runifpoint
Generate N Uniform Random Points
Generate a random point pattern containing \(n\) independent uniform random points.
Usage
runifpoint(n, win=owin(c(0,1),c(0,1)), giveup=1000, warn=TRUE, …,
nsim=1, drop=TRUE, ex=NULL)Arguments
- n
Number of points.
- win
Window in which to simulate the pattern. An object of class
"owin"or something acceptable toas.owin.- 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
nis very large. See Details.- …
Ignored.
- nsim
Number of simulated realisations to be generated.
- drop
Logical. If
nsim=1anddrop=TRUE(the default), the result will be a point pattern, rather than a list containing a point pattern.- ex
Optional. A point pattern to use as the example. If
exis given andnandwinare missing, thennandwinwill be calculated from the point patternex.
Details
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
winis a rectangle then \(n\) independent random points, uniformly distributed in the rectangle, are generated by assigning uniform random values to their cartesian coordinates.If
winis a binary image mask, then a random sequence of pixels is selected (usingsample) with equal probabilities. Then for each pixel in the sequence we generate a uniformly distributed random point in that pixel.If
winis 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 whengiveup * ntests have been performed without yieldingnsuccesses.
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.
Value
A point pattern (an object of class "ppp")
if nsim=1, or a list of point patterns if nsim > 1.
See Also
Examples
# NOT RUN {
# 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)
# }