summary.acomp {compositions} | R Documentation |

## Summarizing a compositional dataset in terms of ratios

### Description

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.

### Usage

```
## S3 method for class 'acomp'
summary( object, ... ,robust=getOption("robust"))
```

### Arguments

`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. |

### Details

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.

### Value

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 |

### Author(s)

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

### References

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

### See Also

### Examples

```
data(SimulatedAmounts)
summary(acomp(sa.lognormals))
```

*compositions*version 2.0-8 Index]