split.ppp
Divide Point Pattern into Sub-patterns
Divides a point pattern into several sub-patterns, according to their marks, or according to any user-specified grouping.
Usage
## S3 method for class 'ppp':
split(x, f = marks(x), drop=FALSE, un=NULL, ...)
## S3 method for class 'ppp':
split(x, f = marks(x), drop=FALSE, un=missing(f), ...) <- valueArguments
- x
- A two-dimensional point pattern.
An object of class
"ppp". - f
- Data determining the grouping. Either a factor, a pixel image with factor values, or a tessellation.
- 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.
- value
- List of point patterns.
Details
The function split.ppp
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 offdetermine the destination of each point inx. Theith point ofxwill be placed in the sub-patternsplit.ppp(x)$lwherel = f[i]. - a pixel image (object of class
"im") with factor values. The pixel value offat each point ofxwill be used as the classifying variable. - a tessellation (object of class
"tess"). Each point ofxwill be classified according to the tile of the tessellation into which it falls.
f is missing, then x must be a multitype point pattern
(a marked point pattern whose marks vector is a factor).
Then the effect is that the points of each type
are separated into different point patterns. Some of the sub-patterns created by the split
may be empty. If drop=TRUE, then empty sub-patterns will
be deleted from the list. If drop=FALSE then they are retained.
The argument un determines how to handle marks
in the case where x is a marked point pattern.
If un=TRUE then the marks of the
points will be discarded when they are split into groups,
while if un=FALSE then the marks will be retained.
The result of split.ppp has class "splitppp"
and can be plotted using plot.splitppp.
The assignment function split<-.ppp
updates the point pattern x so that
it satisfies split(x, f, drop, un) = value. The argument value
is expected to be a list of point patterns, one for each level of
f. These point patterns are expected to be compatible with the
type of data in the original pattern x.
Splitting can also be undone by the function superimpose.
Value
- The value of
split.pppis a list of point patterns. The components of the list are named by the levels off. The list also has the class"splitppp".The assignment form
split<-.pppreturns the updated point patternx.
See Also
Examples
# (1) Splitting by marks
# Multitype point pattern: separate into types
data(amacrine)
u <- split(amacrine)
# plot them
plot(split(amacrine))
# the following are equivalent:
amon <- split(amacrine)$on
amon <- unmark(amacrine[amacrine$marks == "on"])
# the following are equivalent:
amon <- split(amacrine, un=FALSE)$on
amon <- amacrine[amacrine$marks == "on"]
# Scramble the locations of the 'on' cells
u <- split(amacrine)
u$on <- runifpoint(amon$n, amon$window)
split(amacrine) <- u
# Point pattern with continuous marks
data(longleaf)
<testonly># smaller dataset
longleaf <- longleaf[seq(1, longleaf$n, by=80)]</testonly>
# cut the range of tree diameters into three intervals
# using cut.ppp
long3 <- cut(longleaf, breaks=3)
# now split them
long3split <- split(long3)
# (2) Splitting by a factor
# Unmarked point pattern
data(swedishpines)
# cut & split according to nearest neighbour distance
f <- cut(nndist(swedishpines), 3)
u <- split(swedishpines, f)
# (3) Splitting over a tessellation
tes <- tess(xgrid=seq(0,96,length=5),ygrid=seq(0,100,length=5))
v <- split(swedishpines, tes)