Perform the generalized Cholesky decompostion of a real symmetric matrix. The function and R documentation are included from the archived
kinship package that can be found at http://cran.r-project.org/web/packages/kinship/index.html.
- the symmetric matrix to be factored
- the numeric tolerance for detection of singular columns in x.
A symmetric matrix A can be decomposed as LDL', where L is a lower triangular matrix with 1's on the diagonal, L' is the transpose of L, and D is diagonal. The inverse of L is also lower-triangular, with 1's on the diagonal. If all elements of D are positive, then A must be symmetric positive definite (SPD), and the solution can be reduced the usual Cholesky decomposition U'U where U is upper triangular and U = sqrt(D) L'.
The main advantage of the generalized form is that it admits of matrices that are not of full rank: D will contain zeros marking the redundant columns, and the rank of A is the number of non-zero columns. If all elements of D are zero or positive, then A is a non-negative definite (NND) matrix. The generalized form also has the (quite minor) numerical advantage of not requiring square roots during its calculation. To extract the components of the decompostion, use the
an object of class
gchol containing the generalized Cholesky decompostion. It has the appearance of a lower triangular matrix.
Documentation reproduced from package GWAF, version 2.0. License: GPL (>= 2)