plot.kppm | R Documentation |
Plots a fitted cluster point process model, displaying the fitted intensity and the fitted K-function.
## S3 method for class 'kppm' plot(x, ..., what=c("intensity", "statistic", "cluster"), pause=interactive(), xname)
x |
Fitted cluster point process model.
An object of class |
... |
Arguments passed to |
what |
Character vector determining what will be plotted. |
pause |
Logical value specifying whether to pause between plots. |
xname |
Optional. Character string. The name of the object |
This is a method for the generic function
plot
for the class "kppm"
of fitted
cluster point process models.
The argument x
should be a cluster point process model
(object of class "kppm"
) obtained using
the function kppm
.
The choice of plots (and the order in which they are
displayed) is controlled by the argument what
.
The options (partially matched) are "intensity"
,
"statistic"
and "cluster"
.
This command is capable of producing three different plots:
specifies the fitted intensity of the model,
which is plotted using plot.ppm
. By default this plot
is not produced for stationary models.
specifies the empirical and fitted summary
statistics, which are plotted using plot.fv
. This is
only meaningful if the model has been fitted using the Method of
Minimum Contrast, and it is turned off otherwise.
specifies a fitted cluster, which is computed
by clusterfield
and plotted by
plot.im
. It is only meaningful for Poisson cluster
(incl. Neyman-Scott) processes, and it is turned off for
log-Gaussian Cox processes (LGCP). If the model is stationary (and
non-LGCP) this option is turned on by default and shows a fitted
cluster positioned at the centroid of the observation window. For
non-stationary (and non-LGCP) models this option is only invoked if
explicitly told so, and in that case an additional argument
locations
(see clusterfield
) must be given to
specify where to position the parent point(s) .
Alternatively what="all"
selects all available options.
Null.
and \ege
kppm
,
plot.ppm
,
data(redwood) fit <- kppm(redwood~1, "Thomas") plot(fit)
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