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An Introduction to Applied Multivariate Analysis with R
The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.
- GenresMathematics
288 pages, Paperback
First published January 1, 2011
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Displaying 1 - 6 of 6 reviews
April 1, 2020
This book is just like your genius but confusing statistics professor. The authors love fancy formulas but keep their focus on examples, unfortunately using fancy and barely replicable R coding, which is fun to look at but not too useful. They also do not use ggplot for their graphics and stay a bit too silent on how to practically conduct, interpret and report the instruments. It is still a decent book to work through the theoretical basics of multivariate statistics but can probably not replace watching countless YouTube tutorials and consulting step-by-step guides to actually carry out the analyses.
December 10, 2018
This is an excellent overview of multivariate techniques. Every topic is covered with appropriate depth. The chapter on visualizing multivariate data was particularly useful. The book is well written and very fun to read. They provide information about the math used for each approach and do an excellent job of explaining the math and the implications (e.g., whether/how scaling affects analyses). I particularly appreciated how carefully the chapters covered assumptions, caveats, and criticisms of techniques or analytic choice options and how they always directed interested readers to more detailed texts. The book doesn't cover MANOVA ("...we are not convinced that MANOVA is now of much more than historical interest...."!) or discriminant function analysis.
November 24, 2022
Multivariate techniques are well described in this book with R codes. It covers the main topics in multivariate analysis.
July 1, 2015
- Read Chapter 1-4, 8
- Skipped Chapter 6 Clustering
- Don't understand Chapter 5 and 7 on Factor Analysis
- Skipped Chapter 6 Clustering
- Don't understand Chapter 5 and 7 on Factor Analysis
May 4, 2016
Decent but uses horrible plotting based on lattice or base. Authors should release a new version with ggplot2 versions.
May 11, 2016
Quick read. Good intro on Multivariate techniques. Covers only the basics. Focus on intuition and examples.
Displaying 1 - 6 of 6 reviews
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