spatstat (version 1.28-2)

quadrat.test.splitppp: Dispersion Test of CSR for Split Point Pattern Based on Quadrat Counts

Description

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, ...)

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.

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.

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.

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