Description Usage Arguments Details Value Author(s) References Examples

Given the matrix *A* of order *nxm*, the generalized singular value decomposition (GSVD) involves the use of two sets of positive square matrices of order *nxn* and *mxm* respectively. These two matrices express constraints imposed, respectively, on the lines and columns of *A*.

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`data` |
Matrix used for decomposition. |

`plin` |
Weight for rows. |

`pcol` |
Weight for columns |

If plin or pcol is not used, it will be calculated as the usual singular value decomposition.

`d` |
Eigenvalues, that is, line vector with singular values of the decomposition. |

`u` |
Eigenvectors referring rows. |

`v` |
Eigenvectors referring columns. |

Paulo Cesar Ossani

Marcelo Angelo Cirillo

ABDI, H. Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition (GSVD). In: SALKIND, N. J. (Ed.). *Encyclopedia of measurement and statistics.* Thousand Oaks: Sage, 2007. p. 907-912.

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