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

`clusterfield(model, locations = NULL, …)` # S3 method for character
clusterfield(model, locations = NULL, …)

# S3 method for function
clusterfield(model, locations = NULL, …, mu = NULL)

# S3 method for kppm
clusterfield(model, locations = NULL, …)

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.

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

).

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`

.

`density.ppp`

and `kppm`

Simulation algorithms for cluster models:
`rCauchy`

`rMatClust`

`rThomas`

`rVarGamma`

# NOT RUN { # 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) # }