[R] GLM and Neg. Binomial models

[D_Tomas]

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    
 



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