Karen Moore <kmoore <at> tcd.ie> writes: > > I'm dealing with count data that's nested and has spatial dependence. > I ran a glmm in lmer with a random factor for nestedness. Spatial dependence > seems to have been accommodated by model. However I can't add a variance > strcuture to this model (to accommodate heterogeneity). > > Is there a model that can have a poisson distribution *AND* a variance > structure *AND* have AIC in output (for model comparison and selection)? > Some we've looked at that can't: > > - glmmPQL - can add structures BUT can't have AIC (you can calculate it > but it doesn't give correct AIC with this model) > - glmm in lme4 (lmer) - won't allow variance structure > - gls - can add variance but can't have Poisson
[Any further discussion should probably go to r-sig-mixed-mod...@r-project.org ...] I'm not sure I know what you mean by Poisson + variance structure -- if the data are really Poisson (not overdispersed in some way), then the variance structure is completely defined. If you want to deal with overdispersion, and have a well-defined AIC, you may be able to add a per-observation random effect in lme4. Alternatively, you could just use a weights= argument in glmmPQL to set some sensible mean-variance relationship, overlooking the fact that the data are discrete and positive rather than being normally distributed with an equivalent variance structure. <http://glmm.wikidot.com/faq> may also be useful. ______________________________________________ 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.
