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From |
Darcy Hannibal <dlhannibal@ucdavis.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Re: question about mixed-effects ordinal regression in STATA 13 |

Date |
Fri, 14 Feb 2014 11:58:38 -0800 |

On 2/14/2014 10:25 AM, Stas Kolenikov wrote:

I am pretty sure I am missing something (proportional odds sounds like coming from biometric literature, and I am used to look at the ordinal models as econometric/social science models, and see the outcome as a categorized continuous variable, rather than a bunch of different outcomes "Low", "Medium", "High", "Very high"), but if the alternative model is that with each category freely estimated, I think there are several potential ways to fit a multinomial model with (correlated) random effects, or a set of binary models for each category, using user-written -gllamm- and/or -cmp- (the latter with random effects, on top of what's in the award winning Stata Journal paper). P.S. Stata is not an acronym, so you don't have to shout STATA. -- Stas Kolenikov, PhD, PStat (ASA, SSC) -- Principal Survey Scientist, Abt SRBI -- Opinions stated in this email are mine only, and do not reflect the position of my employer -- http://stas.kolenikov.name On Fri, Feb 14, 2014 at 12:58 PM, Darcy Hannibal <dlhannibal@ucdavis.edu> wrote:Although I did not receive response to this form the statlist, I have had several people contact me who have the same question and want to know if I received a response or found out anything more. I am writing this email to post for the record my response to the latest inquiry so that it might be helpful or expanded on by anyone familiar with mixed ordinal regression. Please see below: "No one responded, but after doing quite a bit more reading it is clear that meologit and meglm use an algorithm that does assume proportional odds. Unfortunately STATA does not provide a way to test the proportional model with a mixed model. Non-proportional odds separate out the coefficients of the predictors by each category of the ordinal outcome and you can force STATA to do this by recoding your outcome variable into multiple separate binary outcomes for your cutpoints. There is a program called SuperMix that will do it and allows you to do proportional odds models, non-proportional odds models, or partial-proportional odds models. It is not very user friendly and you will have to read an extensive amount of documentation to use it. I ended up using that to assess the variables in my models for the proportional-odds assumption and then ran the final model in STATA to produce predicted probabilities and make graphs. Fortunately, all of the variables in my final model met the assumption. You can get estimates out of SuperMix, but it is pretty time consuming. There is a free version of SuperMix you can download from their website at: http://www.ssicentral.com/supermix/downloads.html. [additional note: the non-free version is super expensive at $425 but so super useful I would buy it at a more affordable price of say $100-$200; unless, of course, STATA 14 makes it possible to do everything SuperMix can do]. Although the website states it limits the size of the model you can analyze, I did not have any problems and had a data set much larger than what is supposed to be allowed in the student version. Although they state they do not provide support for the student version, they were willing to respond to the few questions I asked, but that was after I read much of the documentation and had figured most of it out. So, I think they are willing to help those who've already done as much as possible on their own and are just a little bit stuck." I hope that helps, Darcy On 12/5/2013 1:15 PM, Darcy Hannibal wrote:Hello, I have a question about the assumptions for the models using either the meologit or meglm (ordinal family) commands. None of the documentation I have found for these new commands available in version 13 mention anything about testing for whether the data meet the proportional odds assumption. Since there are different varieties of ordinal models and not all of them are constrained by proportional odds I am wondering if the models used in these two commands do not assume proportional odds. The brant command will test proportional odds, but it only works after the ologit command. Can anyone tell me if the proportional odds assumption applies to meologit and meglm (ordinal family). If so, is there a simple way to test for this in STATA or does it have to be done by hand? Thank you in advance for any advice you can give. --Darcy-- Darcy L. Hannibal, PhD Staff Research Associate III Supervisor McCowan Animal Behavior Laboratory for Welfare and Conservation Department of Population Health and Reproduction Behavior Management Brain, Mind, and Behavior Unit California National Primate Research Center University of California at Davis Office: 3029-B CNPRC Phone: 530-752-1586 * * 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/

-- Darcy L. Hannibal, PhD Staff Research Associate III Supervisor McCowan Animal Behavior Laboratory for Welfare and Conservation Department of Population Health and Reproduction Behavior Management Brain, Mind, and Behavior Unit California National Primate Research Center University of California at Davis Office: 3029-B CNPRC Phone: 530-752-1586 * * 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: Re: question about mixed-effects ordinal regression in STATA 13***From:*Darcy Hannibal <dlhannibal@ucdavis.edu>

**Re: st: Re: question about mixed-effects ordinal regression in STATA 13***From:*Stas Kolenikov <skolenik@gmail.com>

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