distfun.lpp
Distance Map on Linear Network
Compute the distance function of a point pattern on a linear network.
Usage
# S3 method for lpp
distfun(X, ..., k=1)Arguments
- X
A point pattern on a linear network (object of class
"lpp").- k
An integer. The distance to the
kth nearest point will be computed.- …
Extra arguments are ignored.
Details
On a linear network \(L\), the “geodesic distance function”
of a set of points \(A\) in \(L\) is the
mathematical function \(f\) such that, for any
location \(s\) on \(L\),
the function value f(s)
is the shortest-path distance from \(s\) to \(A\).
The command distfun.lpp is a method for the generic command
distfun
for the class "lpp" of point patterns on a linear network.
If X is a point pattern on a linear network,
f <- distfun(X) returns a function
in the R language that represents the
distance function of X. Evaluating the function f
in the form v <- f(x,y), where x and y
are any numeric vectors of equal length containing coordinates of
spatial locations, yields the values of the distance function at these
locations. More efficiently f can be called in the form
v <- f(x, y, seg, tp) where seg and tp are the local
coordinates on the network. It can also be called as
v <- f(x) where x is a point pattern on the same linear
network.
The function f obtained from f <- distfun(X)
also belongs to the class "linfun".
It can be printed and plotted immediately as shown in the Examples.
It can be
converted to a pixel image using as.linim.
Value
A function with arguments x,y and optional
arguments seg,tp.
It also belongs to the class "linfun" which has methods
for plot, print etc.
See Also
To identify which point is the nearest neighbour, see
nnfun.lpp.
Examples
# NOT RUN {
data(letterR)
X <- runiflpp(3, simplenet)
f <- distfun(X)
f
plot(f)
# using a distfun as a covariate in a point process model:
Y <- runiflpp(4, simplenet)
fit <- lppm(Y ~D, covariates=list(D=f))
f(Y)
# }