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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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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