Keller | R Documentation |
Keller
calculates a rank p approximation to a correlation matrix according to Keller's method.
Keller's method is based on iterated eigenvalue decompositions that are used to adjust the diagonal of the correlation matrix.
Keller(R, eps = 1e-06, nd = 2, itmax = 10)
R |
A correlation matrix |
eps |
Numerical criterion for convergence (default |
nd |
Number of dimensions used in the spectral decomposition (default |
itmax |
The maximum number of iterations |
A matrix containing the approximation to the correlation matrix-
Jan Graffelman (jan.graffelman@upc.edu)
Keller, J.B. (1962) Factorization of Matrices by Least-Squares. Biometrika, 49(1 and 2) pp. 239–242.
Graffelman, J. and De Leeuw, J. (2023) Improved approximation and visualization of the correlation matrix. The American Statistician pp. 1–20. Available online as latest article \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00031305.2023.2186952")}
ipSymLS
data(Kernels)
R <- cor(Kernels)
Rhat <- Keller(R)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.