densityCOP: Density of a Copula

In wasquith/copBasic: General Bivariate Copula Theory and Many Utility Functions

Density of a Copula

Description

Numerically estimate the copula density for a sequence of u and v probabilities for which each sequence has equal steps that are equal to \Delta(uv). The density c(u,v) of a copula \mathbf{C}(u,v) is numerically estimated by

c(u,v) = \bigl[\mathbf{C}(u_2,v_2) - \mathbf{C}(u_2,v_1) - \mathbf{C}(u_1,v_2) + \mathbf{C}(u_1,v_1)\bigr]\, /\, \bigl[\Delta(uv)\times\Delta(uv)\bigr]\mbox{,}

where c(u,v) \ge 0 (see Nelsen, 2006, p. 10; densityCOPplot). The joint density can be defined by the coupla density for continuous variables and is the ratio of the joint density funcion f(x,y) for random variables X and Y to the product of the marginal densities (f_x(x) and f_y(y)):

c\bigl(F_x(x), F_y(y)\bigr) = \frac{f(x,y)}{f_x(x)f_y(y)}\mbox{,}

where F_x(x) and F_y(y) are the respective cumulative distribution functions of X and Y, and lastly u = F_x(x) and v = F_y(y).

Usage

densityCOP(u,v, cop=NULL, para=NULL, deluv=.Machine$double.eps^0.25,
                truncate.at.zero=TRUE, the.zero=0, sumlogs=FALSE, ...)

Arguments

u	Nonexceedance probability u in the X direction;
v	Nonexceedance probability v in the Y direction;
cop	A copula function;
para	Vector of parameters or other data structure, if needed, to pass to the copula;
deluv	The change in the sequences \{u, v\} \mapsto \delta, \ldots, 1-\delta; \delta = \Delta(uv) probabilities;
truncate.at.zero	A density must be c(u,v) \ge 0, but because this computation is based on a rectangular approximation and not analytical, there exists a possibility that very small rectangles could result in numerical values in R that are less than zero. This usually can be blamed on rounding. This logical if TRUE truncates computed densities to zero, and the default assumes that the user is providing a proper copula. A FALSE value is used by the function isfuncCOP;
the.zero	The value for “the zero” where a small number might be useful for pseudo-maximum likelihood estimation using sumlogs;
sumlogs	Return the \sum{\log c(u,v; \Theta)} where \Theta are the parameters in para and this feature is provided for mleCOP; and
...	Additional arguments to pass to the copula function.

Value

Value(s) for c(u,v) are returned.

Note

The copBasic package does not currently have copula densities as analytical solutions implemented. This is because initial design decisions were entirely on cumulative distribution function (CDF) representations of the copula.

Author(s)

W.H. Asquith

References

Joe, H., 2014, Dependence modeling with copulas: Boca Raton, CRC Press, 462 p.

Nelsen, R.B., 2006, An introduction to copulas: New York, Springer, 269 p.

See Also

simCOP, densityCOPplot, kullCOP, mleCOP

Examples

## Not run: 
# Joe (2014, p. 164) shows the closed form copula density for the Plackett.
"dPLACKETTcop" <- function(u,v,para) {
   uv <- u*v; upv <- u + v; eta <- para - 1
   A <- para*(1+eta*(upv - 2*uv)); B <- ((1+eta*upv)^2 - 4*para*eta*uv)^(3/2)
   return(A/B)
}
dPLACKETTcop(0.32, 0.74,            para=1.3) # 0.9557124
densityCOP(  0.32, 0.74, cop=PLcop, para=1.3) # 0.9557153
## End(Not run)

## Not run: 
# Joe (2014, p. 165) shows the corner densities of the Plackett as Theta.
# copBasic uses numerical density estimation and not analytical formula.
eps <- .Machine$double.eps
densityCOP(0,0, cop=PLcop, para=4) # 3.997073  (default eps^0.25)
densityCOP(1,1, cop=PLcop, para=4) # 3.997073  (default eps^0.25)
densityCOP(1,1, cop=PLcop, para=4, deluv=eps)     # 0 (silent failure)
densityCOP(1,1, cop=PLcop, para=4, deluv=eps^0.5) # 4.5
densityCOP(1,1, cop=PLcop, para=4, deluv=eps^0.4) # 4.002069
densityCOP(1,1, cop=PLcop, para=4, deluv=eps^0.3) # 3.999513
# So, we see that the small slicing does have an effect, the default of 0.25 is
# intented for general application by being away enough from machine limits.
## End(Not run)

## Not run: 
# Joe (2014, p. 170) shows the closed form copula density for "Bivariate Joe/B5" copula
"dJOEB5cop" <- function(u, v, para) {
   up <- (1-u)^para; vp <- (1-v)^para; eta <- para - 1
   A <- (up + vp - up*vp); B <- (1-u)^eta * (1-v)^eta; C <- (eta + A)
   return(A^(-2 + 1/para) * B * C)
}
densityCOP(0.32, 0.74, cop=JOcopB5, para=1.3)  # 0.9410653
dJOEB5cop( 0.32, 0.74, para=1.3)               # 0.9410973 
## End(Not run)

wasquith/copBasic documentation built on Dec. 13, 2024, 6:39 p.m.