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Re: st: gologit2 and mlogit coefficients do not agree
From
"Garth Rauscher" <[email protected]>
To
<[email protected]>
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 <[email protected]>
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: [email protected]
WWW: http://www.nd.edu/~rwilliam
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