[R-sig-ME] mhglm as a substitute for glmer in case of quasi-complete separation

[Tibor Kiss]

Hello,

I have a question emerging from a case of quasi-complete separation (as I understand it): I am working on a random-intercept GLMM, where one of my predictors has eight levels, one of which seems to lead to quasi-complete separation, as the dependent variable has a (0/260) distribution for this level. In any case, the standard error for this level is about 20 times as high as its coefficient, and consequently, the the Pr(z) is greater 0.95.

I understand that Firth's penalized likelihood method is the method of choice, and hence used mhglm (from mbest), which allows for glmms with one random factor. The problem with the aforementioned level disappears but the coefficients are differ largely from the one provided by glmer. mhglm deals with the offending level, but also turns other factors that have always received Pr(z) < 0.05 with values above 0.05. 

Here are my questions: Does anybody on this list have experience with mbest and mhglm in particular, or is there another alternative for mixed models? Is there another way to tweak glmer so that Firth's logistic regression can be included into glmer?

Thanks

T.
   

   	 	
Prof. Dr. Tibor Kiss, Sprachwissenschaftliches Institut
Ruhr-Universität Bochum D-44780 Bochum
Office: +49-234-322-5114

 


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