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From |
Maarten buis <[email protected]> |

To |
[email protected] |

Subject |
RE: st: gologit2 |

Date |
Wed, 16 Apr 2008 10:01:25 +0100 (BST) |

--- Richard Williams wrote: > >>>You might also want to consider more > stringent alpha levels (e.g. .01, .001) to reduce the possibility of > capitalizing on chance. You can also try to assess the practical > significance of violations, e.g. do my conclusions and/or predicted > probabilities really change that much if I stick with the model whose > assumptions are violated as opposed to a (possibly much harder to > understand and interpret) model whose assumptions are not > violated.<<< --- "Verkuilen, Jay" wrote: > Right, this is about what I was thinking---be a more stringent about > the test. I wonder if anyone's done good simulation studies to see the > properties of the Brant test? I 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. Because Rich and Jay (and I) think that a potential problem with the Brant test is that it test many things at once (The odds is proportional for all variables and and for all equations) I repeat this eexperiment for 1, 2, .., 10, 12, 14, .., 20 explanatory variables. If our hunch is correct, the Brant test should do worse (reject the true null hypothesis more than 5% of the samples) in models with more explanatory variables. Because running this simulation takes quite a while, I will give the results below: # 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 ---------------------- So, in this simulation the Brant test seems to perform reasonable well upto 10 explanatory variables, but than starts to noticably deviate from the nominal 5%. Do not read to much in this number of covariates: the data were simulated to be very well behaved, in real data that number may be much smaller. Moreover, this number is likely to depend on the number of categories in your dependent variable as well (in the simulation there are four categories). The results seem to be that the Brant test is a bit problematic in larger models, but even if that weren't the case you should not always avoid using -ologit- whenever the Brant test says that it rejects the proportional odds asssumption. The reason is that rejecting the null hypothesis may not be that informative. We (almost) never believe that a null hypothesis is exactly true. This is especially true in case of a test of a model, like the Brant test, because it is the very purpose of a model to be wrong (in a special way). This may be a bit provocative, so let me elobarate: A model exist to simplify your observations, such that you can relate it your theory. You need to simplify because the patterns in the raw data are too complicated to be understood by just looking at the data. Simplifying is just a special case of being wrong. So, the purpose of a model is to be wrong in a special way. So, if the Brant test rejects the proportionality assumption, it is up to you to determine whether the proportionality assumption is still acceptable as a simplification or not. Below is the code I used for this simulation, in case you want to replicate my results, or want to expand on it, for instance by creating a dependent variable with more categories, or with one or more sparse categories (categories with few observations). *--------------------- begin simulation ----------------- 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)) gen y = cond(ystar < -2, 1, /// cond(ystar < 0, 2, /// cond(ystar < 2, 3, 4))) ologit y `x' brant return scalar p = r(p) end simulate p=r(p), reps(10000): sim, nx(1) count if p < .05 matrix res = 1, r(N)/10000 foreach i of numlist 2/10 12(2)20 { simulate p=r(p), reps(10000): sim, nx(`i') count if p < .05 matrix res = res \ `i', r(N)/10000 } matlist res *-------------------- end simulation ---------------------- (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 ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room Z434 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- ___________________________________________________________ Yahoo! For Good helps you make a difference http://uk.promotions.yahoo.com/forgood/ * * 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:*Richard Williams <[email protected]>

**RE: st: gologit2***From:*Richard Williams <[email protected]>

**References**:**RE: st: gologit2***From:*"Verkuilen, Jay" <[email protected]>

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