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RE: st: gologit2


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 18:50:43 -0500

For those of you who have lost track of this thread, Maarten developed some simulations to see how well the brant test does when ologit's proportional odds assumption is NOT violated. Ideally, the null should be rejected 5% of the time. As his simulations using the ologit and brant commands showed, the more vars in the model, the more likely the brant test was to incorrectly reject the null:



# of Xs  | % reject H0
----------------------
 1       |  5.18
 2       |  5.55
 3       |  5.19
 4       |  5.49
 5       |  5.61
 6       |  5.39
 7       |  5.73
 8       |  5.88
 9       |  6.09
10       |  6.33
12       |  7.06
14       |  7.58
16       |  8.67
18       |  9.75
20       | 11.85
----------------------
The -omodel- command is an alternative to brant. Here are the simulation results using -omodel- :

# of Xs | % reject H0
----------------------
1 | 5.19
2 | 5.14
3 | 4.97
4 | 4.86
5 | 4.80
6 | 4.64
7 | 4.90
8 | 4.77
9 | 5.01
10 | 5.59
12 | 6.36
14 | 7.06
16 | 8.91
18 | 9.91
20 | 11.71
----------------------

The omodel test appears to be slightly more conservative than the brant test, but overall the pattern is the same; once you get past 10 vars, the test is more and more likely to reject the null, at least in this particular simulation.

I'll try it next using the likelihood ratio test that can be generated using gologit2 and report back later. My guess is it will do about the same, although I hope the fact that it is a LR test lets it do a little better.

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