rStraussHard

0th

Percentile

Perfect Simulation of the Strauss-Hardcore Process

Generate a random pattern of points, a simulated realisation of the Strauss-Hardcore process, using a perfect simulation algorithm.

Usage
rStraussHard(beta, gamma = 1, R = 0, H = 0, W = owin(), expand=TRUE, nsim=1)
Arguments
beta
intensity parameter (a positive number).
gamma
interaction parameter (a number between 0 and 1, inclusive).
R
interaction radius (a non-negative number).
H
hard core distance (a non-negative number smaller than R).
W
window (object of class "owin") in which to generate the random pattern. Currently this must be a rectangular window.
expand
Logical. If FALSE, simulation is performed in the window W, which must be rectangular. If TRUE (the default), simulation is performed on a larger window, and the result is clipped to the original wind
nsim
Number of simulated realisations to be generated.
Details

This function generates a realisation of the Strauss-Hardcore point process in the window W using a perfect simulation algorithm.

The Strauss-Hardcore process is described in StraussHard.

The simulation algorithm used to generate the point pattern is dominated coupling from the past as implemented by Berthelsen and latex{Mller{Moller} (2002, 2003). This is a perfect simulation or exact simulation algorithm, so called because the output of the algorithm is guaranteed to have the correct probability distribution exactly (unlike the Metropolis-Hastings algorithm used in rmh, whose output is only approximately correct).

A limitation of the perfect simulation algorithm is that the interaction parameter $\gamma$ must be less than or equal to $1$. To simulate a Strauss-hardcore process with $\gamma > 1$, use rmh.

There is a tiny chance that the algorithm will run out of space before it has terminated. If this occurs, an error message will be generated. } If nsim = 1, a point pattern (object of class "ppp"). If nsim > 1, a list of point patterns. Berthelsen, K.K. and latex{Mller{Moller}, J. (2002) A primer on perfect simulation for spatial point processes. Bulletin of the Brazilian Mathematical Society 33, 351-367.

Berthelsen, K.K. and latex{Mller{Moller}, J. (2003) Likelihood and non-parametric Bayesian MCMC inference for spatial point processes based on perfect simulation and path sampling. Scandinavian Journal of Statistics 30, 549-564.

latex{Mller{Moller}, J. and Waagepetersen, R. (2003). Statistical Inference and Simulation for Spatial Point Processes. Chapman and Hall/CRC. } [object Object],[object Object] Z <- rStraussHard(100,0.7,0.05,0.02) rmh, rStrauss, StraussHard.

spatial datagen

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

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