pcaCoDa {robCompositions}R Documentation

Robust principal component analysis for compositional data

Description

This function performs out a robust principal component analysis for compositional data.

Usage

pcaCoDa(x, method = "robust")

Arguments

x compositional data
method either “robust” (default) or ‘standard’

Details

The compositional data set is transformed using the ilr tranformation. Afterwards, robust principal component analysis is performed. Resulting loadings and scores are back-transformed to the clr space where the compositional biplot can be shown.

Value

scores scores in clr space
loadings loadings in clr space
eigenvalues eigenvalues of the clr covariance matrix
method method
princompOutputClr output of princomp needed in plot.pcaCoDa

Author(s)

K. Hron, P. Filzmoser, M. Templ

References

Filzmoser, P., Hron, K., Reimann, C. (2009) Principal Component Analysis for Compositional Data with Outliers. Environmetrics, accepted for publication.

See Also

print.pcaCoDa, plot.pcaCoDa

Examples

data(aitchison395)
p1 <- pcaCoDa(aitchison395)
p1
plot(p1)

[Package robCompositions version 1.2.2 Index]