spatstat (version 1.3-2)

rNeymanScott: Simulate Neyman-Scott Process

Description

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

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

Value

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

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.

See Also

rpoispp, rMatClust

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)