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From |
sjsamuels@gmail.com |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Evaluation of Logistic Regression of Complex Survey Data |

Date |
Sun, 5 Jul 2009 10:49:57 -0400 |

-- To plot the ROC curve, see:http://groups.google.de/group/statalistrss/browse_thread/thread/512f852d8f66a711/2a20319878808e92 Roger Newson's -somersd- (downloadable from SSC) will compute and compare the areas under ROC curves with weighted, clustered data together. See caveats at: http://www.stata.com/statalist/archive/2007-06/msg00166.html To mimic Stata's -linktest-, see: http://www.stata.com/statalist/archive/2008-10/msg00882.html The original topic was about -svy: reg- . After logistic regression, make sure that you generate the linear predictor, not the probability with "predict yhat, xb" -Steve On Fri, Jul 3, 2009 at 11:52 AM, Hisako Kobayashi<hisakoko@usc.edu> wrote: > I am analyzing a logistic regression with svy command. As you > know, with “svy” many statistics, i.e. dbeta, dx2, ddeviance, ROC, > etc. etc. for assessment of model are not calculated in logit > postestimation, except “svylogitgof”. This may be a stupid > question.. but my question is how I can assess the fit of svy logit > regression model? > Here are two approaches that I can think of: > 1. According to Hosmer and Lemeshow, one approach is to compare > design-based analysis with model-based analysis. > 2. The other approach would be to estimate those statistics only with > cluster (without pweight) and evaluate them. > > Is there any better approach to evaluate survey logit model? If no, > which approach is better? -- Steven Samuels sjsamuels@gmail.com 18 Cantine's Island Saugerties NY 12477 USA 845-246-0774 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: Evaluation of Logistic Regression of Complex Survey Data***From:*"Hisako Kobayashi" <hisakoko@usc.edu>

**References**:**st: Evaluation of Logistic Regression of Complex Survey Data***From:*Hisako Kobayashi <hisakoko@usc.edu>

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