Introductory Statistics with R

✍ Scribed by Dalgaard, Peter

• Publisher: Springer New York
• Year: 2008
• Tongue: English
• Leaves: 370
• Series: Statistics and Computing
• Category: Library

✦ Synopsis

This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.

✦ Table of Contents

Preface......Page 7
Contents......Page 11
1.1 First steps......Page 17
1.1.2 Assignments......Page 19
1.1.3 Vectorized arithmetic......Page 20
1.1.4 Standard procedures......Page 22
1.1.5 Graphics......Page 23
1.2.1 Expressions and objects......Page 25
1.2.2 Functions and arguments......Page 27
1.2.3 Vectors......Page 28
1.2.4 Quoting and escape sequences......Page 29
1.2.6 Functions that create vectors......Page 30
1.2.7 Matrices and arrays......Page 32
1.2.8 Factors......Page 34
1.2.9 Lists......Page 35
1.2.10 Data frames......Page 36
1.2.11 Indexing......Page 37
1.2.12 Conditional selection......Page 38
1.2.13 Indexing of data frames......Page 39
1.2.14 Grouped data and data frames......Page 41
1.2.15 Implicit loops......Page 42
1.2.16 Sorting......Page 43
1.3 Exercises......Page 44
2.1.1 The workspace......Page 46
2.1.2 Textual output......Page 47
2.1.3 Scripting......Page 48
2.1.4 Getting help......Page 49
2.1.6 Built-in data......Page 50
2.1.7 attach and detach......Page 51
2.1.8 subset, transform, and within......Page 52
2.2.1 Plot layout......Page 54
2.2.2 Building a plot from pieces......Page 55
2.2.4 Combining plots......Page 57
2.3.1 Flow control......Page 59
2.4 Data entry......Page 61
2.4.1 Reading from a text file......Page 62
2.4.2 Further details on read.table......Page 65
2.4.3 The data editor......Page 66
2.4.4 Interfacing to other programs......Page 67
2.5 Exercises......Page 68
3.1 Random sampling......Page 69
3.2 Probability calculations and combinatorics......Page 70
3.3 Discrete distributions......Page 71
3.4 Continuous distributions......Page 72
3.5.1 Densities......Page 73
3.5.2 Cumulative distribution functions......Page 76
3.5.3 Quantiles......Page 77
3.5.4 Random numbers......Page 78
3.6 Exercises......Page 79
4.1 Summary statistics for a single group......Page 80
4.2.1 Histograms......Page 84
4.2.2 Empirical cumulative distribution......Page 86
4.2.3 Q–Q plots......Page 87
4.3 Summary statistics by groups......Page 88
4.4.1 Histograms......Page 92
4.4.2 Parallel boxplots......Page 93
4.4.3 Stripcharts......Page 94
4.5.1 Generating tables......Page 96
4.5.2 Marginal tables and relative frequency......Page 100
4.6.1 Barplots......Page 102
4.6.2 Dotcharts......Page 104
4.6.3 Piecharts......Page 105
4.7 Exercises......Page 106
5.1 One-sample t test......Page 108
5.2 Wilcoxon signed-rank test......Page 112
5.3 Two-sample t test......Page 113
5.5 Two-sample Wilcoxon test......Page 116
5.6 The paired t test......Page 117
5.7 The matched-pairs Wilcoxon test......Page 119
5.8 Exercises......Page 120
6.1 Simple linear regression......Page 121
6.2 Residuals and fitted values......Page 125
6.3 Prediction and confidence bands......Page 129
6.4 Correlation......Page 132
6.4.1 Pearson correlation......Page 133
6.4.2 Spearman’s p......Page 135
6.5 Exercises......Page 136
7.1 One-way analysis of variance......Page 138
7.1.1 Pairwise comparisons and multiple testing......Page 142
7.1.2 Relaxing the variance assumption......Page 144
7.1.3 Graphical presentation......Page 145
7.2 Kruskal–Wallis test......Page 147
7.3 Two-way analysis of variance......Page 148
7.3.1 Graphics for repeated measurements......Page 151
7.5 The ANOVA table in regression analysis......Page 152
7.6 Exercises......Page 154
8.1 Single proportions......Page 155
8.2 Two independent proportions......Page 157
8.3 k proportions, test for trend......Page 159
8.4 r × c tables......Page 161
8.5 Exercises......Page 163
9.1 The principles of power calculations......Page 165
9.1.1 Power of one-sample and paired t tests......Page 166
9.1.3 Approximate methods......Page 168
9.2 Two-sample problems......Page 169
9.4 Comparison of proportions......Page 171
9.5 Exercises......Page 172
10.1.1 The cut function......Page 173
10.1.2 Manipulating factor levels......Page 175
10.1.3 Working with dates......Page 176
10.1.4 Recoding multiple variables......Page 179
10.2 Conditional calculations......Page 180
10.3 Combining and restructuring data frames......Page 181
10.3.1 Appending frames......Page 182
10.3.2 Merging data frames......Page 183
10.3.3 Reshaping data frames......Page 185
10.4 Per-group and per-case procedures......Page 188
10.5 Time splitting......Page 189
10.6 Exercises......Page 193
11.1 Plotting multivariate data......Page 195
11.2 Model specification and output......Page 197
11.3 Model search......Page 200
11.4 Exercises......Page 203
12 Linear models......Page 205
12.1 Polynomial regression......Page 206
12.2 Regression through the origin......Page 208
12.3 Design matrices and dummy variables......Page 210
12.4 Linearity over groups......Page 212
12.5 Interactions......Page 216
12.6 Two-way ANOVA with replication......Page 217
12.7 Analysis of covariance......Page 218
12.7.1 Graphical description......Page 219
12.7.2 Comparison of regression lines......Page 222
12.8 Diagnostics......Page 228
12.9 Exercises......Page 234
13 Logistic regression......Page 236
13.1 Generalized linear models......Page 237
13.2 Logistic regression on tabular data......Page 238
13.2.1 The analysis of deviance table......Page 243
13.2.2 Connection to test for trend......Page 244
13.3 Likelihood profiling......Page 246
13.5 Logistic regression using raw data......Page 248
13.6 Prediction......Page 250
13.7 Model checking......Page 251
13.8 Exercises......Page 256
14.1 Essential concepts......Page 258
14.2 Survival objects......Page 259
14.3 Kaplan–Meier estimates......Page 260
14.4 The log-rank test......Page 263
14.5 The Cox proportional hazards model......Page 265
14.6 Exercises......Page 267
15.1 Basic ideas......Page 268
15.1.2 Survival analysis with constant hazard......Page 269
15.2 Fitting Poisson models......Page 271
15.3 Computing rates......Page 275
15.4 Models with piecewise constant intensities......Page 279
15.5 Exercises......Page 283
16 Nonlinear curve fitting......Page 284
16.1 Basic usage......Page 285
16.2 Finding starting values......Page 287
16.3 Self-starting models......Page 293
16.4 Profiling......Page 294
16.5 Finer control of the fitting algorithm......Page 296
16.6 Exercises......Page 297
A Obtaining and installing......Page 298
B Data sets in the ISwR package1......Page 301
C Compendium......Page 332
D Answers to exercises......Page 344
Bibliography......Page 361
Index......Page 363

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