spatstat (version 1.31-3)

clarkevans.test: Clark and Evans Test

Description

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

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.

Value

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

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.

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.

See Also

clarkevans

Examples

# Example of a clustered pattern
  data(redwood)
  clarkevans.test(redwood)
  clarkevans.test(redwood, alternative="less")