<>
I can only speculate, but what you are doing is you are estimating a "naive"
model, sort of "constant only" - which may entail special problems for the
initial values. Even in this case, the official examples do coincide in
their results, though...
*************
webuse bangladesh, clear
// Random-intercept model,
// analogous to xtlogit
xtmelogit c_use /*
*/|| district:
xtlogit c_use /*
*/, i(district)/*
*/ nolog
*************
Also note http://www.stata.com/meeting/2sweden/gutierrez_sweden07.pdf
HTH
Martin
-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von Thomas Klausch
Gesendet: Dienstag, 11. August 2009 16:51
An: [email protected]
Betreff: Re: st: RE: Reproducing xtlogit with xtmelogit
Thanks for the initial guidelines. However, if I apply the same syntax
to my data set, that is
xtset respnr
xtlogit dummy
//equivalent xtlogit model should be:
xtmelogit dummy || respnr:
STATA returns the error code r1400 "initial values not feasible"
(numerical overflow). However for xtlogit the algorithm finds a
solution.
So I understand that the algorithms used by the two programs (xtlogit
and xtmelogit) are not the same. Is there anything I can do about this
numerical overflow?
Many thanks
Thomas Klausch
2009/8/10 Martin Weiss <[email protected]>:
>
> <>
>
>
> The equivalance should be obvious from this:
>
> ***
> webuse bangladesh, clear
>
> // Random-intercept model,
> // analogous to xtlogit
> xtmelogit c_use urban age /*
> */child* || district:, nolog
>
> xtlogit c_use urban /*
> */age child*, i(district)/*
> */ nolog
> ***
>
>
>
> HTH
> Martin
>
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of Thomas Klausch
> Sent: Montag, 10. August 2009 18:56
> To: [email protected]
> Subject: st: Reproducing xtlogit with xtmelogit
>
> Hello stata list member,
>
> As I understand it should be possible to reproduce a random effects
> logistic regression model that was fitted with xtlogit by using the
> multilevel syntax of xtmelogit. But for my case it just does not seem
> to work.
>
> I have longitudinal data, i.e. respondents nested in days. First step
> is that I want to fit an empty random intercept model, that is:
>
> xtset respondents days
> xtlogit dummy, re
>
> Which gives me a fit using Gauss-Hermite Quadrature in reasonable
> time, say 3 minutes for NxT=60,000.
>
> Then giving
>
> xtmelogit dummy || respondents:
>
> Does give no fit. I was not sure what would be the correct syntax, maybe
it
> is
>
> xtmelogit dummy || respondents: ||days:
>
> This takes forever to fit (I am not sure if the algorithm is ever
> going to stop, It's still running after 1h waiting).
>
> a. Am I specifying xtmelogit correctly?
> b. Does xtmelogit use the same optimization algorithm as xtlogit?
> c. What should be changed to yield the same results as with xtlogit?
>
> Many thanks in advance.
>
> Kind regards
>
> Thomas
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