fCopulae (version 3000.79)

EllipticalModelling: Bivariate Elliptical Copulae

Description

A collection and description of functions to investigate bivariate elliptical copulae. Elliptical Copulae Functions: ll{ ellipticalCopulaSim simulates an elliptical copula, ellipticalCopulaFit fits the parameters of an elliptical copula. }

Usage

ellipticalCopulaSim(n, rho = 0.75, param = NULL, type = c("norm", "cauchy", "t"))
ellipticalCopulaFit(u, v, type = c("norm", "cauchy", "t"), ...)

Arguments

n

[rellipticalCopula][ellipticalCopulaSim] - the number of random deviates to be generated, an integer value.

rho

[*ellipticalCopula] - is the numeric value setting the correlation strength, ranging between minus one and one.

param

[*ellipticalCopula][gfunc] - additional distributional parameters: for the Sudent-t distribution this is "nu", for the Kotz distribution this is "r", and for the Exponential Power distribution these are "r" and "s". If the argumen

type

[*ellipticalCopula][gfunc] - the type of the elliptical copula. A character string selected from: "norm", "cauchy", "t", "logistic", "laplace", "kotz", or "epower". [*ellipticalSlider] - a character string which indicates what

u, v

[*ellipticalCopula] - two numeric values or vectors of the same length at which the copula will be computed. If u is a list then the the $x and $y elements will be used as u and

...

[ellipticalCopulaFit] - arguments passed to the optimization function nlminb.

Value

• Copula Functions: The functions [rpd]ellipticalCopula return a numeric vector of random variates, probabilities, or densities for the specified copula computed at grid coordinates u|v. The functions [rpd]ellipticalSlider display an interactive graph of an perspective copula plot either for random variates, probabilities or densities. Alternatively, an image underlayed contour plot can be shown. Copula Dependence Measures: The functions ellipticalTau and ellipticalRho return a numericc value for Kendall's Tau and Spearman's Rho. Copula Tail Coefficient: The function ellipticalTailCoeff returns the coefficient of tail dependence for a specified copula. The function ellipticalTailPlot displays a whole plot for the upper or alternatively for the lower tail dependence as a function of u for a set of nine rho values. Copula Generator Function: The function gfunc computes the generator function for the specified copula, by default the normal copula. If the argument x is missing, then the normalization constand lambda will be returned, otherwise if x is specified the values for the function g(x) will be freturned. The selected type of copula is added to the output as an attribute named "control". The function gfuncSlider allows to display interactively the generator function, the marginal density, the marginal probability, and the contours of the the bivariate density. Copula Simulation and Parameter Fitting: The function ellipticalCopulaSim returns a numeric two-column matrix with randomly generated variates for the specified copula. The function ellipticalCopulaFit returns a fit to empirical data for the specified copula. The returned object is a list with elements from the function nlminb.

Examples

## [rp]ellipticalCopula -
   # Default Normal Copula:
   rellipticalCopula(10)
   pellipticalCopula(10)

## [rp]ellipticalCopula -   
   # Student-t Copula Probability and Density:
   u = grid2d(x = (0:25)/25)
   pellipticalCopula(u, rho = 0.75, param = 4, 
     type = "t", output = "list")
   d = dellipticalCopula(u, rho = 0.75, param = 4, 
     type = "t", output = "list")   
   persp(d, theta = -40, phi = 30, col = "steelblue")
   
## ellipticalTau -
## ellipticalRho -
   # Dependence Measures:
   ellipticalTau(rho = -0.5)
   ellipticalRho(rho = 0.75, type = "logistic", subdivisions = 100)
   
## ellipticalTailCoeff -
   # Student-t Tail Coefficient:
   ellipticalTailCoeff(rho = 0.25, param = 3, type = "t")

## gfunc -
   # Generator Function:
   plot(gfunc(x = 0:10), main = "Generator Function")
   
## ellipticalCopulaSim -
## ellipticalCopulaSim -
   # Simualtion and Parameter Fitting:
   rv = ellipticalCopulaSim(n = 100, rho = 0.75)
   ellipticalCopulaFit(rv)