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st: RE: All-possible-regressions procedure


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: All-possible-regressions procedure
Date   Fri, 19 Sep 2003 17:43:44 +0100

Jim.Seward@sunhealth.org
 
> Does Stata have an all-possible-regressions procedure, for 
> use in model
> building in multiple regression?

Sort of. 

I wrote a program -allpossible- 
which does this to a limited extent. 
It's really a wrapper that runs 
lots of regressions or similar 
and prints out selected results, but 
it has strict limits. 

I also wrote a program -selectvars- 
which is, indirectly, one of the 
tools you need if you want to knit 
your own and cycle through subsets 
of predictors. 

Both are on SSC. 

However, I have not written anything 
to select "best" regressions in any 
sense whatsoever, not only because
that is much more difficult to program, 
but also because it's against my religion. 
(I am not aware anyone else did either.) 

In fact, before someone starts thinking 
"gun manufacturer" I will stress that my 
own motivation for writing -allpossible- 
was the complete opposite of what I take 
to be a common motivation for using
such programs. I wanted to show that 
a variety of models were, in a class of 
problems, almost equally good in terms 
of various criteria. The naive opposite 
is, naturally, the idea that one can 
automate the selection process completely. 

I'll add a standard rider. No such program 
can cope adequately with the combinatorial 
explosion(*) of possibilities. Even 
trimming matters down to whether each 
predictor is in or out of the model, 
then 20 predictors give you 2^20 ~ 10^6 
models, and it's pretty hard to keep 
track of a million sets of model results. 
(20 predictors is perhaps a small model 
by many analysts' standards). Even 2^10 
is more than I want to compare. 

Nick 
n.j.cox@durham.ac.uk 

(*) A nice term. When was it introduced? 

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