# rmatrixit: Matrix inverted-t sampler In matrixsampling: Simulations of Matrix Variate Distributions

## Description

Samples the matrix inverted-t distribution.

## Usage

 1 rmatrixit(n, nu, M, U, V, checkSymmetry = TRUE, keep = TRUE)

## Arguments

 n sample size, a positive integer nu degrees of freedom, any positive number or an integer strictly greater than 1-nrow(M) M mean matrix, without constraints U columns covariance matrix, a positive semidefinite matrix of order equal to nrow(M) V rows covariance matrix, a positive semidefinite matrix of order equal to ncol(M) checkSymmetry logical, whether to check the symmetry of U and V keep logical, whether to return an array with class keep

## Value

A numeric three-dimensional array; simulations are stacked along the third dimension (see example).

## Examples

 1 2 3 4 5 6 7 8 9 10 11 nu <- 0 m <- 2 p <- 3 M <- matrix(1, m, p) U <- toeplitz(m:1) V <- toeplitz(p:1) ITsims <- rmatrixit(10000, nu, M, U, V) dim(ITsims) # 2 3 10000 apply(ITsims, 1:2, mean) # approximates M vecITsims <- t(apply(ITsims, 3, function(X) c(t(X)))) round(cov(vecITsims),2) # approximates 1/(nu+m+p-1) * kronecker(U,V)

matrixsampling documentation built on Aug. 25, 2019, 1:03 a.m.