GenParetoPars: Generalised Pareto distribution parameter estimates

In UKFE: UK Flood Estimation

Generalised Pareto distribution parameter estimates

Description

Estimated parameters from a sample (with Lmoments or maximum likelihood estimation) or from L1 (first L-moment), Lcv (linear coefficient of variation), and LSkew (linear skewness)

Usage

GenParetoPars(x = NULL, mle = FALSE, L1, LCV, LSKEW)

Arguments

x	numeric vector. The sample
mle	logical argument with a default of FALSE. If FALSE the parameters are estimated with Lmoments, if TRUE the parameters are estimated by maximum likelihood estimation
L1	first Lmoment
LCV	linear coefficient of variation
LSKEW	linear skewness

Details

The L-moment estimated parameters are by the method detailed in 'Hosking J. Wallis J. 1997 Regional Frequency Analysis: An Approach Based on L-moments. Cambridge University Press, New York'

Value

Parameter estimates (location, scale, shape)

Author(s)

Anthony Hammond

Examples

#Get a peaks over threshold sample and estimate the parameters using Lmoments
ThamesPOT <- ThamesPOT <- POTextract(ThamesPQ[,c(1,3)], thresh = 0.90)
GenParetoPars(ThamesPOT$peak)
#Estimate parameters using MLE
GenParetoPars(ThamesPOT$peak, mle = TRUE)
#calculate Lmoments and estimate the parmeters with L1, Lcv and Lskew
Lmoms(ThamesPOT$peak)
#store linear moments in an object
LPars <- as.numeric(Lmoms(ThamesPOT$peak))[c(1,5,6)]
GenParetoPars(L1 = LPars[1], LCV = LPars[2], LSKEW = LPars[3])

UKFE documentation built on Nov. 6, 2023, 1:07 a.m.