Generating Random Matrices
rmatrix(x, ...)
## S4 method for signature 'numeric'
rmatrix(x, y = NULL, dist = runif,
byrow = FALSE, dimnames = NULL, ...)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.
#---------- # rmatrix,numeric-method #---------- ## 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,ANY-method #---------- # random matrix of the same dimension as another matrix x <- matrix(3,4) dim(rmatrix(x)) #---------- # 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: # 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)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.