applynbd(X, FUN, N, R, criterion, exclude=FALSE, ...)"ppp",
or data which can be converted into
this format by as.ppp.FUN are described under Details.X is defined to consist of the
N points of X which are closest to it.
This argument is incompatible with R and critX is defined to consist of
all points of X which lie within a distance R
of it.
This argument is incompatible wiX is determined by
evaluating this function. See under Details.
This argument is incompatible with N and R.TRUE then the point currently being visited
is excluded from its own neighbourhood.FUN.
They must be given in the form name=value.apply.
If each call to FUN returns a single numeric value,
the result is a vector of dimension X$n, the number of points
in X.
If each call to FUN returns a vector of the same length
m, then the result is a matrix of dimensions c(m,n);
note the transposition of the indices, as usual for the family of
apply functions.
If the calls to FUN return vectors of different lengths,
the result is a list of length X$n.apply
for point patterns. It visits each point in the point pattern X,
determines which points of X are ``neighbours'' of the current
point, applies the function FUN to this neighbourhood,
and collects the values returned by FUN. The definition of ``neighbours'' depends on the arguments
N, R and criterion, exactly one of which
must be given.
Also the argument exclude determines whether
the current point is excluded from its own neighbourhood.
Nis given, then the neighbours of the current
point are theNpoints ofXwhich are closest to
the current point (including the current point itself
unlessexclude=TRUE).Ris given, then the neighbourhood of the current point
consists of all points ofXwhich lie closer than a distanceRfrom the current point.criterionis given, then it must be a function
with two argumentsdistanddrankwhich will be
vectors of equal length.
The interpretation is thatdist[i]will be the
distance of a point from the current point, anddrank[i]will be the rank of that distance (the three points
closest to the current point will have rank 1, 2 and 3).
This function must return a logical vector of the same length
asdistanddrankwhosei-th entry isTRUEif the corresponding point should be included in
the neighbourhood. See the examples below. When applynbd is executed,
each point of X is visited, and the following happens
for each point:
Y;FUNis called as:FUN(Y=Y, current=current, dists=dists, dranks=dranks, ...)wherecurrentis the location of the current point
(in a format explained below),distsis a vector of distances from the current
point to each of the points inY,dranksis a vector of the ranks of these distances
with respect to the full point patternX,
and...are the arguments passed from the call toapplynbd;FUNis stored.FUN are collected and returned
according to the usual rules for apply and its
relatives. See the Value section of this help file. The format of the argument current is as follows.
If X is an unmarked point pattern, then current is a
vector of length 2 containing the coordinates of the current point.
If X is marked, then current is a point pattern
containing exactly one point, so that current$x is its
$x$-coordinate and current$marks is its mark value.
In either case, the coordinates of the current point can be referred to as
current$x and current$y.
Note that FUN will be called exactly as described above,
with each argument named explicitly. Care is required when writing the
function FUN to ensure that
the arguments will match up. See the Examples.
See markstat for a common use of this function.
To simply tabulate the marks in every R-neighbourhood, use
marktable.
ppp.object,
apply,
markstat,
marktabledata(redwood)
# count the number of points within radius 0.2 of each point of X
nneighbours <- applynbd(redwood, R=0.2, function(Y, ...){Y$n-1})
# equivalent to:
nneighbours <- applynbd(redwood, R=0.2, function(Y, ...){Y$n}, exclude=TRUE)
# compute the distance to the second nearest neighbour of each point
secondnndist <- applynbd(redwood, N = 2,
function(dists, ...){max(dists)},
exclude=TRUE)
# marked point pattern
data(longleaf)
<testonly># smaller dataset
longleaf <- longleaf[seq(1, longleaf$n, by=80)]</testonly>
# compute the median of the marks of all neighbours of a point
# (see also 'markstat')
dbh.med <- applynbd(longleaf, R=90, exclude=TRUE,
function(Y, ...) { median(Y$marks)})
# ANIMATION explaining the definition of the K function
# (arguments `fullpicture' and 'rad' are passed to FUN)
showoffK <- function(Y, current, dists, dranks, fullpicture,rad) {
plot(fullpicture, main="")
points(Y, cex=2)
u <- current
points(u$x,u$y,pch="+",cex=3)
theta <- seq(0,2*pi,length=100)
polygon(u$x+ rad * cos(theta),u$y+rad*sin(theta))
text(u$x+rad/3,u$y+rad/2,Y$n,cex=3)
Sys.sleep(if(runif(1) < 0.1) 1.5 else 0.3)
return(Y$n - 1)
}
applynbd(redwood, R=0.2, showoffK, fullpicture=redwood, rad=0.2, exclude=TRUE)
# animation explaining the definition of the G function
showoffG <- function(Y, current, dists, dranks, fullpicture) {
plot(fullpicture, main="")
points(Y, cex=2)
u <- current
points(u[1],u[2],pch="+",cex=3)
v <- c(Y$x[1],Y$y[1])
segments(u[1],u[2],v[1],v[2],lwd=2)
w <- (u + v)/2
nnd <- dists[1]
text(w[1],w[2],round(nnd,3),cex=2)
Sys.sleep(if(runif(1) < 0.1) 1.5 else 0.3)
return(nnd)
}
data(cells)
applynbd(cells, N=1, showoffG, exclude=TRUE, fullpicture=cells)Run the code above in your browser using DataLab