# partR2-package: partR2: Partitioning R2 in generalized linear mixed models In partR2: Partitioning R2 in GLMMs

 partR2-package R Documentation

## partR2: Partitioning R2 in generalized linear mixed models

### Description

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:

### 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.

partR2 documentation built on May 29, 2024, 2:29 a.m.