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Re: st: RE: Re: predict after Poisson

From   Richard Goldstein <>
Subject   Re: st: RE: Re: predict after Poisson
Date   Tue, 11 Oct 2005 14:18:19 -0400

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?



German Rodriguez wrote:


I believe everything depends on whether your Poisson model includes an
offset. This is explained in the help system if you type

help poisson postestimation, marker(predict)
If the model doesn't have an offset, then |predict ir, ir| gives the fitted
count. This is the same as |predict mu| (with no options), and the same as
|gen mu = exp(xb)| after |predict xb, xb|.
If the model has an offset, then |predict ir, ir| gives the predicted rate,
which is the number of events divided by exposure. This is not the same as
|predict mu|, which gives the expected count taking exposure into account.
Note also that |predict xb, xb| gives the linear predictor *including* the
offset. Another way to obtain the incidence rate is |gen rate =
events/exposure| after |predict events|, assuming your exposure variable is
called exposure.

In either case it is possible for the rate to exceed one; you could expect
more than one event (no offset) or more than one event per unit of exposure
(with offset). For example the number of children ever born per woman can be
more than one.

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