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Re: st: ologit, gologit2, mlogit?
John Antonakis <John.Antonakis@unil.ch>
Re: st: ologit, gologit2, mlogit?
Mon, 08 Jul 2013 19:53:00 +0200
Thanks for your note--I have looked at your very helpful webpage. I
used gologit2, I tried simplifying the model (using fewer regressors)--I
only have 5 outcome categories and it does not make theoretical sense to
combine them. I have tried gologit2 with autofit and uses different
links too. I keep getting negative predicted probabilities.
I will look into slogit or just stick with mlogit, I guess.
Thanks so much for your help.
Professor of Organizational Behavior
Director, Ph.D. Program in Management
Faculty of Business and Economics
University of Lausanne
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
The Leadership Quarterly
On 08.07.2013 19:56, Richard Williams wrote:
> mlogit may be your best bet (although there are a few other choices,
such as slogit). But, just from what you say, I wonder if your model is
too complicated and/or you are spreading your data too thin. You might
consider combining categories of the ordinal variable (at least if some
have very small frequencies), or using fewer variables, or (if using
autofit with gologit2) use the .01 level of significance. gologit2
troubleshooting tips are at
> You might also want to assess how substantively important the
violations are -- you give up a lot of parsimony when you switch from
ologit to mlogit, and it may not be worth it if the violations are
substantively trivial. You might consider using BIC tests as an
alternative criterion for rejecting the proportional odds model, e.g. do
> gologit2 y x1 x2 x3, pl sto(ologit)
> gologit2 y x1 x2 x3, npl sto(gologit)
> lrtest ologit gologit, stats
> The likelihood ratio test is similar to the Brant test. But, you'll
also get BIC stats reported, and it is possible that the BICs might
favor the ologit model even if the LR test says to reject it.
> At 03:57 AM 7/8/2013, John Antonakis wrote:
>> I have model, having a dependent variable with 5 categories that are
ordered. The model violates the assumptions of the -brant- test (after
ologit estimation). It also produces predicted probabilities of less
than zero in the user-written command (from SSC) -gologit2- (generalized
ordered logit); in addition, I can only estimate a partial model because
I can't estimate the full model with some dummy controls.
>> I think that the only option I have available is to estimate a
-mlogit- model, which makes no assumptions on the ordering or on the
proporational odds assumption (brant).
>> Does anyone know of any literature to support my intuition on this
>> Or are there any other ways to estimate this model with Stata?
>> John Antonakis
>> Professor of Organizational Behavior
>> Director, Ph.D. Program in Management
>> Faculty of Business and Economics
>> University of Lausanne
>> Internef #618
>> CH-1015 Lausanne-Dorigny
>> Tel ++41 (0)21 692-3438
>> Fax ++41 (0)21 692-3305
>> Associate Editor
>> The Leadership Quarterly
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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: Richard.A.Williams.5@ND.Edu
> WWW: http://www.nd.edu/~rwilliam
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