VineCopula (version 2.6.0)

BiCopKDE: Kernel estimate of a Bivariate Copula Density

Description

A kernel density estimate of the copula density is visualized. The function provides the same options as plot.BiCop(). Further arguments can be passed to kdecopula::kdecop() to modify the estimate. The kdecopula::kdecopula-package() must be installed to use this function.

Usage

BiCopKDE(u1, u2, type = "contour", margins, size, kde.pars = list(), ...)

Arguments

u1, u2

numeric vectors of equal length with values in \([0,1]\).

type

plot type; either "contour" or "surface" (partial matching is activated) for a contour or perspective/surface plot respectively.

margins

only relevant for types "contour" and "surface"; options are: "unif" for the original copula density, "norm" for the transformed density with standard normal margins, "exp" with standard exponential margins, and "flexp" with flipped exponential margins. Default is "norm" for type = "contour", and "unif" for type = "surface". "norm" for the transformed density with standard normal margins (partial matching is activated). Default is "norm" for type = "contour", and "unif" for type = "surface".

size

integer; the plot is based on values on a size x size grid; default is 100 for type = "contour", and 25 for type = "surface".

kde.pars

list of arguments passed to kdecopula::kdecop().

...

optional arguments passed to contour() or lattice::wireframe().

Author

Thomas Nagler

Details

For further details on estimation see kdecopula::kdecop().

Examples

# simulate data from Joe copula
cop <- BiCop(3, tau = 0.3)
u <- BiCopSim(1000, cop)
contour(cop)  # true contours

# kernel contours with standard normal margins
BiCopKDE(u[, 1], u[, 2])
BiCopKDE(u[, 1], u[, 2], kde.pars = list(mult = 0.5))  # undersmooth
BiCopKDE(u[, 1], u[, 2], kde.pars = list(mult = 2))  # oversmooth

# kernel density with uniform margins
BiCopKDE(u[, 1], u[, 2], type = "surface", zlim = c(0, 4))
plot(cop, zlim = c(0, 4))  # true density

# kernel contours are also used in pairs.copuladata
data(daxreturns)
data <- as.copuladata(daxreturns)
pairs(data[c(4, 5, 14, 15)])