Massive‐scale digitization of specimens promises to further enable phenological research, especially the ability to move towards a deeper understanding of drivers of change and how trait‐environment interactions shape phenological sensitivity. Natural history collections (NHCs) have been indispensable to understanding longer‐term trends of the timing of seasonal events. And Rlik2 is most appropriate to assess the importance of different components within the same model applied to the same data, because it is most closely associated with statistical significance tests. Rresid2 is most appropriate for comparing models fit to different datasets, because it does not depend on sample sizes. However, Rpred2 gives the most direct answer to the question of how much variance in the data is explained by a model. Rresid2, Rpred2, and Rlik2 all have similar performance in describing the variance explained by different components of models. Because the R2s are designed broadly for any model for correlated data, the R2s were also compared for LMMs and GLMMs. ![]() The properties of the R2s for phylogenetic models were assessed using simulations for continuous and binary response data (phylogenetic generalized least squares and phylogenetic logistic regression). Because partial R2s compare a full model with a reduced model without components of the full model, they are distinct from marginal R2s that partition additive components of the variance. These three R2s are formulated as partial R2s, making it possible to compare the contributions of predictor variables and variance components (phylogenetic signal and random effects) to the fit of models. Rlik2 is based on the likelihood of fitted models and therefore reflects the amount of information that the models contain. R2pred is based on predicting each residual from the fitted model and computing the variance between observed and predicted values. ![]() Rresid2 is an extension of the ordinary least-squares R2 that weights residuals by variances and covariances estimated by the model it is closely related to Rglmm2 presented by Nakagawa and Schielzeth (2013). (ii) Researchers may want the R2 to include the variance explained by the covariances by asking questions such as "How much of the data is explained by phylogeny?" Here, I investigate three R2s for phylogenetic and mixed models. (i) It is unclear how to measure the variance explained by predictor (independent) variables when the model contains covariances. When the model includes correlation among data, such as phylogenetic models and mixed models, defining an R2 faces two conceptual problems. Many researchers want to report an R2 to measure the variance explained by a model.
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