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

```
clarkevans.test(X, ...,
correction="none",
clipregion=NULL,
alternative=c("two.sided", "less", "greater",
"clustered", "regular"),
nsim=999)
```

An object of class `"htest"`

representing the result of the test.

- 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.

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

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.

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`

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

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