Re: st: ordered logistic integration problems

[Richard Williams <richardwilliams.ndu@gmail.com>]

Occasionally adding the -difficult- option will work miracles.

My guess, that you are spreading the data too thin. If I follow you, 
the DV has 12 values, and 90% of the cases are a 1, which means the 
other 11 values average less than 1% of the cases. With gologit2 you 
are estimating 11 sets of coefficients. I am not surprised you have 
to collapse to only 3 categories.

But why are you using an ordinal model in the first place? Why not a 
model specifically designed for proportions? See, for example,

http://www.stata.com/support/faqs/statistics/logit-transformation/

http://www.ats.ucla.edu/stat/stata/faq/proportion.htm

At 06:04 PM 3/20/2013, Bontempo, Daniel E wrote:
> 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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-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
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