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

[Stas Kolenikov <skolenik@gmail.com>]

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/