Component And Correspondence Analysis
DOWNLOAD
Download Component And Correspondence Analysis PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Component And Correspondence Analysis book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Component And Correspondence Analysis
DOWNLOAD
Author : J. L. A. Van Rijckevorsel
language : en
Publisher:
Release Date : 1988-05-25
Component And Correspondence Analysis written by J. L. A. Van Rijckevorsel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988-05-25 with Mathematics categories.
Contributions to this work begin from a homogeneous-analytic point of view, then go further to analyze continuous variables, extending the geometrical approach of Gifi, and applying functional analytic techniques to problems involving replicated time series data (which are not subject to classical correspondence analysis and principal component analysis). Closing chapters address probability coding (which is related to fuzzy coding), and two approaches to component analysis: optimal scaling (which uses montone splines) embedded in a more classical statistical framework; and the connection between explorative multivariate data analysis and confirmation analysis based on statistical modelling.
Principal Component And Correspondence Analyses Using R
DOWNLOAD
Author : Hervé Abdi
language : en
Publisher: Springer
Release Date : 2015-05-05
Principal Component And Correspondence Analyses Using R written by Hervé Abdi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-05 with Computers categories.
With the right R packages, R is uniquely suited to perform Principal Component Analysis (PCA), Correspondence Analysis (CA), Multiple Correspondence Analysis (MCA), and metric multidimensional scaling (MMDS). The analyses depicted in this book use several packages specially developed for theses analyses and include (among others): the ExPosition suite, FactoMiner , ade4, and ca. The authors present each technique with one or several small examples that demonstrate how to enter the data, perform the standard analyses, and obtain professional quality graphics. Through explanations of the major options for how to carry out each method, readers can tailor the content of this book to their particular goals. Explanations include the effects of using particular packages. ExPosition is a great choice for the methods as it was written specifically for this book. However, options abound and are illustrated within unique scenarios. The first chapter includes installation of the packages. At the end of the book, a short appendix presents critical mathematical material for readers who want to go deeper into the theory.
Principal Component Analysis
DOWNLOAD
Author : I.T. Jolliffe
language : en
Publisher: Springer Science & Business Media
Release Date : 2002-10
Principal Component Analysis written by I.T. Jolliffe and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-10 with Mathematics categories.
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.
Principal Components Analysis
DOWNLOAD
Author : George H. Dunteman
language : en
Publisher: SAGE
Release Date : 1989-05
Principal Components Analysis written by George H. Dunteman and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989-05 with Mathematics categories.
For anyone in need of a concise, introductory guide to principal components analysis, this book is a must. Through an effective use of simple mathematical-geometrical and multiple real-life examples (such as crime statistics, indicators of drug abuse, and educational expenditures) -- and by minimizing the use of matrix algebra -- the reader can quickly master and put this technique to immediate use.
An Introduction To Correspondence Analysis
DOWNLOAD
Author : Eric J. Beh
language : en
Publisher: John Wiley & Sons
Release Date : 2021-04-09
An Introduction To Correspondence Analysis written by Eric J. Beh and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-09 with Mathematics categories.
Master the fundamentals of correspondence analysis with this illuminating resource An Introduction to Correspondence Analysis assists researchers in improving their familiarity with the concepts, terminology, and application of several variants of correspondence analysis. The accomplished academics and authors deliver a comprehensive and insightful treatment of the fundamentals of correspondence analysis, including the statistical and visual aspects of the subject. Written in three parts, the book begins by offering readers a description of two variants of correspondence analysis that can be applied to two-way contingency tables for nominal categories of variables. Part Two shifts the discussion to categories of ordinal variables and demonstrates how the ordered structure of these variables can be incorporated into a correspondence analysis. Part Three describes the analysis of multiple nominal categorical variables, including both multiple correspondence analysis and multi-way correspondence analysis. Readers will benefit from explanations of a wide variety of specific topics, for example: Simple correspondence analysis, including how to reduce multidimensional space, measuring symmetric associations with the Pearson Ratio, constructing low-dimensional displays, and detecting statistically significant points Non-symmetrical correspondence analysis, including quantifying asymmetric associations Simple ordinal correspondence analysis, including how to decompose the Pearson Residual for ordinal variables Multiple correspondence analysis, including crisp coding and the indicator matrix, the Burt Matrix, and stacking Multi-way correspondence analysis, including symmetric multi-way analysis Perfect for researchers who seek to improve their understanding of key concepts in the graphical analysis of categorical data, An Introduction to Correspondence Analysis will also assist readers already familiar with correspondence analysis who wish to review the theoretical and foundational underpinnings of crucial concepts.
Nonlinear Principal Component Analysis And Its Applications
DOWNLOAD
Author : Yuichi Mori
language : en
Publisher: Springer
Release Date : 2016-12-09
Nonlinear Principal Component Analysis And Its Applications written by Yuichi Mori and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-09 with Mathematics categories.
This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology. In the applications part of the book, four applications are introduced: variable selection for mixed measurement levels data, sparse MCA, joint dimension reduction and clustering methods for categorical data, and acceleration of ALS computation. The variable selection methods in PCA that originally were developed for numerical data can be applied to any types of measurement levels by using nonlinear PCA. Sparseness and joint dimension reduction and clustering for nonlinear data, the results of recent studies, are extensions obtained by the same matrix operations in nonlinear PCA. Finally, an acceleration algorithm is proposed to reduce the problem of computational cost in the ALS iteration in nonlinear multivariate methods. This book thus presents the usefulness of nonlinear PCA which can be applied to different measurement levels data in diverse fields. As well, it covers the latest topics including the extension of the traditional statistical method, newly proposed nonlinear methods, and computational efficiency in the methods.
Principal Component Analysis
DOWNLOAD
Author : I.T. Jolliffe
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Principal Component Analysis written by I.T. Jolliffe and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Mathematics categories.
Principal component analysis is probably the oldest and best known of the It was first introduced by Pearson (1901), techniques ofmultivariate analysis. and developed independently by Hotelling (1933). Like many multivariate methods, it was not widely used until the advent of electronic computers, but it is now weIl entrenched in virtually every statistical computer package. The central idea of principal component analysis is to reduce the dimen sionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Computation of the principal components reduces to the solution of an eigenvalue-eigenvector problem for a positive-semidefinite symmetrie matrix. Thus, the definition and computation of principal components are straightforward but, as will be seen, this apparently simple technique has a wide variety of different applications, as weIl as a number of different deri vations. Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some quite broad topics which are related to principal component analysis receive no more than a brief mention in the final two chapters.
Theory And Applications Of Correspondence Analysis
DOWNLOAD
Author : Michael J. Greenacre
language : en
Publisher:
Release Date : 1984
Theory And Applications Of Correspondence Analysis written by Michael J. Greenacre and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984 with Mathematics categories.
Geometric concepts in multidimensional space; Simple illustrations of correspondence analysis; Theory of correspondence analysis and equivalent approaches; Multiple correspondence analysis; Correspondence analysis of ratings and preferences; Use of correspondence analysis in discriminant analysis, classification, regression and cluster analysis; Special topics; Applications of correspondence analysis.
Multivariate Analysis Future Directions 2
DOWNLOAD
Author : C.M. Cuadras
language : en
Publisher: Elsevier
Release Date : 2014-05-21
Multivariate Analysis Future Directions 2 written by C.M. Cuadras and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-21 with Mathematics categories.
The contributions in this volume, made by distinguished statisticians in several frontier areas of research in multivariate analysis, cover a broad field and indicate future directions of research. The topics covered include discriminant analysis, multidimensional scaling, categorical data analysis, correspondence analysis and biplots, association analysis, latent variable models, bootstrap distributions, differential geometry applications and others. Most of the papers propose generalizations or new applications of multivariate analysis. This volume will be of great interest to statisticians, probabilists, data analysts and scientists working in the disciplines such as biology, biometry, ecology, medicine, econometry, psychometry and marketing. It will be a valuable guide to professors, researchers and graduate students seeking new and promising lines of statistical research.
Practical Guide To Principal Component Methods In R
DOWNLOAD
Author : Alboukadel KASSAMBARA
language : en
Publisher: STHDA
Release Date : 2017-08-23
Practical Guide To Principal Component Methods In R written by Alboukadel KASSAMBARA and has been published by STHDA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-23 with Education categories.
Although there are several good books on principal component methods (PCMs) and related topics, we felt that many of them are either too theoretical or too advanced. This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R. The visualization is based on the factoextra R package that we developed for creating easily beautiful ggplot2-based graphs from the output of PCMs. This book contains 4 parts. Part I provides a quick introduction to R and presents the key features of FactoMineR and factoextra. Part II describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. These methods include: Principal Component Analysis (PCA, for continuous variables), simple correspondence analysis (CA, for large contingency tables formed by two categorical variables) and Multiple CA (MCA, for a data set with more than 2 categorical variables). In Part III, you'll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA). Part IV covers hierarchical clustering on principal components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables.