Matrix (version 1.0-4)

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,
             symmetric = FALSE, index1 = TRUE,
             giveCsparse = TRUE, check = 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.

symmetric

logical indicating if the resulting matrix should be symmetric. In that case, only the lower or upper triangle needs to be specified via $(i/j/p)$.

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

giveCsparse

logical indicating if the result should be a CsparseMatrix or a TsparseMatrix. The default, TRUE is very often more efficient subsequently, but not a

check

logical indicating if a validity check is performed; do not set to FALSE unless you know what you're doing!

Value

• A sparse matrix, by default (see giveCsparse) 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 by default 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={}))

## a symmetric one:
(sy <- sparseMatrix(i= c(2,4,3:5), j= c(4,7:5,5), x = 1:5,
                    dims = c(7,7), symmetric=TRUE))
stopifnot(isSymmetric(sy))

## pointers example in converting from other sparse matrix representations.
if(require(SparseM) && packageVersion("SparseM") >= 0.87 &&
   nzchar(dfil <- system.file("textdata", "rua_32_ax.rua",
                              package = "SparseM"))) {
  X <- model.matrix(read.matrix.hb(dfil))
  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))
}