gpdDiag: Diagnostic plots for a fit to the Generalized Pareto...

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

Description

Diagnostic plots for a fit to the Generalized Pareto distribution

Usage

gpdDiag(z, conf = 0.95, method = c("delta", "profile"))

Arguments

z	A class object returned from ‘gpdFit’.
conf	Confidence level used in the return level plot.
method	The method to compute the return level confidence interval - either delta method (default) or profile likelihood. Choosing profile likelihood may be quite slow.

Details

See the reference for details on how return levels are calculated.

Value

For stationary models, provides return level, density, probability, and quantile plots for the GPD exceedances. The overlaid density is the ‘true’ density for the estimated parameters. For nonstationary models, provides residual probability and quantile plots. In addition, nonstationary models provide plots of the residuals vs. the parameter covariates.

References

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

Examples

## Not run: 
x <- rgpd(10000, loc = 0.5, scale = 1, shape = 0.1)
z <- gpdFit(x, nextremes = 500)
plot(z)

## End(Not run)

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