Nearest neighbour distances in three dimensions
Computes the distance from each point to its nearest neighbour in a three-dimensional point pattern. Alternatively computes the distance to the second nearest neighbour, or third nearest, etc.
## S3 method for class 'pp3' nndist(X, ..., k=1, by=NULL)
X |
Three-dimensional point pattern
(object of class |
... |
Ignored. |
k |
Integer, or integer vector. The algorithm will compute the distance to the
|
by |
Optional. A factor, which separates |
This function computes the Euclidean distance from each point
in a three-dimensional
point pattern to its nearest neighbour (the nearest other
point of the pattern). If k is specified, it computes the
distance to the kth nearest neighbour.
The function nndist is generic; this function
nndist.pp3 is the method for the class "pp3".
The argument k may be a single integer, or an integer vector.
If it is a vector, then the kth nearest neighbour distances are
computed for each value of k specified in the vector.
If there is only one point (if x has length 1),
then a nearest neighbour distance of Inf is returned.
If there are no points (if x has length zero)
a numeric vector of length zero is returned.
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 compute, for each point of X,
the distance to the nearest neighbour in each subset.
To identify which point is the nearest neighbour of a given point,
use nnwhich.
To use the nearest neighbour distances for statistical inference,
it is often advisable to use the edge-corrected empirical distribution,
computed by G3est.
To find the nearest neighbour distances from one point pattern
to another point pattern, use nncross.
Numeric vector or matrix containing the nearest neighbour distances for each point.
If k = 1 (the default), the return value is a
numeric vector v such that v[i] is the
nearest neighbour distance for the ith data point.
If k is a single integer, then the return value is a
numeric vector v such that v[i] is the
kth nearest neighbour distance for the
ith data point.
If k is a vector, then the return value is a
matrix m such that m[i,j] is the
k[j]th nearest neighbour distance for the
ith data point.
An infinite or NA value is returned if the
distance is not defined (e.g. if there is only one point
in the point pattern).
Adrian Baddeley Adrian.Baddeley@curtin.edu.au
based on code for two dimensions by Pavel Grabarnik
X <- pp3(runif(40), runif(40), runif(40), box3(c(0,1))) # nearest neighbours d <- nndist(X) # second nearest neighbours d2 <- nndist(X, k=2) # first, second and third nearest d1to3 <- nndist(X, k=1:3) # distance to nearest point in each group marks(X) <- factor(rep(letters[1:4], 10)) dby <- nndist(X, by=marks(X))
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