```
library(stats, pos = "package:base", verbose = FALSE)
dd <- data.frame(a = gl(3,4), b = gl(4,1,12))# balanced 2-way
options("contrasts") # the default: "contr.treatment"
sparse.model.matrix(~ a + b, dd)
sparse.model.matrix(~ -1+ a + b, dd)# no intercept --> even sparser
sparse.model.matrix(~ a + b, dd, contrasts = list(a="contr.sum"))
sparse.model.matrix(~ a + b, dd, contrasts = list(b="contr.SAS"))
## Sparse method is equivalent to the traditional one :
stopifnot(all(sparse.model.matrix(~ a + b, dd) ==
Matrix(model.matrix(~ a + b, dd), sparse=TRUE)),
all(sparse.model.matrix(~0 + a + b, dd) ==
Matrix(model.matrix(~0 + a + b, dd), sparse=TRUE)))
(ff <- gl(3,4,, c("X","Y", "Z")))
fac2sparse(ff) # 3 x 12 sparse Matrix of class "dgCMatrix"
##
## X 1 1 1 1 . . . . . . . .
## Y . . . . 1 1 1 1 . . . .
## Z . . . . . . . . 1 1 1 1
## can also be computed via sparse.model.matrix():
f30 <- gl(3,0 )
f12 <- gl(3,0, 12)
stopifnot(
all.equal(t( fac2sparse(ff) ),
sparse.model.matrix(~ 0+ff),
tolerance = 0, check.attributes=FALSE),
is(M <- fac2sparse(f30, drop= TRUE),"CsparseMatrix"), dim(M) == c(0, 0),
is(M <- fac2sparse(f30, drop=FALSE),"CsparseMatrix"), dim(M) == c(3, 0),
is(M <- fac2sparse(f12, drop= TRUE),"CsparseMatrix"), dim(M) == c(0,12),
is(M <- fac2sparse(f12, drop=FALSE),"CsparseMatrix"), dim(M) == c(3,12)
)
```

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