clarkevans.test(X, ..., correction="none", clipregion=NULL, alternative=c("two.sided", "less", "greater", "clustered", "regular"), nsim=999)"ppp").
clarkevans
"owin").
See clarkevans
"htest" representing the result of the test.
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.
clarkevans,
hopskel.test
# Redwood data - clustered
clarkevans.test(redwood)
clarkevans.test(redwood, alternative="clustered")
Run the code above in your browser using DataLab