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.
Version: 0.1.0 Imports: dplyr, magrittr, stats, tidyr, ggplot2, boot, purrr, utils Suggests: knitr, rmarkdown, testthat (≥ 3.0.0), rlang, spelling Published: 2025-07-16 DOI: 10.32614/CRAN.package.rwa Author: Martin Chan [aut, cre] Maintainer: Martin Chan <martinchan53 at gmail.com> BugReports: https://github.com/martinctc/rwa/issues License: GPL-3 URL: https://martinctc.github.io/rwa/, https://github.com/martinctc/rwa NeedsCompilation: no Language: en-US Materials: README, NEWS CRAN checks: rwa results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=rwa to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4