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In GLMs, quasi-likelihood estimation is a way to allow over- or under-dispersion by choosing an appropriate variance function.
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Accounting for overdispersion in binomial glm using proportions, without quasibinomial
I am doing binomial GLM using relative abundance, for example:
model<-glm(cbind(number_pres,number_abs)~Var1+Var2+Var3+Var4...,
family=binomial, data=Data).
My sample size is about 700, and I hav …