Dear Ben, First of all, many thanks for your reply. I am highly appreciative of that.
I am still unsure about some issues.... The dispersion parameter is that which is estimated by sum(residuals(fit,type="pearson")^2)/fit$df.res. This is what a quasipoisson model estimates. This corresponds to the theoretical notion that Var Y=phi*mu where phi is the dispersion parameter which is > 1 in the over-dispersion case. This is 2.4 if I fit the Poisson model. This is the same value i get for the quasipoisson (as you suggested). However, in the summary() of the quasipoisson i also get the same theta=0.17 which i do not understand... Does it have to do with the scale or shape parameter? However if I fit my negative binomial model i obtain sum(residuals(fit,type="pearson")^2)/fit$df.res= 1.4. Different to the above. Also i get different estimates and different standard errors between my Neg. Binomial and Poisson models (I thought estimates should remain the same but standard errors be different....) And to cap it all, when i do sum(fitted(poisson.model)) I obtain the same count as my data but when I do sum(fitted(neg.binomial.model)) it is much greater!!! :S I would be extremely pleased were you to have a moment to reply to this post. :) Many thanks, Tomas -- View this message in context: http://r.789695.n4.nabble.com/GLM-and-Neg-Binomial-models-tp3902173p3915009.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
