ANOVA for Fitted Multiple Point Process Models
Performs analysis of deviance for two or more fitted multiple point process models.
## S3 method for class 'mppm': anova(object, \dots, test=NULL, override=FALSE)
- A fitted multiple point process model
(object of class
- One or more fitted multiple point process models.
- Type of hypothesis test to perform.
A 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
mppm). At the time of writing, there is no statistical
theory to support inferential interpretation of log pseudolikelihood
override option is provided for research purposes
test determines which hypothesis test, if any, will
be performed to compare the models. The argument
should be a character string, partially matching one of
NULL. The first option
the likelihood ratio test based on the asymptotic chi-squared
distribution of the deviance difference.
The meaning of the other options is explained in
For random effects models, only
available, and again gives the likelihood ratio test.
- An object of class
data(waterstriders) H <- hyperframe(X=waterstriders) mod0 <- mppm(X~1, H, Poisson()) modx <- mppm(X~x, H, Poisson()) anova.mppm(mod0, modx, test="Chi")