SparseM.ontology: Sparse Matrix Class Ontology

Description

This group of functions evaluates and coerces changes in class structure.

Usage

as.matrix.csr(x, nrow, ncol, eps = .Machine$double.eps, ...)
# S4 method for matrix.csr.chol
as.matrix.csr(x, nrow, ncol, eps, upper.tri=TRUE, ...)
# S4 method for matrix.csr
as.matrix.csc(x, nrow = 1, ncol = 1, eps = .Machine$double.eps)
# S4 method for matrix.coo
as.matrix.ssr(x, nrow = 1, ncol = 1, eps = .Machine$double.eps)
# S4 method for matrix.csc
as.matrix.ssc(x, nrow = 1, ncol = 1, eps = .Machine$double.eps)
# S4 method for matrix.csr
as.matrix.coo(x, nrow = 1, ncol = 1, eps = .Machine$double.eps)
is.matrix.csr(x)
is.matrix.csc(x)
is.matrix.ssr(x)
is.matrix.ssc(x)
is.matrix.coo(x)

Arguments

x: is a matrix, or vector object, of either dense or sparse form
nrow: number of rows of matrix
ncol: number of columns of matrix
eps: A tolerance parameter: elements of x such that abs(x) < eps set to zero. This argument is only relevant when coercing matrices from dense to sparse form. Defaults to eps = .Machine$double.eps
upper.tri: logical, to choose upper or lower triangular matrix result.
...: other arguments

Details

The function matrix.csc acts like matrix to coerce a vector object to a sparse matrix object of class matrix.csr. This aspect of the code is in the process of conversion from S3 to S4 classes. For the most part the S3 syntax prevails. An exception is the code to coerce vectors to diagonal matrix form which uses as(v,"matrix.diag.csr". The generic functions as.matrix.xxx coerce a matrix x into a matrix of storage class matrix.xxx. The argument matrix x may be of conventional dense form, or of any of the four supported classes: matrix.csr, matrix.csc, matrix.ssr, matrix.ssc. The generic functions is.matrix.xxx evaluate whether the argument is of class matrix.xxx. The function as.matrix transforms a matrix of any sparse class into conventional dense form. The primary storage class for sparse matrices is the compressed sparse row matrix.csr class. An n by m matrix A with real elements $a_{i j}$ , stored in matrix.csr format consists of three arrays:

ra: a real array of nnz elements containing the non-zero elements of A, stored in row order. Thus, if i<j, all elements of row i precede elements from row j. The order of elements within the rows is immaterial.
ja: an integer array of nnz elements containing the column indices of the elements stored in ra.
ia: an integer array of n+1 elements containing pointers to the beginning of each row in the arrays ra and ja. Thus ia[i] indicates the position in the arrays ra and ja where the ith row begins. The last, (n+1)st, element of ia indicates where the n+1 row would start, if it existed.

The compressed sparse column class matrix.csc is defined in an analogous way, as are the matrix.ssr, symmetric sparse row, and matrix.ssc, symmetric sparse column classes.

References

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

Examples

Run this code

t(m5 <- as.matrix.csr(c(-1:1,0,0)))
t(M4 <- as.matrix.csc(c(0:2,0), 4))
 (S3 <- as.matrix.ssr(diag(x = 0:2))) # *symmetric*
stopifnot(identical(dim(m5), c(5L, 1L)),
          identical(dim(M4), c(4L, 1L)),
          identical(dim(S3), c(3L, 3L)))

n1 <- 10
p <- 5
a <- round(rnorm(n1*p), 2)
a[abs(a) < 0.7] <- 0
A <- matrix(a,n1,p)
B <- t(A) %*% A      
A.csr <- as.matrix.csr(A)
A.csc <- as.matrix.csc(A)
B.ssr <- as.matrix.ssr(B)
B.ssc <- as.matrix.ssc(B)
stopifnot(exprs = {
  is.matrix.csr(A.csr) # -> TRUE
  is.matrix.csc(A.csc) # -> TRUE
  is.matrix.ssr(B.ssr) # -> TRUE
  is.matrix.ssc(B.ssc) # -> TRUE
})
as.matrix(A.csr)
as.matrix(A.csc)
as.matrix(B.ssr)
as.matrix(B.ssc)
as.matrix.csr(0, 2,3)    # sparse matrix of all zeros
## Diagonal (sparse) : 
as(4,   "matrix.diag.csr") # identity matrix of dimension 4
as(2:0, "matrix.diag.csr") # diagonal 3x3 matrix

Run the code above in your browser using DataLab

Data Engineering and BI courses are free this week!