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\name{as.data.frame.lintess}
\alias{as.data.frame.lintess}
\title{Convert Network Tessellation to Data Frame}
\description{
Converts a tessellation on a linear network into a data frame.
}
\usage{
\method{as.data.frame}{lintess}(x, \dots)
}
\arguments{
\item{x}{
Tessellation on a linear network (object of class \code{"lintess"}).
}
\item{\dots}{Further arguments passed to
\code{\link[base:as.data.frame]{as.data.frame.default}}
to determine the row names and other features.
}
}
\details{
A tessellation on a linear network is a partition of the
network into non-overlapping pieces (tiles). Each tile consists of one
or more line segments which are subsets of the line segments making up
the network. A tile can consist of several disjoint pieces.
This function converts the tessellation \code{x} to a data frame.
Each row of the data frame specifies one sub-segment of the network,
and allocates it to a particular tile. The data frame has the
following columns:
\itemize{
\item The \code{seg} column specifies which line segment of the network
contains the sub-segment. Values of \code{seg} are integer indices
for the network segments in \code{as.psp(as.linnet(x))}.
\item The \code{t0} and \code{t1} columns specify the start and end points
of the sub-segment. They are numeric values between 0 and 1
inclusive, where the values 0 and 1 representing the network vertices
that are joined by this network segment.
\item The \code{tile} column specifies which tile of the tessellation
includes this sub-segment. It is a factor whose
levels are the names of the tiles.
}
The tessellation may have marks, which are attached to the \emph{tiles}
of the tessellation. If marks are present, the resulting data frame
includes columns containing, for each sub-segment,
the mark value of the corresponding tile.
}
\value{
A data frame with columns named \code{seg}, \code{t0}, \code{t1},
\code{tile}, and possibly other columns.
}
\author{
\spatstatAuthors.
}
\seealso{
\code{\link{lintess}}
}
\examples{
X <- lineardirichlet(runiflpp(3, simplenet))
marks(X) <- letters[1:3]
as.data.frame(X)
}
\keyword{spatial}
\keyword{methods}
\concept{Linear network}
\concept{Tessellation}
|
