# clarkevans.test

0th

Percentile

##### Clark and Evans Test

Performs the Clark-Evans test of aggregation for a spatial point pattern.

Keywords
spatial, nonparametric
##### Usage
clarkevans.test(X, ...,
correction="none",
clipregion=NULL,
alternative=c("two.sided", "less", "greater"),
nsim=1000)
##### Arguments
X
A spatial point pattern (object of class "ppp").
...
Ignored.
correction
Character string. The type of edge correction to be applied. See clarkevans
clipregion
Clipping region for the guard area correction. A window (object of class "owin"). See clarkevans
alternative
String indicating the type of alternative for the hypothesis test.
nsim
Number of Monte Carlo simulations to perform, if a Monte Carlo p-value is required.
##### Details

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"oralternative="clustered": the alternative hypothesis is that$R < 1$corresponding to a clustered point pattern;
• alternative="greater"oralternative="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", 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.

For other edge corrections, the $p$-value for the test is computed by Monte Carlo simulation of nsim realisations of Complete Spatial Randomness.

##### Value

• An object of class "htest" representing the result of the test.

##### References

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

##### Aliases
• clarkevans.test
##### Examples
# Example of a clustered pattern
data(redwood)
clarkevans.test(redwood)
clarkevans.test(redwood, alternative="less")
Documentation reproduced from package spatstat, version 1.25-5, License: GPL (>= 2)

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