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From |
"German Rodriguez" <grodri@Princeton.EDU> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: RE: Re: predict after Poisson |

Date |
Tue, 11 Oct 2005 14:56:04 -0400 |

Rich is concerned about a predicted Poisson rate above one (see below). If the data consist of counts of successes out of a certain number of attempts, I would use a binomial model, which in Stata can be fit using blogit or the glm command with family binomial. In both cases you get to specify the binomial denominator (the number of trials or attempts) and the fitted counts will never exceed that number. The Poisson distribution can be viewed as an approximation to the binomial for rare events, typically large number of attempts with small probability of success in each one. In that case the fitted count will rarely exceed the number of attempts. But why use an approximation when Stata can do the right thing? Cheers, German -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Richard Goldstein Sent: Tuesday, October 11, 2005 2:18 PM To: statalist@hsphsun2.harvard.edu Subject: Re: st: RE: Re: predict after Poisson Thank you for both explanations. I assume that your point about an IR above 1 still holds if exposure is a number of attempts (and the numerator is the number of successes), correct? I ask this because that is my situation and here an IR above 1 means a predicted value of successes that is greater than the number of trials (the exposure). Are there sensible constraints that I could use on the model to keep the predicted values to no more than the exposure? Are there any alternative models that I should look into? Thanks, Rich [earlier bits of exchange deleted] * * 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: predict after Poisson***From:*Richard Goldstein <richgold@ix.netcom.com>

**References**:**Re: st: RE: Re: predict after Poisson***From:*Richard Goldstein <richgold@ix.netcom.com>

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