stats (version 3.6.2)

rWishart: Random Wishart Distributed Matrices

Description

Generate n random matrices, distributed according to the Wishart distribution with parameters Sigma and df, $W_p(\mathrm{\Sigma },m),m=\text{code}df,\mathrm{\Sigma }=\text{code}Sigma$.

Usage

rWishart(n, df, Sigma)

Arguments

n

integer sample size.

df

numeric parameter, “degrees of freedom”.

Sigma

positive definite ($p\times p$) “scale” matrix, the matrix parameter of the distribution.

Value

a numeric array, say R, of dimension $p\times p\times n$, where each R[,,i] is a positive definite matrix, a realization of the Wishart distribution $W_p(\mathrm{\Sigma },m),m=\text{code}df,\mathrm{\Sigma }=\text{code}Sigma$.

Details

If $X_1,\dots ,X_m,X_i\in {\mathbf{R}}^p$ is a sample of $m$ independent multivariate Gaussians with mean (vector) 0, and covariance matrix $\mathrm{\Sigma }$, the distribution of $M=X^{\prime }X$ is $W_p(\mathrm{\Sigma },m)$.

Consequently, the expectation of $M$ is $$E[M]=m\times \mathrm{\Sigma }.$$ Further, if Sigma is scalar ($p=1$), the Wishart distribution is a scaled chi-squared (${\chi }^2$) distribution with df degrees of freedom, $W_1({\sigma }^2,m)={\sigma }^2{\chi }_m^2$.

The component wise variance is $$\mathrm{Var}(M_{ij})=m({\mathrm{\Sigma }}_{ij}^2+{\mathrm{\Sigma }}_{ii}{\mathrm{\Sigma }}_{jj}).$$

References

Mardia, K. V., J. T. Kent, and J. M. Bibby (1979) Multivariate Analysis, London: Academic Press.

See Also

cov, rnorm, rchisq.

Examples

# NOT RUN {
## Artificial
S <- toeplitz((10:1)/10)
set.seed(11)
R <- rWishart(1000, 20, S)
dim(R)  #  10 10  1000
mR <- apply(R, 1:2, mean)  # ~= E[ Wish(S, 20) ] = 20 * S
stopifnot(all.equal(mR, 20*S, tolerance = .009))

## See Details, the variance is
Va <- 20*(S^2 + tcrossprod(diag(S)))
vR <- apply(R, 1:2, var)
stopifnot(all.equal(vR, Va, tolerance = 1/16))
# }