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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. > >> > >> > >> * > >> * 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: Richard.A.Williams.5@ND.Edu > > 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/ >* >* 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/ > > > >* >* 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: Richard.A.Williams.5@ND.Edu 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/ * * 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/

**References**:**st: ordered logistic integration problems***From:*"Bontempo, Daniel E" <deb193@ku.edu>

**Re: st: ordered logistic integration problems***From:*Richard Williams <richardwilliams.ndu@gmail.com>

**Re: st: ordered logistic integration problems***From:*Stas Kolenikov <skolenik@gmail.com>

**RE: st: ordered logistic integration problems***From:*"Bontempo, Daniel E" <deb193@ku.edu>

**RE: st: ordered logistic integration problems***From:*Richard Williams <richardwilliams.ndu@gmail.com>

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