Computes the distances between pairs of points taken from two different point patterns on the same linear network.
# S3 method for lpp
crossdist(X, Y, …, method="C", check=TRUE)Point patterns on a linear network (objects of class "lpp").
They must lie on the same network.
Ignored.
String specifying which method of calculation to use
when the network data use the non-sparse representation.
Values are "C" and "interpreted".
Logical value specifying whether to check that X and Y
are defined on the same network. Default is check=TRUE.
Setting check=FALSE will save time,
but should only be used if it is certain that the
two networks are identical.
A matrix whose [i,j] entry is the distance
from the i-th point in X
to the j-th point in Y.
Matrix entries are nonnegative numbers or infinity (Inf).
Distances are accurate within the numerical tolerance of the
network, summary(X)$toler.
For network data stored in the non-sparse
representation described in linnet,
then pairwise distances are computed using the matrix of path distances
between vertices of the network, using R code if
method = "interpreted", or using C code if
method="C" (the default).
For networks stored in the sparse representation,
the argument method has no effect, and the distances are
computed using an efficient C algorithm.
Given two point patterns on a linear network, this function computes the distance from each point in the first pattern to each point in the second pattern, measuring distance by the shortest path along the network.
This is a method for the generic function crossdist
for the class of point patterns on a linear network
(objects of class "lpp").
This function expects two point pattern objects X and Y
on the same linear network,
and returns the matrix whose [i,j] entry is the
shortest-path distance from X[i] to Y[j].
If two points cannot be joined by a path,
the distance between them is infinite (Inf).
The argument method is not normally used. It is
retained only for developers to check the validity of the software.
# NOT RUN {
v <- split(chicago)
X <- v$cartheft
Y <- v$burglary
d <- crossdist(X, Y)
d[1:3,1:4]
# }
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