| Title: | Perform a Relative Weights Analysis |
|---|---|
| Description: | 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]> |
| Maintainer: | Martin Chan <[email protected]> |
| License: | GPL-3 |
| Version: | 0.0.3 |
| Built: | 2025-02-17 05:31:26 UTC |
| Source: | https://github.com/martinctc/rwa |
rwa()
Pass the output of rwa() and plot a bar chart of the rescaled importance values.
Signs are always calculated and taken into account, which is equivalent to setting the applysigns
argument to TRUE in rwa().
plot_rwa(rwa)plot_rwa(rwa)
rwa |
Direct list output from |
library(ggplot2) diamonds %>% rwa(outcome = "price", predictors = c("depth","carat", "x", "y", "z"), applysigns = TRUE) %>% plot_rwa()library(ggplot2) diamonds %>% rwa(outcome = "price", predictors = c("depth","carat", "x", "y", "z"), applysigns = TRUE) %>% plot_rwa()
Pass a data frame and returns a version where all columns made up of entirely missing values are removed.
remove_all_na_cols(df)remove_all_na_cols(df)
df |
Data frame to be passed through. |
This is used within rwa().
This function creates a Relative Weights Analysis (RWA) and returns a list of outputs.
RWA provides a heuristic method for estimating the relative weight of predictor variables in multiple regression, which involves
creating a multiple regression with on a set of transformed predictors which are orthogonal to each other but
maximally related to the original set of predictors.
rwa() is optimised for dplyr pipes and shows positive / negative signs for weights.
rwa(df, outcome, predictors, applysigns = FALSE, plot = TRUE)rwa(df, outcome, predictors, applysigns = FALSE, plot = TRUE)
df |
Data frame or tibble to be passed through. |
outcome |
Outcome variable, to be specified as a string or bare input. Must be a numeric variable. |
predictors |
Predictor variable(s), to be specified as a vector of string(s) or bare input(s). All variables must be numeric. |
applysigns |
Logical value specifying whether to show an estimate that applies the sign. Defaults to |
plot |
Logical value specifying whether to plot the rescaled importance metrics. |
rwa() produces raw relative weight values (epsilons) as well as rescaled weights (scaled as a percentage of predictable variance)
for every predictor in the model.
Signs are added to the weights when the applysigns argument is set to TRUE.
See https://relativeimportance.davidson.edu/multipleregression.html for the original implementation that inspired this package.
rwa() returns a list of outputs, as follows:
predictors: character vector of names of the predictor variables used.
rsquare: the rsquare value of the regression model.
result: the final output of the importance metrics.
The Rescaled.RelWeight column sums up to 100.
The Sign column indicates whether a predictor is positively or negatively correlated with the outcome.
n: indicates the number of observations used in the analysis.
lambda:
RXX: Correlation matrix of all the predictor variables against each other.
RXY: Correlation values of the predictor variables against the outcome variable.
library(ggplot2) rwa(diamonds,"price",c("depth","carat"))library(ggplot2) rwa(diamonds,"price",c("depth","carat"))