Sorry I meant to say over-dispersed not over-fitted. Apologies for
any confusion.
Roger
On 3 Oct 03, at 15:27, Nick Cox wrote:
I pass on this. There's lots of people on Statalist
who can advise much better on this than I can.
Nick
[email protected]
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]]On Behalf Of roger webb
> Sent: 03 October 2003 15:17
> To: [email protected]
> Subject: Re: st: RE: re. Poisson Regression Goodness of Fit Tests
>
>
> Thanks Nick
>
> Would you advise that I assume that the model is over-fitted and
> run a negative binomial regression model (nbreg) instead?
>
> Roger
>
> On 3 Oct 03, at 15:05, Nick Cox wrote:
>
> roger webb wrote:
>
> > >I'm generating Poisson regression models with an aggregated data
> > >set (i.e. each record in the data set represents a stratum of
> > >aggregated numbers of deaths and person-years of observation).
> > >
> > >I wish to check that the models are not over-dispersed.
> The manual
> > >tells me that I can use either the 'poisgof' or the
> > 'poisgof, pearson'
> > >command. These produce the following contradictory results:
> > >
> > >poisgof
> > >
> > > Goodness-of-fit chi2 = 1191.579
> > > Prob > chi2 (5304) = 1.0000
> > >
> > >poisgof, pearson
> > >
> > > Goodness-of-fit chi2 = 29207.21
> > > Prob > chi2 (5304) = 0.0000
> > >
> > >A colleague has told me that these results have no meaning for my
> > >data set, because the degrees of freedon are incorrect (I
> > think). He
> > >says that I should instead apply the Breslow adjusted score test
> > >(Breslow NE. Generalized linear models: checking assumptions
> > >and strengthening conclusion. Statistica Applicata 1996;
> > 8: 23-41).
>
> Irrespective of whether it's the correct test -- I am happy
> to understand that it's not -- the difference in results is
> a nice (or rather nasty) _numerical_ example of how which
> chi-square measure you use can be crucial. That is, the two
> sample statistics differ by a factor of 29207.21 / 1191.579 ~ 25.
> Presumably lots and lots of small expected frequencies
> are blowing up the Pearson measure.
>
> And, as far as P-values are concerned, partly because
> of the incorrect number of df, you flip from one tail to
> the other...
>
> Nick
> [email protected]
>
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
