Performs analysis of deviance for two or more fitted point process models on a linear network.
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
This is a method for
fitted point process models on a linear network
(objects of class
usually generated by the model-fitting function
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
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
~x+y, so these models are nested. However
the two point process
models with formulae
~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.
There may be an error message from
“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.
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.
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