Package: rwa 0.0.3

rwa: Perform a Relative Weights Analysis

Perform a Relative Weights Analysis (RWA) (a.k.a. Key Drivers Analysis) as per the method described in Tonidandel & LeBreton (2015) <doi:10.1007/s10869-014-9351-z>, with its original roots in Johnson (2000) <doi:10.1207/S15327906MBR3501_1>. In essence, RWA decomposes the total variance predicted in a regression model into weights that accurately reflect the proportional contribution of the predictor variables, which addresses the issue of multi-collinearity. In typical scenarios, RWA returns similar results to Shapley regression, but with a significant advantage on computational performance.

Authors:Martin Chan <[email protected]>

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NEWS

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

Bug tracker:https://github.com/martinctc/rwa/issues

On CRAN:

Conda-Forge:

relative-weightsrwa

4.11 score 13 stars 10 scripts 356 downloads 1 mentions 4 exports 36 dependencies

Last updated 16 days agofrom:100b48ae86. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 19 2025
R-4.5-winOKFeb 19 2025
R-4.5-macOKFeb 19 2025
R-4.5-linuxOKFeb 19 2025
R-4.4-winOKFeb 19 2025
R-4.4-macOKFeb 19 2025
R-4.3-winOKFeb 19 2025
R-4.3-macOKFeb 19 2025

Exports:%>%plot_rwaremove_all_na_colsrwa

Dependencies:clicolorspacecpp11dplyrfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpurrrR6RColorBrewerrlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr