# quadrat.test.splitppp

##### Chi-Squared Test of CSR for Split Point Pattern

Performs a chi-squared test of Complete Spatial Randomness for each of the component patterns in a split point pattern.

##### 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`

.

##### 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.

##### 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.

##### 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
```

*Documentation reproduced from package spatstat, version 1.27-0, License: GPL (>= 2)*