Generates simulated realisations from a fitted cluster point process model.

```
# S3 method for 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 `"kppm"`

.

nsim

Number of simulated realisations.

seed

an object specifying whether and how to initialise
the random number generator. Either `NULL`

or an integer that will
be used in a call to `set.seed`

before simulating the point patterns.

…

Additional arguments passed to the relevant random generator. See Details.

window

Optional. Window (object of class `"owin"`

) in which the
model should be simulated.

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 `"owin"`

)
specifying the region which must contain exactly `n.cond`

points.
See the section on *Conditional Simulation*.

verbose

Logical. Whether to print progress reports (when `nsim > 1`

).

retry

Number of times to repeat the simulation if it fails (e.g. because of insufficient memory).

drop

Logical. If `nsim=1`

and `drop=TRUE`

, the
result will be a point pattern, rather than a list
containing a point pattern.

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.

This function is a method for the generic function
`simulate`

for the class `"kppm"`

of fitted
cluster point process models.

Simulations are performed by
`rThomas`

,
`rMatClust`

,
`rCauchy`

,
`rVarGamma`

or `rLGCP`

depending on the model.

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`

.

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.

`kppm`

,
`rThomas`

,
`rMatClust`

,
`rCauchy`

,
`rVarGamma`

,
`rLGCP`

,
`simulate.ppm`

,
`simulate`

```
# NOT RUN {
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()
# }
```

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