GEE.var.kc: A Modified GEE Variance Estimator Proposed by Kauermann and...

In geesmv: Modified Variance Estimators for Generalized Estimating Equations

Description

Calculate the modified GEE variance estimator proposed by Kauermann and Carroll (2001).

Usage

GEE.var.kc(formula,id,family=gaussian,data,corstr="independence")

Arguments

formula	an object of class "formula" (or one that can be coerced to that class): a formula expression as for other regression models to be fitted, of the form response ~ predictors. The details of formula specification can be seen in glm() and gee().
id	a vector which identifies the clusters. The length of id should be the same as the total number of observations. Data is assumed to be sorted so that observations on a cluster are contiguous rows for all entities in the formula.
family	a family object: a list of functions and expressions for defining link and variance functions to be used in the model. This can be a character string naming a family function including "gaussian", "binomial", and "poisson". See family for details of family functions in glm() and gee().
data	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which glm is called.
corstr	a character string specifying working correlation structure: "independence", "AR-M","exchangeable", "unstructured" are possible.

Details

Fit the model by GEE and calculate the bias-corrected sandwich variance estimator proposed by Kauermann and Carroll(2001).

Value

cov.beta	estimate of the variance for beta
cov.var	estimate of the variance-covariance matrix for the variance estimator

Author(s)

Ming Wang <[e-mail]>

Maintainer: Zheng Li <[e-mail]>

References

Kauermann G and Carroll RJ. A note on the efficiency of sandwich covariance matrix estimation. Journal of the American Statistical Association 2001;96: 1387-1398.

Examples

data(dental)
data_alt <- reshape(dental, direction="long", timevar="Time", 
        varying=names(dental)[3:6], v.names="response", times=c(8,10,12,14))
data_alt <- data_alt[order(data_alt$subject),]
data_alt$gender <- as.numeric(data_alt$gender)
data_alt$Time <- sqrt(data_alt$Time)
formula <- response~Time+gender
kc.ind <- GEE.var.kc(formula,id="subject",family=gaussian,
        data_alt,corstr="independence") ##Independence correlation structure;
kc.exch <- GEE.var.kc(formula,id="subject",family=gaussian,
        data_alt,corstr="exchangeable") ##Exchangeable correlation structure;                   
kc.ar1 <- GEE.var.kc(formula,id="subject",family=gaussian,
        data_alt,corstr="AR-M") ##AR-1 correlation structure;                   
kc.unstr <- GEE.var.kc(formula,id="subject",family=gaussian,
        data_alt,corstr="unstructured") ##Unstructured correlation structure;                  

Example output

Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
(Intercept)        Time      gender 
   6.077172    4.319197    2.321023 
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
(Intercept)        Time      gender 
   6.077172    4.319197    2.321023 
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
(Intercept)        Time      gender 
   6.077172    4.319197    2.321023 
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
(Intercept)        Time      gender 
   6.077172    4.319197    2.321023 

geesmv documentation built on May 2, 2019, 9:40 a.m.