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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: quasi-complete separation |

Date |
Sun, 28 Aug 2011 17:29:02 +0100 |

I don't know where 48.5 comes from so I can't comment on that. . input y x y x 1. 0 1 2. 0 2 3. 0 3 4. 0 4 5. 1 1 6. 1 2 7. 1 3 8. 1 4 9. 1 5 10. 1 6 11. 1 7 12. end . logit y x Iteration 0: log likelihood = -7.2102995 Iteration 1: log likelihood = -6.3453449 Iteration 2: log likelihood = -6.31452 Iteration 3: log likelihood = -6.314268 Iteration 4: log likelihood = -6.314268 Logistic regression Number of obs = 11 LR chi2(1) = 1.79 Prob > chi2 = 0.1807 Log likelihood = -6.314268 Pseudo R2 = 0.1243 ------------------------------------------------------------------------------ y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .5126268 .4305072 1.19 0.234 -.3311519 1.356405 _cons | -1.076253 1.436463 -0.75 0.454 -3.891669 1.739162 ------------------------------------------------------------------------------ In my ignorance I was not aware until now of the terminology of "quasi-complete separation" although Googling reveals several long discussions. Evidently there are datasets which are difficult or impossible to model with -logit- or -probit-. So, what else is new? Whether it helps to use this terminology I don't know. It just sounds like giving the problem a name to me. Others may be able to add deeper comments. Nick On Sun, Aug 28, 2011 at 5:01 PM, Sabrina Helmut <vitamint@hotmail.de> wrote: > Nick, > thanks! You are right, logit works but the coefficient for the concerned variable is extremely high (48.5..) I will need an explanation for this. So, do you think my example shows quasi-complete separation which could be an explanation for the high coefficient? > > ---------------------------------------- >> Date: Sun, 28 Aug 2011 16:43:36 +0100 >> Subject: Re: st: quasi-complete separation >> From: njcoxstata@gmail.com >> To: statalist@hsphsun2.harvard.edu >> >> Sabrina, and indeed anybody else: Please do not send, or attempt to >> send, attachments to Statalist. >> See http://www.stata.com/support/faqs/res/statalist.html#toask where >> this is explained, twice over. >> >> Sabrina: -logit y x- will work with this dataset, but there is only a >> weak relationship. >> >> Nick >> >> On Sun, Aug 28, 2011 at 4:24 PM, Sabrina Helmut <vitamint@hotmail.de> wrote: >> > I am sorry, the scatter has not been send. Thus, an example for you: >> > >> > binary dependent variable y >> > continuous variable x >> > >> > y x >> > 0 1 >> > 0 2 >> > 0 3 >> > 0 4 >> > 1 1 >> > 1 2 >> > 1 3 >> > 1 4 >> > 1 5 >> > 1 6 >> > 1 7 >> > >> > Thus, values of the independent variable being higher than 4 are only captured by y=1. >> > So, is this a problem of quasi-complete separation? Thank you very much. >> > >> > >> > ---------------------------------------- >> >> From: vitamint@hotmail.de >> >> >> >> I provided a scatter for you. Am I right with the assumption that it shows the problem of quasi-complete separation? Thanks. >> * * 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/

**References**:**st: quasi-complete separation***From:*Sabrina Helmut <vitamint@hotmail.de>

**RE: st: quasi-complete separation***From:*Sabrina Helmut <vitamint@hotmail.de>

**Re: st: quasi-complete separation***From:*Nick Cox <njcoxstata@gmail.com>

**RE: st: quasi-complete separation***From:*Sabrina Helmut <vitamint@hotmail.de>

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