"logLik"(object, ...)
"AIC"(object, ..., k=2)
"extractAIC"(fit, scale=0, k=2, ...)
"nobs"(object, ...)"kppm".
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.
logLik,
extractAIC and
nobs
for the class "kppm". An object of class "kppm" represents a fitted
Cox or cluster point process model.
It is obtained from the model-fitting function kppm.
These methods apply only when the model was fitted
by maximising the Palm likelihood (Tanaka et al, 2008)
by calling kppm with the argument method="palm".
The method logLik.kppm computes the
maximised value of the log Palm likelihood for the fitted model object.
The methods AIC.kppm and extractAIC.kppm 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 $edf$ is the effective degrees of freedom
of the model.
The method nobs.kppm returns the number of points
in the original data point pattern to which the model was fitted.
The R function step uses these methods.
kppm,
logLik.ppm
fit <- kppm(redwood ~ x, "Thomas", method="palm")
nobs(fit)
logLik(fit)
extractAIC(fit)
AIC(fit)
step(fit)
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