An expansion of R's 'stats' random wishart matrix generation. This package allows the user to generate singular, Uhlig and Harald (1994) <doi:10.1214/aos/1176325375>, and pseudo wishart, Diaz-Garcia, et al.(1997) <doi:10.1006/jmva.1997.1689>, matrices. In addition the user can generate wishart matrices with fractional degrees of freedom, Adhikari (2008) <doi:10.1061/(ASCE)0733-9399(2008)134:12(1029)>, commonly used in volatility modeling. Users can also use this package to create random covariance matrices.
Generate n
random matrices, distributed according to the Wishart distribution with parameters Sigma
and df
, W_p(Sigma, df).
rWishart(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).
# NOT RUN {
rWishart(2, 5, diag(1, 20))
# }
Run the code above in your browser using DataCamp Workspace