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 |
|
M |
|
A |
|
Sigma |
|
Psi |
|
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
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]