A Comprehensive Workflow for Compositional Data Analysis in Archaeometry, with Code in R DOI Creative Commons
Michael Greenacre, Jonathan R. Wood

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 2, 2024

Abstract Compositional data, which have relative rather than absolute meaning, are common in quantitative archaeological research. Such multivariate data usually expressed as proportions, summing to 1, or equivalently percentages. We present a comprehensive and defensible workflow for processing compositional archaeometry, using both the original values their transformation logratios. The most useful logratio transformations illustrated how they affect interpretation of final results context unsupervised supervised learning. is on from bronze ritual vessels provide fingerprints Shang Zhou periods Chinese Bronze Age. Predictions, with caveats, fabrication age made -- effect, typological seriation bronzes. In Supplementary Material, we further explore effect zeros dataset compare logaratio analyses chiPower approach, where replace any value determined being below detection limit instruments element, zeros. R code reproducing all provided Material online.

Language: Английский

GeoCoDA: Recognizing and validating structural processes in geochemical data. A workflow on compositional data analysis in lithogeochemistry DOI Creative Commons
Eric Grunsky, Michael Greenacre, B A Kjarsgaard

et al.

Applied Computing and Geosciences, Journal Year: 2024, Volume and Issue: 22, P. 100149 - 100149

Published: Jan. 2, 2024

Geochemical data are compositional in nature and subject to the problems typically associated with that restricted real non-negative number space constant-sum constraint, is, simplex. Geochemistry can be considered a proxy for mineralogy, comprised of atomically ordered structures define placement abundance elements mineral lattice structure. Based on innovative contributions John Aitchison, who introduced logratio transformation into analysis, this contribution provides systematic workflow assessing geochemical simple efficient way, such significant (mineralogical) processes recognized validated. This workflow, called GeoCoDA presented here form tutorial, enables recognition from which models constructed based associations reflect mineralogy. Both original values their logratios considered. These rock-forming processes, metamorphism, alteration ore mineralization. Moreover, machine learning methods, both unsupervised supervised, applied an optimized set subcompositions data, provide systematic, accurate, defensible approach analysis. The is illustrated lithogeochemical exploration Star kimberlite, consisting series eruptions five phases.

Language: Английский

Citations

10
Compositional and Machine Learning Tools to Model Plant Nutrition: Overview and Perspectives DOI Creative Commons
Léon‐Étienne Parent

Horticulturae, Journal Year: 2025, Volume and Issue: 11(2), P. 161 - 161

Published: Feb. 3, 2025

The ceteris paribus assumption that all features are equal except the one(s) being examined limits reliability of nutrient diagnosis and fertilizer recommendations. objective is to review machine learning (ML) compositional data analysis (CoDa) tools make management feature specific. average accuracy ML methods was 84% across crops. additive orthogonal log ratios CoDa reduce a D-parts soil composition D-1 variables, alleviating redundancy in predictive models. Using Brazilian onion (Allium cepa) database, combined returned crop response patterns, allowing feature-specific recommendations be made. centered ratio (clr) diagnoses plant nutrients as (CND). Quebec database vegetable crops, mean variance clr variables (VAR¯) allowed comparing total among species growth stages. While summation equally weighted dual ratios, may show unequal importance regarding performance. RReliefF scores or gain can provide weighting coefficients for each ratio. widely contrasting (wlr) improved models muck database. models, VAR¯ wlr, advanced improve diagnosis.

Language: Английский

Citations

1
Kent feature embedding for classification of compositional data with zeros DOI
Shan Lu, Wenjing Wang, Rong Guan

et al.

Statistics and Computing, Journal Year: 2024, Volume and Issue: 34(2)

Published: Jan. 31, 2024

Language: Английский

Citations

4
Turning Points in the Core–Periphery Displacement of Systemic Risk in the Eurozone: Constrained Weighted Compositional Clustering DOI Creative Commons
Anna Maria Fiori, Germà Coenders

Risks, Journal Year: 2025, Volume and Issue: 13(2), P. 21 - 21

Published: Jan. 24, 2025

Investigating how systemic risk originates and spreads across the financial system poses an inherently compositional question, i.e., a question concerning joint distribution of relative share several interdependent contributors. To address this we propose weighted clustering approach aimed at tackling trajectories turning points in Eurozone, from both chronological geographical perspective. The cluster profiles emerging our analysis indicate progressive shift Northern Europe towards Euro-Mediterranean region coordinate center compositions. This matures as outcome complex interactions between core peripheral EU countries that methods have merit capturing unifying self-contained multivariate framework.

Language: Английский

Citations

0
Unveiling Land Use Dynamics: Insights from a Hierarchical Bayesian Spatio-Temporal Modelling of Compositional Data DOI Creative Commons
Mario Figueira,

Carmen Guarner,

David Conesa

et al.

Journal of Agricultural Biological and Environmental Statistics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 12, 2025

Language: Английский

Citations

0
Proposal of Nutritional Standards for the Assessment of the Nutritional Status of Grapevines in Subtropical and Temperate Regions DOI Creative Commons
Danilo Eduardo Rozane,

Moreno Toselli,

Gustavo Brunetto

et al.

Plants, Journal Year: 2025, Volume and Issue: 14(5), P. 698 - 698

Published: Feb. 24, 2025

The necessity for nutritional standards to evaluate the status of grapevines is a critical concern viticulturists worldwide. This study addressed lack multinutrient that consider specific genetic and environmental factors by proposing regional based on data collected under different growing conditions. Using compositional nutrient diagnosis (CND) method multivariate analyses, leaf samples from 585 commercial vineyards in Emilia-Romagna, Italy, states São Paulo Rio Grande do Sul, Brazil, were evaluated. results confirmed significant variations among regions cultivars, emphasizing need adjustments fertilization recommendations. work proposes levels, sufficiency ranges, can improve grapevine assessments, promoting greater precision management. findings reinforce importance standards, avoiding use unsuitable universal

Language: Английский

Citations

0
Nutrient Balance of Citrus Across the Mandarin Belts of India DOI Creative Commons
Anoop Kumar Srivastava,

A.D. Huchche,

Léon‐Étienne Parent

et al.

Horticulturae, Journal Year: 2025, Volume and Issue: 11(3), P. 254 - 254

Published: Feb. 27, 2025

India is a major producer of mandarin oranges. However, the average fruit yield remains below potential due in part to multiple nutrient deficiencies. Our objective was elaborate compositional diagnosis (CND) log-ratio standards accounting for interactions and dilution leaf tissue. We hypothesized that equally or unequally weighted dual log ratios integrated into centered (clr) (wlr) influence accuracy CND diagnosis. The database comprised 494 observations on ‘Nagpur’, ‘Khasi’, ‘Kinnow’ cultivars surveyed contrasting agroecosystems India. Weights were provided by gain indicated importance ratio crop performance. cutoff set at upper high-yield quarter each variety. Centered (clrs) (wlrs) returned accuracies 0.7–0.8 depending machine learning classification model. not contrasted enough make difference between clr wlr. derived wlr following Gradient Boosting In case study, similar diagnoses. capacity generalize unseen cases correct imbalance should be further verified fertilizer trials. could also conducted local scale, thanks Euclidian geometry additivity variables.

Language: Английский

Citations

0
Compositional risk capital allocations DOI Creative Commons
Anna Maria Fiori, Emanuela Rosazza Gianin

Statistical Methods & Applications, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Language: Английский

Citations

0
Statistical Approaches for Modeling Correlated Grade and Tonnage Distributions and Applications for Mineral Resource Assessments DOI Creative Commons
Joshua M. Rosera, Graham Lederer,

John H. Schuenemeyer

et al.

Applied Computing and Geosciences, Journal Year: 2025, Volume and Issue: unknown, P. 100240 - 100240

Published: April 1, 2025

Language: Английский

Citations

0
A flexible Bayesian tool for CoDa mixed models: logistic-normal distribution with Dirichlet covariance DOI Creative Commons
Joaquín Martínez‐Minaya, Håvard Rue

Statistics and Computing, Journal Year: 2024, Volume and Issue: 34(3)

Published: April 16, 2024

Abstract Compositional Data Analysis (CoDa) has gained popularity in recent years. This type of data consists values from disjoint categories that sum up to a constant. Both Dirichlet regression and logistic-normal have become popular as CoDa analysis methods. However, fitting this kind multivariate models presents challenges, especially when structured random effects are included the model, such temporal or spatial effects. To overcome these we propose Model (LNDM). We seamlessly incorporate approach into R-INLA package, facilitating model prediction within framework Latent Gaussian Models. Moreover, explore metrics like Deviance Information Criteria, Watanabe Akaike information criterion, cross-validation measure conditional predictive ordinate for selection CoDa. Illustrating LNDM through two simulated examples with an ecological case study on Arabidopsis thaliana Iberian Peninsula, underscore its potential effective tool managing large databases.

Language: Английский

Citations

3