plotGPD: Graphical Diagnostic: the Univariate GPD Model In POT: Generalized Pareto Distribution and Peaks Over Threshold

 plot.uvpot R Documentation

Graphical Diagnostic: the Univariate GPD Model

Description

Produces QQ-plot, Probability Plot and a Density Plot of the fitted model versus the empirical one. Another function computes the Return Level Plot of the fitted model.

Usage

```## S3 method for class 'uvpot'
plot(x, npy, main, which = 1:4, ask = nb.fig <
length(which) && dev.interactive(),ci = TRUE, ...)
```

Arguments

 `x` A fitted object of class `'uvpot'`. Generally, an object return by `fitgpd` `npy` The mean Number of events Per Year - or more generally a block. `main` optional. A string vector corresponding to the title of each plot. `which` a numeric vector which specifies which plot must be drawn : `'1'` for Probability Plot, `'2'` for QQ-Plot,`'3'` for Density Plot and `'4'` for a Return Level Plot. `ask` Logical. If `TRUE`, user is asked before each plot. `ci` Logical. If `TRUE`, the simulated 95% confidence interval is plotted. `...` Other parameters to pass to the `plot` function.

Mathieu Ribatet

Examples

```data(ardieres)
ardieres <- clust(ardieres, 4, 10 / 365, clust.max = TRUE)
fgpd <- fitgpd(ardieres[, "obs"], 6, 'mle')
npy <- fgpd\$nat / 33.4 ##33.4 is the total record length (in year)
par(mfrow=c(2,2))
plot(fgpd, npy = npy)
```

POT documentation built on April 14, 2022, 5:07 p.m.