Performs the Clark-Evans test of aggregation for a spatial point pattern.
clarkevans.test(X, ...,
correction="none",
clipregion=NULL,
alternative=c("two.sided", "less", "greater",
"clustered", "regular"),
nsim=999)An object of class "htest" representing the result of the test.
A spatial point pattern (object of class "ppp").
Ignored.
Character string.
The type of edge correction to be applied.
See clarkevans
Clipping region for the guard area correction.
A window (object of class "owin").
See clarkevans
String indicating the type of alternative for the hypothesis test. Partially matched.
Number of Monte Carlo simulations to perform, if a Monte Carlo p-value is required.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au
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 Clark-Evans index is computed by the function
clarkevans. See the help for clarkevans
for information about the Clark-Evans index \(R\) and about
the arguments correction and clipregion.
This command performs a hypothesis test of clustering or ordering of
the point pattern X. 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 Clark-Evans index \(R\) is computed for the data
as described in clarkevans.
If correction="none" and nsim is missing,
the \(p\)-value for the test is computed by standardising
\(R\) as proposed by Clark and Evans (1954) and referring the
statistic to the standard Normal distribution.
Otherwise, the \(p\)-value for the test is computed
by Monte Carlo simulation of nsim realisations of
Complete Spatial Randomness conditional on the
observed number of points.
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.
clarkevans,
hopskel.test
# Redwood data - clustered
clarkevans.test(redwood)
clarkevans.test(redwood, alternative="clustered")
clarkevans.test(redwood, correction="cdf", nsim=39)
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