# simulate.lppm

##### Simulate a Fitted Point Process Model on a Linear Network

Generates simulated realisations from a fitted Poisson point process model on a linear network.

##### Usage

```
# S3 method for lppm
simulate(object, nsim=1, ...,
new.coef=NULL,
progress=(nsim > 1),
drop=FALSE)
```

##### Arguments

- object
Fitted point process model on a linear network. An object of class

`"lppm"`

.- nsim
Number of simulated realisations.

- progress
Logical flag indicating whether to print progress reports for the sequence of simulations.

- new.coef
New values for the canonical parameters of the model. A numeric vector of the same length as

`coef(object)`

.- …
Arguments passed to

`predict.lppm`

to determine the spatial resolution of the image of the fitted intensity used in the simulation.- drop
Logical. If

`nsim=1`

and`drop=TRUE`

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

##### Details

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

for the class `"lppm"`

of fitted
point process models on a linear network.

Only Poisson process models are supported so far.

Simulations are performed by `rpoislpp`

.

##### Value

A list of length `nsim`

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

) on the same linear network as the
original data used to fit the model.
The result also belongs to the class `"solist"`

, so that it can be
plotted, and the class `"timed"`

, so that the total computation
time is recorded.

##### See Also

##### Examples

```
# NOT RUN {
fit <- lppm(unmark(chicago) ~ y)
simulate(fit)[[1]]
# }
```

*Documentation reproduced from package spatstat, version 1.55-1, License: GPL (>= 2)*