Description Usage Arguments Details Value Author(s) See Also Examples
Generate a random point pattern containing n independent uniform random points.
1 2 
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
.
and \rolf
ppp.object
,
owin.object
,
rpoispp
,
rpoint
1 2 3 4 5 6 7 8 9 10 11 12  # 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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