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RE: st: ordered logistic integration problems
From
"Bontempo, Daniel E" <[email protected]>
To
"[email protected]" <[email protected]>
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: [email protected] [mailto:[email protected]] On Behalf Of Richard Williams
Sent: Thursday, March 21, 2013 12:59 PM
To: [email protected]; [email protected]
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: [email protected]
>[mailto:[email protected]] On Behalf Of Stas
>Kolenikov
>Sent: Wednesday, March 20, 2013 8:26 PM
>To: [email protected]
>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
><[email protected]> 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.
> >>
> >>
> >> *
> >> * For searches and help try:
> >> * http://www.stata.com/help.cgi?search
> >> * http://www.stata.com/support/faqs/resources/statalist-faq/
> >> * 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: [email protected]
> > WWW: http://www.nd.edu/~rwilliam
> >
> >
> > *
> > * For searches and help try:
> > * http://www.stata.com/help.cgi?search
> > * http://www.stata.com/support/faqs/resources/statalist-faq/
> > * http://www.ats.ucla.edu/stat/stata/
>*
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>* http://www.ats.ucla.edu/stat/stata/
>
>
>
>*
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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: [email protected]
WWW: http://www.nd.edu/~rwilliam
*
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*
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