Menelaos Stavrinides <menstav <at> gmail.com> writes: > > I am running a binomial glm with response variable the no of mites of two > species y->cbind(mitea,miteb) against two continuous variables (temperature > and predatory mites) - see below. My model shows overdispersion as the > residual deviance is 48.81 on 5 degrees of freedom. If I use quasibinomial > to account for overdispersion the dispersion parameter estimate is 2501139, > which seems unrealistic. Any ideas as to why I am getting such a huge > dispersion parameter? >
The dispersion parameter depends on the Pearson residuals, not the deviance residuals (i.e., scaled by expected variance). I haven't checked into this in great detail, but the Pearson residual of your first data set is huge, probably because the fitted value is tiny (and hence the expected variance is tiny) and the observed value is 0.2. dfr <- df.residual(model2) deviance(model2)/dfr d2 <- sum(residuals(model2,"pearson")^2) (disp2 <- d2/dfr) fitted(model2) residuals(model2,"pearson") Ben Bolker ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
