FGM function

The Eyraud Farlie Gumbel Morgenstern Distribution

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

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 $P[X \le x]$, otherwise, $P[X > x]$.

Details

The FGM is defined by the following distribution function

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

Returns

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.

References

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

Author(s)

Christophe Dutang

Examples

#####
# (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))