# quadrat.test.splitppp

##### 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.

##### 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.

##### See Also

`quadrat.test`

,
`quadratcount`

,
`quadrats`

,
`quadratresample`

,
`chisq.test`

,
`kstest`

.

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
```

*Documentation reproduced from package spatstat, version 1.30-0, License: GPL (>= 2)*