cauchy.estK {spatstat.model}

Fit the Neyman-Scott cluster process with Cauchy kernel

Description

Fits the Neyman-Scott Cluster point process with Cauchy kernel to a point pattern dataset by the Method of Minimum Contrast.

Usage

cauchy.estK(X, startpar=c(kappa=1,scale=1), lambda=NULL,
            q = 1/4, p = 2, rmin = NULL, rmax = NULL, ...)

Arguments

X

Data to which the model will be fitted. Either a point pattern or a summary statistic. See Details.

startpar

Vector of starting values for the parameters of the model.

lambda

Optional. An estimate of the intensity of the point process.

q, p

Optional. Exponents for the contrast criterion.

rmin, rmax

Optional. The interval of r values for the contrast criterion.

...

Optional arguments passed to optim to control the optimisation algorithm. See Details.

Details

This algorithm fits the Neyman-Scott cluster point process model with Cauchy kernel to a point pattern dataset by the Method of Minimum Contrast, using the K function.

The argument X can be either

a point pattern:

An object of class "ppp" representing a point pattern dataset. The K function of the point pattern will be computed using Kest, and the method of minimum contrast will be applied to this.

a summary statistic:

An object of class "fv" containing the values of a summary statistic, computed for a point pattern dataset. The summary statistic should be the K function, and this object should have been obtained by a call to Kest or one of its relatives.

The algorithm fits the Neyman-Scott cluster point process with Cauchy kernel to X, by finding the parameters of the Matern Cluster model which give the closest match between the theoretical K function of the Matern Cluster process and the observed K function. For a more detailed explanation of the Method of Minimum Contrast, see mincontrast.

The model is described in Jalilian et al (2013). It is a cluster process formed by taking a pattern of parent points, generated according to a Poisson process with intensity \kappa, and around each parent point, generating a random number of offspring points, such that the number of offspring of each parent is a Poisson random variable with mean \mu, and the locations of the offspring points of one parent follow a common distribution described in Jalilian et al (2013).

If the argument lambda is provided, then this is used as the value of the point process intensity \lambda. Otherwise, if X is a point pattern, then \lambda will be estimated from X. If X is a summary statistic and lambda is missing, then the intensity \lambda cannot be estimated, and the parameter \mu will be returned as NA.

The remaining arguments rmin,rmax,q,p control the method of minimum contrast; see mincontrast.

The corresponding model can be simulated using rCauchy.

For computational reasons, the optimisation procedure uses the parameter eta2, which is equivalent to 4 * scale^2 where scale is the scale parameter for the model as used in rCauchy.

Homogeneous or inhomogeneous Neyman-Scott/Cauchy models can also be fitted using the function kppm and the fitted models can be simulated using simulate.kppm.

The optimisation algorithm can be controlled through the additional arguments "..." which are passed to the optimisation function optim. For example, to constrain the parameter values to a certain range, use the argument method="L-BFGS-B" to select an optimisation algorithm that respects box constraints, and use the arguments lower and upper to specify (vectors of) minimum and maximum values for each parameter.

Value

An object of class "minconfit". There are methods for printing and plotting this object. It contains the following main components:

par

Vector of fitted parameter values.

fit

Function value table (object of class "fv") containing the observed values of the summary statistic (observed) and the theoretical values of the summary statistic computed from the fitted model parameters.

Author(s)

Abdollah Jalilian and Rasmus Waagepetersen. Adapted for spatstat by Adrian Baddeley [e-mail]

References

Ghorbani, M. (2013) Cauchy cluster process. Metrika 76, 697–706.

Jalilian, A., Guan, Y. and Waagepetersen, R. (2013) Decomposition of variance for spatial Cox processes. Scandinavian Journal of Statistics 40, 119-137.

Waagepetersen, R. (2007) An estimating function approach to inference for inhomogeneous Neyman-Scott processes. Biometrics 63, 252–258.

See Also

kppm, cauchy.estpcf, lgcp.estK, thomas.estK, vargamma.estK, mincontrast, Kest, Kmodel.

rCauchy to simulate the model.

Examples

    u <- cauchy.estK(redwood)
    u
    plot(u)