Dear statalisters and personally Stas, Maarten and J. Verkuilen:
Apologies for my late reply to your helpful suggestions -due to a strong (non PC) virus.
The problem has been resolved by Sophia Rabe-Hesqueth a few days ago; it has been the result of reversing the order of the expand() option variables, namely patt and chosen. With the correct order, instability has stopped.
As a matter of fact, the random effects correlation equal to 1 problem has not ceased to exist and I would be much obliged if you could post me any references you might have come accross on this.
Much obliged,
Dino Konstantaras
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On 11/24/08, Stas Kolenikov [[email protected]] wrote:
Dino,
Give your -mlogit- results, too; did you feed them as starting values to -gllamm-? Keep in mind that introducing random effects changes the scaling of your coefficients; see http://www.citeulike.org/user/ctacmo/article/3057661.
I'd fully agree with earlier reactions that your model does not appear to be identified well. Correlation of 1 are troublesome; in your particular case this may mean that there is no difference between two of the three alternatives -- is this making sense? If one of the categories is quite rare, or present in some weird patterns wrt units producing random effects, it may create those numeric problems, too.
On 11/24/08, KONSTANTARAS KONSTANTINOS <[email protected]> wrote:
Dear statalisters,
In my model runs (using Intercooled Stata 9.2) I experience
tremendous instability in gllamm results, higher than has been
elsewhere reported at
statalist: I run a simple multinomial logit model with random effects,
using the results as initial matrix for exactly the same model, to get
as a result an extremely different log likelihood and coefficients
etc. The resulting loglikelihood differs by large margins (3-9%). I
have also tried to use exactly the same initial values from mlogit but
not only does the overall loglikelihood continues to be unstable but
also the same happens with the loglikelihood obtained without random
effects –running gllamm with init option-. Nevertheless the model
invariably converges, albeit to an entirely different estimate.
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