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 pairwise 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 two-sigma-factor it needs to be squared. 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

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 0.9-10 Index]