Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/clusterfunctions.R
Calculate the superposition of cluster kernels at the location of a point pattern.
1 2 3 4 5 6 7 8 9 10  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, ...)

model 
Cluster model. Either a fitted cluster model (object of class

locations 
A point pattern giving the locations of the kernels. Defaults to the
centroid of the observation window for the 
... 
Additional arguments passed to 
mu 
Mean number of offspring per cluster. A single number or a pixel image. 
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 NeymanScott cluster process with
Cauchy kernel, or model="VarGamma"
for the NeymanScott
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
.
A pixel image (object of class "im"
).
, \rolf
and \ege .
density.ppp
and kppm
Simulation algorithms for cluster models:
rCauchy
rMatClust
rThomas
rVarGamma
1 2 3 4 5 6 7 8 9 10 11 12 13  # 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)

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