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The R Package geepack for Generalized Estimating Equations

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  • Højsgaard, Søren
  • Halekoh, Ulrich
  • Yan, Jun

Abstract

This paper describes the core features of the R package geepack, which implements the generalized estimating equations (GEE) approach for fitting marginal generalized linear models to clustered data. Clustered data arise in many applications such as longitudinal data and repeated measures. The GEE approach focuses on models for the mean of the correlated observations within clusters without fully specifying the joint distribution of the observations. It has been widely used in statistical practice. This paper illustrates the application of the GEE approach with geepack through an example of clustered binary data.

Suggested Citation

  • Højsgaard, Søren & Halekoh, Ulrich & Yan, Jun, 2005. "The R Package geepack for Generalized Estimating Equations," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i02).
    • Handle: RePEc:jss:jstsof:v:015:i02
    DOI: http://hdl.handle.net/10.18637/jss.v015.i02
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    References listed on IDEAS

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    1. Lumley, Thomas, 1996. "XLISP-Stat Tools for Building Generalised Estimating Equation Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 1(i03).
    2. N. Rao Chaganty & Harry Joe, 2004. "Efficiency of generalized estimating equations for binary responses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 851-860, November.
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    Cited by:

    1. Francis L. Huang, 2022. "Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations," Journal of Educational and Behavioral Statistics, , vol. 47(1), pages 101-125, February.
    2. Nooraee, Nazanin & Molenberghs, Geert & van den Heuvel, Edwin R., 2014. "GEE for longitudinal ordinal data: Comparing R-geepack, R-multgee, R-repolr, SAS-GENMOD, SPSS-GENLIN," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 70-83.
    3. Marchese, Scott & Diao, Guoqing, 2018. "Joint regression analysis of mixed-type outcome data via efficient scores," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 156-170.

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