| sparseMatrix-class {Matrix} | 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.
(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().
signature(x = "sparseMatrix"), ....
The format method for sparse matrices, see
formatSpMatrix() for details such as the extra
optional arguments.
(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.
(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.
(x = *, y = *): binding matrices together
row-wise, see cbind2 above.
(x = "sparseMatrix", logarithm=TRUE):
determinant() methods for sparse matrices typically
work via Cholesky or lu decompositions.
(x = "sparseMatrix"): extracts the diagonal of a
sparse matrix.
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)).
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.
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]))