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From |
Maarten buis <[email protected]> |

To |
[email protected] |

Subject |
Re: st: discriminatory power of a probit model |

Date |
Sun, 2 Mar 2008 19:36:43 +0000 (GMT) |

--- Martin Weiss <[email protected]> wrote: > ********* > sysuse auto, clear > probit foreign weight, nolog > estat clas > probit foreign weight mpg, nolog > estat clas > ********* > > can I prefer one specification of covariates in a probit model > over the other on the basis of the correctly classified cases as > provided at the bottom of the classification table? If so, is there > a confidence interval that would let me judge whether the difference > between two models is significant? There is another option: model 2 is model 1 when the coefficient of mpg is equal to 0. This is an assumption you can test using the wald test (the test that is immediately displayed in the output of -probit-), or if you have multiple variables, the likelihood ratio test (-lrtest-). The problem with the proportion correctly classified is that it depends on the distribution of your dependent variable: if success is rare and everybody is classified as a failure than the proportion correclty specified is still large. In that case, adding an explanatory variable isn't going to do much. This characteristic of the proportion correctly specified is illustrated in the example below. The effect of x is the same in each probit, all that is different is the constant, that is, the proportion of successes. This dramatically influences how much adding x to the model increasses the proportion correctly specified, even though the effect of x is the same in all models. *------------ begin example --------------------- set more off capture program drop sim program define sim, rclass drop _all set obs 500 gen x = invnorm(uniform()) gen byte y1 = uniform() < normal(x) probit y1 estat class local p1 = r(P_corr) probit y1 x estat class return scalar diff1 = r(P_corr) - `p1' gen byte y2 = uniform() < normal(x-1) probit y2 estat class local p2 = r(P_corr) probit y2 x estat class return scalar diff2 = r(P_corr) - `p2' gen byte y3 = uniform() < normal(x-2) probit y3 estat class local p3 = r(P_corr) probit y3 x estat class return scalar diff3 = r(P_corr) - `p3' end simulate diff1=r(diff1) /// diff2=r(diff2) /// diff3=r(diff3), /// reps(100): sim sum *---------------- end example ---------------------- (For more on how to use examples I sent to the Statalist, see http://home.fsw.vu.nl/m.buis/stata/exampleFAQ.html ) In general when it comes to selecting a model I would not rely on a single statistic. Some quotes along this line can be found here: http://www.stata.com/statalist/archive/2004-09/msg00535.html The book "Regression Models for Categorical Dependent Variables Using Stata" by J. Scott Long and Jeremy Freese http://www.stata.com/bookstore/regmodcdvs.html contains a good discussion of all the things you should take into account when selecting a model. 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/ ----------------------------------------- ___________________________________________________________ Rise to the challenge for Sport Relief with Yahoo! For Good 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: discriminatory power of a probit model***From:*"Verkuilen, Jay" <[email protected]>

**References**:**st: discriminatory power of a probit model***From:*"Martin Weiss" <[email protected]>

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