contributionByLevel

Retrieve the average contribution by level for each predictor in a dominance analysis.
The average contribution defines conditional dominance.

Dominance analysis is a method that allows to compare the
relative importance of predictors in multiple regression models:
ordinary least squares, generalized linear models,
hierarchical linear models, beta regression and dynamic linear models.
The main principles and methods of
dominance analysis are described in
Budescu, D. V. (1993) <doi:10.1037/0033-2909.114.3.542> and
Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129>
for ordinary least squares regression. Subsequently, the extensions
for multivariate regression, logistic regression and
hierarchical linear models were described in
Azen, R., & Budescu, D. V. (2006) <doi:10.3102/10769986031002157>,
Azen, R., & Traxel, N. (2009) <doi:10.3102/1076998609332754> and
Luo, W., & Azen, R. (2013) <doi:10.3102/1076998612458319>,
respectively.

Claudio Bustos Navarrete

dominanceanalysis

Dominance Analysis

Filipa Coutinho Soares

contributionByLevel function

<dl><dt>da.object</dt>
<dd>dominanceAnalysis object</dd>
<dt>fit.functions</dt>
<dd>name of the fit indices to retrieve. If NULL, all fit indices will be retrieved</dd></dl>

Arguments

Retrieve average contribution by level for each predictor — contributionByLevel

<dl>

<dt>da.object</dt>
<dd>dominanceAnalysis object</dd>


<dt>fit.functions</dt>
<dd>name of the fit indices to retrieve. If NULL, all fit indices will be retrieved</dd>

</dl>

contributionByLevel: Retrieve average contribution by level for each predictor

Description

Usage

Value

Arguments

See Also

Examples