R help - Aug 2000 - Testing for parallel slopes

I'm running a series of simple bivariate linear regressions on grouped 
data.  I want to test the slopes to see if they are parallel.  I normally 
use analysis of covariance to do so, looking at interaction between the 
covariate and the factor to make this determination.

VR3 pp.149 - 154 has a very nice example of an ANOCOVA, ending with a 
discussion of this very operation.

My question has to do with the issue of parametrization.  When I use the 
example data set "whiteside", I get the same results that VR get (see
p.
154) except I'm getting them *without* reparametrizing the model. (In fact, 
I get the same answers with the default parametrization and the 
alternative).  Since I'm only interested in the interaction term, is it 
even necessary to change the structure of the model matrix, or will I get 
the answer I need using the default contrast matrices (i.e. 
"contr.helmert", "contr.poly")?




====================Dr. Marc R. Feldesman
Professor and Chairman
Anthropology Department
Portland State University
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email:  feldesmanm at pdx.edu
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fax:    503-725-3905
http://odin.cc.pdx.edu/~h1mf
=====================
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If we're looking for the source of our troubles, we shouldn't test
people
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On Tue, 1 Aug 2000, Marc Feldesman wrote:

> 
> My question has to do with the issue of parametrization.  When I use the 
> example data set "whiteside", I get the same results that VR get
(see p.
> 154) except I'm getting them *without* reparametrizing the model. (In
fact,
> I get the same answers with the default parametrization and the 
> alternative).  Since I'm only interested in the interaction term, is it
> even necessary to change the structure of the model matrix, or will I get 
> the answer I need using the default contrast matrices (i.e. 
> "contr.helmert", "contr.poly")?
In R "contr.helmert" is *not* the default. Type 
  options("contrasts")
to see what contrasts you are using.  

This and other differences are described in both the "R Complements"
to
V&R3 (see http://www.stats.ox.ac.uk/pub/MASS3) and the R FAQ.

	-thomas

Thomas Lumley
Assistant Professor, Biostatistics
University of Washington, Seattle

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