lmomplot {POT}

Threshold Selection: The L-moments Plot

Description

Plots of sample L-Skewness ans L-Kurtosis estimates at various thresholds for peaks over threshold modelling, using the Generalized Pareto parametrization.

Usage

lmomplot(data, u.range, nt = max(50, length(data)), identify = TRUE,
...)

Arguments

data

A numeric vector.

u.range

A numeric vector of length two, giving the limits for the thresholds at which the model is fitted.

nt

The number of thresholds at which the sample L-moments are evaluated.

identify

Logical. If TRUE, points on the plot are identify using identify function.

...

Other arguments to be passed to the model fit function fitgpd.

Details

For each thresholds, sample L-skewness and L-kurtosis are computed. If data are GP distributed, one have :

\tau_4 = \frac{\tau_3 \left( 1 + 5 \tau_3 \right)}{5 + \tau_3}

So, a threshold is acceptable if sample \left(\tau_3, \tau_4\right) are near the theoretical curve.

Warnings

L-moments plot are really difficult to interpret. It can help us to say if the GP distribution is suited to model data.

Author(s)

Mathieu Ribatet

References

Hosking, J. R. M. and Wallis, J. R. (1997) Regional Frequency Analysis. Cambridge University Press.

Begueria, S. (2005) Uncertainties in partial duration series modelling of extremes related to the choice of the threshold value. Journal of Hydrology, 303(1-4): 215–230.

See Also

fitgpd, mrlplot, tcplot

Examples

data(ardieres)
ardieres <- clust(ardieres, 4, 10 / 365, clust.max = TRUE)
flows <- ardieres[, "obs"]
lmomplot(flows, identify = FALSE)