rbing.O2: Simulate a 2*2 Orthogonal Random Matrix

In rstiefel: Random Orthonormal Matrix Generation and Optimization on the Stiefel Manifold

Description

Simulate a 2*2 random orthogonal matrix from the Bingham distribution using a rejection sampler.

Usage

rbing.O2(A, B, a = NULL, E = NULL)

Arguments

A	a symmetric matrix.
B	a diagonal matrix with decreasing entries.
a	sum of the eigenvalues of A, multiplied by the difference in B-values.
E	eigenvectors of A.

Value

A random 2x2 orthogonal matrix simulated from the Bingham distribution.

Author(s)

Peter Hoff

References

Hoff(2009)

Examples

## The function is currently defined as
function (A, B, a = NULL, E = NULL) 
{
    if (is.null(a)) {
        trA <- A[1, 1] + A[2, 2]
        lA <- 2 * sqrt(trA^2/4 - A[1, 1] * A[2, 2] + A[1, 2]^2)
        a <- lA * (B[1, 1] - B[2, 2])
        E <- diag(2)
        if (A[1, 2] != 0) {
            E <- cbind(c(0.5 * (trA + lA) - A[2, 2], A[1, 2]), 
                c(0.5 * (trA - lA) - A[2, 2], A[1, 2]))
            E[, 1] <- E[, 1]/sqrt(sum(E[, 1]^2))
            E[, 2] <- E[, 2]/sqrt(sum(E[, 2]^2))
        }
    }
    b <- min(1/a^2, 0.5)
    beta <- 0.5 - b
    lrmx <- a
    if (beta > 0) {
        lrmx <- lrmx + beta * (log(beta/a) - 1)
    }
    lr <- -Inf
    while (lr < log(runif(1))) {
        w <- rbeta(1, 0.5, b)
        lr <- a * w + beta * log(1 - w) - lrmx
    }
    u <- c(sqrt(w), sqrt(1 - w)) * (-1)^rbinom(2, 1, 0.5)
    x1 <- E %*% u
    x2 <- (x1[2:1] * c(-1, 1) * (-1)^rbinom(1, 1, 0.5))
    cbind(x1, x2)
  }

rstiefel documentation built on June 15, 2021, 5:07 p.m.