CIC {glmtoolbox}

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))

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).

Value

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

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.

See Also

QIC, GHYC, RJC, AGPC, SGPC

Examples

###### 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)