Description Usage Arguments Methods Examples
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
.
1 2 3 4 5 
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 
byrow 
a logical passed in the internal call to the
function 
dimnames 

... 
extra arguments passed to the distribution
function 
signature(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...
signature(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, ...)
.
signature(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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56  #
# rmatrix,numericmethod
#
## Generate a random matrix of a given size
rmatrix(5, 3)
## Generate a random matrix of the same dimension of a template matrix
a < matrix(1, 3, 4)
rmatrix(a)
## 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,ANYmethod
#
# random matrix of the same dimension as another matrix
x < matrix(3,4)
dim(rmatrix(x))
#
# rmatrix,NMFmethod
#
# 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:
# show heatmap of the noisy target matrix: block patterns should be clear
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)
# add more noise to the true model (drawn from uniform [0,10])
res < nmf(rmatrix(x, max=10), 3)
summary(res)

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