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Lecturer(s)
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Hron Karel, prof. RNDr. Ph.D.
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Course content
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1. Sample space, compositional data as a methodological concept 2. Geometric properties of compositional data 3. Exploratory data analysis and visualization 4. Multivariate statistics with compositional data: cluster analysis, PCA, correlation analysis, classification 5. Regression analysis 6. Methods for high-dimensional compositional data 7. Compositional tables 8. Preprocessing of compositional data 9. Bayes spaces: a tool for analyzing probability densities, implications for Bayesian statistics
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
- Preparation for the Course Credit
- 20 hours per semester
- Attendace
- 26 hours per semester
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Learning outcomes
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Modeling and analysis of data carrying relative information (percentages, proportions, concentrations etc.).
Comprehension Understand the concepts and methods of compositional data analysis.
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Prerequisites
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Basic knowledge of multivariate statistics.
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Assessment methods and criteria
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Seminar Work
Credit: concise analysis of a chosen data set
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Recommended literature
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A. Buccianti, V. Pawlowsky-Glahn. (2011). Compositional data analysis: Theory and applications. Wiley, Chichester.
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J. Aitchison. (1986). The statistical analysis of compositional data. Chapman and Hall, London.
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K.G. van den Boogaart, R. Tolosana-Delgado. (2013). Analyzing compositional data with R. Springer, Heidelberg.
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P. Filzmoser, K. Hron, M. Templ. (2018). Applied compositional data analysis. Springer, Cham.
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V. Pawlowsky-Glahn, J.J. Egozcue, R. Tolosana-Delgado. (2011). Modeling and analysis of compositional data. Wiley, Chichester.
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