Keller: Program 'Keller' calculates a rank p approximation to a...

In Correlplot: A Collection of Functions for Graphing Correlation Matrices

Program Keller calculates a rank p approximation to a correlation matrix according to Keller's method.

Description

Keller's method is based on iterated eigenvalue decompositions that are used to adjust the diagonal of the correlation matrix.

Usage

Keller(R, eps = 1e-06, nd = 2, itmax = 10)

Arguments

R	A correlation matrix
eps	Numerical criterion for convergence (default eps=1e-06)
nd	Number of dimensions used in the spectral decomposition (default nd=2)
itmax	The maximum number of iterations

Value

A matrix containing the approximation to the correlation matrix-

Author(s)

Jan Graffelman ([e-mail])

References

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")}

See Also

ipSymLS

Examples

data(Kernels)
R <- cor(Kernels)
Rhat <- Keller(R)

Correlplot documentation built on June 22, 2024, 11:34 a.m.