Ben Bolker wrote :
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
For the record
residuals(model)
1 2 3 4 5
5.55860143 -0.00073852 2.49255235 -1.41987341 -0.00042425
6 7 8
-0.94389158 2.72987046 -1.15760836
residuals(model, "pearson")
1 2 3
Menelaos Stavrinides 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 48.8
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
t
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