MixGHD (version 2.3.7)

dMSGHD: Density of a mulitple-scaled generalized hyperbolic distribution (MSGHD).

Description

Compute the density of a p dimensional mulitple-scaled generalized hyperbolic distribution.

Usage

dMSGHD(data,p,mu=rep(0,p),alpha=rep(0,p),sigma=diag(p),omegav=rep(1,p),
 lambdav=rep(0.5,p),gam=NULL,phi=NULL,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

omegav

(optional) the p dimensional concentration parameter omega

lambdav

(optional) the p dimensional index parameter lambda

gam

(optional) the pxp gamma matrix

phi

(optional) the p dimensional vector phi

log

(optional) if TRUE returns the log of the density

Value

A n dimensional vector with the density from a multiple-scaled 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

C. Tortora, B.C. Franczak, R.P. Browne, and P.D. McNicholas (2019). A Mixture of Coalesced Generalized Hyperbolic Distributions. Journal of Classification (to appear).

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] =  dMSGHD(xy,2) 
      
    }
  }
contour(x=x,y=y,z=xyS1, levels=seq(.005,.25,by=.005), main="MSGHD")

# }