markvario.Rd
\name{markvario}
\alias{markvario}
\title{Mark Variogram}
\description{
Estimate the mark variogram of a marked point pattern.
}
\usage{
markvario(X, correction = c("isotropic", "Ripley", "translate"),
r = NULL, method = "density", ..., normalise=FALSE)
}
\arguments{
\item{X}{The observed point pattern.
An object of class \code{"ppp"} or something acceptable to
\code{\link{as.ppp}}. It must have marks which are numeric.
}
\item{correction}{
A character vector containing any selection of the
options \code{"isotropic"}, \code{"Ripley"} or \code{"translate"}.
It specifies the edge correction(s) to be applied.
}
\item{r}{numeric vector. The values of the argument \eqn{r}
at which the mark variogram
\eqn{\gamma(r)}{gamma(r)}
should be evaluated.
There is a sensible default.
}
\item{method}{
A character vector indicating the user's choice of
density estimation technique to be used. Options are
\code{"density"},
\code{"loess"},
\code{"sm"} and \code{"smrep"}.
}
\item{\dots}{
Arguments passed to the density estimation routine
(\code{\link{density}}, \code{\link{loess}} or \code{sm.density})
selected by \code{method}.
}
\item{normalise}{If \code{TRUE}, normalise the variogram by
dividing it by the estimated mark variance.
}
}
\details{
The mark variogram \eqn{\gamma(r)}{gamma(r)}
of a marked point process \eqn{X}
is a measure of the dependence between the marks of two
points of the process a distance \eqn{r} apart.
It is informally defined as
\deqn{
\gamma(r) = E[\frac 1 2 (M_1 - M_2)^2]
}{
gamma(r) = E[(1/2) * (M1 - M2)^2 ]
}
where \eqn{E[ ]} denotes expectation and \eqn{M_1,M_2}{M1,M2}
are the marks attached to two points of the process
a distance \eqn{r} apart.
The mark variogram of a marked point process is analogous,
but \bold{not equivalent}, to the variogram of a random field
in geostatistics. See Waelder and Stoyan (1996).
}
\value{
An object of class \code{"fv"} (see \code{\link{fv.object}}).
Essentially a data frame containing numeric columns
\item{r}{the values of the argument \eqn{r}
at which the mark variogram \eqn{\gamma(r)}{gamma(r)}
has been estimated
}
\item{theo}{the theoretical value of \eqn{\gamma(r)}{gamma(r)}
when the marks attached to different points are independent;
equal to the sample variance of the marks
}
together with a column or columns named
\code{"iso"} and/or \code{"trans"},
according to the selected edge corrections. These columns contain
estimates of the function \eqn{\gamma(r)}{gamma(r)}
obtained by the edge corrections named.
}
\references{
Cressie, N.A.C. (1991)
\emph{Statistics for spatial data}.
John Wiley and Sons, 1991.
Mase, S. (1996)
The threshold method for estimating annual rainfall.
\emph{Annals of the Institute of Statistical Mathematics}
\bold{48} (1996) 201-213.
Waelder, O. and Stoyan, D. (1996)
On variograms in point process statistics.
\emph{Biometrical Journal} \bold{38} (1996) 895-905.
}
\seealso{
Mark correlation function \code{\link{markcorr}} for numeric marks.
Mark connection function \code{\link{markconnect}} and
multitype K-functions \code{\link{Kcross}}, \code{\link{Kdot}}
for factor-valued marks.
}
\examples{
# Longleaf Pine data
# marks represent tree diameter
data(longleaf)
# Subset of this large pattern
swcorner <- owin(c(0,100),c(0,100))
sub <- longleaf[ , swcorner]
# mark correlation function
mv <- markvario(sub)
plot(mv)
}
\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}