# rsparsematrix: Random Sparse Matrix In Matrix: Sparse and Dense Matrix Classes and Methods

 rsparsematrix R Documentation

## Random Sparse Matrix

### Description

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`.

### 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 `repr`.

### 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`.

Martin Maechler

### Examples

```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, repr = "T")