# clarkevans.test

0th

Percentile

##### Clark and Evans Test

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

Keywords
htest, spatial, nonparametric
##### Usage
clarkevans.test(X, ...,
correction="none",
clipregion=NULL,
alternative=c("two.sided", "less", "greater",
"clustered", "regular"),
nsim=999)
##### 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. Partially matched.

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" 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.

##### 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, hopskel.test

##### Aliases
• clarkevans.test
##### Examples
# NOT RUN {
# Redwood data - clustered
clarkevans.test(redwood)
clarkevans.test(redwood, alternative="clustered")
clarkevans.test(redwood, correction="cdf", nsim=39)
# }

Documentation reproduced from package spatstat, version 1.62-2, License: GPL (>= 2)

### Community examples

Looks like there are no examples yet.