sparseMatrix-class | R Documentation |

Virtual Mother Class of All Sparse Matrices

`Dim`

:Object of class

`"integer"`

- the dimensions of the matrix - must be an integer vector with exactly two non-negative values.`Dimnames`

:a list of length two - inherited from class

`Matrix`

, see`Matrix`

.

Class `"Matrix"`

, directly.

- show
`(object = "sparseMatrix")`

: The`show`

method for sparse matrices prints*“structural”*zeroes as`"."`

using`printSpMatrix()`

which allows further customization.`signature(x = "sparseMatrix")`

, ....

The`print`

method for sparse matrices by default is the same as`show()`

but can be called with extra optional arguments, see`printSpMatrix()`

.- format
`signature(x = "sparseMatrix")`

, ....

The`format`

method for sparse matrices, see`formatSpMatrix()`

for details such as the extra optional arguments.- summary
`(object = "sparseMatrix", uniqT=FALSE)`

: Returns an object of S3 class`"sparseSummary"`

which is basically a`data.frame`

with columns`(i,j,x)`

(or just`(i,j)`

for`nsparseMatrix`

class objects) with the stored (typically non-zero) entries. The`print`

method resembles Matlab's way of printing sparse matrices, and also the MatrixMarket format, see`writeMM`

.- cbind2
`(x = *, y = *)`

: several methods for binding matrices together, column-wise, see the basic`cbind`

and`rbind`

functions.

Note that the result will typically be sparse, even when one argument is dense and larger than the sparse one.- rbind2
`(x = *, y = *)`

: binding matrices together row-wise, see`cbind2`

above.- determinant
`(x = "sparseMatrix", logarithm=TRUE)`

:`determinant()`

methods for sparse matrices typically work via`Cholesky`

or`lu`

decompositions.- diag
`(x = "sparseMatrix")`

: extracts the diagonal of a sparse matrix.- dim<-
`signature(x = "sparseMatrix", value = "ANY")`

: allows to*reshape*a sparse matrix to a sparse matrix with the same entries but different dimensions.`value`

must be of length two and fulfill`prod(value) == prod(dim(x))`

.- coerce
`signature(from = "factor", to = "sparseMatrix")`

: Coercion of a factor to`"sparseMatrix"`

produces the matrix of indicator**rows**stored as an object of class`"dgCMatrix"`

. To obtain columns representing the interaction of the factor and a numeric covariate, replace the`"x"`

slot of the result by the numeric covariate then take the transpose. Missing values (`NA`

) from the factor are translated to columns of all`0`

s.

See also `colSums`

, `norm`

,
...
for methods with separate help pages.

In method selection for multiplication operations (i.e. `%*%`

and the two-argument form of `crossprod`

)
the sparseMatrix class takes precedence in the sense that if one
operand is a sparse matrix and the other is any type of dense matrix
then the dense matrix is coerced to a `dgeMatrix`

and the
appropriate sparse matrix method is used.

`sparseMatrix`

, and its references, such as
`xtabs(*, sparse=TRUE)`

, or
`sparse.model.matrix()`

,
for constructing sparse matrices.

`T2graph`

for conversion of `"graph"`

objects
(package graph) to and from sparse matrices.

showClass("sparseMatrix") ## and look at the help() of its subclasses M <- Matrix(0, 10000, 100) M[1,1] <- M[2,3] <- 3.14 M ## show(.) method suppresses printing of the majority of rows data(CAex); dim(CAex) # 72 x 72 matrix determinant(CAex) # works via sparse lu(.) ## factor -> t( <sparse design matrix> ) : (fact <- gl(5, 3, 30, labels = LETTERS[1:5])) (Xt <- as(fact, "sparseMatrix")) # indicator rows ## missing values --> all-0 columns: f.mis <- fact i.mis <- c(3:5, 17) is.na(f.mis) <- i.mis Xt != (X. <- as(f.mis, "sparseMatrix")) # differ only in columns 3:5,17 stopifnot(all(X.[,i.mis] == 0), all(Xt[,-i.mis] == X.[,-i.mis]))

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