partR2-package | R Documentation |

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:

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.

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.

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