rStraussHard(beta, gamma = 1, R = 0, H = 0, W = owin(), expand=TRUE, nsim=1, drop=TRUE)
R
).
"owin"
) in which to
generate the random pattern. Currently this must be a rectangular
window.
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
window W
.
Alternatively expand
can be an object of class
"rmhexpand"
(see rmhexpand
)
determining the expansion method.
nsim=1
and drop=TRUE
(the default), the
result will be a point pattern, rather than a list
containing a point pattern.
nsim = 1
, a point pattern (object of class "ppp"
).
If nsim > 1
, a list of point patterns.
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 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.
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.
rmh
,
StraussHard
. Z <- rStraussHard(100,0.7,0.05,0.02)
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