Package: vita 1.0.0

Ender Celik

vita: Variable Importance Testing Approaches

Implements the novel testing approach by Janitza et al.(2015) <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4> 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) <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4> 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.

Authors:Ender Celik [aut, cre]

vita_1.0.0.tar.gz
vita_1.0.0.tar.gz(r-4.5-noble)vita_1.0.0.tar.gz(r-4.4-noble)
vita_1.0.0.tgz(r-4.4-emscripten)vita_1.0.0.tgz(r-4.3-emscripten)
vita.pdf |vita.html
vita/json (API)

# Install 'vita' in R:
install.packages('vita', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.27 score 2 packages 31 scripts 147 downloads 1 mentions 6 exports 2 dependencies

Last updated 9 years agofrom:177e2c3f74. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 22 2024
R-4.5-linux-x86_64OKNov 22 2024

Exports:compVarImpCVPVINTAPIMPPimpTestVarImpCVl

Dependencies:randomForestRcpp