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 ANOVA using the robust package

After fitting a linear regression model using lm, we can feed that into the anova function. Thus, we get the corresponding sum of squares and F-tests. But since analysis of variance (ANOVA) relies on a sum of squares (or linear regression residuals) it also suffers from the presence of outliers.

The mechanics here are quite similar to the non-robust/standard way: first, we do the robust regression model, and then we pass the estimated model into the anova.lmrob function.

Getting ready

In order to run this example, the robust package needs to be installed using install.packages("robust").

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