SparseM.ops {SparseM} | R Documentation |
Basic linear algebra operations for sparse matrices
of class matrix.csr
.
x |
matrix of class matrix.csr . |
y |
matrix of class matrix.csr or a dense matrix or vector. |
value |
replacement values. |
i,j |
vectors of elements to extract or replace. |
nrow |
optional number of rows for the result. |
lag |
an integer indicating which lag to use. |
differences |
an integer indicating the order of the difference. |
Linear algebra operations for matrices of class
matrix.csr
are designed to behave exactly as for
regular matrices. In particular, matrix multiplication, kronecker
product, addition,
subtraction and various logical operations should work as with the conventional
dense form of matrix storage, as does indexing, rbind, cbind, and diagonal
assignment and extraction. The method diag may be used to extract the
diagonal of a matrix.csr
object, to create a sparse diagonal see
SparseM.ontology
.
The function det
computes the determinant of the argument
matrix. If the matrix is of class matrix.csr
then it must
be symmetric, or an error will be returned. If the matrix is of
class matrix.csr.chol
then the determinant of the Cholesky
factor is returned, ie the product of the diagonal elements.
The function norm
is used to check for symmetry by
computing the maximum of the elements of the difference between
the matrix and its transpose. Optionally, this sup norm can
be replaced by the Hilbert-Schmidt norm, or the l1 norm.
Koenker, R and Ng, P. (2002). SparseM: A Sparse Matrix Package for R,
http://www.econ.uiuc.edu/~roger/research
slm
for sparse linear model fitting.
SparseM.ontology
for coercion and other class relations involving the
sparse matrix classes.
n1 <- 10 n2 <- 10 p <- 6 y <- rnorm(n1) a <- rnorm(n1*p) a[abs(a)<0.5] <- 0 A <- matrix(a,n1,p) A.csr <- as.matrix.csr(A) b <- rnorm(n2*p) b[abs(b)<1.0] <- 0 B <- matrix(b,n2,p) B.csr <- as.matrix.csr(B) # matrix transposition and multiplication A.csr%*%t(B.csr) # kronecker product A.csr %x% matrix(1:4,2,2)