quadrat.test.splitppp
Dispersion Test of CSR for Split Point Pattern Based on Quadrat Counts
Performs a test of Complete Spatial Randomness for each of the component patterns in a split point pattern, based on quadrat counts. By default performs chi-squared tests; can also perform Monte Carlo based tests.
Usage
## S3 method for class 'splitppp':
quadrat.test(X, ..., df=NULL, df.est=NULL, Xname=NULL)
Arguments
- X
- A split point pattern (object of class
"splitppp"
), each component of which will be subjected to the goodness-of-fit test. - ...
- Arguments passed to
quadrat.test.ppp
. - df,df.est,Xname
- Arguments passed to
pool.quadrattest
.
Details
The function quadrat.test
is generic, with methods for
point patterns (class "ppp"
), split point patterns
(class "splitppp"
) and point process models
(class "ppm"
).
If X
is a split point pattern, then for each of the
component point patterns (taken separately) we test
the null hypotheses of Complete Spatial Randomness,
then combine the result into a single test.
The method quadrat.test.ppp
is applied to each
component point pattern. Then the results are pooled using
pool.quadrattest
to obtain a single test.
Value
- An object of class
"quadrattest"
which can be printed and plotted.
See Also
quadrat.test
,
quadratcount
,
quadrats
,
quadratresample
,
chisq.test
,
cdf.test
.
To test a Poisson point process model against a specific Poisson alternative,
use anova.ppm
.
Examples
data(humberside)
qH <- quadrat.test(split(humberside), 2, 3)
plot(qH)
qH