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Variable Importance Testing Approaches

Installation

About

Implements the novel testing approach by Janitza et al.(2015) for the permutation variable importance measure in a random forest and the PIMP-algorithm by Altmann et al.(2010) doi:10.1093/bioinformatics/btq134. Janitza et al.(2015) do not use the "standard" permutation variable importance but the cross-validated permutation variable importance for the novel test approach. The cross-validated permutation variable importance is not based on the out-of-bag observations but uses a similar strategy which is inspired by the cross-validation procedure. The novel test approach can be applied for classification trees as well as for regression trees. However, the use of the novel testing approach has not been tested for regression trees so far, so this routine is meant for the expert user only and its current state is rather experimental.

Key Metrics

Version 1.0.0
R ≥ 3.1.0
Published 2015-12-14 3065 days ago
Needs compilation? yes
License GPL-2
License GPL-3
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Maintainer

Maintainer

Ender Celik

celik.p.ender@gmail.com

Authors

Ender Celik

aut / cre

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Depends

R ≥ 3.1.0

Imports

Rcpp ≥ 0.11.6
parallel
randomForest
stats

Suggests

mnormt

LinkingTo

Rcpp