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From |
Richard Williams <Richard.A.Williams.5@ND.edu> |

To |
statalist@hsphsun2.harvard.edu, statalist@hsphsun2.harvard.edu |

Subject |
RE: st: gologit2 |

Date |
Wed, 16 Apr 2008 10:24:50 -0500 |

At 04:01 AM 4/16/2008, Maarten buis wrote:

Thanks much for doing this Maarten. I'll note that there are at least two other ways to test the proportional odds assumption in Stata. First, you can use the -omodel- command, available from SSC. To test this, I believe you drop Maarten's ologit, brant and return commands and replace them withI don't know of any such simulation in the literature, but you can use -simulate- to do it yourself. In the example below I draw at random from a population in which the proportional odds assumption holds, and than use the Brant test to test that assumption. For this I use the -brant- command which is part of the -spost- package (see: -findit spost-). I than repeat this 10,000 times. If the Brant test works then it should reject the null hypothesis in 5% of the draws.

omodel logit y `x'

return scalar p = chi2tail($S_2, $S_1)

I'm running that right now and, assuming I've done it right, I'll report the results tonight or tomorrow.

The other approach is to use gologit2, which is also interesting because the Likelihood Ratio test that follows is (as far as I can tell) identical to the test SPSS PLUM reports for parallel lines. I think the code is

gologit2 y `x', pl store(m1)

gologit2 y `x', npl store(m2)

lrtest m1 m2

return scalar p = r(p)

gologit2 is no speed demon so this may take a few months to run!

I have no particular reason for believing that any of these approaches is clearly superior or inferior to another, but it would be interesting to find out if there are clear differences.

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Richard Williams, Notre Dame Dept of Sociology

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**Follow-Ups**:**RE: st: gologit2***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**References**:**RE: st: gologit2***From:*"Verkuilen, Jay" <JVerkuilen@gc.cuny.edu>

**RE: st: gologit2***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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