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.
# S3 method for splitppp
quadrat.test(X, ..., df=NULL, df.est=NULL, Xname=NULL)An object of class "quadrattest" which can be printed and
plotted.
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.
Arguments passed to pool.quadrattest.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au and Rolf Turner r.turner@auckland.ac.nz
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.
quadrat.test,
quadratcount,
quadrats,
quadratresample,
chisq.test,
cdf.test.
To test a Poisson point process model against a specific Poisson alternative,
use anova.ppm.
qH <- quadrat.test(split(humberside), 2, 3)
plot(qH)
qH
Run the code above in your browser using DataLab