https://github.com/cran/spatstat
Revision 4fe059206e698a4b7135d792f3d533b173ecfe77 authored by Adrian Baddeley on 16 May 2012, 12:44:15 UTC, committed by cran-robot on 16 May 2012, 12:44:15 UTC
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Tip revision: 4fe059206e698a4b7135d792f3d533b173ecfe77 authored by Adrian Baddeley on 16 May 2012, 12:44:15 UTC
version 1.27-0
version 1.27-0
Tip revision: 4fe0592
localpcf.Rd
\name{localpcf}
\alias{localpcf}
\alias{localpcfinhom}
\title{Local pair correlation function}
\description{
Computes individual contributions to the
pair correlation function from each data point.
}
\usage{
localpcf(X, ..., delta=NULL, rmax=NULL, nr=512, stoyan=0.15)
localpcfinhom(X, ..., delta=NULL, rmax=NULL, nr=512, stoyan=0.15,
lambda=NULL, sigma=NULL, varcov=NULL)
}
\arguments{
\item{X}{A point pattern (object of class \code{"ppp"}).}
\item{delta}{
Smoothing bandwidth for pair correlation.
The halfwidth of the Epanechnikov kernel.
}
\item{rmax}{
Optional. Maximum value of distance \eqn{r} for which
pair correlation values \eqn{g(r)} should be computed.
}
\item{nr}{
Optional. Number of values of distance \eqn{r} for which
pair correlation \eqn{g(r)} should be computed.
}
\item{stoyan}{
Optional. The value of the constant \eqn{c} in Stoyan's rule
of thumb for selecting the smoothing bandwidth \code{delta}.
}
\item{lambda}{
Optional.
Values of the estimated intensity function, for the
inhomogeneous pair correlation.
Either a vector giving the intensity values
at the points of the pattern \code{X},
a pixel image (object of class \code{"im"}) giving the
intensity values at all locations, a fitted point process model
(object of class \code{"ppm"}) or a \code{function(x,y)} which
can be evaluated to give the intensity value at any location.
}
\item{sigma,varcov,\dots}{
These arguments are ignored by \code{localpcf}
but are passed by \code{localpcfinhom} (when \code{lambda=NULL})
to the function \code{\link{density.ppp}}
to control the kernel smoothing estimation of \code{lambda}.
}
}
\details{
\code{localpcf} computes the contribution, from each individual
data point in a point pattern \code{X}, to the
empirical pair correlation function of \code{X}.
These contributions are sometimes known as LISA (local indicator
of spatial association) functions based on pair correlation.
\code{localpcfinhom} computes the corresponding contribution
to the \emph{inhomogeneous} empirical pair correlation function of \code{X}.
Given a spatial point pattern \code{X}, the local pcf
\eqn{g_i(r)}{g[i](r)} associated with the \eqn{i}th point
in \code{X} is computed by
\deqn{
g_i(r) = \frac a {2 \pi n} \sum_j k(d_{i,j} - r)
}{
g[i](r) = (a/(2 * pi * n) * sum[j] k(d[i,j] - r)
}
where the sum is over all points \eqn{j \neq i}{j != i},
\eqn{a} is the area of the observation window, \eqn{n} is the number
of points in \code{X}, and \eqn{d_{ij}}{d[i,j]} is the distance
between points \code{i} and \code{j}. Here \code{k} is the
Epanechnikov kernel,
\deqn{
k(t) = \frac 3 { 4\delta} \max(0, 1 - \frac{t^2}{\delta^2}).
}{
k(t) = (3/(4*delta)) * max(0, 1 - t^2/delta^2).
}
Edge correction is performed using the border method
(for the sake of computational efficiency):
the estimate \eqn{g_i(r)}{g[i](r)} is set to \code{NA} if
\eqn{r > b_i}{r > b[i]}, where \eqn{b_i}{b[i]}
is the distance from point \eqn{i} to the boundary of the
observation window.
The smoothing bandwidth \eqn{\delta}{delta} may be specified.
If not, it is chosen by Stoyan's rule of thumb
\eqn{\delta = c/\hat\lambda}{delta = c/lambda}
where \eqn{\hat\lambda = n/a}{lambda = n/a} is the estimated intensity
and \eqn{c} is a constant, usually taken to be 0.15.
The value of \eqn{c} is controlled by the argument \code{stoyan}.
For \code{localpcfinhom}, the optional argument \code{lambda}
specifies the values of the estimated intensity function.
If \code{lambda} is given, it should be either a
numeric vector giving the intensity values
at the points of the pattern \code{X},
a pixel image (object of class \code{"im"}) giving the
intensity values at all locations, a fitted point process model
(object of class \code{"ppm"}) or a \code{function(x,y)} which
can be evaluated to give the intensity value at any location.
If \code{lambda} is not given, then it will be estimated
using a leave-one-out kernel density smoother as described
in \code{\link{pcfinhom}}.
}
\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{K} has been estimated
}
\item{theo}{the theoretical value \eqn{K(r) = \pi r^2}{K(r) = pi * r^2}
or \eqn{L(r)=r} for a stationary Poisson process
}
together with columns containing the values of the
local pair correlation function for each point in the pattern.
Column \code{i} corresponds to the \code{i}th point.
The last two columns contain the \code{r} and \code{theo} values.
}
\seealso{
\code{\link{localK}},
\code{\link{localKinhom}},
\code{\link{pcf}},
\code{\link{pcfinhom}}
}
\examples{
data(ponderosa)
X <- ponderosa
g <- localpcf(X, stoyan=0.5)
colo <- c(rep("grey", npoints(X)), "blue")
a <- plot(g, main=c("local pair correlation functions", "Ponderosa pines"),
legend=FALSE, col=colo, lty=1)
# plot only the local pair correlation function for point number 7
plot(g, est007 ~ r)
gi <- localpcfinhom(X, stoyan=0.5)
a <- plot(gi, main=c("inhomogeneous local pair correlation functions",
"Ponderosa pines"),
legend=FALSE, col=colo, lty=1)
}
\author{Adrian Baddeley
\email{Adrian.Baddeley@csiro.au}
\url{http://www.maths.uwa.edu.au/~adrian/}
and Rolf Turner
\email{r.turner@auckland.ac.nz}
}
\keyword{spatial}
\keyword{nonparametric}
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