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From |
Joseph Coveney <jcoveney@bigplanet.com> |

From |
Richard Williams wrote: |

To |
Statalist <statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: Lrtest versus Test |

Date |
Mon, 22 Dec 2003 12:12:29 +0900 |

------------------------------------------------------------------------------- A couple of questions about Lrtest: * When using routines like Logistic Regression, LrTest is generally supposed to be better than Test. In practice, does it tend to make much difference, other than in maybe borderline cases? And if so, are there any particular sets of circumstances when Test would be especially flawed? Test is generally easier to use. * If you use LrTest with OLS regression, does it produce equivalent results to Test? It seems to give virtually identical results with some 1 d.f. tests I have tried, but I'm not sure about more complicated tests. For example, the docs show how to do chow tests with logistic regression and LrTest, so I'm wondering if the same thing works ok with OLS regression. -------------------------------------------------------------------------------- With logistic regression, especially, I believe that the likelihood ratio test is generally preferred: there is an uncommon set of circumstances with logistic regression that lead to a likelihood function that renders the Wald test wildly inaccurate. It's known as the Hauck-Donner Phenomenon or Hauck- Donner Effect. (google brings up information on it.) If you'd like to see this phenomenon in action with Stata, you can find a description of an example of a dataset that displays this effect at www.math.yorku.ca/Who/Faculty/Monette/S-news/0034.html . Another case where I've stumbled across a dataset for which the Wald test gives inaccurate results compared to -lrtest- is Alan Agresti's AZT and race dataset from Table 5.5 of his _An Introduction to Categorical Data Analysis_; the dataset and SAS code can be downloaded from www.stat.ufl.edu/~aa/intro-cda/appendix.html . (The example is also used in his _Categorical Data Analysis_.) If you're typically more involved with hypothesis testing than with prediction (where overfitting is a concern), you might typically fit a model with interaction terms unless a particular term is not scientifically warranted or not implied by the study's objective and design. If you fit a full model to this dataset (AZT, race and AZT-by-race interaction), then the Wald tests for the main effects are inaccurate. -lrtest-, however, provides test statistics that are consistent among saturated and reduced models. Joseph Coveney * * 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: Lrtest versus Test***From:*Richard Williams <Richard.A.Williams.5@nd.edu>

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