quadrat.test.splitppp
Chi-Squared Test of CSR for Split Point Pattern
Performs a chi-squared test of Complete Spatial Randomness for each of the component patterns in a split point pattern.
Usage
## S3 method for class 'splitppp':
quadrat.test(X, ...)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.
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.
The method quadrat.test.ppp is applied to each
component point pattern.
The return value is a list of objects, each giving the result of one of the tests.
Value
- A list of objects, each giving the result of one of the
hypothesis tests. Each component object is of class
"htest"and"quadrat.test". The list itself is of class"listof"so that it can be printed and plotted.
See Also
quadrat.test,
quadratcount,
quadrats,
quadratresample,
chisq.test,
kstest.
To test a Poisson point process model against a specific alternative,
use anova.ppm.
Examples
data(humberside)
qH <- quadrat.test(split(humberside), 2, 3)
plot(qH)
qH