# 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 lppm
anova(object, …, test=NULL)
```

##### 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"`

.

##### 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 the deviance difference is replaced by the
adjusted composite likelihood ratio (Pace et al, 2011;
Baddeley et al, 2014).

##### Value

An object of class `"anova"`

, or `NULL`

.

##### Errors and warnings

- models not nested:
There may be an error message that the models are not “nested”. For an Analysis of Deviance the models must be nested, i.e. one model must be a special case of the other. For example the point process model with formula

`~x`

is a special case of the model with formula`~x+y`

, so these models are nested. However the two point process models with formulae`~x`

and`~y`

are not nested.If you get this error message and you believe that the models should be nested, the problem may be the inability of R to recognise that the two formulae are nested. Try modifying the formulae to make their relationship more obvious.

- different sizes of dataset:
There may be an error message from

`anova.glmlist`

that “models were not all fitted to the same size of dataset”. This generally occurs when the point process models are fitted on different linear networks.

##### 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.
*Scandinavian Journal of Statistics* **39**, 591--617.

Baddeley, A., Turner, R. and Rubak, E. (2015)
Adjusted composite likelihood ratio test for Gibbs point processes.
*Journal of Statistical Computation and Simulation*
**86** (5) 922--941.
DOI: 10.1080/00949655.2015.1044530.

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

Pace, L., Salvan, A. and Sartori, N. (2011)
Adjusting composite likelihood ratio statistics.
*Statistica Sinica* **21**, 129--148.

##### See Also

##### Examples

```
# NOT RUN {
X <- runiflpp(10, simplenet)
mod0 <- lppm(X ~1)
modx <- lppm(X ~x)
anova(mod0, modx, test="Chi")
# }
```

*Documentation reproduced from package spatstat, version 1.63-0, License: GPL (>= 2)*