r2glmm: Computes R Squared for Mixed (Multilevel) Models
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) <DOI:10.1002/sim.3429> is extended to the GLMM using penalized quasi-likelihood (PQL) estimation (see Jaeger et al. 2016 <DOI:10.1080/02664763.2016.1193725>). 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) <DOI:10.1111/j.2041-210x.2012.00261.x> and later extended by Johnson (2014) <DOI:10.1111/2041-210X.12225>. 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.
- Byron Jaeger [aut, cre]
- Date of publication
- 2016-11-28 22:10:47
- Byron Jaeger <firstname.lastname@example.org>
- Compute the standardized generalized variance (SGV) of a...
- Compute r2beta with a specified C matrix Compute R2 with a...
- Compute PQL estimates for fixed effects from a generalized...
- Checks if a matrix is Compound Symmetric.
- Generate partial contrast matrices
- Visualize standardized effect sizes and model R squared
- Print the contents of an R2 object
- R Squared Difference Test (R2DT). Test for a statistically...
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