MixGHD (version 2.3.7)

dGHD: Density of a generalized hyperbolic distribution (GHD).

Description

Compute the density of a p dimensional generalized hyperbolic distribution.

Usage

dGHD(data,p, mu=rep(0,p),alpha=rep(0,p),sigma=diag(p),omega=1,lambda=0.5, log=FALSE)

Arguments

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

Value

A n dimensional vector with the density from a generilzed hyperbolic distribution

Details

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.

References

R.P. Browne, and P.D. McNicholas (2015). A Mixture of Generalized Hyperbolic Distributions. Canadian Journal of Statistics, 43.2 176-198

Examples

# NOT RUN {


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))




# }