# 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 in`z`

to factor values in the output. See`cut.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 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.

##### 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)
# }
```

*Documentation reproduced from package spatstat, version 1.64-1, License: GPL (>= 2)*