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E-raamat: SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition 2nd edition [Taylor & Francis e-raamat]

(Amherst College, Amherst, MA), (Harvard University, Boston, Massachusetts, USA)
  • Formaat: 470 pages, 8 Tables, black and white; 48 Illustrations, black and white
  • Ilmumisaeg: 17-Jul-2014
  • Kirjastus: CRC Press Inc
  • ISBN-13: 9780429169106
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  • Taylor & Francis e-raamat
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SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition 2nd editionSuurem pilt
  • Formaat: 470 pages, 8 Tables, black and white; 48 Illustrations, black and white
  • Ilmumisaeg: 17-Jul-2014
  • Kirjastus: CRC Press Inc
  • ISBN-13: 9780429169106
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9780429169106
An excellent addition for one's statistical programming bookshelf, this textbook, now in its second edition, fills in an important pedagogical gap. It dos not purport to teach the reader how to program in SAR and R, but, rather, provides the all-important information on how to use these powerful programs. Plenty of code snippets illustrate specific statistical and mathematical functions, interfaces, the all-important issues of input and output (often sadly neglected)the management of data, regression and linear models, a wealth on information on graphing in general as well as graphical options and configuration and simulation. A section is dedicated to case studies, illustrating the concepts described previously. Appendices provide a basic introduction to R and SAS--but some previous knowledge of the syntax of those languages and at least some programming experience are required. The new edition draws on explorations and discussions from a blog that followed the first edition; much of the new material is adapted form the blog entries, and material in general is reorganized and streamlined. In particular, usage of R is now dependent on RStudio, a powerful programming environment, and several new and expanded R packages. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)

An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent Tasks
The first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition explains how to easily perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and graphics, along with more complex applications.

New to the Second Edition
This edition now covers RStudio, a powerful and easy-to-use interface for R. It incorporates a number of additional topics, including using application program interfaces (APIs), accessing data through database management systems, using reproducible analysis tools, and statistical analysis with Markov chain Monte Carlo (MCMC) methods and finite mixture models. It also includes extended examples of simulations and many new examples.

Enables Easy Mobility between the Two Systems
Through the extensive indexing and cross-referencing, users can directly find and implement the material they need. SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Numerous example analyses demonstrate the code in action and facilitate further exploration. The datasets and code are available for download on the book’s website.

List of figures xvii
List of tables xix
Preface to the second edition xxi
Preface to the first edition xxiii
1 Data input and output 1(16)
1.1 Input
1(10)
1.1.1 Native dataset
1(1)
1.1.2 Fixed format text files
2(1)
1.1.3 Other fixed files
3(1)
1.1.4 Reading more complex text files
3(1)
1.1.5 Comma separated value (CSV) files
4(1)
1.1.6 Read sheets from an Excel file
5(1)
1.1.7 Read data from R into SAS
5(1)
1.1.8 Read data from SAS into R
6(1)
1.1.9 Reading datasets in other formats
6(1)
1.1.10 Reading data with a variable number of words in a field
7(1)
1.1.11 Read a file byte by byte
8(1)
1.1.12 Access data from a URL
9(1)
1.1.13 Read an XML-formatted file
9(1)
1.1.14 Manual data entry
10(1)
1.2 Output
11(4)
1.2.1 Displaying data
11(1)
1.2.2 Number of digits to display
11(1)
1.2.3 Save a native dataset
12(1)
1.2.4 Creating datasets in text format
12(1)
1.2.5 Creating Excel spreadsheets
12(1)
1.2.6 Creating files for use by other packages
13(1)
1.2.7 Creating HTML formatted output
14(1)
1.2.8 Creating XML datasets and output
14(1)
1.3 Further resources
15(2)
2 Data management 17(36)
2.1 Structure and meta-data
17(2)
2.1.1 Access variables from a dataset
17(1)
2.1.2 Names of variables and their types
17(1)
2.1.3 Values of variables in a dataset
18(1)
2.1.4 Label variables
18(1)
2.1.5 Add comment to a dataset or variable
19(1)
2.2 Derived variables and data manipulation
19(10)
2.2.1 Add derived variable to a dataset
19(1)
2.2.2 Rename variables in a dataset
19(1)
2.2.3 Create string variables from numeric variables
20(1)
2.2.4 Create categorical variables from continuous variables
20(1)
2.2.5 Recode a categorical variable
21(1)
2.2.6 Create a categorical variable using logic
21(1)
2.2.7 Create numeric variables from string variables
22(1)
2.2.8 Extract characters from string variables
23(1)
2.2.9 Length of string variables
23(1)
2.2.10 Concatenate string variables
24(1)
2.2.11 Set operations
24(1)
2.2.12 Find strings within string variables
25(1)
2.2.13 Find approximate strings
25(1)
2.2.14 Replace strings within string variables
26(1)
2.2.15 Split strings into multiple strings
26(1)
2.2.16 Remove spaces around string variables
27(1)
2.2.17 Upper to lower case
27(1)
2.2.18 Lagged variable
28(1)
2.2.19 Formatting values of variables
28(1)
2.2.20 Perl interface
29(1)
2.2.21 Accessing databases using SQL (structured query language)
29(1)
2.3 Merging, combining, and subsetting datasets
29(8)
2.3.1 Subsetting observations
30(1)
2.3.2 Drop or keep variables in a dataset
30(1)
2.3.3 Random sample of a dataset
31(1)
2.3.4 Observation number
32(1)
2.3.5 Keep unique values
32(1)
2.3.6 Identify duplicated values
32(1)
2.3.7 Convert from wide to long (tall) format
33(1)
2.3.8 Convert from long (tall) to wide format
34(1)
2.3.9 Concatenate and stack datasets
35(1)
2.3.10 Sort datasets
35(1)
2.3.11 Merge datasets
35(2)
2.4 Date and time variables
37(2)
2.4.1 Create date variable
37(1)
2.4.2 Extract weekday
38(1)
2.4.3 Extract month
38(1)
2.4.4 Extract year
38(1)
2.4.5 Extract quarter
38(1)
2.4.6 Create time variable
39(1)
2.5 Further resources
39(1)
2.6 Examples
39(14)
2.6.1 Data input and output
39(4)
2.6.2 Data display
43(1)
2.6.3 Derived variables and data manipulation
44(7)
2.6.4 Sorting and subsetting datasets
51(2)
3 Statistical and mathematical functions 53(18)
3.1 Probability distributions and random number generation
53(6)
3.1.1 Probability density function
53(1)
3.1.2 Quantiles of a probability density function
54(1)
3.1.3 Setting the random number seed
55(1)
3.1.4 Uniform random variables
55(1)
3.1.5 Multinomial random variables
56(1)
3.1.6 Normal random variables
56(1)
3.1.7 Multivariate normal random variables
56(2)
3.1.8 Truncated multivariate normal random variables
58(1)
3.1.9 Exponential random variables
58(1)
3.1.10 Other random variables
58(1)
3.2 Mathematical functions
59(4)
3.2.1 Basic functions
59(1)
3.2.2 Trigonometric functions
60(1)
3.2.3 Special functions
60(1)
3.2.4 Integer functions
60(1)
3.2.5 Comparisons of floating point variables
61(1)
3.2.6 Complex numbers
61(1)
3.2.7 Derivatives
62(1)
3.2.8 Integration
62(1)
3.2.9 Optimization problems
62(1)
3.3 Matrix operations
63(5)
3.3.1 Create matrix from vector
63(1)
3.3.2 Combine vectors or matrices
63(1)
3.3.3 Matrix addition
64(1)
3.3.4 Transpose matrix
64(1)
3.3.5 Find the dimension of a matrix or dataset
64(1)
3.3.6 Matrix multiplication
65(1)
3.3.7 Invert matrix
65(1)
3.3.8 Component-wise multiplication
66(1)
3.3.9 Create submatrix
66(1)
3.3.10 Create a diagonal matrix
66(1)
3.3.11 Create a vector of diagonal elements
67(1)
3.3.12 Create a vector from a matrix
67(1)
3.3.13 Calculate the determinant
67(1)
3.3.14 Find eigenvalues and eigenvectors
67(1)
3.3.15 Find the singular value decomposition
68(1)
3.4 Examples
68(3)
3.4.1 Probability distributions
68(3)
4 Programming and operating system interface 71(12)
4.1 Control flow, programming, and data generation
71(6)
4.1.1 Looping
71(1)
4.1.2 Conditional execution
72(1)
4.1.3 Sequence of values or patterns
73(1)
4.1.4 Referring to a range of variables
74(1)
4.1.5 Perform an action repeatedly over a set of variables
74(1)
4.1.6 Grid of values
75(1)
4.1.7 Debugging
76(1)
4.1.8 Error recovery
76(1)
4.2 Functions and macros
77(1)
4.2.1 SAS macros
77(1)
4.2.2 R functions
78(1)
4.3 Interactions with the operating system
78(5)
4.3.1 Timing commands
78(1)
4.3.2 Suspend execution for a time interval
79(1)
4.3.3 Execute a command in the operating system
79(1)
4.3.4 Command history
80(1)
4.3.5 Find working directory
80(1)
4.3.6 Change working directory
80(1)
4.3.7 List and access files
81(2)
5 Common statistical procedures 83(30)
5.1 Summary statistics
83(4)
5.1.1 Means and other summary statistics
83(1)
5.1.2 Other moments
84(1)
5.1.3 Trimmed mean
84(1)
5.1.4 Quantiles
85(1)
5.1.5 Centering, normalizing, and scaling
85(1)
5.1.6 Mean and 95% confidence interval
86(1)
5.1.7 Proportion and 95% confidence interval
86(1)
5.1.8 Maximum likelihood estimation of parameters
86(1)
5.2 Bivariate statistics
87(3)
5.2.1 Epidemiologic statistics
87(1)
5.2.2 Test characteristics
87(2)
5.2.3 Correlation
89(1)
5.2.4 Kappa (agreement)
89(1)
5.3 Contingency tables
90(2)
5.3.1 Display cross-classification table
90(1)
5.3.2 Displaying missing value categories in a table
90(1)
5.3.3 Pearson chi-square statistic
91(1)
5.3.4 Cochran—Mantel—Haenszel test
91(1)
5.3.5 Cramer's V
91(1)
5.3.6 Fisher's exact test
92(1)
5.3.7 McNemar's test
92(1)
5.4 Tests for continuous variables
92(3)
5.4.1 Tests for normality
92(1)
5.4.2 Student's t test
93(1)
5.4.3 Test for equal variances
93(1)
5.4.4 Nonparametric tests
94(1)
5.4.5 Permutation test
94(1)
5.4.6 Logrank test
95(1)
5.5 Analytic power and sample size calculations
95(2)
5.6 Further resources
97(1)
5.7 Examples
97(16)
5.7.1 Summary statistics and exploratory data analysis
97(4)
5.7.2 Bivariate relationships
101(2)
5.7.3 Contingency tables
103(4)
5.7.4 Two sample tests of continuous variables
107(4)
5.7.5 Survival analysis: logrank test
111(2)
6 Linear regression and ANOVA 113(36)
6.1 Model fitting
113(5)
6.1.1 Linear regression
113(1)
6.1.2 Linear regression with categorical covariates
114(1)
6.1.3 Changing the reference category
114(1)
6.1.4 Parameterization of categorical covariates
115(1)
6.1.5 Linear regression with no intercept
116(1)
6.1.6 Linear regression with interactions
117(1)
6.1.7 One-way analysis of variance
117(1)
6.1.8 Analysis of variance with two or more factors
117(1)
6.2 Tests, contrasts, and linear functions of parameters
118(2)
6.2.1 Joint null hypotheses: several parameters equal 0
118(1)
6.2.2 Joint null hypotheses: sum of parameters
118(1)
6.2.3 Tests of equality of parameters
119(1)
6.2.4 Multiple comparisons
119(1)
6.2.5 Linear combinations of parameters
120(1)
6.3 Model diagnostics
120(4)
6.3.1 Predicted values
120(1)
6.3.2 Residuals
121(1)
6.3.3 Standardized and Studentized residuals
121(1)
6.3.4 Leverage
122(1)
6.3.5 Cook's D
122(1)
6.3.6 DFFITS
123(1)
6.3.7 Diagnostic plots
123(1)
6.3.8 Heteroscedasticity tests
124(1)
6.4 Model parameters and results
124(4)
6.4.1 Parameter estimates
124(1)
6.4.2 Standardized regression coefficients
124(1)
6.4.3 Standard errors of parameter estimates
125(1)
6.4.4 Confidence interval for parameter estimates
125(1)
6.4.5 Confidence limits for the mean
125(1)
6.4.6 Prediction limits
126(1)
6.4.7 R-squared
127(1)
6.4.8 Design and information matrix
127(1)
6.4.9 Covariance matrix of parameter estimates
127(1)
6.4.10 Correlation matrix of parameter estimates
128(1)
6.5 Further resources
128(1)
6.6 Examples
128(21)
6.6.1 Scatterplot with smooth fit
129(1)
6.6.2 Linear regression with interaction
130(5)
6.6.3 Regression diagnostics
135(3)
6.6.4 Fitting the regression model separately for each value of another variable
138(1)
6.6.5 Two-way ANOVA
139(5)
6.6.6 Multiple comparisons
144(2)
6.6.7 Contrasts
146(3)
7 Regression generalizations and modeling 149(62)
7.1 Generalized linear models
149(5)
7.1.1 Logistic regression model
149(2)
7.1.2 Conditional logistic regression model
151(1)
7.1.3 Exact logistic regression
152(1)
7.1.4 Ordered logistic model
152(1)
7.1.5 Generalized logistic model
152(1)
7.1.6 Poisson model
153(1)
7.1.7 Negative binomial model
153(1)
7.1.8 Log-linear model
153(1)
7.2 Further generalizations
154(2)
7.2.1 Zero-inflated Poisson model
154(1)
7.2.2 Zero-inflated negative binomial model
154(1)
7.2.3 Generalized additive model
155(1)
7.2.4 Nonlinear least squares model
155(1)
7.3 Robust methods
156(1)
7.3.1 Quantile regression model
156(1)
7.3.2 Robust regression model
156(1)
7.3.3 Ridge regression model
156(1)
7.4 Models for correlated data
157(6)
7.4.1 Linear models with correlated outcomes
157(1)
7.4.2 Linear mixed models with random intercepts
158(1)
7.4.3 Linear mixed models with random slopes
158(1)
7.4.4 More complex random coefficient models
159(1)
7.4.5 Multilevel models
160(1)
7.4.6 Generalized linear models with correlated outcomes
160(1)
7.4.7 Generalized linear mixed models
161(1)
7.4.8 Generalized estimating equations
161(1)
7.4.9 MANOVA
162(1)
7.4.10 Time series model
162(1)
7.5 Survival analysis
163(3)
7.5.1 Proportional hazards (Cox) regression model
163(1)
7.5.2 Proportional hazards (Cox) model with frailty
163(1)
7.5.3 Nelson—Aalen estimate of cumulative hazard
164(1)
7.5.4 Testing the proportionality of the Cox model
164(1)
7.5.5 Cox model with time-varying predictors
165(1)
7.6 Multivariate statistics and discriminant procedures
166(2)
7.6.1 Cronbach's α
166(1)
7.6.2 Factor analysis
166(1)
7.6.3 Recursive partitioning
166(1)
7.6.4 Linear discriminant analysis
167(1)
7.6.5 Latent class analysis
167(1)
7.6.6 Hierarchical clustering
168(1)
7.7 Complex survey design
168(1)
7.8 Model selection and assessment
169(3)
7.8.1 Compare two models
169(1)
7.8.2 Log-likelihood
170(1)
7.8.3 Akaike Information Criterion (AIC)
170(1)
7.8.4 Bayesian Information Criterion (BIC)
170(1)
7.8.5 LASSO model
171(1)
7.8.6 Hosmer—Lemeshow goodness of fit
171(1)
7.8.7 Goodness of fit for count models
171(1)
7.9 Further resources
172(1)
7.10 Examples
172(39)
7.10.1 Logistic regression
172(4)
7.10.2 Poisson regression
176(2)
7.10.3 Zero-inflated Poisson regression
178(2)
7.10.4 Negative binomial regression
180(1)
7.10.5 Quantile regression
181(1)
7.10.6 Ordered logistic
182(1)
7.10.7 Generalized logistic model
183(2)
7.10.8 Generalized additive model
185(2)
7.10.9 Reshaping a dataset for longitudinal regression
187(3)
7.10.10 Linear model for correlated data
190(3)
7.10.11 Linear mixed (random slope) model
193(4)
7.10.12 Generalized estimating equations
197(2)
7.10.13 Generalized linear mixed model
199(1)
7.10.14 Cox proportional hazards model
200(1)
7.10.15 Cronbach's α
201(1)
7.10.16 Factor analysis
202(3)
7.10.17 Recursive partitioning
205(1)
7.10.18 Linear discriminant analysis
206(2)
7.10.19 Hierarchical clustering
208(3)
8 A graphical compendium 211(30)
8.1 Univariate plots
211(4)
8.1.1 Barplot
211(1)
8.1.2 Stem-and-leaf plot
212(1)
8.1.3 Dotplot
212(1)
8.1.4 Histogram
213(1)
8.1.5 Density plots
213(1)
8.1.6 Empirical cumulative probability density plot
214(1)
8.1.7 Boxplot
214(1)
8.1.8 Violin plots
215(1)
8.2 Univariate plots by grouping variable
215(2)
8.2.1 Side-by-side histograms
215(1)
8.2.2 Side-by-side boxplots
215(1)
8.2.3 Overlaid density plots
216(1)
8.2.4 Bar chart with error bars
216(1)
8.3 Bivariate plots
217(4)
8.3.1 Scatterplot
217(1)
8.3.2 Scatterplot with multiple y values
218(1)
8.3.3 Scatterplot with binning
219(1)
8.3.4 Transparent overplotting scatterplot
219(1)
8.3.5 Bivariate density plot
220(1)
8.3.6 Scatterplot with marginal histograms
220(1)
8.4 Multivariate plots
221(2)
8.4.1 Matrix of scatterplots
221(1)
8.4.2 Conditioning plot
221(1)
8.4.3 Contour plots
222(1)
8.4.4 3-D plots
222(1)
8.5 Special purpose plots
223(7)
8.5.1 Choropleth maps
223(1)
8.5.2 Interaction plots
223(1)
8.5.3 Plots for categorical data
224(1)
8.5.4 Circular plot
224(1)
8.5.5 Plot an arbitrary function
224(1)
8.5.6 Normal quantile-quantile plot
225(1)
8.5.7 Receiver operating characteristic (ROC) curve
225(1)
8.5.8 Plot confidence intervals for the mean
226(1)
8.5.9 Plot prediction limits from a simple linear regression
226(1)
8.5.10 Plot predicted lines for each value of a variable
226(1)
8.5.11 Kaplan—Meier plot
227(1)
8.5.12 Hazard function plotting
228(1)
8.5.13 Mean-difference plots
228(2)
8.6 Further resources
230(1)
8.7 Examples
230(11)
8.7.1 Scatterplot with multiple axes
230(2)
8.7.2 Conditioning plot
232(1)
8.7.3 Scatterplot with marginal histograms
232(2)
8.7.4 Kaplan—Meier plot
234(1)
8.7.5 ROC curve
235(1)
8.7.6 Pairs plot
236(2)
8.7.7 Visualize correlation matrix
238(3)
9 Graphical options and configuration 241(20)
9.1 Adding elements
241(9)
9.1.1 Arbitrary straight line
242(1)
9.1.2 Plot symbols
242(1)
9.1.3 Add points to an existing graphic
243(1)
9.1.4 Jitter points
243(1)
9.1.5 Regression line fit to points
244(1)
9.1.6 Smoothed line
244(1)
9.1.7 Normal density
245(1)
9.1.8 Marginal rug plot
245(1)
9.1.9 Titles
246(1)
9.1.10 Footnotes
246(1)
9.1.11 Text
246(1)
9.1.12 Mathematical symbols
247(1)
9.1.13 Arrows and shapes
247(1)
9.1.14 Add grid
248(1)
9.1.15 Legend
248(1)
9.1.16 Identifying and locating points
249(1)
9.2 Options and parameters
250(6)
9.2.1 Graph size
250(1)
9.2.2 Grid of plots per page
250(1)
9.2.3 More general page layouts
251(1)
9.2.4 Fonts
252(1)
9.2.5 Point and text size
252(1)
9.2.6 Box around plots
252(1)
9.2.7 Size of margins
253(1)
9.2.8 Graphical settings
253(1)
9.2.9 Axis range and style
253(1)
9.2.10 Axis labels, values, and tick marks
254(1)
9.2.11 Line styles
254(1)
9.2.12 Line widths
255(1)
9.2.13 Colors
255(1)
9.2.14 Log scale
255(1)
9.2.15 Omit axes
256(1)
9.3 Saving graphs
256(5)
9.3.1 PDF
256(1)
9.3.2 Postscript
256(1)
9.3.3 RTF
257(1)
9.3.4 JPEG
258(1)
9.3.5 Windows Metafile (WMF)
258(1)
9.3.6 Bitmap image file (BMP)
258(1)
9.3.7 Tagged image file format (TIFF)
259(1)
9.3.8 Portable Network Graphics (PNG)
259(1)
9.3.9 Closing a graphic device
260(1)
10 Simulation 261(20)
10.1 Generating data
261(13)
10.1.1 Generate categorical data
261(2)
10.1.2 Generate data from a logistic regression
263(1)
10.1.3 Generate data from a generalized linear mixed model
264(3)
10.1.4 Generate correlated binary data
267(2)
10.1.5 Generate data from a Cox model
269(2)
10.1.6 Sampling from a challenging distribution
271(3)
10.2 Simulation applications
274(6)
10.2.1 Simulation study of Student's t test
274(2)
10.2.2 Diploma (or hat-check) problem
276(2)
10.2.3 Monty Hall problem
278(2)
10.3 Further resources
280(1)
11 Special topics 281(34)
11.1 Processing by group
281(3)
11.2 Simulation-based power calculations
284(3)
11.3 Reproducible analysis and output
287(3)
11.4 Advanced statistical methods
290(23)
11.4.1 Bayesian methods
290(6)
11.4.2 Propensity scores
296(7)
11.4.3 Bootstrapping
303(1)
11.4.4 Missing data
304(7)
11.4.5 Finite mixture models with concomitant variables
311(2)
11.5 Further resources
313(2)
12 Case studies 315(26)
12.1 Data management and related tasks
315(6)
12.1.1 Finding two closest values in a vector
315(2)
12.1.2 Tabulate binomial probabilities
317(1)
12.1.3 Calculate and plot a running average
318(2)
12.1.4 Create a Fibonacci sequence
320(1)
12.2 Read variable format files
321(3)
12.3 Plotting maps
324(5)
12.3.1 Massachusetts counties, continued
324(1)
12.3.2 Bike ride plot
325(2)
12.3.3 Choropleth maps
327(2)
12.4 Data scraping and visualization
329(7)
12.4.1 Scraping data from HTML files
330(1)
12.4.2 Reading data with two lines per observation
331(2)
12.4.3 Plotting time series data
333(1)
12.4.4 URL APIs-and truly random numbers
334(2)
12.5 Manipulating bigger datasets
336(1)
12.6 Constrained optimization: the knapsack problem
337(4)
A Introduction to SAS 341(16)
A.1 Installation
341(1)
A.2 Running SAS and a sample session
341(5)
A.3 Learning SAS and getting help
346(1)
A.4 Fundamental elements of SAS syntax
347(2)
A.5 Work process: The cognitive style of SAS
349(1)
A.6 Useful SAS background
349(2)
A.6.1 Dataset options
349(1)
A.6.2 Subsetting
350(1)
A.6.3 Formats and informats
350(1)
A.7 Output Delivery System
351(4)
A.7.1 Saving output as datasets and controlling output
351(4)
A.7.2 Output file types and ODS destinations
355(1)
A.8 SAS macro variables
355(1)
A.9 Miscellanea
356(1)
B Introduction to R and RStudio 357(22)
B.1 Installation
358(2)
B.1.1 Installation under Windows
358(1)
B.1.2 Installation under Mac OS X
359(1)
B.1.3 RStudio
359(1)
B.1.4 Other graphical interfaces
359(1)
B.2 Running R and sample session
360(2)
B.2.1 Replicating examples from the book and sourcing commands
361(1)
B.2.2 Batch mode
362(1)
B.3 Learning R and getting help
362(3)
B.4 Fundamental structures and objects
365(4)
B.4.1 Objects and vectors
365(1)
B.4.2 Indexing
365(1)
B.4.3 Operators
366(1)
B.4.4 Lists
366(1)
B.4.5 Matrices
367(1)
B.4.6 Dataframes
367(2)
B.4.7 Attributes and classes
369(1)
B.4.8 Options
369(1)
B.5 Functions
369(2)
B.5.1 Calling functions
369(1)
B.5.2 The apply family of functions
370(1)
B.6 Add-ons: packages
371(6)
B.6.1 Introduction to packages
371(1)
B.6.2 CRAN task views
372(1)
B.6.3 Installed libraries and packages
373(1)
B.6.4 Packages referenced in this book
374(3)
B.6.5 Datasets available with R
377(1)
B.7 Support and bugs
377(2)
C The HELP study dataset 379(6)
C.1 Background on the HELP study
379(1)
C.2 Roadmap to analyses of the HELP dataset
379(2)
C.3 Detailed description of the dataset
381(4)
References 385(14)
Subject index 399(20)
SAS index 419(12)
R index 431
Ken Kleinman, Nicholas J. Horton
Püsilink: https://www.kriso.ee/db/9780429169106_pe.html
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