The Partial Least Squares (PLS) biplot for Generalized Linear...

Takes in a set of predictor variables and a set of response variables and produces a zoomed-in display of the coefficient points in the PLS biplot for the (univariate) GLMs.

1 | ```
PLS.GLM.biplot_bvec(X, y, algorithm = NULL, ax.tickvec.b = NULL, ...)
``` |

`X` |
A (NxP) predictor matrix |

`y` |
A (Nx1) response vector |

`algorithm` |
The PLS.GLM_SIMPLS algorithm |

`ax.tickvec.b` |
(purple) tick marker length for the y-variable axis in the biplot |

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

A zoomed-in display of the coefficient points in the PLS biplot of a GLM 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 15 16 17 | ```
if(require(robustbase))
possum.mat
y = as.matrix(possum.mat[,1], ncol=1)
dimnames(y) = list(paste("S", 1:nrow(possum.mat), seq=""), "Diversity")
X = as.matrix(possum.mat[,2:14], ncol=13)
dimnames(X) = list(paste("S", 1:nrow(possum.mat), seq=""), colnames(possum.mat[,2:14]))
PLS.GLM.biplot_bvec(X, y, algorithm=PLS.GLM, ax.tickvec.b=10)
#Pima.tr data
if(require(MASS))
data(Pima.tr, package="MASS")
X = as.matrix(cbind(Pima.tr[,1:7]))
dimnames(X) = list(1:nrow(X), colnames(X))
y = as.matrix(as.numeric(Pima.tr$type)-1, ncol=1)
#0=No and 1=Yes
dimnames(y) = list(1:nrow(y), paste("type"))
PLS.GLM.biplot_bvec(X, y, algorithm=PLS.binomial.GLM,ax.tickvec.b=10)
``` |

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