lite (version 1.1.1)

logLikVector: Functions for log-likelihood contributions

Description

Generic function for calculating log-likelihood contributions from individual observations for a fitted model object.

Usage

logLikVector(object, ...)
# S3 method for Bernoulli
logLikVector(object, pars = NULL, ...)
# S3 method for GP
logLikVector(object, pars = NULL, ...)
# S3 method for logLikVector
logLik(object, ...)

Value

For logLikVector: an object of class logLikVec. This is a numeric vector of length \(n\) containing contributions to the the independence log-likelihood from \(n\) observations, with attributes

"df" (degrees of freedom), giving the number of estimated parameters in the model, and "nobs", giving the number observations used to perform the estimation.

For logLik.logLikVector: an object of class logLik. This is a number with the attributes "df" and "nobs" as described above.

Arguments

object

A fitted model object.

...

Further arguments. None are used for either logLikVector.Bernoulli or
logLikVector.GP.

pars

A numeric parameter vector.

For logLikVector.Bernoulli this is a vector of length 1 containing a value of the Bernoulli success probability.

For logLikVector.GP this is a numeric vector of length 2 containing the values of the generalised Pareto scale (\(\sigma_u\)) and shape (\(\xi\)) parameters.

Details

A logLikVector method is used to construct a log-likelihood function to supply as the argument loglik to the function adjust_loglik, which performs log-likelihood adjustment for parts 1 and 2 of the inferences performed by flite.

The logLik method logLik.LogLikVector sums the log-likelihood contributions from individual observations.

See Also

Bernoulli for maximum likelihood inference for the Bernoulli distribution.

generalisedPareto for maximum likelihood inference for the generalised Pareto distribution.

Examples

# logLikVector.Bernoulli
bfit <- fitBernoulli(c(exdex::cheeseboro) > 45)
bvec <- logLikVector(bfit)
head(bvec)
logLik(bvec)
logLik(bfit)

# estfun.generalisedPareto
gpfit <- fitGP(c(exdex::cheeseboro), u = 45)
gpvec <- logLikVector(gpfit)
head(gpvec)
logLik(gpvec)
logLik(gpfit)