The R Package geepack for Generalized Estimating Equations

By Søren Højsgaard, U. Halekoh, Jun Yan. 2005

2,387 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/997c7c7d2218585e8594f2f9abec7dc97e5ce3b1

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Citations in the corpus

Sound production and associated behavior of tagged fin whales (Balaenoptera physalus) in the Southern California Bight (2015, positive): “0 [35] using the MRSea package [36] and the geepack package [37].”
Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations (2021, methodology): “…matrix (see Figure 1D) that does not impose any kind of correlation restrictions between units is more computationally demanding, can lead to convergence issues in data sets with highly unbalanced cluster sizes (Hardin & Hilbe, 2013), and can cause software to crash (Højsgaard et al., 2005).”
Interference-Based Forgetting in Verbal Short-Term Memory. (2008, methodology): “geepack within R ( Hojsgaard, Halekoh, & Yan, 2006 ), the GEE parameter estimates for the two-slope model were found to differ from the conven-”
Does a minimal intervention approach threaten the biodiversity of protected areas? A multi-taxa short-term response to intervention in temperate oak-dominated forests (2015, methodology): “The models were fitted using the Generalized Estimating Equations algorithm in the geepack package (Hojsgaard et al., 2006) in R 2.14.2 (R Development Core Team, 2012), the effect of habitat type being tested by Chi-squared distibution.”
Ontario freshwater fishes demonstrate differing range‐boundary shifts in a warming climate (2014, methodology): “GEEs allowed us to account for correlation between observations taken in different survey periods at the same lakes (Halekoh et al., 2006).”
ipw: An R Package for Inverse Probability Weighting (2011, background): “For further details see Halekoh et al. (2005). trunc is an optional truncation fraction for the weights (between 0 and 0.”
Nonparametric Analysis of Longitudinal Data in Factorial Experiments (2003, methodology): “…lme() function in nlme (Pinheiro, Bates, DebRoy, Sarkar, and R Development Core Team 2012), and the lmer() function in lme4 (Bates, Maechler, and Bolker 2012); while GEE is available through gee (Carey, Lumley, and Ripley 2012) and geepack (Halekoh, Højsgaard, and Yan 2006, and references therein).”
The composition and stability of the vaginal microbiota of normal pregnant women is different from that of non-pregnant women (2014, methodology): “The model fitting was performed using the geepack package [238] in R (version 2.”
Regression Models for Count Data in R (2008, background): “Therefore, starting from Lambert (1992) various zero-inflated regression models have been suggested that extend the basic count data models by augmenting them with a point mass at zero—see Cameron and Trivedi (1998, 2005) for an overview.”