RT Journal Article
SR Electronic
T1 partR2: Partitioning R^{2} in generalized linear mixed models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.07.26.221168
DO 10.1101/2020.07.26.221168
A1 Stoffel, Martin A.
A1 Nakagawa, Shinichi
A1 Schielzeth, Holger
YR 2020
UL http://biorxiv.org/content/early/2020/07/26/2020.07.26.221168.abstract
AB The coefficient of determination R2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. The R2 of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part) R2 and structure coefficients, but this is rarely done due to a lack of software implementing these statistics. Here, we introduce partR2, an R package that quantifies part R2 for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest and monitors the change in R2 as a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors. partR2 also estimates structure coefficients as the correlation between a predictor and fitted values, which provide an estimate of the total contribution of a fixed effect to the overall prediction, independent of other predictors. Structure coefficients are converted to the total variance explained by a predictor, termed ‘inclusive’ R2, as the square of the structure coefficients times total R2. Furthermore, the package reports beta weights (standardized regression coefficients). Finally, partR2 implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR2 with real example datasets for Gaussian and binomials GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors.Competing Interest StatementThe authors have declared no competing interest.