Matrix (version 0.999375-46)

sparseMatrix: General Sparse Matrix Construction from Nonzero Entries

Description

User friendly construction of a compressed, column-oriented, sparse matrix, inheriting from class CsparseMatrix, from locations (and values) of its nonzero entries.

This is the recommended user interface rather than direct new("***Matrix", ....) calls.

Usage

sparseMatrix(i = ep, j = ep, p, x, dims, dimnames, index1 = TRUE)

Arguments

i,j

integer vectors of the same length specifying the locations (row and column indices) of the non-zero (or non-TRUE) entries of the matrix.

p

numeric (integer valued) vector of pointers, one for each column (or row), to the initial (zero-based) index of elements in the column (or row). Exactly one of i, j or p must be missing.

x

optional values of the matrix entries. If specified, must be of the same length as i / j, or of length one where it will be recycled to full length. If missing, the resulting matrix will be a 0/1 pattern matr

dims

optional, non-negative, integer, dimensions vector of length 2. Defaults to c(max(i), max(j)).

dimnames

optional list of dimnames; if not specified, none, i.e., NULL ones, are used.

index1

logical scalar. If TRUE, the default, the index vectors i and/or j are 1-based, as is the convention in R. That is, counting of rows and columns starts at 1. If FALSE the index vectors are

Value

• A sparse matrix in compressed, column-oriented form, as an Robject inheriting from both CsparseMatrix and generalMatrix.

Details

Exactly one of the arguments i, j and p must be missing.

In typical usage, p is missing, i and j are vectors of positive integers and x is a numeric vector. These three vectors, which must have the same length, form the triplet representation of the sparse matrix.

If i or j is missing then p must be a non-decreasing integer vector whose first element is zero. It provides the compressed, or pointer representation of the row or column indices, whichever is missing. The expanded form of p, rep(seq_along(dp),dp) where dp <- diff(p), is used as the (1-based) row or column indices.

The values of i, j, p and index1 are used to create 1-based index vectors i and j from which a TsparseMatrix is constructed, with numerical values given by x, if non-missing. The CsparseMatrix derived from this triplet form is returned.

The reason for returning a CsparseMatrix object instead of the triplet format is that the compressed column form is easier to work with when performing matrix operations. In particular, if there are no zeros in x then a CsparseMatrix is a unique representation of the sparse matrix.

See Also

Matrix(*, sparse=TRUE) for the more usual constructor of such matrices; further bdiag and Diagonal for (block-)diagonal and bandSparse for banded sparse matrix constructors.

Consider CsparseMatrix and similar class definition help files.

Examples

## simple example
i <- c(1,3:8); j <- c(2,9,6:10); x <- 7 * (1:7)
(A <- sparseMatrix(i, j, x = x))
summary(A)
str(A) # note that *internally* 0-based row indices are used

## dims can be larger than the maximum row or column indices
(AA <- sparseMatrix(c(1,3:8), c(2,9,6:10), x = 7 * (1:7), dims = c(10,20)))
summary(AA)

## i, j and x can be in an arbitrary order, as long as they are consistent
set.seed(1); (perm <- sample(1:7))
(A1 <- sparseMatrix(i[perm], j[perm], x = x[perm]))
stopifnot(identical(A, A1))

## the (i,j) pairs can be repeated, in which case the x's are summed
(args <- data.frame(i = c(i, 1), j = c(j, 2), x = c(x, 2)))
(Aa <- do.call(sparseMatrix, args))

dn <- list(LETTERS[1:3], letters[1:5])
## pointer vectors can be used, and the (i,x) slots are sorted if necessary:
m <- sparseMatrix(i = c(3,1, 3:2, 2:1), p= c(0:2, 4,4,6), x = 1:6, dimnames = dn)
m
str(m)
stopifnot(identical(dimnames(m), dn))

sparseMatrix(x = 2.72, i=1:3, j=2:4) # recycling x
sparseMatrix(x = TRUE, i=1:3, j=2:4) # recycling x, |--> "lgCMatrix"

## no 'x' --> patter*n* matrix:
(n <- sparseMatrix(i=1:6, j=rev(2:7)))# -> ngCMatrix

## an empty sparse matrix:
(e <- sparseMatrix(dims = c(4,6), i={}, j={}))

## pointers example in converting from other sparse matrix representations.
if(require(SparseM)) {
  X <- model.matrix(read.matrix.hb(system.file("data", "rua_32_ax.rua",
					       package = "SparseM")))
  XX <- sparseMatrix(j = X@ja, p = X@ia - 1L, x = X@ra, dims = X@dimension)
  validObject(XX)

  ## Alternatively, and even more user friendly :
  X. <- as(X, "Matrix")  # or also
  X2 <- as(X, "sparseMatrix")
  stopifnot(identical(XX, X.), identical(X., X2))
}