spatstat.geom (version 2.0-1)

Extract.ppp: Extract or Replace Subset of Point Pattern

Description

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.

Usage

# S3 method for ppp
[(x, i, j, drop=FALSE, …, clip=FALSE)
  # S3 method for 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 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.

value

Replacement value for the subset. A point pattern.

j

Redundant. Included for backward compatibility.

drop

Logical value indicating whether to remove unused levels of the marks, if the marks are a factor.

clip

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.

Value

A point pattern (of class "ppp").

Warnings

The function does not check whether i is a subset of Window(x). Nor does it check whether value lies inside Window(x).

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 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.

See Also

subset.ppp.

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

Examples

# 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)
# }