CIC: Correlation Information Criterion for Generalized Estimating Equations

Description

Computes the Correlation Information Criterion (CIC) for one or more objects of the class glmgee.

Usage

CIC(..., verbose = TRUE, digits = max(3, getOption("digits") - 2))

Value

A data.frame with the values of the CIC for each glmgee object in the input.

Arguments

...: one or several objects of the class glmgee.
verbose: an (optional) logical switch indicating if should the report of results be printed. As default, verbose is set to TRUE.
digits: an (optional) integer indicating the number of digits to print. As default, digits is set to max(3, getOption("digits") - 2).

References

Hin L.-Y., 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.

Vanegas L.H., Rondon L.M., Paula G.A. (2023) Generalized Estimating Equations using the new R package glmtoolbox. The R Journal 15:105-133.

Examples

Run this code

###### Example 1: Effect of ozone-enriched atmosphere on growth of sitka spruces
data(spruces)
mod1 <- size ~ poly(days,4) + treat
fit1 <- glmgee(mod1, id=tree, family=Gamma(log), data=spruces)
fit2 <- update(fit1, corstr="AR-M-dependent")
fit3 <- update(fit1, corstr="Stationary-M-dependent(2)")
fit4 <- update(fit1, corstr="Exchangeable")
CIC(fit1, fit2, fit3, fit4)

###### Example 2: Treatment for severe postnatal depression
data(depression)
mod2 <- depressd ~ visit + group
fit1 <- glmgee(mod2, id=subj, family=binomial(logit), data=depression)
fit2 <- update(fit1, corstr="AR-M-dependent")
fit3 <- update(fit1, corstr="Stationary-M-dependent(2)")
fit4 <- update(fit1, corstr="Exchangeable")
CIC(fit1, fit2, fit3, fit4)

###### Example 3: Treatment for severe postnatal depression (2)
mod3 <- dep ~ visit*group
fit1 <- glmgee(mod3, id=subj, family=gaussian(identity), data=depression)
fit2 <- update(fit1, corstr="AR-M-dependent")
fit3 <- update(fit1, corstr="Exchangeable")
CIC(fit1, fit2, fit3)