# split.ppx

##### Divide Multidimensional Point Pattern into Sub-patterns

Divides a multidimensional point pattern into several sub-patterns, according to their marks, or according to any user-specified grouping.

##### Usage

```
# S3 method for ppx
split(x, f = marks(x), drop=FALSE, un=NULL, …)
```

##### Arguments

- x
A multi-dimensional point pattern. An object of class

`"ppx"`

.- f
Data determining the grouping. Either a factor, or the name of one of the columns of marks.

- drop
Logical. Determines whether empty groups will be deleted.

- un
Logical. Determines whether the resulting subpatterns will be unmarked (i.e. whether marks will be removed from the points in each subpattern).

- …
Other arguments are ignored.

##### Details

The generic command `split`

allows a dataset to be separated
into subsets according to the value of a grouping variable.

The function `split.ppx`

is a method for the generic
`split`

for the class `"ppx"`

of multidimensional
point patterns. It divides up the points of the point pattern `x`

into several sub-patterns according to the values of `f`

.
The result is a list of point patterns.

The argument `f`

may be

a factor, of length equal to the number of points in

`x`

. The levels of`f`

determine the destination of each point in`x`

. The`i`

th point of`x`

will be placed in the sub-pattern`split.ppx(x)$l`

where`l = f[i]`

.a character string, matching the name of one of the columns of marks, if

`marks(x)`

is a data frame. This column should be a factor.

If `f`

is missing, then it will be determined by the
marks of the point pattern. The pattern `x`

can be either

a multitype point pattern (a marked point pattern whose marks vector is a factor). Then

`f`

is taken to be the marks vector. The effect is that the points of each type are separated into different point patterns.a marked point pattern with a data frame or hyperframe of marks, containing at least one column that is a factor. The first such column will be used to determine the splitting factor

`f`

.

Some of the sub-patterns created by the split
may be empty. If `drop=TRUE`

, then empty sub-patterns will
be deleted from the list. If `drop=FALSE`

then they are retained.

The argument `un`

determines how to handle marks
in the case where `x`

is a marked point pattern.
If `un=TRUE`

then the marks of the
points will be discarded when they are split into groups,
while if `un=FALSE`

then the marks will be retained.

If `f`

and `un`

are both missing,
then the default is `un=TRUE`

for multitype point patterns
and `un=FALSE`

for marked point patterns with a data frame of
marks.

The result of `split.ppx`

has class `"splitppx"`

and `"anylist"`

. There are methods for `print`

,
`summary`

and `plot`

.

##### Value

A list of point patterns.
The components of the list are named by the levels of `f`

.
The list also has the class `"splitppx"`

and `"anylist"`

.

##### See Also

##### Examples

```
# NOT RUN {
df <- data.frame(x=runif(4),y=runif(4),t=runif(4),
age=rep(c("old", "new"), 2),
size=runif(4))
X <- ppx(data=df, coord.type=c("s","s","t","m","m"))
X
split(X)
# }
```

*Documentation reproduced from package spatstat, version 1.56-1, License: GPL (>= 2)*