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From |
"Martin Weiss" <martin.weiss@uni-tuebingen.de> |

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

Subject |
RE: st: gologit2 |

Date |
Fri, 18 Apr 2008 15:33:40 +0200 |

Very much apart from the heated discussion in this thread, could anyone tell me what - list retokenize- does. Cannot find it anywhere. -findit list retokenize- does not throw up anything, either. Furthermore, I would be interested how long Maarten`s original code (10,000 reps) a couple of days ago took to execute for you. I ran it last night on 2GB Ram, Vista SP1 32-bit, with AMD 64 2.2 GhZ dual core under Stata 10.0 MP/2, and it took 7.3 hours (26212 seconds). Martin Weiss _________________________________________________________________ Diplom-Kaufmann Martin Weiss Mohlstrasse 36 Room 415 72074 Tuebingen Germany Fon: 0049-7071-2978184 Home: http://www.wiwi.uni-tuebingen.de/cms/index.php?id=1130 Publications: http://www.wiwi.uni-tuebingen.de/cms/index.php?id=1131 SSRN: http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=669945 -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Richard Williams Sent: Friday, April 18, 2008 4:18 PM To: statalist@hsphsun2.harvard.edu Subject: RE: st: gologit2 For anyone who is still following this thread - Maarten and I have been conversing offline and I think we both agree the original simulations of the brant test and its counterparts were flawed and the test works much better than we initially thought. Here are new simulations. For each simulation, the expected value for reject H0 is .05, i.e. 50 false rejections in 1000 tries using the .05 level of significance. Brant Simulations, 1000 reps # of Xs | reject H0 ----------+----------- 1 | .051 2 | .042 3 | .054 4 | .051 5 | .057 6 | .054 7 | .048 8 | .056 9 | .050 10 | .056 12 | .067 14 | .038 16 | .048 18 | .041 20 | .036 Altogether, the observed number of false rejections of the null totaled 749; the expected number of rejections was 750, so I'd say the simulations worked pretty good. Most critically, you don't see an increased tendency to reject the null as the number of x variables increases. For anyone who is interested as to what has changed - in Maarten's original code, as the number of Xs increased, the distribution of Y also changed. Cases were less likely to be in the middle categories of 2 and 3 and more likely to be in the extreme categories of 1 and 4. I don't think this would necessarily be bad, except that it does result in some very thin cell counts in some simulations which probably results in poor estimation. I tweaked Maarten's code so that the distribution of Y was always the same no matter how many Xs were in the model. This seems more realistic anyway, i.e. the distribution of the observed y shouldn't change just because you have more explanatory variables. That produced the results above. Also, Maarten reports that he tweaked his original code, increasing the sample size, and the brant test also performed better then. Here is the modified code. set more off set seed 12345 capture program drop sim program define sim, rclass syntax, [nx(integer 1)] drop _all set obs 500 forvalues i = 1/`nx' { gen x`i' = invnorm(uniform()) local x `x' x`i' } local x : list retokenize x local xsum : subinstr local x " " " + ", all gen u = uniform() gen ystar = `xsum' + ln(u/(1-u)) * original code for y. This code causes * the distribution of y to change as the * number of x vars increases, probably * resulting in very small cell counts in * some simulations. *gen y = cond(ystar < -2, 1, /// * cond(ystar < 0, 2, /// * cond(ystar < 2, 3, 4))) * New code for y. Distribution for y is * the same no matter how many Xs there are. egen y = cut(ystar), group(4) replace y = y + 1 * Uncomment the section you want * brant test code ologit y `x' brant return scalar p = r(p) * omodel test code *omodel logit y `x' *return scalar p = chi2tail($S_2, $S_1) * gologit2 test code *ologit y `x' *est store m1 *gologit2 y `x', npl store(m2) *lrtest m1 m2, force *return scalar p = r(p) end simulate p=r(p), reps(1000): sim, nx(1) count if p < .05 matrix res = 1, r(N)/1000 foreach i of numlist 2/10 12(2)20 { simulate p=r(p), reps(1000): sim, nx(`i') count if p < .05 matrix res = res \ `i', r(N)/1000 } matlist res * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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

**list retokenize {was:RE: st: gologit2]***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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

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

**RE: st: gologit2***From:*David Jacobs <jacobs.184@sociology.osu.edu>

**RE: st: gologit2***From:*Richard Williams <Richard.A.Williams.5@ND.edu>

**RE: st: gologit2***From:*Richard Williams <Richard.A.Williams.5@ND.edu>

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