Nearest Neighbours in Any Dimensions
Finds the nearest neighbour of each point in a multi-dimensional point pattern.
## S3 method for class 'ppx' nnwhich(X, ..., k=1)
X |
Multi-dimensional point pattern
(object of class |
... |
Arguments passed to |
k |
Integer, or integer vector. The algorithm will compute the distance to the
|
For each point in the given multi-dimensional
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. This is the method
for the class "ppx".
If there are no points in the pattern,
a numeric vector of length zero is returned.
If there is only one point,
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.
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.
By default, both spatial and temporal coordinates are extracted.
To obtain the spatial distance between points in a space-time point
pattern, set temporal=FALSE.
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.
A value of NA is returned if there is only one point
in the point pattern.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au
df <- data.frame(x=runif(5),y=runif(5),z=runif(5),w=runif(5)) X <- ppx(data=df) m <- nnwhich(X) m2 <- nnwhich(X, k=2)
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