linregEst | R Documentation |

Function that performs multivariate multiple linear regression modelling
(*Y = XB + E*) according to a principal component regression (PCR)
approach where the number of components equals the number of nonzero
eigenvalues (generalised inverse).

linregEst(X, Y)

`X` |
regressor matrix |

`Y` |
response matrix |

`rank_lim` |
tuning parameter for the rank. The default value corresponds to the rank function in Matlab. |

`Umodel` |
this matrix is returned by |

The function `linregEst`

performs the calculations in two steps by
calling `linregStart`

and `linregEnd`

. The former functions
function makes all calculations that can be done without knowing *Y*.
The singular value decomposition (SVD) is an essential part of the
calculations and some of the output variables are named according to SVD
(U, S and V).

`linregEst`

returns a list with seven components. The first
three components is returned by `linregStart`

- the rest by
`linregEnd`

.

`Umodel` |
Matrix of score values according to the PCR model. |

`VmodelDivS` |
Matrix that can be used to calculate |

`VextraDivS1` |
Matrix that can be used to check estimability. That is,
predictions for a new X cannot be made if |

`BetaU` |
Matrix of regression parameters according to the PCR model. |

`msError` |
Mean square error of each response |

`errorObs` |
Error observations that can be used in multivariate testing |

`Yhat` |
Fitted values. Equals |

When the number of error degrees of freedom exceeds the number of
linearly independent responses, then the matrix of error observations is
made so that several rows are zero. In this case the zero rows are omitted
and a list with components `errorObs`

and `df_error`

is returned.

Øyvind Langsrud and Bjørn-Helge Mevik

`ffmanova`

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