rbvgpd: Parametric Bivariate GPD

In POT: Generalized Pareto Distribution and Peaks Over Threshold

Parametric Bivariate GPD

Description

Density, distribution function and random generation for six different parametric bivariate GPD

Usage

rbvgpd(n, alpha, model = "log", asCoef, asCoef1, asCoef2, mar1 =
c(0,1,0), mar2 = mar1)
pbvgpd(q, alpha, model = "log", asCoef, asCoef1, asCoef2, mar1 =
c(0,1,0), mar2 = mar1, lower.tail = TRUE)

Arguments

n	The number of observations to be simulated.
q	A matrix or vector with two columns at which the distribution is computed.
alpha	Dependence parameter for the logistic, asymmetric logistic, negative logistic, asymmetric negative logistic, mixed and asymmetric mixed models.
model	The specified model; a character string. Must be either "log" (the default), "alog", "nlog", "anlog", "mix" or "amix", for the logistic, asymmetric logistic, negative logistic, asymmetric negative logistic, mixed and asymmetric mixed models respectively.
asCoef, asCoef1, asCoef2	The asymmetric coefficients for the asymmetric logistic, asymmetric negative logistic and asymmetric mixed models.
mar1, mar2	Vectors of length 3 giving the marginal parameters.
lower.tail	Logical. If TRUE (the default), P[X \leq x] is computed, else P[X \geq x].

Details

The logistic and asymmetric logistic models respectively are simulated using bivariate versions of Algorithms 1.1 and 1.2 in Stephenson(2003). All other models are simulated using a root finding algorithm to simulate from the conditional distributions.

Value

Generate a random vector of length n.

Author(s)

Mathieu Ribatet (Alec Stephenson for the C codes)

References

Stephenson, A. G. (2003) Simulating multivariate extreme value distributions of logistic type. Extremes, 6(1), 49–60.

Examples

x <- rbvgpd(1000, alpha = 0.25, model = "log", mar1 = c(0,1,0.25), mar2
= c(2,0.5, -0.15))
y <- rbvgpd(1000, alpha = 0.75, model = "nlog", mar1 = c(0,1,0.25), mar2
= c(2,0.5, -0.15))
par(mfrow=c(1,2))
plot(x);plot(y)

POT documentation built on Oct. 17, 2024, 5:06 p.m.