redecompose_Omegas {gmvarkit} | R Documentation |
In the decomposition of the covariance matrices (Muirhead, 1982, Theorem A9.9), change the order of the covariance matrices.
Description
redecompose_Omegas
exchanges the order of the covariance matrices in
the decomposition of Muirhead (1982, Theorem A9.9) and returns the new decomposition.
Usage
redecompose_Omegas(M, d, W, lambdas, perm = 1:sum(M))
Arguments
M |
|
d |
the number of time series in the system. |
W |
a length |
lambdas |
a length |
perm |
a vector of length |
Details
We consider the following decomposition of positive definite covariannce matrices:
\Omega_1 = WW'
, \Omega_m = W\Lambda_{m}W'
, m=2,..,M
where \Lambda_{m} = diag(\lambda_{m1},...,\lambda_{md})
contains the strictly postive eigenvalues of \Omega_m\Omega_1^{-1}
and the column of the invertible W
are the
corresponding eigenvectors. Note that this decomposition does not necessarily exists for M > 2
.
See Muirhead (1982), Theorem A9.9 for more details on the decomposition and the source code for more details on the reparametrization.
Value
Returns a d^2 + (M - 1)*d x 1
vector of the form c(vec(new_W), new_lambdas)
where the lambdas parameters are in the regimewise order (first regime 2, then 3, etc) and the
"new W" and "new lambdas" are constitute the new decomposition with the order of the covariance
matrices given by the argument perm
. Notice that if the first element of perm
is one, the W matrix will be the same and the lambdas are just re-ordered.
Note that unparametrized zero elements ARE present in the returned W!
Warning
No argument checks! Does not work with dimension d=1
or with only
one mixture component M=1
.
References
Muirhead R.J. 1982. Aspects of Multivariate Statistical Theory, Wiley.
Examples
d <- 2
M <- 2
Omega1 <- matrix(c(2, 0.5, 0.5, 2), nrow=d)
Omega2 <- matrix(c(1, -0.2, -0.2, 1), nrow=d)
# Decomposition with Omega1 as the first covariance matrix:
decomp1 <- diag_Omegas(Omega1, Omega2)
W <- matrix(decomp1[1:d^2], nrow=d, ncol=d)
lambdas <- decomp1[(d^2 + 1):length(decomp1)]
tcrossprod(W) # = Omega1
W%*%tcrossprod(diag(lambdas), W) # = Omega2
# Reorder the covariance matrices in the decomposition so that now
# the first covariance matrix is Omega2:
decomp2 <- redecompose_Omegas(M=M, d=d, W=as.vector(W), lambdas=lambdas,
perm=2:1)
new_W <- matrix(decomp2[1:d^2], nrow=d, ncol=d)
new_lambdas <- decomp2[(d^2 + 1):length(decomp2)]
tcrossprod(new_W) # = Omega2
new_W%*%tcrossprod(diag(new_lambdas), new_W) # = Omega1