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Re: st: [Stata8] Model building and stepwise


From   Joseph Coveney <jcoveney@bigplanet.com>
To   Statalist <statalist@hsphsun2.harvard.edu>
Subject   Re: st: [Stata8] Model building and stepwise
Date   Fri, 29 Apr 2005 11:10:25 +0900

Ethan Corona wrote:

As part of an epidemiology class project, I have to employ stepwise for
model selection. I can do that with no problems. My problem lies with the
lack of output that -stepwise- generates compared to the output that SAS
provides. Short of performing stepwise by hand, are there any model building
ADOs that give more detailed output of each intermediate model?
Specifically, I'm looking for type I and type III sum of squares as well as
regression output for each intermediate model.

I looked at -allpossible-, and while it provides all of the intermediate
models using the -details- information, it doesn't provide the sum of
squares that I'm looking for.

. . . 

Hello again,

I realized that I failed to mention that I am doing logistic regression, so
-reg_ss- won't help me, either.

--------------------------------------------------------------------------------

What do Type I and Type III sum of squares look like for logistic regression in 
SAS?  

Is SAS using weighted least squares estimation on grouped data, as in -glogit-? 
Nick Cox mentioned -allpossible-; you should be able to do something with 
-allpossible glogit- after construction of the indicator variables to get the 
contrasts that you're looking for.  The technique for constructing the 
difference contrast indicator variable to get -regress- to match -anova , 
partial- (Type III sums of squares) is shown in Sophia Rabe-Hesketh and Brian 
Everitt, _A Handbook of Statistical Analyses using Stata_ Second Edition. (Boca 
Raton: Chapman & Hall/CRC, 2000), pp. 73-75. The use of the technique with 
-glogit- will be directly analogous to that with -regress-.  You can also use 
-logit- with the same indicator variables to get an analogous contrast to that 
in -anova, partial-.  I think that the SAS manuals variously call this a 
"Type 3 test" or "Type III test," or something, outside the contexts of PROC 
ANOVA and PROC GLM.

Joseph Coveney

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