Description Usage Arguments Value References Examples
This function converts a non-positive-definite correlation matrix to a positive-definite matrix using the
adjusted gradient updating method with initial matrix B1
.
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Sigma |
the non-PD correlation matrix |
B1 |
the initial matrix for algorithm; if NULL, uses a scaled initial matrix with diagonal elements |
tau |
parameter used to calculate theta |
tol |
maximum error for Frobenius norm distance between new matrix and original matrix |
steps |
maximum number of steps for k (default = 100) |
msteps |
maximum number of steps for m (default = 10) |
list with Sigma2
the new correlation matrix, dist
the Frobenius norm distance between Sigma2
and Sigma
,
eig0
original eigenvalues of Sigma
, eig2
eigenvalues of Sigma2
S Maree (2012). Correcting Non Positive Definite Correlation Matrices. BSc Thesis Applied Mathematics, TU Delft. http://resolver.tudelft.nl/uuid:2175c274-ab03-4fd5-85a9-228fe421cdbf.
JF Yin and Y Zhang (2013). Alternative gradient algorithms for computing the nearest correlation matrix. Applied Mathematics and Computation, 219(14): 7591-7599. https://doi.org/10.1016/j.amc.2013.01.045.
Y Zhang and JF Yin. Modified alternative gradients algorithm for computing the nearest correlation matrix. Internal paper of the Tongji University, Shanghai.
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