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From |
"Eva Poen" <[email protected]> |

To |
Statalist <[email protected]> |

Subject |
st: interaction effect in logit model with orthogonal predictors (-inteff-) |

Date |
Thu, 13 Mar 2008 11:34:55 +0000 |

Hello, I have a question concerning interaction effects in logit models. In my model, a subset of the regressors are mutually exclusive dummy variables (i.e. orthogonal). They represent different categories of a single variable. The regression looks like this: - xi: logit y i.x*i.cat1 cat2 cat3 other regressors - where the dependent variable y is a dummy, x is a dummy (take female, for instance), cat1-cat3 represent three categories of a variable where the base category has been omitted, and other regressors can be dummies or continuous variables. To compute the interaction effect of x and cat1 I can use the -inteff- routine. Looking at inteff.ado and logit_dd.ado, I can see that in order to compute that effect, four predictions are obtained from the model for each observation: one for each combination of x and cat1 being 1 or 0. However, this means that for each observation, cat1 is set to 1 for some of the computations, leaving the values of cat2 and cat3 unchanged. But it is impossible for an observation to have cat1=1 and cat2=1 at the same time. The interaction effects for these observations seem rather hypothetical to me. What is the appropriate thing to do? Taking the interaction effects from -inteff- and not worrying about orthogonal predictors, or changing the routine to take care of orthogonal predictors accordingly (i.e. changing cat2 and cat3 to zero if appropriate in the calculations). I tried the latter, and it does make a difference with my data. Thanks, Eva * * 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: interaction effect in logit model with orthogonal predictors (-inteff-)***From:*Johannes Geyer <[email protected]>

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