Multivariate Multiple Linear Regression (MMLR)

Takes in a set of predictor variables and a set of response variables and gives the Partial Least Squares (PLS) parameters.

1 | ```
mod.NIPALS(X, Y, A, ...)
``` |

`X` |
A (NxP) predictor matrix |

`Y` |
A (NxM) response matrix |

`A` |
The number of PLS components |

`...` |
Other arguments. Currently ignored |

The PLS parameters using the NIPALS algorithm

Opeoluwa F. Oyedele and Sugnet Gardner-Lubbe

1 2 3 4 5 6 7 8 9 10 11 | ```
if(require(pls))
data(oliveoil, package="pls")
X = as.matrix(oliveoil$chemical, ncol=5) #predictors
dimnames(X) = list(paste(c("G1","G2","G3","G4","G5","I1","I2","I3","I4","I5",
"S1","S2","S3","S4","S5","S6")),
paste(c("Acidity","Peroxide","K232","K270","DK")))
Y = as.matrix(oliveoil$sensory, ncol=6) #responses
dimnames(Y) = list(paste(c("G1","G2","G3","G4","G5","I1","I2","I3","I4","I5",
"S1","S2","S3","S4","S5","S6")),
paste(c("Yellow","Green","Brown","Glossy","Transp","Syrup")))
mod.NIPALS(X, Y, A=5)
``` |

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