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