| diag_Omegas {sstvars} | R Documentation |
Simultaneously diagonalize two covariance matrices
Description
diag_Omegas Simultaneously diagonalizes two covariance matrices using
eigenvalue decomposition.
Usage
diag_Omegas(Omega1, Omega2)
Arguments
Omega1 |
a positive definite |
Omega2 |
another positive definite |
Details
See the return value and Muirhead (1982), Theorem A9.9 for details.
Value
Returns a length d^2 + d vector where the first d^2 elements
are vec(W) with the columns of W being (specific) eigenvectors of
the matrix \Omega_2\Omega_1^{-1} and the rest d elements are the
corresponding eigenvalues "lambdas". The result satisfies WW' = Omega1 and
Wdiag(lambdas)W' = Omega2.
If Omega2 is not supplied, returns a vectorized symmetric (and pos. def.)
square root matrix of Omega1.
Warning
No argument checks! Does not work with dimension d=1!
References
Muirhead R.J. 1982. Aspects of Multivariate Statistical Theory, Wiley.
Examples
# Create two (2x2) coviance matrices using the parameters W and lambdas:
d <- 2 # The dimension
W0 <- matrix(1:(d^2), nrow=2) # W
lambdas0 <- 1:d # The eigenvalues
(Omg1 <- W0%*%t(W0)) # The first covariance matrix
(Omg2 <- W0%*%diag(lambdas0)%*%t(W0)) # The second covariance matrix
# Then simultaneously diagonalize the covariance matrices:
res <- diag_Omegas(Omg1, Omg2)
# Recover W:
W <- matrix(res[1:(d^2)], nrow=d, byrow=FALSE)
tcrossprod(W) # == Omg1, the first covariance matrix
# Recover lambdas:
lambdas <- res[(d^2 + 1):(d^2 + d)]
W%*%diag(lambdas)%*%t(W) # == Omg2, the second covariance matrix