Description Usage Arguments Details Value Warnings Author(s) See Also Examples
Computes the distance from each point to its nearest neighbour in a multidimensional point pattern. Alternatively computes the distance to the second nearest neighbour, or third nearest, etc.
1 2 
X 
Multidimensional point pattern
(object of class 
... 
Arguments passed to 
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 multidimensional
point pattern to its nearest neighbour (the nearest other
point of the pattern). If k
is specified, it computes the
distance to the k
th nearest neighbour.
The function nndist
is generic; this function
nndist.ppx
is the method for the class "ppx"
.
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 find the nearest neighbour distances 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 spacetime point
pattern, set temporal=FALSE
.
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 i
th data point.
If k
is a single integer, then the return value is a
numeric vector v
such that v[i]
is the
k
th nearest neighbour distance for the
i
th 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
i
th 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).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  df < data.frame(x=runif(5),y=runif(5),z=runif(5),w=runif(5))
X < ppx(data=df)
# nearest neighbours
d < nndist(X)
# second nearest neighbours
d2 < nndist(X, k=2)
# first, second and third nearest
d1to3 < nndist(X, k=1:3)
# nearest neighbour distances to each group
marks(X) < factor(c("a","a", "b", "b", "b"))
nndist(X, by=marks(X))
nndist(X, by=marks(X), k=1:2)

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