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, override=FALSE)
- 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
- Logical flag indicating whether to proceed even when there is no statistical theory to support the calculation.
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 there is no statistical theory available to support
a similar analysis. The function issues a warning,
and (by default) returns a
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
override option is provided for research purposes
- 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. 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.
example(lpp) mod0 <- lppm(X, ~1) modx <- lppm(X, ~x) anova(mod0, modx, test="Chi")