On Thu, Aug 30, 2012 at 5:25 PM, Lynne Clay <lynne.clay at xtra.co.nz> wrote:
Dear Prof Bates, I'm a doctoral candidate in NZ trying to analyse survey data with random effects with my outcome being a count. I discovered your lme4 package and have been using this with success, however, I need to check for overdispersion and it is at this point I am having problems. The formula I have used before has been (1/df)*deviance and if I use this my model is highly overdispersed. I read on one of the discussion boards that adding an extra random effect (1|id#) addresses the overdispersion problem which I have included but overdispersion continues. Can overdispersion be calculated in this manner?
I'm sorry but I know nothing about overdispersion. To me it is completely artificial because there is no probability distribution on which to base a statistical model with these properties.
Do you have any suggestions of how to deal with this?
Sorry but I don't. I have taken the liberty of sending a copy of this reply to the R-SIG-Mixed-Models mailing list in the hope that readers of that list can help you.
Lynne Lynne Clay PhD Candidate School of Physiotherapy University of Otago PO Box 56 Dunedin 9054 New Zealand