anova.lppm

0th

Percentile

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.

Keywords
models, methods, spatial
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

lppm

Aliases
  • anova.lppm
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 1.55-1, License: GPL (>= 2)

Community examples

Looks like there are no examples yet.