# rNeymanScott

0th

Percentile

##### Simulate Neyman-Scott Process

Generate a random point pattern using the Neyman-Scott cluster process.

Keywords
spatial
##### Usage
rNeymanScott(lambda, rmax, rcluster, win = owin(c(0,1),c(0,1)), ...)
##### Arguments
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
##### Details

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.

##### Value

• The simulated point pattern (an object of class "ppp").

##### See Also

rpoispp, rMatClust

• rNeymanScott
##### Examples
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)
Documentation reproduced from package spatstat, version 1.1-3, License: GPL version 2 or newer

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