Generalised Pareto distribution parameter estimates
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)
GenParetoPars(x = NULL, mle = FALSE, L1, LCV, LSKEW)
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 |
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'
Parameter estimates (location, scale, shape)
Anthony Hammond
#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) GenParetoPars(L1 = 282.2071, LCV = 0.1753749, LSKEW = 0.2576356)
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