SparseM.ontology: Sparse Matrix Class

Description

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

Usage

## S3 method for class 'matrix.csr':
as(x, nrow = 1, ncol = 1, eps = .Machine$double.eps, ...)
## S3 method for class 'matrix.csc':
as(x, nrow = 1, ncol = 1, eps = .Machine$double.eps, ...)
## S3 method for class 'matrix.ssr':
as(x, nrow = 1, ncol = 1, eps = .Machine$double.eps, ...)
## S3 method for class 'matrix.ssc':
as(x, nrow = 1, ncol = 1, eps = .Machine$double.eps, ...)
## S3 method for class 'matrix.csr':
is(x, ...)
## S3 method for class 'matrix.csc':
is(x, ...)
## S3 method for class 'matrix.ssr':
is(x, ...)
## S3 method for class 'matrix.ssc':
is(x, ...)

Arguments

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

...

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 $a_{ij}$, stored in matrix.csr format consists of three arrays:

ra: a real array ofnnzelements containing the non-zero elements ofA, stored in row order. Thus, ifi, all elements of rowiprecede elements from rowj. The order of elements within the rows is immaterial.
ja: an integer array ofnnzelements containing the column indices of the elements stored inra.
ia: an integer array ofn+1elements containing pointers to the beginning of each row in the arraysraandja. Thusia[i]indicates the position in the arraysraandjawhere theith row begins. The last,(n+1)st, element ofiaindicates where then+1row 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

Examples

Run this code

n1 <- 10
p <- 5
a <- rnorm(n1*p)
a[abs(a)<0.5] <- 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)
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(rep(0,9),3,3) #sparse matrix of all zeros
as(4,"matrix.diag.csr") #identity matrix of dimension 4