rPsuedoWishart

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Random Psuedo Wishart Matrix

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

Usage
rPsuedoWishart(n, df, Sigma, covariance = FALSE, simplify = "array")
Arguments
n

integer: the number of replications.

df

numeric parameter, “degrees of freedom”.

Sigma

positive definite (\(p\times p\)) “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]]).

Details

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).

Value

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)

References

Diaz-Garcia, Jose A, Ramon Gutierrez Jaimez, and Kanti V Mardia. 1997. <U+201C>Wishart and Pseudo-Wishart Distributions and Some Applications to Shape Theory.<U+201D> Journal of Multivariate Analysis 63 (1): 73<U+2013>87. doi:10.1006/jmva.1997.1689.

Aliases
  • rPsuedoWishart
Examples
# NOT RUN {
rPsuedoWishart(2, 5, diag(1, 20))
# }
Documentation reproduced from package rWishart, version 0.1.1, License: GPL-2

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