gevrDiag: Diagnostic plots for a fit to the GEVr distribution.

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

Description

Diagnostic plots for a fit to the GEVr distribution.

Usage

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

Arguments

z	A class object returned from ‘gevrFit’.
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

In certain cases the quantile plot may fail, because it requires solving a root equation. See the references for details.

Value

For stationary models, provides return level plot and density, probability, and quantile plots for each marginal order statistic. The overlaid density is the ‘true’ marginal 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

Tawn, J. A. (1988). An extreme-value theory model for dependent observations. Journal of Hydrology, 101(1), 227-250.

Smith, R. L. (1986). Extreme value theory based on the r largest annual events. Journal of Hydrology, 86(1), 27-43.

Examples

## Not run: 
x <- rgevr(500, 2, loc = 0.5, scale = 1, shape = 0.1)
z <- gevrFit(x)
plot(z)

## End(Not run)

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