Re-Post: Combining Factors in model.matrix

On Sat, 24 Jan 2004 paul.boutros at utoronto.ca wrote:

I didn't get any response on this before, leading me to believe
I've missed
something fundamental.  Can anybody guide me in the correct
direction for more help on this?

Thanks for your reply:

You will need to explain to us why the object you list is `the design
matrix': have *you* a reference for that?  R is doing the conventional
thing, and I at least have no idea where your example comes from.

Perhaps I have used the wrong terminology?  My understanding of a design
matrix is that it identifies the factors are present for a given experiment.

Here, I have a two factor experiment, where each factor has two levels.
In the case I gave:
   t1 t2
1   1  0
2   1  1
3   0  0
4   0  1

I had expected this to represent four distinct experiments where
factor one is present in the first two and absent in the second two.

You seem to have coded variables t1 and t2 the opposite ways (the
reference level is 2 for t1 and 1 for t2), and your model has the fit at
levels t1=2,t1=1 constrained to pass through the origin.  I don't think R
has a simple syntax for that (although you can fake anything), and I find
it hard to believe that is actually what you want.

That wasn't my intention, I want to retain the intercept term and
not constrain it to pass through the origin.

Paul

Paul

=================================================
I want to be able to create a design matrix with two factors.

For instance, if

I have:

t1 <- factor(c(1,1,2,2));
t2 <- factor(c(1,2,1,2));
design <- model.matrix(~ -1 + (t1+t2));
design;

  t11 t12 t22
1   1   0   0
2   1   0   1
3   0   1   0
4   0   1   1

But the design matrix I want is:
   t1 t2
1   1  0
2   1  1
3   0  0
4   0  1

Actually, in general I'm struggling with the syntax for

formulating a design

matrix I can write down on paper.  Is there a reference for

this beyond the R

documentation?

Chapter 6 of MASS has the most complete exposition (by Bill
Venables) that
I know of, and the White Book (Chambers & Hastie, 1992) goes well beyind
the R documentation (which uses it as the reference).

--
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