R: Fit the Matern Cluster Point Process by Minimum Contrast...

matclust.estpcf {spatstat.core} | R Documentation

## Fit the Matern Cluster Point Process by Minimum Contrast Using Pair Correlation

### Description

Fits the Matern Cluster point process to a point pattern dataset by the Method of Minimum Contrast using the pair correlation function.

### Usage

    matclust.estpcf(X, startpar=c(kappa=1,scale=1), lambda=NULL,
                q = 1/4, p = 2, rmin = NULL, rmax = NULL, ...,
                pcfargs=list())

### Arguments

`X` | Data to which the Matern Cluster model will be fitted. Either a point pattern or a summary statistic. See Details.
`startpar` | Vector of starting values for the parameters of the Matern Cluster process.
`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.
`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 Matern Cluster point process model to a point pattern dataset by the Method of Minimum Contrast, using the pair correlation function.

The argument `X` can be either

a point pattern:

  An object of class `"ppp"` representing a point pattern dataset. The pair correlation function of the point pattern will be computed using `pcf`, 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 pair correlation function, and this object should have been obtained by a call to `pcf` or one of its relatives.

The algorithm fits the Matern Cluster point process to `X`, by finding the parameters of the Matern Cluster model which give the closest match between the theoretical pair correlation function of the Matern Cluster process and the observed pair correlation function. For a more detailed explanation of the Method of Minimum Contrast, see `mincontrast`.

The Matern Cluster point process is described in Moller and Waagepetersen (2003, p. 62). 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 are independent and uniformly distributed inside a circle of radius R centred on the parent point, where R is equal to the parameter `scale`. The named vector of stating values can use either `R` or `scale` as the name of the second component, but the latter is recommended for consistency with other cluster models.

The theoretical pair correlation function of the Matern Cluster process is

g(r) = 1 + h(r/(2*R))/(4 * pi * R * kappa * r)

where the radius R is the parameter `scale` and

h(z) = (16/pi) * ((z * arccos(z) - z^2 * sqrt(1 - z^2))

for z <= 1, and h(z) = 0 for z > 1. The theoretical intensity of the Matern Cluster process is lambda=kappa* mu.

In this algorithm, the Method of Minimum Contrast is first used to find optimal values of the parameters kappa and R. Then the remaining parameter mu is inferred from the estimated intensity lambda.

If the argument `lambda` is provided, then this is used as the value of 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 Matern Cluster process can be simulated, using `rMatClust`.

Homogeneous or inhomogeneous Matern Cluster models can also be fitted using the function `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)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

### References

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

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

### See Also

`kppm`, `matclust.estK`, `thomas.estpcf`, `thomas.estK`, `lgcp.estK`, `mincontrast`, `pcf`, `rMatClust` to simulate the fitted model.

### Examples

        data(redwood)
        u <- matclust.estpcf(redwood, c(kappa=10, R=0.1))
        u
        plot(u, legendpos="topright")

[Package spatstat.core version 2.4-4 Index]