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\name{linearKEuclid}
\alias{linearKEuclid}
\title{
Linear K Function Using Euclidean Distance
}
\description{
Computes an estimate of the linear \eqn{K} function
based on Euclidean distances,
for a point pattern on a linear network.
}
\usage{
linearKEuclid(X, r = NULL, ...)
}
\arguments{
\item{X}{
Point pattern on linear network (object of class \code{"lpp"}).
}
\item{r}{
Optional. Numeric vector of values of the function argument \eqn{r}.
There is a sensible default.
}
\item{\dots}{
Ignored.
}
}
\details{
This command computes an estimate of the
linear \eqn{K} function based on Euclidean distances
between the points, as described by
Rakshit, Nair and Baddeley (2017).
This is different from the linear \code{K} function
based on shortest-path distances, which is computed by
\code{\link{linearK}}.
The linear \eqn{K} function based on Euclidean distances
is defined in equation (20) of Rakshit, Nair and Baddeley (2017).
The estimate is computed from the point pattern as described in equation (25).
}
\value{
Function value table (object of class \code{"fv"}).
}
\references{
Rakshit. S., Nair, G. and Baddeley, A. (2017)
Second-order analysis of point patterns on a network
using any distance metric. \emph{Spatial Statistics} \bold{22} (1) 129--154.
}
\author{
\adrian.
}
\seealso{
\code{\link{linearpcfEuclid}}, \code{\link{linearKEuclidInhom}}.
See \code{\link{linearK}} for the corresponding function
based on shortest-path distances.
}
\examples{
X <- rpoislpp(5, simplenet)
K <- linearKEuclid(X)
}
\keyword{spatial}
\keyword{nonparametric}
\concept{Linear network}
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