vargamma.estpcf

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Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel

Fits the Neyman-Scott cluster point process, with Variance Gamma kernel, to a point pattern dataset by the Method of Minimum Contrast, using the pair correlation function.

Keywords
models, spatial
Usage
vargamma.estpcf(X, startpar=c(kappa=1,eta=1), nu.ker = -1/4, lambda=NULL,
            q = 1/4, p = 2, rmin = NULL, rmax = NULL, nu.pcf=NULL,
            ..., pcfargs = list())
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.
nu.ker
Numerical value controlling the shape of the tail of the clusters. A number greater than -1/2.
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.
nu.pcf
Alternative specification of the shape parameter. See Details.
...
Optional arguments passed to optim to control the optimisation algorithm. See Details.
pcfargs
Optional list containing arguments passed to pcf.ppp to control the smoothing in the estimation of the pair correlation function.
Details

This algorithm fits the Neyman-Scott Cluster point process model with Variance Gamma kernel (Jalilian et al, 2013) to a point pattern dataset by the Method of Minimum Contrast, using the pair correlation function.

The argument X can be either [object Object],[object Object]

The algorithm fits the Neyman-Scott Cluster point process with Variance Gamma kernel to X, by finding the parameters of the model which give the closest match between the theoretical pair correlation function of the model and the observed pair correlation function. For a more detailed explanation of the Method of Minimum Contrast, see mincontrast. The Neyman-Scott cluster point process with Variance Gamma kernel 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 have a common distribution described in Jalilian et al (2013).

The shape of the kernel is determined by the dimensionless index nu.ker. This is the parameter $\nu^\prime = \alpha/2-1$ appearing in equation (12) on page 126 of Jalilian et al (2013). Instead of specifying nu.ker the user can specify nu.pcf which is the parameter $\nu=\alpha-1$ appearing in equation (13), page 127 of Jalilian et al (2013). These are related by nu.pcf = 2 * nu.ker + 1 and nu.ker = (nu.pcf - 1)/2. Exactly one of nu.ker or nu.pcf must be specified. 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 rVarGamma. The parameter eta appearing in startpar is equivalent to the scale parameter omega used in rVarGamma. Homogeneous or inhomogeneous Neyman-Scott/VarGamma 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:
  • parVector of fitted parameter values.
  • fitFunction 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.

References

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, vargamma.estK, lgcp.estpcf, thomas.estpcf, cauchy.estpcf, mincontrast, pcf, pcfmodel.

rVarGamma to simulate the model.

Aliases
  • vargamma.estpcf
Examples
u <- vargamma.estpcf(redwood)
    u
    plot(u, legendpos="topright")
Documentation reproduced from package spatstat, version 1.36-0, License: GPL (>= 2)

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