lax (version 1.1.0)

POT: Loglikelihood adjustment for POT fits

Description

S3 alogLik method to perform loglikelihood adjustment for fitted extreme value model objects returned from fitGPD function in the POT package. The model must have been fitted using maximum likelihood estimation.

Usage

# S3 method for uvpot
alogLik(x, cluster = NULL, use_vcov = TRUE, ...)

Arguments

x

A fitted model object with certain associated S3 methods. See Details.

cluster

A vector or factor indicating from which cluster the respective loglikelihood contributions from loglik originate. This must have the same length as the vector returned by the logLikVec method for an object like x. If cluster is not supplied (i.e. is NULL) then it is assumed that each observation forms its own cluster. See Details.

use_vcov

A logical scalar. Should we use the vcov S3 method for x (if this exists) to estimate the Hessian of the independence loglikelihood to be passed as the argument H to adjust_loglik? Otherwise, H is estimated inside adjust_loglik using optimHess.

...

Further arguments to be passed to the functions in the sandwich package meat (if cluster = NULL), or meatCL (if cluster is not NULL).

Value

An object inheriting from class "chandwich". See adjust_loglik.

class(x) is c("lax", "chandwich", "POT", "pot", "gpd").

Details

See alogLik for details.

References

Chandler, R. E. and Bate, S. (2007). Inference for clustered data using the independence loglikelihood. Biometrika, 94(1), 167-183. http://doi.org/10.1093/biomet/asm015

Zeleis (2006) Object-Oriented Computation and Sandwich Estimators. Journal of Statistical Software, 16, 1-16. http://doi.org/10.18637/jss.v016.i09

See Also

alogLik: loglikelihood adjustment for model fits.

Examples

# NOT RUN {
# We need the POT package
got_POT <- requireNamespace("POT", quietly = TRUE)

if (got_POT) {
  library(POT)
  # An example from the POT::fitgpd documentation.
  set.seed(4082019)
  x <- POT::rgpd(200, 1, 2, 0.25)
  fit <- fitgpd(x, 1, "mle")
  adj_fit <- alogLik(fit)
}
# }