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runifpoint(n, win=owin(c(0,1),c(0,1)), giveup=1000, warn=TRUE, ..., nsim=1, drop=TRUE, ex=NULL)"owin"
or something acceptable to as.owin.
n is very large.
See Details.
nsim=1 and drop=TRUE (the default), the
result will be a point pattern, rather than a list
containing a point pattern.
ex is given and n and win are missing,
then n and win will be calculated from
the point pattern ex.
"ppp")
if nsim=1, or a list of point patterns if nsim > 1.
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:
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.
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.
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.
ppp.object,
owin.object,
rpoispp,
rpoint
# 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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