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From |
jverkuilen <jverkuilen@gc.cuny.edu> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Re: Brant test |

Date |
Sun, 19 Oct 2008 10:43:49 -0400 |

I don't see how the Brant test could do well with a sparse table. No asymptotic based test does because the statistic isn't close to being chi square, though whether it or other asymptotically equivalent tests will agree in a given sample is, of course, an empirical matter. In particular Wald tests and CIs can be quite inaccurate when the sampling distribution isn't reasonably normal whereas the LR test seems to work better. Models like -ologit- need sufficient cases in a given category to ensure the thresholds for it be well-defined. Beware many categorical predictor variables; perfect prediction is never far away. -----Original Message----- From: "Maarten buis" <maartenbuis@yahoo.co.uk> To: statalist@hsphsun2.harvard.edu Sent: 10/18/2008 12:05 PM Subject: RE: st: Re: Brant test In the series of posts starting here Richard and I were exploring the properties of the Brant test and -omodel-: http://www.stata.com/statalist/archive/2008-04/msg00660.html I think that the main conclusion was that these tests don't do too well in the presence of small categories (I initially attributed it to the number of categories, but Rich later pointed out that this had more to do with the fact that my simulation created a number of very sparse categories). The easiest way of handling small categories is to merge them with neighboring categories. If that is not an option you might try to bootstrap -omodel- to get an empirical estimate of the sampling distribution of the test statistic and use that to compute the p-value, like in the example below: *------------------ begin example ---------------------- sysuse auto, clear recode rep78 1/2=3 omodel logit rep78 foreign mpg capture program drop bootomodel program define bootomodel, rclass omodel logit rep78 foreign mpg return scalar chi2 = $S_1 end keep if !missing(rep78, foreign, mpg) tempfile res bootstrap chi2=r(chi2), saving(`res') reps(10000): bootomodel use `res', clear local chi2 = _b[chi2] hist chi2 count if chi2 > `chi2' & chi2 < . local rej = r(N) coun if chi2 < . di "p is " `rej'/r(N) *-------------------------- end example ----------------------- (For more on how to use examples/simulations I sent to the Statalist, see http://home.fsw.vu.nl/m.buis/stata/exampleFAQ.html ) Hope this helps, Maarten --- Richard Williams <Richard.A.Williams.5@ND.edu> wrote: > At 08:24 AM 10/18/2008, Gao LIU wrote: > >The results are similar, but not the same. What might cause the > difference? > > > >Gao > > Brant and gologit2 take different approaches. They generally are > pretty close on the global test but might differ on tests of > individual variables. You can also try out -omodel- from SSC. > > The way to do a global test in gologit2 is something like > > gologit2 y x1 x2 x3, pl store(m1) > gologit2 y x1 x2 x3, npl store(m2) > lrtest m1 m2, stats > > For more on gologit2, see > > http://www.nd.edu/~rwilliam/gologit2/index.html ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room N515 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- Send instant messages to your online friends http://uk.messenger.yahoo.com * * 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/ * * 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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