Description Usage Arguments Details Value Author(s) References See Also Examples

Partial Least Squares Regression for numerical variables.

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`Y` |
Matrix of Dependent Variables |

`X` |
Matrix of Independent Variables |

`S` |
Dimension of the solution |

`InitTransform` |
Initial transformation of the independent variables. |

`grouping` |
Fator when the init transformation is the standardization with the within groups deviation. |

`centerY` |
Should the dependent variables be centered? |

`scaleY` |
Should the dependent variables be standadized? |

`tolerance` |
Tolerance for the algorithm |

`maxiter` |
Maximum number of iterations |

`show` |
Show the progress of the algorithm? |

`Validation` |
Validation (None, Cross, Bootstrap) |

`nB` |
number of samples for the bottstrap validation |

Partial Least Squares Regression for numerical variables.

An object of class plsr with fiends

`Method` |
PLSR |

`X` |
The X matrix |

`Y` |
The Y matrix |

`centerY` |
Is the Y matrix centered |

`scaleY` |
Is the Y matrix scaled |

`Initial_Transformation` |
Initial transformation of the Y matrix |

`ScaledX` |
Transformed X matrix |

`ScaledY` |
Transformed Y matrix |

`Intercept` |
Intercept of the model |

`XScores` |
Scores for the individals from the X matrix |

`XWeights` |
Weigths for the X set |

`XLoadings` |
Loadings for the X set |

`YScores` |
Scores for the individals from the Y matrix |

`YWeights` |
Weigths for the Y set |

`YLoadings` |
Loadings for the Y set |

`RegParameters` |
Final Regression Parameters |

`ExpectedY` |
Expected values of Y |

`R2` |
R-squared |

`XStructure` |
Relation of the X variables with its structure |

`YStructure` |
Relation of the Y variables with its structure |

`YXStructure` |
Relation of the Y variables with the X components |

Jose Luis Vicente Villardon

H. Abdi, Partial least squares regression and projection on latent structure regression (PLS regression), WIREs Comput. Stat. 2 (2010), pp. 97-106.

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