clarkevans | R Documentation |
Computes the Clark and Evans aggregation index R for a spatial point pattern.
clarkevans(X, correction=c("none", "Donnelly", "cdf"), clipregion=NULL)
X |
A spatial point pattern (object of class |
correction |
Character vector. The type of edge correction(s) to be applied. |
clipregion |
Clipping region for the guard area correction.
A window (object of class |
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.
Without correction for edge effects, the value of R
will be
positively biased. Edge effects arise because, for a point of X
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.
The argument correction
specifies an edge correction
or several edge corrections to be applied. It is a character vector
containing one or more of the options
"none"
, "Donnelly"
, "guard"
and "cdf"
(which are recognised by partial matching).
These edge corrections are:
No edge correction is applied.
Edge correction of Donnelly (1978), available for rectangular windows only. The theoretical expected value of mean nearest neighbour distance under a Poisson process is adjusted for edge effects by the edge correction of Donnelly (1978). The value of R is the ratio of the observed mean nearest neighbour distance to this adjusted theoretical mean.
Guard region or buffer area method.
The observed mean nearest neighbour distance
for the point pattern X
is re-defined by averaging only over those points of X
that fall inside the sub-window clipregion
.
Cumulative Distribution Function method.
The nearest neighbour distance distribution function G(r)
of the stationary point process is estimated by Gest
using the Kaplan-Meier type edge correction. Then the mean of the
distribution is calculated from the cdf.
Alternatively correction="all"
selects all options.
If the argument clipregion
is given, then the selected
edge corrections will be assumed to include correction="guard"
.
To perform a test based on the Clark-Evans index,
see clarkevans.test
.
A numeric value, or a 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 Donnelly
component will be NA
if the window of X
is not a rectangle.
John Rudge rudge@esc.cam.ac.uk with modifications by \adrian
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 I. Hodder (ed.) Simulation studies in archaeology, Cambridge/New York: Cambridge University Press, pp 91–95.
clarkevans.test
,
hopskel
,
nndist
,
Gest
# Example of a clustered pattern clarkevans(redwood) # Example of an ordered pattern 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 <- erosion(Window(cells), 0.1) clarkevans(cells, clipregion=clip1) clarkevans(cells, clipregion=clip2)
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