| rpoint {spatstat} | R Documentation |
Generate a random point pattern containing n independent, identically distributed random points with any specified distribution.
rpoint(n, f, fmax=NULL, win=unit.square(), ..., giveup=1000, verbose=FALSE)
n |
Number of points to generate. |
f |
The probability density of the points,
possibly un-normalised.
Either a constant,
a function f(x,y,...), or a pixel image object.
|
fmax |
An upper bound on the values of f.
If missing, this number will be estimated.
|
win |
Window in which to simulate the pattern.
Ignored if f is a pixel image.
|
... |
Arguments passed to the function f.
|
giveup |
Number of attempts in the rejection method after which the algorithm should stop trying to generate new points. |
verbose |
Flag indicating whether to report details of performance of the simulation algorithm. |
This function generates n independent, identically distributed
random points with common probability density proportional to
f.
The argument f may be
win with probability density proportional
to f(x,y,...) where x and y are the cartesian
coordinates. The function f must accept
two vectors of coordinates x,y and return the corresponding
vector of function values. Additional arguments ... of any kind
may be passed to the function.
f is a pixel image object
of class "im" (see im.object) then
random points will be generated
in the window of this pixel image, with probability density
proportional to the pixel values of f.
The algorithm is as follows:
f is a constant, we invoke runifpoint.
f is a function, then we use the rejection method.
Proposal points are generated from the uniform distribution.
A proposal point (x,y) is accepted with probability
f(x,y,...)/fmax and otherwise rejected.
The algorithm continues until n points have been
accepted. It gives up after giveup * n proposals
if there are still fewer than n points.
f is a pixel image, then a random sequence of
pixels is selected (using sample)
with probabilities proportional to the
pixel values of f. Then for each pixel in the sequence
we generate a uniformly distributed random point in that pixel.
The algorithm for pixel images is more efficient than that for functions.
The simulated point pattern (an object of class "ppp").
Adrian Baddeley adrian@maths.uwa.edu.au http://www.maths.uwa.edu.au/~adrian/ and Rolf Turner rolf@math.unb.ca http://www.math.unb.ca/~rolf
ppp.object,
owin.object,
runifpoint
# 100 uniform random points in the unit square
X <- rpoint(100)
# 100 random points with probability density proportional to x^2 + y^2
X <- rpoint(100, function(x,y) { x^2 + y^2}, 1)
# `fmax' may be omitted
X <- rpoint(100, function(x,y) { x^2 + y^2})
# irregular window
data(letterR)
X <- rpoint(100, function(x,y) { x^2 + y^2}, win=letterR)
# make a pixel image
Z <- setcov(letterR)
# 100 points with density proportional to pixel values
X <- rpoint(100, Z)