rankUsingMatrix

<p>Dominance of each factor over others.
Dominance requires that a variable have higher values on all submodels.
This method allows to visualize those relation.</p>

internal

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

rankUsingMatrix function

Dominance of each factor over others.
Dominance requires that a variable have higher values on all submodels.
This method allows to visualize those relation. — rankUsingMatrix

Dominance of each factor over others.
Dominance requires that a variable have higher values on all submodels.
This method allows to visualize those relation.

rankUsingMatrix: Dominance of each factor over others. Dominance requires that a variable have higher values on all submodels. This method allows to visualize those relation.

Description

Usage

Arguments

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