Re: st: Stepwise Regression using Multiple Imputed Data - stata 11

[Richard Williams <richardwilliams.ndu@gmail.com>]

At 09:34 PM 8/8/2011, Russell Simco wrote:
> Hi,
>
>
>
> The "stepwise" model selection program is not available when using 
> the "mi estimate:" prefix in stata-11. I would like to do this 
> manually using "mi estimate: ologit".

Besides all the usual concerns with stepwise, with MI there would be 
the added problem that arbitrary or minor changes in your imputation 
procedure, e.g. using a different random number seed, would produce 
different imputed data sets and hence could lead to different 
variable selection. It wouldn't surprise me if there are other 
reasons why the use of sw with mi is invalid. You might take a look 
at the svy & stepwise FAQ for other ideas on how to proceed:

http://www.stata.com/support/faqs/stat/stepsvy.html

But, if you are bound and determined to do stepwise manually anyway, 
it doesn't seem that hard (other than possibly being extremely 
tedious). Suppose you have x1-x10. Run 10 bivariate regressions of y 
with each X. The X with the largest T value wins the first round 
(assuming it meets your criterion for entry, e.g. is significant at 
the .05 level). Lets say x1 wins.

In the 2nd round, run 9 regressions, e.g. y with x1 and x2, y with x1 
and x3, etc. Winner (i.e. the added X with the largest significant T 
value) advances to the 3rd round.

Repeat process until no more x's are statistically significant.

Or, work backwards. Regress Y on x1-x10. The least significant x 
(assuming at least one meets your criteria for removal) gets dropped. 
Repeat as needed.

The manual (at least in Stata 12) also describes the logic behind stepwise.

Overall, I am not sure why you would want to do this, I think it is 
probably a bad idea to do it, and if I was going to do it I'd be 
tempted to do it with the original un-imputed data (at least then my 
fate isn't being decided by an arbitrary random number seed). But if 
you really really want to do it seems straightforward albeit tedious.


-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
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