plotGPD: Graphical Diagnostic: the Univariate GPD Model

In POT: Generalized Pareto Distribution and Peaks Over Threshold

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.

Author(s)

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, 3:03 a.m.