nncross.lpp  R Documentation 
Given two point patterns X
and Y
on a linear network,
finds the nearest neighbour in Y
of each point of X
using the shortest path in the network.
## S3 method for class 'lpp' nncross(X, Y, iX=NULL, iY=NULL, what = c("dist", "which"), ..., k = 1, method="C")
X,Y 
Point patterns on a linear network (objects of class 
iX, iY 
Optional identifiers, used to determine whether a point in

what 
Character string specifying what information should be returned.
Either the nearest neighbour distance ( 
... 
Ignored. 
k 
Integer, or integer vector. The algorithm will compute the distance to the

method 
Internal use only. 
Given two point patterns X
and Y
on the same linear
network, this function finds, for each point of X
,
the nearest point of Y
, measuring distance by the shortest path
in the network. The distance between these points
is also computed.
The return value is a data frame, with rows corresponding to
the points of X
. The first column gives the nearest neighbour
distances (i.e. the i
th entry is the distance
from the i
th point of X
to the nearest element of
Y
). The second column gives the indices of the nearest
neighbours (i.e.\ the i
th entry is the index of
the nearest element in Y
.)
If what="dist"
then only the vector of distances is returned.
If what="which"
then only the vector of indices is returned.
Note that this function is not symmetric in X
and Y
.
To find the nearest neighbour in X
of each point in Y
,
use nncross(Y,X)
.
The arguments iX
and iY
are used when
the two point patterns X
and Y
have some points in
common. In this situation nncross(X, Y)
would return some zero
distances. To avoid this, attach a unique integer identifier to
each point, such that two points are identical if their
identifying numbers are equal. Let iX
be the vector of
identifier values for the points in X
, and iY
the vector of identifiers for points in Y
. Then the code
will only compare two points if they have different values of the
identifier. See the Examples.
The k
th nearest neighbour may be undefined, for example
if there are fewer than k+1
points in the dataset, or if
the linear network is not connected.
In this case, the k
th nearest neighbour distance is infinite.
By default (if what=c("dist", "which")
and k=1
)
a data frame with two columns:
dist 
Nearest neighbour distance 
which 
Nearest neighbour index in 
If what="dist"
, a vector of nearest neighbour distances.
If what="which"
, a vector of nearest neighbour indices.
If k
is a vector of integers, the result is a matrix
with one row for each point in X
,
giving the distances and/or indices of the k
th nearest
neighbours in Y
.
nndist.lpp
for nearest neighbour
distances in a single point pattern.
nnwhich.lpp
to identify which points are nearest
neighbours in a single point pattern.
# two different point patterns X < runiflpp(3, simplenet) Y < runiflpp(5, simplenet) nn < nncross(X,Y) nn plot(simplenet, main="nncross") plot(X, add=TRUE, cols="red") plot(Y, add=TRUE, cols="blue", pch=16) XX < as.ppp(X) YY < as.ppp(Y) i < nn$which arrows(XX$x, XX$y, YY[i]$x, YY[i]$y, length=0.15) # nearest and secondnearest neighbours nncross(X, Y, k=1:2) # two patterns with some points in common X < Y[1:2] iX < 1:2 iY < 1:5 nncross(X,Y, iX, iY)
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