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From |
"Nick Cox" <[email protected]> |

To |
<[email protected]> |

Subject |
st: RE: re. Poisson Regression Goodness of Fit Tests |

Date |
Fri, 3 Oct 2003 15:05:06 +0100 |

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/

**Follow-Ups**:**Re: st: RE: re. Poisson Regression Goodness of Fit Tests***From:*"roger webb" <[email protected]>

**References**:**st: re. Poisson Regression Goodness of Fit Tests***From:*Michel Camus <[email protected]>

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