spatialEco (version 2.0-2)

o.ring: Inhomogeneous O-ring

Description

Calculates the inhomogeneous O-ring point pattern statistic (Wiegand & Maloney 2004)

Usage

o.ring(x, inhomogeneous = FALSE, ...)

Value

plot of o-ring and data.frame with plot labels and descriptions

Arguments

x

spatstat ppp object

inhomogeneous

(FALSE/TRUE) Run homogeneous (pcf) or inhomogeneous (pcfinhom)

...

additional arguments passed to pcf or pcfinhom

Author

Jeffrey S. Evans <[e-mail]>

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.

References

Wiegand T., and K. A. Moloney (2004) Rings, circles and null-models for point pattern analysis in ecology. Oikos 104:209-229

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")
}