Extract.ppp

0th

Percentile

Extract or Replace Subset of Point Pattern

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.

Keywords
manip, spatial
Usage
## S3 method for class 'ppp':
[(x, i, j, drop, ...)
  ## S3 method for class 'ppp':
[(x, i, j) <- value
Arguments
x
A two-dimensional point pattern. An object of class "ppp".
i
Subset index. Either a valid subset index in the usual Rsense, indicating which points should be retained, or a window (an object of class "owin") delineating a subset of the original observation window.
value
Replacement value for the subset. A point pattern.
j
Redundant. Included for backward compatibility.
drop, ...
Ignored. These arguments are required for compatibility with the generic function.
Details

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 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.

Value

  • A point pattern (of class "ppp").

Warnings

The function does not check whether window is a subset of x$window. Nor does it check whether value lies inside x$window.

See Also

ppp.object, owin.object, unmark, split.ppp, cut.ppp

Aliases
  • [.ppp
  • [<-.ppp
Examples
data(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)
Documentation reproduced from package spatstat, version 1.23-3, License: GPL (>= 2)

Community examples

Looks like there are no examples yet.