[R] How do I obtain the design matrix of an lm()?

[Gavin Simpson]

On Tue, 2007-10-09 at 20:04 +0530, Ajay Shah wrote:
> I am using the clever formula notation of R to first do an OLS. E.g. I
> say
> 
>   m <- lm(y ~ x + f)
> 
> where f is a factor, and R automatically constructs the dummy
> variables. Very nice.
> 
> I need to then go on to do some other ML estimation using the same
> design matrix that's used for the OLS. I could, of course, do this
> manually. But it seems that lm() has done all this hard work. I wonder
> if there's a way to ask him nicely so as to get it. :-)

model.matrix()

> f <- factor(sample(1:10, 100, replace = TRUE))
> x <- rnorm(100)
> y <- rnorm(100)
> m <- lm(y ~ x + f)
> model.matrix(m)
    (Intercept)             x f2 f3 f4 f5 f6 f7 f8 f9 f10
1             1  1.8256930313  0  0  0  0  0  1  0  0   0
2             1  0.5129526275  0  0  0  1  0  0  0  0   0
3             1  0.6791212194  0  0  1  0  0  0  0  0   0
4             1  2.4711715848  0  0  0  0  0  0  1  0   0
5             1  0.5646964940  0  1  0  0  0  0  0  0   0
6             1  0.2640087965  0  0  0  0  1  0  0  0   0
###<snip />

You can also do this directly without the lm step:

model.matrix(y ~ x + f)

HTH

G
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