run_rwa_bootstrap

internal

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.

Martin Chan

Perform a Relative Weights Analysis

run_rwa_bootstrap function

<dl><dt>data</dt>
<dd>Data frame</dd>
<dt>outcome</dt>
<dd>Outcome variable</dd>
<dt>predictors</dt>
<dd>Predictor variables</dd>
<dt>n_bootstrap</dt>
<dd>Number of bootstrap samples</dd>
<dt>conf_level</dt>
<dd>Confidence level</dd>
<dt>focal</dt>
<dd>Focal variable for comparisons (optional)</dd>
<dt>comprehensive</dt>
<dd>Whether to run comprehensive analysis</dd>
<dt>include_rescaled</dt>
<dd>Whether to bootstrap rescaled weights</dd></dl>

Arguments

Run bootstrap analysis for RWA — run_rwa_bootstrap

<dl>

<dt>data</dt>
<dd>Data frame</dd>


<dt>outcome</dt>
<dd>Outcome variable</dd>


<dt>predictors</dt>
<dd>Predictor variables</dd>


<dt>n_bootstrap</dt>
<dd>Number of bootstrap samples</dd>


<dt>conf_level</dt>
<dd>Confidence level</dd>


<dt>focal</dt>
<dd>Focal variable for comparisons (optional)</dd>


<dt>comprehensive</dt>
<dd>Whether to run comprehensive analysis</dd>


<dt>include_rescaled</dt>
<dd>Whether to bootstrap rescaled weights</dd>

</dl>

run_rwa_bootstrap: Run bootstrap analysis for RWA

Description

Usage

Value

Arguments