The R Book by Crawley, Michael J. -- Read -- Imperial Library of Trantor

Index

Cover Title Page Copyright Preface

Acknowledgements

Chapter 1: Getting Started

1.1 How to use this book 1.2 Installing R 1.3 Running R 1.4 The Comprehensive R Archive Network 1.5 Getting help in R 1.6 Packages in R 1.7 Command line versus scripts 1.8 Data editor 1.9 Changing the look of the R screen 1.10 Good housekeeping 1.11 Linking to other computer languages

Chapter 2: Essentials of the R Language

2.1 Calculations 2.2 Logical operations 2.3 Generating sequences 2.4 Membership: Testing and coercing in R 2.5 Missing values, infinity and things that are not numbers 2.6 Vectors and subscripts 2.7 Vector functions 2.8 Matrices and arrays 2.9 Random numbers, sampling and shuffling 2.10 Loops and repeats 2.11 Lists 2.12 Text, character strings and pattern matching 2.13 Dates and times in R 2.14 Environments 2.15 Writing R functions 2.16 Writing from R to file 2.17 Programming tips

Chapter 3: Data Input

3.1 Data input from the keyboard 3.2 Data input from files 3.3 Input from files using scan 3.4 Reading data from a file using readLines 3.5 Warnings when you attach the dataframe 3.6 Masking 3.7 Input and output formats 3.8 Checking files from the command line 3.9 Reading dates and times from files 3.10 Built-in data files 3.11 File paths 3.12 Connections 3.13 Reading data from an external database

Chapter 4: Dataframes

4.1 Subscripts and indices 4.2 Selecting rows from the dataframe at random 4.3 Sorting dataframes 4.4 Using logical conditions to select rows from the dataframe 4.5 Omitting rows containing missing values, NA 4.6 Using order and !duplicated to eliminate pseudoreplication 4.7 Complex ordering with mixed directions 4.8 A dataframe with row names instead of row numbers 4.9 Creating a dataframe from another kind of object 4.10 Eliminating duplicate rows from a dataframe 4.11 Dates in dataframes 4.12 Using the match function in dataframes 4.13 Merging two dataframes 4.14 Adding margins to a dataframe 4.15 Summarizing the contents of dataframes

Chapter 5: Graphics

5.1 Plots with two variables 5.2 Plotting with two continuous explanatory variables: Scatterplots 5.3 Adding other shapes to a plot 5.4 Drawing mathematical functions 5.5 Shape and size of the graphics window 5.6 Plotting with a categorical explanatory variable 5.7 Plots for single samples 5.8 Plots with multiple variables 5.9 Special plots 5.10 Saving graphics to file 5.11 Summary

Chapter 6: Tables

6.1 Tables of counts 6.2 Summary tables 6.3 Expanding a table into a dataframe 6.4 Converting from a dataframe to a table 6.5 Calculating tables of proportions with prop.table 6.6 The scale function 6.7 The expand.grid function 6.8 The model.matrix function 6.9 Comparing table and tabulate

Chapter 7: Mathematics

7.1 Mathematical functions 7.2 Probability functions 7.3 Continuous probability distributions 7.4 Discrete probability distributions 7.5 Matrix algebra 7.6 Solving systems of linear equations using matrices 7.7 Calculus

Chapter 8: Classical Tests

8.1 Single samples 8.2 Bootstrap in hypothesis testing 8.3 Skew and kurtosis 8.4 Two samples 8.5 Tests on paired samples 8.6 The sign test 8.7 Binomial test to compare two proportions 8.8 Chi-squared contingency tables 8.9 Correlation and covariance 8.10 Kolmogorov–Smirnov test 8.11 Power analysis 8.12 Bootstrap

Chapter 9: Statistical Modelling

9.1 First things first 9.2 Maximum likelihood 9.3 The principle of parsimony (Occam's razor) 9.4 Types of statistical model 9.5 Steps involved in model simplification 9.6 Model formulae in R 9.7 Multiple error terms 9.8 The intercept as parameter 1 9.9 The update function in model simplification 9.10 Model formulae for regression 9.11 Box–Cox transformations 9.12 Model criticism 9.13 Model checking 9.14 Influence 9.15 Summary of statistical models in R 9.16 Optional arguments in model-fitting functions 9.17 Akaike's information criterion 9.18 Leverage 9.19 Misspecified model 9.20 Model checking in R 9.21 Extracting information from model objects 9.22 The summary tables for continuous and categorical explanatory variables 9.23 Contrasts 9.24 Model simplification by stepwise deletion 9.25 Comparison of the three kinds of contrasts 9.26 Aliasing 9.27 Orthogonal polynomial contrasts: contr.poly 9.28 Summary of statistical modelling

Chapter 10: Regression

10.1 Linear regression 10.2 Polynomial approximations to elementary functions 10.3 Polynomial regression 10.4 Fitting a mechanistic model to data 10.5 Linear regression after transformation 10.6 Prediction following regression 10.7 Testing for lack of fit in a regression 10.8 Bootstrap with regression 10.9 Jackknife with regression 10.10 Jackknife after bootstrap 10.11 Serial correlation in the residuals 10.12 Piecewise regression 10.13 Multiple regression

Chapter 11: Analysis of Variance

11.1 One-way ANOVA 11.2 Factorial experiments 11.3 Pseudoreplication: Nested designs and split plots 11.4 Variance components analysis 11.5 Effect sizes in ANOVA: aov or lm? 11.6 Multiple comparisons 11.7 Multivariate analysis of variance

Chapter 12: Analysis of Covariance

12.1 Analysis of covariance in R 12.2 ANCOVA and experimental design 12.3 ANCOVA with two factors and one continuous covariate 12.4 Contrasts and the parameters of ANCOVA models 12.5 Order matters in summary.aov

Chapter 13: Generalized Linear Models

13.1 Error structure 13.2 Linear predictor 13.3 Link function 13.4 Proportion data and binomial errors 13.5 Count data and Poisson errors 13.6 Deviance: Measuring the goodness of fit of a GLM 13.7 Quasi-likelihood 13.8 The quasi family of models 13.9 Generalized additive models 13.10 Offsets 13.11 Residuals 13.12 Overdispersion 13.13 Bootstrapping a GLM 13.14 Binomial GLM with ordered categorical variables

Chapter 14: Count Data

14.1 A regression with Poisson errors 14.2 Analysis of deviance with count data 14.3 Analysis of covariance with count data 14.4 Frequency distributions 14.5 Overdispersion in log-linear models 14.6 Negative binomial errors

Chapter 15: Count Data in Tables

15.1 A two-class table of counts 15.2 Sample size for count data 15.3 A four-class table of counts 15.4 Two-by-two contingency tables 15.5 Using log-linear models for simple contingency tables 15.6 The danger of contingency tables 15.7 Quasi-Poisson and negative binomial models compared 15.8 A contingency table of intermediate complexity 15.9 Schoener's lizards: A complex contingency table 15.10 Plot methods for contingency tables 15.11 Graphics for count data: Spine plots and spinograms

Chapter 16: Proportion Data

16.1 Analyses of data on one and two proportions 16.2 Count data on proportions 16.3 Odds 16.4 Overdispersion and hypothesis testing 16.5 Applications 16.6 Averaging proportions 16.7 Summary of modelling with proportion count data 16.8 Analysis of covariance with binomial data 16.9 Converting complex contingency tables to proportions

Chapter 17: Binary Response Variables

17.1 Incidence functions 17.2 Graphical tests of the fit of the logistic to data 17.3 ANCOVA with a binary response variable 17.4 Binary response with pseudoreplication

Chapter 18: Generalized Additive Models

18.1 Non-parametric smoothers 18.2 Generalized additive models 18.3 An example with strongly humped data 18.4 Generalized additive models with binary data 18.5 Three-dimensional graphic output from gam

Chapter 19: Mixed-Effects Models

19.1 Replication and pseudoreplication 19.2 The lme and lmer functions 19.3 Best linear unbiased predictors 19.4 Designed experiments with different spatial scales: Split plots 19.5 Hierarchical sampling and variance components analysis 19.6 Mixed-effects models with temporal pseudoreplication 19.7 Time series analysis in mixed-effects models 19.8 Random effects in designed experiments 19.9 Regression in mixed-effects models 19.10 Generalized linear mixed models

Chapter 20: Non-Linear Regression

20.1 Comparing Michaelis–Menten and asymptotic exponential 20.2 Generalized additive models 20.3 Grouped data for non-linear estimation 20.4 Non-linear time series models (temporal pseudoreplication) 20.5 Self-starting functions 20.6 Bootstrapping a family of non-linear regressions

Chapter 21: Meta-Analysis

21.1 Effect size 21.2 Weights 21.3 Fixed versus random effects 21.4 Random-effects meta-analysis of binary data

Chapter 22: Bayesian Statistics

22.1 Background 22.2 A continuous response variable 22.3 Normal prior and normal likelihood 22.4 Priors 22.5 Bayesian statistics for realistically complicated models 22.6 Practical considerations 22.7 Writing BUGS models 22.8 Packages in R for carrying out Bayesian analysis 22.9 Installing JAGS on your computer 22.10 Running JAGS in R 22.11 MCMC for a simple linear regression 22.12 MCMC for a model with temporal pseudoreplication 22.13 MCMC for a model with binomial errors

Chapter 23: Tree Models

23.1 Background 23.2 Regression trees 23.3 Using rpart to fit tree models 23.4 Tree models as regressions 23.5 Model simplification 23.6 Classification trees with categorical explanatory variables 23.7 Classification trees for replicated data 23.8 Testing for the existence of humps

Chapter 24: Time Series Analysis

24.1 Nicholson's blowflies 24.2 Moving average 24.3 Seasonal data 24.4 Built-in time series functions 24.5 Decompositions 24.6 Testing for a trend in the time series 24.7 Spectral analysis 24.8 Multiple time series 24.9 Simulated time series 24.10 Time series models

Chapter 25: Multivariate Statistics

25.1 Principal components analysis 25.2 Factor analysis 25.3 Cluster analysis 25.4 Hierarchical cluster analysis 25.5 Discriminant analysis 25.6 Neural networks

Chapter 26: Spatial Statistics

26.1 Point processes 26.2 Nearest neighbours 26.3 Tests for spatial randomness 26.4 Packages for spatial statistics 26.5 Geostatistical data 26.6 Regression models with spatially correlated errors: Generalized least squares 26.7 Creating a dot-distribution map from a relational database

Chapter 27: Survival Analysis

27.1 A Monte Carlo experiment 27.2 Background 27.3 The survivor function 27.4 The density function 27.5 The hazard function 27.6 The exponential distribution 27.7 Kaplan–Meier survival distributions 27.8 Age-specific hazard models 27.9 Survival analysis in R 27.10 Parametric analysis 27.11 Cox's proportional hazards 27.12 Models with censoring

Chapter 28: Simulation Models

28.1 Temporal dynamics: Chaotic dynamics in population size 28.2 Temporal and spatial dynamics: A simulated random walk in two dimensions 28.3 Spatial simulation models 28.4 Pattern generation resulting from dynamic interactions

Chapter 29: Changing the Look of Graphics

29.1 Graphs for publication 29.2 Colour 29.3 Cross-hatching 29.4 Grey scale 29.5 Coloured convex hulls and other polygons 29.6 Logarithmic axes 29.7 Different font families for text 29.8 Mathematical and other symbols on plots 29.9 Phase planes 29.10 Fat arrows 29.11 Three-dimensional plots 29.12 Complex 3D plots with wireframe 29.13 An alphabetical tour of the graphics parameters 29.14 Trellis graphics

References and Further Reading Index

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