[R] Making slope coefficients ``relative to 0''.

[Bert Gunter]

 Either lm(y~ a-1 + a:x) or lm (y~ a + (a-1):x) or lm(y~ a+ a:(x-1))

-- Bert Gunter
Genentech

-----Original Message-----
From: bgunter 
Sent: Friday, May 16, 2008 8:39 AM
To: Rolf Turner
Subject: RE: [R] Making slope coefficients ``relative to 0''.

lm(y ~ a + a:(x-1)) 

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Rolf Turner
Sent: Thursday, May 15, 2008 8:55 PM
To: R-help forum
Subject: [R] Making slope coefficients ``relative to 0''.


I am interested in whether the slopes in a linear model are different  
from 0.

I.e. I would like to obtain the slope estimates, and their standard  
errors,
``relative to 0'' for each group, rather than relative to some baseline.

Explicitly I would like to write/represent the model as

	y = a_i + b_i*x + E

i = 1, ..., K, where x is a continuous variate and i indexes groups
(levels of a factor with K levels).

The ``usual'' structure (using ``treatment contrasts'') gives

	y = a + a_i + b*x + b_i*x + E

i = 2, ..., K.  (So that b is the slope for the baseline group, and  
b_i measures
how much the slope for group i differs from that for the baseline group.

I can force the *intercepts* to be ``relative to 0'' by putting a  
``-1'' into the formula:

	lm(y ~ g*x-1)

But I don't really care about the intercepts; it's the slopes I'm  
interested in.

And there doesn't seem to a way to do the thing equivalent to the  
``-1'' trick
for slopes.  Or is there?

There are of course several work-arounds.  (E.g. calculate my b_i- 
hats and their
standard errors from the information obtained from the usual model  
structure.
Or set up my own dummy variable to regress upon.  Easy enough, and I  
could do that.)

I just wanted to know for sure that there wasn't a sexier way, using  
some aspect
of the formula machinery with which I am not yet familiar.

Thanks for any insights.

	cheers,

		Rolf Turner

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