عنوان

An R companion to applied regression /

1544336470

9781544336473

An R companion to applied regression /

[Book]

John Fox, McMaster University, Sanford Weisberg, University of Minnesota.

Third edition.

Thousand Oaks, California :

SAGE Publications, Inc.,

[2019]

xxx, 577 pages ;

26 cm

Includes bibliographical references and indexes.

Machine generated contents note: What Is R? -- Obtaining and Installing R and RStudio -- Installing R on a Windows System -- Installing R on a macOS System -- Installing RStudio -- Installing and Using R Packages -- Optional: Customizing R -- Optional: Installing LATEX -- Using This Book -- Chapter Synopses -- Typographical Conventions -- New in the Third Edition -- The Website for the R Companion -- Beyond the R Companion -- Acknowledgments -- 1.1.Projects in RStudio -- 1.2.R Basics -- 1.2.1.Interacting With R Through the Console -- 1.2.2.Editing R Commands in the Console -- 1.2.3.R Functions -- 1.2.4.Vectors and Variables -- 1.2.5.Nonnumeric Vectors -- 1.2.6.Indexing Vectors -- 1.2.7.User-Defined Functions -- 1.3.Fixing Errors and Getting Help -- 1.3.1.When Things Go Wrong -- 1.3.2.Getting Help and Information -- 1.4.Organizing Your Work in R and RStudio and Making It Reproducible -- 1.4.1.Using the RStudio Editor With R Script Files -- 1.4.2.Writing R Markdown Documents

Note continued: 1.5.An Extended Illustration: Duncan's Occupational-Prestige Regression -- 1.5.1.Examining the Data -- 1.5.2.Regression Analysis -- 1.5.3.Regression Diagnostics -- 1.6.R Functions for Basic Statistics -- 1.7.Generic Functions and Their Methods* -- 2.1.Data Input -- 2.1.1.Accessing Data From a Package -- 2.1.2.Entering a Data Frame Directly -- 2.1.3.Reading Data From Plain-Text Files -- 2.1.4.Files and Paths -- 2.1.5.Exporting or Saving a Data Frame to a File -- 2.1.6.Reading and Writing Other File Formats -- 2.2.Other Approaches to Reading and Managing Data Sets in R -- 2.3.Working With Data Frames -- 2.3.1.How the R Interpreter Finds Objects -- 2.3.2.Missing Data -- 2.3.3.Modifying and Transforming Data -- 2.3.4.Binding Rows and Columns -- 2.3.5.Aggregating Data Frames -- 2.3.6.Merging Data Frames -- 2.3.7.Reshaping Data -- 2.4.Working With Matrices, Arrays, and Lists -- 2.4.1.Matrices -- 2.4.2.Arrays -- 2.4.3.Lists -- 2.4.4.Indexing

Note continued: 10.3.5.Miscellaneous Matrix Computations -- 10.4.Program Control With Conditionals, Loops, and Recursion -- 10.4.1.Conditionals -- 10.4.2.Iteration (Looping) -- 10.4.3.Recursion -- 10.5.Avoiding Loops: apply () and Its Relatives -- 10.5.1.To Loop or Not to Loop? -- 10.6.Optimization Problems* -- 10.6.1.Zero-Inflated Poisson Regression -- 10.7.Monte-Carlo Simulation* -- 10.7.1.Testing Regression Models Using Simulation -- 10.8.Debugging R Code* -- 10.9.Object-Oriented Programming in R* -- 10.10.Writing Statistical-Modeling Functions in R* -- 10.11.Organizing Code for R Functions -- 10.12.Complementary Reading and References.

Note continued: 2.5.Dates and Times -- 2.6.Character Data -- 2.7.Large Data Sets in R* -- 2.7.1.How Large Is "Large"? -- 2.7.2.Reading and Saving Large Data Sets -- 2.8.Complementary Reading and References -- 3.1.Examining Distributions -- 3.1.1.Histograms -- 3.1.2.Density Estimation -- 3.1.3.Quantile-Comparison Plots -- 3.1.4.Boxplots -- 3.2.Examining Relationships -- 3.2.1.Scatterplots -- 3.2.2.Parallel Boxplots -- 3.2.3.More on the plot () Function -- 3.3.Examining Multivariate Data -- 3.3.1.Three-Dimensional Plots -- 3.3.2.Scatterplot Matrices -- 3.4.Transforming Data -- 3.4.1.Logarithms: The Champion of Transformations -- 3.4.2.Power Transformations -- 3.4.3.Transformations and Exploratory Data Analysis -- 3.4.4.Transforming Restricted-Range Variables -- 3.4.5.Other Transformations -- 3.5.Point Labeling and Identification -- 3.5.1.The identify () Function -- 3.5.2.Automatic Point Labeling -- 3.6.Scatterplot Smoothing -- 3.7.Complementary Reading and References

Note continued: 4.1.The Linear Model -- 4.2.Linear Least-Squares Regression -- 4.2.1.Simple Linear Regression -- 4.2.2.Multiple Linear Regression -- 4.2.3.Standardized Regression Coefficients -- 4.3.Predictor Effect Plots -- 4.4.Polynomial Regression and Regression Splines -- 4.4.1.Polynomial Regression -- 4.4.2.Regression Splines* -- 4.5.Factors in Linear Models -- 4.5.1.A Linear Model With One Factor: One-Way Analysis of Variance -- 4.5.2.Additive Models With Numeric Predictors and Factors -- 4.6.Linear Models With Interactions -- 4.6.1.Interactions Between Numeric Predictors and Factors -- 4.6.2.Shortcuts for Writing Linear-Model Formulas -- 4.6.3.Multiple Factors -- 4.6.4.Interactions Between Numeric Predictors* -- 4.7.More on Factors -- 4.7.1.Dummy Coding -- 4.7.2.Other Factor Codings -- 4.7.3.Ordered Factors and Orthogonal-Polynomial Contrasts -- 4.7.4.User-Specified Contrasts* -- 4.7.5.Suppressing the Intercept in a Model With Factors*

Note continued: 4.8.Too Many Regressors* -- 4.9.The Arguments of the lm () Function -- 4.9.1.formula -- 4.9.2.data -- 4.9.3.subset -- 4.9.4.weights -- 4.9.5.na.action -- 4.9.6.method, model, x, y, qr* -- 4.9.7.singular.ok* -- 4.9.8.contrasts -- 4.9.9.offset -- 4.10.Complementary Reading and References -- 5.1.Coefficient Standard Errors -- 5.1.1.Conventional Standard Errors of Least-Squares Regression Coefficients -- 5.1.2.Robust Regression Coefficient Standard Errors -- 5.1.3.Using the Bootstrap to Compute Standard Errors -- 5.1.4.The Delta Method for Standard Errors of Nonlinear Functions* -- 5.2.Confidence Intervals -- 5.2.1.Wald Confidence Intervals -- 5.2.2.Bootstrap Confidence Intervals -- 5.2.3.Confidence Regions and Data Ellipses* -- 5.3.Testing Hypotheses About Regression Coefficients -- 5.3.1.Wald Tests -- 5.3.2.Likelihood-Ratio Tests and the Analysis of Variance -- 5.3.3.Sequential Analysis of Variance -- 5.3.4.The Anova () Function

Note continued: 5.3.5.Testing General Linear Hypotheses* -- 5.4.Complementary Reading and References -- 6.1.Review of the Structure of GLMs -- 6.2.The glm () Function in R -- 6.3.GLMs for Binary Response Data -- 6.3.1.Example: Women's Labor Force Participation -- 6.3.2.Example: Volunteering for a Psychological Experiment -- 6.3.3.Predictor Effect Plots for Logistic Regression -- 6.3.4.Analysis of Deviance and Hypothesis Tests for Logistic Regression -- 6.3.5.Fitted and Predicted Values -- 6.4.Binomial Data -- 6.5.Poisson GLMs for Count Data -- 6.6.Loglinear Models for Contingency Tables -- 6.6.1.Two-Dimensional Tables -- 6.6.2.Three-Dimensional Tables -- 6.6.3.Sampling Plans for Loglinear Models -- 6.6.4.Response Variables -- 6.7.Multinomial Response Data -- 6.8.Nested Dichotomies -- 6.9.The Proportional-Odds Model -- 6.9.1.Testing for Proportional Odds -- 6.10.Extensions -- 6.10.1.More on the Anova () Function -- 6.10.2.Gamma Models

Note continued: 6.10.3.Quasi-Likelihood Estimation -- 6,10.4.Overdispersed Binomial and Poisson Models -- 6.11.Arguments to glm() -- 6.11.1.weights -- 6.11.2.start, etastart, mustart -- 6.11.3.offset -- 6.11.4.control -- 6.11.5.model, method, x, y -- 6.12.Fitting GLMs by Iterated Weighted Least Squares* -- 6.13.Complementary Reading and References -- 7.1.Background: The Linear Model Revisited -- 7.1.1.The Linear Model in Matrix Form* -- 7.2.Linear Mixed-Effects Models -- 7.2.1.Matrix Form of the Linear Mixed-Effects Model* -- 7.2.2.An Application to Hierarchical Data -- 7.2.3.Wald Tests for Linear Mixed-Effects Models -- 7.2.4.Examining the Random Effects: Computing BLUPs -- 7.2.5.An Application to Longitudinal Data -- 7.2.6.Modeling the Errors -- 7.2.7.Sandwich Standard Errors for Least-Squares Estimates -- 7.3.Generalized Linear Mixed Models -- 7.3.1.Matrix Form of the GLMM* -- 7.3.2.Example: Minneapolis Police Stops -- 7.4.Complementary Reading -- 8.1.Residuals

Note continued: 8.10.Complementary Reading and References -- 9.1.A General Approach to R Graphics -- 9.1.1.Defining a Coordinate System: plot () -- 9.1.2.Graphics Parameters: par () -- 9.1.3.Adding Graphical Elements: axis (), points (), lines (), text (), et al. -- 9.1.4.Specifying Colors -- 9.2.Putting It Together: Explaining Local Linear Regression -- 9.2.1.Finer Control Over Plot Layout -- 9.3.Other R Graphics Packages -- 9.3.1.The lattice Package -- 9.3.2.The ggplot2 Package -- 9.3.3.Maps -- 9.3.4.Other Notable Graphics Packages -- 9.4.Complementary Reading and References -- 10.1.Why Learn to Program in R? -- 10.2.Defining Functions: Preliminary Examples -- 10.2.1.Lagging a Variable -- 10.2.2.Creating an Influence Plot -- 10.3.Working With Matrices* -- 10.3.1.Basic Matrix Arithmetic -- 10.3.2.Matrix Inversion and the Solution of Linear Simultaneous Equations -- 10.3.3.Example: Linear Least-Squares Regression -- 10.3.4.Eigenvalues and Eigenvectors

Note continued: 8.2.Basic Diagnostic Plots -- 8.2.1.Plotting Residuals -- 8.2.2.Marginal-Model Plots -- 8.2.3.Added-Variable Plots -- 8.2.4.Marginal-Conditional Plots -- 8.3.Unusual Data -- 8.3.1.Outliers and Studentized Residuals -- 8.3.2.Leverage: Hat-Values -- 8.3.3.Influence Measures -- 8.4.Transformations After Fitting a Regression Model -- 8.4.1.Transforming the Response -- 8.4.2.Predictor Transformations -- 8.5.Nonconstant Error Variance -- 8.5.1.Testing for Nonconstant Error Variance -- 8.6.Diagnostics for Generalized Linear Models -- 8.6.1.Residuals and Residual Plots -- 8.6.2.Influence Measures -- 8.6.3.Graphical Methods: Added-Variable Plots, Component-Plus-Residual Plots, and Effect Plots With Partial Residuals -- 8.7.Diagnostics for Mixed-Effects Models -- 8.7.1.Mixed-Model Component-Plus-Residual Plots -- 8.7.2.Influence Diagnostics for Mixed Models -- 8.8.Collinearity and Variance Inflation Factors -- 8.9.Additional Regression Diagnostics

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R (Computer program language)

Regression analysis-- Data processing.

Mathematical Computing.

Regression Analysis.

R (Computer program language)

Regression analysis-- Data processing.

Fox, John,1947-

Weisberg, Sanford,1947-

20200823042640.0

rda

[Book]

Y