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, ... )

### Arguments

`object` | a data matrix of compositions, not necessarily closed
`...` | not used, only here for generics

### Details

It is quite difficult to summarize a composition in a consistent and interpretable way. We tried to provide such a summary here.

### Value

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

`mean` | the `mean.acomp` composition
`mean.ratio` | a matrix containing the geometric mean of the pairwise ratios
`variation` | the variation matrix of the dataset (`{variation.acomp}`)
`expsd` | a matrix containing the one-sigma factor for each ratio, computed as `exp(sqrt(variation.acomp(W)))`. To obtain a two-sigma-factor, one has to take its squared value. To obtain the reverse bound we compute 1/expsd
`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

### References

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

### See Also

`acomp`

### Examples

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

[Package compositions version 0.91-6 Index]