# rsparsematrix

0th

Percentile

##### Random Sparse Matrix

Generate a random sparse matrix efficiently. The default has rounded gaussian non-zero entries, and rand.x = NULL generates random pattern matrices, i.e. inheriting from '>nsparseMatrix.

Keywords
distribution, array
##### Usage
rsparsematrix(nrow, ncol, density, nnz = round(density * maxE),
symmetric = FALSE,
rand.x = function(n) signif(rnorm(n), 2), …)
##### Arguments
nrow, ncol

number of rows and columns, i.e., the matrix dimension (dim).

density

optional number in $[0,1]$, the density is the proportion of non-zero entries among all matrix entries. If specified it determines the default for nnz, otherwise nnz needs to be specified.

nnz

number of non-zero entries, for a sparse matrix typically considerably smaller than nrow*ncol. Must be specified if density is not.

symmetric

logical indicating if result should be a matrix of class '>symmetricMatrix. Note that in the symmetric case, nnz denotes the number of non zero entries of the upper (or lower) part of the matrix, including the diagonal.

rand.x

NULL or the random number generator for the x slot, a function such that rand.x(n) generates a numeric vector of length n. Typical examples are rand.x = rnorm, or rand.x = runif; the default is nice for didactical purposes.

optionally further arguments passed to sparseMatrix(), notably giveCsparse.

##### Details

The algorithm first samples “encoded” $(i,j)$s without replacement, via one dimensional indices, if not symmetric sample.int(nrow*ncol, nnz), then---if rand.x is not NULL---gets x <- rand.x(nnz) and calls sparseMatrix(i=i, j=j, x=x, ..). When rand.x=NULL, sparseMatrix(i=i, j=j, ..) will return a pattern matrix (i.e., inheriting from '>nsparseMatrix).

##### Value

a '>sparseMatrix, say M of dimension (nrow, ncol), i.e., with dim(M) == c(nrow, ncol), if symmetric is not true, with nzM <- nnzero(M) fulfilling nzM <= nnz and typically, nzM == nnz.

##### Aliases
• rsparsematrix
##### Examples
# NOT RUN {
set.seed(17)# to be reproducible
M <- rsparsematrix(8, 12, nnz = 30) # small example, not very sparse
M
M1 <- rsparsematrix(1000, 20,  nnz = 123,  rand.x = runif)
summary(M1)

## a random *symmetric* Matrix
(S9 <- rsparsematrix(9, 9, nnz = 10, symmetric=TRUE)) # dsCMatrix
nnzero(S9)# ~ 20: as 'nnz' only counts one "triangle"

## a random patter*n* aka boolean Matrix (no 'x' slot):
(n7 <- rsparsematrix(5, 12, nnz = 10, rand.x = NULL))

## a [T]riplet representation sparseMatrix:
T2 <- rsparsematrix(40, 12, nnz = 99, giveCsparse=FALSE)