Matrix (version 1.7-6)

sparse.model.matrix: Construct Sparse Design / Model Matrices

Description

Construct a sparse model or “design” matrix, from a formula and data frame (sparse.model.matrix) or a single factor (fac2sparse).

The fac2[Ss]parse() functions are utilities, also used internally in the principal user level function sparse.model.matrix().

Usage

sparse.model.matrix(object, data = environment(object),
		    contrasts.arg = NULL, xlev = NULL, transpose = FALSE,
		    drop.unused.levels = FALSE, row.names = TRUE,
		    sep = "", verbose = FALSE, ...)
fac2sparse(from, to = c("d", "l", "n"),
	   drop.unused.levels = TRUE, repr = c("C", "R", "T"), giveCsparse)
fac2Sparse(from, to = c("d", "l", "n"),
	   drop.unused.levels = TRUE, repr = c("C", "R", "T"), giveCsparse,
	   factorPatt12, contrasts.arg = NULL)

Value

a sparse matrix, extending CsparseMatrix (for

fac2sparse() if repr = "C" as per default; a

TsparseMatrix or RsparseMatrix, otherwise).

For fac2Sparse(), a list of length two, both components with the corresponding transposed model matrix, where the corresponding factorPatt12 is true.

fac2sparse(), the basic workhorse of

sparse.model.matrix(), returns the transpose

(t) of the model matrix.

See Also

model.matrix in package stats, part of base R.

model.Matrix in package MatrixModels; see ‘Note’.

as(f, "sparseMatrix") (see coerce(from = "factor", ..) in the class doc sparseMatrix) produces the transposed sparse model matrix for a single factor f (and no contrasts).

Examples

 
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)
 )