cut.ppp
Classify Points in a Point Pattern
Classifies the points in a point pattern into distinct types according to the numerical marks in the pattern, or according to another variable.
Usage
# S3 method for ppp
cut(x, z=marks(x), ...)Arguments
- x
A two-dimensional point pattern. An object of class
"ppp".- z
Data determining the classification. A numeric vector, a factor, a pixel image, a window, a tessellation, or a string giving the name of a column of marks or the name of a spatial coordinate.
- …
Arguments passed to
cut.default. They determine the breakpoints for the mapping from numerical values inzto factor values in the output. Seecut.default.
Details
This function has the effect of classifying each point in the point
pattern x into one of several possible types. The
classification is based on the dataset z, which may be either
a factor (of length equal to the number of points in
z) determining the classification of each point inx. Levels of the factor determine the classification.a numeric vector (of length equal to the number of points in
z). The range of values ofzwill be divided into bands (the number of bands is determined by…) andzwill be converted to a factor usingcut.default.a pixel image (object of class
"im"). The value ofzat each point ofxwill be used as the classifying variable.a tessellation (object of class
"tess", seetess). Each point ofxwill be classified according to the tile of the tessellation into which it falls.a window (object of class
"owin"). Each point ofxwill be classified according to whether it falls inside or outside this window.a character string, giving the name of one of the columns of
marks(x), if this is a data frame.a character string
"x"or"y"identifying one of the spatial coordinates.
The default is to take z to be the vector of marks in
x (or the first column in the data frame of marks of x,
if it is a data frame). If the marks are numeric, then the range of values
of the numerical marks is divided into several intervals, and each
interval is associated with a level of a factor.
The result is a
marked point pattern, with the same window and point locations as
x, but with the numeric mark of each point discretised
by replacing it by the factor level.
This is a convenient way to transform a marked point pattern
which has numeric marks into a multitype point pattern,
for example to plot it or analyse it. See the examples.
To select some points from a point pattern, use the subset operators
[.ppp or subset.ppp instead.
Value
A multitype point pattern, that is, a point pattern object
(of class "ppp") with a marks vector that is a factor.
See Also
Examples
# NOT RUN {
# (1) cutting based on numeric marks of point pattern
trees <- longleaf
# Longleaf Pines data
# the marks are positive real numbers indicating tree diameters.
# }
# NOT RUN {
# }
# NOT RUN {
plot(trees)
# }
# NOT RUN {
# cut the range of tree diameters into three intervals
long3 <- cut(trees, breaks=3)
# }
# NOT RUN {
plot(long3)
# }
# NOT RUN {
# adult trees defined to have diameter at least 30 cm
long2 <- cut(trees, breaks=c(0,30,100), labels=c("Sapling", "Adult"))
plot(long2)
plot(long2, cols=c("green","blue"))
# (2) cutting based on another numeric vector
# Divide Swedish Pines data into 3 classes
# according to nearest neighbour distance
swedishpines
plot(cut(swedishpines, nndist(swedishpines), breaks=3))
# (3) cutting based on tessellation
# Divide Swedish Pines study region into a 4 x 4 grid of rectangles
# and classify points accordingly
tes <- tess(xgrid=seq(0,96,length=5),ygrid=seq(0,100,length=5))
plot(cut(swedishpines, tes))
plot(tes, lty=2, add=TRUE)
# (4) inside/outside a given region
with(murchison, cut(gold, greenstone))
# (5) multivariate marks
finpines
cut(finpines, "height", breaks=4)
# }