dmvvg {mvvg}R Documentation

Calculate Matrix-Variate Variance Gamma Density

Description

Determines density of observations from a Matrix-variate variance gamma (MVVG) distribution, under the identifiability constraint set by [].

Usage

dmvvg(X, M, A, Sigma, Psi, gamma, log = FALSE)

Arguments

X

p \times q observed matrix value

M

p \times q location matrix

A

p \times q skewness matrix

Sigma

p \times p covariance matrix

Psi

q \times q covariance matrix

gamma

scalar mixing parameter

log

returns log-likelihood if TRUE, default is FALSE.

Details

MVVG samples are formulated through the normal variance-mean mixture M + WA + \sqrt{W}Z, where W \sim Gamma(\gamma, \gamma).

Gamma must be >0. Sigma and Psi must be positive definite covariance matrices.

Value

dmvvg returns the probability density corresponding to the inputted values and parameters.

Author(s)

Samuel Soon

See Also

rmvvg

Examples

M <- cbind(rep(1, 5), c(1, 0, 1, 0, 1))
A <- matrix(c(1,2), 5, 2, byrow = TRUE)
Sigma <- diag(5)
Psi <- matrix(c(4,2,2,3), 2, 2)
gamma <- 3

X <- rmvvg(1, M, A, Sigma, Psi, gamma)[[1]]
dmvvg(X, M, A, Sigma, Psi, gamma)

[Package mvvg version 0.1.0 Index]