Re: st: Relative Importance of predictors in regression

[David Hoaglin <dchoaglin@gmail.com>]

Dear Nikos,

The article by Nathans et al. looks helpful, though I have not yet
read all of it.

I would add a note of caution, however.  Nathans et al. (and many
others) interpret a beta weight (or a regression coefficient more
generally) in a way that involves holding all the other predictor
variables constant.  The "held constant" part of that interpretation
is not correct.  Straightforward mathematics shows that it does not
reflect the way that multiple regression actually works.

David Hoaglin

On Mon, Nov 4, 2013 at 12:03 PM, Nikos Kakouros <nkakouros@gmail.com> wrote:
> Dear Statalisters,
>
> I have a multiple regression in Stata and want to concisely show the
> relative contribution of each predictor to the final model. I would
> have thought this is easy but I'm finding it difficult to figure out
> how to go about it.
> I used
> regress Y x1 x2 x3...x7, beta vce(robust)
>
> I think, however, that the beta coefficients do not really tell the whole story.
>
> I found this document by Nathans et al that I thought does a great job
> of discussing the problem:
> http://pareonline.net/pdf/v17n9.pdf
>
> I really like the idea of repartitioning the overall model R2 between
> the predictors by Relative Weight Analysis. I looked around and found
> there's a package for this for R but not Stata.
>
> I would be most grateful for your recommendations.
>
> Many thanks
>
> Nikos
*
*   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/