| nndist {spatstat} | R Documentation |
Computes the distance from each point to its nearest neighbour in a point pattern.
nndist(X, ..., method="C") ## S3 method for class 'ppp': nndist(X, ..., method="C") ## Default S3 method: nndist(X, Y=NULL, ..., method="C")
X,Y |
Arguments specifying the locations of
a set of points.
For nndist.ppp, the argument X should be a point
pattern (object of class "ppp").
For nndist.default, 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 x and y,
or a matrix with two columns.
|
... |
Ignored by nndist.ppp
and nndist.default.
|
method |
String specifying which method of calculation to use.
Values are "C" and "interpreted".
|
This function computes the Euclidean distance from each point in a point pattern to its nearest neighbour (the nearest other point of the pattern).
The function nndist is generic, with
a method for point patterns (objects of class "ppp")
and a default method.
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.
Numeric vector of the nearest neighbour distances for each point.
An infinite value is returned if there is only one point in the point pattern.
Pavel Grabarnik pavel.grabar@issp.serpukhov.su and Adrian Baddeley adrian@maths.uwa.edu.au http://www.maths.uwa.edu.au/~adrian/
pairdist,
Gest,
nnwhich,
nncross.
data(cells) d <- nndist(cells) x <- runif(100) y <- runif(100) d <- nndist(x, y) # Stienen diagram plot(cells %mark% (nndist(cells)/2), markscale=1)