Comparisons of Some Graphical Methods for Exploratory Multivariate Data Analysis

The role of permutation tests in exploratory multivariate data analysis

In this paper, we propose tools for studying biophysical characteristics of a person by processing polygraph charts. We present the results produced by two methods of multivariate data analysis: the principal component analysis (PCA) and projection on latent structures (PLS). Data of biophysical state of a person during the polygraph testing - galvanic skin response (GSP), photoplethysmogram, blood pressure, and other biophysical characteristics – are processed and analyzed.It is shown that load diagrams are the preferable method to identify the relationship between the biophysical characteristics of a person. The principal component analysis provides an opportunity to assess minor changes in biophysical characteristics of a person and to take into account situational significant identifiers. They can attest to the fact that a possibly significant physical-chemical process runs in a human body.It is demonstrated that the projection on latent structures should be used to develop regression models that can constantly be used in polygraph testing.It is concluded that proposed methods of multivariate data analysis can help in an investigation of biophysical characteristics of a person using polygraph data. Also, the proposed methods are able to detect possible malfunctions in the responsible physical-chemical processes. The obtained results enable solving practical problems of information security matters, for example, the identification of an insider.DOI 10.14258/izvasu(2018)1-05

The performance of neural networks in classifying mass spectral data is evaluated and compared to methods of multivariate data analysis and pattern recognition. Back propagation networks are matched with linear discriminant analysis, Kohonen feature maps are compared to the knearest neighbour clustering algorithm. Eight classifiers were trained, in order to discriminate mass spectra of steroids from eight distinct classes of chemical compounds. The results obtained show slightly better performance of Kohonen networks compared to k-nearest neighbour clustering and equal performance of multi-layer perceptrons and discriminant analysis.

Multivariate data analysis for Raman imaging of a model pharmaceutical tablet

This article is intended as a guideline to the use of two exploratory data analysis methods, namely STATICO and COSTATIS. Both techniques have already been used in the field of ecological data analysis, and we present a rapid survey of the ecological literature on three-ways analysis methods. Here, we wish to share some advanced computation and graphical display scripts to help ecologists use these methods. We first recall the main principles of these two methods for the analysis of the relationships between the structures of two series of data tables. In the context of ecology, these two series can be for example (1) a series of species data tables and (2) a series of environmental parameters tables. A detailed, real-size example is presented to show how this strategy can be put in place using the ade4 and adegraphics packages for R. This example relates to the ecology of aquatic Heteroptera in the Medjerda watershed (Tunisia). We show how the outputs of the two methods can be used to interpret the relationships between aquatic Heteroptera species distribution and environmental parameters. Several R scripts to conduct the computations and draw suitable graphical displays are reproduced and explained in the text and in five appendices.

Five multivariate data analysis methods, namely principal components analysis, non-linear mapping, hierarchical clustering, minimal spanning tree and mode analysis, were used to analyse the natural structure of red wines from Tarragona (Catalonia, Spain). Seventeen parameters were analysed for each sample to determine the multi-dimensional spectrum in which the wines are studied. In spite of the homogeneity of the wine samples which resulted from the similarity of the edaphic, varietal factors as well as the cultural conditions of the producing areas, two different clusters can be distinguished on the basis of all the methods used. The groups of wine samples obtained coincide with two viticultural zones that differ with regard to climate and geographical conditions. Therefore, a natural distinction can bei made between the wines produced in each adjacent zone, forming the basis for future differentiation according to brand name.

The purpose of the research was to identify the method of preparation and the composition of aceclofenac based nanoparticles using various multivariate data analysis methods. Top down and bottom up approaches were used for the preparation of the nanoparticles and polymers like hydroxylpropyl methylcellulose and polyvinyl pyrrolidone, surfactants like sodium lauryl sulphate and Tween 80 were screened for the purpose of formulation. Different combinations of these factors were used for designing of the experiments and the obtained products were evaluated for particle size, poly dispersibility index, zeta potential, drug content and entrapment efficiency. Principal component analysis, agglomerative hierarchical clustering, composite desirability index aided to identify the best possible combination. Nanoprecipitation along with sonication as process and poly vinyl pyrrolidone as stabilizer gave the highest values of desirability index. The formulation composition of nanosuspension was oscillated around the settings for the zeroing in on optimum quantitative formulation. The nanosuspension was gelled using Carbopol. The gel was studied for in vitro and ex vivo permeation characteristics. The optimized formulation was found to be stable and had acceptable levels of irritation as studied in wistar rats. Thus various data analysis methods could help identify the right combination of process and formulation composition.

Contents and enantiomeric compositions of monoterpene hydrocarbons in xylem oleoresins from four Pinus species growing in Cuba. Comparison of trees unattacked and attacked by Dioryctria horneana

An important determinant of the level of development of each country and the whole Europe is the care about an adequate level of well-being and quality of life of all citizens, above all those to whom the future belongs - our children. In the times of demographic changes it is particularly important to understand specific needs and rights of the youngest generation of Europeans. Investing in children is investing in our future, the shape of which will depend precisely on whether the youngest generation will be healthy, well educated and able to participate in the development of their societies. The aim of this study is to identify territorial differentiation of children’s well-being in Europe at the end of the first decade of the 21st century using the methods of multivariate data analysis. The study was based on data published by Eurostat (among others EU-SILC), OECD (PISA), UNICEF, WORLD BANK and WHO (HBSC) for the years 2009 and 2010.

This project aims to present a methodology to study the relationships between physiological measures and evaluation metrics for 3D interaction techniques using methods for multivariate data analysis. Physiological responses, such as heart rate and skin conductance, offer objective data about the user stress during interaction. This could be useful, for instance, to evaluate qualitative aspects of interaction techniques without relying on solely subjective data. Moreover, these data could contribute to improve task performance analysis by measuring different responses to 3D interaction techniques. With this in mind, we propose a methodology that defines a testing protocol, a normalization procedure and statistical techniques, considering the use of physiological measures during the evaluation process. A case study comparison between two 3D interaction techniques (ray-casting and HOMER) shows promising results, pointing to heart rate variability, as measured by the NN50 parameter, as a potential index of task performance. Further studies are needed in order to establish guidelines for evaluation processes based on welldefined associations between human behaviors and human actions realized in 3D user interfaces.

PurposeThe purpose of this paper aims at investigating which lean supply chain management (LSCM) practices efficiently improve the supply chain performance.Design/methodology/approachTo achieve that, a cross-sectional survey was carried out with 113 manufacturing companies undergoing a lean implementation. The proposed method combines complementary methods of multivariate data analysis in order to determine which bundles of LSCM practices more efficiently entail improvements on supply chain performance.FindingsThe findings justify why some LSCM initiatives may find larger barriers than others, compromising their success due to misguided implementation efforts according to the desired performance improvement.Originality/valueThe empirical examination on the efficiency of LSCM practices with regards to a certain set of performance indicators provides guidelines with respect to LSCM implementation depending on which performance indicators are envisioned for improvement.

This paper reviewed the classical principal components analysis methods for multivariate data analysis and feature extraction in pattern classification. A kernel-based extension to the classical PCA models was discussed to cope with nonlinear data dependencies. Kernel PCA was implicitly performing a linear PCA in some high-dimensional kernel feature space that was nonlinearly related to input space by using a suitable nonlinear kernel function mapping. And then, the conjunction of kernel PCA method and RBF neural networks was proposed in practical and algorithmic considerations. Finally, we illustrate the usefulness of kernel PCA algorithms by discussing kernel PCA RBF neural networks application in handwritten digit classification

The goal of this work is the comparison of differences in the lipidomic compositions of human cell lines derived from normal and cancerous breast tissues, and tumor vs. normal tissues obtained after the surgery of breast cancer patients. Hydrophilic interaction liquid chromatography/electrospray ionization mass spectrometry (HILIC/ESI-MS) using the single internal standard approach and response factors is used for the determination of relative abundances of individual lipid species from five lipid classes in total lipid extracts of cell lines and tissues. The supplementary information on the fatty acyl composition is obtained by gas chromatography/mass spectrometry (GC/MS) of fatty acid methyl esters. Multivariate data analysis (MDA) methods, such as nonsupervised principal component analysis (PCA), hierarchical clustering analysis (HCA) and supervised orthogonal partial least-squares discriminant analysis (OPLS-DA), are used for the visualization of differences between normal and tumor samples and the correlation of similarity between cell lines and tissues either for tumor or normal samples. MDA methods are used for differentiation of sample groups and also for identification of the most up- and downregulated lipids in tumor samples in comparison to normal samples. Observed changes are subsequently generalized and correlated with data from tumor and normal tissues of breast cancer patients. In total, 123 lipid species are identified based on their retention behavior in HILIC and observed ions in ESI mass spectra, and relative abundances are determined. MDA methods are applied for a clear differentiation between tumor and normal samples both for cell lines and tissues. The most upregulated lipids are phospholipids (PL) with a low degree of unsaturation (e.g., 32:1 and 34:1) and also some highly polyunsaturated PL (e.g., 40:6), while the most downregulated lipids are PL containing polyunsaturated fatty acyls (e.g., 20:4), plasmalogens and ether lipids. Copyright © 2016 John Wiley & Sons, Ltd.

Variation patterns of nitric oxide in Catalonia during the period from 2001 to 2006 using multivariate data analysis methods

Multivariate data analysis methods, although widely diffused since the 1960’s, are really much older. Hotelling founded both the Principal Component Analysis and the Canonical Analysis in the 30’s, by further developing previous studies made by Spearman and Pearson at the beginning of the century. These methods were subsequently expanded and enriched, but remained beyond the reach of the common practitioner because of the massive calculations they required. With the birth, and subsequent dramatic development of computers, multivariate data analysis methods have finally become practicable (Bouroche and Saporta, 1983).KeywordsFactor ScoremUltivariate MethodMultivariate Data AnalysisAmes TestHydrazine SulfateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.