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Date:   Wed, 10 Dec 2008 19:02:59 -0200
Reply-To:   Hector Maletta <hmaletta@fibertel.com.ar>
Sender:   "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:   Hector Maletta <hmaletta@fibertel.com.ar>
Subject:   Re: validating a logistic regression model
Comments:   To: J P <jp7837@yahoo.com>
In-Reply-To:   <117042.20858.qm@web59909.mail.ac4.yahoo.com>
Content-Type:   text/plain; charset="iso-8859-1"

Whatever you use, please do not use the percentage of correctly predicted individual cases. In my opinion it means little or nothing (even if you choose a "correct" cutoff point, which is itself a difficult and --from some viewpoints-- an unsolvable problem). Probability prediction by logistic regression is not predicated of individuals but of populations or groups or similar individuals: any individual outcome is compatible with the prediction. For instance, if you predict a 90% probability that I (or more exactly, people with my values in the chosen predictors) would die within a year, my eventual survival for another 45 years is perfectly compatible with that prediction. What you were actually predicting is that out of a large number of people like me, 90 out of every 100 die within one year. I just happened to be in the lucky 10% living longer. Even the estimated 90% probability is itself subjet to estimation error: the "true" population probability might be higher or lower, with certain probability of error (you may have, say, a 95% chance that the true probability is between 0.85 and 0.95, and 5% chance that is it either lower or higher. In other words, the true probability might be much lower. The probability (observed or predicted), whatever its value happens to be, is an attribute of the group, not an attribute of each subject. This is, of course, the frequentist interpretation of probability, but it is arguably the only consistent one. Individual outcomes of random variables are strictly indeterminate: it is the group aggregate outcome which is subject to the prediction. With these caveats in mind, you may turn for example to Hosmer and Lemeshow's book, Applied Logistic Regression for detailed information about significance tests and goodness of fit tests for logistic regression, and about applying a logistic regression solution (obtained from one dataset) to a second dataset with a validation purpose. Hope this helps.

Hector -----Original Message----- From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of J P Sent: 10 December 2008 18:33 To: SPSSX-L@LISTSERV.UGA.EDU Subject: validating a logistic regression model

Dear Colleagues,

I am attempting to learn how to valide a logististic regression model. I've been reading about bootstrapping and cross validation, etc. But have found no instruction on how to actually conduct the anaysis and interpret the results. Any references on this subject or advice on how to perform this with SPSS is greatly appreciated.

Here is an example of what I am talking about....http://symptomresearch.nih.gov/chapter_8/sec7/cess7pg14.htm

Thanks! John

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