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 DOI: 10.32614/CRAN.package.randomForestVIP 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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=randomForestVIP to link to this page.
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