Regression slopes

No modification needed.  Fit either of

lm(y-x ~ x + z)
lm(y ~ x + z + offset(x))

and the t-test in the summary will be a test of the coefficient of x being 
one.

You can also use such models to do an anova against a modle with unit 
coefficient.

Sorry if this is an obvious one, but is there a simple way to modify the lm 
function to test whether a slope coefficient is significantly different 
from 1 instead of different from 0?

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)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

Hi,
Sorry if this is an obvious one, but is there a simple way to modify the lm 
function to test whether a slope coefficient is significantly different 
from 1 instead of different from 0?

Thanks,

There might be an easier way, but the brute force method would be:

R> set.seed(1)
R> x <- data.frame(x = 1:10, y = 1:10 + rnorm(10))
R> lm.x <- lm(y ~ x, data = x)
R> coef.x <- summary(lm.x)$coef
R> # t-statistic comparing slope to 1
R> t.x <- (coef.x["x","Estimate"]-1)/coef.x["x","Std. Error"]
R> # p-value
R> 2 * pt(abs(t.x), lm.x$df, lower = FALSE)
[1] 0.5559868

Hope this helps,

Sundar