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 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")
Sorry to dig that one from one year ago, but it seems it is still
interesting (at least to me).
From summary.glm, it looks like the deviance is obtained by the working
type deviance, which gave close results to pearson type (when working
residuals are multiplied by the weights). I couldn't find a lot of
information on the working type. How is it computed (from ?glm, I know
that these are «the residuals in the final iteration of the IWLS fit») ?
sum(model2$weights * residuals(model2, type="working")^2)
sum(residuals(model2, type="pearson")^2)
Maybe I'm wrong, but could someone clarify that (which type is used and
what difference it makes) ?
Thank you in advance,
Etienne
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