| rNeymanScott {spatstat} | R Documentation |
Generate a random point pattern using the Neyman-Scott cluster process.
rNeymanScott(lambda, rmax, rcluster, win = owin(c(0,1),c(0,1)), ...)
lambda |
Intensity of the Poisson process of cluster centres. A single positive number. |
rmax |
Maximum radius of a random cluster. |
rcluster |
A function which generates random clusters. |
win |
Window in which to simulate the pattern.
An object of class "owin"
or something acceptable to as.owin.
|
... |
Arguments passed to rcluster
|
This algorithm generates a realisation of the
general Neyman-Scott process, with the cluster mechanism
given by the function rcluster.
The clusters must have a finite maximum possible radius rmax.
We algorithm
generates a uniform Poisson point process of ``parent'' points
with intensity lambda. Then each parent point is
replaced by a random cluster of points, created by calling the
function rcluster.
The function rcluster should expect to be called as
rcluster(xp[i],yp[i],...) for each parent point at a location
(xp[i],yp[i]). The return value of rcluster
should be a list with elements
x,y which are vectors of equal length giving the absolute
x and y coordinates of the points in the cluster.
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
library(spatstat)
nclust <- function(x0, y0, radius, n) {
return(runifdisc(n, radius, x0, y0))
}
X <- rNeymanScott(10, 0.2, nclust, radius=0.2, n=5)