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r2glmm  

Computes R Squared for Mixed (Multilevel) Models
View on CRAN: Click here


Download and install r2glmm package within the R console
Install from CRAN:
install.packages("r2glmm")

Install from Github:
library("remotes")
install_github("cran/r2glmm")

Install by package version:
library("remotes")
install_version("r2glmm", "0.1.2")



Attach the package and use:
library("r2glmm")
Maintained by
Byron Jaeger
[Scholar Profile | Author Map]
First Published: 2016-09-15
Latest Update: 2017-08-05
Description:
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. First, 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. Second, 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.
How to cite:
Byron Jaeger (2016). r2glmm: Computes R Squared for Mixed (Multilevel) Models. R package version 0.1.2, https://cran.r-project.org/web/packages/r2glmm. Accessed 16 Apr. 2025.
Previous versions and publish date:
0.1.0 (2016-09-15 16:16), 0.1.1 (2016-11-28 22:10)
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Complete documentation for r2glmm
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