in varImp package.

Value

Vector with computed permutation importance for each variable.

Author(s)

Nicolas Robette

References

Janitza, S., Strobl, C. & Boulesteix, A.-L. An AUC-based permutation variable importance measure for random forests. BMC Bioinform. 14, 119 (2013).

See Also

varImpAUC, fastvarImp, cforest, fastcforest

Examples

  data(iris)
  iris2 = iris
  iris2$Species = factor(iris$Species == "versicolor")
  iris.cf = party::cforest(Species ~ ., data = iris2,
            control = party::cforest_unbiased(mtry = 2, ntree = 50))
  fastvarImpAUC(object = iris.cf, parallel = FALSE)