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.
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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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