R: Dispersion Test of CSR for Split Point Pattern Based on...

quadrat.test.splitppp {spatstat.core} | R Documentation

## 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, ..., df=NULL, df.est=NULL, Xname=NULL)

### 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`.

### 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.

### Value

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

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

and Rolf Turner r.turner@auckland.ac.nz

### 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

     data(humberside)
     qH <- quadrat.test(split(humberside), 2, 3)
     plot(qH)
     qH

[Package spatstat.core version 2.4-4 Index]