R: Clark and Evans Test

clarkevans.test {spatstat.core} | R Documentation

## 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)

### 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 != 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.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

### 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)

[Package spatstat.core version 2.3-0 Index]