partR2: partitioning R in generalized linear mixed models

Stoffel MA, Nakagawa S, Schielzeth H (2021)
PeerJ 9: e11414.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Stoffel, Martin A.; Nakagawa, Shinichi; Schielzeth, Holger
Abstract / Bemerkung
The coefficient of determinationR2quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatabilityR(intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. TheR2of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part)R2and structure coefficients, but this is rarely done due to a lack of software implementing these statistics. Here, we introducepartR2, an R package that quantifies partR2for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest from the model and monitors the change in the variance of the linear predictor. The difference to the full model gives a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors.partR2also 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 can be converted to the total variance explained by a predictor, here called ‘inclusive’R2, as the square of the structure coefficients times totalR2. Furthermore, the package reports beta weights (standardized regression coefficients). Finally,partR2implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use ofpartR2with real example datasets for Gaussian and binomial GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors.
Erscheinungsjahr
2021
Zeitschriftentitel
PeerJ
Band
9
Art.-Nr.
e11414
eISSN
2167-8359
Page URI
https://pub.uni-bielefeld.de/record/2955281

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Stoffel MA, Nakagawa S, Schielzeth H. partR2: partitioning R in generalized linear mixed models. PeerJ. 2021;9: e11414.
Stoffel, M. A., Nakagawa, S., & Schielzeth, H. (2021). partR2: partitioning R in generalized linear mixed models. PeerJ, 9, e11414. https://doi.org/10.7717/peerj.11414
Stoffel, M. A., Nakagawa, S., and Schielzeth, H. (2021). partR2: partitioning R in generalized linear mixed models. PeerJ 9:e11414.
Stoffel, M.A., Nakagawa, S., & Schielzeth, H., 2021. partR2: partitioning R in generalized linear mixed models. PeerJ, 9: e11414.
M.A. Stoffel, S. Nakagawa, and H. Schielzeth, “partR2: partitioning R in generalized linear mixed models”, PeerJ, vol. 9, 2021, : e11414.
Stoffel, M.A., Nakagawa, S., Schielzeth, H.: partR2: partitioning R in generalized linear mixed models. PeerJ. 9, : e11414 (2021).
Stoffel, Martin A., Nakagawa, Shinichi, and Schielzeth, Holger. “partR2: partitioning R in generalized linear mixed models”. PeerJ 9 (2021): e11414.

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