# 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

 ```1 2``` ```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

 ```1 2 3 4 5``` ```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] 6.614254

\$CI
[1] 5.562765 7.665743

\$Period
[1] 50

\$ConfLevel
[1] 0.95

\$Estimate
[1] 6.614254

\$CI
[1] 5.922375 8.563130

\$Period
[1] 50

\$ConfLevel
[1] 0.95
```

eva documentation built on Oct. 18, 2018, 5:04 p.m.