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 pp3
nndist(X, …, k=1)Three-dimensional point pattern
(object of class "pp3").
Ignored.
Integer, or integer vector. The algorithm will compute the distance to the
kth nearest neighbour.
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).
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 \(k\)th 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.
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.
# NOT RUN {
X <- runifpoint3(40)
# nearest neighbours
d <- nndist(X)
# second nearest neighbours
d2 <- nndist(X, k=2)
# first, second and third nearest
d1to3 <- nndist(X, k=1:3)
# }
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