| kcopula {ks} | R Documentation |
Kernel copula and copula density estimator for 2-dimensional data.
kcopula(x, H, hs, gridsize, gridtype, xmin, xmax, supp=3.7, eval.points, binned=FALSE, bgridsize, w, verbose=FALSE, marginal="kernel") kcopula.de(x, H, Hfun, hs, gridsize, gridtype, xmin, xmax, supp=3.7, eval.points, binned=FALSE, bgridsize, w, verbose=FALSE, compute.cont=FALSE, approx.cont=TRUE, boundary.supp, marginal="kernel", Hfun.pilot="dscalar")
x |
matrix of data values |
H,hs |
bandwidth matrix. If these are missing, |
Hfun |
bandwidth matrix function. If missing, |
Hfun.pilot |
pilot bandwidth matrix - see |
gridsize |
vector of number of grid points |
gridtype |
not yet implemented |
xmin,xmax |
vector of minimum/maximum values for grid |
supp |
effective support for standard normal |
eval.points |
points at which estimate is evaluated |
binned |
flag for binned estimation. Default is FALSE. |
bgridsize |
vector of binning grid sizes |
w |
vector of weights. Default is a vector of all ones. |
verbose |
flag to print out progress information. Default is FALSE. |
marginal |
"kernel" = kernel cdf or "empirical" = empirical cdf to calculate pseudo-uniform values. Default is "kernel". |
compute.cont |
flag for computing 1% to 99% probability contour levels. Default is FALSE. |
approx.cont |
flag for computing approximate probability contour levels. Default is TRUE. |
boundary.supp |
scaled boundary region is [0, boundary.supp*h] or [1-boundary.supp*h,1] on [0,1]. Default is 1. |
For kernel copula estimates, a transformation approach is used to
account for the boundary effects. If H is missing, the default
is Hpi.kcde; if hs are missing, the default is
hpi.kcde.
For kernel copula density estimates, for those points which are in
the interior region, the usual normal kernel density estimator
(kde) is used. For those points in the boundary region,
a product beta kernel based on the boundary corrected univariate beta
kernel of Chen (1999) is used. If H is missing, the default
is Hpi.kcde; if hs are missing, the default is
hpi.
The effective support, binning, grid size, grid range parameters are
the same as for kde.
A kernel copula estimate, output from kcopula, is an object of
class kcopula. A kernel copula density estimate, output from
kcopula.de, is an object of class kde. These two classes
of objects have the same fields as kcde and kde objects
respectively, except for
x |
pseudo-uniform data points |
x.orig |
data points - same as input |
marginal |
marginal function used to compute pseudo-uniform data |
boundary |
flag for data points in the boundary region
( |
Duong, T. (2014) Optimal data-based smoothing for non-parametric estimation of copula functions and their densities. Submitted.
Chen, S.X. (1999). Beta kernel estimator for density functions. Computational Statistics & Data Analysis, 31, 131–145.
library(MASS)
data(fgl)
x <- fgl[,c("RI", "Na")]
Chat <- kcopula(x=x)
plot(Chat, disp="persp", thin=3, col="white", border=1)
chat <- kcopula.de(x=x)
plot(chat, disp="persp", thin=3, theta=40, phi=30, col="white", border=1)