compositions

R package compositions:Compositional data analysis with R and the package compositions. This paper is a hands-on introduction and shows how to perform basic tasks in the analysis of compositional data following {it J. Aitchison}’s philosophy [The statistical analysis of compositional data. London-New York: Chapman and Hall (1986; Zbl 0688.62004)] within the statistical package `$R$’ and using a contributed package (called `compositions’), which is devoted specially to compositional data analysis. The studied asks are: descriptive statistics and plots (ternary diagrams, boxplots), principal component analysis (using biplots), cluster analysis with Aitchison distance, analysis of variance (ANOVA) of a dependent composition, some transformations and operations between compositions in the simplex.


References in zbMATH (referenced in 22 articles )

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  1. Graf, Monique: Regression for compositions based on a generalization of the Dirichlet distribution (2020)
  2. Pawlowsky-Glahn, V.; Egozcue, J. J.: Compositional data in geostatistics: a log-ratio based framework to analyze regionalized compositions (2020)
  3. Boonen, Tim J.; Guillen, Montserrat; Santolino, Miguel: Forecasting compositional risk allocations (2019)
  4. Bruch, Christian: Applying the rescaling bootstrap under imputation: a simulation study (2019)
  5. Tolosana-Delgado, Raimon; Mueller, Ute; van den Boogaart, K. Gerald: Geostatistics for compositional data: an overview (2019)
  6. Nicholas E. Hamilton, Michael Ferry: ggtern: Ternary Diagrams Using ggplot2 (2018) not zbMATH
  7. Xia, Yinglin; Sun, Jun; Chen, Ding-Geng: Statistical analysis of microbiome data with R (2018)
  8. Director, Hannah M.; Gattiker, James; Lawrence, Earl; van der Wiel, Scott: Efficient sampling on the simplex with a self-adjusting logit transform proposal (2017)
  9. Di Palma, M. A.; Gallo, M.: A co-median approach to detect compositional outliers (2016)
  10. Tolosana-Delgado, R.; van den Boogaart, K. G.; Fišerová, E.; Hron, K.; Dunkl, I.: Joint compositional calibration: an example for U-Pb geochronology (2016)
  11. White, Arthur; Murphy, Thomas Brendan: Exponential family mixed membership models for soft clustering of multivariate data (2016)
  12. Ros-Freixedes, Roger; Estany, Joan: On the compositional analysis of fatty acids in pork (2014)
  13. Schaeben, Helmut: Potential modeling: conditional independence matters (2014)
  14. Leininger, Thomas J.; Gelfand, Alan E.; Allen, Jenica M.; Silander, John A. jun.: Spatial regression modeling for compositional data with many zeros (2013)
  15. van den Boogaart, K. Gerald; Tolosana-Delgado, Raimon: Analyzing compositional data with R (2013)
  16. Conversano, Claudio; Vistocco, Domenico: Analysis of mutual funds’ management styles: a modeling, ranking and visualizing approach (2010)
  17. Hron, K.; Templ, M.; Filzmoser, P.: Imputation of missing values for compositional data using classical and robust methods (2010)
  18. Cuevas, Antonio; Fraiman, Ricardo: On depth measures and dual statistics. A methodology for dealing with general data (2009)
  19. Rodrigues, Paulo C.; Lima, Ana T.: Analysis of a European Union election using principal component analysis (2009)
  20. Porzio, Giovanni C.; Ragozini, Giancarlo; Vistocco, Domenico: On the use of archetypes as benchmarks (2008)

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