clarkevans(X, correction=c("none", "Donnelly", "cdf"), clipregion=NULL)
"ppp"
).
"owin"
).
See Details.
correction
). The value of the Donnelly
component will be NA
if the window of X
is not a rectangle.
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:
X
is re-defined by averaging only over those points of X
that fall inside the sub-window clipregion
.
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
.
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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