randomForestVIP: Tune Random Forests Based on Variable Importance & Plot Results

Functions for assessing variable relations and associations prior to modeling with a Random Forest algorithm (although these are relevant for any predictive model). Metrics such as partial correlations and variance inflation factors are tabulated as well as plotted for the user. A function is available for tuning the main Random Forest hyper-parameter based on model performance and variable importance metrics. This grid-search technique provides tables and plots showing the effect of the main hyper-parameter on each of the assessment metrics. It also returns each of the evaluated models to the user. The package also provides superior variable importance plots for individual models. All of the plots are developed so that the user has the ability to edit and improve further upon the plots. Derivations and methodology are described in Bladen (2022) <https://digitalcommons.usu.edu/etd/8587/>.

Version: 0.1.3
Depends: R (≥ 4.0.0)
Imports: car, dplyr, ggplot2, gridExtra, minerva, randomForest, stats, tidyr
Suggests: EZtune, e1071, knitr, MASS, rmarkdown, rpart, testthat (≥ 3.0.0)
Published: 2023-07-19
Author: Kelvyn Bladen [aut, cre], D. Richard Cutler [aut]
Maintainer: Kelvyn Bladen <kelvyn.bladen at usu.edu>
License: GPL-3
URL: https://github.com/KelvynBladen/randomForestVIP
NeedsCompilation: no
Materials: README
CRAN checks: randomForestVIP results

Documentation:

Reference manual: randomForestVIP.pdf
Vignettes: randomForestVIP Vignette

Downloads:

Package source: randomForestVIP_0.1.3.tar.gz
Windows binaries: r-prerel: randomForestVIP_0.1.3.zip, r-release: randomForestVIP_0.1.3.zip, r-oldrel: randomForestVIP_0.1.3.zip
macOS binaries: r-prerel (arm64): randomForestVIP_0.1.3.tgz, r-release (arm64): randomForestVIP_0.1.3.tgz, r-oldrel (arm64): randomForestVIP_0.1.3.tgz, r-prerel (x86_64): randomForestVIP_0.1.3.tgz, r-release (x86_64): randomForestVIP_0.1.3.tgz

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