Book Image

R Statistics Cookbook

By : Francisco Juretig
2 (2)
Book Image

R Statistics Cookbook

2 (2)
By: Francisco Juretig

Overview of this book

R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and not-so-common challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cutting-edge statistical tools. You'll start by implementing data modeling, data analysis, and machine learning to solve real-world problems. You'll then understand how to work with nonparametric methods, mixed effects models, and hidden Markov models. This book contains recipes that will guide you in performing univariate and multivariate hypothesis tests, several regression techniques, and using robust techniques to minimize the impact of outliers in data.You'll also learn how to use the caret package for performing machine learning in R. Furthermore, this book will help you understand how to interpret charts and plots to get insights for better decision making. By the end of this book, you will be able to apply your skills to statistical computations using R 3.5. You will also become well-versed with a wide array of statistical techniques in R that are extensively used in the data science industry.
Table of Contents (12 chapters)

Robust principal components

In this recipe, we will work with robust principal components. Principal components are used to project data into a smaller subspace that is easier to work with. It is probably the most important dimensionality reduction technique.

Analyzing and working with lots of features is usually complicated for two main reasons:

  • It's difficult to find patterns between them, because combinations of them might be relatively correlated.
  • Modeling variables to predict another variable sometimes carries a significant amount of noise. Ideally, we would like to compress some of the information contained in the data in order to have a simpler model.

In order to introduce principal components, let's review a basic example. Let's assume we have a football and rugby score for some students:

Person

Football

Rugby

John

10

8

Michael

3...