Clark and Evans Test
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"), nsim=1000)
- A spatial point pattern (object of class
- Character string.
The type of edge correction to be applied.
- Clipping region for the guard area correction.
A window (object of class
- String indicating the type of alternative for the hypothesis test.
- Number of Monte Carlo simulations to perform, if a Monte Carlo p-value is required.
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
for information about the Clark-Evans index $R$ and about
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="clustered": the alternative hypothesis is that$R < 1$corresponding to a clustered point pattern;
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 $p$-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.
- An object of class
"htest"representing the result of the test.
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.
# Example of a clustered pattern data(redwood) clarkevans.test(redwood) clarkevans.test(redwood, alternative="less")