Using R for data management, statistical analysis, and graphics / Nicholas J. Horton, Ken Kleinman. — Boca Raton : CRC Press, c2011. – (73.962/H823)

Contents

            Contents
    
    List of Tables
    List of Figures
    Preface
    1 Introduction to R
    1.1 Installation
    1.2 Running R and sample session
    1.3 Using the R Commander graphical interface
    1.4 Learning R and getting help
    1.5 Fundamental structures: Objects， classes， and related concepts
    1.6 Built-in and user-defined functions
    1.7 Add-ons: Libraries and packages
    1.8 Support and bugs
    2 Data management
    2.1 Input
    2.2 Output
    2.3 Structure and meta-data
    2.4 Derived variables and data manipulation
    2.5 Merging， combining， and subsetting datasets 42
    2.6 Date and time variables 46
    2.7 Interactions with the operating system 47
    2.8 Mathematical functions
    2.9 Matrix operations
    2.10 Probability distributions and random number generation
    2.11 Control flow and programming
    2.12 Further resources
    2.13 HELP examples.
    3 Common statistical procedures
    3.1 Summary statistics
    3.2 Contingency tables
    3.3 Bivariate statistics
    3.4 Two sample tests for continuous variables
    3.5 Further resources
    3.6 HELP examples
    4 Linear regression and ANOVA
    4.1 Model fitting
    4.2 Model comparison and selection
    4.3 Tests， contrasts， and linear functions
    4.4 Model diagnostics
    4.5 Model parameters and results
    4.6 Further resources
    4.7 HELP examples
    5 Regression generalizations
    5.1 Generalized linear models
    5.2 Models for correlated data
    5.3 Survival analysis
    5.4 Further generalizations to regression models
    5.5 Multivariate statistics
    5.6 Further resources
    5.7 HELP examples
    6 Graphics
    6.1 A compendium of useful plots
    6.2 Adding elements
    6.3 Options and parameters
    6.4 Saving graphs
    6.5 Further resources
    6.6 HELP examples
    7 Advanced applications
    7.1 Power and sample size calculations
    7.2 Simulations and data generation
    7.3 Data management and related tasks
    7.4 Read geocoded data and draw maps
    7.5 Data scraping and visualization
    7.6 Account for missing data
    7.7 Propensity score modeling
    7.8 Empirical problem solving
    7.9 Further resources
    Appendix The HELP study dataset
    A.1 Background on the HELP study
    A.2 Road map to analyses of the HELP dataset
    A.3 Detailed description of the dataset
    Bibliography
    Indices
    Subject index
    R index