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.

- 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`

.

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
```

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