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From |
Richard Goldstein <richgold@ix.netcom.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Tue, 11 Oct 2005 15:14:35 -0400 |

Thank you (I'm embarrassed that I forgot about the grouped logit estimator) Rich German Rodriguez wrote:

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]

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**References**:**RE: st: RE: Re: predict after Poisson***From:*"German Rodriguez" <grodri@Princeton.EDU>

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