Contents
1.4 The Comprehensive R Archive Network
1.7 Command line versus scripts
1.9 Changing the look of the R screen
1.11 Linking to other computer languages
Chapter 2: Essentials of the R Language
2.4 Membership: Testing and coercing in R
2.5 Missing values, infinity and things that are not numbers
2.9 Random numbers, sampling and shuffling
2.12 Text, character strings and pattern matching
3.1 Data input from the keyboard
3.3 Input from files using scan
3.4 Reading data from a file using readLines
3.5 Warnings when you attach the dataframe
3.8 Checking files from the command line
3.9 Reading dates and times from files
3.13 Reading data from an external database
4.2 Selecting rows from the dataframe at random
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
4.12 Using the match function in dataframes
4.14 Adding margins to a dataframe
4.15 Summarizing the contents of dataframes
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
5.8 Plots with multiple variables
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
6.9 Comparing table and tabulate
7.3 Continuous probability distributions
7.4 Discrete probability distributions
7.6 Solving systems of linear equations using matrices
8.2 Bootstrap in hypothesis testing
8.7 Binomial test to compare two proportions
8.8 Chi-squared contingency tables
8.9 Correlation and covariance
Chapter 9: Statistical Modelling
9.3 The principle of parsimony (Occam's razor)
9.4 Types of statistical model
9.5 Steps involved in model simplification
9.8 The intercept as parameter 1
9.9 The update function in model simplification
9.10 Model formulae for regression
9.15 Summary of statistical models in R
9.16 Optional arguments in model-fitting functions
9.17 Akaike's information criterion
9.21 Extracting information from model objects
9.22 The summary tables for continuous and categorical explanatory variables
9.24 Model simplification by stepwise deletion
9.25 Comparison of the three kinds of contrasts
9.27 Orthogonal polynomial contrasts: contr.poly
9.28 Summary of statistical modelling
10.2 Polynomial approximations to elementary functions
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
Chapter 11: Analysis of Variance
11.3 Pseudoreplication: Nested designs and split plots
11.4 Variance components analysis
11.5 Effect sizes in ANOVA: aov or lm?
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
13.4 Proportion data and binomial errors
13.5 Count data and Poisson errors
13.6 Deviance: Measuring the goodness of fit of a GLM
13.8 The quasi family of models
13.9 Generalized additive models
13.14 Binomial GLM with ordered categorical variables
14.1 A regression with Poisson errors
14.2 Analysis of deviance with count data
14.3 Analysis of covariance with count data
14.5 Overdispersion in log-linear models
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
16.1 Analyses of data on one and two proportions
16.2 Count data on proportions
16.4 Overdispersion and hypothesis testing
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
17.2 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
18.2 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)
20.6 Bootstrapping a family of non-linear regressions
21.3 Fixed versus random effects
21.4 Random-effects meta-analysis of binary data
Chapter 22: Bayesian Statistics
22.2 A continuous response variable
22.3 Normal prior and normal likelihood
22.5 Bayesian statistics for realistically complicated models
22.8 Packages in R for carrying out Bayesian analysis
22.9 Installing JAGS on your computer
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
23.3 Using rpart to fit tree models
23.4 Tree models as regressions
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
24.4 Built-in time series functions
24.6 Testing for a trend in the time series
Chapter 25: Multivariate Statistics
25.1 Principal components analysis
25.4 Hierarchical cluster analysis
Chapter 26: Spatial Statistics
26.3 Tests for spatial randomness
26.4 Packages for spatial statistics
26.6 Regression models with spatially correlated errors: Generalized least squares
26.7 Creating a dot-distribution map from a relational database
27.6 The exponential distribution
27.7 Kaplan–Meier survival distributions
27.8 Age-specific hazard models
27.11 Cox's proportional hazards
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
29.5 Coloured convex hulls and other polygons
29.7 Different font families for text
29.8 Mathematical and other symbols on plots
29.12 Complex 3D plots with wireframe
29.13 An alphabetical tour of the graphics parameters