Generate n random matrices, distributed according to the Wishart distribution with parameters Sigma and df, W_p(Sigma, df).
rFractionalWishart(n, df, Sigma, covariance = FALSE, simplify = "array")integer: the number of replications.
numeric parameter, “degrees of freedom”.
positive definite (\(p\times p\)) “scale” matrix, the matrix parameter of the distribution.
logical on whether a covariance matrix should be generated
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]]).
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)
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).
Adhikari, S. (2008). Wishart random matrices in probabilistic structural mechanics. Journal of engineering mechanics, 134(12), doi:10.1061/(ASCE)0733-9399(2008)134:12(1029).
# NOT RUN {
rFractionalWishart(2, 22.5, diag(1, 20))
# }
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