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.
}
A point pattern (object of class "ppp"
).
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