Simulate a Fitted Cluster Point Process Model
Generates simulated realisations from a fitted cluster point process model.
## S3 method for class 'kppm'
simulate(object, nsim = 1, seed=NULL, ...,
window=NULL, covariates=NULL,
n.cond = NULL, w.cond = NULL,
verbose=TRUE, retry=10,
drop=FALSE)object |
Fitted cluster point process model. An object of class |
nsim |
Number of simulated realisations. |
seed |
an object specifying whether and how to initialise
the random number generator. Either |
... |
Additional arguments passed to the relevant random generator. See Details. |
window |
Optional. Window (object of class |
covariates |
Optional. A named list containing new values for the covariates in the model. |
n.cond |
Optional. Integer specifying a fixed number of points. See the section on Conditional Simulation. |
w.cond |
Optional. Conditioning region. A window (object of class |
verbose |
Logical. Whether to print progress reports (when |
retry |
Number of times to repeat the simulation if it fails (e.g. because of insufficient memory). |
drop |
Logical. If |
This function is a method for the generic function
simulate for the class "kppm" of fitted
cluster point process models.
Additional arguments ... are passed to the relevant function
performing the simulation.
For example the argument saveLambda is recognised by all of the
simulation functions.
The return value is a list of point patterns.
It also carries an attribute "seed" that
captures the initial state of the random number generator.
This follows the convention used in
simulate.lm (see simulate).
It can be used to force a sequence of simulations to be
repeated exactly, as shown in the examples for simulate.
A list of length nsim containing simulated point patterns
(objects of class "ppp"). (For conditional simulation,
the length of the result may be shorter than nsim).
The return value also carries an attribute "seed" that
captures the initial state of the random number generator.
See Details.
If n.cond is specified, it should be a single integer.
Simulation will be conditional on the event
that the pattern contains exactly n.cond points
(or contains exactly n.cond points inside
the region w.cond if it is given).
Conditional simulation uses the rejection algorithm described
in Section 6.2 of Moller, Syversveen and Waagepetersen (1998).
There is a maximum number of proposals which will be attempted.
Consequently the return value may contain fewer
than nsim point patterns.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk
Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press.
Moller, J., Syversveen, A. and Waagepetersen, R. (1998) Log Gaussian Cox Processes. Scandinavian Journal of Statistics 25, 451–482.
if(offline <- !interactive()) {
spatstat.options(npixel=32, ndummy.min=16)
}
fit <- kppm(redwood ~x, "Thomas")
simulate(fit, 2)
simulate(fit, n.cond=60)
if(offline) reset.spatstat.options()Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.