I'd like asking your help to understand a statistical issue from my data set. I ran a GLM with proportional data, using a binomial distribution. However, I've found underdispersion in my model and I don't know how to deal with that. I'm aware that a solution for overdispersion is fit a model using a quasibinomial distribution, but I couldn't find a solution to my problem in the literature.
I'm comparing the differences between continuous forest sites and fragments regarding the proportion of richness and abundance of specialist species. So, the models are:
M1 <- glm(prop_rich_speci ~ LandscapeBin, family = binomial, weights=rich_total_sp, data = envir.all) M2 <- glm(prop_abu_speci ~ LandscapeBin, family = binomial, weights=abu_total_sp, data = envir.all)
I think using a quasibinomial distribution I can solve my problem (underdispersion), as Ben suggested.