Table of contents for Analyzing linguistic data : a practical introduction to statistics using R / R. H. Baayen.


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1  An introduction to R                                             1
1.1  R as a calculator                                          2
1.2  Getting data into and out of R                             4
1.3   Accessing information in data frames                      6
1.4  Operations on data frames                                  10
1.4.1 Sorting a data frame by one or more columns         10
1.4.2 Changing information in a data frame                12
1.4.3 Extracting contingency tables from data frames      13
1.4.4 Calculations on data frames                         15
1.5  Session management                                        18
2  Graphical data exploration                                      20
2.1   Random variables                                          20
2.2   Visualizing single random variables                       21
2.3   Visualizing two or more variables                         32
2.4   Trellis graphics                                          37
3  Probability distributions                                       44
3.1   Distributions                                            44
3.2   Discrete distributions                                   44
3.3   Continuous distributions                                  57
3.3.1 The normal distribution                             58
3.3.2 The t, F, and X2 distributions                      63
4  Basic statistical methods                                       68
4.1   Tests for single vectors                                  71
4.1.1 Distribution tests                                  71
4.1.2 Tests for the mean                                  75
4.2   Tests for two independent vectors                         77
4.2.1 Are the distributions the same?                     78
4.2.2 Are the means the same?                             79
4.2.3 Are the variances the same?                         81
4.3   Paired vectors                                            82
4.3.1 Are the means or medians the same?                  82
4.3.2 Functional relations: linear regression             84
4.3.3 What does the joint density look like?            97
4.4   A numerical vector and a tfactor: analysis of variance  101
4.4. 1 Two numerical vectors and a factor: analysis
of covariance                                     108
4.5   Two vectors with counts                                11I
4.6  A note on statistical significance                      114
5  Clustering and classification                                118
5.1  Clustering                                              118
5.1.1 Tables with measurements: principal components analysis 118
5.1.2 Fables with measurements: factor analysis        126
5.1.3 Tables with counts: correspondence analysis      128
5.1.4 Tables with distances: multidimensional scaling  136
5.1.5 Tables with distances: hierarchical cluster analysis  138
5.2  Classification                                          148
5.2.1 Classification trees                             148
5.2.2 Discriminant analysis                            154
5.2.3 Support vector machines                          160
6  Regression modeling                                         165
6.1  Introduction                                            165
6.2   Ordinary least squares regression                      169
6.2.1 Nonlinearities                                   174
6.2.2 Collinearity                                     181
6.2.3 Model criticism                                  188
6.2.4 Validation                                       193
6.3   Generalized linear models                              105
6.3.1 Logistic regression                              195
6.3.2 Ordinal logistic regression                      2108
6.4   Regression with breakpoints                            214
6.5   Models for lexical richness                            22
6.6   General considerations                                 236
7  Mixed models                                                241
7.1   Modeling data with fixed and random effects            242
7.2  A comparison with traditional analyses                  259
7.2.1 Mixed-effects models and quasi-F                 260
7.2.2 Mixed-effects models and Latin Square designs    266
7.2.3 Regression kN ith subjects and items             260
7.3   Shrinkage in inixed-effects models                     275
7.4  Generalized linear mixed models                         278
7.5  Case studies                                            284
7.5.1 Primed lexical decision latencies foi Dutch neologisms  284
7.5.2 Self-paced reading latencies for Dutch neologisms  287
7.5.3 Visual lexical decision latencies of Dutch
eight-year-olds                                   289
7.5.4 Mixed-effects models in corpus linguistics       295
Appendix A    Solutions to the exercises              303
Appendix B    Overview of R functions                 335



Library of Congress subject headings for this publication: Mathematical linguistics, R (Computer program language)Linguistics Statistical methods, Computational linguistics, R (computerprogramma) gttStatistische methoden, gttLinguïstiek, gtt