Partial Least Squares-Generalized Linear Model (PLS-GLM)...

Takes in a set of predictor variables and a set of response variables and produces a PLS biplot.

1 2 | ```
PLS.biplot(X, Y, algorithm = NULL, ax.tickvec.X = NULL,
ax.tickvec.Y = NULL, ...)
``` |

`X` |
A (NxP) predictor matrix |

`Y` |
A (NxM) response matrix |

`algorithm` |
Any of the PLS algorithms ("mod.NIPALS", "mod.KernelPLS_R", "mod.KernelPLS_L", "mod.SIMPLS") |

`ax.tickvec.X` |
tick marker length for each X-variable axis in the PLS biplot |

`ax.tickvec.Y` |
tick marker length for each Y-variable axis in the PLS biplot |

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

The PLS biplot of D=[X Y] with some parameters

Opeoluwa F. Oyedele and Sugnet Gardner-Lubbe

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
if(require(pls))
data(oliveoil, package="pls")
X = as.matrix(oliveoil$chemical, ncol=5)
dimnames(X) = list(paste(c("G1","G2","G3","G4","G5","I1","I2","I3","I4","I5",
"S1","S2","S3","S4","S5","S6")),
paste(c("Acidity","Peroxide","K232","K270","DK")))
Y = as.matrix(oliveoil$sensory, ncol=6)
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")))
#SIMPLS biplot
PLS.biplot(X, Y, algorithm=mod.SIMPLS, ax.tickvec.X=c(8,5,5,5,5), ax.tickvec.Y=c(5,8,5,6,9,8))
#Kernel PLS biplot
PLS.biplot(X, Y, algorithm=mod.KernelPLS_R, ax.tickvec.X=c(3,3,4,5,2), ax.tickvec.Y=c(3,3,5,6,7,6))
``` |

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