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## S3 method for class 'ppp':
[(x, i, j, drop, ...)
## S3 method for class 'ppp':
[(x, i, j) <- value"ppp"."owin")
delineating a subset of the original observation window."ppp").window is a subset of
x$window. Nor does it check whether value lies
inside x$window. 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 Rsense:
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 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 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.
ppp.object,
owin.object,
unmark,
split.ppp,
cut.pppdata(longleaf)
# Longleaf pines data
plot(longleaf)
<testonly>longleaf <- longleaf[seq(1,longleaf$n,by=10)]</testonly>
# adult trees defined to have diameter at least 30 cm
adult <- (longleaf$marks >= 30)
longadult <- longleaf[adult]
plot(longadult)
# note that the marks are still retained.
# Use unmark(longadult) to remove the marks
# New Zealand trees data
data(nztrees)
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]
plot(nzsub)
# Redwood data
data(redwood)
plot(redwood)
# Random thinning: delete 60\% of data
retain <- (runif(redwood$n) < 0.4)
thinred <- redwood[retain]
plot(thinred)
# Scramble 60\% of data
modif <- (runif(redwood$n) < 0.6)
scramble <- function(x) { runifpoint(x$n, x$window) }
redwood[modif] <- scramble(redwood[modif])
# Lansing woods data - multitype points
data(lansing)
<testonly>lansing <- lansing[seq(1, lansing$n, length=100)]</testonly>
# Hickory trees
hicks <- split(lansing)$hickory
# Trees in subwindow
win <- owin(c(0.3, 0.6),c(0.2, 0.5))
lsub <- lansing[win]
# Scramble the locations of trees in subwindow, retaining their marks
lansing[win] <- scramble(lsub) %mark% (lsub$marks)Run the code above in your browser using DataLab