| spMatrix {Matrix} | R Documentation |
User friendly construction of a sparse matrix (inheriting from class
TsparseMatrix) from the triplet representation.
This is much less flexible than sparseMatrix() and hence
somewhat deprecated.
spMatrix(nrow, ncol, i = integer(), j = integer(), x = numeric())
nrow, ncol |
integers specifying the desired number of rows and columns. |
i,j |
integer vectors of the same length specifying the locations
of the non-zero (or non- |
x |
atomic vector of the same length as |
A sparse matrix in triplet form, as an R object inheriting from both
TsparseMatrix and
generalMatrix.
The matrix M will have
M[i[k], j[k]] == x[k], for k = 1,2,…, n, where
n = length(i) and
M[ i', j' ] == 0 for all other pairs (i',j').
Matrix(*, sparse=TRUE) for the more usual
constructor of such matrices. Then, sparseMatrix
is more general and flexible than spMatrix() and by default
returns a CsparseMatrix which is often slightly
more desirable. Further, bdiag and
Diagonal for (block-)diagonal matrix constructors.
Consider TsparseMatrix and similar class
definition help files.
## simple example
A <- spMatrix(10,20, i = c(1,3:8),
j = c(2,9,6:10),
x = 7 * (1:7))
A # a "dgTMatrix"
summary(A)
str(A) # note that *internally* 0-based indices (i,j) are used
L <- spMatrix(9, 30, i = rep(1:9, 3), 1:27,
(1:27) %% 4 != 1)
L # an "lgTMatrix"
## A simplified predecessor of Matrix' rsparsematrix() function :
rSpMatrix <- function(nrow, ncol, nnz,
rand.x = function(n) round(rnorm(nnz), 2))
{
## Purpose: random sparse matrix
## --------------------------------------------------------------
## Arguments: (nrow,ncol): dimension
## nnz : number of non-zero entries
## rand.x: random number generator for 'x' slot
## --------------------------------------------------------------
## Author: Martin Maechler, Date: 14.-16. May 2007
stopifnot((nnz <- as.integer(nnz)) >= 0,
nrow >= 0, ncol >= 0, nnz <= nrow * ncol)
spMatrix(nrow, ncol,
i = sample(nrow, nnz, replace = TRUE),
j = sample(ncol, nnz, replace = TRUE),
x = rand.x(nnz))
}
M1 <- rSpMatrix(100000, 20, nnz = 200)
summary(M1)