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.
## S3 method for class 'splitppp': quadrat.test(X, ..., df=NULL, df.est=NULL, Xname=NULL)
quadrat.test is generic, with methods for
point patterns (class
"ppp"), split point patterns
"splitppp") and point process models
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.
quadrat.test.ppp is applied to each
component point pattern. Then the results are pooled using
pool.quadrattest to obtain a single test.
- An object of class
"quadrattest"which can be printed and plotted.
To test a Poisson point process model against a specific Poisson alternative,
data(humberside) qH <- quadrat.test(split(humberside), 2, 3) plot(qH) qH