Extracts the log Palm likelihood, deviance, and AIC of a fitted Cox or cluster point process model.
# S3 method for kppm
logLik(object, ...)
# S3 method for kppm
AIC(object, …, k=2)
# S3 method for kppm
extractAIC(fit, scale=0, k=2, …)
# S3 method for kppm
nobs(object, ...)
Fitted point process model.
An object of class "kppm"
.
Ignored.
Ignored.
Numeric value specifying the weight of the equivalent degrees of freedom in the AIC. See Details.
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.
These functions are methods for the generic commands
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 \(\mbox{edf}\) 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.
Tanaka, U. and Ogata, Y. and Stoyan, D. (2008) Parameter estimation and model selection for Neyman-Scott point processes. Biometrical Journal 50, 43--57.
# NOT RUN {
fit <- kppm(redwood ~ x, "Thomas", method="palm")
nobs(fit)
logLik(fit)
extractAIC(fit)
AIC(fit)
step(fit)
# }
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