| logLik.dppm {spatstat.model} | R Documentation |
Log Likelihood and AIC for Fitted Determinantal Point Process Model
Description
Extracts the log Palm likelihood, deviance, and AIC of a fitted determinantal point process model.
Usage
## S3 method for class 'dppm'
logLik(object, ...)
## S3 method for class 'dppm'
AIC(object, ..., k=2)
## S3 method for class 'dppm'
extractAIC(fit, scale=0, k=2, ...)
## S3 method for class 'dppm'
nobs(object, ...)
Arguments
object, fit |
Fitted point process model.
An object of class |
... |
Ignored. |
scale |
Ignored. |
k |
Numeric value specifying the weight of the equivalent degrees of freedom in the AIC. See Details. |
Details
These functions are methods for the generic commands
logLik,
extractAIC and
nobs
for the class "dppm".
An object of class "dppm" represents a fitted
Cox or cluster point process model.
It is obtained from the model-fitting function dppm.
These methods apply only when the model was fitted
by maximising the Palm likelihood (Tanaka et al, 2008)
by calling dppm with the argument method="palm".
The method logLik.dppm computes the
maximised value of the log Palm likelihood for the fitted model object.
The methods AIC.dppm and extractAIC.dppm compute the
Akaike Information Criterion AIC for the fitted model
based on the Palm likelihood (Tanaka et al, 2008)
AIC = -2 \log(PL) + k \times \mbox{edf}
where PL is the maximised Palm likelihood of the fitted model,
and \mbox{edf} is the effective degrees of freedom
of the model.
The method nobs.dppm returns the number of points
in the original data point pattern to which the model was fitted.
The R function step uses these methods, but it does
not work for determinantal models yet due to a missing implementation
of update.dppm.
Value
logLik returns a numerical value, belonging to the class
"logLik", with an attribute "df" giving the degrees of
freedom.
AIC returns a numerical value.
extractAIC returns a numeric vector of length 2
containing the degrees of freedom and the AIC value.
nobs returns an integer value.
Author(s)
Adrian Baddeley Adrian.Baddeley@curtin.edu.au
Rolf Turner rolfturner@posteo.net
and Ege Rubak rubak@math.aau.dk
References
Tanaka, U. and Ogata, Y. and Stoyan, D. (2008) Parameter estimation and model selection for Neyman-Scott point processes. Biometrical Journal 50, 43–57.
See Also
Examples
fit <- dppm(swedishpines ~ x, dppGauss(), method="palm")
nobs(fit)
logLik(fit)
extractAIC(fit)
AIC(fit)