PARETOcop | R Documentation |
The Pareto copula (Nelsen, 2006, pp. 33) is
\mathbf{C}_{\Theta}(u,v) = \mathbf{PA}(u,v) = \bigl[(1-u)^{-\Theta}+(1-v)^{-\Theta}\bigr]^{-1/\Theta}\mbox{,}
where \Theta \in [0, \infty)
. As \Theta \rightarrow 0^{+}
, the copula limits to the \mathbf{\Pi}
copula (P
) and the \mathbf{M}
copula (M
). The parameterization here has assocation increasing with increasing \Theta
, which differs from Nelsen (2006), and also the Pareto copula is formed with right-tail increasing reflection of the Nelsen (2006) presentation because it is anticipated that copBasic users are more likely to have right-tail dependency situations (say large maxima [right tail] coupling in earth-system data but not small maxima [left tail] coupling).
PARETOcop(u, v, para=NULL, ...)
PAcop(u, v, para=NULL, ...)
u |
Nonexceedance probability |
v |
Nonexceedance probability |
para |
A vector (single element) of parameters—the |
... |
Additional arguments to pass. |
Value(s) for the copula are returned.
The Pareto copula is used in a demonstration of Kendall Function L-moment ratio diagram construction (see kfuncCOPlmoms
).
W.H. Asquith
Nelsen, R.B., 2006, An introduction to copulas: New York, Springer, 269 p.
M
, P
## Not run:
z <- seq(0.01,0.99, by=0.01) # Both copulas have Kendall Tau = 1/3
plot( z, kfuncCOP(z, cop=PAcop, para=1), lwd=2, col="black",
xlab="z <= Z", ylab="F_K(z)", type="l")
lines(z, kfuncCOP(z, cop=GHcop, para=1.5), lwd=2, col="red") # red line
# All extreme value copulas have the same Kendall Function [F_K(z)], the
# Gumbel-Hougaard is such a copula and the F_K(z) for the Pareto does not
# plot on top and thus is not an extreme value but shares a "closeness."
## End(Not run)
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