Using Observations as Random Effect in GLMM?
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."