Compositional Data Analysis

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Plots variables and cases in the same plot, based on a principal component analysis.

1 2 3 4 5 6 7 8 9 10 11 | ```
biplot3D(x,...)
## Default S3 method:
biplot3D(x,y,var.axes=TRUE,col=c("green","red"),cex=c(2,2),
xlabs = NULL, ylabs = NULL, expand = 1,arrow.len = 0.1,
...,add=FALSE)
## S3 method for class 'princomp'
biplot3D(x,choices=1:3,scale=1,...,
comp.col=1,comp.labs=paste("Comp.",1:3),
scale.scores=lambda[choices]^(1-scale),
scale.var=scale.comp, scale.comp=sqrt(lambda[choices]),
scale.disp=1/scale.comp)
``` |

`x` |
princomp object or matrix of point locations to be drawn (typically, cases) |

`choices` |
Which principal components should be used? |

`scale` |
a scaling parameter like in |

`scale.scores` |
a vector giving the scaling applied to the scores |

`scale.var` |
a vector giving the scaling applied to the variables |

`scale.comp` |
a vector giving the scaling applied to the unit length of each component |

`scale.disp` |
a vector giving the scaling of the display in the directions of the components |

`comp.col` |
color to draw the axes of the components, defaults to black |

`comp.labs` |
labels for the components |

`...` |
further plotting parameters as defined in |

`y` |
matrix of second point/arrow-head locations (typically, variables) |

`var.axes` |
logical, TRUE draws arrows and FALSE points for y |

`col` |
vector/list of two elements the first giving the color/colors for the first data set and the second giving color/colors for the second data set. |

`cex` |
vector/list of two elements the first giving the size for the first data set and the second giving size for the second data set. |

`xlabs` |
labels to be plotted at x-locations |

`ylabs` |
labels to be plotted at y-locations |

`expand` |
the relative expansion of the y data set with respect to x |

`arrow.len` |
The length of the arrows as defined in |

`add` |
logical, adding to existing plot or making a new one? |

This "biplot" is a triplot, relating data, variables and principal components. The relative scaling of the components is still experimental, meant to mimic the behavior of classical biplots.

nothing

K.Gerald v.d. Boogaart http://www.stat.boogaart.de

1 2 3 4 5 6 | ```
data(SimulatedAmounts)
pc <- princomp(acomp(sa.lognormals5))
pc
summary(pc)
plot(pc) #plot(pc,type="screeplot")
biplot3D(pc)
``` |

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