| Preface | p. x |
| An introduction to R | p. 1 |
| R as a calculator | p. 2 |
| Getting data into and out of R | p. 4 |
| Accessing information in data frames | p. 6 |
| Operations on data frames | p. 10 |
| Sorting a data frame by one or more columns | p. 10 |
| Changing information in a data frame | p. 12 |
| Extracting contingency tables from data frames | p. 13 |
| Calculations on data frames | p. 15 |
| Session management | p. 18 |
| Graphical data exploration | p. 20 |
| Random variables | p. 20 |
| Visualizing single random variables | p. 21 |
| Visualizing two or more variables | p. 32 |
| Trellis graphics | p. 37 |
| Probability distributions | p. 44 |
| Distributions | p. 44 |
| Discrete distributions | p. 44 |
| Continuous distributions | p. 57 |
| The normal distribution | p. 58 |
| The t, F, and X[superscript 2] distributions | p. 63 |
| Basic statistical methods | p. 68 |
| Tests for single vectors | p. 71 |
| Distribution tests | p. 71 |
| Tests for the mean | p. 75 |
| Tests for two independent vectors | p. 77 |
| Are the distributions the same? | p. 78 |
| Are the means the same? | p. 79 |
| Are the variances the same? | p. 81 |
| Paired vectors | p. 82 |
| Are the means or medians the same? | p. 82 |
| Functional relations: linear regression | p. 84 |
| What does the joint density look like? | p. 97 |
| A numerical vector and a factor: analysis of variance | p. 101 |
| Two numerical vectors and a factor: analysis of covariance | p. 108 |
| Two vectors with counts | p. 111 |
| A note on statistical significance | p. 114 |
| Clustering and classification | p. 118 |
| Clustering | p. 118 |
| Tables with measurements: principal components analysis | p. 118 |
| Tables with measurements: factor analysis | p. 126 |
| Tables with counts: correspondence analysis | p. 128 |
| Tables with distances: multidimensional scaling | p. 136 |
| Tables with distances: hierarchical cluster analysis | p. 138 |
| Classification | p. 148 |
| Classification trees | p. 148 |
| Discriminant analysis | p. 154 |
| Support vector machines | p. 160 |
| Regression modeling | p. 165 |
| Introduction | p. 165 |
| Ordinary least squares regression | p. 169 |
| Nonlinearities | p. 174 |
| Collinearity | p. 181 |
| Model criticism | p. 188 |
| Validation | p. 193 |
| Generalized linear models | p. 195 |
| Logistic regression | p. 195 |
| Ordinal logistic regression | p. 208 |
| Regression with breakpoints | p. 214 |
| Models for lexical richness | p. 222 |
| General considerations | p. 236 |
| Mixed models | p. 241 |
| Modeling data with fixed and random effects | p. 242 |
| A comparison with traditional analyses | p. 259 |
| Mixed-effects models and quasi-F | p. 260 |
| Mixed-effects models and Latin Square designs | p. 266 |
| Regression with subjects and items | p. 269 |
| Shrinkage in mixed-effects models | p. 275 |
| Generalized linear mixed models | p. 278 |
| Case studies | p. 284 |
| Primed lexical decision latencies for Dutch neologisms | p. 284 |
| Self-paced reading latencies for Dutch neologisms | p. 287 |
| Visual lexical decision latencies of Dutch eight-year-olds | p. 289 |
| Mixed-effects models in corpus linguistics | p. 295 |
| Solutions to the exercises | p. 303 |
| Overview of R functions | p. 335 |
| References | p. 342 |
| Index | p. 347 |
| Index of data sets | p. 347 |
| Index of R | p. 347 |
| Index of topics | p. 349 |
| Index of authors | p. 352 |
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