The model R squared and semi-partial R squared for the linear and generalized linear mixed model (LMM and GLMM) are computed with confidence limits. The R squared measure from Edwards et.al (2008) is extended to the GLMM using penalized quasi-likelihood (PQL) estimation (see Jaeger et al. 2016 ). Three methods of computation are provided and described as follows. Firstly, The Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest' package and the 'glmmPQL' function, the Kenward-Roger approach in the 'r2glmm' package is limited to the LMM. Secondly, The method introduced by Nakagawa and Schielzeth (2013) and later extended by Johnson (2014) . The 'r2glmm' package only computes marginal R squared for the LMM and does not generalize the statistic to the GLMM; however, confidence limits and semi-partial R squared for fixed effects are useful additions. Lastly, an approach using standardized generalized variance (SGV) can be used for covariance model selection. Package installation instructions can be found in the readme file.

Documentation

Manual: r2glmm.pdf
Vignette: None available.

Maintainer: Byron Jaeger <byron.jaeger at gmail.com>

Author(s): Byron Jaeger*

Install package and any missing dependencies by running this line in your R console:

install.packages("r2glmm")

Depends
Imports mgcv, lmerTest, Matrix, pbkrtest, ggplot2, afex, stats, MASS, gridExtra, grid, data.table, dplyr
Suggests lme4, nlme, testthat
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Package r2glmm
Materials
URL https://github.com/bcjaeger/r2glmm
Task Views
Version 0.1.1
Published 2016-11-28
License GPL-2
BugReports https://github.com/bcjaeger/r2glmm/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks r2glmm check results
Package source r2glmm_0.1.1.tar.gz