st: ordered logistic integration problems

["Bontempo, Daniel E" <deb193@ku.edu>]

Can anyone explain the kind of data conditions that cause gllamm or glogit2 to spit out:

flat or discontinuous region encountered
numerical derivatives are approximate
nearby values are missing
could not calculate numerical derivatives
missing values encountered
r(430);


I have a colleague with proportion data that only has about 12 discrete values between 0 and 1 with about 90% 1's. Skew -3.27, Kurtosis>15. 

We want to model for 3 groups (between) and 3 occasions (within). Prior work published in 2000, had similar proportions and used HML (Gaussian) and got interpretable results. After looking at the distributions, I suggested ologit might be more appropriate than regress.

I was already concerned about these proportion DVs because my colleague has calculated proportion correct of however many scorable events there were, and the number of events differs a lot from subject to subject. Some have 2 some have 10. BUT - my question for the moment is technical difficulty with numerical derivatives.

Since there is occasion nested within person, I was interested in gllamm with the ologit link, as well as robust ologit with "cluster(subject)". I also tried glogit2 because I was unsure the parallel regression assumption was met.

I easily get ologit to run. However both gllamm and glogit2 make similar complaints about missing or discontinuous numerical derivatives and do not complete. I tried the log-log link in glogit2 since the values rise slowly from 0 and suddenly go to 1. I kept rounding to get fewer levels.

I have to collapse to only 3 levels to get glogit2 to run. gllamm keeps telling me to use trace and check initial model, but when I do I see reasonable fixed effect values.

Is ologit able to use an estimation method that avoids these integration issues?

I am trying to get the disaggregated data so multilevel logistic regressions can be done, but it is not clear disaggregated data will be available.

Any pointers, advice, suggestions, references ... all appreciated.


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