nndist(X, ...)
## S3 method for class 'ppp':
nndist(X, \dots, k=1, by=NULL, method="C")
## S3 method for class 'default':
nndist(X, Y=NULL, \dots, k=1, by=NULL, method="C")nndist.ppp, the argument X should be a point
pattern (object of class "ppp").
For nndist.default, typically X and <nndist.ppp
and nndist.default.kth nearest neighbour.X into groups.
The algorithm will compute the distance to
the nearest point in each group."C" and "interpreted". 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.
If the argument by is given, then the result is a data frame
containing the distances described above, from each point of X,
to the nearest point in each subset of X
defined by the factor by.
X is a multitype point pattern
and by=marks(X), then the algorithm will compute,
for each point of X, the distance to the nearest neighbour
of each type. See the Examples.NA value is returned if the
distance is not defined (e.g. if there is only one point
in the point pattern).k is specified, it computes the
distance to the kth 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 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 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.
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.
nndist.psp,
pairdist,
Gest,
nnwhich,
nncross.data(cells)
# nearest neighbours
d <- nndist(cells)
# second nearest neighbours
d2 <- nndist(cells, k=2)
# first, second and third nearest
d1to3 <- nndist(cells, k=1:3)
x <- runif(100)
y <- runif(100)
d <- nndist(x, y)
# Stienen diagram
plot(cells %mark% (nndist(cells)/2), markscale=1)
# distance to nearest neighbour of each type
head(nndist(ants, by=marks(ants)))Run the code above in your browser using DataLab