Calculate the superposition of cluster kernels at the location of a point pattern.
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Cluster model. Either a fitted cluster model (object of class
A point pattern giving the locations of the kernels. Defaults to the
centroid of the observation window for the
Additional arguments passed to
Mean number of offspring per cluster. A single number or a pixel image.
The actual calculations are preformed by
... arguments are passed thereto for control over the pixel
resolution etc. (These arguments are then passed on to
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
(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
scale is required and passed through
well as the parameter
clusterfield.function so the parameter
may also be passed through
... and will be interpreted as
The kppm method extracts the relevant information from the fitted
mu) and calls
A pixel image (object of class
and \ege .
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Loading required package: nlme Loading required package: rpart spatstat 1.52-1 (nickname: 'Apophenia') For an introduction to spatstat, type 'beginner' Note: spatstat version 1.52-1 is out of date by more than 4 months; we recommend upgrading to the latest version. real-valued pixel image 100 x 100 pixel array (ny, nx) enclosing rectangle: [0, 1] x [-1, 0] units Warning message: In density.ppp(locations, kernel = model, ...) : Bandwidth selection will be based on Gaussian kernel real-valued pixel image 128 x 128 pixel array (ny, nx) enclosing rectangle: [0, 1] x [0, 1] units Warning message: In density.ppp(locations, kernel = model, ...) : Bandwidth selection will be based on Gaussian kernel
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