dMSGHD | R Documentation |
Compute the density of a p dimensional mulitple-scaled generalized hyperbolic distribution.
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)
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 |
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 multiple-scaled generilzed hyperbolic distribution
Cristina Tortora, Aisha ElSherbiny, Ryan P. Browne, Brian C. Franczak, and Paul D. McNicholas. Maintainer: Cristina Tortora <cristina.tortora@sjsu.edu>
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).
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")
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