book 2014/5/6 15:21 page v #3 List of figures List of tables Preface to the second edition Preface to the first edition

Transcription

1 book 2014/5/6 15:21 page v #3 Contents List of figures List of tables Preface to the second edition Preface to the first edition xvii xix xxi xxiii 1 Data input and output Input Native dataset Fixed format text files Other fixed files Reading more complex text files Comma separated value (CSV) files Read sheets from an Excel file Read data from R into SAS Read data from SAS into R Reading datasets in other formats Reading data with a variable number of words in a field Read a file byte by byte Access data from a URL Read an XML-formatted file Manual data entry Output Displaying data Number of digits to display Save a native dataset Creating datasets in text format Creating Excel spreadsheets Creating files for use by other packages Creating HTML formatted output Creating XML datasets and output Further resources Data management Structure and meta-data Access variables from a dataset Names of variables and their types Values of variables in a dataset v

2 book 2014/5/6 15:21 page vi #4 vi Label variables Add comment to a dataset or variable Derived variables and data manipulation Add derived variable to a dataset Rename variables in a dataset Create string variables from numeric variables Create categorical variables from continuous variables Recode a categorical variable Create a categorical variable using logic Create numeric variables from string variables Extract characters from string variables Length of string variables Concatenate string variables Set operations Find strings within string variables Find approximate strings Replace strings within string variables Split strings into multiple strings Remove spaces around string variables Upper to lower case Lagged variable Formatting values of variables Perl interface Accessing databases using SQL (structured query language) Merging, combining, and subsetting datasets Subsetting observations Drop or keep variables in a dataset Random sample of a dataset Observation number Keep unique values Identify duplicated values Convert from wide to long (tall) format Convert from long (tall) to wide format Concatenate and stack datasets Sort datasets Merge datasets Date and time variables Create date variable Extract weekday Extract month Extract year Extract quarter Create time variable Further resources Examples Data input and output Data display Derived variables and data manipulation Sorting and subsetting datasets

3 book 2014/5/6 15:21 page vii #5 vii 3 Statistical and mathematical functions Probability distributions and random number generation Probability density function Quantiles of a probability density function Setting the random number seed Uniform random variables Multinomial random variables Normal random variables Multivariate normal random variables Truncated multivariate normal random variables Exponential random variables Other random variables Mathematical functions Basic functions Trigonometric functions Special functions Integer functions Comparisons of floating point variables Complex numbers Derivatives Integration Optimization problems Matrix operations Create matrix from vector Combine vectors or matrices Matrix addition Transpose matrix Find the dimension of a matrix or dataset Matrix multiplication Invert matrix Component-wise multiplication Create submatrix Create a diagonal matrix Create a vector of diagonal elements Create a vector from a matrix Calculate the determinant Find eigenvalues and eigenvectors Find the singular value decomposition Examples Probability distributions Programming and operating system interface Control flow, programming, and data generation Looping Conditional execution Sequence of values or patterns Referring to a range of variables Perform an action repeatedly over a set of variables Grid of values Debugging Error recovery

4 book 2014/5/6 15:21 page viii #6 viii 4.2 Functions and macros SAS macros R functions Interactions with the operating system Timing commands Suspend execution for a time interval Execute a command in the operating system Command history Find working directory Change working directory List and access files Common statistical procedures Summary statistics Means and other summary statistics Other moments Trimmed mean Quantiles Centering, normalizing, and scaling Mean and 95% confidence interval Proportion and 95% confidence interval Maximum likelihood estimation of parameters Bivariate statistics Epidemiologic statistics Test characteristics Correlation Kappa (agreement) Contingency tables Display cross-classification table Displaying missing value categories in a table Pearson chi-square statistic Cochran Mantel Haenszel test Cramér s V Fisher s exact test McNemar s test Tests for continuous variables Tests for normality Student s t test Test for equal variances Nonparametric tests Permutation test Logrank test Analytic power and sample size calculations Further resources Examples Summary statistics and exploratory data analysis Bivariate relationships Contingency tables Two sample tests of continuous variables Survival analysis: logrank test

5 book 2014/5/6 15:21 page ix #7 ix 6 Linear regression and ANOVA Model fitting Linear regression Linear regression with categorical covariates Changing the reference category Parameterization of categorical covariates Linear regression with no intercept Linear regression with interactions One-way analysis of variance Analysis of variance with two or more factors Tests, contrasts, and linear functions of parameters Joint null hypotheses: several parameters equal Joint null hypotheses: sum of parameters Tests of equality of parameters Multiple comparisons Linear combinations of parameters Model diagnostics Predicted values Residuals Standardized and Studentized residuals Leverage Cook s D DFFITS Diagnostic plots Heteroscedasticity tests Model parameters and results Parameter estimates Standardized regression coefficients Standard errors of parameter estimates Confidence interval for parameter estimates Confidence limits for the mean Prediction limits R-squared Design and information matrix Covariance matrix of parameter estimates Correlation matrix of parameter estimates Further resources Examples Scatterplot with smooth fit Linear regression with interaction Regression diagnostics Fitting the regression model separately for each value of another variable Two-way ANOVA Multiple comparisons Contrasts

6 book 2014/5/6 15:21 page x #8 x 7 Regression generalizations and modeling Generalized linear models Logistic regression model Conditional logistic regression model Exact logistic regression Ordered logistic model Generalized logistic model Poisson model Negative binomial model Log-linear model Further generalizations Zero-inflated Poisson model Zero-inflated negative binomial model Generalized additive model Nonlinear least squares model Robust methods Quantile regression model Robust regression model Ridge regression model Models for correlated data Linear models with correlated outcomes Linear mixed models with random intercepts Linear mixed models with random slopes More complex random coefficient models Multilevel models Generalized linear models with correlated outcomes Generalized linear mixed models Generalized estimating equations MANOVA Time series model Survival analysis Proportional hazards (Cox) regression model Proportional hazards (Cox) model with frailty Nelson Aalen estimate of cumulative hazard Testing the proportionality of the Cox model Cox model with time-varying predictors Multivariate statistics and discriminant procedures Cronbach s α Factor analysis Recursive partitioning Linear discriminant analysis Latent class analysis Hierarchical clustering Complex survey design Model selection and assessment Compare two models Log-likelihood Akaike Information Criterion (AIC) Bayesian Information Criterion (BIC) LASSO model Hosmer Lemeshow goodness of fit

7 book 2014/5/6 15:21 page xi #9 xi Goodness of fit for count models Further resources Examples Logistic regression Poisson regression Zero-inflated Poisson regression Negative binomial regression Quantile regression Ordered logistic Generalized logistic model Generalized additive model Reshaping a dataset for longitudinal regression Linear model for correlated data Linear mixed (random slope) model Generalized estimating equations Generalized linear mixed model Cox proportional hazards model Cronbach s α Factor analysis Recursive partitioning Linear discriminant analysis Hierarchical clustering A graphical compendium Univariate plots Barplot Stem-and-leaf plot Dotplot Histogram Density plots Empirical cumulative probability density plot Boxplot Violin plots Univariate plots by grouping variable Side-by-side histograms Side-by-side boxplots Overlaid density plots Bar chart with error bars Bivariate plots Scatterplot Scatterplot with multiple y values Scatterplot with binning Transparent overplotting scatterplot Bivariate density plot Scatterplot with marginal histograms Multivariate plots Matrix of scatterplots Conditioning plot Contour plots D plots Special purpose plots

8 book 2014/5/6 15:21 page xii #10 xii Choropleth maps Interaction plots Plots for categorical data Circular plot Plot an arbitrary function Normal quantile-quantile plot Receiver operating characteristic (ROC) curve Plot confidence intervals for the mean Plot prediction limits from a simple linear regression Plot predicted lines for each value of a variable Kaplan Meier plot Hazard function plotting Mean-difference plots Further resources Examples Scatterplot with multiple axes Conditioning plot Scatterplot with marginal histograms Kaplan Meier plot ROC curve Pairs plot Visualize correlation matrix Graphical options and configuration Adding elements Arbitrary straight line Plot symbols Add points to an existing graphic Jitter points Regression line fit to points Smoothed line Normal density Marginal rug plot Titles Footnotes Text Mathematical symbols Arrows and shapes Add grid Legend Identifying and locating points Options and parameters Graph size Grid of plots per page More general page layouts Fonts Point and text size Box around plots Size of margins Graphical settings Axis range and style

9 book 2014/5/6 15:21 page xiii #11 xiii Axis labels, values, and tick marks Line styles Line widths Colors Log scale Omit axes Saving graphs PDF Postscript RTF JPEG Windows Metafile (WMF) Bitmap image file (BMP) Tagged image file format (TIFF) Portable Network Graphics (PNG) Closing a graphic device Simulation Generating data Generate categorical data Generate data from a logistic regression Generate data from a generalized linear mixed model Generate correlated binary data Generate data from a Cox model Sampling from a challenging distribution Simulation applications Simulation study of Student s t test Diploma (or hat-check) problem Monty Hall problem Further resources Special topics Processing by group Simulation-based power calculations Reproducible analysis and output Advanced statistical methods Bayesian methods Propensity scores Bootstrapping Missing data Finite mixture models with concomitant variables Further resources Case studies Data management and related tasks Finding two closest values in a vector Tabulate binomial probabilities Calculate and plot a running average Create a Fibonacci sequence Read variable format files Plotting maps

10 book 2014/5/6 15:21 page xiv #12 xiv Massachusetts counties, continued Bike ride plot Choropleth maps Data scraping and visualization Scraping data from HTML files Reading data with two lines per observation Plotting time series data URL APIs and truly random numbers Manipulating bigger datasets Constrained optimization: the knapsack problem A Introduction to SAS 341 A.1 Installation A.2 Running SAS and a sample session A.3 Learning SAS and getting help A.4 Fundamental elements of SAS syntax A.5 Work process: The cognitive style of SAS A.6 Useful SAS background A.6.1 Dataset options A.6.2 Subsetting A.6.3 Formats and informats A.7 Output Delivery System A.7.1 Saving output as datasets and controlling output A.7.2 Output file types and ODS destinations A.8 SAS macro variables A.9 Miscellanea B Introduction to R and RStudio 357 B.1 Installation B.1.1 Installation under Windows B.1.2 Installation under Mac OS X B.1.3 RStudio B.1.4 Other graphical interfaces B.2 Running R and sample session B.2.1 Replicating examples from the book and sourcing commands B.2.2 Batch mode B.3 Learning R and getting help B.4 Fundamental structures and objects B.4.1 Objects and vectors B.4.2 Indexing B.4.3 Operators B.4.4 Lists B.4.5 Matrices B.4.6 Dataframes B.4.7 Attributes and classes B.4.8 Options B.5 Functions B.5.1 Calling functions B.5.2 The apply family of functions B.6 Add-ons: packages B.6.1 Introduction to packages

11 book 2014/5/6 15:21 page xv #13 xv B.6.2 CRAN task views B.6.3 Installed libraries and packages B.6.4 Packages referenced in this book B.6.5 Datasets available with R B.7 Support and bugs C The HELP study dataset 379 C.1 Background on the HELP study C.2 Roadmap to analyses of the HELP dataset C.3 Detailed description of the dataset References 385 Subject index 399 SAS index 419 R index 431