# anova.lppm

##### ANOVA for Fitted Point Process Models on Linear Network

Performs analysis of deviance for two or more fitted point process models on a linear network.

##### Usage

```
## S3 method for class 'lppm':
anova(object, \dots, test=NULL, override=FALSE)
```

##### Arguments

- object
- A fitted point process model on a linear network
(object of class
`"lppm"`

). - ...
- One or more fitted point process models on the same linear network.
- test
- Character string, partially matching one of
`"Chisq"`

,`"F"`

or`"Cp"`

. - override
- Logical flag indicating whether to proceed even when there is no statistical theory to support the calculation.

##### Details

This is a method for `anova`

for
fitted point process models on a linear network
(objects of class `"lppm"`

,
usually generated by the model-fitting function `lppm`

).

If the fitted models are all Poisson point processes,
then this function performs an Analysis of Deviance of
the fitted models. The output shows the deviance differences
(i.e. 2 times log likelihood ratio),
the difference in degrees of freedom, and (if `test="Chi"`

)
the two-sided p-values for the chi-squared tests. Their interpretation
is very similar to that in `anova.glm`

.

If some of the fitted models are *not* Poisson point processes,
then there is no statistical theory available to support
a similar analysis. The function issues a warning,
and (by default) returns a `NULL`

value.

However if `override=TRUE`

,
then a kind of analysis of deviance table will be printed.
The `deviance' differences in this table are equal to 2 times the differences
in the maximised values of the log pseudolikelihood (see
`ppm`

). At the time of writing, there is no statistical
theory to support inferential interpretation of log pseudolikelihood
ratios. The `override`

option is provided for research purposes
only!

##### Value

- An object of class
`"anova"`

, or`NULL`

.

##### References

Ang, Q.W. (2010)
*Statistical methodology for events on a network*.
Master's thesis, School of Mathematics and Statistics, University of
Western Australia.
Ang, Q.W., Baddeley, A. and Nair, G. (2012)
Geometrically corrected second-order analysis of
events on a linear network, with applications to
ecology and criminology.
To appear in *Scandinavian Journal of Statistics*.

McSwiggan, G., Nair, M.G. and Baddeley, A. (2012) Fitting Poisson point process models to events on a linear network. Manuscript in preparation.

##### See Also

##### Examples

```
example(lpp)
mod0 <- lppm(X, ~1)
modx <- lppm(X, ~x)
anova(mod0, modx, test="Chi")
```

*Documentation reproduced from package spatstat, version 1.28-1, License: GPL (>= 2)*