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

• Published in: Chemometrics and intelligent laboratory systems 2015-04, Vol.143, p.85-96
• Main Authors: Palarea-Albaladejo, Javier, Martín-Fernández, Josep Antoni
• Format: Article
• Language: English
• Subjects: Censored data; Compositional data; Imputation; Log-ratio analysis; Nondetects; Zeros
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2015.02.019

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. •Unified, coherent and well-principled imputation of multivariate nondetects and zeros in compositional data sets•Ability to deal with single and multiple limits of detection, consistent treatment of closed and non-closed data sets•Single and multiple imputation methods. Maximum likelihood, MCMC, robust and non-parametric choices•Treatment of zeros in compositional count data•Freely available for Windows, Linux and Apple OSX systems as an R package