# clarkevans.test

##### 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.

`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.

##### See Also

##### Examples

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

*Documentation reproduced from package spatstat, version 1.41-1, License: GPL (>= 2)*