# simulate.ppm

From spatstat v1.16-2
by Adrian Baddeley

##### Simulate a Fitted Gibbs Point Process Model

Generates simulated realisations from a fitted Gibbs or Poisson point process model.

##### Usage

```
## S3 method for class 'ppm':
simulate(object, nsim=1, ...,
start = NULL,
control = rmhcontrol(),
project=TRUE,
verbose=FALSE, progress=TRUE)
```

##### Arguments

- object
- Fitted point process model.
An object of class
`"ppm"`

. - nsim
- Number of simulated realisations.
- start
- Data determining the initial state
of the Metropolis-Hastings algorithm. See
`rmhstart`

for description of these arguments. Defaults to`list(x.start=data.ppm(model))`

- control
- Data controlling the running of
the Metropolis-Hastings algorithm. See
`rmhcontrol`

for description of these arguments. - ...
- Ignored.
- verbose
- Logical flag indicating whether to print progress reports
from
`rmh.ppm`

during the simulation of each point pattern. - progress
- Logical flag indicating whether to print progress reports for the sequence of simulations.
- project
- Logical flag indicating what to do if the fitted model is
invalid (in the sense that the values of the fitted coefficients do not
specify a valid point process).
If
`project=TRUE`

the closest valid model will be simulated; if

##### Details

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

for the class `"ppm"`

of fitted
point process models.
Simulations are performed by `rmh.ppm`

.

##### Value

- A list of length
`nsim`

containing simulated point patterns (objects of class`"ppp"`

).

##### See Also

##### Examples

```
data(japanesepines)
fit <- ppm(japanesepines, ~1, Strauss(0.1))
simulate(fit, 2)
```

*Documentation reproduced from package spatstat, version 1.16-2, License: GPL (>= 2)*

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