kdecopula: hkdecop – R documentation

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hkdecop

H-function and inverse of a kdecop() fit

Description

Evaluates the h-function (or its inverse) corresponding to a kdecopula object. H-functions are conditional distribution functions obtained by integrating the copula density w.r.t. to one of its arguments (see also VineCopula::BiCopHfunc().

Usage

hkdecop(u, obj, cond.var, inverse = FALSE)

Arguments

`u`	n x 2 matrix of evaluation points.
`obj`	`kdecopula` object.
`cond.var`	integer; `cond.var = 1` conditions on the first variable, `cond.var = 2` on the second.
`inverse`	logical; indicates whether the h-function or its inverse shall be calculated.

Value

A length n vector of the (inverse) h-function evaluated at u.

Author(s)

Thomas Nagler

Examples

## load data and transform with empirical cdf
data(wdbc)
udat <- apply(wdbc[, -1], 2, function(x) rank(x) / (length(x) + 1))

## estimation of copula density of variables 5 and 6
fit <- kdecop(udat[, 5:6])
plot(fit) 

## evaluate h-function estimate and its inverse at (u1|u2) = (0.123 | 0.321)
hkdecop(c(0.123, 0.321), fit, cond.var = 2) 
hkdecop(c(0.123, 0.321), fit, cond.var = 2, inverse = TRUE)

kdecopula

Kernel Smoothing for Bivariate Copula Densities

v0.9.2

GPL-3

Authors

Thomas Nagler [aut, cre], Kuangyu Wen [ctb]

Initial release