Re: st: After non-convergence with xtmixed

[Clyde Schechter <cschecht@aecom.yu.edu>]

Thanks to Maarten Buis for his suggestions.

I wasn't actually thinking about using the results of an uncompleted 
attempt at estimation as a final result to report in a paper.  It was 
precisely for the purpose of diagnosing what is wrong with the model 
that I thought the results corresponding to the final state of the 
gradient-based estimation might be more helpful than the final 
EM-iteration results.

In particular, in addition to the kinds of modeling problems Maarten 
pointed out, xtmixed can fail to converge because of a boundary 
problem when one of the random effects being estimated is close to 
zero.  In some of my models this might be the case.  But the final EM 
results may be fairly far from the correct values and could fail to 
display this problem, could they not?

Of course, it is simple enough to re-estimate the model leaving out 
the suspected offending random effect.  But the fact that the reduced 
model converges isn't really evidence that the omitted component is 
close to zero, is it?  So I'm left not really knowing if the reduced 
model is adequate.

That's why I thought that seeing the estimates based on the 
incomplete gradient-estimation would be more helpful, because 
typically the log likelihood ratio is much bigger and I would imagine 
that the corresponding random effect estimate would be a better way 
to judge if I'm up against a boundary problem.

Clyde Schechter


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