I used the glmulti
function in the glmulti
package to obtain the best glm model for poisson error distributed data. No problems there. Once I had obtained the best model, I used the Chi-square test to obtain p-values and test statistics for each of the variables entered into the model. The only problem I am encountering is that the data is overdispersed and the Zuur book and Crawley both suggest using the quasi family function to correct for overdispersion. This in itself is not a problem except that the glmulti function does not allow fits to quasi functions.
The question I have, is whether obtaining my best model using glmulti with a poisson error distribution and then fitting the best model output to a quasi function is the incorrect way to go about doing things and if there are any other suggestions anyone could offer.
I also ran glmulti for normally distributed data (specifying family as gaussian and link as identity) and this did work, but if I am violating any major rules, please do let me know.