Compositional data analysis with R

Examples of compositional data. The simplex, a suitable sample space for compositional data and Aitchison's geometry. R, a free language and environment for statistical computing and graphics

An R Library for Compositional Data Analysis in Archaeometry

Compositional data naturally arises from the scientific analysis of the chemicalcomposition of archaeological material such as ceramic and glass artefacts. Data of thistype can be explored using a variety of techniques, from standard multivariate methodssuch as principal components analysis and cluster analysis, to methods based upon theuse of log-ratios. The general aim is to identify groups of chemically similar artefactsthat could potentially be used to answer questions of provenance.This paper will demonstrate work in progress on the development of a documentedlibrary of methods, implemented using the statistical package R, for the analysis ofcompositional data. R is an open source package that makes available very powerfulstatistical facilities at no cost. We aim to show how, with the aid of statistical softwaresuch as R, traditional exploratory multivariate analysis can easily be used alongside, orin combination with, specialist techniques of compositional data analysis.The library has been developed from a core of basic R functionality, together withpurpose-written routines arising from our own research (for example that reported atCoDaWork'03). In addition, we have included other appropriate publicly availabletechniques and libraries that have been implemented in R by other authors. Availablefunctions range from standard multivariate techniques through to various approaches tolog-ratio analysis and zero replacement. We also discuss and demonstrate a smallselection of relatively new techniques that have hitherto been little-used inarchaeometric applications involving compositional data. The application of the libraryto the analysis of data arising in archaeometry will be demonstrated; results fromdifferent analyses will be compared; and the utility of the various methods discussed

A compositional data analysis package for R providing multiple approaches

”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 andpositive 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 workingin coordinates, and the object-oriented programming paradigm of R. In this way,called functions automatically select the most appropriate type of analysis as a functionof the geometry. The graphical capabilities include ternary diagrams and tetrahedrons,various compositional plots (boxplots, barplots, piecharts) and extensive graphical toolsfor principal components. Afterwards, ortion and proportion lines, straight lines andellipses in all geometries can be added to plots. The package is accompanied by ahands-on-introduction, documentation for every function, demos of the graphical capabilitiesand plenty of usage examples. It allows direct and parallel computation inall four vector spaces and provides the beginner with a copy-and-paste style of dataanalysis, while letting advanced users keep the functionality and customizability theydemand of R, as well as all necessary tools to add own analysis routines. A completeexample is included in the appendix

Y-90 Ibritumomab Tiuxetan (Zevalin (R)) Consolidation of First Remission In Advanced Stage Follicular Non Hodgkin's Lymphoma Updated Results After a Median Follow up of 662 Months From the International, Randomized, Phase III First Line Indolent Trial (FIT) In 414 Patients

The FIT trial was conducted to evaluate the safety and efficacy of 90Y-ibritumomab tiuxetan (0.4 mCi/kg; maximum dose 32 mCi) when used as consolidation of first complete or partial remission in patients with previously untreated, advanced-stage follicular lymphoma (FL). Patients were randomly assigned to either 90Y-ibritumomab treatment (n = 207) or observation (n = 202) within 3 months (mo) of completing initial induction therapy (chemotherapy only: 86%; rituximab in combination with chemotherapy: 14%). Response status prior to randomization did not differ between the groups: 52% complete response (CR)/CR unconfirmed (CRu) to induction therapy and 48% partial response (PR) in the 90Y-ibritumomab arm vs 53% CR/CRu and 44% PR in the control arm. The primary endpoint was progression-free survival (PFS) of the intent-to-treat (ITT) population. Results from the first extended follow-up after a median of 3.5 years revealed a significant improvement in PFS from the time of randomization with 90Y-ibritumomab consolidation compared with control (36.5 vs 13.3 mo, respectively; P < 0.0001; Morschhauser et al. JCO. 2008; 26:5156-5164). Here we report a median follow-up of 66.2 mo (5.5 years). Five-year PFS was 47% in the 90Y-ibritumomab group and 29% in the control group (hazard ratio (HR) = 0.51, 95% CI 0.39-0.65; P < 0.0001). Median PFS in the 90Y-ibritumomab group was 49 mo vs 14 mo in the control group. In patients achieving a CR/CRu after induction, 5-year PFS was 57% in the 90Y-ibritumomab group, and the median had not yet been reached at 92 months, compared with a 43% 5-year PFS in the control group and a median of 31 mo (HR = 0.61, 95% CI 0.42-0.89). For patients in PR after induction, the 5-year PFS was 38% in the 90Y-ibritumomab group with a median PFS of 30 mo vs 14% in the control group with a median PFS of 6 mo (HR = 0.38, 95% CI 0.27-0.53). Patients who had received rituximab as part of induction treatment had a 5-year PFS of 64% in the 90Y-ibritumomab group and 48% in the control group (HR = 0.66, 95% CI 0.30-1.47). For all patients, time to next treatment (as calculated from the date of randomization) differed significantly between both groups; median not reached at 99 mo in the 90Y-ibritumomab group vs 35 mo in the control group (P < 0.0001). The majority of patients received rituximab-containing regimens when treated after progression (63/82 [77%] in the 90Y-ibritumomab group and 102/122 [84%] in the control group). Overall response rate to second-line treatment was 79% in the 90Y-ibritumomab group (57% CR/CRu and 22% PR) vs 78% in the control arm (59% CR/CRu, 19% PR). Five-year overall survival was not significantly different between the groups; 93% and 89% in the 90Y-ibritumomab and control groups, respectively (P = 0.561). To date, 40 patients have died; 18 in the 90Y-ibritumomab group and 22 in the control group. Secondary malignancies were diagnosed in 16 patients in the 90Y-ibritumomab arm vs 9 patients in the control arm (P = 0.19). There were 6 (3%) cases of myelodysplastic syndrome (MDS)/acute myelogenous leukemia (AML) in the 90Y-ibritumomab arm vs 1 MDS in the control arm (P = 0.063). In conclusion, this extended follow-up of the FIT trial confirms the benefit of 90Y-ibritumomab consolidation with a nearly 3 year advantage in median PFS. A significant 5-year PFS improvement was confirmed for patients with a CR/CRu or a PR after induction. Effective rescue treatment with rituximab-containing regimens may explain the observed no difference in overall survival between both patient groups who were - for the greater part - rituximab-naïve.

Compositional Data Analysis with R

R from http://www.r-project.org/ is ‘GNU S’ – a language and environment for statistical computingand graphics. The environment in which many classical and modern statistical techniques havebeen implemented, but many are supplied as packages. There are 8 standard packages and many moreare 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 isa MixeR package for compositional data analysis that provides support foroperations on compositions: perturbation and power multiplication, subcomposition with or withoutresiduals, 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 ofsubsets 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 simpleand 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

News from compositions, the R package

The R-package “compositions”is a tool for advanced compositional analysis. Its basicfunctionality has seen some conceptual improvement, containing now some facilitiesto work with and represent ilr bases built from balances, and an elaborated subsys-tem for dealing with several kinds of irregular data: (rounded or structural) zeroes,incomplete observations and outliers. The general approach to these irregularities isbased on subcompositions: for an irregular datum, one can distinguish a “regular��� sub-composition (where all parts are actually observed and the datum behaves typically)and a “problematic” subcomposition (with those unobserved, zero or rounded parts, orelse where the datum shows an erratic or atypical behaviour). Systematic classificationschemes are proposed for both outliers and missing values (including zeros) focusing onthe nature of irregularities in the datum subcomposition(s).To compute statistics with values missing at random and structural zeros, a projectionapproach is implemented: a given datum contributes to the estimation of the desiredparameters only on the subcompositon where it was observed. For data sets withvalues below the detection limit, two different approaches are provided: the well-knownimputation technique, and also the projection approach.To compute statistics in the presence of outliers, robust statistics are adapted to thecharacteristics of compositional data, based on the minimum covariance determinantapproach. The outlier classification is based on four different models of outlier occur-rence and Monte-Carlo-based tests for their characterization. Furthermore the packageprovides special plots helping to understand the nature of outliers in the dataset.Keywords: coda-dendrogram, lost values, MAR, missing data, MCD estimator,robustness, rounded zeros

Evaluation of the GlideScope (R) for tracheal intubation in patients with cervical spine immobilization by a semi-rigid collar

Background: In patients with cervical spine injury, a cervical collar may prevent cervical spine movements but renders tracheal intubation with a standard laryngoscope difficult if not impossible. We hypothesized that despite the presence of a semi-rigid cervical collar and with the patient's head taped to the trolley, we would be able to intubate all patients with the GlideScopeR and its dedicated stylet. Methods: 50 adult patients (ASA 1 or 2, BMI ≤35 kg/m2) scheduled for elective surgical procedures requiring tracheal intubation were included. After standardized induction of general anesthesia and neuromuscular blockade, the neck was immobilized with an appropriately sized semi-rigid Philadelphia Patriot® cervical collar, the head was taped to the trolley. Laryngoscopy was attempted using a Macintosh laryngoscope blade 4 and the modified Cormack Lehane grade was noted. Subsequently, laryngoscopy with the GlideScopeR was graded and followed by oro-tracheal intubation. Results: All patients were successfully intubated with the GlideScopeR and its dedicated stylet. The median intubation time was 50 sec [43; 61]. The modified Cormack Lehane grade was 3 or 4 at direct laryngoscopy. It was significantly reduced with the GlideScopeR (p <0.0001), reaching 2a in most of patients. Maximal mouth opening was significantly reduced with the cervical collar applied, 4.5 cm [4.5; 5.0] vs. 2.0 cm [1.8; 2.0] (p <0.0001). Conclusions: The GlideScope® allows oro-tracheal intubation in patients having their cervical spine immobilized by a semi-rigid collar and their head taped to the trolley. It furthermore decreases significantly the modified Cormack Lehane grade.

Implementació dels mòduls SD i MM del sistema SAP R/3 en una empresa del sector metal·lúrgic

SAP R/3 és un software estàndard de gestió empresarial que engloba tots els processos iactivitats d’una companyia i permet el disseny de l’aplicació a les necessitats de l’empresa.Diferents avantatges, com ara disposar d’una base de dades única, han fet escollir aquestsistema a una empresa del sector metal·lúrgic com a eina de gestió:Aquest projecte té com a objecte la implantació de dos dels mòduls que inclou el sistemaSAP R/3, concretament el mòdul SD (Sales and Distribution), amb el qual es controlen lesvendes de l’empresa, i el mòdul MM (Material Management), mòdul amb el qual es controlatot el que fa refer��ncia als materials, la gestió d’estocs i tota la gestió de compres del’empresa

Validation interne de la version française du NEO-PI-R et influence de l'âge, du sexe et de la profession

We have examined the internal validity of the French translation of the NEO PI-R personality test which measures the « big five » (Rolland, 1993). The impact of age, gender and professional categories on the NEO PI-R scales was assessed. A large sample (n=731) of subjects of different age, gender and profession and a sample of Swiss students (n=261) responding anonymously were used. Factor analyses confirmed the structure of the instrument (5 domains) and the structures of the domains in terms of facets (six facets within each domain). On the other hand, the age has a significant impact on all the domains of the NEO PI-R; the gender has an impact on the scores on N (neuroticism), O (openness) and A (agreeableness), and the profession has an impact on the domains E (extraversion), O (openness) and A (agreeableness). The scores on several facets are also affected by those three variables. Our study gives the researchers and the practitioner a reference score table according to the studied variables.