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R Statistics Cookbook

By : Juretig

4.3 (4)

R Statistics Cookbook

4.3 (4)

By: 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.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Sections

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Free Chapter

Getting Started with R and Statistics

Getting Started with R and Statistics

Introduction

Technical requirements

Maximum likelihood estimation

Calculating densities, quantiles, and CDFs

Creating barplots using ggplot

Generating random numbers from multiple distributions

Complex data processing with dplyr

3D visualization with the plot3d package

Formatting tabular data with the formattable package

Simple random sampling

Creating diagrams via the DiagrammeR package

C++ in R via the Rcpp package

Interactive plots with the ggplot GUI package

Animations with the gganimate package

Using R6 classes

Modeling sequences with the TraMineR package

Clustering sequences with the TraMineR package

Displaying geographical data with the leaflet package

Univariate and Multivariate Tests for Equality of Means

Univariate and Multivariate Tests for Equality of Means

Introduction

The univariate t-test

The Fisher-Behrens problem

Paired t-test

Calculating ANOVA sum of squares and F tests

Two-way ANOVA

Type I, Type II, and Type III sum of squares

Random effects

Repeated measures

Multivariate t-test

MANOVA

Linear Regression

Linear Regression

Introduction

Computing ordinary least squares estimates

Reporting results with the sjPlot package

Finding correlation between the features

Testing hypothesis

Testing homoscedasticity

Implementing sandwich estimators

Variable selection

Ridge regression

Working with LASSO

Leverage, residuals, and influence

Bayesian Regression

Bayesian Regression

Introduction

Getting the posterior density in STAN

Formulating a linear regression model

Assigning the priors

Doing MCMC the manual way

Evaluating convergence with CODA

Bayesian variable selection

Using a model for prediction

GLMs in JAGS

Nonparametric Methods

Nonparametric Methods

Introduction

The Mann-Whitney test

Estimating nonparametric ANOVA

The Spearman's rank correlation test

LOESS regression

Finding the best transformations via the acepack package

Nonparametric multivariate tests using the npmv package

Semiparametric regression with the SemiPar package

Robust Methods

Robust Methods

Introduction

Robust linear regression

Estimating robust covariance matrices

Robust logistic regression

Robust ANOVA using the robust package

Robust principal components

Robust Gaussian mixture models with the qclust package

Robust clustering

Time Series Analysis

Time Series Analysis

Introduction

The general ARIMA model

Seasonality and SARIMAX models

Choosing the best model with the forecast package

Vector autoregressions (VARs)

Facebook's automatic Prophet forecasting

Modeling count temporal data

Imputing missing values in time series

Anomaly detection

Spectral decomposition of time series

Mixed Effects Models

Mixed Effects Models

Introduction

The standard model and ANOVA

Some useful plots for mixed effects models

Nonlinear mixed effects models

Crossed and nested designs

Robust mixed effects models with robustlmm

Choosing the best linear mixed model

Mixed generalized linear models

Predictive Models Using the Caret Package

Predictive Models Using the Caret Package

Introduction

Data splitting and general model fitting

Preprocessing

Variable importance and feature selection

Model tuning

Classification in caret and ROC curves

Gradient boosting and class imbalance

Lasso, ridge, and elasticnet in caret

Logic regression

Bayesian Networks and Hidden Markov Models

Bayesian Networks and Hidden Markov Models

Introduction

A discrete Bayesian network via bnlearn

Conditional independence tests

Continuous and hybrid Bayesian networks via bnlearn

Interactive visualization of BNs with the bnviewer package

An introductory hidden Markov model

Regime switching in financial data via HMM

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Preprocessing

The caret package allows us to do a variety of things for preprocessing our data, such as scaling, centering, removing variables with very low variability, and projecting it via principal components. The main workhorse for this is the preProcess() function.

In this recipe, we will explore how to undertake several data transformation steps, before modeling using the Boston dataset (included in the MASS package). This is a famous dataset containing house price indexes for several areas in Boston. The objective is to use several metrics for each area and predict the price index there. We will explain how to do it using random forests.

There are essentially two ways of doing this in caret:

By calling the preProcess= argument in the train function (this is less flexible, but can be used with cross-validation)
By calling the preProcess() function before calling train...

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