logLik.ppm {spatstat} | R Documentation |
Extracts the log likelihood, deviance, and AIC of a fitted Poisson point process model, or analogous quantities based on the pseudolikelihood for a fitted Gibbs point process model.
## S3 method for class 'ppm' logLik(object, ..., warn=TRUE) ## S3 method for class 'ppm' extractAIC(fit, scale=0, k=2, ...) ## S3 method for class 'ppm' nobs(object, ...)
object,fit |
Fitted point process model.
An object of class |
... |
Ignored. |
warn |
If |
scale |
Ignored. |
k |
Numeric value specifying the weight of the equivalent degrees of freedom in the AIC. See Details. |
These functions are methods for the generic commands
logLik
,
extractAIC
and
nobs
for the class "ppm"
.
An object of class "ppm"
represents a fitted
Poisson or Gibbs point process model.
It is obtained from the model-fitting function ppm
.
The method logLik.ppm
computes the
maximised value of the log likelihood for the fitted model object
(as approximated by quadrature using the Berman-Turner approximation)
is extracted. If object
is not a Poisson process, the maximised log
pseudolikelihood is returned, with a warning (if warn=TRUE
).
The Akaike Information Criterion AIC for a fitted model is defined as
AIC = -2 * log(L) + k * edf
where L is the maximised likelihood of the fitted model,
and edf is the effective degrees of freedom
of the model.
The method extractAIC.ppm
returns the analogous quantity
AIC* in which L is replaced by L*,
the quadrature approximation
to the likelihood (if fit
is a Poisson model)
or the pseudolikelihood (if fit
is a Gibbs model).
The method nobs.ppm
returns the number of points
in the original data point pattern to which the model was fitted.
The R functions AIC
and step
use
these methods.
A numerical value.
Adrian Baddeley Adrian.Baddeley@csiro.au http://www.maths.uwa.edu.au/~adrian/ and Rolf Turner r.turner@auckland.ac.nz
ppm
,
as.owin
,
coef.ppm
,
fitted.ppm
,
formula.ppm
,
model.frame.ppm
,
model.matrix.ppm
,
plot.ppm
,
predict.ppm
,
residuals.ppm
,
simulate.ppm
,
summary.ppm
,
terms.ppm
,
update.ppm
,
vcov.ppm
.
data(cells) fit <- ppm(cells, ~x) nobs(fit) logLik(fit) extractAIC(fit) AIC(fit) step(fit)