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 |
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.
1 | rSingularWishart(2, 5, diag(1, 20))
|
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