GPD: Generalised Pareto Distribution

In Renext: Renewal Method for Extreme Values Extrapolation

Generalised Pareto Distribution

Description

Density function, distribution function, quantile function, random generation, hazard and cumulative hazard functions for the Generalised Pareto Distribution.

Usage

   dGPD(x, loc = 0.0, scale = 1.0, shape = 0.0, log = FALSE)
   pGPD(q, loc = 0.0, scale = 1.0, shape = 0.0, lower.tail = TRUE)
   qGPD(p, loc = 0.0, scale = 1.0, shape = 0.0, lower.tail = TRUE)
   rGPD(n, loc = 0.0, scale = 1.0, shape = 0.0)
   hGPD(x, loc = 0.0, scale = 1.0, shape = 0.0)
   HGPD(x, loc = 0.0, scale = 1.0, shape = 0.0) 

Arguments

x, q	Vector of quantiles.
p	Vector of probabilities.
n	Number of observations.
loc	Location parameter \mu.
scale	Scale parameter \sigma.
shape	Shape parameter \xi.
log	Logical; if TRUE, the log density is returned.
lower.tail	Logical; if TRUE (default), probabilities are \textrm{Pr}[X <= x], otherwise, \textrm{Pr}[X > x].

Details

Let \mu, \sigma and \xi denote loc, scale and shape. The distribution values y are \mu \leq y < y_{\textrm{max}}.

When \xi \neq 0, the survival function value for y \geq \mu is given by

S(y) = \left[1 + \xi(y - \mu)/\sigma\right]^{-1/ \xi} \qquad \mu < y < y_{\textrm{max}}

where the upper end-point is y_{\textrm{max}} = \infty for \xi >0 and y_{\textrm{max}} = \mu -\sigma/ \xi for \xi <0.

When \xi = 0, the distribution is exponential with survival

S(y) = \exp\left[- (y - \mu)/\sigma\right] \qquad \mu \leq y.

Value

dGPD gives the density function, pGPD gives the distribution function, qGPD gives the quantile function, and rGPD generates random deviates. The functions hGPD and HGPD return the hazard rate and the cumulative hazard.

Note

The functions are slight adaptations of the [r,d,p,q]gpd functions in the evd package. The main difference is that these functions return NaN when shape is negative, as it might be needed in unconstrained optimisation. The quantile function can be used with p=0 and p=1, then returning the lower and upper end-point.

See Also

fGPD to fit such a distribution by Maximum Likelihood.

Examples

qGPD(p = c(0, 1), shape = -0.2)
shape <- -0.3
xlim <- qGPD(p = c(0, 1), shape = shape)
x <- seq(from = xlim[1], to = xlim[2], length.out = 100)
h <- hGPD(x, shape = shape)
plot(x, h, type = "o", main = "hazard rate for shape < 0")
shape <- 0.2
xlim <- qGPD(p = c(0, 1 - 1e-5), shape = shape)
x <- seq(from = xlim[1], to = xlim[2], length.out = 100)
h <- hGPD(x, shape = shape)
plot(x, h, type = "o", main = "hazard rate shape > 0 ")

Renext documentation built on Aug. 30, 2023, 1:06 a.m.