Joseph,
Thanks. Yes, and no. Yes in that xtmelogit for crossed-level model uses Laplace approximation. However, when I tried specifying the intpoints option, STATA generates an error message as below:
. xtmelogit fail refinance || _all:R.year || _all:R.hub || _all:R.originator_n, intpoints(5 5 5)
wrong number of intpoints() specifications
r(198);
Could you tell me what went wrong?
Thanks,
Albert.
________________________________________
From: [email protected] [[email protected]] On Behalf Of Joseph Coveney [[email protected]]
Sent: Tuesday, December 29, 2009 1:13 AM
To: [email protected]
Subject: st: Re: Predicting Random Effects from a Crossed-Level Model using xtmelogit
Albert Lee wrote:
This may be obvious. If so, I apologize in advance.
I'm estimating a logistic model with crossed-level random effects. The outcome
binary variable is fail with one fixed-effect independent binary variable
refinance. The three crossed-level random effects are VAR1, VAR2 and VAR3. The
STATA command I used is as below:
xtmelogit fail refinance || _all:R.VAR1 || _all:R.VAR2 || _all:R.VAR3
After estimating this model, I tried to recover the random effects of VAR3
using:
predict u, reffects level(VAR3)
However, u only contains missing values.
I would very much appreciate if someone can help me recovering these predicted
random effects.
--------------------------------------------------------------------------------
Doesn't -xtmelogit- use Laplace approximation by default when fitting a
cross-classified model? I believe that you would need to specify at least
three, or so, integration points (abscissa weights) in order to get empirical
Bayes predictions of individual random effects.
Joseph Coveney
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