nndist
Nearest neighbour distances
Computes the distance from each point to its nearest neighbour in a point pattern. Alternatively computes the distance to the second nearest neighbour, or third nearest, etc.
Usage
nndist(X, ..., method="C")
## S3 method for class 'ppp':
nndist(X, \dots, k=1, method="C")
## S3 method for class 'default':
nndist(X, Y=NULL, \dots, k=1, method="C")
Arguments
- X,Y
- Arguments specifying the locations of
a set of points.
For
nndist.ppp
, the argumentX
should be a point pattern (object of class"ppp"
). Fornndist.default
, typicallyX
and < - ...
- Ignored by
nndist.ppp
andnndist.default
. - k
- Integer. The algorithm will compute the distance to the
k
th nearest neighbour. - method
- String specifying which method of calculation to use.
Values are
"C"
and"interpreted"
.
Details
This function computes the Euclidean distance from each point
in a 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, with
a method for point patterns (objects of class "ppp"
),
and a default method for coordinate vectors.
There is also a method for line segment patterns, nndist.psp
.
The method for point patterns expects a single
point pattern argument X
and returns the vector of its
nearest neighbour distances.
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 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.
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 Gest
.
To find the nearest neighbour distances from one point pattern
to another point pattern, use nncross
.
Value
- Numeric vector of the (
k
th) nearest neighbour distances for each point.
Warnings
An infinite value is returned if there is only one point
in the point pattern (or in general if there are fewer than
k+1
points).
See Also
nndist.psp
,
pairdist
,
Gest
,
nnwhich
,
nncross
.
Examples
data(cells)
d <- nndist(cells)
d2 <- nndist(cells, k=2)
x <- runif(100)
y <- runif(100)
d <- nndist(x, y)
# Stienen diagram
plot(cells %mark% (nndist(cells)/2), markscale=1)