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From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: lincom command |

Date |
Wed, 13 Mar 2013 09:36:49 +0100 |

On Tue, Mar 12, 2013 at 10:54 PM, Ari Samaranayaka wrote: > Thank you Maarten for the comment. Your idea was my very first try, it was > given up later for the difficulty of interpreting results. What you are > saying is to treat A,B,C,D as 4 separate binary variables. Problem with that > is overlap between those binary variables is high, therefore colinearity. > For this reason estimated IRR for ethnicity A and B can be quite different > eventhough overlap of people between ethnicities A and B are quite large. In > that situation I am unable to interpret (understand) estimates. You should get exactly the same effects if you correctly specify your alternative model with -lincom-, so that won't solve anything. Anyhow, the fact that there is a large overlap between categories does not mean that their effects should be the same, so I don't see why the behaviour you report represents a problem. However, a pattern like the one you report would make me want to look at the effects of those with only A, only B, and A and B, as I would suspect that the combination A and B would in such situations represent a separate category in its own right. But that is an empirical question, so estimate both models and see if the results make sense. -- Maarten --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: lincom command***From:*Ari Samaranayaka <ari.samaranayaka@ipru.otago.ac.nz>

**Re: st: lincom command***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: lincom command***From:*Ari Samaranayaka <ari.samaranayaka@ipru.otago.ac.nz>

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