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Re: st: gologit2 and mlogit coefficients do not agree


From   "Garth Rauscher" <garthr@uic.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   Re: st: gologit2 and mlogit coefficients do not agree
Date   Mon, 13 Feb 2012 09:45:13 -0600

Richard, Thank you for that elaboration-that makes perfect sense to me now.
Garth

------------------------------
Date: Sun, 12 Feb 2012 23:14:30 -0500
From: Richard Williams <richardwilliams.ndu@gmail.com>
Subject: Re: st: gologit2 and mlogit coefficients do not agree

At 07:54 PM 2/12/2012, Rauscher, Garth wrote:
>Dear listservers,
>
>I am unable to reproduce the coefficients that I obtain from mlogit when I
>attempt to run the same model in gologit2. As a simplified example of the
>problem, my dependent variable (Y) has 3 categories (0,1,2) and I have a
>single binary independent variable X (0,1). Mlogit gave me the same result
>I obtained when I ran separate logistic regressions comparing Y=1 and Y=2
>separately with Y=0, but gologit2 did not. My results are below. At first
>I thought that gologit2 might be giving the inverse of mlogit but that is
>not the case.  I like the flexibility of gologit2 but am not sure how to
>interpret it's results.
>
>Thanks for listening, Garth
>
>
>. mlogit   y x , rrr baseoutcome(2)
>
>Multinomial logistic          Number of obs   =    730
>                               LR chi2(2)      =  25.52
>                               Prob > chi2     = 0.0000
>Log likelihood = -754.39125   Pseudo R2       = 0.0166
>
>-------------------------------------------------------
>             y |        RRR   Std. Err.      z    P>|z|
>-------------+-----------------------------------------
>0           x |   .3853242   .1040091    -3.53   0.000
>1           x |   .3950005   .0858599    -4.27   0.000
>2             |  (base outcome)
>-------------------------------------------------------
>
>
>. gologit2 y x, npl or
>
>Generalized Ordered Logit    Number of obs   =    730
>                              LR chi2(2)      =  25.52
>                              Prob > chi2     = 0.0000
>Log likelihood = -754.39125  Pseudo R2       = 0.0166
>
>-------------------------------------------------------
>            y | Odds Ratio   Std. Err.      z    P>|z|
>-------------+-----------------------------------------
>0           x |   1.822296   .4744057     2.31   0.021
>1           x |   2.554348   .4826326     4.96   0.000
>-------------------------------------------------------

To elaborate on my earlier message -- mlogit is basically 0 vs 2 and 
1 vs 2. But gologit2 is like 0 versus 1 and 2 followed by 0 and 1 
versus 2. With unconstrained models like this the fits are often 
identical or nearly identical, but the parameterizations are different.


- -------------------------------------------
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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