gpd.diag: Diagnostic Plots for GPD Models

In ismev: An Introduction to Statistical Modeling of Extreme Values

Description

Produces diagnostic plots for GPD models using the output of the function gpd.fit.

Usage

gpd.diag(z)

Arguments

z	An object returned by gpd.fit.

Value

For stationary models four plots are produced; a probability plot, a quantile plot, a return level plot and a histogram of data with fitted density.

For non-stationary models two plots are produced; a residual probability plot and a residual quantile plot.

See Also

gpd.fit, gpd.prof, pp.fit

Examples

data(rain)
rnfit <- gpd.fit(rain, 10)
gpd.diag(rnfit)

Example output

Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-20. For overview type 'help("mgcv-package")'.
$threshold
[1] 10

$nexc
[1] 2003

$conv
[1] 0

$nllh
[1] 6123.465

$mle
[1] 7.43768624 0.05045225

$rate
[1] 0.1142547

$se
[1] 0.23606472 0.02256649

ismev documentation built on May 1, 2019, 9:10 p.m.