Extract or replace a subset of a point pattern. Extraction of a subset has the effect of thinning the points and/or trimming the window.
# S3 method for ppp
[(x, i, j, drop=FALSE, …, clip=FALSE)
# S3 method for ppp
[(x, i, j) <- valueA two-dimensional point pattern.
An object of class "ppp".
Subset index. Either a valid subset index in the usual R sense,
indicating which points should be retained, or a window
(an object of class "owin")
delineating a subset of the original observation window,
or a pixel image with logical values defining a subset of the
original observation window.
Replacement value for the subset. A point pattern.
Redundant. Included for backward compatibility.
Logical value indicating whether to remove unused levels of the marks, if the marks are a factor.
Logical value indicating how to form the window of the resulting
point pattern, when i is a window.
If clip=FALSE (the default), the result has window
equal to i. If clip=TRUE, the resulting window
is the intersection between the window of x and the
window i.
Ignored. This argument is required for compatibility with the generic function.
A point pattern (of class "ppp").
The function does not check whether i is a subset of
Window(x). Nor does it check whether value lies
inside Window(x).
These functions extract a designated subset of a point pattern, or replace the designated subset with another point pattern.
The function [.ppp is a method for [ for the
class "ppp". It extracts a designated subset of a point pattern,
either by ``thinning''
(retaining/deleting some points of a point pattern)
or ``trimming'' (reducing the window of observation
to a smaller subregion and retaining only
those points which lie in the subregion) or both.
The pattern will be ``thinned''
if i is a subset index in the usual R sense:
either a numeric vector
of positive indices (identifying the points to be retained),
a numeric vector of negative indices (identifying the points
to be deleted) or a logical vector of length equal to the number of
points in the point pattern x. In the latter case,
the points (x$x[i], x$y[i]) for which
subset[i]=TRUE will be retained, and the others
will be deleted.
The pattern will be ``trimmed''
if i is an object of class
"owin" specifying a window of observation.
The points of x lying inside the new
window i will be retained. Alternatively i may be a
pixel image (object of class "im") with logical values;
the pixels with the value TRUE will be interpreted as a window.
The argument drop determines whether to remove
unused levels of a factor, if the point pattern is multitype
(i.e. the marks are a factor) or if the marks are a data frame
in which some of the columns are factors.
The function [<-.ppp is a method for [<- for the
class "ppp". It replaces the designated
subset with the point pattern value.
The subset of x to be replaced is designated by
the argument i as above.
The replacement point pattern value must lie inside the
window of the original pattern x.
The ordering of points in x will be preserved
if the replacement pattern value has the same number of points
as the subset to be replaced. Otherwise the ordering is
unpredictable.
If the original pattern x has marks, then the replacement
pattern value must also have marks, of the same type.
Use the function unmark to remove marks from a
marked point pattern.
Use the function split.ppp to select those points
in a marked point pattern which have a specified mark.
# NOT RUN {
# Longleaf pines data
lon <- longleaf
if(human <- interactive()) {
plot(lon)
}
# }
# NOT RUN {
# adult trees defined to have diameter at least 30 cm
longadult <- subset(lon, marks >= 30)
if(human){
plot(longadult)
}
# note that the marks are still retained.
# Use unmark(longadult) to remove the marks
# New Zealand trees data
if(human){
plot(nztrees) # plot shows a line of trees at the far right
abline(v=148, lty=2) # cut along this line
}
nzw <- owin(c(0,148),c(0,95)) # the subwindow
# trim dataset to this subwindow
nzsub <- nztrees[nzw]
if(human){
plot(nzsub)
}
# Redwood data
if(human){
plot(redwood)
}
# Random thinning: delete 60% of data
retain <- (runif(npoints(redwood)) < 0.4)
thinred <- redwood[retain]
if(human){
plot(thinred)
}
# Scramble 60% of data
if(require(spatstat.core)) {
X <- redwood
modif <- (runif(npoints(X)) < 0.6)
X[modif] <- runifpoint(ex=X[modif])
}
# Lansing woods data - multitype points
lan <- lansing
# }
# NOT RUN {
# Hickory trees
hicks <- split(lansing)$hickory
# Trees in subwindow
win <- owin(c(0.3, 0.6),c(0.2, 0.5))
lsub <- lan[win]
if(require(spatstat.core)) {
# Scramble the locations of trees in subwindow, retaining their marks
lan[win] <- runifpoint(ex=lsub) %mark% marks(lsub)
}
# Extract oaks only
oaknames <- c("redoak", "whiteoak", "blackoak")
oak <- lan[marks(lan) %in% oaknames, drop=TRUE]
oak <- subset(lan, marks %in% oaknames, drop=TRUE)
# To clip or not to clip
X <- unmark(demopat)
B <- owin(c(5500, 9000), c(2500, 7400))
opa <- par(mfrow=c(1,2))
plot(X, main="X[B]")
plot(X[B], add=TRUE,
cols="blue", col="pink", border="blue",
show.all=TRUE, main="")
plot(Window(X), add=TRUE)
plot(X, main="X[B, clip=TRUE]")
plot(B, add=TRUE, lty=2)
plot(X[B, clip=TRUE], add=TRUE,
cols="blue", col="pink", border="blue",
show.all=TRUE, main="")
par(opa)
# }