rSingularWishart: Random Singular Wishart Matrix
In BenBarnard/rWishart: Random Wishart Matrix Generation

Description Usage Arguments Details Value References Examples

View source: R/rSingularWishart.R

Generate n random matrices, distributed according to the Wishart distribution with parameters Sigma and df, W_p(Sigma, df).

1	rSingularWishart(n, df, Sigma, covariance = FALSE, simplify = "array")

`n`	integer: the number of replications.
`df`	numeric parameter, “degrees of freedom”.
`Sigma`	positive definite (p p*) “scale” matrix, the matrix parameter of the distribution.
`covariance`	logical on whether a covariance matrix should be generated
`simplify`	logical or character string; should the result be simplified to a vector, matrix or higher dimensional array if possible? For `sapply` it must be named and not abbreviated. The default value, `TRUE`, returns a vector or matrix if appropriate, whereas if `simplify = "array"` the result may be an `array` of “rank” (=`length(dim(.))`) one higher than the result of `FUN(X[[i]])`.

If X_1, ..., X_m is a sample of m independent multivariate Gaussians with mean vector 0, and covariance matrix Sigma, the distribution of M = X'X is W_p(Sigma, m).

A numeric array of dimension p * p * n, where each array is a positive semidefinite matrix, a realization of the Wishart distribution W_p(Sigma, df)

Uhlig, Harald. 1994. “On Singular Wishart and Singular Multivariate Beta Distributions.” The Annals of Statistics 22 (1): 395–405. doi:10.1214/aos/1176325375.