Statistical Models in S 1st edition Edited By Chambers, Hastie – Ebook PDF Instant Download/Delivery: 0412830402 , 9780412830402
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ISBN 10: 0412830402
ISBN 13: 9780412830402
Author: Chambers, Hastie
Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance, generalized linear models, additive models, local regression, and tree-based models. The contributions of the ten authors-most of whom work in the statistics research department at AT&T Bell Laboratories-represent results of research in both the computational and statistical aspects of modeling data.
Statistical Models in S 1st Table of contents:
1 An Appetizer
John M. Chambers, Trevor J. Hastie
A Manufacturing Experiment
Models for the Experimental Results
A Second Experiment
Summary
2 Statistical Models
John M. Chambers, Trevor J. Hastie
Thinking About Models
Models and Data
Creating Statistical Models
Model Formulas in S
Data of Different Types in Formulas
Interaction
Combining Data and Formula
More on Models
Formulas in Detail
Coding Factors by Contrasts
Internal Organization of Models
Rules for Coding Expanded Formulas
Formulas and Terms
Terms and the Model Matrix
Bibliographic Notes
Data for Mdels
John M. Chambers
Examples of Data Frames
Example: Automobile Data
Example: A Manufacturing Experiment
Example: A Marketing Study
Computations on Data Frames
Variables in Data Frames; Factors
Creating Data Frames
Using and Modifying Data Frames
Summaries and Plots
Advanced Computations on Data
Methods for Data Frames
Data Frames as Databases or Evaluation Frames
Model Frames and Model Matrices
Parameterized Data Frames
4 Linear Models
John M. Chambers
Linear Models in Statistics
S Functions and Objects
Fitting the Model
Basic Summaries
Prediction
Options in Fitting
Updating Models
Specializing and Extending the Computations
Repeated Fitting of Similar Models
Adding and Dropping Terms
Influence of Individual Observations
Numerical and Statistical Methods
Mathematical and Statistical Results
Numerical Methods
Overdetermined and Ill-determined Models
5 Analysis of Variance; Designed Experiments
John M. Chambers, Anne E. Freeny, Richard M. Heiberger
Models for Experiments: The Analysis of Variance
S Functions and Objects
Analysis of Variance Models
Graphical Methods and Diagnostics
Generating Designs
The S Functions: Advanced Use
Parameterization; Contrasts
More on Aliasing
Anova Models as Projections
Computational Techniques
Basic Computational Theory Aliasing; Rank-deficiency
Error Terms
Computations for Projection
6 Generalized Linear Models
Trevor J. Hastie, Daryl Pregibon
Statistical Methods
S Functions and Objects
Fitting the Model
Specifying the Link and Variance Functions
Updating Models
Analysis of Deviance Tables
Chi-squared Analyses
Plotting
Specializing and Extending the Computations
Other Arguments to glm()
Coding Factors for GLMs
More on Families
Diagnostics Stepwise Model Selection
Prediction
Statistical and Numerical Methods
Likelihood Inference
Quadratic Approximations
Algorithms
Initial Values
7 Generalized Additive Models
Trevor J. Hastie
Statistical Methods
Data Analysis and Additive Models
Fitting Generalized Additive Models
S Functions and Objects
Fitting the Models
Plotting the Fitted Models
Further Details on gam()
Parametric Additive Models: bs() and ns()
An Example in Detail
Specializing and Extending the Computations
Stepwise Model Selection
Missing Data
Prediction
Smoothers in gam()
More on Plotting
Numerical and Computational Details
Scatterplot Smoothing
Fitting Simple Additive Models
Fitting Generalized Additive Models
Standard Errors and Degrees of Freedom
Implementation Details
8 Local Regression Models
William S. Cleveland, Eric Grosse, William M. Shyu
Statistical Models and Fitting
Definition of Local Regression Models
Loess: Fitting Local Regression Models
S Functions and Objects
Gas Data
Ethanol Data
Air Data
Galaxy Velocities
Fuel Comparison Data
Specializing and Extending the Computations
Computation
Inference
Graphics
Statistical and Computational Methods
9 Tree-Based Models
Linda A. Clark, Daryl Pregibon
Tree-Based Models in Statistics
Numeric Response and a Single Numeric Predictor
Factor Response and Numeric Predictions
Factor Response and Mixed Predictor Variables
S Functions and Objects
Growing a Tree
Functions for Diagnostics
Examining Subtrees
Examining Nodes
Examining Splits
Examining Leaves
Specializing the Computations
Numerical and Statistical Methods
Handling Missing Values
Some Computational Issues
Extending the Computations
10 Nonlinear Models
Douglas M. Bates, John M. Chambers
Statistical Methods
S Functions
Fitting the Models
Summaries
Derivatives
Profiling the Objective Function
Partially Linear Models
Some Details
Controlling the Fitting
Examining the Model
Weighted Nonlinear Regression
Programming Details
Optimization Algorithm
Nonlinear Least-Squares Algorithm
A Classes and Methods: Object-oriented Programming in S
John M. Chambers
A.1 Motivation
A.2 Background
A.3 The Mechanism
A.4 An Example of Designing a Class
A.5 Inheritance
A.6 The Frames for Methods
A.7 Group Methods; Methods for Operators
A.8 Replacement Methods
A.9 Assignment Methods
A.10 Generic Functions
A.11 Comment
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Tags: Chambers, Hastie, Statistical Models