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| Acknowledgments | |
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| Preface | |
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| Acronyms | |
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| Basic Tools | |
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| Goals of inference | |
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| Population or process? | |
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| Probability samples | |
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| Sampling weights | |
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| Design effects | |
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| An introduction to the data | |
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| Real surveys | |
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| Populations | |
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| Obtaining the software | |
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| Obtaining R | |
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| Obtaining the survey package | |
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| Using R | |
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| Reading plain text data | |
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| Reading data from other packages | |
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| Simple computations | |
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| Exercises | |
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| Simple and Stratified sampling | |
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| Analyzing simple random samples | |
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| Confidence intervals | |
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| Describing the sample to R | |
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| Stratified sampling | |
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| Replicate weights | |
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| Specifying replicate weights to R | |
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| Creating replicate weights in R | |
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| Other population summaries | |
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| Quantiles | |
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| Contingency tables | |
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| Estimates in subpopulations | |
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| Design of stratified samples | |
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| Exercises | |
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| Cluster sampling | |
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| Introduction | |
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| Why clusters: the NHANES II design | |
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| Single-stage and multistage designs | |
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| Describing multistage designs to R | |
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| Strata with only one PSU | |
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| How good is the single-stage approximation? | |
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| Replicate weights for multistage samples | |
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| Sampling by size | |
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| Loss of information from sampling clusters | |
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| Repeated measurements | |
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| Exercises | |
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| Graphics | |
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| Why is survey data different? | |
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| Plotting a table | |
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| One continuous variable | |
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| Graphs based on the distribution function | |
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| Graphs based on the density | |
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| Two continuous variables | |
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| Scatterplots | |
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| Aggregation and smoothing | |
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| Scatterplot smoothers | |
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| Conditioning plots | |
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| Maps | |
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| Design and estimation issues | |
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| Drawing maps in R | |
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| Exercises | |
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| Ratios and linear regression | |
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| Ratio estimation | |
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| Estimating ratios | |
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| Ratios for subpopulation estimates | |
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| Ratio estimators of totals | |
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| Linear regression | |
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| The least-squares slope as an estimated population summary | |
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| Regression estimation of population totals | |
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| Confounding and other criteria for model choice | |
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| Linear models in the survey package | |
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| Is weighting needed in regression models? | |
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| Exercises | |
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| Categorical data regression | |
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| Logistic regression | |
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| Relative risk regression | |
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| Ordinal regression | |
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| Other cumulative link models | |
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| Loglinear models | |
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| Choosing models | |
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| Linear-association models | |
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| Exercises | |
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| Post-stratification, raking and calibration | |
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| Introduction | |
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| Post-stratification | |
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| Raking | |
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| Generalized raking, GREG estimation, and calibration | |
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| Calibration in R | |
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| Basu's elephants | |
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| Selecting auxiliary variables for non-response | |
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| Direct standardization | |
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| Standard error estimation | |
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| Exercises | |
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| Two-phase sampling | |
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| Multistage and multiphase sampling | |
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| Sampling for stratification | |
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| The case-control design | |
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| *Simulations: efficiency of the design-based estimator | |
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| Frequency matching | |
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| Sampling from existing cohorts | |
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| Logistic regression | |
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| Two-phase case-control designs in R | |
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| Survival analysis | |
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| Case-cohort designs in R | |
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| Using auxiliary information from phase one | |
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| Population calibration for regression models | |
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| Two-phase designs | |
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| Some history of the two-phase calibration estimator | |
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| Exercises | |
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| Missing data | |
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| Item non-response | |
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| Two-phase estimation for missing data | |
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| Calibration for item non-response | |
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| Models for response probability | |
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| Effect on precision | |
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| *Doubly-robust estimators | |
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| Imputation of missing data | |
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| Describing multiple imputations to R | |
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| Example: NHANES III imputations | |
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| Exercises | |
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| *Causal inference | |
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| IPTW estimators | |
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| Randomized trials and calibration | |
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| Estimated weights for IPTW | |
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| Double robustness | |
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| Marginal Structural Models | |
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| Analytic Details | |
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| Asymptotics | |
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| Embedding in an infinite sequence | |
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| Asymptotic unbiasedness | |
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| Asymptotic normality and consistency | |
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| Variances by linearization | |
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| Subpopulation inference | |
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| Tests in contingency tables | |
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| Multiple imputation | |
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| Calibration and influence functions | |
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| Calibration in randomized trials and ANCOVA | |
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| Basic R | |
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| Reading data | |
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| Plain text data | |
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| Data manipulation | |
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| Merging | |
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| Factors | |
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| Randomness | |
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| Methods and objects | |
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| *Writing functions | |
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| Repetition | |
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| Strings | |
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| Computational details | |
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| Linearization | |
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| Generalized linear models and expected information | |
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| Replicate weights | |
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| Choice of estimators | |
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| Hadamard matrices | |
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| Scatterplot smoothers | |
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| Quantiles | |
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| Bug reports and feature requests | |
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| Database-backed design objects | |
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| Large data | |
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| Setting up database interfaces | |
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| ODBC | |
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| DBI | |
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| Extending the package | |
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| A case study: negative binomial regression | |
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| Using a Poisson model | |
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| Replicate weights | |
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| Linearization | |
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| References | |
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| Author Index | |
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| Topic Index | |