\name{Lest}
\alias{Lest}
\title{L-function}
\description{
Calculates an estimate of Ripley's L-function
for a spatial point pattern.
}
\usage{
Lest(...)
}
\arguments{
\item{\dots}{
Arguments passed to \code{\link{Kest}}
to estimate the K-function.
}
}
\details{
This command computes an estimate of the L-function
for a spatial point pattern.
The L-function is a transformation of Ripley's K-function,
\deqn{L(r) = \sqrt{\frac{K(r)}{\pi}}}{L(r) = sqrt(K(r)/pi)}
where \eqn{K(r)} is the K-function.
See \code{\link{Kest}} for information
about Ripley's K-function.
The command \code{Lest} first calls
\code{\link{Kest}} to compute the estimate of the K-function,
and then applies the square root transformation.
For a completely random (uniform Poisson) point pattern,
the theoretical value of the L-function is \eqn{L(r) = r}.
The square root also has the effect of stabilising
the variance of the estimator, so that L is more appropriate
for use in simulation envelopes and hypothesis tests.
}
\value{
An object of class \code{"fv"}, see \code{\link{fv.object}},
which can be plotted directly using \code{\link{plot.fv}}.
Essentially a data frame containing columns
\item{r}{the vector of values of the argument \eqn{r}
at which the function \eqn{L} has been estimated
}
\item{theo}{the theoretical value \eqn{L(r) = r}
for a stationary Poisson process
}
together with columns named
\code{"border"}, \code{"bord.modif"},
\code{"iso"} and/or \code{"trans"},
according to the selected edge corrections. These columns contain
estimates of the function \eqn{L(r)} obtained by the edge corrections
named.
}
\seealso{
\code{\link{Kest}},
\code{\link{pcf}}
}
\examples{
data(cells)
L <- Lest(cells)
plot(L)
plot(L, main="L function for cells")
}
\author{Adrian Baddeley
\email{adrian@maths.uwa.edu.au}
\url{http://www.maths.uwa.edu.au/~adrian/}
and Rolf Turner
\email{r.turner@auckland.ac.nz}
}
\keyword{spatial}
\keyword{nonparametric}