Data Analysis and Graphics Using R

✍ Scribed by Matthew Norman

• Publisher: Cambridge University Press
• Year: 2003
• Tongue: English
• Leaves: 384
• Series: Springer Professional Computing
• Edition: 1
• Category: Library

✦ Synopsis

Using modern statistical software systems requires training both in the software itself and in the underlying statistical methods. Concentrating on the freely available R system, this volume demonstrates recently implemented approaches and methods in statistical analysis. The authors introduce elementary concepts in statistics through examples of real-world data analysis drawn from their experience as teachers and as consultants. R code and data sets for all examples are available on the Internet. This emphasis on practical methodology combined with a tutorial approach makes the book accessible to anyone with a knowledge of undergraduate-level statistics. The methods demonstrated are suitable for use in a wide variety of disciplines, from social sciences to medicine, engineering and science.

✦ Table of Contents

Cover......Page 1
Data Analysis and Graphics Using R - an Example-based Approach......Page 3
Contents......Page 6
Preface......Page 14
A Chapter by Chapter Summary......Page 18
1.1 A Short R Session......Page 23
1.2 The Uses of R......Page 27
1.3 The R Language......Page 28
1.4 Vectors in R......Page 30
1.5 Data Frames......Page 33
1.7 Looping......Page 36
1.8 R Graphics......Page 37
1.9 Additional Points on the Use of R in This Book......Page 45
1.10 Further Reading......Page 47
1.11 Exercises......Page 48
2.1 Revealing Views of the Data......Page 51
2.2 Data Summary......Page 64
2.3 Statistical Analysis Strategies......Page 69
2.4 Recap......Page 71
2.6 Exercises......Page 72
3 Statistical Models......Page 74
3.1 Regularities......Page 75
3.2 Distributions: Models for the Random Component......Page 79
3.3 The Uses of Random Numbers......Page 82
3.4 Model Assumptions......Page 84
3.6 Further Reading......Page 90
3.7 Exercises......Page 91
4.1 Standard Errors......Page 93
4.2 Calculations Involving Standard Errors: the t Distribution......Page 96
4.3 Confidence Intervals and Hypothesis Tests......Page 99
4.4 Contingency Tables......Page 106
4.5 One-Way Unstructured Comparisons......Page 110
4.6 Response Curves......Page 115
4.7 Data with a Nested Variation Structure......Page 116
4.8 Resampling Methods for Tests and Confidence Intervals......Page 118
4.9 Further Comments on Formal Inference......Page 122
4.11 Further Reading......Page 124
4.12 Exercises......Page 125
5.1 Fitting a Line to Data......Page 129
5.2 Outliers, Influence and Robust Regression......Page 136
5.3 Standard Errors and Confidence Intervals......Page 138
5.4 Regression versus Qualitative anova Comparisons......Page 141
5.5 Assessing Predictive Accuracy......Page 143
5.6 A Note on Power Transformations......Page 148
5.7 Size and Shape Data......Page 149
5.8 The Model Matrix in Regression......Page 152
5.9 Recap......Page 153
5.1 1 Exercises......Page 154
6.1 Basic Ideas: Book Weight and Brain Weight Examples......Page 156
6.2 Multiple Regression Assumptions and Diagnostics......Page 164
6.3 A Strategy for Fitting Multiple Regression'Models......Page 167
6.4 Measures for the Comparison of Regression Models......Page 174
6.5 Interpreting Regression Coefficients - the Labor Training Data......Page 177
6.6 Problems with Many Explanatory Variables......Page 183
6.7 Multicollinearity......Page 186
6.8 Multiple Regression Models - Additional Points......Page 190
6.9 Further Reading......Page 193
6.10 Exercises......Page 194
7.1 Levels of a Factor - Using Indicator Variables......Page 197
7.2 Polynomial Regression......Page 201
7.3 Fitting Multiple Lines......Page 205
7.4 Methods for Passing Smooth Curves through Data......Page 209
7.5 Smoothing Terms in Multiple Linear Models......Page 214
7.7 Exercises......Page 216
8.1 Generalized Linear Models......Page 219
8.2 Logistic Multiple Regression......Page 224
8.3 Logistic Models for Categorical Data - an Example......Page 232
8.4 Poisson and Quasi-Poisson Regression......Page 233
8.5 Ordinal Regression Models......Page 238
8.6 Other Related Models......Page 242
8.7 Transformations for Count Data......Page 243
8.8 Further Reading......Page 244
8.9 Exercises......Page 245
9.1 Introduction......Page 246
9.2 Example - Survey Data, with Clustering......Page 247
9.3 A Multi-level Experimental Design......Page 252
9.4 Within and Between Subject Effects - an Example......Page 261
9.5 Time Series - Some Basic Ideas......Page 264
9.6 Regression modeling with moving average errors - an example......Page 268
9.7 Repeated Measures in Time - Notes on the Methodology......Page 274
9.8 Further Notes on Multi-level Modeling......Page 277
9.9 Further Reading......Page 278
9.10 Exercises......Page 280
10.1 The Uses of Tree-based Methods......Page 281
10.2 Detecting Email Spam - an Example......Page 283
10.3 Terminology and Methodology......Page 286
10.4 Assessments of Predictive Accuracy......Page 292
10.5 A Strategy for Choosing the Optimal Tree......Page 293
10.6 Detecting Email Spam - the Optimal Tree......Page 295
10.7 Interpretation and Presentation of the rpart Output......Page 297
10.8 Additional Notes......Page 300
10.9 Further Reading......Page 301
10.10 Exercises......Page 302
11 Multivariate Data Exploration and Discrimination......Page 303
11.1 Multivariate Exploratory Data Analysis......Page 304
11.2 Discriminant Analysis......Page 307
11.3 Principal Component Scores in Regression......Page 313
11.4 Propensity Scores in Regression Comparisons - Labor Training Data......Page 317
11.5 Further Reading......Page 319
11.6 Exercises......Page 320
12.1 Graphs in R......Page 322
12.2 Functions - Some Further Details......Page 325
12.3 Data Input and Output......Page 332
12.4 Factors - Additional Comments......Page 337
12.5 Missing Values......Page 339
12.6 Lists and Data Frames......Page 342
12.7* Matrices and Arrays......Page 346
12.8 Classes and Methods......Page 350
12.9 Databases and Environments......Page 352
12.10 Manipulation of Language Constructs......Page 355
12.11 Further Reading......Page 356
12.12 Exercises......Page 357
Epilogue - Models......Page 360
The Handling of NAs......Page 363
Graphics......Page 364
Data sets......Page 365
Differences that relate to Chapter 12......Page 366
References......Page 368
Index of R Symbols and Functions......Page 374
Index of Terms......Page 378
Index of Names......Page 383