clarkevans.test  R Documentation 
Performs the ClarkEvans test of aggregation for a spatial point pattern.
clarkevans.test(X, ...,
correction,
clipregion=NULL,
alternative=c("two.sided", "less", "greater",
"clustered", "regular"),
method=c("asymptotic", "MonteCarlo"),
nsim=999)
X 
A spatial point pattern (object of class 
... 
Ignored. 
correction 
Character string.
The type of edge correction to be applied.
See 
clipregion 
Clipping region for the guard area correction.
A window (object of class 
alternative 
String indicating the type of alternative for the hypothesis test. Partially matched. 
method 
Character string (partially matched) specifying how to calculate
the 
nsim 
Number of Monte Carlo simulations to perform, if a Monte Carlo

This command uses the Clark and Evans (1954) aggregation index R
as the basis for a crude test of clustering or ordering of a point pattern.
The ClarkEvans aggregation index R
is computed by the separate
function clarkevans
.
This command clarkevans.text
performs a hypothesis test of clustering or ordering of
the point pattern X
based on the ClarkEvans index R
.
The null hypothesis is Complete
Spatial Randomness, i.e.\ a uniform Poisson process. The alternative
hypothesis is specified by the argument alternative
:
alternative="less"
or alternative="clustered"
:
the alternative hypothesis
is that R < 1
corresponding to a clustered point pattern;
alternative="greater"
or alternative="regular"
:
the alternative hypothesis
is that R > 1
corresponding to a regular or ordered point pattern;
alternative="two.sided"
:
the alternative hypothesis is that R \neq 1
corresponding to a clustered or regular pattern.
The ClarkEvans index R
is first computed for the point pattern
dataset X
using the edge correction determined by
the arguments correction
and clipregion
. These arguments
are documented in the help file for clarkevans
.
If method="asymptotic"
(the default),
the p
value for the test is computed by standardising
R
as proposed by Clark and Evans (1954) and referring the
standardised statistic to the standard Normal distribution.
For this asymptotic test, the default edge correction is
correction="Donnelly"
if the window of X
is a rectangle,
and correction="cdf"
otherwise. It is strongly recommended
to avoid using correction="none"
which would lead to a severely
biased test.
If method="MonteCarlo"
, the p
value for the test is computed
by comparing the observed value of R
to the
results obtained from nsim
simulated realisations of
Complete Spatial Randomness conditional on the
observed number of points. This test is theoretically exact
for any choice of edge correction, but may have lower power
than the asymptotic test.
For this Monte Carlo test, the default edge correction
is correction="none"
for computational efficiency.
An object of class "htest"
representing the result of the test.
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 edgeeffect of total nearest neighbour distance. In Simulation methods in archaeology, Cambridge University Press, pp 91–95.
clarkevans
,
hopskel.test
# Redwood data  clustered
clarkevans.test(redwood)
clarkevans.test(redwood, alternative="clustered")
clarkevans.test(redwood, correction="cdf", method="MonteCarlo", nsim=39)
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