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Re: st: ordered logistic integration problems


From   Stas Kolenikov <skolenik@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: ordered logistic integration problems
Date   Wed, 20 Mar 2013 20:25:43 -0500

I second Richard. The message probably comes from the difficulty of
identifying the threshold parameters for the categories with the
fewest observations, especially if they interact in some odd ways with
the random effects and/or variance parameters. For as much as you
(understandably) hate to run this as a linear model, this may be a
better option, as the prior work did. Or, at the other extreme, create
a dummy "less than 100%", which will only have 10% non-trivial values.

-- Stas Kolenikov, PhD, PStat (SSC)
-- Senior Survey Statistician, Abt SRBI
-- Opinions stated in this email are mine only, and do not reflect the
position of my employer
-- http://stas.kolenikov.name



On Wed, Mar 20, 2013 at 6:20 PM, Richard Williams
<richardwilliams.ndu@gmail.com> wrote:
> 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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>> *   http://www.ats.ucla.edu/stat/stata/
>
>
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
> HOME:   (574)289-5227
> EMAIL:  Richard.A.Williams.5@ND.Edu
> WWW:    http://www.nd.edu/~rwilliam
>
>
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