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From |
Maarten Buis <[email protected]> |

To |
[email protected] |

Subject |
Re: st: too good to be true : lr test in mlogit? |

Date |
Fri, 13 May 2011 10:43:14 +0200 |

On Fri, May 13, 2011 at 10:12 AM, John Litfiba <[email protected]> wrote: > 1) It is normal to obtain such a negative log likelihood when we use > very large sample, right ? (n=2 millions here) Yes, that is perfectly normal. It is the log of the probability of observing the data given the parameters. This probability is the product of the probabilities of observing each individual given the parameters (assuming independent sampling...). A probability is a number between 0 and 1, so the product of two such probabilities results in a smaller number than either of the individual probabilities. Do that multiplication 2 million times and you end up with an extremely tiny number indeed. Take the log of such a tiny number and you end up with a very negative number. > 2) if the association (for example given by tabulation) show that > there is strong association between yvar and xvar1 then it is > plausible to obtain this fastastic LR statistic of... 140000 ?? I would not call that fantastic. It just test whether the effects of all covariates are all equal to 0, so you would expect that with such a large sample size you would thoroughly reject that hypothesis, which you do. Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * 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/

**Follow-Ups**:**Re: st: too good to be true : lr test in mlogit?***From:*John Litfiba <[email protected]>

**References**:**st: too good to be true : lr test in mlogit?***From:*John Litfiba <[email protected]>

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