| applynbd {spatstat} | R Documentation |
Visit each point in a point pattern, find the neighbouring points, and apply a given function to them.
applynbd(X, FUN, N, R, criterion, exclude=FALSE, ...)
X |
Point pattern.
An object of class "ppp",
or data which can be converted into
this format by as.ppp.
|
FUN |
Function to be applied to each neighbourhood.
The arguments of FUN are described under Details.
|
N |
Integer. If this argument is present,
the neighbourhood of a point of X is defined to consist of the
N points of X which are closest to it.
This argument is incompatible with R and criterion.
|
R |
Nonnegative numeric value. If this argument is present,
the neighbourhood of a point of X is defined to consist of
all points of X which lie within a distance R
of it.
This argument is incompatible with N and criterion.
|
criterion |
Function. If this argument is present,
the neighbourhood of a point of X is determined by
evaluating this function. See under Details.
This argument is incompatible with N and R.
|
exclude |
Logical. If TRUE then the point currently being visited
is excluded from its own neighbourhood.
|
... |
extra arguments passed to the function FUN.
They must be given in the form name=value.
|
This is an analogue of 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.
If N is given, then the neighbours of the current
point are the N points of X which are closest to
the current point (including the current point itself
unless exclude=TRUE).
If R is given, then the neighbourhood of the current point
consists of all points of X which lie closer than a distance R
from the current point.
If criterion is given, then it must be a function
with two arguments dist and drank which will be
vectors of equal length.
The interpretation is that dist[i] will be the
distance of a point from the current point, and
drank[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
as dist and drank whose i-th entry is
TRUE if the corresponding point should be included in
the neighbourhood. See the examples below.
Each point of X is visited; the neighbourhood
of the current point is determined, and stored as a point pattern
Y; then the function FUN is called as:
FUN(Y=Y, current=current, dists=dists, dranks=dranks, ...)
where current is a vector of length 2 giving the coordinates of the
current point, dists is a vector of distances from the current
point to each of the points in Y,
dranks is a vector of the ranks of these distances
with respect to the full point pattern X,
and ... are the arguments passed from the call to
applynbd.
The results of each call to FUN are collected and returned
according to the usual rules for apply and its
relatives. See Value above.
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.
Similar to the result of 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.
Adrian Baddeley adrian@maths.uwa.edu.au http://www.maths.uwa.edu.au/~adrian/ and Rolf Turner rolf@math.unb.ca http://www.math.unb.ca/~rolf
data(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)
# 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$marks)})
# ANIMATION explaining the definition of the K function
# (arguments `fullpicture' and 'rad' are passed to FUN)
## Not run:
showoffK <- function(Y, current, dists, dranks, fullpicture,rad) {
plot(fullpicture, main="")
points(Y, cex=2)
u <- current
points(u[1],u[2],pch="+",cex=3)
theta <- seq(0,2*pi,length=100)
polygon(u[1]+ rad * cos(theta),u[2]+rad*sin(theta))
text(u[1]+rad/3,u[2]+rad/2,Y$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)
## End(Not run)