# clusterfield

##### Field of clusters

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

- Keywords
- spatial

##### Usage

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

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

## S3 method for class 'kppm':
clusterfield(model, locations = NULL, \dots)

##### 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"`

).

##### 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)
```

*Documentation reproduced from package spatstat, version 1.41-1, License: GPL (>= 2)*