Kappa3Pars: Kappa3 distribution parameter estimates
| Kappa3Pars | R Documentation |
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)
Arguments
x |
numeric vector. The sample |
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. 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).
Value
Parameter estimates (location, scale, shape)
Author(s)
Anthony Hammond
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])
