Correlation Information Criterion for Generalized Estimating Equations
Computes the Correlation Information Criterion (CIC) for one or more objects of the class glmgee.
CIC(...)
... |
one or several objects of the class glmgee which are obtained from the fit of generalized estimating equations. |
A matrix with the values of the CIC for each glmgee object provided in the input.
Hin L.Y. and Wang Y.G. (2009) Working-Correlation-Structure Identification in Generalized Estimating Equations. Statistics in Medicine, 28, 642-658.
Hin L-Y, Carey V.J., Wang Y-G (2007) Criteria for Working–Correlation–Structure Selection in GEE: Assessment via Simulation. The American Statistician 61, 360–364.
## Example 1
mod <- size ~ poly(days,4) + treat
fit1 <- glmgee(mod, id=tree, family=Gamma("log"), data=spruce, corstr="AR-1")
fit2 <- update(fit1, corstr="Exchangeable")
fit3 <- update(fit1, corstr="Stationary-M-dependent(2)")
fit4 <- update(fit1, corstr="Independence")
CIC(fit1, fit2, fit3, fit4)
## Example 2
mod <- dep ~ visit + group
fit1 <- glmgee(mod, id=subj, family=gaussian, corstr="Exchangeable", data=depression)
fit2 <- update(fit1, corstr="Exchangeable")
fit3 <- update(fit1, corstr="Non-Stationary-M-dependent(2)")
fit4 <- update(fit1, corstr="Independence")
CIC(fit1, fit2, fit3, fit4)
## Example 3
mod <- depressd ~ visit + group
fit1 <- glmgee(mod, id=subj, family=binomial, corstr="Exchangeable", data=depression)
fit2 <- update(fit1, corstr="Exchangeable")
fit3 <- update(fit1, corstr="Stationary-M-dependent(2)")
fit4 <- update(fit1, corstr="Independence")
CIC(fit1, fit2, fit3, fit4)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.