dkdecop: Working with 'kdecopula' objects
In kdecopula: Kernel Smoothing for Bivariate Copula Densities

Description Usage Arguments Value Author(s) References See Also Examples

The function kdecop() stores it's result in object of class kdecopula. The density estimate can be evaluated on arbitrary points with dkdecop(); the cdf with pkdecop(). Furthermore, synthetic data can be simulated with rkdecop().

dkdecop(u, obj, stable = FALSE)

pkdecop(u, obj)

rkdecop(n, obj, quasi = FALSE)

`u`	`mx2` matrix of evaluation points.
`obj`	`kdecopula` object.
`stable`	logical; option for stabilizing the estimator: the estimated density is cut off at 50.
`n`	integer; number of observations.
`quasi`	logical; the default (`FALSE`) returns pseudo-random numbers, use `TRUE` for quasi-random numbers (generalized Halton, see `qrng::ghalton()`).

A numeric vector of the density/cdf or a n x 2 matrix of simulated data.

Thomas Nagler

#' Nagler, T. (2018) kdecopula: An R Package for the Kernel Estimation of Bivariate Copula Densities. Journal of Statistical Software 84(7), 1-22

#' Geenens, G., Charpentier, A., and Paindaveine, D. (2017). Probit transformation for nonparametric kernel estimation of the copula density. Bernoulli, 23(3), 1848-1873.

Nagler, T. (2014). Kernel Methods for Vine Copula Estimation. Master's Thesis, Technische Universitaet Muenchen, https://mediatum.ub.tum.de/node?id=1231221

Cambou, T., Hofert, M., Lemieux, C. (2015). A primer on quasi-random numbers for copula models, arXiv:1508.03483

kdecop, plot.kdecopula, ghalton

## 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 density estimate at (u1,u2)=(0.123,0.321)
dkdecop(c(0.123, 0.321), fit) 

## evaluate cdf estimate at (u1,u2)=(0.123,0.321)
pkdecop(c(0.123, 0.321), fit) 

## simulate 500 samples from density estimate
plot(rkdecop(500, fit))