Given certain variables, we usually want to find clusters of observations. These clusters should be as different as possible, and should contain "similar" observations inside. Suppose we had the following pairs of values [height=170,weight=50], [height=180,weight=70],[height=190,weight=90] and [height=200,weight=100] and we wanted to cluster them. A reasonable 2-cluster configuration would have the following centroids: [height=175,weight=60],[height=195,weight=95]. Obviously, the first two observations would fall under the first cluster, and the other two should fall under the second cluster. The simplest and most preferred algorithm for clustering is k-means. It works by picking k centroids at random and assigning each observation to the nearest centroid. The mean/center for each centroid is updated, and the procedure is repeated for the other variables...
R Statistics Cookbook
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R Statistics Cookbook
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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)
Preface
Free Chapter
Getting Started with R and Statistics
Univariate and Multivariate Tests for Equality of Means
Linear Regression
Bayesian Regression
Nonparametric Methods
Robust Methods
Time Series Analysis
Mixed Effects Models
Predictive Models Using the Caret Package
Bayesian Networks and Hidden Markov Models
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