gpdRl: GPD Return Level Estimate and Confidence Interval for...

In eva: Extreme Value Analysis with Goodness-of-Fit Testing

Description

Computes stationary m-period return level estimate and interval for the Generalized Pareto distribution, using either the delta method or profile likelihood.

Usage

gpdRl(
  z,
  period,
  conf = 0.95,
  method = c("delta", "profile"),
  plot = TRUE,
  opt = c("Nelder-Mead")
)

Arguments

z	An object of class ‘gpdFit’.
period	The number of periods to use for the return level.
conf	Confidence level. Defaults to 95 percent.
method	The method to compute the confidence interval - either delta method (default) or profile likelihood.
plot	Plot the profile likelihood and estimate (vertical line)?
opt	Optimization method to maximize the profile likelihood if that is selected. Argument passed to optim. The default method is Nelder-Mead.

Details

Caution: The profile likelihood optimization may be slow for large datasets.

Value

Estimate	Estimated m-period return level.
CI	Confidence interval for the m-period return level.
Period	The period length used.
ConfLevel	The confidence level used.

References

Coles, S. (2001). An introduction to statistical modeling of extreme values (Vol. 208). London: Springer.

Examples

x <- rgpd(5000, loc = 0, scale = 1, shape = -0.1)
# Compute 50-period return level.
z <- gpdFit(x, nextremes = 200)
gpdRl(z, period = 50, method = "delta")
gpdRl(z, period = 50, method = "profile")

Example output

$Estimate
[1] 6.38063

$CI
[1] 5.304495 7.456765

$Period
[1] 50

$ConfLevel
[1] 0.95

$Estimate
[1] 6.38063

$CI
[1] 5.647561 8.233872

$Period
[1] 50

$ConfLevel
[1] 0.95

eva documentation built on Jan. 13, 2021, 8:34 p.m.