anova.lppm {spatstat} | R Documentation |
Performs analysis of deviance for two or more fitted point process models on a linear network.
## S3 method for class 'lppm' anova(object, ..., test=NULL, override=FALSE)
object |
A fitted point process model on a linear network
(object of class |
... |
One or more fitted point process models on the same linear network. |
test |
Character string, partially matching one of
|
override |
Logical flag indicating whether to proceed even when there is no statistical theory to support the calculation. |
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 there is no statistical theory available to support
a similar analysis. The function issues a warning,
and (by default) returns a NULL
value.
However if override=TRUE
,
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
ratios. The override
option is provided for research purposes
only!
An object of class "anova"
, or NULL
.
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.
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.
Adrian Baddeley Adrian.Baddeley@csiro.au http://www.maths.uwa.edu.au/~adrian/
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")