[R] linear regression: evaluating the result Q

Prof Brian Ripley ripley at stats.ox.ac.uk
Thu Sep 16 18:53:02 CEST 2004

```On Thu, 16 Sep 2004, RenE J.V. Bertin wrote:

> On Thu, 16 Sep 2004 17:03:09 +0100 (BST), Prof Brian Ripley <ripley at stats.ox.ac.uk> wrote regarding
> "Re: [R] linear regression: evaluating the result Q"
>
> Thank you, that should get me going into the right direction!
>
> 8-) Well, for rlm no, as it is not least-squares fitting and R^2 is very
> 8-) suseptible to outliers.  For glm, not really unless it is a Gaussian
> 8-) model.
>
> 	This is what I feared. How then would one evaluate the goodness of
> an rlm fit, on a comparable 0-1 scale?

Via the estimated robust scales.

> 8-) > Aside from question 2), what is the best way to compare
> 8-) > the calculated slope with another slope (say of the unity line)?
> 8-)
> 8-) Use offset, as in y ~ x + offset(x) and test for the coefficient of x to
> 8-) be zero.  (That's R only, BTW.)
>
> offset seems to be ignored by rlm(), is that correct? (Which isn't too
> much of a problem as long as confint operates correctly on rlm objects.)

Yes -- rlm was written before R existed.

--
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

```