rDiggleGratton {spatstat.random}

Perfect Simulation of the Diggle-Gratton Process

Description

Generate a random pattern of points, a simulated realisation of the Diggle-Gratton process, using a perfect simulation algorithm.

Usage

  rDiggleGratton(beta, delta, rho, kappa=1, W = owin(),
                 expand=TRUE, nsim=1, drop=TRUE)

Arguments

beta

intensity parameter (a positive number).

delta

hard core distance (a non-negative number).

rho

interaction range (a number greater than delta).

kappa

interaction exponent (a non-negative number).

W

window (object of class "owin") in which to generate the random pattern. Currently this must be a rectangular window.

expand

Logical. If FALSE, simulation is performed in the window W, which must be rectangular. If TRUE (the default), simulation is performed on a larger window, and the result is clipped to the original window W. Alternatively expand can be an object of class "rmhexpand" (see rmhexpand) determining the expansion method.

nsim

Number of simulated realisations to be generated.

drop

Logical. If nsim=1 and drop=TRUE (the default), the result will be a point pattern, rather than a list containing a point pattern.

Details

This function generates a realisation of the Diggle-Gratton point process in the window W using a ‘perfect simulation’ algorithm.

Diggle and Gratton (1984, pages 208-210) introduced the pairwise interaction point process with pair potential h(t) of the form

h(t) = \left( \frac{t-\delta}{\rho-\delta} \right)^\kappa \quad\quad \mbox{ if } \delta \le t \le \rho

with h(t) = 0 for t < \delta and h(t) = 1 for t > \rho. Here \delta, \rho and \kappa are parameters.

Note that we use the symbol \kappa where Diggle and Gratton (1984) use \beta, since in spatstat we reserve the symbol \beta for an intensity parameter.

The parameters must all be nonnegative, and must satisfy \delta \le \rho.

The simulation algorithm used to generate the point pattern is ‘dominated coupling from the past’ as implemented by Berthelsen and Moller (2002, 2003). This is a ‘perfect simulation’ or ‘exact simulation’ algorithm, so called because the output of the algorithm is guaranteed to have the correct probability distribution exactly (unlike the Metropolis-Hastings algorithm used in rmh, whose output is only approximately correct).

There is a tiny chance that the algorithm will run out of space before it has terminated. If this occurs, an error message will be generated.

Value

If nsim = 1, a point pattern (object of class "ppp"). If nsim > 1, a list of point patterns.

Author(s)

Adrian Baddeley [e-mail]

based on original code for the Strauss process by Kasper Klitgaard Berthelsen.

References

Berthelsen, K.K. and Moller, J. (2002) A primer on perfect simulation for spatial point processes. Bulletin of the Brazilian Mathematical Society 33, 351-367.

Berthelsen, K.K. and Moller, J. (2003) Likelihood and non-parametric Bayesian MCMC inference for spatial point processes based on perfect simulation and path sampling. Scandinavian Journal of Statistics 30, 549-564.

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.

Moller, J. and Waagepetersen, R. (2003). Statistical Inference and Simulation for Spatial Point Processes. Chapman and Hall/CRC.

See Also

rmh, rStrauss, rHardcore, rStraussHard, rDGS, rPenttinen.

For fitting the model, see DiggleGratton.

Examples

   X <- rDiggleGratton(50, 0.02, 0.07)
   Z <- rDiggleGratton(50, 0.02, 0.07, 2, nsim=2)