summary.acomp: 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 pairwise ratio in a log geometry.

Usage

## S3 method for class 'acomp':
summary( object, \dots )

Arguments

object

a data.matrix of compositions, not necessarily closed

...

not used, only here for generics

Value

The result is an object of type "summary.acomp"
meanThe mean.acomp composition
mean.ratioA matrix containing the geometric mean of the pairwise ratios
variationThe variation matrix of the dataset ({variation.acomp})
expsdA matrix containing the one-sigma factor for each ratio, computed as exp(sqrt(variation.acomp(W))). To obtain two-sigma-factor it needs to be squared. To obtain the reverse bound we compute 1/expsd
minA matrix containing the minimum of each of the pairwise ratios
q1A matrix containing the 1-Quartile of each of the pairwise ratios
medianA matrix containing the median of each of the pairwise ratios
q1A matrix containing the 3-Quartile of each of the pairwise ratios
maxA matrix containing the maximum of each of the pairwise ratios

Details

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

References

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

Examples

Run this code

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

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