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.
## S3 method for class 'lppm': anova(object, \dots, test=NULL)
- A fitted point process model on a linear network
(object of class
- One or more fitted point process models on the same linear network.
- Character string, partially matching one of
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
the two-sided p-values for the chi-squared tests. Their interpretation
is very similar to that in
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).
- An object of class
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. (2014) Adjusted composite likelihood ratio test for Gibbs point processes. Submitted for publication.
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.
example(lpp) mod0 <- lppm(X, ~1) modx <- lppm(X, ~x) anova(mod0, modx, test="Chi")