Description Usage Arguments Details Value Author(s) References See Also Examples

Summaries in terms of compositions are quite different from classical ones. Instead of analysing each variable individually, we must analyse each pair-wise ratio in a log geometry.

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`object` |
a data matrix of compositions, not necessarily closed |

`...` |
not used, only here for generics |

`robust` |
A robustness description. See robustnessInCompositions for details. The parameter can be null for avoiding any estimation. |

It is quite difficult to summarize a composition in a consistent and interpretable way. We tried to provide such a summary here, based on the idea of the variation matrix.

The result is an object of type `"summary.acomp"`

`mean` |
the |

`mean.ratio` |
a matrix containing the geometric mean of the pairwise ratios |

`variation` |
the variation matrix of the dataset ( |

`expsd` |
a matrix containing the one-sigma factor for
each ratio, computed as |

`invexpsd` |
the inverse of the preceding one, giving the reverse bound. Additionally, it can be "almost" intepreted as a correlation coefficient, with values near one indicating high proportionality between the components. |

`min` |
a matrix containing the minimum of each of the pairwise ratios |

`q1` |
a matrix containing the 1-Quartile of each of the pairwise ratios |

`median` |
a matrix containing the median of each of the pairwise ratios |

`q1` |
a matrix containing the 3-Quartile of each of the pairwise ratios |

`max` |
a matrix containing the maximum of each of the pairwise ratios |

K.Gerald v.d. Boogaart http://www.stat.boogaart.de, R. Tolosana-Delgado

Aitchison, J. (1986) *The Statistical Analysis of Compositional
Data* Monographs on Statistics and Applied Probability. Chapman &
Hall Ltd., London (UK). 416p.

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