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From |
"JVerkuilen (Gmail)" <jvverkuilen@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Odd ratio / relative risk in logistic regression |

Date |
Tue, 9 Apr 2013 08:36:53 -0400 |

On Tue, Apr 9, 2013 at 12:06 AM, Ching Wong <ching.y.wong@student.adelaide.edu.au> wrote: > <snip> In general I'm dubious of screening by univariate statistics before running a model, but I guess if you need to do that David Hoaglin's advice is spot-on: Don't set the criterion for inclusion too high. > And I have got the following output. > > Iteration 1: deviance = 113.0721 > Iteration 2: deviance = 92.10798 > Iteration 3: deviance = 87.45499 > Iteration 4: deviance = 86.88055 > Iteration 5: deviance = 86.86395 > Iteration 6: deviance = 86.86393 > Iteration 7: deviance = 86.86393 > Generalized linear models No. of obs = > 297 > Optimization : MQL Fisher scoring Residual df = > 294 > (IRLS EIM) Scale parameter = > 1 > Deviance = 86.86392755 (1/df) Deviance = > .2954555 > Pearson = 311.8670508 (1/df) Pearson = > 1.060772 Note that it's unusual for there to be such a substantial discrepancy between Pearson chi square and deviance, which makes me think there's something up with this model. > In this case, I can tell var 1 is significant in the logistic > regression model, since it has a p-value =0.006. However, how can I > find out the odd ratio or the relative risk of this model? Did I use > the wrong command? -help binreg- gives the options, of which there are several including odds ratio. I think one could make the case for using -glm- pretty much all the time. Next time I teach categorical I intend to push -glm- as the "one stop shop". That's not quite true but it's pretty close and the fact that the post-estimation is consistent is a big plus. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Odd ratio / relative risk in logistic regression***From:*Ching Wong <ching.y.wong@student.adelaide.edu.au>

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