rmatdata: Random matrix-variate data

In jijiadong/MVDN: Matrix-variate Differential Network Analysis

Description

The function provide a random number generator for the Matrix-variate data.

Usage

rmatdata(n, mean = matrix(0, nrow = nrow(sigmaS), ncol = ncol(sigmaT)), sigmaS, sigmaT, method = c("eigen", "svd", "chol"), distribution = c("uniform", "t"), df)

Arguments

n	number of observations.
mean	mean matrix, default is matrix(0, nrow=nrow(sigmaS), ncol=ncol(sigmaT)).
sigmaS	covariance matrix between rows.
sigmaT	covariance matrix between columns.
method	string specifying the matrix decomposition used todetermine the matrix root of sigmaS and sigmaT. Possible methods are eigenvalue decomposition ("eigen", default), singular value decomposition ("svd"), and Cholesky decomposition ("chol").
distribution	the distribution used to generate random number. uniform distribution or t distribution.
df	runif(-df,df) used if distribution is "uniform". degrees of freedom used if distribution is "t".

Value

returns a p*q*n array if n > 1. returns a p*q matrix if n = 1.

See Also

rmatnorm

Examples

p <- 5; q <- 6; n <- 10
set.seed(1212)
s1 <- 0.5^abs(outer(1:p,1:p,"-"))
s2 <- 0.4^abs(outer(1:q,1:q,"-"))
x <- rmatdata(n,mean=matrix(1,p,q),sigmaS=s1,sigmaT=s2,distribution="t", df=3)

jijiadong/MVDN documentation built on Sept. 6, 2020, 7:15 p.m.