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spatstat.explore (version 3.3-1)

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 splitppp
quadrat.test(X, ..., df=NULL, df.est=NULL, Xname=NULL)

Value

An object of class "quadrattest" which can be printed and plotted.

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.

Author

Adrian Baddeley Adrian.Baddeley@curtin.edu.au and Rolf Turner rolfturner@posteo.net

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.

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

Run this code
 qH <- quadrat.test(split(humberside), 2, 3)
 plot(qH)
 qH

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