robCompositions (version 1.6.3)

pcaCoDa: Robust principal component analysis for compositional data

Description

This function applies robust principal component analysis for compositional data.

Usage

pcaCoDa(x, method = "robust")

Arguments

x

compositional data

method

either robust (default) or standard

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

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.

References

Filzmoser, P., Hron, K., Reimann, C. (2009) Principal Component Analysis for Compositional Data with Outliers. Environmetrics, 20, 621-632.

See Also

print.pcaCoDa, plot.pcaCoDa

Examples

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