The Principal Component Analysis (PCA) biplot

Takes in a samples by variables data matrix and produces a PCA biplot, where the labels of the samples points excluded.

1 | ```
PCA.biplot_no.SN(D, method = NULL, ax.tickvec.D = NULL, ...)
``` |

`D` |
A samples by variables data matrix |

`method` |
the mod.PCA algorithm |

`ax.tickvec.D` |
tick marker length per axis in the PCA biplot |

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

The PCA biplot of D 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 | ```
if(require(pls))
data(oliveoil, package="pls")
Dmat = as.matrix(oliveoil) #(16x11) overall original data matrix
dimnames(Dmat) = 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","Yellow","Green","Brown",
"Glossy","Transp","Syrup")))
PCA.biplot_no.SN(D=Dmat, method=mod.PCA, ax.tickvec.D=c(8,5,5,7,6,4,5,5,8,7,7))
#glass data
if(require(chemometrics))
data(glass, package="chemometrics")
Dmat = matrix(glass,ncol=13)
dimnames(Dmat) = list(1:180, paste(c("Na2O", "MgO", "Al2O3", "SiO2",
"P2O5", "SO3", "Cl", "K2O", "CaO", "MnO", "Fe2O3", "BaO", "PbO")))
PCA.biplot_no.SN(D=Dmat, method=mod.PCA, ax.tickvec.D=rep(5,ncol(Dmat)))
``` |

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