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.
## S3 method for class 'ppp' cut(x, z=marks(x), ...)
x |
A two-dimensional point pattern.
An object of class |
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 |
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 in x.
Levels of the factor determine the classification.
a numeric vector (of length equal to the number of points in
z). The range of values of z will be divided into
bands (the number of bands is determined by ...)
and z will be converted to a factor using
cut.default.
a pixel image (object of class "im").
The value of z at each point of x will be
used as the classifying variable.
a tessellation (object of class "tess", see
tess). Each point of x will be classified
according to the tile of the tessellation into which it falls.
a window (object of class "owin").
Each point of x will 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.
A multitype point pattern, that is, a point pattern object
(of class "ppp") with a marks vector that is a factor.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
# (1) cutting based on numeric marks of point pattern
trees <- longleaf
# Longleaf Pines data
# the marks are positive real numbers indicating tree diameters.
if(interactive()) {
plot(trees)
}
# cut the range of tree diameters into three intervals
long3 <- cut(trees, breaks=3)
if(interactive()) {
plot(long3)
}
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.