lmmR2

Calculates several measures of fit for Linear Mixed Models
based on Lou and Azen (2013) text.
Models could be lmer or lme models.

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

lmmR2 function

<dl><dt>m.null</dt>
<dd>Null model (only with random intercept effects)</dd>
<dt>m.full</dt>
<dd>Full model</dd></dl>

Arguments

Calculates several measures of fit for Linear Mixed Models
based on Lou and Azen (2013) text.
Models could be lmer or lme models. — lmmR2

<dl>

<dt>m.null</dt>
<dd>Null model (only with random intercept effects)</dd>


<dt>m.full</dt>
<dd>Full model</dd>

</dl>

lmmR2: Calculates several measures of fit for Linear Mixed Models based on Lou and Azen (2013) text. Models could be lmer or lme models.

Description

Usage

Value

Arguments