# FGM: The Eyraud Farlie Gumbel Morgenstern Distribution In RTDE: Robust Tail Dependence Estimation

## Description

Density function, distribution function, quantile function, random generation.

## Usage

 1 2 3 4 dFGM(u, v, alpha, log = FALSE) pFGM(u, v, alpha, lower.tail=TRUE, log.p = FALSE) qFGM(p, alpha, lower.tail=TRUE, log.p = FALSE) rFGM(n, alpha)

## Arguments

 u, v vector of quantiles. p vector of probabilities. n number of observations. If length(n) > 1, the length is taken to be the number required. alpha shape parameter. log, log.p logical; if TRUE, probabilities p are given as log(p). lower.tail logical; if TRUE (default), probabilities are P[X ≤ x], otherwise, P[X > x].

## Details

The FGM is defined by the following distribution function

C(u,v) = u*v*(1+α*(1-u)*(1-v))

for all u,v in [0,1] and α in [0,1]. When lower.tail=FALSE, pFGM returns the survival copula P(U > u, V > v).

## Value

dFGM gives the density, pFGM gives the distribution function, qFGM gives the quantile function, and rFGM generates random deviates.

The length of the result is determined by n for rFGM, and is the maximum of the lengths of the numerical parameters for the other functions.

The numerical parameters other than n are recycled to the length of the result. Only the first elements of the logical parameters are used.

## Author(s)

Christophe Dutang

## References

Nelsen, R. (2006), An Introduction to Copula, Second Edition, Springer.

## Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ##### # (1) density function u <- v <- seq(0, 1, length=25) cbind(u, v, dFGM(u, v, 1/2)) cbind(u, v, outer(u, v, dFGM, alpha=1/2)) ##### # (2) distribution function cbind(u, v, pFGM(u, v, 1/2)) cbind(u, v, outer(u, v, pFGM, alpha=1/2))

RTDE documentation built on Jan. 8, 2020, 5:09 p.m.