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MulvariateRandomForestVarImp
View on CRAN: Click
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Download and install MulvariateRandomForestVarImp package within the R console
Install from CRAN:
install.packages("MulvariateRandomForestVarImp")
Install from Github:
library("remotes")
install_github("cran/MulvariateRandomForestVarImp")
Install by package version:
library("remotes")
install_version("MulvariateRandomForestVarImp", "0.0.2")
Attach the package and use:
library("MulvariateRandomForestVarImp")
Maintained by
Dogonadze Nika
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-12-03
Latest Update: 2021-12-15
Description:
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.
How to cite:
Dogonadze Nika (2021). MulvariateRandomForestVarImp: Variable Importance Measures for Multivariate Random Forests. R package version 0.0.2, https://cran.r-project.org/web/packages/MulvariateRandomForestVarImp
Previous versions and publish date:
0.0.1 (2021-12-03 19:50)
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Other R packages that MulvariateRandomForestVarImp depends,
imports, suggests or enhances
Functions, R codes and Examples using
the MulvariateRandomForestVarImp R package
Some associated functions: MeanOutcomeDifference . MeanSplitImprovement .
Some associated R codes: common.R . mean_outcome_difference.R . mean_split_improvement.R . Full MulvariateRandomForestVarImp package functions and examples
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