VineCopula: BiCopKDE – R documentation

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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 `wireframe()`.

Details

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

Author(s)

Thomas Nagler

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)])

VineCopula

Statistical Inference of Vine Copulas

v2.4.1

GPL (>= 2)

Authors

Thomas Nagler [aut, cre], Ulf Schepsmeier [aut], Jakob Stoeber [aut], Eike Christian Brechmann [aut], Benedikt Graeler [aut], Tobias Erhardt [aut], Carlos Almeida [ctb], Aleksey Min [ctb, ths], Claudia Czado [ctb, ths], Mathias Hofmann [ctb], Matthias Killiches [ctb], Harry Joe [ctb], Thibault Vatter [ctb]

Initial release