Finds the nearest neighbour of each point in a point pattern.
nnwhich(X, ...)
# S3 method for ppp
nnwhich(X, ..., k=1, by=NULL, method="C", metric=NULL)
# S3 method for default
nnwhich(X, Y=NULL, ..., k=1, by=NULL, method="C")Numeric vector or matrix giving, for each point,
the index of its nearest neighbour (or kth nearest neighbour).
If k = 1 (the default), the return value is a
numeric vector v giving the indices of the nearest neighbours
(the nearest neighbout of the ith point is
the jth point where j = v[i]).
If k is a single integer, then the return value is a
numeric vector giving the indices of the
kth nearest neighbours.
If k is a vector, then the return value is a
matrix m such that m[i,j] is the
index of the k[j]th nearest neighbour for the
ith data point.
If the argument by is given, then it should be a factor
which separates X into groups (or any type of data acceptable to
split.ppp that determines the grouping).
The result is a data frame
containing the indices described above, from each point of X,
to the nearest point in each subset of X
defined by the factor by.
Arguments specifying the locations of
a set of points.
For nnwhich.ppp, the argument X should be a point
pattern (object of class "ppp").
For nnwhich.default, typically X and Y would be
numeric vectors of equal length. Alternatively Y may be
omitted and X may be
a list with two components x and y,
or a matrix with two columns.
Ignored by nnwhich.ppp
and nnwhich.default.
Integer, or integer vector. The algorithm will compute the distance to the
kth nearest neighbour.
Optional. A factor, which separates X into groups.
The algorithm will find the nearest neighbour in each group.
See Details.
String specifying which method of calculation to use.
Values are "C" and "interpreted".
Optional. A metric (object of class "metric")
that will be used to define and compute the distances.
If X is a multitype point pattern
and by=marks(X), then the algorithm will find,
for each point of X, the nearest neighbour
of each type. See the Examples.
A value of NA is returned if there is only one point
in the point pattern.
Pavel Grabarnik pavel.grabar@issp.serpukhov.su and Adrian Baddeley Adrian.Baddeley@curtin.edu.au
For each point in the given point pattern, this function finds
its nearest neighbour (the nearest other point of the pattern).
By default it returns a vector giving, for each point,
the index of the point's
nearest neighbour. If k is specified, the algorithm finds
each point's kth nearest neighbour.
The function nnwhich is generic, with
method for point patterns (objects of class "ppp")
and a default method which are described here, as well as a method for
three-dimensional point patterns (objects of class "pp3",
described in nnwhich.pp3.
The method nnwhich.ppp expects a single
point pattern argument X.
The default method expects that X and Y will determine
the coordinates of a set of points. Typically X and
Y would be numeric vectors of equal length. Alternatively
Y may be omitted and X may be a list with two components
named x and y, or a matrix or data frame with two columns.
The argument k may be a single integer, or an integer vector.
If it is a vector, then the \(k\)th nearest neighbour distances are
computed for each value of \(k\) specified in the vector.
If the argument by is given, it should be a factor,
of length equal to the number of points in X.
This factor effectively partitions X into subsets,
each subset associated with one of the levels of X.
The algorithm will then find, for each point of X,
the nearest neighbour in each subset.
If there are no points (if x has length zero)
a numeric vector of length zero is returned.
If there is only one point (if x has length 1),
then the nearest neighbour is undefined, and a value of NA
is returned. In general if the number of points is less than or equal
to k, then a vector of NA's is returned.
The argument method is not normally used. It is
retained only for checking the validity of the software.
If method = "interpreted" then the distances are
computed using interpreted R code only. If method="C"
(the default) then C code is used.
The C code is faster by two to three orders of magnitude
and uses much less memory.
To evaluate the distance between a point and its nearest
neighbour, use nndist.
To find the nearest neighbours from one point pattern
to another point pattern, use nncross.
nndist,
nncross
plot(cells)
m <- nnwhich(cells)
m2 <- nnwhich(cells, k=2)
# plot nearest neighbour links
b <- cells[m]
arrows(cells$x, cells$y, b$x, b$y, angle=15, length=0.15, col="red")
# find points which are the neighbour of their neighbour
self <- (m[m] == seq(m))
# plot them
A <- cells[self]
B <- cells[m[self]]
plot(cells)
segments(A$x, A$y, B$x, B$y)
# nearest neighbours of each type
head(nnwhich(ants, by=marks(ants)))
Run the code above in your browser using DataLab