Description Usage Arguments Details Value Author(s) See Also Examples
Classifies the points in a point pattern into distinct types according to the numerical marks in the pattern, or according to another variable.
1 2 
x 
A twodimensional 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.
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  # (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)

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