Is rwa superior to multiple regression analysis? about rwa HOT 2 CLOSED

Hi @mizumot, thanks for your question! I think this question is beyond my expertise but I will try my best to answer. In an ideal situation where predictors are not correlated, there should be no difference between interpreting the correlations of the predictors and the outcomes for how important they are. When there is multi-collinearity, methods such as Shapley or Relative Weights are more appropriate. I do not know if there is a point where it becomes 'correct' to use a method rather than another; in practical terms, I would always opt to apply multiple methods (e.g. methods from random forest, correlations, dominance analysis, or from the package vip) to explore the implications.

This paper is also a pretty helpful guide for me:
https://www.researchgate.net/profile/Jeff_Johnson/publication/40455316_Determining_the_Statistical_Significance_of_Relative_Weights/links/09e4150cb66c7c099a000000/Determining-the-Statistical-Significance-of-Relative-Weights.pdf

from rwa.

Thanks a lot for your answer and suggestions! Now it is all clear to me. I'll look into the paper.

from rwa.