Generic function for calculating log-likelihood contributions from individual observations for a fitted model object.
logLikVector(object, ...)
# S3 method for Bernoulli
logLikVector(object, pars = NULL, ...)
# S3 method for GP
logLikVector(object, pars = NULL, ...)
# S3 method for logLikVector
logLik(object, ...)A fitted model object.
Further arguments. None are used for either
logLikVector.Bernoulli or logLikVector.GP.
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.
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.
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.
Bernoulli for maximum likelihood inference for the
Bernoulli distribution.
generalisedPareto for maximum likelihood inference
for the generalised Pareto distribution.
# logLikVector.Bernoulli
bfit <- fitBernoulli(c(exdex::cheeseboro) > 45)
bvec <- logLikVector(bfit)
head(bvec)
#> [1] -0.02810136 -0.02810136 -0.02810136 -0.02810136 -0.02810136 -0.02810136
logLik(bvec)
#> 'log Lik.' -937.2539 (df=1)
logLik(bfit)
#> 'log Lik.' -937.2539 (df=1)
# estfun.generalisedPareto
gpfit <- fitGP(c(exdex::cheeseboro), u = 45)
gpvec <- logLikVector(gpfit)
head(gpvec)
#> [1] -2.626236 -2.424665 -2.524926 -2.626236 -3.149341 -2.524926
logLik(gpvec)
#> 'log Lik.' -642.3738 (df=2)
logLik(gpfit)
#> 'log Lik.' -642.3738 (df=2)