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

Compositional data and Simpson's paradox

Simpson's paradox, also known as amalgamation or aggregation paradox, appears whendealing with proportions. Proportions are by construction parts of a whole, which canbe interpreted as compositions assuming they only carry relative information. TheAitchison inner product space structure of the simplex, the sample space of compositions, explains the appearance of the paradox, given that amalgamation is a nonlinearoperation within that structure. Here we propose to use balances, which are specificelements of this structure, to analyse situations where the paradox might appear. Withthe proposed approach we obtain that the centre of the tables analysed is a naturalway to compare them, which avoids by construction the possibility of a paradox.Key words: Aitchison geometry, geometric mean, orthogonal projection

A longitudinal study of environmental tobacco smoke exposure in children: parental self reports versus age dependent biomarkers

Background: Awareness of the negative effects of smoking on children's health prompted a decrease in the self-reporting of parental tobacco use in periodic surveys from most industrialized countries. Our aim is to assess changes between ETS exposure at the end of pregnancy and at 4 years of age determined by the parents' self-report and measurement of cotinine in age related biological matrices.Methods: The prospective birth cohort included 487 infants from Barcelona city (Spain). Mothers were asked about maternal and household smoking habit. Cord serum and children's urinary cotinine were analyzed in duplicate using a double antibody radioimmunoassay. Results: At 4 years of age, the median urinary cotinine level in children increased 1.4 or 3.5 times when father or mother smoked, respectively. Cotinine levels in children's urine statistically differentiated children from smoking mothers (Geometric Mean (GM) 19.7 ng/ml; 95% CI 16.83–23.01) and exposed homes (GM 7.1 ng/ml; 95% CI 5.61–8.99) compared with non-exposed homes (GM 4.5 ng/ml; 95% CI 3.71–5.48). Maternal self-reported ETS exposure in homes declined in the four year span between the two time periods from 42.2% to 31.0% (p < 0.01). Nevertheless, most of the children considered non-exposed by their mothers had detectable levels of cotinine above 1 ng/mL in their urine.Conclusion: We concluded that cotinine levels determined in cord blood and urine, respectively, were useful for categorizing the children exposed to smoking and showed that a certain increase in ETS exposure during the 4-year follow-up period occurred.

A nonparametric method for the measurement of size diversity with emphasis on data standardization

The most suitable method for estimation of size diversity is investigated. Size diversity is computed on the basis of the Shannon diversity expression adapted for continuous variables, such as size. It takes the form of an integral involving the probability density function (pdf) of the size of the individuals. Different approaches for the estimation of pdf are compared: parametric methods, assuming that data come from a determinate family of pdfs, and nonparametric methods, where pdf is estimated using some kind of local evaluation. Exponential, generalized Pareto, normal, and log-normal distributions have been used to generate simulated samples using estimated parameters from real samples. Nonparametric methods include discrete computation of data histograms based on size intervals and continuous kernel estimation of pdf. Kernel approach gives accurate estimation of size diversity, whilst parametric methods are only useful when the reference distribution have similar shape to the real one. Special attention is given for data standardization. The division of data by the sample geometric mean is proposedas the most suitable standardization method, which shows additional advantages: the same size diversity value is obtained when using original size or log-transformed data, and size measurements with different dimensionality (longitudes, areas, volumes or biomasses) may be immediately compared with the simple addition of ln k where kis the dimensionality (1, 2, or 3, respectively). Thus, the kernel estimation, after data standardization by division of sample geometric mean, arises as the most reliable and generalizable method of size diversity evaluation

Geometric and long run aspects of Granger causality

This paper extends multivariate Granger causality to take into account the subspacesalong which Granger causality occurs as well as long run Granger causality. The propertiesof these new notions of Granger causality, along with the requisite restrictions, are derivedand extensively studied for a wide variety of time series processes including linear invertibleprocess and VARMA. Using the proposed extensions, the paper demonstrates that: (i) meanreversion in L2 is an instance of long run Granger non-causality, (ii) cointegration is a specialcase of long run Granger non-causality along a subspace, (iii) controllability is a special caseof Granger causality, and finally (iv) linear rational expectations entail (possibly testable)Granger causality restriction along subspaces.

Nanocrystalline M-type hexaferrite powders: preparation, geometric and magnetic propertiess

Co-Ti-Sn-Ge substituted M-type bariumhexaferrite powders with mean grain sizes between about 10 nm and about 1 ¿m and a narrow size distribution were prepared reproducibly by means of a modified glass crystallization method. At annealing temperatures between 560 and 580°C of the amorphous flakes nanocrystalline particles grow. They behave superparamagnetically at room temperature and change into stable magnetic single domains at lower temperatures. The magnetic volume of the powders is considerably less than the geometric one. However, the effective anisotropy fields are larger by a Factor of two to three.

Nanocrystalline M-type hexaferrite powders: preparation, geometric and magnetic propertiess

Co-Ti-Sn-Ge substituted M-type bariumhexaferrite powders with mean grain sizes between about 10 nm and about 1 ¿m and a narrow size distribution were prepared reproducibly by means of a modified glass crystallization method. At annealing temperatures between 560 and 580°C of the amorphous flakes nanocrystalline particles grow. They behave superparamagnetically at room temperature and change into stable magnetic single domains at lower temperatures. The magnetic volume of the powders is considerably less than the geometric one. However, the effective anisotropy fields are larger by a Factor of two to three.

Geometric tools to determine the hyperbolicity of limit cycles

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The mean dehn functions of Abelian groups

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Euclidean geometric invariant of framed knots in manifolds

We present an invariant of a three dimensional manifold with a framed knot in it based on the Reidemeister torsion of an acyclic complex of Euclidean geometric origin. To show its nontriviality, we calculate the invariant for some framed (un)knots in lens spaces. An important feature of our work is that we are not using any nontrivial representation of the manifold fundamental group or knot group.

A computational and geometric approach to phase resetting curves and surfaces

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Mean-payoff games and propositional proofs

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A geometric mechanism of diffusion: Rigorous verification in a priori unstable Hamiltonian systems

In this paper we consider a representative a priori unstable Hamiltonian system with 2+1/2 degrees of freedom, to which we apply the geometric mechanism for diffusion introduced in the paper Delshams et al., Mem.Amer.Math. Soc. 2006, and generalized in Delshams and Huguet, Nonlinearity 2009, and provide explicit, concrete and easily verifiable conditions for the existence of diffusing orbits. The simplification of the hypotheses allows us to perform explicitly the computations along the proof, which contribute to present in an easily understandable way the geometric mechanism of diffusion. In particular, we fully describe the construction of the scattering map and the combination of two types of dynamics on a normally hyperbolic invariant manifold.

Changes in wage structure in Mexico going beyond the mean: An analysis of differences in distribution, 1987-2008

This paper conducts an empirical analysis of the relationship between wage inequality, employment structure, and returns to education in urban areas of Mexico during the past two decades (1987-2008). Applying Melly’s (2005) quantile regression based decomposition, we find that changes in wage inequality have been driven mainly by variations in educational wage premia. Additionally, we find that changes in employment structure, including occupation and firm size, have played a vital role. This evidence seems to suggest that the changes in wage inequality in urban Mexico cannot be interpreted in terms of a skill-biased change, but rather they are the result of an increasing demand for skills during that period.

Kriging coordinates: what does that mean?

Kriging is an interpolation technique whose optimality criteria are based on normality assumptions either for observed or for transformed data. This is the case of normal, lognormal and multigaussian kriging.When kriging is applied to transformed scores, optimality of obtained estimators becomes a cumbersome concept: back-transformed optimal interpolations in transformed scores are not optimal in the original sample space, and vice-versa. This lack of compatible criteria of optimality induces a variety of problems in both point and block estimates. For instance, lognormal kriging, widely used to interpolate positivevariables, has no straightforward way to build consistent and optimal confidence intervals for estimates.These problems are ultimately linked to the assumed space structure of the data support: for instance, positive values, when modelled with lognormal distributions, are assumed to be embedded in the whole real space, with the usual real space structure and Lebesgue measure