http://hdl.handle.net/10256/636 Fri, 01 Aug 2025 03:50:48 GMT 2025-08-01T03:50:48Z http://hdl.handle.net/10256/682 New Features of CoDaPack. An Userfriendly Compositional Data Package Thió i Fernández de Henestrosa, Santiago; Tolosana Delgado, Raimon; Gómez, O. Mateu i Figueras, Glòria; Barceló i Vidal, Carles The statistical analysis of compositional data is commonly used in geological studies. As is well-known, compositions should be treated using logratios of parts, which are difficult to use correctly in standard statistical packages. In this paper we describe the new features of our freeware package, named CoDaPack, which implements most of the basic statistical methods suitable for compositional data. An example using real data is presented to illustrate the use of the package Sat, 01 Oct 2005 00:00:00 GMT http://hdl.handle.net/10256/682 2005-10-01T00:00:00Z http://hdl.handle.net/10256/681 Compositional Data Analysis with R Bren, Matevž; Batagelj, Vladimir Mateu i Figueras, Glòria; Barceló i Vidal, Carles R from http://www.r-project.org/ is ‘GNU S’ – a language and environment for statistical computing and graphics. The environment in which many classical and modern statistical techniques have been implemented, but many are supplied as packages. There are 8 standard packages and many more are available through the cran family of Internet sites http://cran.r-project.org . We started to develop a library of functions in R to support the analysis of mixtures and our goal is a MixeR package for compositional data analysis that provides support for operations on compositions: perturbation and power multiplication, subcomposition with or without residuals, centering of the data, computing Aitchison’s, Euclidean, Bhattacharyya distances, compositional Kullback-Leibler divergence etc. graphical presentation of compositions in ternary diagrams and tetrahedrons with additional features: barycenter, geometric mean of the data set, the percentiles lines, marking and coloring of subsets of the data set, theirs geometric means, notation of individual data in the set . . . dealing with zeros and missing values in compositional data sets with R procedures for simple and multiplicative replacement strategy, the time series analysis of compositional data. We’ll present the current status of MixeR development and illustrate its use on selected data sets Sat, 01 Oct 2005 00:00:00 GMT http://hdl.handle.net/10256/681 2005-10-01T00:00:00Z http://hdl.handle.net/10256/670 A compositional data analysis package for R providing multiple approaches Boogaart, K. Gerald van den; Tolosana Delgado, Raimon Mateu i Figueras, Glòria; Barceló i Vidal, Carles ”compositions” is a new R-package for the analysis of compositional and positive data. It contains four classes corresponding to the four different types of compositional and positive geometry (including the Aitchison geometry). It provides means for computation, plotting and high-level multivariate statistical analysis in all four geometries. These geometries are treated in an fully analogous way, based on the principle of working in coordinates, and the object-oriented programming paradigm of R. In this way, called functions automatically select the most appropriate type of analysis as a function of the geometry. The graphical capabilities include ternary diagrams and tetrahedrons, various compositional plots (boxplots, barplots, piecharts) and extensive graphical tools for principal components. Afterwards, ortion and proportion lines, straight lines and ellipses in all geometries can be added to plots. The package is accompanied by a hands-on-introduction, documentation for every function, demos of the graphical capabilities and plenty of usage examples. It allows direct and parallel computation in all four vector spaces and provides the beginner with a copy-and-paste style of data analysis, while letting advanced users keep the functionality and customizability they demand of R, as well as all necessary tools to add own analysis routines. A complete example is included in the appendix Sat, 01 Oct 2005 00:00:00 GMT http://hdl.handle.net/10256/670 2005-10-01T00:00:00Z http://hdl.handle.net/10256/669 An R Library for Compositional Data Analysis in Archaeometry Beardah, C.C.; Baxter, M.J. Mateu i Figueras, Glòria; Barceló i Vidal, Carles Compositional data naturally arises from the scientific analysis of the chemical composition of archaeological material such as ceramic and glass artefacts. Data of this type can be explored using a variety of techniques, from standard multivariate methods such as principal components analysis and cluster analysis, to methods based upon the use of log-ratios. The general aim is to identify groups of chemically similar artefacts that could potentially be used to answer questions of provenance. This paper will demonstrate work in progress on the development of a documented library of methods, implemented using the statistical package R, for the analysis of compositional data. R is an open source package that makes available very powerful statistical facilities at no cost. We aim to show how, with the aid of statistical software such as R, traditional exploratory multivariate analysis can easily be used alongside, or in combination with, specialist techniques of compositional data analysis. The library has been developed from a core of basic R functionality, together with purpose-written routines arising from our own research (for example that reported at CoDaWork'03). In addition, we have included other appropriate publicly available techniques and libraries that have been implemented in R by other authors. Available functions range from standard multivariate techniques through to various approaches to log-ratio analysis and zero replacement. We also discuss and demonstrate a small selection of relatively new techniques that have hitherto been little-used in archaeometric applications involving compositional data. The application of the library to the analysis of data arising in archaeometry will be demonstrated; results from different analyses will be compared; and the utility of the various methods discussed Sat, 01 Oct 2005 00:00:00 GMT http://hdl.handle.net/10256/669 2005-10-01T00:00:00Z