Cited by 704Geochemistry and Geologic MappingAdvanced Statistical Methods and ModelsStatistical Methods and Applications

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Compositional Data Analysis: Theory and Applications

V. Pawlowsky Glahn, Antonella Buccianti·2011·Wiley eBooks

DOI 10.1002/9781119976462

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Abstract

It is difficult to imagine that the statistical analysis of compositional data has been a major issue of concern for more than 100 years. It is even more difficult to realize that so many statisticians and users of statistics are unaware of the particular problems affecting compositional data, as well as their solutions. The issue of spurious correlation'', as the situation was phrased by Karl Pearson back in 1897, affects all data that measures parts of some whole, such as percentages, proportions, ppm and ppb. Such measurements are present in all fields of science, ranging from geology, biology, environmental sciences, forensic sciences, medicine and hydrology. This book presents the history and development of compositional data analysis along with Aitchison's log-ratio approach. Compositional Data Analysis describes the state of the art both in theoretical fields as well as applications in the different fields of science.

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“Recently, the main focus is devoted to find interpretable orthonormal coordinates, guaranteeing isometry between the Aitchison geometry and the Euclidean real space, that would reflect the needs of a particular statistical problem of interest.[2,6] One possibility are logcontrasts, each one within a different set of o…”
Background
Geochemical characteristics of rare earth elements (REEs) in soils developed on different parent materials, in the Baoshan area, Yunnan Province, SW China
Zhang, L., Han, W., Peng, M., et al. (2021) · Geochemistry Exploration Environment Analysis · doi:10.1144/geochem2019-082 · Cited by 9
A Simultaneous Feature Selection and Compositional Association Test for Detecting Sparse Associations in High-Dimensional Metagenomic Data
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“While logratio transformations are well-known and routinely applied in some fields (Pawlowsky-Glahn and Buccianti, 2011), their use in metagenomic datasets has been limited.”
Background
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Vermeesch, P. (2022) · Geochronology · doi:10.5194/gchron-4-561-2022 · Cited by 9
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“First, the nature of the dependent variable (i.e., compositions, having a fixed range, [0,1]) makes it inappropriate to conduct the analysis using a straightforward least squares regression (see e.g., Aitchison, 1986; Pawlowsky-Glahn and Buccianti, 2011).”
Method
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Biddau, R., Cidu, R., Ghiglieri, G., Pelo, S. D., Carletti, A., & Pittalis, D. (2016) · Italian Journal of Geosciences · doi:10.3301/ijg.2016.03 · Cited by 9
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Background
Compositional Data Analysis
Garrigós, J. B. I. (2018) · The Encyclopedia of Archaeological Sciences · doi:10.1002/9781119188230.saseas0103 · Cited by 9
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“Therefore, it is easy to perform multivariate statistical analysis on them (Aitchison 1984; Reimann et al. 2008; Pawlowsky-Glahn 2011; Pawlowsky-Glahn and Egozcue 2020).”
Method
Geochemical Characteristics of Soils to the Impact of Diamond Mining in Siberia (Russia)
Гололобова, А. Г., Legostaeva, Y., Popov, V. F., Makarov, V. S., & Shadrinova, O. V. (2022) · Minerals · doi:10.3390/min12121518 · Cited by 8
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Graphics for relatedness research
Galván‐Femenía, I., Graffelman, J., & Barceló‐i‐Vidal, C. (2017) · Molecular Ecology Resources · doi:10.1111/1755-0998.12674 · Cited by 8
“…perturbation and powering operators (analogous to translation and scalar multiplication in the real space, respectively), forms the structure of the simplex in a two-dimensional metric space (Aitchison, Barcel o-Vidal, Mart ınFern andez, & Pawlowsky-Glahn, 2000; Pawlowsky-Glahn & Buccianti, 2011).”
BackgroundMethod
Distributions on the Simplex Revisited
Mateu‐Figueras, G., Monti, G. S., & Egozcue, J. J. (2021) · Springer eBooks · doi:10.1007/978-3-030-71175-7_4 · Cited by 8
The maximum entropy principle for compositional data
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Logratio approach to statistical analysis of 2×2 compositional tables
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“The theory of compositional tables is closely connected to compositional data analysis [2,16] and the concept of the Aitchison geometry [6].”
Method
Differential Models for Evolutionary Compositions
Egozcue, J. J., & Jarauta‐Bragulat, E. (2014) · Mathematical Geosciences · doi:10.1007/s11004-014-9533-2 · Cited by 8
Metals and Metalloids in Wild Asparagus at Uncontaminated and Mining‐Contaminated Sites
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“CoDA is a framework to deal with compositional data, which are defined as positive values representing part of a whole and summing to a constant (e.g., percentages) (Pawlowsky-Glahn and Buccianti 2011).”
Background
Detecting and modeling changes in a time series of proportions
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“From there, standard multivariate methods are utilized on the transformed data (see Pawlowsky-Glahn and Buccianti, 2011).”
Method
Exploring Compositional Data with the Robust Compositional Biplot
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Background
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Moreva, O., & Schlather, M. (2022) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2022.105099 · Cited by 7
“8 ppm for zinc), we analyze the dataset in a non-compositional way, following [36] and [20], rather then employ a compositional approach ([1], [33], [34]).”
Method
Assessment of heavy metal pollution of drain sediments in the urban area of Mexicali, Mexico
Quiñonez-Plaza, A., Temores-Peña, J., García-Flores, E., et al. (2020) · Environmental Earth Sciences · doi:10.1007/s12665-020-09193-y · Cited by 7
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“Most likely, there is no general approach to the “zero problem” because the presence of zero values in a compositional data set can be caused by multiple different reasons, and there are three types of zeros: rounded, count and essential (Pawlowsky-Glahn and Buccianti, 2011).”
BackgroundInfluential
Tentative application of compositional data analysis to the fatty acid profiles of green Spanish-style Gordal table olives
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“In compositional data, another important exploratory tool is the balance dendrogram developed by Pawlowsky-Glahn and Buccianti (2011) which is a tree of ratios (binary partitions) and their respective variances.”
BackgroundMethod
Sliced inverse regression method for multivariate compositional data modeling
Wang, H., Wang, Z., & Wang, S. (2019) · Statistical Papers · doi:10.1007/s00362-019-01093-z · Cited by 7
Building geochemically based quantitative analogies from soil classification systems using different compositional datasets
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Hron, K., Brito, P., & Filzmoser, P. (2016) · Advances in Data Analysis and Classification · doi:10.1007/s11634-016-0245-y · Cited by 7
First steps of integrated spatial modeling of titanium, zirconium, and rare earth element resources within the Coastal Plain sediments of the southeastern United States
Ellefsen, K. J., Gosen, B. S. V., Fey, D. L., et al. (2015) · Antarctica A Keystone in a Changing World · doi:10.3133/ofr20151111 · Cited by 7
“Readers who are unfamiliar with these methods might consult Aitchison (1986) and Pawlowsky-Glahn and Buccianti (2011).”
Method
Phylofactorization - theory and challengesPreprint
Washburne, A. D. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/196378 · Cited by 7
“The ilr transform can be defined through a sequential binary partition defining which groups of parts to contrast for each coordinate but, in choosing which isometric log-ratio transform to use for analysis and cross-validation across datasets, one encounters similar considerations as those encountered when choosing s…”
MethodInfluential
Holocene paleoenvironmental changes recorded in the sediments of the karst Krka River estuary (eastern Adriatic coast, Croatia)
Smrkulj, N., Miko, S., Razum, I., et al. (2025) · CATENA · doi:10.1016/j.catena.2025.108811 · Cited by 6
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Mo, Sn and W patterns in topsoils of the Campania Region, Italy
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“Several log-ratio transformations have been elaborated to ‘open’ the data structure and treat them in the Euclidean space (Aitchison 1986; Pawlowsky-Glahn & Buccianti 2011).”
Background
Robust prediction of domain compositions from uncertain data using isometric logratio transformations in a penalized multivariate Fay–Herriot model
Krause, J., Burgard, J. P., & Morales, D. (2021) · Statistica Neerlandica · doi:10.1111/stan.12253 · Cited by 6
“For a more extended discussion of the geometrical properties of these transformations, we refer to chapter 1 of Pawlowsky-Glahn and Buccianti (2011).”
Method
Bayesian Multilevel Compositional Data Analysis with the R Package multilevelcoda
Le, F., Dumuid, D., Stanford, T., & Wiley, J. F. (2025) · Multivariate Behavioral Research · doi:10.1080/00273171.2025.2565598 · Cited by 8
“Instead, compositional data analysis (CoDA; Aitchison, 1986; Pawlowsky-Glahn & Buccianti, 2011) utilizes the relative information contained in compositional data using log-ratios.”
Chemical composition and morphometric traits and yield ofcarrots grown in organic and integrated farming systems
Šink, N., Mikulič-Petkovšek, M., Veberič, R., & Maršić, N. K. (2017) · TURKISH JOURNAL OF AGRICULTURE AND FORESTRY · doi:10.3906/tar-1705-8 · Cited by 6
Partial Least Squares-Discriminant Analysis of the Major and Trace Elements and their Evolutionary Characteristics from the Jinchuan Ni-Cu-(PGE) Sulfide Deposit, NW China
Wang, Y., Lai, J., Cao, Y., et al. (2022) · Minerals · doi:10.3390/min12101301 · Cited by 6
Principal component analysis for probabilistic symbolic data: a more generic and accurate algorithm
Chen, M., Wang, H., & Qin, Z. (2014) · Advances in Data Analysis and Classification · doi:10.1007/s11634-014-0178-2 · Cited by 6
“…methods for complex situations such as symbolic data (Diday 1987; Bock and Diday 2000; Billard and Diday 2006; Diday and Noirhomme-Fraiture 2008), functional data (Ramsay 1982, 2005; Ferraty and Vieu 2006), compositional data (Aitchison 1982, 1986; Pawlowsky-Glahn and Buccianti 2011) and so forth.”
Method
Distribution of potentially toxic metals (As, Cu, Hg, Pb and Zn) in the topsoil of the Pannonian Basin System and associated parts of the surrounding orogens
Ivanišević, D., & Malvić, T. (2015) · Journal of Maps · doi:10.1080/17445647.2015.1101624 · Cited by 6
“This means that standard statistical methods based on Euclidean geometry are not applicable for compositional data (Pawlowsky-Glahn & Egozcue, 2006) (more information about compositional data can be found in e.g. Buccianti, Mateu-Figueras, & Pawlowsky-Glahn, 2006; Pawlowsky-Glahn & Buccianti, 2011).”
Method
Sparse PCA and investigation of multi-elements compositional repositories: theory and applications
Gallo, M., Trendafilov, N. T., & Buccianti, A. (2016) · Environmental and Ecological Statistics · doi:10.1007/s10651-016-0346-y · Cited by 6
Self-citation
Bayesian Hierarchical Compositional Models for Analysing Longitudinal Abundance Data from Microbiome Studies
Creus-Martí, I., Moyá, A., & Santonja, F. J. (2022) · Complexity · doi:10.1155/2022/4907527 · Cited by 6
Machine Learning Workflow
Petrelli, M. (2023) · Springer textbooks in earth sciences, geography and environment · doi:10.1007/978-3-031-35114-3_3 · Cited by 6
The fish farm of origin is assigned by the element profile of Atlantic salmon (Salmo salar L.) scales in a simulated escape event
Flem, B., Benden, T. F., Finne, T. E., et al. (2018) · Fisheries Research · doi:10.1016/j.fishres.2018.04.025 · Cited by 6
Multivariate analysis of log-ratio transformed data and its priority in mining science, porphyry and polymetallic vein deposits case studies
Darabi-Golestan, F., & Hezarkhani, A. (2018) · Bulletin Of The Mineral Research and Exploration · doi:10.19111/bulletinofmre.456958 · Cited by 6
“Whatever, the Ilr transformation does not show any clear simplest or canonical basis, but it has significant conceptual advantages (Pawlowsky-Glahn and Buccianti, 2011).”
Background
Estimation of a proportion in survey sampling using the logratio approach
Hron, K., Templ, M., & Filzmoser, P. (2012) · Metrika · doi:10.1007/s00184-012-0416-6 · Cited by 6
Compositional analysis of trade flows structure
Hrůzová, K., Rypka, M., & Hron, K. (2017) · Austrian Journal of Statistics · doi:10.17713/ajs.v46i2.569 · Cited by 6
Numerical Reconstruction of the Covariance Matrix of a Spherically Truncated Multinormal Distribution
Palombi, F., Toti, S., & Filippini, R. (n.d.) · RePEc: Research Papers in Economics · Cited by 6
A comprehensive workflow for compositional data analysis in archaeometry, with code in R
Greenacre, M. J., & Wood, J. R. (2024) · Archaeological and Anthropological Sciences · doi:10.1007/s12520-024-02070-w · Cited by 5
Reframing natural organic matter research through compositional data analysis
Kida, M., Merder, J., Dittmar, T., Pawlowsky-Glahn, V., & Egozcue, J. J. (2026) · Frontiers in Environmental Science · doi:10.3389/fenvs.2026.1724142 · Cited by 6
Self-citation
An XRPD and EPR spectroscopy study of microcrystalline calcite bioprecipitated by Bacillus subtilis
Perito, B., Romanelli, M., Buccianti, A., et al. (2018) · Physics and Chemistry of Minerals · doi:10.1007/s00269-018-0974-x · Cited by 5
“…= 1 √ 2 ln � aiso Δ6 � , (2)ilr2 = 1 √ 6 ln � aiso × Δ6 � W6 �2 � , The function ilr: SD → RD−1 is an isometry of vector spaces and it allows to move data with D components (variables) from the constrained sample space, called simplex SD, to the real space RD−1 (Pawlowsky-Glahn and Buccianti 2011).”
Self-citation
Partial linear regression of compositional data
Han, H., & Yu, K. (2022) · Journal of the Korean Statistical Society · doi:10.1007/s42952-022-00177-1 · Cited by 5
“Pawlowsky-Glahn and Buccianti, (2011) showed < ⋅, ⋅ >a is an inner product.”
Impacts of the COVID-19 lockdown measures on coarse and fine atmospheric aerosol particles (PM) in the city of Rome (Italy): compositional data analysis approach
Speranza, A., & Caggiano, R. (2022) · Air Quality Atmosphere & Health · doi:10.1007/s11869-022-01235-7 · Cited by 5
“The compositional data analysis began with Aitchison (1982, 1986) and has since been used in many practical applications relating to geoscience, fire emission, and particulate matter (Pawlowsky-Glahn and Buccianti 2002; Pawlowsky-Glahn and Buccianti 2011; Speranza et al.”
Method
IUPAC/CITAC Guide: Evaluation of risks of false decisions in conformity assessment of a substance or material with a mass balance constraint (IUPAC Technical Report)
Pennecchi, F. R., Kuselman, I., & Hibbert, D. B. (2023) · Pure and Applied Chemistry · doi:10.1515/pac-2022-0801 · Cited by 5
Multi-scale (whole-rock, grain size, and particles) distribution of potentially hazardous elements (PHEs) in sedimentary phosphorites from Algerian deposits: Geochemical and environmental implications
Saouli, S., Kechiched, R., Olivier Bruguier, B., Mongelli, G., Delphine Bosch, A., Ameur-Zaimeche, O., Boumaza, B., & Laouar, R. (2025) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2025.138414 · Cited by 5
Size Fraction Effects on Planktonic Foraminifera Assemblages: A Compositional Contribution to the Golden Sieve Rush
Donato, V. D., Daunis‐i‐Estadella, P., Fernández, J. A. M., et al. (2014) · Mathematical Geosciences · doi:10.1007/s11004-014-9529-y · Cited by 5
“Over the past few years, important developments have taken place in the statistical methods for such kind of data (Pawlowsky-Glahn and Buccianti 2011), referring to the analysis of compositional data (CoDa).”
Background
Improved Intensity-Based Label-Free Quantification via Proximity-Based Intensity Normalization (PIN)
Riper, S. K. V., de Jong, E. P., Higgins, L., Carlis, J. V., & Griffin, T. J. (2014) · Journal of Proteome Research · doi:10.1021/pr400866r · Cited by 5
Structural Analysis of the National Geochemical Survey of Australia Data
Mueller, U. A., Lo, J., de Caritat, P., et al. (2013) · Lecture notes in earth system sciences · doi:10.1007/978-3-642-32408-6_24 · Cited by 5
A systematic approach for the comparison of PM10, PM2.5, and PM1 mass concentrations of characteristic environmental sites
Speranza, A., Caggiano, R., & Summa, V. (2019) · Environmental Monitoring and Assessment · doi:10.1007/s10661-019-7828-y · Cited by 5
“This kind of statistical analysis began with Aitchison (1982, 1986) and has since undergone several developments and many practical applications leading to it being considered a consolidated statistical technique (Pawlowsky-Glahn and Buccianti 2002; Pawlowsky-Glahn and Buccianti 2011).”
Method
Beyond Dichotomy: Dynamics of Choice in Compositional Space
Jensen, G. (2014) · doi:10.7916/d8nc5zc4 · Cited by 5
Logistic Normal Multinomial Factor Analyzers for Clustering Microbiome Data
Tu, W., & Subedi, S. D. (2023) · Journal of Classification · doi:10.1007/s00357-023-09452-0 · Cited by 5
“In contrast, the CLR transformation preserves distances but leads to a singular covariance matrix (Pawlowsky-Glahn & Buccianti, 2011; Pawlowsky-Glahn et al., 2007).”
Background
Dead but not forgotten: complexity of Acropora palmata colonies increases with greater composition of dead coral
Engleman, A., Cox, K., & Brooke, S. D. (2023) · PeerJ · doi:10.7717/peerj.16101 · Cited by 5
“…( Bacon-Shone, 1992 ; Pawlowsky-Glahn & Egozcue, 2001 ; von Eynatten, Pawlowsky-Glahn & Egozcue, 2002 ; Aitchison, 2003b ; Egozcue et al., 2003 ; Aitchison & Egozcue, 2005 ; Pawlowsky-Glahn & Buccianti, 2011 ; Pawlowsky-Glahn, Egozcue & Tolosana-Delgado, 2015 ; Filzmoser, Hron & Templ, 2018 ).”
A Three-Component Approach to Model and Forecast Age-at-Death Distributions
Basellini, U., & Camarda, C. G. (2020) · The Springer series on demographic methods and population analysis · doi:10.1007/978-3-030-42472-5_6 · Cited by 4
“We thus employ a Compositional Data methodology to model and forecast the time series of component-specific probability masses (Aitchison 1986; Pawlowsky-Glahn and Buccianti 2011).”
Method
Water Chemistry: Are New Challenges Possible from CoDA (Compositional Data Analysis) Point of View?
Buccianti, A. (2018) · Springer eBooks · doi:10.1007/978-3-319-78999-6_16 · Cited by 5
“A recent thorough analysis of the “compositional problem” can be found in Pawlowsky-Glahn and Buccianti (2011) and Pawlowsky-Glahn et al. (2015).”
BackgroundSelf-citation
A tour of regression models for explaining sharesPreprint
Morais, J., Thomas‐Agnan, C., & Simioni, M. (2016) · RePEc: Research Papers in Economics · doi:10.13140/rg.2.2.20965.40163 · Cited by 5
In vivo microstructural heterogeneity of white matter lesions in Alzheimer’s disease using tissue compositional analysis of diffusion MRI dataPreprint
Mito, R., Dhollander, T., Xia, Y., Raffelt, D. A., Salvado, O., Churilov, L. P., Rowe, C. C., Brodtmann, A. G., Villemagne, V. L., & Connelly, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/623124 · Cited by 5
“Therefore, we adopted the Compositional Data Analysis (CoDA) framework (Aitchison, 1982; Pawlowsky-Glahn and Buccianti, 2011).”
Method
Human immune system variation during one yearPreprint
Lakshmikanth, T., Muhammad, S. A., Olin, A., Chen, Y., Mikeš, J., Fagerberg, L., Gummesson, A., Bergström, G. M. L., Uhlén, M., & Brodin, P. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.22.915025 · Cited by 5
Methodology for microbiome data analysis: An overview
Creus-Martí, I., Moyá, A., & Gómez, F. J. S. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110157 · Cited by 9
Representation Learning of Compositional Data
Avalos, M., Nock, R., Ong, C. S., et al. (2018) · ANU Open Research (Australian National University) · Cited by 4
Compositional Data Analysis in E-Tourism Research
Ferrer-Rosell, B., Coenders, G., & Martín-Fuentes, E. (2021) · Springer eBooks · doi:10.1007/978-3-030-05324-6_136-1 · Cited by 4
Mints not Mines: a macroscale investigation of Roman silver coinage
Wood, J. R., Ponting, M. J., & Butcher, K. (2023) · Internet Archaeology · doi:10.11141/ia.61.10 · Cited by 4
Stream sediment pollution: a compositional baseline assessment
Albuquerque, T., Fonseca, R., Araújo, J. F., Bustos, N. S., & Araújo, A. (2024) · Euro-Mediterranean Journal for Environmental Integration · doi:10.1007/s41207-024-00470-x · Cited by 4
“These initial contributions were explained and expanded into general-purpose works such as those of Pawlowsky-Glahn et al. (2015), Boogaart van den and Tolosana-Delgado (2013), Filzmoser and Hron (2011), Pawlowsky-Glahn and Buccianti (2011), and Pawlowsky-Glahn and Serra (2019).”
Multivariate Poisson lognormal distribution for modeling counts from modern biological data: An overview
Subedi, S. D., & Dang, U. J. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.03.017 · Cited by 4
Statistical monitoring for continual quality control of railway ballast
Hofer, V., & Bach, H. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.07.011 · Cited by 4
Things that make us different: analysis of deviance with time-use data
Chapela, J. G. (2013) · Journal of Applied Statistics · doi:10.1080/02664763.2013.789097 · Cited by 4
Which Country Epitomizes the World? A Study from the Perspective of Demographic Composition
Yoshida, T., Er-rbib, R., & Tsutsumi, M. (2019) · Sustainability · doi:10.3390/su11226404 · Cited by 6
Compositional Data Analysis using Kernels in mass cytometry data
Rudra, P., Baxter, R. M., Hsieh, E. W. Y., & Ghosh, D. (2022) · Bioinformatics Advances · doi:10.1093/bioadv/vbac003 · Cited by 4
“Note, however, that the alrtransformation is asymmetric in the components, and the alr-coordinates are non-isometric in nature (Pawlowsky-Glahn and Buccianti, 2011).”
Influential
The impact of ActiGraph’s low-frequency extension filter on measurement of children’s physical activity
Filanowski, P. M., Slade, E., Iannotti, R. J., et al. (2022) · Journal of Sports Sciences · doi:10.1080/02640414.2022.2081404 · Cited by 4
Archaeometric Characterization of the Industrial Production of Porcelains in the Vieillard & Co. Manufactory (Bordeaux, France, 19th Century)
Beauvoit, E., Cantin, N., Lemasson, Q., et al. (2022) · Materials · doi:10.3390/ma15155311 · Cited by 4
“Principal component analysis (PCA) after transformation into centered log-ratio (clr) of the concentrations obtained by SEM-EDS (Na2O, Al2O3, SiO2, K2O, CaO, and Fe2O3) allows for the visualization of the projection of the individuals and variables [21].”
Method
Bias and Variance in Multiparty Election Polls
Selb, P., Chen, S., Körtner, J., & Bosch, P. (2023) · Public Opinion Quarterly · doi:10.1093/poq/nfad046 · Cited by 5
Kent feature embedding for classification of compositional data with zeros
Lu, S., Wang, W., & Guan, R. (2024) · Statistics and Computing · doi:10.1007/s11222-024-10382-z · Cited by 4
Meta-analysis of lettuce (Lactuca sativa L.) response to added N in organic soils1
Gonzalez, M. Q., Pellerin, A., & Parent, L. E. (2016) · Canadian Journal of Plant Science · doi:10.1139/cjps-2015-0301 · Cited by 4
“/2D−1 possible balances for a D-parts composition (Pawlowsky-Glahn and Buccianti 2011).”
Background
Sexual dimorphism in the Drosophila melanogaster (Diptera: Drosophilidae) metabolome increases throughout development
Ingleby, F. C., & Morrow, E. H. (2017) · European Journal of Entomology · doi:10.14411/eje.2017.030 · Cited by 4
“…25 compounds listed in Table 1 were used to calculate standardised peak areas using a centred log ratio transformation on proportional peak areas (Pawlowsky-Glahn & Buccianti, 2011), as follows: standardisedn=ln prop (trait n) (∏ n=1 k prop (trait n)) ½ (1) where the divisor is the geometric…”
Method
Composition of pelagic microbial communities in Mediterranean coastal aquatic ecosystems under extreme drought conditions
Àvila, N., López‐Flores, R., & Quintana, X. D. (2018) · Estuarine Coastal and Shelf Science · doi:10.1016/j.ecss.2018.01.018 · Cited by 4
Comments on: Compositional data: the sample space and its structure
Filzmoser, P., & Hron, K. (2019) · Test · doi:10.1007/s11749-019-00671-5 · Cited by 4
Why Do We Do What We Do?
Siems, M., & Síthigh, D. M. (2017) · Cambridge University Press eBooks · doi:10.1017/9781316442906.002 · Cited by 4
A Bibliometric Analysis of the 35th anniversary of the paper "The Statistical Analysis of Compositional Data" by John Aitchison (1982)
Navarro, C., González-Morcillo, S., Mulet-Forteza, C., et al. (2021) · Austrian Journal of Statistics · doi:10.17713/ajs.v50i2.1066 · Cited by 4
“The second event is the proliferation of tutorials and textbooks on the theory (Aitchison (1986), Aitchison (1994), Valls (2018), Pawlowsky-Glahn and Buccianti (2011), Van den Boogaart and Tolosana-Delgado (2013), Pawlowsky-Glahn, Egozcue, and Tolosana-Delgado (2015), Greenacre (2017) and…”
Background
Analyse de données de composition et identification des altérations géochimiques des matériaux céramiques : le cas des productions d’un atelier ibérique (Teruel, Espagne ; iie-ier siècles avant J.-C.)
Frèrebeau, N., Amara, A. B., & Cantin, N. (2020) · ArchéoSciences · doi:10.4000/archeosciences.7220 · Cited by 4
“Cette étape est rendue nécessaire par la nature de ces données dites de composition, qui décrivent les parties d’un tout comme mutuellement exclusives et exhaustives (Aitchison, 1986 ; Pawlowsky-Glahn & Buccianti, 2011).”
Background

Authors: Glahn, V. P., & Buccianti, A.
ORCID: Antonella Buccianti
Venue: Wiley eBooks
Publisher: Wiley
Published: 2011-07-28
DOI: 10.1002/9781119976462
OpenAlex: W571770348
Cited by: 704
References: 0
Citations by year: 2026: 30 · 2025: 38 · 2024: 34 · 2023: 40 · 2022: 45 · 2021: 71 · 2020: 71 · 2019: 46 · 2018: 61 · 2017: 60 · 2016: 52 · 2015: 42 · 2014: 47 · 2013: 37 · 2012: 22
FWCI: 14.85
Type: book
Language: en
Open access: closed
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
Locations: Wiley eBooks (publishedVersion)
Florence Research (University of Florence) (submittedVersion)
Topics: Physical Sciences › Earth and Planetary Sciences › Geochemistry and Petrology › Geochemistry and Geologic Mapping
Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords: compositional data analysis, spurious correlations, parts and wholes

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