Prediction variance for GLMM
You might consider using glmmTMB::glmmTMB(), which has an `se.fit=TRUE` argument. Note the ability, with allow.new.levels=TRUE, to predict for a new random factor level. glmmTMB() has the advantange of allowing a wider range of error familiies. For betabinomial, negative binomial, and other such families, it allows the modeling of the ?dispersion? parameter (NB that this is not ?dispersion? as defined for glm quasi models, albeit one can be expressed as a function of the other.) There is the standard warning that the SEs are contingent on the model assumptions, and on distributional theory approximations. (E.g., what DF assumptions will give a good approximation to the coef/SE distributions.) The lme4 parametric bootstrap function bootMer() should now work to give SEs that pretty much get around issues with distributional theory approximations, but not around model assumptions more generally. John Maindonald email: john.maindonald at anu.edu.au<mailto:john.maindonald at anu.edu.au>
On 6/01/2019, at 07:48, Levine, Michael <mlevins at purdue.edu<mailto:mlevins at purdue.edu>> wrote:
Dear all, I would like to ask the following question. Is it possible to obtain prediction variances for GLMMs in the package lme4 , based e.g. on the function glmer? I know that it is possible to do it with "pure" GLM's but I don't see any options for GLMM's. I realize there is a problem there because such a variance can be defined in several different ways... Let me know and thanks a lot in advance! Yours, Michael Levine Associate Professor, Statistics Department of Statistics Purdue University 250 North University Street West Lafayette, IN 47907 USA email: mlevins at purdue.edu<mailto:mlevins at purdue.edu> Phone: +1-765-496-7571 Fax: +1-765-494-0558 URL: www.stat.purdue.edu/~mlevins _______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models