GEV: Create a GEV distribution (Generalized Extreme Value)

In benRenard/disTRIMbution: Estimation of Trimmed (Either Truncated or Rectified) Distributions

Create a GEV distribution (Generalized Extreme Value)

Description

Create a GEV distribution (Generalized Extreme Value)

Usage

GEV(loc = 0, scale = 1, shape = 0)

Arguments

loc	The location parameter, often noted \mu. Can be any real number. Defaults to '0'.
scale	The scale parameter, often noted \sigma. Can be any positive number. Defaults to '1'.
shape	The shape parameter, often noted \xi. Can be any real number. Defaults to '0', in which case the GEV distribution is also known as the Gumbel distribution. Note that the 'statistical' convention (also used in package evd, in Wikipedia and in many textbooks) is used here: \xi > 0 correspond to heavy-tailed, left-bounded distributions (aka Frechet Family or Type-II distributions). \xi < 0 correspond to light-tailed, right-bounded distributions (aka Weibull Family or Type-III distributions). Be aware that the opposite convention is used in some disciplines (typically, Hydrology).

Value

A 'GEV' object.

See Also

Other continuous distributions: GEVmin2(), GEVmin(), GPDmin2(), GPDmin(), GPD(), KDB4(), KDB(), Triangular()

Examples

set.seed(27)

X <- GEV(loc=0,scale=1,shape=0.2)
X

random(X, 10)

pdf(X, 0.7)
log_pdf(X, 0.7)

cdf(X, 0.7)
quantile(X, 0.7)

cdf(X, quantile(X, 0.7))
quantile(X, cdf(X, 0.7))

benRenard/disTRIMbution documentation built on July 1, 2023, 4:24 a.m.