as.linfun.Rd
\name{as.linfun}
\alias{as.linfun}
\alias{as.linfun.linim}
\alias{as.linfun.lintess}
\title{
Convert Data to a Function on a Linear Network
}
\description{
Convert some kind of data to an object of class \code{"linfun"}
representing a function on a linear network.
}
\usage{
as.linfun(X, \dots)
\method{as.linfun}{linim}(X, \dots)
\method{as.linfun}{lintess}(X, \dots, values, navalue=NA)
}
\arguments{
\item{X}{
Some kind of data to be converted.
}
\item{\dots}{
Other arguments passed to methods.
}
\item{values}{
Optional. Vector of function values,
one entry associated with each tile of the tessellation.
}
\item{navalue}{
Optional. Function value associated with locations that
do not belong to a tile of the tessellation.
}
}
\details{
An object of class \code{"linfun"} represents
a function defined on a linear network.
The function \code{as.linfun} is generic. The method \code{as.linfun.linim}
converts objects of class \code{"linim"} (pixel images on a linear
network) to functions on the network.
The method \code{as.linfun.lintess} converts a tessellation
on a linear network into a function with a different value
on each tile of the tessellation.
If the argument \code{values} is missing or null,
then the function returns factor values identifying which tile
contains each given point. If \code{values} is given,
it should be a vector with one entry for each tile of the tessellation:
any point lying in tile number \code{i} will return the value \code{v[i]}.
}
\value{
Object of class \code{"linfun"}.
}
\author{
\spatstatAuthors.
}
\seealso{
\code{\link{linfun}}
}
\examples{
X <- runiflpp(2, simplenet)
Y <- runiflpp(5, simplenet)
# image on network
D <- density(Y, 0.1, verbose=FALSE)
f <- as.linfun(D)
f
f(X)
# tessellation on network
Z <- lineardirichlet(Y)
g <- as.linfun(Z)
g(X)
h <- as.linfun(Z, values = runif(5))
h(X)
}
\keyword{spatial}
\keyword{manip}