Re: st: checking model fit with svy estimation on

[Bart van Rijsbergen <bartvanrijsbergen@student.ru.nl>]

This article in the stata journal might be helpfull;
http://www.stata-journal.com/abstracts/st0099.pdf

2011/2/18 Cheney <furber.cini@gmail.com>
>
> Hi,
> I am having some troubles checking if my model is good (goodness of fit, no
> influential outliers etc) and comparing model 1 to model 2 (with other iv's
> or w iv's expanded), because of the limitations in stata for post est with
> svy...
> (eg.   svy: logistic outcome iv1 iv2 iv3 iv4 iv5 iv1*iv3) My outcome is
> binary and my IV's vary between categorical and binary (gender). I also know
> you can test the model to see if you should have the iv's expanded into
> dummy variables, I think you run one model, then another (with vars
> expanded) then lrtest (I think- i haven't done it for a while) but you can't
> do this with svy. I know you can do something with a wald test- but I don't
> fully understand how to interpret that. Anyone have any suggestions? I need
> a goodness of fit test...
>
> What I have done so far:
> checked for interactions- found then added to the model.
> checked for collinearity- found, centered vars and re ran model with
> centered vars.
> checked for outliers- (plotted pearson resid's) but I couldn't estimate
> deviance residuals (because of svy) so I'm not sure if just one plot is
> enough? I had only one outlier.
> specification error- i did a linktest which gave me this output:
>        _hat |   1.145419    .274488     4.17   0.000      .607336
> 1.683502
>      _hatsq |   .0298467   .0544568     0.55   0.584    -.0769059
> .1365993
> which might mean that i don't have a problem with specification error. but I
> wanted to do the 'boxtid' to check for non linear relationships, however
> this is not supported with pweights... so I just ran the regression without
> the svy etstimation and ran the boxtid. I ended up with non linear reasults
> coming up, but none of them significant (in the example online they are
> significant of course) so I didn't know if this meant they are non linear
> but not to an extent that matters?
> age      |   .4471324   .0736975      6.067   Nonlin. dev. 0.241   (P =
> 0.624)
>       p1 |    2.85128   3.722516      0.766
>
> Any other suggestions for checking the model is sound?
> thanks in advance!
>
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