Produces a scatterplot matrix of the pixel values in two or more pixel images.
# S3 method for im
pairs(..., plot=TRUE)
Any number of arguments, each of which is either
a pixel image (object of class "im"
)
or a named argument to be passed to pairs.default
.
Logical. If TRUE
, the scatterplot matrix is plotted.
Invisible. A data.frame
containing the
corresponding pixel values for each image.
The return value also belongs to the class plotpairsim
which has
a plot method, so that it can be re-plotted.
Since the scatterplots may show very dense concentrations of points,
it may be useful to set panel=panel.image
or panel=panel.contour
to draw a colour image or contour plot
of the kernel-smoothed density of the scatterplot in each panel.
The argument panel
is passed
to pairs.default
. See the help for
panel.image
and panel.contour
.
To control the appearance of the individual scatterplot panels,
see pairs.default
, points
or par
.
To control the plotting symbol for the points in the scatterplot,
use the arguments pch
, col
, bg
as described
under points
(because
the default panel plotter is the function points
).
To suppress the tick marks on the plot axes,
type par(xaxt="n", yaxt="n")
before calling pairs
.
This is a method for the generic function pairs
for the class of pixel images.
It produces a square array of plot panels, in which each panel shows a scatterplot of the pixel values of one image against the corresponding pixel values of another image.
At least two of the arguments …
should be pixel images (objects of class "im"
).
Their spatial domains must overlap, but need not have the same
pixel dimensions.
First the pixel image domains are intersected, and converted to a
common pixel resolution. Then the corresponding pixel values of each image are
extracted. Then pairs.default
is called to
plot the scatterplot matrix.
Any arguments in …
which are not pixel images will be
passed to pairs.default
to control the plot.
pairs
,
pairs.default
,
panel.contour
,
panel.image
,
plot.im
,
im
,
par
# NOT RUN {
X <- density(rpoispp(30))
Y <- density(rpoispp(40))
Z <- density(rpoispp(30))
p <- pairs(X,Y,Z)
p
plot(p)
# }
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