rNeymanScott {spatstat}R Documentation

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

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").

Author(s)

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

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)

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