Description Usage Arguments Details Value Author(s) References Examples

View source: R/Dir_Elimination.R

Direct Elimination allows to give an analytic solution for equality constrained least squares problem (LSE). Requires MASS and pracma library.

1 | ```
Dir_Elimination(A,C,b,d)
``` |

`A` |
Design matrix, m rows and n columns. |

`C` |
Constraint matrix, p rows and n columns. |

`b` |
Response vector for A, Ax=b, m rows and 1 column. |

`d` |
Response vector for C, Cx=d, p rows and 1 column. |

Direct Elimination method gives a numerical vector as the solution of a least squares problem (Ax=b), when impose some restrictions (additional information, extramuestral information or a priori information) that lead to another linear equality system (Cx=d). See significance constraint (x=0) or inclusion restriction (x+y=1), etc.

Numerical vector for a LSE problem.

Sergio Andrés Cabrera Miranda Statician sergio05acm@gmail.com

Lawson, C. L., & Hanson, R. J. (1974). Linear least squares with linear inequality constraints. Solving least squares problems, 158-173.

Van Benthem, M. H., Keenan, M. R., & Haaland, D. M. (2002). Application of equality constraints on variables during alternating least squares procedures. Journal of Chemometrics: A Journal of the Chemometrics Society, 16(12), 613-622.

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