R: Simulate a Fitted Cluster Point Process Model

simulate.kppm {spatstat.core} | R Documentation

## Simulate a Fitted Cluster Point Process Model

### Description

Generates simulated realisations from a fitted cluster point process model.

### Usage

      ## 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)

### Arguments

`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.

### Details

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`.

### Value

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.

### Conditional Simulation

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.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk

### References

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.

### See Also

`kppm`, `rThomas`, `rMatClust`, `rCauchy`, `rVarGamma`, `rLGCP`, `simulate.ppm`, `simulate`

### Examples

      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()

[Package spatstat.core version 2.4-4 Index]