derCOPinv2 | R Documentation |
Compute the inverse of a numerical partial derivative for U
with respect to V
of a copula, which is a conditional quantile function for nonexceedance probability t
, or
t = c_v(u) = \mathbf{C}^{(-1)}_{1 \mid 2}(u \mid v) = \frac{\delta \mathbf{C}(u,v)}{\delta v}\mbox{,}
and solving for u
. Nelsen (2006, pp. 13, 40–41) shows that this inverse is quite important for random variable generation using the conditional distribution method. This function is not vectorized and will not be so.
derCOPinv2(cop=NULL, v, t, trace=FALSE,
delv=.Machine$double.eps^0.50, para=NULL, ...)
cop |
A copula function; |
v |
A single nonexceedance probability |
t |
A single nonexceedance probability level |
trace |
A logical controlling a |
delv |
The |
para |
Vector of parameters or other data structure, if needed, to pass to |
... |
Additional arguments to pass to the copula. |
Value(s) for the derivative inverse are returned.
W.H. Asquith
Nelsen, R.B., 2006, An introduction to copulas: New York, Springer, 269 p.
derCOP2
u <- runif(1); t <- runif(1)
derCOPinv2(u,t, cop=W) # perfect negative dependence
derCOPinv2(u,t, cop=P) # independence
derCOPinv2(u,t, cop=M) # perfect positive dependence
derCOPinv2(u,t, cop=PSP) # a parameterless copula example
## Not run:
# Simulate 500 values from product (independent) copula
plot(NA,NA, type="n", xlim=c(0,1), ylim=c(0,1), xlab="U", ylab="V")
for(i in 1:500) {
v <- runif(1); t <- runif(1)
points(derCOPinv2(cop=P, v, t),v, cex=0.5, pch=16) # black dots
}
# Simulate 500 of a Frechet Family copula and note crossing singularities.
for(i in 1:500) {
v <- runif(1); t <- runif(1)
u <- derCOPinv2(v, t, cop=FRECHETcop, para=list(alpha=0.7, beta=0.169))
points(u,v, cex=2, pch=16, col=2) # red dots
} #
## End(Not run)
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