Kest.Rd
\name{Kest}
\alias{Kest}
\title{K-function}
\description{
Estimates Ripley's reduced second moment function \eqn{K(r)}
from a point pattern in a window of arbitrary shape.
}
\usage{
Kest(X, \dots, r=NULL, breaks=NULL,
correction=c("border", "isotropic", "Ripley", "translate"),
nlarge=3000, domain=NULL)
}
\arguments{
\item{X}{The observed point pattern,
from which an estimate of \eqn{K(r)} will be computed.
An object of class \code{"ppp"}, or data
in any format acceptable to \code{\link{as.ppp}()}.
}
\item{\dots}{Ignored.}
\item{r}{
Optional. Vector of values for the argument \eqn{r} at which \eqn{K(r)}
should be evaluated. Users are advised \emph{not} to specify this
argument; there is a sensible default.
}
\item{breaks}{
Optional. An alternative to the argument \code{r}.
Not normally invoked by the user. See the \bold{Details} section.
}
\item{correction}{
Optional. A character vector containing any selection of the
options \code{"none"}, \code{"border"}, \code{"bord.modif"},
\code{"isotropic"}, \code{"Ripley"} or \code{"translate"}.
It specifies the edge correction(s) to be applied.
}
\item{nlarge}{
Optional. Efficiency threshold.
If the number of points exceeds \code{nlarge}, then only the
border correction will be computed, using a fast algorithm.
}
\item{domain}{
Optional. Calculations will be restricted to this subset
of the window. See Details.
}
}
\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}
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{K(r)} obtained by the edge corrections
named.
}
\details{
The \eqn{K} function (variously called ``Ripley's K-function''
and the ``reduced second moment function'')
of a stationary point process \eqn{X} is defined so that
\eqn{\lambda K(r)}{lambda K(r)} equals the expected number of
additional random points within a distance \eqn{r} of a
typical random point of \eqn{X}. Here \eqn{\lambda}{lambda}
is the intensity of the process,
i.e. the expected number of points of \eqn{X} per unit area.
The \eqn{K} function is determined by the
second order moment properties of \eqn{X}.
An estimate of \eqn{K} derived from a spatial point pattern dataset
can be used in exploratory data analysis and formal inference
about the pattern (Cressie, 1991; Diggle, 1983; Ripley, 1988).
In exploratory analyses, the estimate of \eqn{K} is a useful statistic
summarising aspects of inter-point ``dependence'' and ``clustering''.
For inferential purposes, the estimate of \eqn{K} is usually compared to the
true value of \eqn{K} for a completely random (Poisson) point process,
which is \eqn{K(r) = \pi r^2}{K(r) = pi * r^2}.
Deviations between the empirical and theoretical \eqn{K} curves
may suggest spatial clustering or spatial regularity.
This routine \code{Kest} estimates the \eqn{K} function
of a stationary point process, given observation of the process
inside a known, bounded window.
The argument \code{X} is interpreted as a point pattern object
(of class \code{"ppp"}, see \code{\link{ppp.object}}) and can
be supplied in any of the formats recognised by
\code{\link{as.ppp}()}.
The estimation of \eqn{K} is hampered by edge effects arising from
the unobservability of points of the random pattern outside the window.
An edge correction is needed to reduce bias (Baddeley, 1998; Ripley, 1988).
The corrections implemented here are
\describe{
\item{border}{the border method or
``reduced sample'' estimator (see Ripley, 1988). This is
the least efficient (statistically) and the fastest to compute.
It can be computed for a window of arbitrary shape.
}
\item{isotropic/Ripley}{Ripley's isotropic correction
(see Ripley, 1988; Ohser, 1983).
This is implemented for rectangular and polygonal windows
(not for binary masks).
}
\item{translate}{Translation correction (Ohser, 1983).
Implemented for all window geometries, but slow for
complex windows.
}
}
For instructional purposes, you can also set
\code{correction="none"} to compute an estimate of the \eqn{K}
function without edge correction. This estimate is biased and should
not be used for data analysis.
The estimates of \eqn{K(r)} are of the form
\deqn{
\hat K(r) = \frac a {(n-1) \pi} \sum_i \sum_j I(d_{ij}\le r) e_{ij}
}{
Kest(r) = (a/((n-1)* pi)) * sum[i,j] I(d[i,j] <= r) e[i,j])
}
where \eqn{a} is the area of the window, \eqn{n} is the number of
data points, and the sum is taken over all ordered pairs of points
\eqn{i} and \eqn{j} in \code{X}.
Here \eqn{d_{ij}}{d[i,j]} is the distance between the two points,
and \eqn{I(d_{ij} \le r)}{I(d[i,j] <= r)} is the indicator
that equals 1 if the distance is less than or equal to \eqn{r}.
The term \eqn{e_{ij}}{e[i,j]} is the edge correction weight (which
depends on the choice of edge correction listed above).
Note that this estimator assumes the process is stationary (spatially
homogeneous). For inhomogeneous point patterns, see
\code{\link{Kinhom}}.
If the point pattern \code{X} contains more than about 3000 points,
the isotropic and translation edge corrections can be computationally
prohibitive. The computations for the border method are much faster,
and are statistically efficient when there are large numbers of
points. Accordingly, if the number of points in \code{X} exceeds
the threshold \code{nlarge}, then only the border correction will be
computed. Setting \code{nlarge=Inf} will prevent this from happening.
Setting \code{nlarge=0} is equivalent to selecting only the border
correction with \code{correction="border"}.
If the argument \code{domain} is given, the calculations will
be restricted to a subset of the data. In the formula for \eqn{K(r)} above,
the \emph{first} point \eqn{i} will be restricted to lie inside
\code{domain}. The result is an approximately unbiased estimate
of \eqn{K(r)} based on pairs of points in which the first point lies
inside \code{domain} and the second point is unrestricted.
This is useful in bootstrap techniques. The argument \code{domain}
should be a window (object of class \code{"owin"}) or something acceptable to
\code{\link{as.owin}}. It must be a subset of the
window of the point pattern \code{X}.
The estimator \code{Kest} ignores marks.
Its counterparts for multitype point patterns
are \code{\link{Kcross}}, \code{\link{Kdot}},
and for general marked point patterns
see \code{\link{Kmulti}}.
Some writers, particularly Stoyan (1994, 1995) advocate the use of
the ``pair correlation function''
\deqn{
g(r) = \frac{K'(r)}{2\pi r}
}{
g(r) = K'(r)/ ( 2 * pi * r)
}
where \eqn{K'(r)} is the derivative of \eqn{K(r)}.
See \code{\link{pcf}} on how to estimate this function.
}
\references{
Baddeley, A.J. Spatial sampling and censoring.
In O.E. Barndorff-Nielsen, W.S. Kendall and
M.N.M. van Lieshout (eds)
\emph{Stochastic Geometry: Likelihood and Computation}.
Chapman and Hall, 1998.
Chapter 2, pages 37--78.
Cressie, N.A.C. \emph{Statistics for spatial data}.
John Wiley and Sons, 1991.
Diggle, P.J. \emph{Statistical analysis of spatial point patterns}.
Academic Press, 1983.
Ohser, J. (1983)
On estimators for the reduced second moment measure of
point processes. \emph{Mathematische Operationsforschung und
Statistik, series Statistics}, \bold{14}, 63 -- 71.
Ripley, B.D. \emph{Statistical inference for spatial processes}.
Cambridge University Press, 1988.
Stoyan, D, Kendall, W.S. and Mecke, J. (1995)
\emph{Stochastic geometry and its applications}.
2nd edition. Springer Verlag.
Stoyan, D. and Stoyan, H. (1994)
Fractals, random shapes and point fields:
methods of geometrical statistics.
John Wiley and Sons.
}
\section{Warnings}{
The estimator of \eqn{K(r)} is approximately unbiased for each fixed \eqn{r}.
Bias increases with \eqn{r} and depends on the window geometry.
For a rectangular window it is prudent to restrict the \eqn{r} values to
a maximum of \eqn{1/4} of the smaller side length of the rectangle.
Bias may become appreciable for point patterns consisting of
fewer than 15 points.
While \eqn{K(r)} is always a non-decreasing function, the estimator
of \eqn{K} is not guaranteed to be non-decreasing. This is rarely
a problem in practice.
}
\seealso{
\code{\link{localK}} to extract individual summands in the \eqn{K}
function.
\code{\link{pcf}} for the pair correlation.
\code{\link{Fest}},
\code{\link{Gest}},
\code{\link{Jest}}
for alternative summary functions.
\code{\link{Kcross}},
\code{\link{Kdot}},
\code{\link{Kinhom}},
\code{\link{Kmulti}} for counterparts of the \eqn{K} function
for multitype point patterns.
\code{\link{reduced.sample}} for the calculation of reduced sample
estimators.
}
\examples{
pp <- runifpoint(50)
K <- Kest(pp)
data(cells)
K <- Kest(cells, correction="isotropic")
plot(K)
plot(K, main="K function for cells")
# plot the L function
plot(K, sqrt(iso/pi) ~ r)
plot(K, sqrt(./pi) ~ r, ylab="L(r)", 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}