[R-sig-ME] Using Observations as Random Effect in GLMM?

[Daniel Hocking]

Hi everyone,

I am having trouble with overdispersion when trying to model count  
data using a GLMM. Beyond going to a negative binomial or Poisson- 
lognormal distribution, I have seen the suggestion (from Ben Bolker I  
believe) to include observation as a random effect. For example using  
the lme4 package my code would look something like this:

glmer(count ~ SoilT + SoilT2 + RH + rain24 + drought +
rain24*SoilT + drought*rain24 + (1 | plot) + (1 | obs), data = Data,
family = poisson)

When I try this I get a fitted vs. residual plot with large residuals  
at low fitted values funneling down to small residuals as the fitted  
values get larger. This indicates heterogeneity. I was wondering if  
that is expected for some reason with observation-level random effects  
or if this model just doesn't meet the assumptions of GLMM for my data?

Thanks,
Dan
------------------------------------------------------------------------------------
Daniel J. Hocking
122 James  Hall
Department of Natural Resources & the Environment
University of New Hampshire
Durham, NH 03824

dhocking at unh.edu
http://sites.google.com/site/danieljhocking/
http://quantitativeecology.blogspot.com/
http://richnessoflife.blogspot.com/

"Without data, you are just another person with an opinion."