# 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)), ..., lmax=NULL)
##### Arguments
lambda
Intensity of the Poisson process of cluster centres. A single positive number, a function, or a pixel image.
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
lmax
Optional. Upper bound on the values of lambda when lambda is a function or pixel image.
##### 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.

First, the algorithm generates a Poisson point process of parent'' points with intensity lambda. Here lambda may be a single positive number, a function lambda(x, y), or a pixel image object of class "im" (see im.object). See rpoispp for details. Second, 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").

rpoispp, rMatClust

• rNeymanScott
##### Examples
require(spatstat)
nclust <-  function(x0, y0, radius, n) {
X <- rNeymanScott(10, 0.2, nclust, radius=0.2, n=5)