sparseMatrix-class: Virtual Class "sparseMatrix" - Mother of Sparse Matrices
In bedatadriven/renjin-matrix: Sparse and Dense Matrix Classes and Methods

Description Slots Extends Methods Note See Also Examples

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.
print: 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"): 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 0s.

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]))