R/mev_gpd.R

In paulnorthrop/lax: Loglikelihood Adjustment for Extreme Value Models

# =============================== mev::fit.gpd ============================== #

# Methods for class laxmev_gpd

#' @export
logLikVec.laxmev_gpd <- function(object, pars = NULL, ...) {
  if (!missing(...)) {
    warning("extra arguments discarded")
  }
  # If the parameter estimates have not been provided in pars then extract
  # them from the fitted object
  if (is.null(pars)) {
    pars <- coef(object)
  }
  n_pars <- length(pars)
  #
  # Threshold exceedances (values that lie above the threshold)
  response_data <- object$threshold + object$exceedances
  sigma <- pars[1]
  xi <- pars[2]
  # Calculate the loglikelihood contributions
  if (sigma <= 0) {
    val <- -Inf
  } else {
    val <- revdbayes::dgp(response_data, loc = object$threshold, scale = sigma,
                          shape = xi, log = TRUE)
  }
  # Return the usual attributes for a "logLik" object
  attr(val, "nobs") <- nobs(object)
  attr(val, "df") <- n_pars
  class(val) <- "logLikVec"
  return(val)
}

#' @export
nobs.laxmev_gpd <- function(object, ...) {
  return(object$nat)
}

#' @export
coef.laxmev_gpd <- function(object, ...) {
  return(object$estimate)
}

#' @export
vcov.laxmev_gpd <- function(object, ...) {
  vc <- object$vcov
  dimnames(vc) <- list(names(coef(object)), names(coef(object)))
  return(vc)
}

#' @export
logLik.laxmev_gpd <- function(object, ...) {
  val <- -object$nllh
  attr(val, "nobs") <- nobs(object)
  attr(val, "df") <- length(coef(object))
  class(val) <- "logLik"
  return(val)
}