Density of a generalized hyperbolic distribution (GHD).
Compute the density of a p dimensional generalized hyperbolic distribution.
dGHD(data,p, mu=rep(0,p),alpha=rep(0,p),sigma=diag(p),omega=1,lambda=0.5, log=FALSE)
data |
n x p data set |
p |
number of variables. |
mu |
(optional) the p dimensional mean |
alpha |
(optional) the p dimensional skewness parameter alpha |
sigma |
(optional) the p x p dimensional scale matrix |
omega |
(optional) the unidimensional concentration parameter omega |
lambda |
(optional) the unidimensional index parameter lambda |
log |
(optional) if TRUE returns the log of the density |
The default values are: 0 for the mean and the skweness parameter alpha, diag(p) for sigma, 1 for omega, and 0.5 for lambda.
A n dimensional vector with the density from a generilzed hyperbolic distribution
Cristina Tortora, Aisha ElSherbiny, Ryan P. Browne, Brian C. Franczak, and Paul D. McNicholas. Maintainer: Cristina Tortora <cristina.tortora@sjsu.edu>
R.P. Browne, and P.D. McNicholas (2015). A Mixture of Generalized Hyperbolic Distributions. Canadian Journal of Statistics, 43.2 176-198
x = seq(-3,3,length.out=50)
y = seq(-3,3,length.out=50)
xyS1 = matrix(0,nrow=length(x),ncol=length(y))
for(i in 1:length(x)){
for(j in 1:length(y)){
xy <- matrix(cbind(x[i],y[j]),1,2)
xyS1[i,j] = dGHD(xy,2)
}
}
contour(x=x,y=y,z=xyS1, levels=c(.005,.01,.025,.05, .1,.25), main="MGHD",ylim=c(-3,3), xlim=c(-3,3))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.