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: | 0.0.3 |
| Imports: | dplyr, magrittr, stats, tidyr, ggplot2 |
| Published: | 2020-11-24 |
| Author: | Martin Chan |
| Maintainer: | Martin Chan <martinchan53 at gmail.com> |
| BugReports: | https://github.com/martinctc/rwa/issues |
| License: | GPL-3 |
| URL: | https://github.com/martinctc/rwa |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | rwa results |
| Reference manual: | rwa.pdf |
| Package source: | rwa_0.0.3.tar.gz |
| Windows binaries: | r-devel: rwa_0.0.3.zip, r-release: rwa_0.0.3.zip, r-oldrel: rwa_0.0.3.zip |
| macOS binaries: | r-release: rwa_0.0.3.tgz, r-oldrel: rwa_0.0.3.tgz |
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