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RE: st: RE: Graphing ploynomial relationships after a regression


From   "Maarten Buis" <M.Buis@fsw.vu.nl>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: RE: Graphing ploynomial relationships after a regression
Date   Wed, 19 Oct 2005 11:19:02 +0200

I find doing these graphs `by hand' particularly useful when polynomials are combined with interactionterms. This method closely conforms to the way I make sense about those models myself: write the model down, and work through various contrasts. Of course it means more typing, but I find that I make less mistakes that way because this link between the syntax and the way I think about these models. Ultimately, I think it is just what you are used to doing (and how you were taught to do this stuff, I had a tough master who deliberately decided not to use Stata because it made this stuff too easy...)

Alternatively, since Tinna intended to use this after a 2sls model, I could imagine a polynomial occurring in an indirect effect. This too would be a case where I would prefer to first write it out, and the results would closely conform to the syntax in the do-file.    

It is true that making graphs with complex models requires careful though, but I find it is very difficult to really explain the results without them. Interactions and indirect effects with a polynomial in it, are cases in point.

On  10/18/05, Nick Cox <n.j.cox@durham.ac.uk> wrote:
 > As a model gets more complex, it is less likely that it can
 > be presented by a graph. y = polynomial(x) is one exception, but
 > in practice even cubics over a whole range are unlikely to be
 > useful, in my experience.
 >
 > But, more to the point, a model gets more complex, you have _more_
 > to type as you spell out each term as _b[varname] * 
 > varname. -predict- does this all for you. And this is error-prone.
 >
 > Other than the occasional pedagogic advantage of underlining
 > what is being done, i.e. plugging estimates into a model
 > formula, I can't understand your preference here.
 >





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