Divides a point pattern into several sub-patterns, according to their marks, or according to any user-specified grouping.
# S3 method for ppp
split(x, f = marks(x), drop=FALSE, un=NULL, reduce=FALSE, ...)
# S3 method for ppp
split(x, f = marks(x), drop=FALSE, un=NULL, ...) <- valueThe value of split.ppp is a list of point patterns.
The components of the list are named by the levels of f.
The list also has the class "splitppp".
The assignment form split<-.ppp returns the updated
point pattern x.
A two-dimensional point pattern.
An object of class "ppp".
Data determining the grouping. Either a factor, a logical vector, a pixel image with factor values, a tessellation, a window, or the name of one of the columns of marks.
Logical. Determines whether empty groups will be deleted.
Logical. Determines whether the resulting subpatterns will be unmarked (i.e. whether marks will be removed from the points in each subpattern).
Logical. Determines whether to delete the column of marks used to split the pattern, when the marks are a data frame.
Other arguments are ignored.
List of point patterns.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
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 of f
determine the destination of each point in x.
The ith point of x will be placed in the sub-pattern
split.ppp(x)$l where l = f[i].
a pixel image (object of class "im") with factor values.
The pixel value of f
at each point of x will be used as the classifying variable.
a tessellation (object of class "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.
the character string "marks", if marks(x) is a factor.
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 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.
If the marks of x are a data frame, then
split(x, reduce=TRUE) will discard only the column of marks
that was used to split the pattern. This applies only when
the argument f is missing.
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,
but this typically changes the ordering of the data.
cut.ppp,
plot.splitppp,
superimpose,
im,
tess,
ppp.object
# (1) Splitting by marks
# Multitype point pattern: separate into types
u <- split(amacrine)
# plot them
plot(split(amacrine))
# the following are equivalent:
amon <- split(amacrine)$on
amon <- unmark(amacrine[amacrine$marks == "on"])
amon <- subset(amacrine, marks == "on", -marks)
# the following are equivalent:
amon <- split(amacrine, un=FALSE)$on
amon <- amacrine[amacrine$marks == "on"]
# Scramble the locations of the 'on' cells
X <- amacrine
u <- split(X)
u$on <- runifrect(npoints(amon), Window(amon))
split(X) <- u
# Point pattern with continuous marks
trees <- longleaf
# \testonly{
# smaller dataset
trees <- trees[seq(1, npoints(trees), by=80)]
# }
# cut the range of tree diameters into three intervals
# using cut.ppp
long3 <- cut(trees, breaks=3)
# now split them
long3split <- split(long3)
# (2) Splitting by a factor
# Unmarked point pattern
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)
# (4) how to apply an operation to selected points:
# split into components, transform desired component, then un-split
# e.g. apply random jitter to 'on' points only
X <- amacrine
Y <- split(X)
Y$on <- rjitter(Y$on, 0.1)
split(X) <- Y
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