Thank you, Steve!
Hisako
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of
[email protected]
Sent: Sunday, July 05, 2009 7:50 AM
To: [email protected]
Subject: Re: st: Evaluation of Logistic Regression of Complex Survey Data
--
To plot the ROC curve,
see:http://groups.google.de/group/statalistrss/browse_thread/thread/512f852d
8f66a711/2a20319878808e92
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<[email protected]> 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
[email protected]
18 Cantine's Island
Saugerties NY 12477
USA
845-246-0774
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