SparseM (version 0.38)

SparseM.ops: Basic Linear Algebra for Sparse Matrices

Description

Basic linear algebra operations for sparse matrices of class matrix.csr.

Arguments

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.

Details

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.

References

Koenker, R and Ng, P. (2002). SparseM: A Sparse Matrix Package for R, http://www.econ.uiuc.edu/~roger/research

See Also

slm for sparse linear model fitting. SparseM.ontology for coercion and other class relations involving the sparse matrix classes.

Examples

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)