The function kdecop stores it's result in ojbect 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.
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
ghalton).
Value
A numeric vector of the density/cdf or a n x 2 matrix of
simulated data.
Author(s)
Thomas Nagler
References
Geenens, G., Charpentier, A., and Paindaveine, D. (2014).
Probit transformation for nonparametric kernel estimation of the copula
density.
arXiv:1404.4414 [stat.ME].
Cambou, T., Hofert, M., Lemieux, C. (2015).
A primer on quasi-random numbers for copula models,
arXiv:1508.03483 [stat.CO]
See Also
kdecop,
plot.kdecopula,
ghalton
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
dens.est <- kdecop(udat[, 5:6])
plot(dens.est)
## evaluate density estimate at (u1,u2)=(0.123,0.321)
dkdecop(c(0.123, 0.321), dens.est)
## evaluate cdf estimate at (u1,u2)=(0.123,0.321)
pkdecop(c(0.123, 0.321), dens.est)
## simulate 500 samples from density estimate
rkdecop(500, dens.est)