R: Field of clusters

clusterfield {spatstat} | R Documentation

## Field of clusters

### Description

Calculate the superposition of cluster kernels at the location of a point pattern.

### Usage

      clusterfield(model, locations = NULL, ...)

      ## S3 method for class 'character'
    clusterfield(model, locations = NULL, ...)

      ## S3 method for class 'function'
    clusterfield(model, locations = NULL, ..., mu = NULL)

      ## S3 method for class 'kppm'
    clusterfield(model, locations = NULL, ...)

### Arguments

`model` | Cluster model. Either a fitted cluster model (object of class `"kppm"`), a character string specifying the type of cluster model, or a function defining the cluster kernel. See Details.
`locations` | A point pattern giving the locations of the kernels. Defaults to the centroid of the observation window for the `"kppm"` method and to the center of a unit square otherwise.
`...` | Additional arguments passed to `density.ppp` or the cluster kernel. See Details.
`mu` | Mean number of offspring per cluster. A single number or a pixel image.

### Details

The actual calculations are preformed by `density.ppp` and `...` arguments are passed thereto for control over the pixel resolution etc. (These arguments are then passed on to `pixellate.ppp` and `as.mask`.)

For the function method the given kernel function should accept vectors of x and y coordinates as its first two arguments. Any additional arguments may be passed through the `...`.

The function method also accepts the optional parameter `mu` (defaulting to 1) specifying the mean number of points per cluster (as a numeric) or the inhomogeneous reference cluster intensity (as an `"im"` object or a `function(x,y)`). The interpretation of `mu` is as explained in the simulation functions referenced in the See Also section below.

For the character method `model` must be one of: `model="Thomas"` for the Thomas process, `model="MatClust"` for the Matern cluster process, `model="Cauchy"` for the Neyman-Scott cluster process with Cauchy kernel, or `model="VarGamma"` for the Neyman-Scott cluster process with Variance Gamma kernel. For all these models the parameter `scale` is required and passed through `...` as well as the parameter `nu` when `model="VarGamma"`. This method calls `clusterfield.function` so the parameter `mu` may also be passed through `...` and will be interpreted as explained above.

The kppm method extracts the relevant information from the fitted model (including `mu`) and calls `clusterfield.function`.

### Value

A pixel image (object of class `"im"`).

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

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

and Ege Rubak rubak@math.aau.dk .

### See Also

`density.ppp` and `kppm`

Simulation algorithms for cluster models: `rCauchy` `rMatClust` `rThomas` `rVarGamma`

### Examples

      # method for fitted model
      fit <- kppm(redwood~1, "Thomas")
      clusterfield(fit, eps = 0.01)

      # method for functions
      kernel <- function(x,y,scal) {
          r <- sqrt(x^2 + y^2)
          ifelse(r > 0,
                 dgamma(r, shape=5, scale=scal)/(2 * pi * r),
                 0)
      }
      X <- runifpoint(10)
      clusterfield(kernel, X, scal=0.05)

[Package spatstat version 1.63-3 Index]