Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Question about interactions |

Date |
Thu, 7 Mar 2013 12:29:56 +0100 |

On Thu, Mar 7, 2013 at 12:03 PM, K Jensen wrote: > Thanks for the replies I received on interactions. They were very > helpful and I think that I understand the model now. > > Say we are looking at the interactions of two variables. A colleague > wondered why we don't just fit the possible group combinations as > indicator variables (i.e. 1-1 as a group (baseline), 1-2, 1-3, 1-4, > 2-1, 2-2, 2-3, 2-4, etc) and get an OR (I am doing logisitic > regression) for each group combination except 1-1 . > > What is the advantage of fitting the conventional model? What your colleague proposes should be equivalent to what you called the conventional model. So you will get the same predicted values and there is no way to statistically choose between the two; they are just two different ways of looking at the same object. One of the advantages of the conventional model is that it is conventional. This may seem trivial, but as you noticed interactions are hard, so it can make a real difference if you stick to patterns that your readers expect so they don't have to rethink the entire model (basically recalculate everything in their mind to fit in their usual pattern). However, this only applies when this pattern is indeed the convention for the people with who you correspond. Another is that in the conventional model you explicitly look at differences (actually ratios) between effects, which is what you typically (but not always) want when you include interaction terms. If you change the setup you need to take care that it is indeed the equivalent model as the conventional model (unless you explicitly want a different model). It is very easy to get this wrong. One check you could perform is predict probabilities for both models and check whether they are the same. If this is the way you want to go, than there are couple of tips in this Stata tip that may help: M.L. Buis (2012) "Stata tip 106: With or without reference", The Stata Journal, 12(1), pp. 162-164. <http://www.maartenbuis.nl/publications/ref_cat.html> In essence, your collegues model was to remove one of the reference categories but leave the other in. Hope this helps, 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/

**Follow-Ups**:**Re: st: Question about interactions***From:*K Jensen <k.x.jensen@gmail.com>

**References**:**st: Question about interactions***From:*K Jensen <k.x.jensen@gmail.com>

**Re: st: Question about interactions***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: Question about interactions***From:*K Jensen <k.x.jensen@gmail.com>

**Re: st: Question about interactions***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: Question about interactions***From:*K Jensen <k.x.jensen@gmail.com>

**Re: st: Question about interactions***From:*K Jensen <k.x.jensen@gmail.com>

- Prev by Date:
**st: generating observations in data set** - Next by Date:
**Re: st: generating observations in data set** - Previous by thread:
**Re: st: Question about interactions** - Next by thread:
**Re: st: Question about interactions** - Index(es):