R: Construct Sparse Design / Model Matrices

sparse.model.matrix {Matrix} | R Documentation

## 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", "i", "l", "n", "z"),
    	   drop.unused.levels = TRUE, repr = c("C","T","R"), giveCsparse)
    fac2Sparse(from, to = c("d", "i", "l", "n", "z"),
    	   drop.unused.levels = TRUE, repr = c("C","T","R"), giveCsparse,
    	   factorPatt12, contrasts.arg = NULL)

### Arguments

`object` | an object of an appropriate class. For the default method, a model formula or terms object.
`data` | a data frame created with `model.frame`. If another sort of object, `model.frame` is called first.
`contrasts.arg` | for `sparse.model.matrix()`: A list, whose entries are contrasts suitable for input to the `contrasts` replacement function and whose names are the names of columns of `data` containing `factor`s. for `fac2Sparse()`: character string or `NULL` or (coercable to) `"sparseMatrix"`, specifying the contrasts to be applied to the factor levels.
`xlev` | to be used as argument of `model.frame` if `data` has no `"terms"` attribute.
`transpose` | logical indicating if the transpose should be returned; if the transposed is used anyway, setting `transpose = TRUE` is more efficient.
`drop.unused.levels` | should factors have unused levels dropped? The default for `sparse.model.matrix` has been changed to `FALSE`, 2010-07, for compatibility with R's standard (dense) `model.matrix()`.
`row.names` | logical indicating if row names should be used.
`sep` | `character` string passed to `paste()` when constructing column names from the variable name and its levels.
`verbose` | logical or integer indicating if (and how much) progress output should be printed.
`...` | further arguments passed to or from other methods.
`from` | (for `fac2sparse()`:) a `factor`.
`to` | a character indicating the “kind” of sparse matrix to be returned. The default, `"d"` is for `double`.
`giveCsparse` | deprecated, replaced with `repr`; logical indicating if the result must be a `CsparseMatrix`.
`repr` | `character` string, one of `"C"`, `"T"`, or `"R"`, specifying the sparse representation to be used for the result, i.e., one from the super classes `CsparseMatrix`, `TsparseMatrix`, or `RsparseMatrix`.
`factorPatt12` | logical vector, say `fp`, of length two; when `fp[1]` is true, return “contrasted” `t(X)`; when `fp[2]` is true, the original (“dummy”) `t(X)`, i.e, the result of `fac2sparse()`.

### 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.

Note that `model.Matrix(*, sparse=TRUE)` from package MatrixModels may be often be preferable to `sparse.model.matrix()` nowadays, as `model.Matrix()` returns `modelMatrix` objects with additional slots `assign` and `contrasts` which relate back to the variables used.

`fac2sparse()`, the basic workhorse of `sparse.model.matrix()`, returns the transpose (`t`) of the model matrix.

### Author(s)

Doug Bates and Martin Maechler, with initial suggestions from Tim Hesterberg.

### See Also

`model.matrix` in standard R's package stats.
`model.Matrix` which calls `sparse.model.matrix` or `model.matrix` depending on its `sparse` argument may be preferred to `sparse.model.matrix`.

`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

    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)
     )

[Package Matrix version 1.3-4 Index]