zCompositions - R Package for multivariate imputation of left-censored data under a compositional approach
zCompositions - R Package for multivariate imputation of left-censored data under a compositional approach
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DOI：
10.1016/j.chemolab.2015.02.019
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发表时间：
2015-04-15
影响因子：
3.9
通讯作者：
Antoni Martin-Fernandez, Josep
中科院分区：
文献类型：
--
作者：
Palarea-Albaladejo, Javier;Antoni Martin-Fernandez, Josep
ZComposes是一个R包，用于在合成方法下对左删失数据进行补偿。当分析师假设相关信息包含在数据的相对变化结构上时，它是相关的。例如，在实验数据同时以与相同总重量或体积相关的数量测量的情况下。这种方法被用于水或沉积岩的地球化学、与空气污染有关的环境研究、法医科学中对玻璃碎片的物理化学分析等许多领域。在这些领域中，四舍五入的零和未检测到的数据通常被认为是左审查数据，这阻碍了任何后续的数据分析。所实现的方法考虑了组成方法的相关性方面，例如尺度不变性、次组成连贯性或保持数据的多变量相对结构。它以可靠的统计框架为基础，包括处理单一和变化的审查阈值的能力，对封闭和非封闭数据的一致处理，探索性工具，多重推算，MCMC，稳健和非参数替代方案，以及最近关于计数数据的提议。讨论了该方法的关键方法学问题、新贡献、计算实现和实际应用。(C)2015爱思唯尔B.V.保留所有权利。
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. (C) 2015 Elsevier B.V. All rights reserved.