FRECHETcop | R Documentation |
The Fréchet Family copula (Durante, 2007, pp. 256–259) is
\mathbf{C}_{\alpha, \beta}(u,v) = \mathbf{FF}(u,v) = \alpha\mathbf{M}(u,v) + (1-\alpha-\beta)\mathbf{\Pi}(u,v)+\beta\mathbf{W}(u,v)\mbox{,}
where \alpha, \beta \ge 0
and \alpha + \beta \le 1
. The Fréchet Family copulas are convex combinations of the fundamental copulas \mathbf{W}
(Fréchet–Hoeffding lower-bound copula; W
), \mathbf{\Pi}
(independence; P
), and \mathbf{M}
(Fréchet–Hoeffding upper-bound copula; M
). The copula is comprehensive because both \mathbf{W}
and \mathbf{M}
can be obtained. The parameters are readily estimated using Spearman Rho (\rho_\mathbf{C}
; rhoCOP
) and Kendall Tau (\tau_\mathbf{C}
; tauCOP
) by
\tau_\mathbf{C} = \frac{(\alpha - \beta)(\alpha + \beta + 2)}{3}\mbox{\ and\ } \rho_\mathbf{C} = \alpha - \beta\mbox{.}
The Fréchet Family copula virtually always has a visible singular component unless \alpha, \beta = 0
. The copula has respective lower- and upper-tail dependency parameters of \lambda^L = \alpha
and \lambda^U = \alpha
(taildepCOP
). Durante (2007, p. 257) reports that the Fréchet Family copula can approximate any bivariate copula in a “unique way” and the error bound can be estimated.
FRECHETcop(u,v, para=NULL, rho=NULL, tau=NULL, par2rhotau=FALSE, ...)
u |
Nonexceedance probability |
v |
Nonexceedance probability |
para |
A vector (two element) of parameters |
rho |
Spearman Rho from which to estimate the parameters; |
tau |
Kendall Tau from which to estimate the parameters; |
par2rhotau |
A logical that if |
... |
Additional arguments to pass. |
The function will check the consistency of the parameters whether given by argument or computed from \rho_\mathbf{C}
and \tau_\mathbf{C}
. The term “Family” is used with this particular copula in copBasic so as to draw distinction to the Fréchet lower- and upper-bound copulas as the two limiting copulas are called.
For no other reason than that it can be easily done and makes a nice picture, loop through a nest of \rho
and \tau
for the Fréchet Family copula and plot the domain of the resulting parameters:
ops <- options(warn=-1) # warning supression because "loops" are dumb taus <- rhos <- seq(-1,1, by=0.01) plot(NA, NA, type="n", xlim=c(0,1), ylim=c(0,1), xlab="Frechet Copula Parameter Alpha", ylab="Frechet Copula Parameter Beta") for(tau in taus) { for(rho in rhos) { fcop <- FRECHETcop(rho=rho, tau=tau) if(! is.na(fcop$para[1])) points(fcop$para[1], fcop$para[2]) } } options(ops)
Value(s) for the copula are returned using the \alpha
and \beta
as set by argument para
; however, if para=NULL
and rho
and tau
are set and compatible with the copula, then \{\rho_\mathbf{C}, \tau_\mathbf{C}\} \rightarrow \{\alpha, \beta\}
, parameter estimation made, and an R list
is returned.
A convex combination (convex2COP
) of \mathbf{\Pi}
and \mathbf{M}
, which is a modification of the Fréchet Family, is the Linear Spearman copula:
\mathbf{C}_\alpha(u,v) = (1-\alpha)\mathbf{\Pi}(u,v) + \alpha\mathbf{M}(u,v)\mbox{,}
for 0 \le \alpha \le 1
, and the parameter is equal to \rho_\mathbf{C}
. When the convex combination is used for construction, the complement of the parameter is equal to \rho_\mathbf{C}
(e.g. 1-\alpha = \rho_\mathbf{C}
; rhoCOP
), which can be validated by
rhoCOP(cop=convex2COP, para=list(alpha=1-0.48, cop1=P, cop2=M)) # 0.4799948
W.H. Asquith
Durante, F., 2007, Families of copulas, Appendix C, in Salvadori, G., De Michele, C., Kottegoda, N.T., and Rosso, R., 2007, Extremes in Nature—An approach using copulas: Springer, 289 p.
M
, P
, W
## Not run:
ppara <- c(0.25, 0.50)
fcop <- FRECHETcop(para=ppara, par2rhotau=TRUE)
RHO <- fcop$rho; TAU <- fcop$tau
level.curvesCOP(cop=FRECHETcop, para=ppara) # Durante (2007, Fig. C.27(b))
mtext("Frechet Family copula")
UV <- simCOP(n=50, cop=FRECHETcop, para=ppara, ploton=FALSE, points=FALSE)
tau <- cor(UV$U, UV$V, method="kendall" ) # sample Kendall Tau
rho <- cor(UV$U, UV$V, method="spearman") # sample Spearman Rho
spara <- FRECHETcop(rho=rho, tau=tau) # a fitted Frechet Family copula
spara <- spara$para
if(is.na(spara[1])) { # now a fittable combination is not guaranteed
warning("sample rho and tau do not provide valid parameters, ",
"try another simulation")
} else { # now if fit, draw some red-colored level curves for comparison
level.curvesCOP(cop=FRECHETcop, para=spara, ploton=FALSE, col=2)
} #
## End(Not run)
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