anova.ppm: ANOVA for Fitted Point Process Models

Description

Performs analysis of deviance for two or more fitted point process models.

Usage

## S3 method for class 'ppm':
anova(object, \dots, test=NULL, adjust=TRUE, warn=TRUE)

Arguments

object

A fitted point process model (object of class "ppm").

...

One or more fitted point process models.

test

Character string, partially matching one of "Chisq", "LRT", "Rao", "F" or "Cp".

adjust

Logical value indicating whether to correct the pseudolikelihood ratio when some of the models are not Poisson processes.

warn

Logical value indicating whether to issue warnings if problems arise.

Value

An object of class "anova", or NULL.

Details

This is a method for anova for fitted point process models (objects of class "ppm", usually generated by the model-fitting function ppm).

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, the `deviance' differences in this table are 'pseudo-deviances' equal to 2 times the differences in the maximised values of the log pseudolikelihood (see ppm). It is not valid to compare these values to the chi-squared distribution. In this case, if adjust=TRUE (the default), the pseudo-deviances will be adjusted using the method of Pace et al (2011) so that the chi-squared test is valid. It is strongly advisable to perform this adjustment.

References

Baddeley, A., Turner, R. and Rubak, E. (2014) Adjusted composite likelihood ratio test for Gibbs point processes. Submitted for publication.

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

Examples

Run this code

mod0 <- ppm(swedishpines ~1)
 modx <- ppm(swedishpines ~x)
 anova(mod0, modx, test="Chi")

 modxy <- ppm(swedishpines ~x + y)
 anova(modxy, test="Chi")

 modP <- ppm(swedishpines ~1, rbord=9)
 modS <- ppm(swedishpines ~1, Strauss(9))
 anova(modP, modS, test="Chi")

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