# simulate.lppm

From spatstat v1.42-2
by Adrian Baddeley

##### 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 class '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

```
fit <- lppm(unmark(chicago) ~ y)
simulate(fit)[[1]]
```

*Documentation reproduced from package spatstat, version 1.42-2, License: GPL (>= 2)*

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