[R] fit linear regression with multiple predictor and constrained intercept

[Prof Brian Ripley]

I think you are looking for y ~ x + x:factor   E.g.

> library(car)
> lm(repwt ~ repht + repht:sex, data=Davis)

Coefficients:
(Intercept)        repht   repht:sexM
   -59.30865      0.71412      0.05694

where the third term is the difference in slope between males and females.

> lm(repwt ~ repht:sex, data=Davis)

Coefficients:
(Intercept)   repht:sexF   repht:sexM
    -59.3086       0.7141       0.7711

for separately reported slopes.

If you want to constrain the intercept, fit with and without and take the 
better fit (or look into package nnls, but that would be overkill here).


On Wed, 28 Nov 2007, robert.ptacnik at niva.no wrote:

> Hi group,
>
> I have this type of data
> x(predictor), y(response), factor (grouping x into many groups, with 6-20
> obs/group)
>
> I want to fit a linear regression with one common intercept. 'factor'
> should only modify the slopes, not the intercept. The intercept is expected
> to be >0.
>
> If I use
> y~ x + factor, I get a different intercept for each factor level, but one
> slope only
>
> if I use
> y~ x * factor, I get the interaction term I want, but the intercept is not
> kept constant.
> Also, if I constrain teh intercept in the regression model (y~a+x*factor),
> I get estimates both for slope and intercept of each factor level.
>
> Robert
>
>
>
>
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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