Generating Random Matrices

Description

The S4 generic rmatrix generates a random matrix from a given object. Methods are provided to generate matrices with entries drawn from any given random distribution function, e.g. runif or rnorm.

rmatrix_int generate an integer matrix, by changing the storage mode of a matrix generated by rmatrix. All its arguments are directly passed to rmatrix.

Usage

rmatrix(x, ...)

S4 (numeric)
`rmatrix`(x, y = NULL, dist = runif, byrow = FALSE, dimnames = NULL, ..., 
  .rng = NULL)

rmatrix_int(...)

Arguments

x
object from which to generate a random matrix
y
optional specification of number of columns
dist
a random distribution function or a numeric seed (see details of method rmatrix,numeric)
byrow
a logical passed in the internal call to the function matrix
dimnames
NULL or a list passed in the internal call to the function matrix
...
extra arguments passed to the distribution function dist, or to rmatrix in the case of rmatrix_int.
.rng
any RNG seed specification supported by setRNG.

Methods

  1. rmatrixsignature(x = "numeric"): Generates a random matrix of given dimensions, whose entries are drawn using the distribution function dist.

    This is the workhorse method that is eventually called by all other methods. It returns a matrix with:

    • x rows and y columns if y is not missing and not NULL;
    • dimension x[1] x x[2] if x has at least two elements;
    • dimension x (i.e. a square matrix) otherwise.

    The default is to draw its entries from the standard uniform distribution using the base function runif, but any other function that generates random numeric vectors of a given length may be specified in argument dist. All arguments in ... are passed to the function specified in dist.

    The only requirement is that the function in dist is of the following form:

    function(n, ...){ # return vector of length n ... }

    This is the case of all base random draw function such as rnorm, rgamma, etc...

  2. rmatrixsignature(x = "ANY"): Default method which calls rmatrix,vector on the dimensions of x that is assumed to be returned by a suitable dim method: it is equivalent to rmatrix(dim(x), y=NULL, ...).

  3. rmatrixsignature(x = "NMF"): Returns the target matrix estimate of the NMF model x, perturbated by adding a random matrix generated using the default method of rmatrix: it is a equivalent to fitted(x) + rmatrix(fitted(x), ...).

    This method can be used to generate random target matrices that depart from a known NMF model to a controlled extend. This is useful to test the robustness of NMF algorithms to the presence of certain types of noise in the data.

Examples



#----------
# rmatrix,numeric-method
#----------
## Generate a random matrix of a given size
rmatrix(5, 3)

##            [,1]      [,2]      [,3]
## [1,] 0.06243016 0.1101419 0.8997295
## [2,] 0.90294478 0.5133422 0.3080472
## [3,] 0.02825208 0.8322932 0.5568540
## [4,] 0.32196286 0.4457090 0.9991947
## [5,] 0.12814938 0.7559115 0.2020942



## Generate a random matrix of the same dimension of a template matrix
a <- matrix(1, 3, 4)
rmatrix(a)

##           [,1]      [,2]      [,3]      [,4]
## [1,] 0.5755040 0.1902230 0.5677783 0.3842288
## [2,] 0.4890726 0.8383846 0.3627005 0.5724400
## [3,] 0.2042821 0.8880539 0.2311141 0.5999191



## Specificy the distribution to use

# the default is uniform
a <- rmatrix(1000, 50)
## Not run:  hist(a) 

# use normal ditribution
a <- rmatrix(1000, 50, rnorm)
## Not run:  hist(a) 

# extra arguments can be passed to the random variate generation function
a <- rmatrix(1000, 50, rnorm, mean=2, sd=0.5)
## Not run:  hist(a) 

#----------
# rmatrix,ANY-method
#----------
# random matrix of the same dimension as another matrix
x <- matrix(3,4)
dim(rmatrix(x))

## [1] 4 1

#----------
# rmatrix,NMF-method
#----------
# generate noisy fitted target from an NMF model (the true model)
gr <- as.numeric(mapply(rep, 1:3, 3))
h <- outer(1:3, gr, '==') + 0
x <- rnmf(10, H=h)
y <- rmatrix(x)
## Not run: 
##D # show heatmap of the noisy target matrix: block patterns should be clear
##D aheatmap(y)
## End(Not run)


# test NMF algorithm on noisy data
# add some noise to the true model (drawn from uniform [0,1])
res <- nmf(rmatrix(x), 3)
summary(res)

## Length  Class   Mode 
##      1 NMFfit     S4

# add more noise to the true model (drawn from uniform [0,10])
res <- nmf(rmatrix(x, max=10), 3)
summary(res)

## Length  Class   Mode 
##      1 NMFfit     S4