A handbook of statistical analyses using R

Brian S. Everitt, Torsten Hothorn9781420010657, 9781584885399, 1584885394

R is dynamic, to say the least. More precisely, it is organic, with new functionality and add-on packages appearing constantly. And because of its open-source nature and free availability, R is quickly becoming the software of choice for statistical analysis in a variety of fields. Doing for R what Everitt’s other Handbooks have done for S-PLUS, STATA, SPSS, and SAS, A Handbook of Statistical Analyses Using R presents straightforward, self-contained descriptions of how to perform a variety of statistical analyses in the R environment. From simple inference to recursive partitioning and cluster analysis, eminent experts Everitt and Hothorn lead you methodically through the steps, commands, and interpretation of the results, addressing theory and statistical background only when useful or necessary. They begin with an introduction to R, discussing the syntax, general operators, and basic data manipulation while summarizing the most important features. Numerous figures highlight R’s strong graphical capabilities and exercises at the end of each chapter reinforce the techniques and concepts presented. All data sets and code used in the book are available as a downloadable package from CRAN, the R online archive. A Handbook of Statistical Analyses Using R is the perfect guide for newcomers as well as seasoned users of R who want concrete, step-by-step guidance on how to use the software easily and effectively for nearly any statistical analysis.

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Table of contents :
A Handbook of Statistical Analyses Using R……Page 2
Dedication……Page 4
Preface……Page 5
List of Figures……Page 7
List of Tables……Page 12
Contents……Page 15
Bibliography……Page 270
1.1 What Is R?……Page 18
1.2.1 The Base System and the First Steps……Page 19
1.2.2 Packages……Page 20
1.3 Help and Documentation……Page 21
1.4 Data Objects in R……Page 22
1.5 Data Import and Export……Page 26
1.6 Basic Data Manipulation……Page 28
1.7 Simple Summary Statistics……Page 31
1.7.1 Simple Graphics……Page 32
1.8 Organising an Analysis……Page 35
Exercises……Page 37
2.1 Introduction……Page 38
2.2.1 Comparing Normal Populations: Student’s t-Tests……Page 42
2.2.2 Non-parametric Analogues of Independent Samples and Paired t-Tests……Page 44
2.2.4 McNemar’s Test……Page 45
2.3.1 Estimating the Width of a Room……Page 46
2.3.2 Wave Energy Device Mooring……Page 49
2.3.3 Mortality and Water Hardness……Page 50
2.3.4 Piston-ring Failures……Page 52
2.3.5 Rearrests of Juveniles……Page 55
2.4 Summary……Page 56
Exercises……Page 57
3.1 Introduction……Page 58
3.2.1 Testing Independence of Two Variables……Page 61
3.3.1 Estimating the Width of a Room Revised……Page 63
3.3.2 Crowds and Threatened Suicide……Page 66
3.3.3 Gastrointestinal Damages……Page 67
3.3.4 Teratogenesis……Page 69
Exercises……Page 70
4.1 Introduction……Page 72
4.2 Analysis of Variance……Page 75
4.3.1 Weight Gain in Rats……Page 76
4.3.2 Foster Feeding of Rats of Different Genotype……Page 80
4.3.3 Water Hardness and Mortality……Page 82
4.3.4 Male Egyptian Skulls……Page 84
Exercises……Page 88
5.1 Introduction……Page 90
5.2 Multiple Linear Regression……Page 91
5.3 Analysis Using R……Page 93
5.3.1 Fitting a Linear Model……Page 94
5.3.2 Regression Diagnostics……Page 100
Exercises……Page 103
6.1 Introduction……Page 105
6.2.1 Logistic Regression……Page 108
6.2.2 The Generalised Linear Model……Page 109
6.3.1 ESR and Plasma Proteins……Page 110
6.3.2 Women’s Role in Society……Page 114
6.3.3 Colonic Polyps……Page 117
6.4 Summary……Page 122
Exercises……Page 123
7.1 Introduction……Page 125
7.2 Density Estimation……Page 127
7.2.1 Kernel Density Estimators……Page 128
7.3.1 A Parametric Density Estimate for the Old Faithful Data……Page 133
Exercises……Page 141
8.2 Recursive Partitioning……Page 146
8.3.1 Forbes 2000 Data……Page 148
8.3.2 Glaucoma Diagnosis……Page 151
8.4 Summary……Page 156
Exercises……Page 157
9.1 Introduction……Page 158
9.2 Survival Analysis……Page 159
9.2.1 The Survivor Function……Page 161
9.2.2 The Hazard Function……Page 162
9.2.3 Cox’s Regression……Page 164
9.3.1 Glioma Radioimmunotherapy……Page 165
9.3.2 Breast Cancer Survival……Page 167
9.4 Summary……Page 172
Exercises……Page 173
10.1 Introduction……Page 174
10.2 Analysing Longitudinal Data……Page 177
10.3 Linear Mixed Effects Models for Repeated Measures Data……Page 178
10.4 Analysis Using R……Page 180
10.5 Prediction of Random Effects……Page 183
10.6 The Problem of Dropouts……Page 184
Exercises……Page 187
11.1 Introduction……Page 189
11.2 Generalised Estimating Equations……Page 191
11.3.1 Beat the Blues Revisited……Page 193
11.3.2 Respiratory Illness……Page 196
11.3.3 Epilepsy……Page 200
Exercises……Page 208
12.1 Introduction……Page 210
12.2 Systematic Reviews and Meta-Analysis……Page 212
12.3.1 Fixed Effects Model – Mantel-Haenszel……Page 214
12.4 Analysis Using R……Page 215
12.5 Meta-Regression……Page 216
12.6 Publication Bias……Page 220
12.7 Summary……Page 224
Exercises……Page 225
13.2 Principal Component Analysis……Page 228
13.3 Analysis Using R……Page 231
13.4 Summary……Page 236
Exercises……Page 238
14.2 Multidimensional Scaling……Page 240
14.2.1 Classical Multidimensional Scaling……Page 243
14.2.2 Non-metric Multidimensional Scaling……Page 245
14.3 Analysis Using R……Page 246
Exercises……Page 252
15.1 Introduction……Page 255
15.2 Cluster Analysis……Page 257
15.2.1 k-Means Clustering……Page 258
15.2.3 Classification Maximum Likelihood……Page 259
15.3 Analysis Using R……Page 260
15.3.1 Model-based Clustering in R……Page 264
15.4 Summary……Page 265
Exercises……Page 267