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Produktbild: Complex Surveys Using R

Complex Surveys Using R A Guide to Analysis Using R

Aus der Reihe Wiley Series in Survey Methodology

104,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

01.03.2010

Abbildungen

Charts: 10 B&W, 0 Color; Drawings: 20 B&W, 0 Color; Screen captures: 40 B&W, 0 Color; Tables: 0 B&W, 0 Color; Graphs: 110 B&W, 0 Color

Verlag

John Wiley & Sons

Seitenzahl

296

Maße (L/B/H)

23,4/15,6/1,7 cm

Gewicht

453 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-470-28430-8

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

01.03.2010

Abbildungen

Charts: 10 B&W, 0 Color; Drawings: 20 B&W, 0 Color; Screen captures: 40 B&W, 0 Color; Tables: 0 B&W, 0 Color; Graphs: 110 B&W, 0 Color

Verlag

John Wiley & Sons

Seitenzahl

296

Maße (L/B/H)

23,4/15,6/1,7 cm

Gewicht

453 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-470-28430-8

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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Die Leseprobe wird geladen.
  • Produktbild: Complex Surveys Using R
  • Acknowledgments.
     
    Preface.
     
    Acronyms.
     
    1 Basic Tools.
     
    1.1 Goals of inference.
     
    1.2 An introduction to the data.
     
    1.3 Obtaining the software.
     
    1.4 Using R.
     
    Exercises.
     
    2 Simple and Stratified sampling.
     
    2.1 Analysing simple random samples.
     
    2.2 Stratified sampling.
     
    2.3 Replicate weights.
     
    2.4 Other population summaries.
     
    2.5 Estimates in subpopulations.
     
    2.6 Design of stratified samples.
     
    Exercises.
     
    3 Cluster sampling.
     
    3.1 Introduction.
     
    3.2 Describing multistage designs to R.
     
    3.3 Sampling by size.
     
    3.4 Repeated measurements.
     
    Exercises.
     
    4 Graphics.
     
    4.1 Why is survey data different?
     
    4.2 Plotting a table.
     
    4.3 One continuous variable.
     
    4.4 Two continuous variables.
     
    4.5 Conditioning plots.
     
    4.6 Maps.
     
    Exercises.
     
    5 Ratios and linear regression.
     
    5.1 Ratio estimation.
     
    5.2 Linear regression.
     
    5.3 Is weighting needed in regression models?
     
    6 Categorical data regression 109.
     
    6.1 Logistic regression 110.
     
    6.2 Ordinal regression 117.
     
    6.3 Loglinear models 123.
     
    7 Poststratification, raking and calibration.
     
    7.1 Introduction.
     
    7.2 Poststratification.
     
    7.3 Raking.
     
    7.4 Generalized raking, GREG estimation, and calibration.
     
    7.5 Basu's elephants.
     
    7.6 Selecting auxiliary variables for nonresponse.
     
    Exercises.
     
    8 Twophase sampling.
     
    8.1 Multistage and multiphase sampling.
     
    8.2 Sampling for stratification.
     
    8.3 The case-control design.
     
    8.4 Sampling from existing cohorts.
     
    8.5 Using auxiliary information from phase one.
     
    Exercises.
     
    9 Missing data.
     
    9.1 Item nonresponse.
     
    9.2 Twophase estimation for missing data.
     
    9.3 Imputation of missing data.
     
    Exercises.
     
    10 Causal inference.
     
    10.1 IPTW estimators.
     
    10.2 Marginal Structural Models.
     
    Appendix A: Analytic details.
     
    A.1 Asymptotics.
     
    A.2 Variances by linearization.
     
    A.3 Tests in contingency tables.
     
    A.4 Multiple imputation.
     
    A.5 Calibration and influence functions.
     
    A.6 Calibration in randomized trials and ANCOVA.
     
    Appendix B: Basic R.
     
    B.1 Reading data.
     
    B.2 Data manipulation.
     
    B.3 Randomness.
     
    B.4 Methods and objects.
     
    B.5 Writing functions.
     
    Appendix C: Computational details.
     
    C.1 Linearization.
     
    C.2 Replicate weights.
     
    C.3 Scatterplot smoothers.
     
    C.4 Quantiles.
     
    C.5 Bug reports and feature requests.
     
    Appendix D: Databasebacked design objects.
     
    D.1 Large data.
     
    D.2 Setting up database interfaces.
     
    Appendix E: Extending the survey package.
     
    E.1 A case study: negative binomial regression.
     
    E.2 Using a Poisson model.
     
    E.3 Replicate weights.
     
    E.4 Linearization.
     
    References.
     
    Author Index.
     
    Topic Index.