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# Re: st: gologit2 and mlogit coefficients do not agree

 From Richard Williams To statalist@hsphsun2.harvard.edu, statalist@hsphsun2.harvard.edu Subject Re: st: gologit2 and mlogit coefficients do not agree Date Sun, 12 Feb 2012 20:12:08 -0500

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.

You are not supposed to be able to get the same results. They are different kinds of models. See the gologit2 support page and troubleshooting page:

http://www.nd.edu/~rwilliam/gologit2/index.html

http://www.nd.edu/~rwilliam/gologit2/tsfaq.html

If you only had a binary dependent variable they would give the same results, but in your case you have three categories, e.g.

use "http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta";, clear
gologit2 yr89 male white age ed prst
mlogit yr89 male white age ed prst

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

Garth H Rauscher
Associate Professor of Epidemiology
UIC School of Public health
(312)413-4317
garthr@uic.edu

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