[R] Model matrix using dummy regressors or deviation regressors

[bluesky315 at gmail.com]

The model matrix for the code at the end the email is shown below.
Since the model matrix doesn't have -1, I think that it is made of
dummy regressors rather than deviation regressors. I'm wondering how
to make a model matrix using deviation regressors. Could somebody let
me know?

> model.matrix(aaov)
   (Intercept) A2 B2 B3 A2:B2 A2:B3
1            1  0  0  0     0     0
2            1  0  0  0     0     0
3            1  0  0  0     0     0
4            1  0  0  0     0     0
5            1  1  0  0     0     0
6            1  1  0  0     0     0
7            1  1  0  0     0     0
8            1  1  0  0     0     0
9            1  0  1  0     0     0
10           1  0  1  0     0     0
11           1  0  1  0     0     0
12           1  0  1  0     0     0
13           1  1  1  0     1     0
14           1  1  1  0     1     0
15           1  1  1  0     1     0
16           1  1  1  0     1     0
17           1  0  0  1     0     0
18           1  0  0  1     0     0
19           1  0  0  1     0     0
20           1  0  0  1     0     0
21           1  1  0  1     0     1
22           1  1  0  1     0     1
23           1  1  0  1     0     1
24           1  1  0  1     0     1
attr(,"assign")
[1] 0 1 2 2 3 3
attr(,"contrasts")
attr(,"contrasts")$A
[1] "contr.treatment"

attr(,"contrasts")$B
[1] "contr.treatment"



#############
a=2
b=3
n=4
A = rep(sapply(1:a,function(x){rep(x,n)}),b)
B = as.vector(sapply(sapply(1:b, function(x){rep(x,n)}), function(x){rep(x,a)}))
Y = A + B + rnorm(a*b*n)

fr = data.frame(Y=Y,A=as.factor(A),B=as.factor(B))
aaov=aov(Y ~ A * B,fr)
summary(aaov)
model.matrix(aaov)