An R and S-Plus Companion to Applied Regression

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SAGE, 2002/06/05 - 312 ページ

"This book fits right into a needed niche: rigorous enough to give full explanation of the power of the S language, yet accessible enough to assign to social science graduate students without fear of intimidation. It is a tremendous balance of applied statistical "firepower" and thoughtful explanation. It meets all of the important mechanical needs: each example is given in detail, code and data are freely available, and the nuances of models are given rather than just the bare essentials. It also meets some important theoretical needs: linear models, categorical data analysis, an introduction to applying GLMs, a discussion of model diagnostics, and useful instructions on writing customized functions. "

—JEFF GILL, University of Florida, Gainesville

 

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目次

Introducing R and SPLUS
1
111 Interacting with the Interpreter
2
112 S Functions
4
113 Vectors and Variables
7
114 UserDefined Functions
12
115 Cleaning Up
14
116 Command Editing and Output Management
15
117 When Things Go Wrong
16
47 More on 1m and Model Formulas
149
472 data
150
473 subset
151
476 method model x y qr
152
479 offset R only
153
Fitting Generalized Linear Models
155
52 Models for Categorial Responses
158
522 Polytomous Data
167

Duncans OccupationalPrestige Regression
18
121 Reading the Data
19
122 Examining the Data
21
123 Regression Analysis
26
124 Regression Diagnostics
28
13 S Functions for Basic Statistics
34
Reading and Manipulating Data
37
21 Data Input
38
212 File Input to a Data Frame
42
213 Importing Data
46
214 Accessing Data in S Libraries
47
215 Getting Data Out of S
48
22 Working with Data Frames
49
222 Missing Data
54
223 Numeric Variables and Factors
60
224 Modifying Data
62
23 Matrices Arrays and Lists
68
231 Matrices
69
232 Arrays
70
234 Indexing
71
24 Data Attributes Models and Classes
78
241 Data in S4
82
Exploring and Transforming Data
85
31 Examining Distributions
86
312 Density Estimates
88
313 QuantileComparison Plots
90
314 Boxplots
91
32 Examining Relationships
93
322 Bivariate Density Estimates
98
323 Parallel Boxplots
100
33 Examining Multivariate Data
102
332 Conditioning Plots
105
34 Transforming Data
106
341 Transformations for Normality and Symmetry
109
342 Transformations to Equalize Spread
113
343 Transformations to Linearity
115
Fitting Linear Models
119
412 Multiple Regression
123
413 Standardized Regression Coefficients
124
42 DummyVariable Regression
126
422 Contrasts
127
423 Ordered Factors
130
425 Dummy Regression with Interactions
133
43 Analysis of Variance Models
136
44 Fitting Additive DummyRegression Models
142
45 General Linear Hypotheses
145
46 Data and Confidence Ellipses
147
53 Poisson Generalized Linear Models for Count Data
177
531 Poisson Regression
178
532 LogLinear Models for Contingency Tables
181
54 Odds and Ends
185
542 Arguments to g1m
188
55 Fitting Generalized Linear Models by Iterated Weighted Least Squares
189
Diagnosing Problems in Linear and Generalized Linear Models
191
61 Unusual Data
192
Hat Values
194
613 Influence Measures
195
62 Nonnormal Errors
201
621 BoxCox Transformation of y
203
622 ConstructedVariable Plot for the BoxCox Transformation
204
63 Nonconstant Error Variance
206
631 Score Tests for Nonconstant Error Variance
208
64 Nonlinearity
210
642 BoxTidwell Transformations of the Predictors
214
643 ConstructedVariable Plots for BoxTidwell Transformations
215
65 Collinearity and Variable Selection
216
652 Variable Selection
220
66 Diagnostics for Generalized Linear Models
225
662 Nonlinearity Diagnostics
230
Drawing Graphs
235
71 A General Approach to S Graphics
236
axis points lines text and so on
239
713 Specifying Colors
246
Effect Displays
247
73 Graphics Devices
255
Writing Programs
257
81 Defining Functions
258
82 Working with Matrices
261
Conditionals Loops and Recursion
268
832 Loops Iteration
269
833 Recursion
272
Binary Logistic Regression
273
84 apply and its Relatives
278
85 ObjectOriented Programming in S
283
852 S Version 4
288
86 Writing S Programs
292
References
295
Index of Data Sets
299
Index of Functions Operators Control Structures and Other Symbols
300
Author Index
305
Subject Index
306
About the Author
著作権

他の版 - すべて表示

An R and S-Plus Companion to Applied Regression
John Fox
プレビュー不可 - 2002

多く使われている語句

abline Added-Variable Plot analysis anonymous Anova argument attach bc bc binomial Box-Cox Transformation boxplot called car library Chapter coefficients computed contrasts Cook's distances data frame data set default defined degrees of freedom deviance dfbeta diagnostics did.not.vote distribution Duncan Estimate Std example extraversion F-statistic factor FALSE FALSE Figure fitted values function in car graph graphics Guyer histogram identify income and education income education interactively Intercept interlocks iterations labels least squares levels likelihood-ratio test linear models log-linear model logit model logit transformation lreg method missing data mouse multinomial logit na.action names neuroticism not.work object observations occupational-prestige data Ornstein's parameter partner.status points Poisson Poisson regression predictors printed produces prof prof prof programming R-Squared recode regressors repwt S-PLUS scatterplot Section specifying standard error statistical studentized residuals subset sums of squares transformation type='n vector weighted least squares women

この書籍への参照

著者について (2002)

John Fox received a BA from the City College of New York and a PhD from the University of Michigan, both in Sociology. He is Professor Emeritus of Sociology at McMaster University in Hamilton, Ontario, Canada, where he was previously the Senator William McMaster Professor of Social Statistics. Prior to coming to McMaster, he was Professor of Sociology, Professor of Mathematics and Statistics, and Coordinator of the Statistical Consulting Service at York University in Toronto. Professor Fox is the author of many articles and books on applied statistics, including emph{Applied Regression Analysis and Generalized Linear Models, Third Edition} (Sage, 2016). He is an elected member of the R Foundation, an associate editor of the Journal of Statistical Software, a prior editor of R News and its successor the R Journal, and a prior editor of the Sage Quantitative Applications in the Social Sciences monograph series.

書誌情報

書籍名An R and S-Plus Companion to Applied Regression
An R and S-Plus Companion to Applied Regression, John Fox
著者John Fox
版イラスト付き
出版社SAGE, 2002
ISBN0761922806, 9780761922803
ページ数312 ページ
  
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