rHardcore {spatstat.random} | R Documentation |
Perfect Simulation of the Hardcore Process
Description
Generate a random pattern of points, a simulated realisation of the Hardcore process, using a perfect simulation algorithm.
Usage
rHardcore(beta, R = 0, W = owin(), expand=TRUE, nsim=1, drop=TRUE)
Arguments
beta |
intensity parameter (a positive number). |
R |
hard core distance (a non-negative number). |
W |
window (object of class |
expand |
Logical. If |
nsim |
Number of simulated realisations to be generated. |
drop |
Logical. If |
Details
This function generates a realisation of the
Hardcore point process in the window W
using a ‘perfect simulation’ algorithm.
The Hardcore process is a model for strong spatial inhibition.
Two points of the process are forbidden to lie closer than
R
units apart.
The Hardcore process is the special case of the Strauss process
(see rStrauss
)
with interaction parameter equal to zero.
The simulation algorithm used to generate the point pattern
is ‘dominated coupling from the past’
as implemented by Berthelsen and 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).
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.
Value
If nsim = 1
, a point pattern (object of class "ppp"
).
If nsim > 1
, a list of point patterns.
Author(s)
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, based on original code for the Strauss process by Kasper Klitgaard Berthelsen.
References
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.
See Also
rmh
,
rStrauss
,
rStraussHard
,
rDiggleGratton
.
rDGS
,
rPenttinen
.
For fitting the model, see
Hardcore
.
Examples
X <- rHardcore(0.05,1.5,square(50))