rWishart {stats} | R Documentation

## Random Wishart Distributed Matrices

### Description

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

### Usage

    rWishart(n, df, Sigma)

### Arguments

`n` | integer sample size.
`df` | numeric parameter, “degrees of freedom”.
`Sigma` | positive definite (p * p) “scale” matrix, the matrix parameter of the distribution.

### Details

If X1,...,Xm, Xi in R^p is a sample of m independent multivariate Gaussians with mean (vector) 0, and covariance matrix Σ, the distribution of M = X'X is W_p(Σ, m).

Consequently, the expectation of M is

E[M] = m * 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

Var(M[i,j]) = m*(S[i,j]^2 + S[i,i] * S[j,j]), where S=Sigma.

### Value

a numeric `array`, say `R`, of dimension p * p * n, where each `R[,,i]` is a positive definite matrix, a realization of the Wishart distribution W_p(Sigma, df).

### Author(s)

Douglas Bates

### References

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

### See Also

`cov`, `rnorm`, `rchisq`.

### Examples

    ## 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, tol = .009))

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

[Package stats version 2.15.1 Index]