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| Preface | |
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| An introduction to R | |
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| R as a calculator | |
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| Getting data into and out of R | |
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| Accessing information in data frames | |
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| Operations on data frames | |
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| Sorting a data frame by one or more columns | |
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| Changing information in a data frame | |
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| Extracting contingency tables from data frames | |
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| Calculations on data frames | |
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| Session management | |
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| Graphical data exploration | |
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| Random variables | |
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| Visualizing single random variables | |
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| Visualizing two or more variables | |
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| Trellis graphics | |
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| Probability distributions | |
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| Distributions | |
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| Discrete distributions | |
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| Continuous distributions | |
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| The normal distribution | |
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| The t, F, and X[superscript 2] distributions | |
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| Basic statistical methods | |
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| Tests for single vectors | |
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| Distribution tests | |
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| Tests for the mean | |
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| Tests for two independent vectors | |
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| Are the distributions the same? | |
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| Are the means the same? | |
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| Are the variances the same? | |
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| Paired vectors | |
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| Are the means or medians the same? | |
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| Functional relations: linear regression | |
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| What does the joint density look like? | |
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| A numerical vector and a factor: analysis of variance | |
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| Two numerical vectors and a factor: analysis of covariance | |
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| Two vectors with counts | |
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| A note on statistical significance | |
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| Clustering and classification | |
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| Clustering | |
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| Tables with measurements: principal components analysis | |
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| Tables with measurements: factor analysis | |
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| Tables with counts: correspondence analysis | |
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| Tables with distances: multidimensional scaling | |
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| Tables with distances: hierarchical cluster analysis | |
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| Classification | |
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| Classification trees | |
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| Discriminant analysis | |
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| Support vector machines | |
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| Regression modeling | |
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| Introduction | |
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| Ordinary least squares regression | |
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| Nonlinearities | |
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| Collinearity | |
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| Model criticism | |
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| Validation | |
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| Generalized linear models | |
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| Logistic regression | |
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| Ordinal logistic regression | |
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| Regression with breakpoints | |
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| Models for lexical richness | |
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| General considerations | |
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| Mixed models | |
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| Modeling data with fixed and random effects | |
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| A comparison with traditional analyses | |
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| Mixed-effects models and quasi-F | |
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| Mixed-effects models and Latin Square designs | |
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| Regression with subjects and items | |
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| Shrinkage in mixed-effects models | |
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| Generalized linear mixed models | |
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| Case studies | |
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| Primed lexical decision latencies for Dutch neologisms | |
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| Self-paced reading latencies for Dutch neologisms | |
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| Visual lexical decision latencies of Dutch eight-year-olds | |
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| Mixed-effects models in corpus linguistics | |
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| Solutions to the exercises | |
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| Overview of R functions | |
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| References | |
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| Index | |
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| Index of data sets | |
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| Index of R | |
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| Index of topics | |
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| Index of authors | |