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Re: Re: st: Comparing model fit of logistic regressions with robust standard errors


From   "Dirk Deichmann" <dirk@dirkdeichmann.de>
To   statalist@hsphsun2.harvard.edu
Subject   Re: Re: st: Comparing model fit of logistic regressions with robust standard errors
Date   Mon, 12 Oct 2009 14:32:01 +0200 (MEST)

Thank you all for your help!

Just for clarification, if I do what Maarten suggested I know whether a model improved in fit when the previously added variable turns out to be significant in the Wald test that I conduct after estimating the logistic regression. But I do not know how much the model fit improved, right?  

Cheers,

Dirk
----- original message --------

Subject: Re: st: Comparing model fit of logistic regressions with robust standard errors
Sent: Mon, 12 Oct 2009
From: Maarten buis<maartenbuis@yahoo.co.uk>

> --- On Mon, 12/10/09, Dirk Deichmann wrote:
> > I am applying a logistic regression model with robust
> > standard errors adjusted for clustering.
> >  
> > I know there have been some posts about this but to me it
> > still is not clear whether and if so how I can assess the
> > improvement in model fit using the Wald chi square values. 
> 
> What you should do is estimate the full model, and use 
> -test- to test the constraints implicit in the nested model.
> So if you want to leave a variable x1 out of your model,
> you type -logit y x1 other_vars, other_options- followed
> by -test x1-.
> 
> Hope this helps,
> Maarten
> 
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
> 
> http://www.maartenbuis.nl
> --------------------------
> 
> Send instant messages to your online friends http://uk.messenger.yahoo.com 
> 
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--- original message end ----


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