Clark and Evans Aggregation Index
Computes the Clark and Evans aggregation index $R$ for a spatial point pattern.
clarkevans(X, correction=c("none", "Donnelly", "cdf"), clipregion=NULL)
- A spatial point pattern (object of class
- Character vector. The type of edge correction(s) to be applied.
- Clipping region for the guard area correction.
A window (object of class
"owin"). See Details.
The Clark and Evans (1954) aggregation index $R$ is a crude measure of clustering or ordering of a point pattern. It is the ratio of the observed mean nearest neighbour distance in the pattern to that expected for a Poisson point process of the same intensity. A value $R>1$ suggests ordering, while $R<1$ suggests="" clustering.<="" p="">
Without correction for edge effects, the value of
R will be
positively biased. Edge effects arise because, for a point of
close to the edge of the window, the true nearest neighbour may
actually lie outside the window. Hence observed nearest neighbour
distances tend to be larger than the true nearest neighbour distances.
correction specifies an edge correction
or several edge corrections to be applied. It is a character vector
containing one or more of the options
(which are recognised by partial matching).
These edge corrections are:
[object Object],[object Object],[object Object],[object Object]
If the argument
clipregion is given, then the selected
edge corrections will be assumed to include
- A numeric value or numeric vector, with named components
naive $R$ without edge correction Donnelly $R$ using Donnelly edge correction guard $R$ using guard region cdf $R$ using cdf method
- (as selected by
correction). The value of the
Donnellycomponent will be
NAif the window of
Xis not a rectangle.
Clark, P.J. and Evans, F.C. (1954) Distance to nearest neighbour as a measure of spatial relationships in populations Ecology 35, 445--453.
Donnelly, K. (1978) Simulations to determine the variance and edge-effect of total nearest neighbour distance. In Simulation methods in archaeology, Cambridge University Press, pp 91--95.
# Example of a clustered pattern data(redwood) clarkevans(redwood) # Example of an ordered pattern data(cells) clarkevans(cells) # Random pattern X <- rpoispp(100) clarkevans(X) # How to specify a clipping region clip1 <- owin(c(0.1,0.9),c(0.1,0.9)) clip2 <- erode.owin(cells$window, 0.1) clarkevans(cells, clipregion=clip1) clarkevans(cells, clipregion=clip2)