Generate N Uniform Random Points
Generate a random point pattern containing n independent uniform random points.
runifpoint(n, win=owin(c(0,1),c(0,1)), giveup=1000, warn=TRUE, ...,
nsim=1, drop=TRUE, ex=NULL)n |
Number of points. |
win |
Window in which to simulate the pattern.
An object of class |
giveup |
Number of attempts in the rejection method after which the algorithm should stop trying to generate new points. |
warn |
Logical. Whether to issue a warning if |
... |
Ignored. |
nsim |
Number of simulated realisations to be generated. |
drop |
Logical. If |
ex |
Optional. A point pattern to use as the example.
If |
This function generates n independent random points,
uniformly distributed in the window win.
(For nonuniform distributions, see rpoint.)
The algorithm depends on the type of window, as follows:
If win is a rectangle then
n independent random points, uniformly distributed
in the rectangle, are generated by assigning uniform random values to their
cartesian coordinates.
If win is a binary image mask, then a random sequence of
pixels is selected (using sample)
with equal probabilities. Then for each pixel in the sequence
we generate a uniformly distributed random point in that pixel.
If win is a polygonal window, the algorithm uses the rejection
method. It finds a rectangle enclosing the window,
generates points in this rectangle, and tests whether they fall in
the desired window. It gives up when giveup * n tests
have been performed without yielding n successes.
The algorithm for binary image masks is faster than the rejection method but involves discretisation.
If warn=TRUE, then a warning will be issued if n is very large.
The threshold is spatstat.options("huge.npoints").
This warning has no consequences,
but it helps to trap a number of common errors.
A point pattern (an object of class "ppp")
if nsim=1, or a list of point patterns if nsim > 1.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au
and Rolf Turner r.turner@auckland.ac.nz
# 100 random points in the unit square pp <- runifpoint(100) # irregular window data(letterR) # polygonal pp <- runifpoint(100, letterR) # binary image mask pp <- runifpoint(100, as.mask(letterR)) ## # randomising an existing point pattern runifpoint(npoints(cells), win=Window(cells)) runifpoint(ex=cells)
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