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zCompositions - R package for multivariate imputation of left-censored data under a compositional approach

zCompositions is an R package for the imputation of left-censored data under a compositional approach. It is pertinent when the analyst assumes that the relevant information is contained on the relative variation structure of the data. For instance, in cases where the experimental data are simultaneously measured in amounts related to a same total weight or volume. The approach is used in fields like geochemistry of waters or sedimentary rocks, environmental studies related to air pollution, physicochemical analysis of glass fragments in forensic science, and among many others. In these fields, rounded zeros and nondetects are usually regarded as left-censored data that hamper any subsequent data analysis. The implemented methods consider aspects of relevance for a compositional approach such as scale invariance, subcompositional coherence or preserving the multivariate relative structure of the data. Based on solid statistical frameworks, it comprises the ability to deal with single and varying censoring thresholds, consistent treatment of closed and non-closed data, exploratory tools, multiple imputation, MCMC, robust and non-parametric alternatives, and recent proposals for count data. Key methodological aspects, new contributions, computational implementation and the practical application of the approach are discussed

This research has been partially supported by the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS), the Spanish Ministry of Economy and Competitiveness under the project "METRICS" (Ref. MTM2012-33236) and the Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR), Generalitat de Catalunya (Ref: 2014SGR551)

Elsevier

Manager: Ministerio de Economía y Competitividad (Espanya)
Generalitat de Catalunya. Agència de Gestió d’Ajuts Universitaris i de Recerca
Author: Palarea Albaladejo, Javier
Martín Fernández, Josep Antoni
Abstract: zCompositions is an R package for the imputation of left-censored data under a compositional approach. It is pertinent when the analyst assumes that the relevant information is contained on the relative variation structure of the data. For instance, in cases where the experimental data are simultaneously measured in amounts related to a same total weight or volume. The approach is used in fields like geochemistry of waters or sedimentary rocks, environmental studies related to air pollution, physicochemical analysis of glass fragments in forensic science, and among many others. In these fields, rounded zeros and nondetects are usually regarded as left-censored data that hamper any subsequent data analysis. The implemented methods consider aspects of relevance for a compositional approach such as scale invariance, subcompositional coherence or preserving the multivariate relative structure of the data. Based on solid statistical frameworks, it comprises the ability to deal with single and varying censoring thresholds, consistent treatment of closed and non-closed data, exploratory tools, multiple imputation, MCMC, robust and non-parametric alternatives, and recent proposals for count data. Key methodological aspects, new contributions, computational implementation and the practical application of the approach are discussed
This research has been partially supported by the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS), the Spanish Ministry of Economy and Competitiveness under the project "METRICS" (Ref. MTM2012-33236) and the Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR), Generalitat de Catalunya (Ref: 2014SGR551)
Document access: http://hdl.handle.net/2072/296142
Language: eng
Publisher: Elsevier
Rights: Tots els drets reservats
Subject: Anàlisi multivariable
Multivariate analysis
Estadística matemàtica
Mathematical statistics
Title: zCompositions - R package for multivariate imputation of left-censored data under a compositional approach
Type: info:eu-repo/semantics/article
Repository: Recercat

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