Multivariate Data Integration Using R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Multivariate Data Integration Using R write by Kim-Anh Lê Cao. This book was released on 2021-11-08. Multivariate Data Integration Using R available in PDF, EPUB and Kindle. Large biological data, which are often noisy and high-dimensional, have become increasingly prevalent in biology and medicine. There is a real need for good training in statistics, from data exploration through to analysis and interpretation. This book provides an overview of statistical and dimension reduction methods for high-throughput biological data, with a specific focus on data integration. It starts with some biological background, key concepts underlying the multivariate methods, and then covers an array of methods implemented using the mixOmics package in R. Features: Provides a broad and accessible overview of methods for multi-omics data integration Covers a wide range of multivariate methods, each designed to answer specific biological questions Includes comprehensive visualisation techniques to aid in data interpretation Includes many worked examples and case studies using real data Includes reproducible R code for each multivariate method, using the mixOmics package The book is suitable for researchers from a wide range of scientific disciplines wishing to apply these methods to obtain new and deeper insights into biological mechanisms and biomedical problems. The suite of tools introduced in this book will enable students and scientists to work at the interface between, and provide critical collaborative expertise to, biologists, bioinformaticians, statisticians and clinicians.
Multivariate Data Integration Using R
Multivariate Data Integration Using R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Multivariate Data Integration Using R write by Kim-Anh Lê Cao. This book was released on 2022. Multivariate Data Integration Using R available in PDF, EPUB and Kindle.
Using R With Multivariate Statistics
Using R With Multivariate Statistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Using R With Multivariate Statistics write by Randall E. Schumacker. This book was released on 2015-07-06. Using R With Multivariate Statistics available in PDF, EPUB and Kindle. Using R with Multivariate Statistics is a quick guide to using R, free-access software available for Windows and Mac operating systems that allows users to customize statistical analysis. Designed to serve as a companion to a more comprehensive text on multivariate statistics, this book helps students and researchers in the social and behavioral sciences get up to speed with using R. It provides data analysis examples, R code, computer output, and explanation of results for every multivariate statistical application included. In addition, R code for some of the data set examples used in more comprehensive texts is included, so students can run examples in R and compare results to those obtained using SAS, SPSS, or STATA. A unique feature of the book is the photographs and biographies of famous persons in the field of multivariate statistics.
Exploratory Multivariate Analysis by Example Using R
Exploratory Multivariate Analysis by Example Using R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Exploratory Multivariate Analysis by Example Using R write by Francois Husson. This book was released on 2010-11-15. Exploratory Multivariate Analysis by Example Using R available in PDF, EPUB and Kindle. Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis. The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualizing objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical. They also illustrate the methods and the ways they can be exploited using examples from various fields. Throughout the text, each result correlates with an R command accessible in the FactoMineR package developed by the authors. All of the data sets and code are available at http://factominer.free.fr/book By using the theory, examples, and software presented in this book, readers will be fully equipped to tackle real-life multivariate data.
Chemometrics with R
Chemometrics with R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Chemometrics with R write by Ron Wehrens. This book was released on 2020-08-20. Chemometrics with R available in PDF, EPUB and Kindle. This book offers readers an accessible introduction to the world of multivariate statistics in the life sciences, providing a comprehensive description of the general data analysis paradigm, from exploratory analysis (principal component analysis, self-organizing maps and clustering) to modeling (classification, regression) and validation (including variable selection). It also includes a special section discussing several more specific topics in the area of chemometrics, such as outlier detection, and biomarker identification. The corresponding R code is provided for all the examples in the book; and scripts, functions and data are available in a separate R package. This second revised edition features not only updates on many of the topics covered, but also several sections of new material (e.g., on handling missing values in PCA, multivariate process monitoring and batch correction).
