R: Define Point Process Model for Metropolis-Hastings...

rmhmodel {spatstat} | R Documentation

## Define Point Process Model for Metropolis-Hastings Simulation.

### Description

Builds a description of a point process model for use in simulating the model by the Metropolis-Hastings algorithm.

### Usage

      rmhmodel(...)

### Arguments

`...` | Arguments specifying the point process model in some format.

### Details

Simulated realisations of many point process models can be generated using the Metropolis-Hastings algorithm `rmh`. The algorithm requires the model to be specified in a particular format: an object of class `"rmhmodel"`.

The function `rmhmodel` takes a description of a point process model in some other format, and converts it into an object of class `"rmhmodel"`. It also checks that the parameters of the model are valid.

The function `rmhmodel` is generic, with methods for

fitted point process models:

  an object of class `"ppm"`, obtained by a call to the model-fitting function `ppm`. See `rmhmodel.ppm`.

lists:

  a list of parameter values in a certain format. See `rmhmodel.list`.

default:

  parameter values specified as separate arguments to `...`. See `rmhmodel.default`.

### Value

An object of class `"rmhmodel"`, which is essentially a list of parameter values for the model.

There is a `print` method for this class, which prints a sensible description of the model chosen.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

and Rolf Turner r.turner@auckland.ac.nz

### References

Diggle, P. J. (2003) Statistical Analysis of Spatial Point Patterns (2nd ed.) Arnold, London.

Diggle, P.J. and Gratton, R.J. (1984) Monte Carlo methods of inference for implicit statistical models. Journal of the Royal Statistical Society, series B 46, 193 – 212.

Diggle, P.J., Gates, D.J., and Stibbard, A. (1987) A nonparametric estimator for pairwise-interaction point processes. Biometrika 74, 763 – 770. Scandinavian Journal of Statistics 21, 359–373.

Geyer, C.J. (1999) Likelihood Inference for Spatial Point Processes. Chapter 3 in O.E. Barndorff-Nielsen, W.S. Kendall and M.N.M. Van Lieshout (eds) Stochastic Geometry: Likelihood and Computation, Chapman and Hall / CRC, Monographs on Statistics and Applied Probability, number 80. Pages 79–140.

### See Also

`rmhmodel.ppm`, `rmhmodel.default`, `rmhmodel.list`, `rmh`, `rmhcontrol`, `rmhstart`, `ppm`, `Strauss`, `Softcore`, `StraussHard`, `Triplets`, `MultiStrauss`, `MultiStraussHard`, `DiggleGratton`, `PairPiece`

[Package spatstat version 1.54-0 Index]