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.
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