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

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Fri, 13 May 2011 09:31:53 +0100 |

I see nothing surprising here. The likelihood is the product of many very small probabilities, so will be very small overall. The P-values are at least in part a side-effect of using a very large sample size. I don't know that a model with such a low pseudo-R-square is "too good to be true", but it depends on your expectations. If this is analysis of data on people, as I wildly guess, the Maarten Buis argument that high levels of "explanation" are not to be expected given what else we know about the many determinants and influences on human behaviour could apply. Present-day significance testing machinery was largely designed in the first few decades of the 20th century to safeguard natural scientists against over-interpreting results from very small samples. Present-day social scientists in the early 21st century need other measures to safeguard themselves against over-interpreting significance tests from very large samples. On Fri, May 13, 2011 at 9:12 AM, John Litfiba <cariboupad@gmx.fr> wrote: > Dear all (again) > > I was wondering if my results seems too good to be true. I run a > multinomial logit for yvar (caterical variable with 4 possible values) > and I obtain the following results : > > 1) It is normal to obtain such a negative log likelihood when we use > very large sample, right ? (n=2 millions here) > 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 ?? > > > Many many thanks in advance > > mlogit yvar xvar1 xvar2 > > Iteration 0: log likelihood = -1953742.5 > Iteration 1: log likelihood = -1900152 > Iteration 2: log likelihood = -1883338.4 > Iteration 3: log likelihood = -1880317 > Iteration 4: log likelihood = -1880312.7 > Iteration 5: log likelihood = -1880312.7 > > Multinomial logistic regression Number of obs = 2227058 > LR chi2(6) = 146859.43 > Prob > chi2 = 0.0000 > Log likelihood = -1880312.7 Pseudo R2 = 0.0376 > > ------------------------------------------------------------------------------ > order | Coef. Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > yvar0 | (base outcome) > -------------+---------------------------------------------------------------- > yvar1 | > xvar1 | -2.137044 .0104876 -203.77 0.000 -2.157599 -2.116489 > xvar2| -.0099444 .0001223 -81.32 0.000 -.0101841 -.0097047 > _cons | 1.708873 .0125759 135.88 0.000 1.684225 1.733522 > -------------+---------------------------------------------------------------- > yvar2 | > xvar1 | .8905294 .0734511 12.12 0.000 .7465678 1.034491 > xvar2 | -.0087927 .0003393 -25.92 0.000 -.0094576 -.0081277 > _cons | -3.672227 .0758592 -48.41 0.000 -3.820908 -3.523546 > -------------+---------------------------------------------------------------- > yvar3 | > xvar1 | -3.826486 .0113315 -337.69 0.000 -3.848695 -3.804276 > xvar2 | -.0054125 .0002488 -21.76 0.000 -.0059002 -.0049249 > _cons | 1.244583 .0180673 68.89 0.000 1.209171 1.279994 > ------------------------------------------------------------------------------ > * * * 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 <cariboupad@gmx.fr>

**References**:**st: too good to be true : lr test in mlogit?***From:*John Litfiba <cariboupad@gmx.fr>

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