# logLik.ppm

0th

Percentile

##### Log Likelihood and AIC for Point Process Model

Extracts the log likelihood and AIC of a fitted Poisson point process model, or analogous quantities based on the pseudolikelihood for a fitted Gibbs point process model.

Keywords
models, spatial
##### Usage
## S3 method for class 'ppm':
logLik(object, ..., warn=TRUE)
## S3 method for class 'ppm':
extractAIC(fit, scale=0, k=2, \dots)
## S3 method for class 'ppm':
nobs(object, ...)
##### Arguments
object,fit
Fitted point process model. An object of class "ppm".
...
Ignored.
warn
If TRUE, a warning is given when the pseudolikelihood is returned instead of the likelihood.
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 "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 $$AIC = -2 \log(L) + k \times \mbox{edf}$$ where $L$ is the maximised likelihood of the fitted model, and $\mbox{edf}$ 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 Rfunctions AIC and step use these methods.

##### Value

• A numerical value.

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.

##### Aliases
• logLik.ppm
• extractAIC.ppm
• nobs.ppm
##### Examples
data(cells)
fit <- ppm(cells, ~x)
nobs(fit)
logLik(fit)
extractAIC(fit)
AIC(fit)
step(fit)
Documentation reproduced from package spatstat, version 1.25-1, License: GPL (>= 2)

### Community examples

Looks like there are no examples yet.