gee: Generalized Estimating Equations

Description

gee performs estimation of parameters in a restricted mean model using standard GEEs with independent working correlation matrix. For clustered data, cluster-robust standard errors are calculated. When cond=TRUE, cluster-specific intercepts are assumed.

Usage

gee(formula, link = c("identity", "log", "logit"), data, cond = FALSE,
clusterid, rootFinder = findRoots, ...)

Arguments

formula

An expression or formula representing the expected outcome conditional on covariates.

link

A character string naming the link function to use. Has to be "identity", "log" or "logit". Default is "identity".

data

A data frame or environment containing the variables appearing in formula. If missing, the variables are expected to be found in the environment of the formula argument.

cond

A logical value indicating whether cluster-specific intercepts should be assumed. Requires a clusterid argument.

clusterid

A cluster-defining variable or a character string naming a cluster-defining variable in the data argument. If it is not found in the data argument, it will be searched for in the calling frame. If missing, each observation

rootFinder

A function to solve a system of non linear equations. Default is findRoots.

...

Further arguments to be passed to the function rootFinder.

Value

gee return an object of class gee containing:
coefficientsEstimates of the parameters.
vcovRobust variance of the estimates.
callThe matched call.
geeDataThe geeData object used in the calculations.
optim.objectAn estimation object returned from the function specified in the rootFinder, if this function is called.
The class methods coef and vcov can be used to extract the estimated parameters and their covariance matrix from a gee object. summary.drgee produces a summary of the calculations.

encoding

latin1

Details

Estimates parameters in a regression model, defined by formula. When cond=FALSE, the estimated coefficients are identical to those obtained with glm, but since no distributional assumptions are made, a robust variance is calculated. When cond=TRUE and link is "identity" or "log", the coefficients will be calculated using conditional estimating equations as described in Goetgeluk and Vansteelandt (2008). When cond=TRUE and link="logit", the coefficients will be calculated by conditional logistic regression (with robust standard errors).

References

Goetgeluk S., & Vansteelandt S. (2008), Conditional generalized estimating equations for the analysis of clustered and longitudinal data. Biometrics, 64(3), pp. 772--780.