cdf_ev: Reversed functions for several Extreme Value Distributions

Description

As several Extreme Value distributions are parameterized for high extreme values, reversed functions for minima (e.g. low flow statistics) are derived. Reversing is done by fitting to the negated data (-x), subtracting probabilities from one (1 - f) and computing the negated probabilities.

Usage

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Arguments

distribution

character vector of length one containing the name of the distribution. The family of the chosen distribution must be supported by the package lmom. See lmom. For example distribution = "gev" directly uses the functions from package lmom, whereas distribution = "gevR" performs reversing.

x

Vector of quantiles.

f

Vector of probabilities.

para

Numeric vector containing the parameters of the distribution, in the order zeta, beta, delta (location, scale, shape).

lmom

Numeric vector containing the L-moments of the distribution or of a data sample. E.g. as returned by samlmu(x).

...

parameters like bound, passed on to the estimating function. E.g. in case of dist = 'wei' to pelwei.

Value

cdf_ev gives the distribution function; qua_ev gives the quantile function.

See Also

lmom, cdfgev, quagev, pelgev.

Examples

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data("ngaruroro")
ng <- as.xts(ngaruroro)
minima <- as.vector(apply.yearly(ng$discharge, min, na.rm = TRUE))

# Weibull distribution and reversed GEV give the same results
distr <- "wei"
qua_ev(distr, seq(0, 1, 0.1), para = pel_ev(distr, samlmu(minima)))

distr <- "gevR"
qua_ev(distr, seq(0, 1, 0.1), para = pel_ev(distr, samlmu(minima)))

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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