An R Companion to Applied Regression

An R Companion to Applied Regression

应用回归之R伴读
新闻理论

售价：

发货周期：预计5-7周发货

作者

John David Fox~Harvey Sanford Weisberg

出版社

Sage

出版时间

2018年10月02日

装帧

平装

ＩＳＢＮ

9781544336473

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页码

608

开本

25.1 x 17.8 x 3 cm

语种

英文

版次

3rd ed.

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An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, and substantial web-based support materials. The Third Edition has been reorganized and includes a new chapter on mixed-effects models, new and updated data sets, and a de-emphasis on statistical programming, while retaining a general introduction to basic R programming. The authors have substantially updated both the car and effects packages for R for this edition, introducing additional capabilities and making the software more consistent and easier to use. They also advocate an everyday data-analysis workflow that encourages reproducible research. To this end, they provide coverage of RStudio, an interactive development environment for R that allows readers to organize and document their work in a simple and intuitive fashion, and then easily share their results with others. Also included is coverage of R Markdown, showing how to create documents that mix R commands with explanatory text.

1. Getting Started with R and RStudio \\ Projects in RStudio \\ R Basics \\ Fixing Errors and Getting Help \\ Organizing Your Work in R and RStudio \\ An Extended Illustration \\ R Functions for Basic Statistics \\ Generic Functions and Their Methods* \\ 2. Reading and Manipulating Data \\ Data Input \\ Managing Data \\ Working With Data Frames \\ Matrices, Arrays, and Lists \\ Dates and Times \\ Character Data \\ Large Data Sets in R* \\ Complementary Reading and References \\ 3. Exploring and Transforming Data \\ Examining Distributions \\ Examining Relationships \\ Examining Multivariate Data \\ Transforming Data \\ Point Labeling and Identication \\ Scatterplot Smoothing \\ Complementary Reading and References \\ 4. Fitting Linear Models \\ The Linear Model \\ Linear Least-Squares Regression \\ Predictor Effect Plots \\ Polynomial Regression and Regression Splines \\ Factors in Linear Models \\ Linear Models with Interactions \\ More on Factors \\ Too Many Regressors* \\ The Arguments of the lm Function \\ Complementary Reading and References \\ 5. Standard Errors, Confidence Intervals, Tests \\ Coefficient Standard Errors \\ Confidence Intervals \\ Testing Hypotheses About Regression Coefficients \\ Complementary Reading and References \\ 6. Fitting Generalized Linear Models \\ The Structure of GLMs \\ The glm() Function in R \\ GLMs for Binary-Response Data \\ Binomial Data \\ Poisson GLMs for Count Data \\ Loglinear Models for Contingency Tables \\ Multinomial Response Data \\ Nested Dichotomies \\ The Proportional-Odds Model \\ Extensions \\ Arguments to glm() \\ Fitting GLMs by Iterated Weighted Least-Squares* \\ Complementary Reading and References \\ 7. Fitting Mixed-Effects Models \\ Background: The Linear Model Revisited \\ Linear Mixed-Effects Models \\ Generalized Linear Mixed Models \\ Complementary Reading \\ 8. Regression Diagnostics \\ Residuals \\ Basic Diagnostic Plots \\ Unusual Data \\ Transformations After Fitting a Regression Model \\ Non-Constant Error Variance \\ Diagnostics for Generalized Linear Models \\ Diagnostics for Mixed-Effects Models \\ Collinearity and Variance-Inflation Factors \\ Additional Regression Diagnostics \\ Complementary Reading and References \\ 9. Drawing Graphs \\ A General Approach to R Graphics \\ Putting It Together: Local Linear Regression \\ Other R Graphics Packages \\ Complementary Reading and References \\ 10. An Introduction to R Programming \\ Why Learn to Program in R? \\ Defining Functions: Preliminary Examples \\ Working With Matrices* \\ Conditionals, Loops, and Recursion \\ Avoiding Loops \\ Optimization Problems* \\ Monte-Carlo Simulations* \\ Debugging R Code* \\ Object-Oriented Programming in R* \\ Writing Statistical-Modeling Functions in R* \\ Organizing Code for R Functions \\ Complementary Reading and References

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