Copula Class

• elliptical (normal and t; ellipCopula)
• Archimedean (Clayton, Gumbel, Frank, Joe, and Ali-Mikhail-Haq; ; archmCopula and acopula)
• extreme value (Gumbel, Husler-Reiss, Galambos, Tawn, and t-EV; evCopula)
• families (Plackett and Farlie-Gumbel-Morgenstern).

Density, CDF, random variable generation

• dCopula, pCopula, rCopula
• bivariate dependence measure (rho, tau, etc)

• perspective and contour plots

    persp(norm.cop, dCopula)
    contour(norm.cop, pCopula)
    scatterplot3d(rCopula(1000, norm.cop))
  

Fitting Model

• Functions (and methods) for fitting copula models including variance estimates (fitCopula).

  example(fitCopula)## fitting Copulas example(fitMvdc) ## fitting multivariate distributions via Copulas

  fitCopula loglikCopula

EllipCopula

ellipCopula (family, param, dim = 2, dispstr = "ex", df = 4, ...)
normalCopula(param, dim = 2, dispstr = "ex")
tCopula (param, dim = 2, dispstr = "ex", df = 4, df.fixed = FALSE)

Implemented structures are “ex” for ex- changeable, “ar1” for AR(1), “toep” for Toeplitz, and “un” for unstructured.

 norm.cop <- normalCopula(c(0.5, 0.6, 0.7), dim = 3, dispstr = "un")
t.cop <- tCopula(c(0.5, 0.3), dim = 3, dispstr = "toep",
                 df = 2, df.fixed = TRUE)
## from the wrapper
norm.cop <- ellipCopula("normal", param = c(0.5, 0.6, 0.7),
                        dim = 3, dispstr = "un")
if(require("scatterplot3d")) {
  ## 3d scatter plot of 1000 random observations
  scatterplot3d(rCopula(1000, norm.cop))
  scatterplot3d(rCopula(1000, t.cop))
  }

mvdc

mvdc(copula, margins, paramMargins, marginsIdentical = FALSE,
     check = TRUE, fixupNames = TRUE)
dMvdc(x, mvdc, log=FALSE)
pMvdc(x, mvdc)
rMvdc(n, mvdc)

Code

file