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:
rwa_0.0.3.tar.gz
rwa_0.0.3.zip(r-4.5)rwa_0.0.3.zip(r-4.4)rwa_0.0.3.zip(r-4.3)
rwa_0.0.3.tgz(r-4.4-any)rwa_0.0.3.tgz(r-4.3-any)
rwa_0.0.3.tar.gz(r-4.5-noble)rwa_0.0.3.tar.gz(r-4.4-noble)
rwa_0.0.3.tgz(r-4.4-emscripten)rwa_0.0.3.tgz(r-4.3-emscripten)
rwa.pdf |rwa.html✨
rwa/json (API)
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
Last updated 4 days agofrom:6ae0582383. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 19 2024 |
| R-4.5-win | OK | Nov 19 2024 |
| R-4.5-linux | OK | Nov 19 2024 |
| R-4.4-win | OK | Nov 19 2024 |
| R-4.4-mac | OK | Nov 19 2024 |
| R-4.3-win | OK | Nov 19 2024 |
| R-4.3-mac | OK | Nov 19 2024 |
Exports:%>%plot_rwaremove_all_na_colsrwa
Dependencies:clicolorspacecpp11dplyrfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpurrrR6RColorBrewerrlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Plot the rescaled importance values from the output of 'rwa()' | plot_rwa |
| Remove any columns where all the values are missing | remove_all_na_cols |
| Create a Relative Weights Analysis (RWA) | rwa |
