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• Cover
• Title Page
• Copyright
• List of Tables
• Preface
• Acknowledgments
• About the Companion Website
• 1 Getting Started
• 1.1 Navigating the book
• 1.2 vs. RStudio
• 1.3 Installing and RStudio
• 1.4 Using RStudio
• 1.5 The Comprehensive Archive Network
• 1.6 Packages in
• 1.7 Getting help in
• 1.8 Good housekeeping
• 1.9 Linking to other computer languages
• References
• 2 Technical Background
• 2.1 Mathematical functions
• 2.2 Matrices
• 2.3 Calculus
• 2.4 Probability
• 2.5 Statistics
• Reference
• 3 Essentials of the Language
• 3.1 Calculations
• 3.2 Naming objects
• 3.3 Factors
• 3.4 Logical operations
• 3.5 Generating sequences
• 3.6 Class membership
• 3.7 Missing values, infinity, and things that are not numbers
• 3.8 Vectors and subscripts
• 3.9 Working with logical subscripts
• 3.10 Vector functions
• 3.11 Matrices and arrays
• 3.12 Random numbers, sampling, and shuffling
• 3.13 Loops and repeats
• 3.14 Lists
• 3.15 Text, character strings, and pattern matching
• 3.16 Dates and times in
• 3.17 Environments
• 3.18 Writing functions
• 3.19 Structure of objects
• 3.20 Writing from to a file
• 3.21 Tips for writing code
• References
• 4 Data Input and Dataframes
• 4.1 Working directory
• 4.2 Data input from files
• 4.3 Data input directly from the web
• 4.4 Built‐in data files
• 4.5 Dataframes
• 4.6 Using the match () function in dataframes
• 4.7 Adding margins to a dataframe
• 5 Graphics
• 5.1 Plotting principles
• 5.2 Plots for single variables
• 5.3 Plots for showing two numeric variables
• 5.4 Plots for numeric variables by group
• 5.5 Plots showing two categorical variables
• 5.6 Plots for three (or more) variables
• 5.7 Trellis graphics
• 5.8 Plotting functions
• References
• 6 Graphics in More Detail
• 6.1 More on colour
• 6.2 Changing the look of graphics
• 6.3 Adding items to plots
• 6.4 The grammar of graphics and ggplot2
• 6.5 Graphics cheat sheet
• References
• 7 Tables
• 7.1 Tabulating categorical or discrete data
• 7.2 Tabulating summaries of numeric data
• 7.3 Converting between tables and dataframes
• Reference
• 8 Probability Distributions in
• 8.1 Probability distributions: the basics
• 8.2 Probability distributions in
• 8.3 Continuous probability distributions
• 8.4 Discrete probability distributions
• 8.5 The central limit theorem
• References
• 9 Testing
• 9.1 Principles
• 9.2 Continuous data
• 9.3 Discrete and categorical data
• 9.4 Bootstrapping
• 9.5 Multiple tests
• 9.6 Power and sample size calculations
• 9.7 A table of tests
• References
• 10 Regression
• 10.1 The simple linear regression model
• 10.2 The multiple linear regression model
• 10.3 Understanding the output
• 10.4 Fitting models
• 10.5 Checking model assumptions
• 10.6 Using the model
• 10.7 Further types of regression modelling
• References
• 11 Generalised Linear Models
• 11.1 How GLMs work
• 11.2 Count data and GLMs
• 11.3 Count table data and GLMs
• 11.4 Proportion data and GLMs
• 11.5 Binary Response Variables and GLMs
• 11.6 Bootstrapping a GLM
• References
• 12 Generalised Additive Models
• 12.1 Smoothing example
• 12.2 Straightforward examples of GAMs
• 12.3 Background to using GAMs
• 12.4 More complex GAM examples
• References
• 13 Mixed‐Effect Models
• 13.1 Regression with categorical covariates
• 13.2 An alternative method: random effects
• 13.3 Common data structures where random effects are useful
• 13.4 packages to deal with mixed effects models
• 13.5 Examples of implementing random effect models
• 13.6 Generalised linear mixed models
• 13.7 Alternatives to mixed models
• References
• 14 Non‐linear Regression
• 14.1 Example: modelling deer jaw bone length
• 14.2 Example: grouped data
• 14.3 Self‐starting functions
• 14.4 Further considerations
• References
• 15 Survival Analysis
• 15.1 Handling survival data
• 15.2 The survival and hazard functions
• 15.3 Modelling survival data
• References
• 16 Designed Experiments
• 16.1 Factorial experiments
• 16.2 Pseudo‐replication
• 16.3 Contrasts
• References
• 17 Meta‐Analysis
• 17.1 Elements of a meta‐analysis
• 17.2 Meta‐analysis in
• 17.3 Examples
• 17.4 Meta‐analysis of categorical data
• References
• 18 Time Series
• 18.1 Moving average
• 18.2 Blowflies
• 18.3 Seasonal data
• 18.4 Multiple time series
• 18.5 Some theoretical background
• 18.6 ARIMA example
• 18.7 Simulation of time series
• Reference
• 19 Multivariate Statistics
• 19.1 Visualising data
• 19.2 Multivariate analysis of variance
• 19.3 Principal component analysis
• 19.4 Factor analysis
• 19.5 Cluster analysis
• 19.6 Hierarchical cluster analysis
• 19.7 Discriminant analysis
• 19.8 Neural networks
• References
• 20 Classification and Regression Trees
• 20.1 How CARTs work
• 20.2 Regression trees
• 20.3 Classification trees
• 20.4 Looking for patterns
• References
• 21 Spatial Statistics
• 21.1 Spatial point processes
• 21.2 Geospatial statistics
• References
• 22 Bayesian Statistics
• 22.1 Components of a Bayesian Analysis
• 22.2 Bayesian analysis in
• 22.3 Examples
• 22.4 MCMC for a model with binomial errors
• References
• 23 Simulation Models
• 23.1 Temporal dynamics
• 23.2 Spatial simulation models
• 23.3 Temporal and spatial dynamics: random walk
• References
• Index
• End User License Agreement