Revision 6ebd6ff5fb7707ef87961f50ee794d8eb7143d0a authored by Adrian Baddeley on 10 August 2005, 00:00:00 UTC, committed by Gabor Csardi on 10 August 2005, 00:00:00 UTC
1 parent 5493ce5
Jmulti.Rd
\name{Jmulti}
\alias{Jmulti}
\title{
Marked J Function
}
\description{
For a marked point pattern,
estimate the multitype \eqn{J} function
summarising dependence between the
points in subset \code{I}
and those in subset \eqn{J}.
}
\synopsis{
Jmulti(X, I, J, eps=NULL, r=NULL, breaks=NULL)
}
\usage{
Jmulti(X, I, J)
Jmulti(X, I, J, eps, r)
Jmulti(X, I, J, eps, breaks)
}
\arguments{
\item{X}{The observed point pattern,
from which an estimate of the multitype distance distribution function
\eqn{J_{IJ}(r)}{JIJ(r)} will be computed.
It must be a marked point pattern.
See under Details.
}
\item{I}{Subset of points of \code{X} from which distances are
measured.
}
\item{J}{Subset of points in \code{X} to which distances are measured.
}
\item{eps}{A positive number.
The pixel resolution of the discrete approximation to Euclidean
distance (see \code{\link{Jest}}). There is a sensible default.
}
\item{r}{numeric vector. The values of the argument \eqn{r}
at which the distribution function
\eqn{J_{IJ}(r)}{JIJ(r)} should be evaluated.
There is a sensible default.
First-time users are strongly advised not to specify this argument.
See below for important conditions on \eqn{r}.
}
\item{breaks}{An alternative to the argument \code{r}.
Not normally invoked by the user. See the \bold{Details} section.
}
}
\value{
An object of class \code{"fv"} (see \code{\link{fv.object}}).
Essentially a data frame containing six numeric columns
\item{r}{the values of the argument \eqn{r}
at which the function \eqn{J_{IJ}(r)}{JIJ(r)} has been estimated
}
\item{rs}{the ``reduced sample'' or ``border correction''
estimator of \eqn{J_{IJ}(r)}{JIJ(r)}
}
\item{km}{the spatial Kaplan-Meier estimator of \eqn{J_{IJ}(r)}{JIJ(r)}
}
\item{un}{the uncorrected estimate of \eqn{J_{IJ}(r)}{JIJ(r)},
formed by taking the ratio of uncorrected empirical estimators
of \eqn{1 - G_{IJ}(r)}{1 - GIJ(r)}
and \eqn{1 - F_{J}(r)}{1 - FJ(r)}, see
\code{\link{Gdot}} and \code{\link{Fest}}.
}
\item{theo}{the theoretical value of \eqn{J_{IJ}(r)}{JIJ(r)}
for a marked Poisson process with the same estimated intensity,
namely 1.
}
}
\details{
The function \code{Jmulti}
generalises \code{\link{Jest}} (for unmarked point
patterns) and \code{\link{Jdot}} and \code{\link{Jcross}} (for
multitype point patterns) to arbitrary marked point patterns.
Suppose \eqn{X_I}{X[I]}, \eqn{X_J}{X[J]} are subsets, possibly
overlapping, of a marked point process. Define
\deqn{J_{IJ}(r) = \frac{1 - G_{IJ}(r)}{1 - F_J(r)}}{
JIJ(r) = (1 - GIJ(r))/(1 - FJ(r))}
where \eqn{F_J(r)}{FJ(r)} is the cumulative distribution function of
the distance from a fixed location to the nearest point
of \eqn{X_J}{X[J]}, and \eqn{G_{IJ}(r)}{GJ(r)}
is the distribution function of the distance
from a typical point of \eqn{X_I}{X[I]} to the nearest distinct point of
\eqn{X_J}{X[J]}.
The argument \code{X} must be a point pattern (object of class
\code{"ppp"}) or any data that are acceptable to \code{\link{as.ppp}}.
The arguments \code{I} and \code{J} specify two subsets of the
point pattern. They may be logical vectors of length equal to
\code{X$n}, or integer vectors with entries in the range 1 to
\code{X$n}, etc.
It is assumed that \code{X} can be treated
as a realisation of a stationary (spatially homogeneous)
random spatial point process in the plane, observed through
a bounded window.
The window (which is specified in \code{X} as \code{X$window})
may have arbitrary shape.
Biases due to edge effects are
treated in the same manner as in \code{\link{Jest}}.
The argument \code{r} is the vector of values for the
distance \eqn{r} at which \eqn{J_{IJ}(r)}{JIJ(r)} should be evaluated.
It is also used to determine the breakpoints
(in the sense of \code{\link{hist}})
for the computation of histograms of distances. The reduced-sample and
Kaplan-Meier estimators are computed from histogram counts.
In the case of the Kaplan-Meier estimator this introduces a discretisation
error which is controlled by the fineness of the breakpoints.
First-time users would be strongly advised not to specify \code{r}.
However, if it is specified, \code{r} must satisfy \code{r[1] = 0},
and \code{max(r)} must be larger than the radius of the largest disc
contained in the window. Furthermore, the successive entries of \code{r}
must be finely spaced.
}
\references{
Van Lieshout, M.N.M. and Baddeley, A.J. (1999)
Indices of dependence between types in multivariate point patterns.
\emph{Scandinavian Journal of Statistics} \bold{26}, 511--532.
}
\seealso{
\code{\link{Jcross}},
\code{\link{Jdot}},
\code{\link{Jest}}
}
\examples{
data(longleaf)
# Longleaf Pine data: marks represent diameter
\testonly{
longleaf <- longleaf[seq(1,longleaf$n, by=50), ]
}
Jm <- Jmulti(longleaf, longleaf$marks <= 15, longleaf$marks >= 25)
plot(Jm)
}
\author{Adrian Baddeley
\email{adrian@maths.uwa.edu.au}
\url{http://www.maths.uwa.edu.au/~adrian/}
and Rolf Turner
\email{rolf@math.unb.ca}
\url{http://www.math.unb.ca/~rolf}
}
\keyword{spatial}
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