partR2-package

partR2: Partitioning R2 in generalized linear mixed models

The partR2 package provides a simple way to estimate R2 in mixed models fitted with lme4 as well as part (semi-partial) R2 for specific predictors and combinations of predictors, among other several other statistics. Here is an overview: package

Details

• Marginal and conditional R2 for LMMs and GLMMs.
• Part (semi-partial) R2 which estimate the explained variance for specific predictors and combinations of predictors.
• Structure coefficients (SC). SC are the correlation between a predictor and the predicted response (called the linear predictor), independent of the other predictors.
• Inclusive R2 (IR2), which estimate the the total variance explained by a predictor independent of other predictors. IR2 is estimated with SC^2 * R2_full_model.
• Beta weights, which are standardised regression coefficients. If beta is a model estimate for variable x, and y is the response,then the beta weight is beta * (sd(x)/sd(y).
• Confidence intervals for all estimates using parametric bootstrapping.

The package has one main function partR2 which takes a fitted model from lme4. At the moment, Gaussian, Poisson and binomial models are supported. For Poisson and non-binary binomial models, partR2 adds an observational level random effect to model additive overdispersion (if an olre is not fitted already).

The summary.partR2 function provides an extended summary with R2s, semi-partial R2s, model estimates and structure coefficients. The forestplot

function provides a means of plotting the results.

References

Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133-142.

Nakagawa, S., Johnson, P. C., & Schielzeth, H. (2017). The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of the Royal Society Interface, 14(134), 20170213.