spatstat.explore (version 3.1-0)

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",
                             "clustered", "regular"),
               nsim=999)

Value

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

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.

Author

Adrian Baddeley

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.

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, hopskel.test

Examples

  # Redwood data - clustered
  clarkevans.test(redwood)
  clarkevans.test(redwood, alternative="clustered")
  clarkevans.test(redwood, correction="cdf", nsim=39)