BiplotGUI: Interactive Biplots in R

Biplots simultaneously provide information on both the samples and the variables of a data matrix in two- or three-dimensional representations. The BiplotGUI package provides a graphical user interface for the construction of, interaction with, and manipulation of biplots in R. The samples are represented as points, with coordinates determined either by the choice of biplot, principal coordinate analysis or multidimensional scaling. Various transformations and dissimilarity metrics are available. Information on the original variables is incorporated by linear or non-linear calibrated axes. Goodness-of-fit measures are provided. Additional descriptors can be superimposed, including convex hulls, alpha-bags, point densities and classification regions. Amongst the interactive features are dynamic variable value prediction, zooming and point and axis drag-and-drop. Output can easily be exported to the R workspace for further manipulation. Three-dimensional biplots are incorporated via the rgl package. The user requires almost no knowledge of R syntax.

Classification PDO olive oils on the basis of their sterol composition by multivariate analysis

The sterol compositions (GLC/FID/capillary column) of monovarietal olive oils (51 samples) from the most important cultivars of northeastern Portugal (Cvs. Cobranc¸osa, Madural and Verdeal Transmontana) and 27 commercial samples of olive oils with protected denomination of origin (PDO) from the same region and cultivars were evaluated. Δ-sitosterol, Δ5-avenasterol and campesterol were the most representative sterols. Cholesterol, stigmasterol, clerosterol and Δ7-stigmastenol were also found in all samples. All studied samples respected EC Regulation N. 2568, and in all cases total sterols were remarkably higher than the minimum limit set by legislation, ranging from 2003 to 2682 mg/kg. Results were analysed with the help of several statistical techniques, including reduction of dimensionality by principal component analysis with cross-validation of the number of components, followed by the use of canonical variate predictive biplots for model development and canonical variate interpolative biplots for approximate classification of monovarietal and PDO olive oils. These biplots proved to be a very interesting solution in the present case study, overcoming the problems of interpretation and classification that arise whenever different multivariate analyses are coupled together

Analisis Variabel Kanonik Biplot Untuk Bank Umum Di Jawa Tengah

Bank Competition in Indonesia increase due to good economic growth and the improvement of the social middle class in Indonesia. Increased bank raises the fierce competition between banks and internal banks themselves. This makes the management of the bank should work seriously to maintain its existence. In this case the assessment of the bank become very important in the banking business to survive in today's banking industry. This study was conducted to determine the competitive commercial banks operating in Central Java with the Canonical Variate Analysis (CVA) Biplot. This analysis can be applied to find out information about the relative position, the similarity between the object characteristics and diversity of variables in the three groups of commercial banks in Central Java, namely state-owned banks, private banks and private banks Non Foreign Exchange, based on the health aspects of the bank. The results obtained are the banks in each group had different characteristics shown in the relative position of the already well-separated in the resulting biplot. Variables that tend to influence the grouping of commercial banks are Capital Adequacy Ratio (CAR). The total assets is variable with the highest level of prediction accuracy on each bank

BiplotGUI: Interactive Biplots in R

Biplots simultaneously provide information on both the samples and the variables ofa data matrix in two- or three-dimensional representations. The BiplotGUI package provides a graphical user interface for the construction of, interaction with, and manipulation of biplots in R. The samples are represented as points, with coordinates determined either by the choice of biplot, principal coordinate analysis or multidimensional scaling. Various transformations and dissimilarity metrics are available. Information on the original variables is incorporated by linear or non-linear calibrated axes. Goodness-of-t measures are provided. Additional descriptors can be superimposed, including convex hulls, alpha-bags, point densities and classication regions. Amongst the interactive features are dynamic variable value prediction, zooming and point and axis drag-and-drop. Output can easily be exported to the R workspace for further manipulation. Three-dimensional biplots are incorporated via the rgl package. The user requires almost no knowledge of R syntax

Contribution biplots

In order to interpret the biplot it is necessary to know which points – usually variables – are the ones that are important contributors to the solution, and this information is available separately as part of the biplot’s numerical results. We propose a new scaling of the display, called the contribution biplot, which incorporates this diagnostic directly into the graphical display, showing visually the important contributors and thus facilitating the biplot interpretation and often simplifying the graphical representation considerably. The contribution biplot can be applied to a wide variety of analyses such as correspondence analysis, principal component analysis, log-ratio analysis and the graphical results of a discriminant analysis/MANOVA, in fact to any method based on the singular-value decomposition. In the contribution biplot one set of points, usually the rows of the data matrix, optimally represent the spatial positions of the cases or sample units, according to some distance measure that usually incorporates some form of standardization unless all data are comparable in scale. The other set of points, usually the columns, is represented by vectors that are related to their contributions to the low-dimensional solution. A fringe benefit is that usually only one common scale for row and column points is needed on the principal axes, thus avoiding the problem of enlarging or contracting the scale of one set of points to make the biplot legible. Furthermore, this version of the biplot also solves the problem in correspondence analysis of low-frequency categories that are located on the periphery of the map, giving the false impression that they are important, when they are in fact contributing minimally to the solution.biplot, contributions, correspondence analysis, discriminant analysis, log-ratio analysis, MANOVA, principal component analysis, scaling, singular value decomposition, weighting.

Analysis of matched matrices

We consider the joint visualization of two matrices which have common rows and columns, for example multivariate data observed at two time points or split accord-ing to a dichotomous variable. Methods of interest include principal components analysis for interval-scaled data, or correspondence analysis for frequency data or ratio-scaled variables on commensurate scales. A simple result in matrix algebra shows that by setting up the matrices in a particular block format, matrix sum and difference components can be visualized. The case when we have more than two matrices is also discussed and the methodology is applied to data from the International Social Survey Program.Correspondence analysis, International Social Survey Program (ISSP), matched matrices, principal component analysis, singular-value decomposition

Assessing the variability of the fatty acid profile and cholesterol content of meat sausages

Eighteen different brands of meat sausages including pork, poultry and the mixture of both meats (pork and poultry) in sausages, were analysed for their nutritional composition (total fat, moisture, crude protein and ash), cholesterol content and fatty acid composition. As expected, the pork Frankfurter sausages presented a higher fat content compared to sausages that include poultry meat in their composition. A multivariate statistical analysis was applied to the data showing the existence of significant differences among samples. Regarding fatty acid composition, significant differences were verified in canonical variate plots when the samples were grouped by sausage type, suggesting that the fatty acid profile is strongly influenced by the type of meats, as well as other ingredients such as vegetable oil and lard, used in its formulation. The group of poultry Frankfurter sausages presented lower levels of SFA and higher levels of PUFA, which can point to a healthier profile compared to the pork and meat mixture sausages. Nevertheless, some poultry sausages showed a higher cholesterol content compared to the pork Frankfurters. The lowest mean cholesterol content was obtained for the group of pork Frankfurters, which somehow contradicts the consumers' idea that pork meat products should be avoided due to its high cholesterol levels.The authors acknowledge the grant no. PEst-C/EQB/LA0006/2011 to FCT - Fundação para a Ciência e a Tecnologia. Sónia Soares is grateful to FCT PhD grant (SFRH/BD/75091/2010).info:eu-repo/semantics/publishedVersio

Detection and Quantification of Grapevine Bunch Rot Using Functional Data Analysis and Canonical Variate Analysis Biplots of Infrared Spectral Data

Grapevine bunch rot assessment has economic significance to wineries. Industrial working conditionsrequire rapid assessment methods to meet the time constraints typically associated with grape intakeat large wineries. Naturally rot-affected and healthy white wine grape bunches were collected overfive vintages (2013 to 2016, 2020). Spectral data of 382 grape must samples were acquired using threedifferent, but same-type attenuated total reflection mid-infrared (ATR-MIR) ALPHA spectrometers. Thepractical industrial problem of wavenumber shifts collected with different spectrometers was overcome byapplying functional data analysis (FDA). FDA improved the data quality and boosted data mining effortsin the sample set. Canonical variate analysis (CVA) biplots were employed to visualise the detection andquantification of rot. When adding 90 % alpha-bags to CVA biplots minimal overlap between rot-affected(Yes) and healthy (No) samples was observed. Several bands were observed in the region 1734 cm-1 to 1722cm-1 which correlated with the separation between rot-affected and healthy grape musts. These bandsconnect to the C=O stretching of the functional groups of carboxylic acids. In addition, wavenumber 1041cm-1, presenting the functional group of ethanol, contributed to the separation between categories (severity% range). ATR-MIR could provide a sustainable alternative for rapid and automated rot assessment.However, qualitative severity quantification of rot was limited to only discriminating between healthy andsevere rot (> 40 %). This study is novel in applying FDA to correct wavenumber shifts in ATR-MIR spectraldata. Furthermore, visualisation of the viticultural data set using CVA biplots is a novel application of thistechnique