Book Image

Learning Predictive Analytics with R

By : Eric Mayor
Book Image

Learning Predictive Analytics with R

By: Eric Mayor

Overview of this book

This book is packed with easy-to-follow guidelines that explain the workings of the many key data mining tools of R, which are used to discover knowledge from your data. You will learn how to perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. All chapters will guide you in acquiring the skills in a practical way. Most chapters also include a theoretical introduction that will sharpen your understanding of the subject matter and invite you to go further. The book familiarizes you with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naïve Bayes, decision trees, and text mining. It also provides a description of visualization techniques using the basic visualization tools of R as well as lattice for visualizing patterns in data organized in groups. This book is invaluable for anyone fascinated by the data mining opportunities offered by GNU R and its packages.
Table of Contents (23 chapters)
Learning Predictive Analytics with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Exercises and Solutions
Index

Robust regression


In the example datasets that we used in this chapter, we have seen that some observations might threaten the reliability of our results, because of the deviations of their residuals from a normal distribution. The Shapiro test performed on the residuals of model1 (nurses dataset) has shown that the distribution of the residuals was not significantly different from a normal distribution. However, let's be particularly cautious and analyze the same data using robust regression.

As we mentioned earlier, robust regression does not require the residuals to be normally distributed, and therefore, fits our purpose. We will not explore the algorithm. For details about this, the reader can consult Robust Regression in R by Fox and Weisberg (2012). Here, we simply perform robust regression using the rlm() function of the MASS package. Let's first install and load it:

install.packages("MASS"); library(MASS)
model1.rr = rlm(Commit ~ Exhaus + Depers + Accompl, data = nurses)
summary(model1...