Applied Multivariate Analysis with R
Applied Multivariate Analysis with R
Step by Step Guide to Perform Multivariate Analysis with R
About the Book
In today's world, Data is everywhere and it is getting easier to produce it , collect it and perform multiple analysis.
This book is designed as a step by step guide on how to perform multivariate analysis with R. It focuses on PCA (Principal Components Analysis) and LDA (Linear Discriminant Analysis).
The book's main idea is to focus on the step by step implementation. It is not necessary to have an advanced knowledge of R but it is recommended to be familiar with the basics of programming, basics of R, Statistics, Math and some Multivariate Methods.
The chapters in the book cover the following:
- Setting Up the R environment.
- A brief introduction to R.
- Using R for Multivariate Analysis.
- Plotting Multivariate Data.
- Calculating Summary Statistics for Multivariate Data.
- Calculating Correlations for Multivariate Data.
- Standardising Variables.
- Principal Component Analysis.
- Linear Discriminant Analysis.
Check out other books from the author:
Other Books by Alejandro
- Recommended Resources
- Who is this book for ?
- What this book covers ?
Setting Up the R environment
- How to check if R is installed on a Windows PC
- Finding out what is the latest version of R
- Installing R on a Windows PC
- How to install R on non-Windows computers (eg. Macintosh or Linux computers)
- Installing R packages
- How to install an R package
- How to install a Bioconductor R package
- Running R
- A brief introduction to R
Using R for Multivariate Analysis
- Reading Multivariate Analysis Data into R
Plotting Multivariate Data
- A Matrix Scatterplot
- A Scatterplot with the Data Points Labelled by their Group
- A Profile Plot
Calculating Summary Statistics for Multivariate Data
- Means and Variances Per Group
- Between-groups Variance and Within-groups Variance for a Variable
- Between-groups Covariance and Within-groups Covariance for Two Variables
- Calculating Correlations for Multivariate Data
- Standardising Variables
Principal Component Analysis
- Deciding How Many Principal Components to Retain
- Loadings for the Principal Components
- Scatterplots of the Principal Components
Linear Discriminant Analysis
- Loadings for the Discriminant Functions
- Separation Achieved by the Discriminant Functions
- A Stacked Histogram of the LDA Values
- Scatterplots of the Discriminant Functions
- Allocation Rules and Misclassification Rate
- Keep developing your Data Science skills
- About the Author
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