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) <- value
```

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.

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) # }