remove_all_na_cols

Pass a data frame and returns a version where all columns
made up of entirely missing values are removed.

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

remove_all_na_cols function

<dl><dt>df</dt>
<dd>Data frame to be passed through.</dd></dl>

Arguments

Remove any columns where all the values are missing — remove_all_na_cols

<dl>

<dt>df</dt>
<dd>Data frame to be passed through.</dd>

</dl>

remove_all_na_cols: Remove any columns where all the values are missing

Description

Usage

Arguments

Details