Modern Applied Statistics with S

Modern Applied Statistics with S by W.N. Venables, B.D. Ripley.

S is a powerful environment for the statistical and graphical analysis of data. It provides the tools to implement many statistical ideas that have been made possible by the widespread availability of workstations having good graphics and computational capabilities. This book is a guide to using S e...

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Bibliographic Details
Main Authors: Venables, W.N (Author), Ripley, B.D (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2002.
Edition:4th ed. 2002.
Series:Statistics and Computing,
Springer eBook Collection.
Subjects:
Probabilities.
Computer mathematics.
Computer software.
Statistics .
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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.

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