UKFE (version 1.0.2)

Kappa3Pars: Kappa3 distribution parameter estimates

Description

Estimated parameters from a sample (using Lmoments) or from user supplied L1 (first L-moment), Lcv (linear coefficient of variation), and LSkew (linear skewness)

Usage

Kappa3Pars(x = NULL, L1 = NULL, LCV = NULL, LSKEW = NULL)

Value

Parameter estimates (location, scale, shape)

Arguments

x

numeric vector. The sample

L1

first Lmoment

LCV

linear coefficient of variation

LSKEW

linear skewness

Author

Anthony Hammond

Details

The L-moment estimated parameters are by the method detailed in 'Hosking J. and Wallis J. 1997 Regional Frequency Analysis: An Approach Based on L-moments. Cambridge University Press, New York'. This is the Kappa3 distribution as defined in Kjeldsen, T (2019), 'The 3-parameter Kappa distribution as an alternative for use with FEH pooling groups.' (Circulation - The Newsletter of the British Hydrological Society, no. 142).

This function applies a probability distribution model which assumes that the sample data is independent and identical, i.e. the assumption is that all observations in the sample would not impact or depend on any other. Furthermore, all observations are from the same underlying process which has not changed over the period of record (stationarity).

Examples

# Get an annual maximum sample and estimate the parameters
am_27090 <- GetAM(27090)
Kappa3Pars(am_27090$Flow)

# Calculate L-moments and estimate the parameters with L1, LCV, and LSKEW
l_pars <- as.numeric(LMoments(am_27090$Flow))[c(1, 5, 6)]
Kappa3Pars(L1 = l_pars[1], LCV = l_pars[2], LSKEW = l_pars[3])