dear list!
i am running an anlysis on proportion data using binomial (quasibinomial
family) error structure. My data comprises of two continuous vars, body
size and range size, as well as of feeding guild, nest placement, nest
type and foragig strata as factors. I hope to model with these variables
the preference of primary forests (#successes) by certain bird species.
My code therefore looks like:
y<-cbind(n_forest,n_trials-n_forest)
model<-glm(y~range+body+nstrata+ntype+forage+feed,family=quasibinomial(link=logit),data=dat)
however plausible the approach may look, overdispersion is prevalent
(dispersion estimated at 6.5). I read up on this and learned that in
case of multiple factors, not all levels may yield good results with
logistic regression (Crawley "The R Book"). I subsequently try to
analyse each feeding guild seperately, but to no avail.overdispersion
remains. Given the number of categorical variables in my study, is there
a convenient way to handle the overdispersion? I was trying tree models
to see the most influential variables but again, to no avail.
BTW: It may well be that the data is just bad...
thanks a lot!
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