Factor Analysis And Dimension Reduction In R

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Factor Analysis and Dimension Reduction in R

Author : G. David Garson
Publisher : Taylor & Francis
ISBN 13 : 1000810593
Total Pages : 547 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis Factor Analysis and Dimension Reduction in R by : G. David Garson

Download or read book Factor Analysis and Dimension Reduction in R written by G. David Garson and published by Taylor & Francis. This book was released on 2022-12-16 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: Factor Analysis and Dimension Reduction in R provides coverage, with worked examples, of a large number of dimension reduction procedures along with model performance metrics to compare them. Factor analysis in the form of principal components analysis (PCA) or principal factor analysis (PFA) is familiar to most social scientists. However, what is less familiar is understanding that factor analysis is a subset of the more general statistical family of dimension reduction methods. The social scientist's toolkit for factor analysis problems can be expanded to include the range of solutions this book presents. In addition to covering FA and PCA with orthogonal and oblique rotation, this book’s coverage includes higher-order factor models, bifactor models, models based on binary and ordinal data, models based on mixed data, generalized low-rank models, cluster analysis with GLRM, models involving supplemental variables or observations, Bayesian factor analysis, regularized factor analysis, testing for unidimensionality, and prediction with factor scores. The second half of the book deals with other procedures for dimension reduction. These include coverage of kernel PCA, factor analysis with multidimensional scaling, locally linear embedding models, Laplacian eigenmaps, diffusion maps, force directed methods, t-distributed stochastic neighbor embedding, independent component analysis (ICA), dimensionality reduction via regression (DRR), non-negative matrix factorization (NNMF), Isomap, Autoencoder, uniform manifold approximation and projection (UMAP) models, neural network models, and longitudinal factor analysis models. In addition, a special chapter covers metrics for comparing model performance. Features of this book include: Numerous worked examples with replicable R code Explicit comprehensive coverage of data assumptions Adaptation of factor methods to binary, ordinal, and categorical data Residual and outlier analysis Visualization of factor results Final chapters that treat integration of factor analysis with neural network and time series methods Presented in color with R code and introduction to R and RStudio, this book will be suitable for graduate-level and optional module courses for social scientists, and on quantitative methods and multivariate statistics courses.

Feature Engineering and Selection

Author : Max Kuhn
Publisher : CRC Press
ISBN 13 : 1351609467
Total Pages : 266 pages
Book Rating : 4.3/5 (516 download)

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Book Synopsis Feature Engineering and Selection by : Max Kuhn

Download or read book Feature Engineering and Selection written by Max Kuhn and published by CRC Press. This book was released on 2019-07-25 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.

Practical Guide To Principal Component Methods in R

Author : Alboukadel KASSAMBARA
Publisher : STHDA
ISBN 13 : 1975721136
Total Pages : 169 pages
Book Rating : 4.9/5 (757 download)

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Book Synopsis Practical Guide To Principal Component Methods in R by : Alboukadel KASSAMBARA

Download or read book Practical Guide To Principal Component Methods in R written by Alboukadel KASSAMBARA and published by STHDA. This book was released on 2017-08-23 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning Techniques for Multimedia

Author : Matthieu Cord
Publisher : Springer Science & Business Media
ISBN 13 : 3540751718
Total Pages : 297 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Machine Learning Techniques for Multimedia by : Matthieu Cord

Download or read book Machine Learning Techniques for Multimedia written by Matthieu Cord and published by Springer Science & Business Media. This book was released on 2008-02-07 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Arising from the EU MUSCLE network, this multidisciplinary book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain.

Univariate, Bivariate, and Multivariate Statistics Using R

Author : Daniel J. Denis
Publisher : John Wiley & Sons
ISBN 13 : 1119549930
Total Pages : 384 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Univariate, Bivariate, and Multivariate Statistics Using R by : Daniel J. Denis

Download or read book Univariate, Bivariate, and Multivariate Statistics Using R written by Daniel J. Denis and published by John Wiley & Sons. This book was released on 2020-04-14 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical source for performing essential statistical analyses and data management tasks in R Univariate, Bivariate, and Multivariate Statistics Using R offers a practical and very user-friendly introduction to the use of R software that covers a range of statistical methods featured in data analysis and data science. The author— a noted expert in quantitative teaching —has written a quick go-to reference for performing essential statistical analyses and data management tasks in R. Requiring only minimal prior knowledge, the book introduces concepts needed for an immediate yet clear understanding of statistical concepts essential to interpreting software output. The author explores univariate, bivariate, and multivariate statistical methods, as well as select nonparametric tests. Altogether a hands-on manual on the applied statistics and essential R computing capabilities needed to write theses, dissertations, as well as research publications. The book is comprehensive in its coverage of univariate through to multivariate procedures, while serving as a friendly and gentle introduction to R software for the newcomer. This important resource: Offers an introductory, concise guide to the computational tools that are useful for making sense out of data using R statistical software Provides a resource for students and professionals in the social, behavioral, and natural sciences Puts the emphasis on the computational tools used in the discovery of empirical patterns Features a variety of popular statistical analyses and data management tasks that can be immediately and quickly applied as needed to research projects Shows how to apply statistical analysis using R to data sets in order to get started quickly performing essential tasks in data analysis and data science Written for students, professionals, and researchers primarily in the social, behavioral, and natural sciences, Univariate, Bivariate, and Multivariate Statistics Using R offers an easy-to-use guide for performing data analysis fast, with an emphasis on drawing conclusions from empirical observations. The book can also serve as a primary or secondary textbook for courses in data analysis or data science, or others in which quantitative methods are featured.

Computational Genomics with R

Author : Altuna Akalin
Publisher : CRC Press
ISBN 13 : 1498781861
Total Pages : 462 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Computational Genomics with R by : Altuna Akalin

Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

Multiple Factor Analysis by Example Using R

Author : Jérôme Pagès
Publisher : CRC Press
ISBN 13 : 1482205483
Total Pages : 272 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Multiple Factor Analysis by Example Using R by : Jérôme Pagès

Download or read book Multiple Factor Analysis by Example Using R written by Jérôme Pagès and published by CRC Press. This book was released on 2014-11-20 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoretical and methodological aspects of MFA. It also inc

Statistics and Data Analysis Through R

Author : César Pérez López
Publisher : Independently Published
ISBN 13 :
Total Pages : 222 pages
Book Rating : 4.5/5 (69 download)

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Book Synopsis Statistics and Data Analysis Through R by : César Pérez López

Download or read book Statistics and Data Analysis Through R written by César Pérez López and published by Independently Published. This book was released on 2020-11-08 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the implementation of statistics and data analysis through R. It deals first with the Exploratory Data Analysis both numerically and graphically, which is always a technique prior to any other statistical analysis. Descriptive statistics and the calculation of probabilities are then developed. Subsequently, the multiple regression model is approached, focusing on the problems of its estimation and diagnosis. It also delves into the generalized linear models and the analysis of variance and covariance models. Dimension reduction techniques are also addressed with special emphasis on principal component analysis and factor analysis. Finally, the segmentation techniques related to hierarchical and non-hierarchical cluster analysis are presented.

Statistical Methods in Social Science Research

Author : S P Mukherjee
Publisher : Springer
ISBN 13 : 9811321469
Total Pages : 152 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Statistical Methods in Social Science Research by : S P Mukherjee

Download or read book Statistical Methods in Social Science Research written by S P Mukherjee and published by Springer. This book was released on 2018-10-05 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents various recently developed and traditional statistical techniques, which are increasingly being applied in social science research. The social sciences cover diverse phenomena arising in society, the economy and the environment, some of which are too complex to allow concrete statements; some cannot be defined by direct observations or measurements; some are culture- (or region-) specific, while others are generic and common. Statistics, being a scientific method – as distinct from a ‘science’ related to any one type of phenomena – is used to make inductive inferences regarding various phenomena. The book addresses both qualitative and quantitative research (a combination of which is essential in social science research) and offers valuable supplementary reading at an advanced level for researchers.

Practical Guide to Cluster Analysis in R

Author : Alboukadel Kassambara
Publisher : STHDA
ISBN 13 : 1542462703
Total Pages : 187 pages
Book Rating : 4.5/5 (424 download)

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Book Synopsis Practical Guide to Cluster Analysis in R by : Alboukadel Kassambara

Download or read book Practical Guide to Cluster Analysis in R written by Alboukadel Kassambara and published by STHDA. This book was released on 2017-08-23 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although there are several good books on unsupervised machine learning, we felt that many of them are too theoretical. This book provides practical guide to cluster analysis, elegant visualization and interpretation. It contains 5 parts. Part I provides a quick introduction to R and presents required R packages, as well as, data formats and dissimilarity measures for cluster analysis and visualization. Part II covers partitioning clustering methods, which subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst. Partitioning clustering approaches include: K-means, K-Medoids (PAM) and CLARA algorithms. In Part III, we consider hierarchical clustering method, which is an alternative approach to partitioning clustering. The result of hierarchical clustering is a tree-based representation of the objects called dendrogram. In this part, we describe how to compute, visualize, interpret and compare dendrograms. Part IV describes clustering validation and evaluation strategies, which consists of measuring the goodness of clustering results. Among the chapters covered here, there are: Assessing clustering tendency, Determining the optimal number of clusters, Cluster validation statistics, Choosing the best clustering algorithms and Computing p-value for hierarchical clustering. Part V presents advanced clustering methods, including: Hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and Density-based clustering.

Mastering Data Analysis with R

Author : Gergely Daroczi
Publisher : Packt Publishing Ltd
ISBN 13 : 1783982039
Total Pages : 397 pages
Book Rating : 4.7/5 (839 download)

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Book Synopsis Mastering Data Analysis with R by : Gergely Daroczi

Download or read book Mastering Data Analysis with R written by Gergely Daroczi and published by Packt Publishing Ltd. This book was released on 2015-09-30 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization About This Book Handle your data with precision and care for optimal business intelligence Restructure and transform your data to inform decision-making Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn Connect to and load data from R's range of powerful databases Successfully fetch and parse structured and unstructured data Transform and restructure your data with efficient R packages Define and build complex statistical models with glm Develop and train machine learning algorithms Visualize social networks and graph data Deploy supervised and unsupervised classification algorithms Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.

Factor analysis and principal component analysis

Author : Di Franco
Publisher : FrancoAngeli
ISBN 13 : 8820457520
Total Pages : 246 pages
Book Rating : 4.8/5 (24 download)

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Book Synopsis Factor analysis and principal component analysis by : Di Franco

Download or read book Factor analysis and principal component analysis written by Di Franco and published by FrancoAngeli. This book was released on 2013 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Principal Component Analysis

Author : I.T. Jolliffe
Publisher : Springer Science & Business Media
ISBN 13 : 1475719043
Total Pages : 283 pages
Book Rating : 4.4/5 (757 download)

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Book Synopsis Principal Component Analysis by : I.T. Jolliffe

Download or read book Principal Component Analysis written by I.T. Jolliffe and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Discovering Statistics Using R

Author : Andy Field
Publisher : SAGE
ISBN 13 : 144628915X
Total Pages : 994 pages
Book Rating : 4.4/5 (462 download)

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Book Synopsis Discovering Statistics Using R by : Andy Field

Download or read book Discovering Statistics Using R written by Andy Field and published by SAGE. This book was released on 2012-03-07 with total page 994 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keeping the uniquely humorous and self-deprecating style that has made students across the world fall in love with Andy Field′s books, Discovering Statistics Using R takes students on a journey of statistical discovery using R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences throughout the world. The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next you discover the importance of exploring and graphing data, before moving onto statistical tests that are the foundations of the rest of the book (for example correlation and regression). You will then stride confidently into intermediate level analyses such as ANOVA, before ending your journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help you gain the necessary conceptual understanding of what you′re doing, the emphasis is on applying what you learn to playful and real-world examples that should make the experience more fun than you might expect. Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, together with hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more. Given this book′s accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software.

Modern Dimension Reduction

Author : Philip D. Waggoner
Publisher : Cambridge University Press
ISBN 13 : 1108991645
Total Pages : 98 pages
Book Rating : 4.1/5 (89 download)

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Book Synopsis Modern Dimension Reduction by : Philip D. Waggoner

Download or read book Modern Dimension Reduction written by Philip D. Waggoner and published by Cambridge University Press. This book was released on 2021-08-05 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github.

Robust Methods for Data Reduction

Author : Alessio Farcomeni
Publisher : CRC Press
ISBN 13 : 9780367783518
Total Pages : 0 pages
Book Rating : 4.7/5 (835 download)

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Book Synopsis Robust Methods for Data Reduction by : Alessio Farcomeni

Download or read book Robust Methods for Data Reduction written by Alessio Farcomeni and published by CRC Press. This book was released on 2021-03-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component analysis, canonical correlation analysis, factor analysis, clustering, double clustering, and discriminant analysis. The first part of the book illustrates how dimension reduction techniques synthesize available information by reducing the dimensionality of the data. The second part focuses on cluster and discriminant analysis. The authors explain how to perform sample reduction by finding groups in the data. Despite considerable theoretical achievements, robust methods are not often used in practice. This book fills the gap between theoretical robust techniques and the analysis of real data sets in the area of data reduction. Using real examples, the authors show how to implement the procedures in R. The code and data for the examples are available on the book's CRC Press web page.

Places Rated Almanac

Author : David Savageau
Publisher : Prentice Hall
ISBN 13 : 9780671849474
Total Pages : 452 pages
Book Rating : 4.8/5 (494 download)

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Book Synopsis Places Rated Almanac by : David Savageau

Download or read book Places Rated Almanac written by David Savageau and published by Prentice Hall. This book was released on 1993 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: This sometimes controversial bestseller, completely updated with all new statistics, is packed with timely facts and unbiased information on more than 300 metropolitan areas in the U.S. and Canada. Each city is ranked according to costs of living, crime rates, cultural life, and environmental factors.