| wolfCOP {copBasic} | R Documentation |
The Schweizer and Wolff Sigma of a Copula
Description
Compute the measure of association known as Schweizer–Wolff Sigma \sigma_\mathbf{C} of a copula according to Nelsen (2006, p. 209) by
\sigma_\mathbf{C} = 12\int\!\!\int_{\mathcal{I}^2} |\mathbf{C}(u,v) - uv|\,\mathrm{d}u\mathrm{d}v\mbox{,}
which is 0 \le \sigma_\mathbf{C} \le 1. It is obvious that this measure of association, without the positive sign restriction, is similar to the following form of Spearman Rho (rhoCOP) of a copula:
\rho_\mathbf{C} = 12\int\!\!\int_{\mathcal{I}^2} [\mathbf{C}(u,v) - uv]\,\mathrm{d}u\mathrm{d}v\mbox{.}
If a copula is positively quadrant dependent (PQD, see isCOP.PQD) then \sigma_\mathbf{C} = \rho_\mathbf{C} and conversely if a copula is negatively quadrant dependent (NQD) then \sigma_\mathbf{C} = -\rho_\mathbf{C}. However, a feature making \sigma_\mathbf{C} especially attractive is that for random variables X and Y, which are not PQD or NQD—copulas that are neither larger nor smaller than \mathbf{\Pi}—is that “\sigma_\mathbf{C} is often a better measure of [dependency] than \rho_\mathbf{C}” (Nelsen, 2006, p. 209).
Usage
wolfCOP(cop=NULL, para=NULL, as.sample=FALSE, brute=FALSE, delta=0.002, ...)
Arguments
cop |
A copula function; |
para |
Vector of parameters or other data structure, if needed, to pass to the copula; |
as.sample |
A logical controlling whether an optional R |
brute |
Should brute force be used instead of two nested |
delta |
The |
... |
Additional arguments to pass. |
Value
The value for \sigma_\mathbf{C} is returned.
Note
A natural estimator for \sigma_\mathbf{C} is through the empirical copula (Póczos et al., 2015) and can be computed as
\hat{\sigma}_\mathbf{C} = \frac{12}{n^2 - 1}
\sum_{i=1}^n\sum_{j=1}^n \bigg|\hat{\mathbf{C}}_n\biggl(\frac{i}{n}, \frac{j}{n}\biggr) -
\frac{i}{n}\times\frac{j}{n}\bigg|\mbox{,}
where \hat{\mathbf{C}}_n is the simplest empirical copula of
\hat{\mathbf{C}}_n\biggl(\frac{i}{n}, \frac{j}{n}\biggr) =
\frac{1}{n}\{\# \mathrm{\ of\ } (U_k \le U_i, V_k \le V_j)\}
An extended example is informative. First declare a composite of two different Plackett copulas (PLcop) and simulate a few hundred values:
para <- list(cop1 =PLcop, cop2=PLcop,
para1=0.145, para2=21.9, alpha=0.81, beta=0.22)
D <- simCOP(n=300, cop=composite2COP, para=para,
cex=0.5, col=rgb(0,0,0,0.2), pch=16)
Second, show that this copula is globally PQD (isCOP.PQD), but there is a significant local NQD part of \mathcal{I}^2 space that clearly is NQD.
PQD <- isCOP.PQD(cop=composite2COP, para=para, uv=D) message(PQD$global.PQD) # TRUE points(D, col=PQD$local.PQD+2, lwd=2)
This composited copula intersects, that is, passes through, the P copula. Hence by the logic of Nelsen (2006), then the \sigma_\mathbf{C} should be larger than \rho_\mathbf{C} as shown below
wolfCOP(cop=composite2COP, para=para) # 0.08373378 (theoretical) rhoCOP(cop=composite2COP, para=para) # 0.02845131 (theoretical) hoefCOP(cop=composite2COP, para=para) # 0.08563823 (theoretical)
In fact, the output above also shows Schweizer–Wolff Sigma to be larger than Blomqvist Beta (blomCOP), Gini Gamma (giniCOP), and Kendall Tau (tauCOP). The Schweizer–Wolff Sigma has captured the fact that although the symbols plot near randomly on the figure, the symbol coloring for PQD and NQD clearly shows local dependency differences. The sample version is triggered by
wolfCOP(para=D, as.sample=TRUE) # 0.09278467 (an example sample)
Author(s)
W.H. Asquith
References
Póczos, Barnabás, Krishner, Sergey, Pál, Szepesvári, Csaba, and Schneider, Jeff, 2015, Robust nonparametric copula based dependence estimators, accessed on August 11, 2015, at https://www.cs.cmu.edu/~bapoczos/articles/poczos11nipscopula.pdf.
Nelsen, R.B., 2006, An introduction to copulas: New York, Springer, 269 p.
See Also
blomCOP, footCOP, giniCOP,
hoefCOP, rhoCOP, tauCOP,
joeskewCOP, uvlmoms
Examples
## Not run:
wolfCOP(cop=PSP) # 0.4784176
## End(Not run)
## Not run:
n <- 1000; UV <- simCOP(n=n, cop=N4212cop, para=7.53, graphics=FALSE)
wolfCOP(cop=N4212cop, para=7.53) # 0.9884666 (theoretical)
wolfCOP(para=UV, as.sample=TRUE) # 0.9884174 (sample)
## End(Not run)
## Not run:
# Redo D from Note section above
para <- list(cop1 =PLcop, cop2=PLcop,
para1=0.145, para2=21.9, alpha=0.81, beta=0.22)
D <- simCOP(n=300, cop=composite2COP, para=para,
cex=0.5, col=rgb(0, 0, 0, 0.2), pch=16)
the.grid <- EMPIRgrid(para=D)
the.persp <- persp(the.grid$empcop, theta=-25, phi=20, shade=TRUE,
xlab="U VARIABLE", ylab="V VARIABLE", zlab="COPULA C(u,v)")
empcop <- EMPIRcopdf(para=D) # data.frame of all points
points(trans3d(empcop$u, empcop$v, empcop$empcop, the.persp), cex=0.7,
col=rgb(0, 1-sqrt(empcop$empcop), 1, sqrt(empcop$empcop)), pch=16)
points(trans3d(empcop$u, empcop$v, empcop$empcop, the.persp),
col=PQD$local.PQD+1, pch=1)
layout(matrix(c(1,2,3,4), 2, 2, byrow = TRUE), respect = TRUE)
PQD.NQD.cop <- gridCOP(cop=composite2COP, para=para)
Pi <- gridCOP(cop=P)
RHO <- PQD.NQD.cop - Pi; SIG <- abs(RHO)
the.persp <- persp(PQD.NQD.cop, theta=-25, phi=20, shade=TRUE, cex=0.5,
xlab="U VARIABLE", ylab="V VARIABLE", zlab="COPULA C(u,v)")
mtext("The Copula that has local PQD and NQD", cex=0.5)
the.persp <- persp(Pi, theta=-25, phi=20, shade=TRUE, cex=0.5,
xlab="U VARIABLE", ylab="V VARIABLE", zlab="COPULA C(u,v)")
mtext("Independence (Pi)", cex=0.5)
the.persp <- persp(RHO, theta=-25, phi=20, shade=TRUE, cex=0.5,
xlab="U VARIABLE", ylab="V VARIABLE", zlab="COPULA C(u,v)")
mtext("Copula delta: Integrand of Spearman Rho", cex=0.5)
the.persp <- persp(SIG, theta=-25, phi=20, shade=TRUE, cex=0.5,
xlab="U VARIABLE", ylab="V VARIABLE", zlab="COPULA C(u,v)")
mtext("abs(Copula delta): Integrand of Schweizer-Wolff Sigma", cex=0.5) #
## End(Not run)