ee.gee.mean: Estimating equation of marginal mean for GEE without...

In ELCIC: The Empirical Likelihood-Based Consistent Information Criterion

Estimating equation of marginal mean for GEE without missingness or missing completely at random

Description

Calculate estimating equation from GEE in ELCIC. This estimating equation is used for marginal mean selection.

Usage

ee.gee.mean(y,x,r,id,beta,rho,phi,dist,corstr)

Arguments

y	A vector containing outcomes.
x	A matrix containing covariates. The first column should be all ones corresponding to the intercept.
r	A vector indicating the observation of outcomes: 1 for observed records, and 0 for unobserved records. The default setup is that all data are observed. See more in details section.
id	A vector indicating subject id.
beta	A plug-in estimator solved by an external estimation procedure, such as GEE.
rho	A correlation coefficients obtained from an external estimation procedure, such as GEE.
phi	An over-dispersion parameter obtained from an external estimation procedure, such as GEE.
dist	A specified distribution. It can be "gaussian", "poisson",and "binomial".
corstr	A candidate correlation structure. It can be "independence","exchangeable", and "ar1".

Details

If the element in argument "r" equals zero, the corresponding rows of "x" and "y" should be all zeros.

Value

A matrix containing values of calculated estimating equations.

Note

corstr should be prespecified.

Examples

## tests
# load data
data(geesimdata)
x<-geesimdata$x
y<-geesimdata$y
id<-geesimdata$id
corstr<-"exchangeable"
dist<-"poisson"
# obtain the estimates
library(geepack)
fit<-geeglm(y~x-1,data=geesimdata,family =dist,id=id,corstr = corstr)
beta<-fit$coefficients
rho<-unlist(summary(fit)$corr[1])
phi<-unlist(summary(fit)$dispersion[1])
r<-rep(1,nrow(x))
ee.matrix<-ee.gee.mean(y,x,r,id,beta,rho,phi,dist,corstr)
apply(ee.matrix,1,mean)

ELCIC documentation built on Feb. 16, 2023, 7:18 p.m.