Principal component analysis for compositional data with outliers

  • Filzmoser P
  • Hron K
  • Reimann C
  • 1


    Mendeley users who have this article in their library.
  • N/A


    Citations of this article.


For compositional data a special transformation is needed prior to applying multivariate data analysis tools. If this is not done, the correlation structure can be completely biased and results become useless. If robust multivariate techniques like principal component analysis (PCA) are used, the transformed data need to have full rank, and thus a transformation like the centered logratio transformation can not be used. In this paper we use the isometric logratio transformation for robust PCA. However, this transformation has the disadvantage that the new variables are not directly interpretable. Here it is shown how the resulting robust scores and loadings can be back-transformed. Using a real data set we demonstrate the difference of the results when not taking appropriate transformations and when applying classical or robust PCA.

Author-supplied keywords

  • compositional data
  • isometric logratio transformation
  • prin- cipal component analysis
  • robust statistics

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

There are no full text links


  • P Filzmoser

  • K Hron

  • C Reimann

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free