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RE: st: ordered logistic integration problems


From   "Bontempo, Daniel E" <deb193@ku.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: ordered logistic integration problems
Date   Thu, 21 Mar 2013 18:36:48 +0000

Thanks again Richard. I did have the default wrong as far as "pl" or "npl" and it is the non-parallel models that always fail. I just got several of the gologit2 models to run using "pl" and did not have to reduce the number of categories.

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Richard Williams
Sent: Thursday, March 21, 2013 12:59 PM
To: statalist@hsphsun2.harvard.edu; statalist@hsphsun2.harvard.edu
Subject: RE: st: ordered logistic integration problems

At 09:28 AM 3/21/2013, Bontempo, Daniel E wrote:
>Thanks Richard. I follow about the difficulty of thresholds between 
>sparse categories, or even when some categories are not at all levels 
>of the IV's.
>
>I do lack insight into why "ologit" quickly picked thresholds and gave 
>results, while gllamm and gologit2 seemed unable to pick thresholds. I 
>am going to avoid thresholds and use the glm with
>link(logit) family(binomial) as suggested in another reply, but it 
>would be great to have more insight into why ologit had no apparent 
>problem and gologit2 failed - even when I used the parallel assumption, 
>and they were both estimating the same model.

If anyone else is interested, -gologit2- is available from SSC. To estimate the same model, I assume you did something like

ologit y x1 x2 x3
gologit2 y x1 x2 x3, pl

If ologit ran fine and gologit2 did not, it may just be because ologit is a better written program! Or, you might try adding the
-difficult- option to gologit2.

If you didn't use the -pl- option with gologit2, then you probably were not estimating the same model.

If you can provide some syntax and output or a replicable problem I can try to see if I can figure anything out. But make sure you really were trying to estimate the exact same model. A generalized ordered logit model potentially estimates far more parameters than an ordered logit model does, which can be difficult to do if you have thin counts in a category and/or many variables.



>-----Original Message-----
>From: owner-statalist@hsphsun2.harvard.edu
>[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Stas 
>Kolenikov
>Sent: Wednesday, March 20, 2013 8:26 PM
>To: statalist@hsphsun2.harvard.edu
>Subject: Re: st: ordered logistic integration problems
>
>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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> >
> >
> > -------------------------------------------
> > 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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-------------------------------------------
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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