Ravi Varadhan <ravi.varadhan <at> jhu.edu> writes: > > Dear All, > I am fitting a model for a binary response variable measured > repeatedly at multiple visits. I am using the binomial GLMM using > the glmer() function in lme4 package. How can I evaluate the model > assumptions (e.g., residual diagnostics, adequacy of random effects > distribution) for a binomial GLMM? Are there any standard checks > that are commonly done? Are there any pedagogical examples or data > sets where model assumptions have been examined for binomial GLMMs? > Any suggestions/guidance is appreciated. > > Thank you, > Ravi
This might be better for r-sig-mixed-mod...@r-project.org. Roughly speaking, you want to do one set of diagnostics on the individual-level residuals similar to those for a binomial GLM (which in turn are adaptations of the diagnostics for linear models) and one on the group-level random effects. As with GLMs, if your binomial values are _binary_ then the individual-level diagnostics will be a bit challenging. Binomial GLMMs with N>1 will be a bit easier. http://rpubs.com/bbolker/glmmchapter may be helpful, especially the second ("Culcita") example. Also http://stats.stackexchange.com/questions/70783/ how-to-assess-the-fit-of-a-binomial-glmm-fitted-with-lme4-1-0/ (broken URL to make Gmane happy) ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.