spatstat (version 1.31-3)

anova.lppm: ANOVA for Fitted Point Process Models on Linear Network

Description

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.

Value

• An object of class "anova", or NULL.

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!

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

lppm

Examples

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