Biplots

Biplots are a graphical method for simultaneously displaying two kinds of information; typically, the variables and sample units described by a multivariate data matrix or the items labelling the rows and columns of a two-way table. This book aims to popularize what is now seen to be a useful and reliable method for the visualization of multidimensional data associated with, for example, principal component analysis, canonical variate analysis, multidimensional scaling, multiplicative interaction and various types of correspondence analysis. Understanding Biplots: • Introduces theory and techniques which can be applied to problems from a variety of areas, including ecology, biostatistics, finance, demography and other social sciences. • Provides novel techniques for the visualization of multidimensional data and includes data mining techniques. • Uses applications from many fields including finance, biostatistics, ecology, demography. • Looks at dealing with large data sets as well as smaller ones. • Includes colour images, illustrating the graphical capabilities of the methods. • Is supported by a Website featuring R code and datasets. Researchers, practitioners and postgraduate students of statistics and the applied sciences will find this book a useful introduction to the possibilities of presenting data in informative ways.

Este libro explica las aplicaciones específicas y las interpretaciones del biplot en muchas áreas del análisis multivariante. regresión, modelos lineales generalizados, análisis de componentes principales, análisis de correspondencias y análisis discriminante.

Biplots are the multivariate analog of scatter plots, approximating the multivariate distribution of a sample in a few dimensions to produce a graphic display. In addition, they superimpose representations of the variables on this display so that the relationships between the sample and the variable can be studied. Like scatter plots, biplots are useful for detecting patterns and for displaying the results found by more formal methods of analysis. In recent years the theory of biplots has been considerably extended. The approach adopted here is geometric, permitting a natural integration of well-known methods, such as components analysis, correspondence analysis, and canonical variate analysis as well as some newer and less well-known methods, such as nonlinear biplots and biadditive models.

Drawing on the author’s experience in social and environmental research, Correspondence Analysis in Practice, Second Edition shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. This completely revised, up-to-date edition features a didactic approach with self-contained chapters, extensive marginal notes, informative figure and table captions, and end-of-chapter summaries. New to the Second Edition • Five new chapters on transition and regression relationships, stacked tables, subset correspondence analysis, analysis of square tables, and canonical correspondence analysis • Substantially more figures and tables than the first edition • A computational appendix that provides the R commands that correspond to most of the analyses featured throughout the book, making it easy for readers to reproduce the analyses With 33 years of CA experience, the expert author demonstrates how to use uncomplicated, relatively nonmathematical techniques to translate complex tabular data into more readable graphical forms. CA and its variants multiple CA (MCA) and joint CA (JCA) are suitable for analyses in various fields, including marketing research, the social and environmental sciences, biochemistry, and more.

Multidimensional scaling covers a variety of statistical techniques in the area of multivariate data analysis. Geared toward dimensional reduction and graphical representation of data, it arose within the field of the behavioral sciences, but now holds techniques widely used in many disciplines. Multidimensional Scaling, Second Edition extends the popular first edition and brings it up to date. It concisely but comprehensively covers the area, summarizing the mathematical ideas behind the various techniques and illustrating the techniques with real-life examples. A computer disk containing programs and data sets accompanies the book.

Regression, analysis of variance, correlation, graphical.

Variety trials are an essential step in crop breeding and production. These trials are a significant investment in time and resources and inform numerous decisions from cultivar development to end-use. Crop Variety Trials: Methods and Analysis is a practical volume that provides valuable theoretical foundations as well as a guide to step-by-step implementation of effective trial methods and analysis in determining the best varieties and cultivars. Crop Variety Trials is divided into two sections. The first section provides the reader with a sound theoretical framework of variety evaluation and trial analysis. Chapters provide insights into the theories of quantitative genetics and principles of analyzing data. The second section of the book gives the reader with a practical step-by-step guide to accurately analyzing crop variety trial data. Combined these sections provide the reader with fuller understanding of the nature of variety trials, their objectives, and user-friendly database and statistical tools that will enable them to produce accurate analysis of data.

The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. It includes core material, current research and a wide range of applications. Its length is nearly double that of the first edition.