On Thu, Apr 22, 2010 at 2:32 AM, Peter Dalgaard <pda...@gmail.com> wrote: > Gabor Grothendieck wrote: >> On Wed, Apr 21, 2010 at 4:26 PM, Peter Dalgaard <pda...@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. >
OK. Thanks. I guess that's fair. > (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....@cbs.dk Priv: pda...@gmail.com > ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
