% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/o.ring.R
\name{o.ring}
\alias{o.ring}
\title{Inhomogeneous O-ring}
\usage{
o.ring(x, inhomogeneous = FALSE, ...)
}
\arguments{
\item{x}{spatstat ppp object}

\item{inhomogeneous}{(FALSE/TRUE) Run homogeneous (pcf) or inhomogeneous 
(pcfinhom)}

\item{...}{additional arguments passed to pcf or pcfinhom}
}
\value{
plot of o-ring and data.frame with plot labels and descriptions
}
\description{
Calculates the inhomogeneous O-ring point pattern statistic 
            (Wiegand & Maloney 2004)
}
\details{
The function K(r) is the expected number of points in a circle of radius r centered 
at an arbitrary point (which is not counted), divided by the intensity l of the pattern. 
The alternative pair correlation function g(r), which arises if the circles of 
Ripley's K-function are replaced by rings, gives the expected number of points at 
distance r from an arbitrary point, divided by the intensity of the pattern. Of special 
interest is to determine whether a pattern is random, clumped, or regular. 

Using rings instead of circles has the advantage that one can isolate specific 
distance classes, whereas the cumulative K-function confounds effects at larger 
distances with effects at shorter distances. Note that the K-function and the O-ring 
statistic respond to slightly different biological questions. The accumulative 
K-function can detect aggregation or dispersion up to a given distance r and is 
therefore appropriate if the process in question (e.g., the negative effect of 
competition) may work only up to a certain distance, whereas the O-ring statistic 
can detect aggregation or dispersion at a given distance r. The O-ring statistic 
has the additional advantage that it is a probability density function (or a 
conditioned probability spectrum) with the interpretation of a neighborhood 
density, which is more intuitive than an accumulative measure.
}
\examples{
if (require(spatstat.explore, quietly = TRUE)) {
data(lansing)
  x  <- spatstat.geom::unmark(split(lansing)$maple)
  o.ring(x)

} else { 
  cat("Please install spatstat.explore package to run example", "\n")
}

}
\references{
Wiegand T., and K. A. Moloney (2004) Rings, circles and null-models for point 
pattern analysis in ecology. Oikos 104:209-229
}
\author{
Jeffrey S. Evans  <jeffrey_evans@tnc.org>
}
