Bolkers GLMM FAQ: Model summaries (goodness-of-fit, decomposition of variance, etc.) http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#how-do-i-compute-a-coefficient-of-determination-r2-or-an-analogue-for-glmms
Listserve thread: http://thread.gmane.org/gmane.comp.lang.r.lme4.devel/3281, including a comment by Doug Bates: http://thread.gmane.org/gmane.comp.lang.r.lme4.devel/3281
Listserve thread: http://thread.gmane.org/gmane.comp.lang.r.lme4.devel/684
A problem that interests me: small marging $R^2$ values for otherwise interesting models: Is it worth reporting small fixed-effect (marginal ), large model (conditional )?
Is (general issues with R2) useful or dangerous?
Misc. references on R2 in GLMMs at Proportion of explained variance in a mixed-effects model
Good explanation of Nakagawa and Schielzeth (2013) at: for mixed models with multiple fixed and random effects
In response to the question Calculating , some re-posts a response from Douglas Bates where he voices his extreme skepticism about R2 for mixed models. in mixed models using Nakagawa & Schielzeth’s (2013) R2glmm method
A related topics: Does the variance of a sum equal the sum of the variances?
R Packages for R2
sjstats:cod “coefficient of discrimination” for logistic regression. See Tjur T (2009) Coefficients of determination in logistic regression models – a new proposal: The coefficient of discrimination. The American Statistician, 63(4): 366-372
sjstats:rsme “root mean square error”
Documentation for sjstats discusses how ICC can be used to investigate amount of variance due to clustering
R Packages for related stuff
Jaeger et al 2017. An R2 statistic for fixed effects in the generalized linear mixed model. http://www.tandfonline.com/doi/abs/10.1080/02664763.2016.1193725?journalCode=cjas20
LaHuis et al. 2014. Explained Variance Measures for Multilevel Models. http://journals.sagepub.com/doi/abs/10.1177/1094428114541701
Tjur T (2009) Coefficients of determination in logistic regression models – a new proposal: The coefficient of discrimination. The American Statistician, 63(4): 366-372
Assessing the Fit of Regression Models