Bugs? when dealing with contrasts

Gabor Grothendieck wrote:

On Wed, Apr 21, 2010 at 4:26 PM, Peter Dalgaard <pdalgd at gmail.com> wrote:

...

I.e., that R reverts to using indicator variables when the intercept is
absent.

Is there any nice way of getting contr.sum coding for the interaction
as opposed to the ugly code in my post that I used to force it? i.e.
cbind(1, model.matrix(~ fac)[,2:3] * scores)

I think not. In general, an interaction like ~fac:scores indicates three
lines with a common intercept and three different slopes, and changing
the parametrization is not supposed to change the model, whereas your
model inserts a restriction that the slopes sum to zero (if I understand
correctly). So if you want to fit "ugly" models, you get to do a little
ugly footwork.

(A similar, simpler, issue arises if you want to have a 2x2 design with
no effect in one column and/or one row (think clinical trial, placebo
vs. active, baseline vs. treated. You can only do this us explicit dummy
variables, not with the two classifications represented as factors.)


--
Peter Dalgaard
Center for Statistics, Copenhagen Business School
Phone: (+45)38153501
Email: pd.mes at cbs.dk ?Priv: PDalgd at gmail.com