Author | Venables, W. N. author |
---|---|
Title | Modern Applied Statistics with S [electronic resource] / by W. N. Venables, B. D. Ripley |
Imprint | New York, NY : Springer New York : Imprint: Springer, 2002 |
Edition | Fourth Edition |
Connect to | http://dx.doi.org/10.1007/978-0-387-21706-2 |
Descript | XII, 498 p. online resource |
1 Introduction -- 1.1 A Quick Overview of S -- 1.2 Using S -- 1.3 An Introductory Session -- 1.4 What Next? -- 2 Data Manipulation -- 2.1 Objects -- 2.2 Connections -- 2.3 Data Manipulation -- 2.4 Tables and Cross-Classification -- 3 The S Language -- 3.1 Language Layout -- 3.2 More on S Objects -- 3.3 Arithmetical Expressions -- 3.4 Character Vector Operations -- 3.5 Formatting and Printing -- 3.6 Calling Conventions for Functions -- 3.7 Model Formulae -- 3.8 Control Structures -- 3.9 Array and Matrix Operations -- 3.10 Introduction to Classes and Methods -- 4 Graphics -- 4.1 Graphics Devices -- 4.2 Basic Plotting Functions -- 4.3 Enhancing Plots -- 4.4 Fine Control of Graphics -- 4.5 Trellis Graphics -- 5 Univariate Statistics -- 5.1 Probability Distributions -- 5.2 Generating Random Data -- 5.3 Data Summaries -- 5.4 Classical Univariate Statistics -- 5.5 Robust Summaries -- 5.6 Density Estimation -- 5.7 Bootstrap and Permutation Methods -- 6 Linear Statistical Models -- 6.1 An Analysis of Covariance Example -- 6.2 Model Formulae and Model Matrices -- 6.3 Regression Diagnostics -- 6.4 Safe Prediction -- 6.5 Robust and Resistant Regression -- 6.6 Bootstrapping Linear Models -- 6.7 Factorial Designs and Designed Experiments -- 6.8 An Unbalanced Four-Way Layout -- 6.9 Predicting Computer Performance -- 6.10 Multiple Comparisons -- 7 Generalized Linear Models -- 7.1 Functions for Generalized Linear Modelling -- 7.2 Binomial Data -- 7.3 Poisson and Multinomial Models -- 7.4 A Negative Binomial Family -- 7.5 Over-Dispersion in Binomial and Poisson GLMs -- 8 Non-Linear and Smooth Regression -- 8.1 An Introductory Example -- 8.2 Fitting Non-Linear Regression Models -- 8.3 Non-Linear Fitted Model Objects and Method Functions -- 8.4 Confidence Intervals for Parameters -- 8.5 Profiles -- 8.6 Constrained Non-Linear Regression -- 8.7 One-Dimensional Curve-Fitting -- 8.8 Additive Models -- 8.9 Projection-Pursuit Regression -- 8.10 Neural Networks -- 8.11 Conclusions -- 9 Tree-Based Methods -- 9.1 Partitioning Methods -- 9.2 Implementation in rpart -- 9.3 Implementation in tree -- 10 Random and Mixed Effects -- 10.1 Linear Models -- 10.2 Classic Nested Designs -- 10.3 Non-Linear Mixed Effects Models -- 10.4 Generalized Linear Mixed Models -- 10.5 GEE Models -- 11 Exploratory Multivariate Analysis -- 11.1 Visualization Methods -- 11.2 Cluster Analysis -- 11.3 Factor Analysis -- 11.4 Discrete Multivariate Analysis -- 12 Classification -- 12.1 Discriminant Analysis -- 12.2 Classification Theory -- 12.3 Non-Parametric Rules -- 12.4 Neural Networks -- 12.5 Support Vector Machines -- 12.6 Forensic Glass Example -- 12.7 Calibration Plots -- 13 Survival Analysis -- 13.1 Estimators of Survivor Curves -- 13.2 Parametric Models -- 13.3 Cox Proportional Hazards Model -- 13.4 Further Examples -- 14 Time Series Analysis -- 14.1 Second-Order Summaries -- 14.2 ARIMA Models -- 14.3 Seasonality -- 14.4 Nottingham Temperature Data -- 14.5 Regression with Autocorrelated Errors -- 14.6 Models for Financial Series -- 15 Spatial Statistics -- 15.1 Spatial Interpolation and Smoothing -- 15.2 Kriging -- 15.3 Point Process Analysis -- 16 Optimization -- 16.1 Univariate Functions -- 16.2 Special-Purpose Optimization Functions -- 16.3 General Optimization -- Appendices -- A Implementation-Specific Details -- A.1 Using S-PLUS under Unix / Linux -- A.2 Using S-PLUS under Windows -- A.3 Using R under Unix / Linux -- A.4 Using R under Windows -- A.5 For Emacs Users -- B The S-PLUS GUI -- C Datasets, Software and Libraries -- C.1 Our Software -- C.2 Using Libraries -- References