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.
rStraussHard(beta, gamma = 1, R = 0, H = 0, W = owin())
- intensity parameter (a positive number).
- interaction parameter (a number between 0 and 1, inclusive).
- interaction radius (a non-negative number).
- hard core distance (a non-negative number smaller than
- window (object of class
"owin") in which to generate the random pattern. Currently this must be a rectangular window.
This function generates a realisation of the
Strauss-Hardcore point process in the window
The Strauss-Hardcore process is described in
The simulation algorithm used to generate the point pattern
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
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.
- A point pattern (object of class
Berthelsen, K.K. and 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 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.
Moller, J. and Waagepetersen, R. (2003). Statistical Inference and Simulation for Spatial Point Processes. Chapman and Hall/CRC.
Z <- rStraussHard(100,0.7,0.05,0.02)