# 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.