rGaussPoisson

From spatstat v1.42-2 by Adrian Baddeley

Simulate Gauss-Poisson Process

Generate a random point pattern, a simulated realisation of the Gauss-Poisson Process.

Keywords: spatial, datagen

Usage

rGaussPoisson(kappa, r, p2, win = owin(c(0,1),c(0,1)),
               ..., nsim=1, drop=TRUE)

Arguments

kappa: Intensity of the Poisson process of cluster centres. A single positive number, a function, or a pixel image.
r: Diameter of each cluster that consists of exactly 2 points.
p2: Probability that a cluster contains exactly 2 points.
win: Window in which to simulate the pattern. An object of class "owin" or something acceptable to as.owin.
...: Ignored.
nsim: Number of simulated realisations to be generated.
drop: Logical. If nsim=1 and drop=TRUE (the default), the result will be a point pattern, rather than a list containing a point pattern.

Details

This algorithm generates a realisation of the Gauss-Poisson point process inside the window win. The process is constructed by first generating a Poisson point process of parent points with intensity kappa. Then each parent point is either retained (with probability 1 - p2) or replaced by a pair of points at a fixed distance r apart (with probability p2). In the case of clusters of 2 points, the line joining the two points has uniform random orientation.

In this implementation, parent points are not restricted to lie in the window; the parent process is effectively the uniform Poisson process on the infinite plane.

Value

A point pattern (an object of class "ppp") if nsim=1, or a list of point patterns if nsim > 1.
Additionally, some intermediate results of the simulation are returned as attributes of the point pattern. See rNeymanScott.

Examples

pp <- rGaussPoisson(30, 0.07, 0.5)

Documentation reproduced from package spatstat, version 1.42-2, License: GPL (>= 2)

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