clusterfield | R Documentation |
Calculate the superposition of cluster kernels at the location of a point pattern.
## 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 |
The actual calculations are performed
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 method clusterfield.function
,
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 method clusterfield.character
, the
argument model
must be one of
the following character strings:
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 method clusterfield.kppm
extracts the relevant information
from the fitted
model (including mu
) and calls clusterfield.function
.
A pixel image (object of class "im"
).
.
density.ppp
and kppm
.
Simulation algorithms for cluster models:
rCauchy
rMatClust
rThomas
rVarGamma
# 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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