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compositional data analysis (CoDA) as a tool to study the (paleo) ecology of coccolithophores from coastal-neritic settings off central Portugal

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SEDIMENTARY GEOLOGY 2015年 319卷 134-146页

作者： Guerreiro, Catarina Cachao, Mario Pawlowsky-Glahn, Vera Oliveira, Anabela Rodrigues, Aurora Univ Bremen Dept Geosci D-28359 Bremen Germany Univ Lisbon IDL P-1740016 Lisbon Portugal Portuguese Hydrog Inst IH P-1249093 Lisbon Portugal Royal Netherlands Inst Sea Res NIOZ Marine Geol Dep Texel Netherlands Univ Lisbon Fac Sci Dept Geol P-1749016 Lisbon Portugal Univ Girona Dept Informat Matemat Aplicada & Estadist E-17071 Girona Spain

Whereas using the species percentages is the standard analytical procedure used to infer species ecological preferences, independently of taphonomical effects, the closure problem associated with closed number systems and subsequent inconsistency of determining percentages may lead to spurious correlations, biased statistical analysis and misleading interpretations. To avoid these problems, we applied compositional data analysis (CoDA) to investigate the (paleo)ecological preferences and spatial distribution of coccolith assemblages preserved in sea- floor sediments, using as a case-study the central Portuguese submarine canyons and adjacent shelf-slope areas. Results from using the isometric log-ratio (ilr) approach from CoDA are compared with results from using classical analytical methods, and further discussed. While providing scale invariance and subcompositional coherence, CoDA is revealed to be a consistent statistical tool to infer the (paleo)ecological preferences of coccolithophores, corroborating earlier work based on from percentage determinations. Results of this study clearly confirmed the coastal-neritic distribution of coccoliths from Gephyrocapsa oceanica, Helicosphaera carteri and Coronosphaera mediterranea, whereas coccoliths from Calcidiscus leptoporus, Umbilicosphaera sibogae, Umbellosphaera irregularis and Rhabdosphaera spp. more typically occur offshore. Differences between canyons and adjacent shelf and slope areas were also confirmed, namely the (a) greater importance of the coastal-neritic assemblage possibly resulting from local and persistent nutrient pumping in these areas, and (b) stronger mixing of coccoliths from coastal and oceanic species in upper canyon reaches, resulting from the focused coastward advection of more oceanic water masses along their axes. Unlike the results from both ilr-coordinates and the percentage approaches, both coccolith concentrations and fluxes showed that spatial trends in which the species ecological inte

关键词： Coccoliths compositional data analysis Isometric log-ratios Relative abundances Nazare and Lisbon-Setubal Canyons Portuguese margin

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compositional data analysis in the study of carcass composition of beef cattle

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LIVESTOCK PRODUCTION SCIENCE 2001年第3期67卷 241-251页

作者： Muldowney, D Connolly, J Keane, MG Natl Univ Ireland Univ Coll Dublin Dept Stat Dublin 4 Ireland TEAGASC Grange Res Ctr Dunsany Meath Ireland

Allometric regression (AR) has been widely used to model changes in the body composition of animals. However, predicted body component proportions based on AR equations do not necessarily sum to 1 and this discrepancy is confounded with treatment effects on components of the composition. Predicted component proportions are not bounded to lie between 0 and 1. An alternative method, compositional data analysis (CDA), which avoids these difficulties is proposed for beef carcass dissection data. For a composition consisting of D components (e.g, muscle, fat and bone) a new set of D - 1 variables is created based on the logarithm of the ratios of components to one of the components (e.g. log(muscle/bone) and log(fat/bone)). Any statistical analysis can be applied on this scale, subject to the assumptions for that method of analysis being true. Regression models with simple interpretations in terms of animal development can be fitted to these logratio variables. Some inferences and interpretations are best made on the scale of component proportions. Predictions made from the models on the logratio scale may be back-transformed to give compositions on the proportional scale which obey the constraints that the component proportions sum to I and individually cannot exceed 1. The method generalises readily to multiple regression models involving factors and variables. CDA provides a fully multivariate framework for dealing with carcass dissection data within which questions on the effects of treatments and covariates on component composition and the differences between components can be addressed. It is a more natural vehicle than AR for analysing part-part relationships as it respects the symmetry between the components being compared. A simple relationship between CDA and AR models is developed. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： beef cattle compositional data analysis allometric regression linear regression carcass composition

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compositional data analysis for elemental data in forensic science

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FORENSIC SCIENCE INTERNATIONAL 2009年第1-3期188卷 81-90页

作者： Campbell, Gareth P. Curran, James M. Miskelly, Gordon M. Coulson, Sally Yaxley, Gregory M. Grunsky, Eric C. Cox, Simon C. Univ Auckland Dept Chem Forens Sci Programme Auckland 1142 New Zealand Univ Auckland Dept Stat Auckland 1142 New Zealand ESR Auckland New Zealand Australian Natl Univ Res Sch Earth Sci Canberra ACT 0200 Australia Geol Survey Canada Nat Resources Canada Ottawa ON Canada GNS Sci Dunedin New Zealand

Discrimination of material based on elemental composition was achieved within a compositional data (CoDa) analysis framework in a form appropriate for use in forensic science. The methods were carried out on example data from New Zealand nephrite. We have achieved good separation of the in situ outcrops of nephrite from within a well-defined area. The most significant achievement of working within the CoDa analysis framework is that the implications of the constraints on the data are acknowledged and dealt with, not ignored. The full composition was reduced based on collinearity of elements, principal components analysis (PCA) and scalings from a backwards linear discriminant analysis (LDA). Thus, a descriptive subcomposition was used for the final discrimination, using LDA, and proved to be more successful than using the full composition. The classification based on the LDA model showed a mean error rate of 2.9% when validated using a 10 repeat, three-fold cross-validation. The methods presented lend objectivity to the process of interpretation, rather than relying on subjective pattern matching type approaches. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

关键词： compositional data analysis ICP-MS Nephrite Forensic science

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compositional data analysis for physical activity, sedentary time and sleep research

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STATISTICAL METHODS IN MEDICAL RESEARCH 2018年第12期27卷 3726-3738页

作者： Dumuid, Dorothea Stanford, Tyman E. Martin-Fernandez, Josep-Antoni Pedisic, Zeljko Maher, Carol A. Lewis, Lucy K. Hron, Karel Katzmarzyk, Peter T. Chaput, Jean-Philippe Fogelholm, Mikael Hu, Gang Lambert, Estelle V. Maia, Jose Sarmiento, Olga L. Standage, Martyn Barreira, Tiago V. Broyles, Stephanie T. Tudor-Locke, Catrine Tremblay, Mark S. Olds, Timothy Univ South Australia Sch Hlth Sci Adelaide SA Australia Univ Adelaide Sch Math Sci Adelaide SA Australia Univ Girona Dept Informat Matemat Aplicada & Estadist Girona Spain Victoria Univ Inst Sport Exercise & Act Living Melbourne Vic Australia Flinders Univ S Australia Sch Hlth Sci Adelaide SA Australia Univ Palackeho Dept Math Anal & Applicat Math Olomouc Czech Republic Pennington Biomed Res Ctr 6400 Perkins Rd Baton Rouge LA 70808 USA Childrens Hosp Eastern Ontario Res Inst Hlth Act Living & Obes Res Ottawa ON Canada Helsingin Yliopisto Dept Food & Environm Sci Helsinki Finland Univ Cape Town Dept Human Biol Cape Town South Africa Univ Porto Fac Desporto Porto Portugal Univ Los Andes Fac Med Bogota Colombia Univ Bath Dept Hlth Bath Avon England Syracuse Univ Dept Exercise Sci Syracuse NY USA Univ Massachusetts Dept Kinesiol Amherst MA 01003 USA

The health effects of daily activity behaviours (physical activity, sedentary time and sleep) are widely studied. While previous research has largely examined activity behaviours in isolation, recent studies have adjusted for multiple behaviours. However, the inclusion of all activity behaviours in traditional multivariate analyses has not been possible due to the perfect multicollinearity of 24-h time budget data. The ensuing lack of adjustment for known effects on the outcome undermines the validity of study findings. We describe a statistical approach that enables the inclusion of all daily activity behaviours, based on the principles of compositional data analysis. Using data from the International Study of Childhood Obesity, Lifestyle and the Environment, we demonstrate the application of compositional multiple linear regression to estimate adiposity from children's daily activity behaviours expressed as isometric log-ratio coordinates. We present a novel method for predicting change in a continuous outcome based on relative changes within a composition, and for calculating associated confidence intervals to allow for statistical inference. The compositional data analysis presented overcomes the lack of adjustment that has plagued traditional statistical methods in the field, and provides robust and reliable insights into the health effects of daily activity behaviours.

关键词： compositional data analysis physical activity sedentary behaviour sleep multicollinearity

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compositional data analysis of Glucose Profiles of Type 1 Diabetes Patients 12

Compositional Data Analysis of Glucose Profiles of Type 1 Di...

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12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS-CAB)

作者： Biagi, Lyvia Bertachi, Arthur Antoni Martin-Fernandez, Josep Vehi, Josep Univ Girona Girona Spain Fed Univ Technol Parana UTFPR Guarapuava Brazil Ctr Invest Biomed Red Diabet & Enfermedades Metab Barcelona Spain

Time spent in different glucose ranges indicate the occurrence of adverse events and measure the quality of glucose control in type one diabetes (T1D) patients. This work proposes a compositional data (CoDa) approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles limited to 6-h duration were analyzed at four different times of the day. These glucose profiles were distributed into time spent in five glucose ranges, which determine the composition. The log-ratio coordinates of the compositions were categorized through a clustering algorithm, which later made possible the obtainment of a linear model that should be used to predict the category of a 6-h period in different times of day. Leave-one-out cross-validation was performed, achieving an average above 90% of correct classification. A probabilistic model of transition between the category of the past 6-h of glucose to the category of the future 6-h period was obtained. Results show that the CoDa approach not only works as new analysis tool and is suitable for the categorization of glucose profiles, but also is a complementary tool for the prediction of different categories of glucose control. This prediction could assist patients to take correction measures in advance to adverse situations. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： compositional data analysis Type 1 Diabetes Biomedical Systems Continuous Glucose Monitoring Decision Support System

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Comparative geochemical study between the tap waters and the bottled mineral waters in Calabria (Southern Italy) by compositional data analysis (CoDA) developments

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APPLIED GEOCHEMISTRY 2019年 107卷 19-33页

作者： Apollaro, Carmine Buccianti, Antonella Vespasiano, Giovanni Varde, Massimiliano Fuoco, Ilaria Barca, Donatella Bloise, Andrea Miriello, Domenico Cofone, Franco Servidio, Alessandro De Rosa, Rosanna Univ Calabria UniCAL Dept Biol Ecol & Earth Sci DiBEST Via P Bucci 4Cubo 15B I-87036 Arcavacata Di Rende CS Italy Univ Florence UniFI Dept Earth Sci Via G La Pira 4 I-50121 Florence Italy Inst Geosci & Earth Resources CNR IGG Via G La Pira 4 I-50121 Florence Italy Inst Dynam Environm Proc CNR IDPA Via Torino 155 I-30172 Venice Mestre Italy Univ Ferrara UniFEJ Dept Chem & Pharmaceut Sci DipSCF Via Luigi Borsari 46 I-44121 Ferrara Italy Inst Nanotechnol CNR NANOTEC Via P Bucci 4Cubo 31C I-87036 Arcavacata Di Rende CS Italy

This work focuses on comparing the inorganic content of tap and bottled mineral waters in the Calabria region (Southern Italy) performed by using robust compositional data analysis (CoDA) methods and related new developments associated with a classical graphical-numerical approach. Thirty-one samples of tap waters scattered throughout the Calabrian territory and twenty-one bottled natural mineral waters of various Calabrian brands were analyzed for major and trace inorganic components. In addition, a very sensitive method based on cold vapour atomic fluorescence spectrometry (CV-AFS) has been employed to determine total mercury (Hg-T) concentrations. compositional data analysis was used to identify the main interrelationships among physico-chemical parameters, highlighting the differences between the tap waters and the bottled mineral waters. Some elements, such as Al, Mn, Fe, Zn, and Pb resulted particularly enriched in the tap waters. This group of enriched elements was probably controlled by corrosion processes from metal pipes, asking for more monitoring on the state of the public distribution water network. Li, Ca, Cr, Ni, and Cd, on the other hand, appear to be mostly contributed by natural sources affecting the overall variability of the bottled mineral waters. In this context, Cr, Cu, Hg, and Pb are mainly enriched in the sparkling bottled mineral waters. Innovative compositional indices did not highlight great differences from the Maximum Allowed Concentrations (MAC) in the two types of water, so that for the health of the consumer it is indifferent to drink tap or bottled mineral water. The results represent a fundamental base to develop monitoring plans in time to check for the maintenance of quality standards.

关键词： Drinking waters Natural mineral waters Major ions Trace elements Mercury Geochemistry compositional data analysis Southern Italy

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Is compositional data analysis a way to see beyond the illusion?

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COMPUTERS & GEOSCIENCES 2013年第Jan.期50卷 165-173页

作者： Buccianti, Antonella Univ Florence I Dept Earth Sci I-50121 Florence I Italy

Notwithstanding the numerous contributions that have been published on theoretical and practical aspects of the management of compositional (constrained) data during the last thirty years, in geochemistry most of the scientific papers in international journals continue to ignore their peculiar features. In order to understand the reasons of the undervaluation of the effects of an incorrect choice of the sample space and, consequently, an incorrect application of the distance concept, case studies of comparison between methodologies will be presented and discussed. The aim is to evaluate the differences in interpretation of geochemical processes affecting rocks, water and gaseous samples, when the two different approaches, classical and compositional, are adopted. If we compare the results of case studies following the two paths it is possible to evaluate which type of error (and consequences) will affect our evaluations in geochemistry. The presence of expected differences between the two approaches indicates that compositional data analysis can be a way to see beyond the illusion due to the constrained space. However, the possibility that the difference is tenuous in some situations, not revealed a priori, may be at the origin of the unconscious choice of the classical approach. Is this condition which some researchers call "common sense" frequently encountered in geochemistry? The paper is aimed to try to answer the proposed question, and to understand the difficulty of diffusion of compositional data analysis even if now simple tools of investigation, for different degrees of knowledge, are available. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： compositional data analysis Geochemistry Worldwide lithology River chemistry Volcanic gases Isometric log-ratio transformation

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The spatial pattern of beryllium and its possible origin using compositional data analysis on a high-density topsoil data set from the Campania Region (Italy)

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APPLIED GEOCHEMISTRY 2018年 91卷 162-173页

作者： Petrik, Attila Albanese, Stefano Lima, Annamaria De Vivo, Benedetto Univ Naples Federico II Dept Earth Environm & Resources Sci Via Cintia Snc I-80126 Naples Italy Pegaso Univ Piazza Trieste & Trento 48 I-80132 Naples Italy BENECON Scarl Dipartimento Ambiente & Terr Via S Maria di Costantinopoli 104 I-80138 Naples Italy

This study demonstrates the spatial distribution of Be and its possible sources by using the high density Campanian topsoil dataset. A combination of univariate and multivariate statistical analysis with multifractal methods were performed on the raw-, and compositionally transformed data set to recognize the spatial patterns of Be and its background values. Specific groups of elements were chosen based on the compositional clr-biplot to implement multiple sequential binary partitions which were used in the calculation of balances. Balances enabled us to reveal the dominance and relation between specific groups of elements. Ratio maps based on balances were also made to better understand the compositional behaviour of Be in a multidimensional space. Index of chemical weathering (CIA) was calculated to explore the advancement of pedogenetic processes which might govern the Be distribution. Enrichment Factor (EF) was used to discover the possible anthropogenic contamination of Be. In addition, different thematic maps (e.g. fault zones, bauxite mineralization spots, hydrothermal springs, pyroclastic and carbonatic rocks distribution) were involved to support and verify our interpretation. Beryllium distribution is influenced by multiple geogenic factors. We proved that Be anomaly in topsoils is not only influenced by the presence of volcanoclastic deposits but the advancement of pedogenetic processes (e.g. chemical weathering, rubification) is equally or even more important. We pointed out that Be anomaly is mainly concentrated in topsoils developed over the oldest pyroclastic deposits in Roccamonfina and over carbonatic massifs where pedogenesis is more advanced. The distribution of pyroclastic deposits in the Campania Region was also demonstrated independently by using different balances (groups of elements). Some high Be anomaly can be interpreted as local peculiarities associated with bauxite mineralization and the presence of fault zones along which hydrothermal d

关键词： Spatial pattern Multifractal methods compositional data analysis Ratio maps Campania region

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Linking compositional data analysis with thermodynamic geochemical modeling: Oilfield brines from the Permian Basin, USA

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JOURNAL OF GEOCHEMICAL EXPLORATION 2014年 141卷 61-70页

作者： Engle, Mark A. Blondes, Madalyn S. US Geol Survey Reston VA 22092 USA Univ Texas El Paso Dept Geol Sci El Paso TX 79968 USA

compositional data analysis (CoDa) of sedimentary basin brines can help reveal the hydrogeochemical history of hydrocarbon producing formations. CoDa uses log-ratio transformations to convert compositional data into Euclidean space, a necessary assumption for many mathematical models and basic data interpretation. This paper demonstrates the novel approach of pairing CoDa with thermodynamic geochemical models for bivariate and multivariate subcompositions for a suite of brines within three producing formations from the Permian Basin, USA. Subcompositional analysis of basin brines using [Na, Cl, H2O] and [Ca, SO4, H2O] with corresponding equilibration models, plotted using isometric log-ratio (ilr) transformations, shows reaction with anhydrite and halite at depth. The [Na, Cl, Br] subcomposition often can discriminate between seawater evaporation and halite dissolution;the sparse Br data suggest that both processes occur. Principal component analysis (PCA) of centered log-ratio (clr) data is useful for visualizing combinations of processes that are not readily apparent in the two-solute models. PCA of the Permian Basin brines reveals distinct groups of data, including samples likely affected by water flooding, and others influenced by a combination of Late Permian seawater evaporation or halite dissolution coupled with Ca + Mg ion exchange for Na on clays. These relationships support a hydrogeologic model in which meteoric inflow occurs in a heterogeneous manner much more complicated than the previously suggested concept of west-to-east piston style flow. Published by Elsevier B.V.

关键词： Permian Basin Produced water Oilfield brine compositional data analysis Geochemical modeling

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Understanding arsenic and manganese enrichment in the aquifers of the Ghaghara river basin, Middle Gangetic Plain (MGP), India: A multivariate statistical, compositional data analysis (CoDA), and receptor model approach

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JOURNAL OF GEOCHEMICAL EXPLORATION 2024年 264卷

作者： Khan, Mohd Usman Rai, Nachiketa Shahwaar, Mohd Musahib, Mohd Rahman, Abdur Indian Inst Technol Dept Earth Sci Roorkee 247667 India Indian Inst Technol Ctr Space Sci & Technol Roorkee 247667 India Eotvos Lornd Univ Dept Geol Budapest Hungary Cent Groundwater Board Dept Water Resources River Dev & Ganga Rejuvenat Ahmadabad India

The focus of this study was to comprehend the spatial distribution, source apportionment, evaluation of natural background level (NBL), and mobilization mechanisms of arsenic (As) and manganese (Mn) in the Ghaghara river basin, located in the middle Gangetic plain (MGP) of India. A combination of analytical tools was employed, including multivariate statistical analysis (MSA), correlation analysis, and receptor models such as positive matrix factorization (PMF) and principal component analysis-multi linear regression (PCA-MLR). Taking into consideration the compositional constraints of the geochemical data, compositional data analysis (CoDA) methods were applied on the raw data prior to the correlation analysis and MSA. The results of MSA, correlation analysis and receptor models showed that major ion chemistry and As, Mn enrichment in groundwater were largely controlled by carbonate weathering, Fe and Mn oxyhydroxides dissolution. Anthropogenic activities, such as the infiltration of dissolved organic matter-rich water from local surface water bodies used as dumping sites and the infiltration of fertilizer-rich water from agricultural lands, were found to notably impact the groundwater geochemistry and release of As in the research area. The lower NBL of As (5.92 mu g/L) compared to its mean concentrations (17.9 mu g/L) in the study area also indicated that the rate of As release in groundwater through natural processes was comparatively low but various anthropogenic activities operating in the study region possibly acted as a trigger for the mobility of As from the mineral phases of the subsurface sediments. This study also highlighted the significance of applying CoDA techniques on geochemical data prior to statistical analysis, and the importance of receptor models, to better understand the nature and contribution of various natural and anthropogenic processes governing the hydrochemistry of groundwater in the alluvial aquifers.

关键词： Ghaghara Basin Arsenic Mobilization Natural Background Level compositional data analysis Receptor models

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