qev: Quantile estimation of a composite extreme value distribution

In evgam: Generalised Additive Extreme Value Models

Quantile estimation of a composite extreme value distribution

Description

Quantile estimation of a composite extreme value distribution

Usage

qev(
  p,
  loc,
  scale,
  shape,
  m = 1,
  alpha = 1,
  theta = 1,
  family,
  tau = 0,
  start = NULL
)

Arguments

p	a scalar giving the quantile of the distribution sought
loc	a scalar, vector or matrix giving the location parameter
scale	as above, but scale parameter
shape	as above, but shape parameter
m	a scalar giving the number of values per return period unit, e.g. 365 for daily data giving annual return levels
alpha	a scalar, vector or matrix of weights if within-block variables not identically distributed and of different frequencies
theta	a scalar, vector or matrix of extremal index values
family	a character string giving the family for which return levels sought
tau	a scalar, vector or matrix of values giving the threshold quantile for the GPD (i.e. 1 - probability of exceedance)
start	a 2-vector giving starting values that bound the return level

Details

If F is the generalised extreme value or generalised Pareto distribution, qev solves

∏_{j=1}^n \big\{F(z)\}^{m α_j θ_j} = p.

For both distributions, location, scale and shape parameters are given by loc, scale and shape. The generalised Pareto distribution, for ξ \neq 0 and z > u, is parameterised as 1 - (1 - τ) [1 + ξ (z - u) / ψ_u]^{-1/ξ}, where u, ψ_u and ξ are its location, scale and shape parameters, respectively, and τ corresponds to argument tau.

Value

A scalar or vector of estimates of p

Examples

qev(0.9, c(1, 2), c(1, 1.1), .1, family="gev")
qev(0.99, c(1, 2), c(1, 1.1), .1, family="gpd", tau=0.9)

evgam documentation built on June 28, 2022, 5:07 p.m.