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


From   "Martin Weiss" <martin.weiss1@gmx.de>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: AW: Comparing model fit of logistic regressions with robust standard errors
Date   Mon, 12 Oct 2009 13:08:16 +0200

<> 



Dirk may also want to look at -nestreg-, in particular -help nestreg,
mark(lr)-



HTH
Martin


-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Dirk Deichmann
Gesendet: Montag, 12. Oktober 2009 12:29
An: statalist@hsphsun2.harvard.edu
Betreff: st: Comparing model fit of logistic regressions with robust
standard errors

Hello everyone,
 
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. 

Can you calculate the change in Wald chi square from a restricted to a full
model and then look up whether this value is significant? Would you just
subtract one Wald chi square value from the other to get to the change in
Wald chi square value? Or is it more meaningful to say that if you assess
model 1 with say x1, x2, and x3  (Wald chi2(3) = 196.63) to model 2 with x1,
x2, x3, and x4 (Wald chi2(4) = 198.9) and both models show Prob > chi2 = 0
that the latter model shows a better fit since it is still significant even
though a new variable has been added? Or how should I think about this?
 
Many thanks for your kind support,
 
Dirk


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