Abstract
The sample space of compositional data is the open simplex. Therefore, zeros in a compositional data set are identified either with below detection limit values, or lead to a division of the data set into different subpopulations with the corresponding lower dimensional sample space. Most multivariate data analysis techniques require complete data matrices, thus calling for a strategy of imputation of zeros in the first case. Existing replacement methods of rounded zeros are reviewed, and a new method is proposed, who’s properties are analyzed and illustrated. The method is applied in a hierarchical cluster analysis of compositional data.
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Martín-Fernández, J.A., Barceló-Vidal, C., Pawlowsky-Glahn, V. (2000). Zero Replacement in Compositional Data Sets. In: Kiers, H.A.L., Rasson, JP., Groenen, P.J.F., Schader, M. (eds) Data Analysis, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59789-3_25
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DOI: https://doi.org/10.1007/978-3-642-59789-3_25
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