Hi all,
recently I stumbled upen a problem in stats::model.matrix that I think is
worth reporting.
When I run:
> dat <- data.frame(
> y = rnorm(8),
> x1 = factor(rep(0:1, each = 4)),
> x2 = factor(rep(rep(0:1, each = 2), 2)),
> x3 = factor(rep(0:1, 4))
> )
>
> stats::model.matrix(y ~ x1+x2+x3 + x1:x2:x3, dat)
I get a matrix with 12 columns, which are linearily dependent and thus not
identified in a linear model:
> summary(lm(y ~ x1+x2+x3 + x1:x2:x3, dat))
Of course, there is usually no need for such a formula that ignores the
two-way interactions, but from my point of view, model.matrix should still
return only 8 columns (or less) in order to produce identified models.
I wonder if this is some sort of intendend behavior or just a side effect
of the way model.matrix handles factors.
Many thanks in advance.
Paul
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