Calculates two sets of post-hoc variable importance measures for multivariate random forests. The first set of variable importance measures are given by the sum of mean split improvements for splits defined by feature j measured on user-defined examples (i.e., training or testing samples). The second set of importance measures are calculated on a per-outcome variable basis as the sum of mean absolute difference of node values for each split defined by feature j measured on user-defined examples (i.e., training or testing samples). The user can optionally threshold both sets of importance measures to include only splits that are statistically significant as measured using an F-test.
|Depends:||R (≥ 2.10)|
|Imports:||MultivariateRandomForest (≥ 1.1.5), MASS (≥ 7.3.0)|
|Suggests:||testthat (≥ 3.0.0)|
|Author:||Sikdar Sharmistha [aut], Hooker Giles [aut], Kadiyali Vrinda [ctb], Dogonadze Nika [cre]|
|Maintainer:||Dogonadze Nika <nika.dogonadze at toptal.com>|
|License:||GPL (≥ 3)|
|CRAN checks:||MulvariateRandomForestVarImp results|
|Windows binaries:||r-devel: MulvariateRandomForestVarImp_0.0.2.zip, r-release: MulvariateRandomForestVarImp_0.0.2.zip, r-oldrel: MulvariateRandomForestVarImp_0.0.2.zip|
|macOS binaries:||r-release (arm64): MulvariateRandomForestVarImp_0.0.2.tgz, r-oldrel (arm64): MulvariateRandomForestVarImp_0.0.2.tgz, r-release (x86_64): MulvariateRandomForestVarImp_0.0.2.tgz, r-oldrel (x86_64): MulvariateRandomForestVarImp_0.0.2.tgz|
|Old sources:||MulvariateRandomForestVarImp archive|
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