mpl
,
plot the spatial trend and conditional intensity of the model.plot.ppm(x, nx = 40, ny = 40, superimpose = T,
trend = T, cif = T, pause = T,
how=c("persp","image", "contour"), ...)
mpl
.
An object of class "ppm"
.nx
by ny
) of pointsnx
aboveTRUE
, the original data point pattern
will be superimposed on the plots.TRUE
, the spatial trend will be plotted.TRUE
, the conditional intensity will be
plotted.pause=FALSE
if plotting to a file.predict.ppm
.plot
method for the class "ppm"
(see ppm.object
for details of this class).
It invokes predict.ppm
to compute the spatial
trend and conditional intensity of the fitted point process model.
See predict.ppm
for more explanation about spatial trend
and conditional intensity.
The spatial trend
and/or the conditional intensity of the fitted spatial point process model
x
are computed at the points of a rectangular grid, and these
values are plotted successively using
persp
, image
and contour
(or only a selection of these three, if how
is given). The default action is to create a rectangular 40 by 40 grid of points
in the observation window of the data point pattern, and evaluate
the spatial trend and conditional intensity at these locations.
If the fitted model had no spatial trend, then the default is
to omit plotting this (flat) surface, unless trend=TRUE
is set explicitly.
If the fitted model was Poisson, so that there were no spatial interactions,
then the conditional intensity and spatial trend are identical, and the
default is to omit the conditional intensity, unless cif=TRUE
is set
explicitly.
If the fitted model was a marked point process, then predictions are made and plotted for each possible mark value in turn.
mpl
,
ppm.object
,
predict.ppm
,
print.ppm
,
persp
,
image
,
contour
,
plot
library(spatstat)
data(cells)
Q <- quadscheme(cells)
m <- mpl(Q, ~1, Strauss(0.05))
plot(m)
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