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R package with tools for fitting generalized linear models with clustered observations using generalized estimating equations.
geeasy is available on CRAN and can be installed as follows:
install.packages("geeasy")To install the development version of geeasy run the following commands from within R (requires that the devtools package is already installed)
devtools::install_github("annennenne/geeasy")geeasy fits generalized linear models on data with correlated/clustered observations by use of generalized estimating equations:
library(geeasy)
# load data
data("respiratory")
respiratory$useid <- interaction(respiratory$center, respiratory$id)
# fit model
m <- geelm(outcome ~ treat + sex + age + baseline, data = respiratory,
id = useid, family = "binomial", corstr = "exchangeable")The syntax is similar to glm(), but a few additional arguments need to be specified:
id: ID for identifying clusters. All observations with the same id are considered to belong to the same cluster.
corstr: The correlation structure that is used within each cluster. Options include “independence” (the default, corresponding to no clustering), “exchangeable” (identical pair-wise correlations between all observations within a cluster) and more, see the documentation of geelm() for more details.
The package includes a selection of functions that can be used to inspect and work with GEE models. These functions can be used both with the output from geelm() and with the output of geeglm() from the geepack R package.
The following functions are implemented in geeasy:
getGEE()plot()confint()drop1()A few more details about the two non-standard functions, getGEE() and plot() are provided below. Furthermore, geeasy imports the following functions from geepack that are also available:
summary()print()anova()QIC()getGEE():
# Get parameter estimates:
getGEE(m, "beta")
# Get standard errors for parameter estimates:
getGEE(m, "beta.se")
# Get estimated alpha (correlation structure parameter):
getGEE(m, "alpha")This function was built to resemble the getME() function from lme4. Note that it can also be accessed by calling getME().
plot():
# Plot estimates and 95% confidence intervals for one geelm model
plot(m)
# Fit a new geelm model with AR1 correlation structure AND a glm
# (corresponding to independent correlation structure)
m_ar1 <- geelm(outcome ~ treat + sex + age + baseline,
data = respiratory, id = useid,
family = "binomial", corstr = "ar1")
m_glm <- glm(outcome ~ treat + sex + age + baseline,
data = respiratory, family = "binomial")
# Plot all three models together for easy comparison
plot(m, m_ar1, m_glm)Note that this plotting function can also be accessed by calling plotEst() and that this function allows for any number of models to be plotted together, and it supports the model types lm, glm, geelm, geeglm, mice and more.
geelm()Changing the output object: geelm() can output a geem object, resembling the output of geem() from the geeM package:
m_outout_geem <- geelm(outcome ~ treat + sex + age + baseline,
data = respiratory, id = useid,
family = "binomial", corstr = "exchangeable",
output = "geem")This does not change the computations performed, only the output object. This means that the output will generally not be identical to that of geeM::geem().
Changing the estimation engine: geelm() allows for choosing to use geepack as its computational engine as follows:
m_engine_geepack <- geelm(outcome ~ treat + sex + age + baseline,
data = respiratory, id = useid,
family = "binomial", corstr = "exchangeable",
engine = "geepack")Note that this does not mean that the id variable is handled as in geepack: Clusters are still constructed by assigning observations with identical values of id to the same cluster.
The geeasy package is based on a modified version of the geeM package and the main estimation code was hence written by Lee McDaniel and Nick Henderson.
The package was modified, updated and extended by Anne Helby Petersen.
Claus Ekstrøm has contributed additional code.
Søren Højsgaard is maintainer of the geeasy package.
If you find bugs or have a request for a new feature, please open an issue.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.