daSubmodels

You could set some variables as constants, limiting the number of models.
Includes, by default, the null model

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

daSubmodels function

<dl><dt>x</dt>
<dd>regression class (lm or lmer)</dd>
<dt>constants</dt>
<dd>vector of constants</dd>
<dt>terms</dt>
<dd>vector of terms. By default, obtained using the formula</dd></dl>

Arguments

Returns all the submodels derived from full models. — daSubmodels

<dl>

<dt>x</dt>
<dd>regression class (lm or lmer)</dd>


<dt>constants</dt>
<dd>vector of constants</dd>


<dt>terms</dt>
<dd>vector of terms. By default, obtained using the formula</dd>

</dl>

daSubmodels: Returns all the submodels derived from full models.

Description

Usage

Value

Arguments