Introductory Statistics with R – Peter Dalgaard – 2nd Edition

Preface
1 Basics
1.1 First steps
1.1.1 An overgrown calculator
1.1.2 Assignments
1.1.3 Vectorized arithmetic
1.1.4 Standard procedures
1.1.5 Graphics
1.2 R language essentials
1.2.1 Expressions and objects
1.2.2 Functions and arguments
1.2.3 Vectors
1.2.4 Quoting and escape sequences
1.2.5 Missing values
1.2.6 Functions that create vectors
1.2.7 Matrices and arrays
1.2.8 Factors
1.2.9 Lists
1.2.10 Data frames
1.2.11 Indexing
1.2.12 Conditional selection
1.2.13 Indexing of data frames
1.2.14 Grouped data and data frames
1.2.15 Implicit loops
1.2.16 Sorting
1.3 Exercises
2 The R environment
2.1 Session management
2.1.1 The workspace
2.1.2 Textual output
2.1.3 Scripting
2.1.4 Getting help
2.1.5 Packages
2.1.6 Built-in data
2.1.7 attach and detach
2.1.8 subset, transform, and within
2.2 The graphics subsystem
2.2.1 Plot layout
2.2.2 Building a plot from pieces
2.2.3 Using par
2.2.4 Combining plots
2.3 R programming
2.3.1 Flow control
2.3.2 Classes and generic functions
2.4 Data entry
2.4.1 Reading from a text file
2.4.2 Further details on read.table
2.4.3 The data editor
2.4.4 Interfacing to other programs
2.5 Exercises
3 Probability and distributions
3.1 Random sampling
3.2 Probability calculations and combinatorics
3.3 Discrete distributions
3.4 Continuous distributions
3.5 The built-in distributions in R
3.5.1 Densities
3.5.2 Cumulative distribution functions
3.5.3 Quantiles
3.5.4 Random numbers
3.6 Exercises
4 Descriptive statistics and graphics
4.1 Summary statistics for a single group
4.2 Graphical display of distributions
4.2.1 Histograms
4.2.2 Empirical cumulative distribution
4.2.3 Q–Q plots
4.2.4 Boxplots
4.3 Summary statistics by groups
4.4 Graphics for grouped data
4.4.1 Histograms
4.4.2 Parallel boxplots
4.4.3 Stripcharts
4.5 Tables
4.5.1 Generating tables
4.5.2 Marginal tables and relative frequency
4.6 Graphical display of tables
4.6.1 Barplots
4.6.2 Dotcharts
4.6.3 Piecharts
4.7 Exercises
5 One- and two-sample tests
5.1 One-sample t test
5.2 Wilcoxon signed-rank test
5.3 Two-sample t test
5.4 Comparison of variances
5.5 Two-sample Wilcoxon test
5.6 The paired t test
5.7 The matched-pairs Wilcoxon test
5.8 Exercises
6 Regression and correlation
6.1 Simple linear regression
6.2 Residuals and fitted values
6.3 Prediction and confidence bands
6.4 Correlation
6.4.1 Pearson correlation
6.4.2 Spearman’s ?
6.4.3 Kendall’s ?
6.5 Exercises
7 Analysis of variance and the Kruskal–Wallis test
7.1 One-way analysis of variance
7.1.1 Pairwise comparisons and multiple testing
7.1.2 Relaxing the variance assumption
7.1.3 Graphical presentation
7.1.4 Bartlett’s test
7.2 Kruskal–Wallis test
7.3 Two-way analysis of variance
7.3.1 Graphics for repeated measurements
7.4 The Friedman test
7.5 The ANOVA table in regression analysis
7.6 Exercises
8 Tabular data
8.1 Single proportions
8.2 Two independent proportions
8.3 k proportions, test for trend
8.4 r × c tables
8.5 Exercises
9 Power and the computation of sample size
9.1 The principles of power calculations
9.1.1 Power of one-sample and paired t tests
9.1.2 Power of two-sample t test
9.1.3 Approximate methods
9.1.4 Power of comparisons of proportions
9.2 Two-sample problems
9.3 One-sample problems and paired tests
9.4 Comparison of proportions
9.5 Exercises
10 Advanced data handling
10.1 Recoding variables
10.1.1 The cut function
10.1.2 Manipulating factor levels
10.1.3 Working with dates
10.1.4 Recoding multiple variables
10.2 Conditional calculations
10.3 Combining and restructuring data frames
10.3.1 Appending frames
10.3.2 Merging data frames
10.3.3 Reshaping data frames
10.4 Per-group and per-case procedures
10.5 Time splitting
10.6 Exercises
11 Multiple regression
11.1 Plotting multivariate data
11.2 Model specification and output
11.3 Model search
11.4 Exercises
12 Linear models
12.1 Polynomial regression
12.2 Regression through the origin
12.3 Design matrices and dummy variables
12.4 Linearity over groups
12.5 Interactions
12.6 Two-way ANOVA with replication
12.7 Analysis of covariance
12.7.1 Graphical description
12.7.2 Comparison of regression lines
12.8 Diagnostics
12.9 Exercises
13 Logistic regression
13.1 Generalized linear models
13.2 Logistic regression on tabular data
13.2.1 The analysis of deviance table
13.2.2 Connection to test for trend
13.3 Likelihood profiling
13.4 Presentation as odds-ratio estimates
13.5 Logistic regression using raw data
13.6 Prediction
13.7 Model checking
13.8 Exercises
14 Survival analysis
14.1 Essential concepts
14.2 Survival objects
14.3 Kaplan–Meier estimates
14.4 The log-rank test
14.5 The Cox proportional hazards model
14.6 Exercises
15 Rates and Poisson regression
15.1 Basic ideas
15.1.1 The Poisson distribution
15.1.2 Survival analysis with constant hazard
15.2 Fitting Poisson models
15.3 Computing rates
15.4 Models with piecewise constant intensities
15.5 Exercises
16 Nonlinear curve fitting
16.1 Basic usage
16.2 Finding starting values
16.3 Self-starting models
16.4 Profiling
16.5 Finer control of the fitting algorithm
16.6 Exercises
A Obtaining and installing R and the ISwR package
B Data sets in the ISwR package
C Compendium
D Answers to exercises
Bibliography
Index