RJC: Rotnitzky–Jewell's Criterion for Generalized Estimating...

In glmtoolbox: Set of Tools to Data Analysis using Generalized Linear Models

Rotnitzky–Jewell's Criterion for Generalized Estimating Equations

Description

Computes the Rotnitzky–Jewell's criterion (RJC) for one or more objects of the class glmgee.

Usage

RJC(..., verbose = TRUE, digits = 3)

Arguments

...	one or several objects of the class glmgee.
verbose	an (optional) logical switch indicating if should the report of results be printed. By default, verbose is set to be TRUE.
digits	an (optional) integer indicating the number of digits to print.

Value

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

References

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.

See Also

QIC, CIC, GHYC, 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")
RJC(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")
RJC(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")
RJC(fit1, fit2, fit3)

glmtoolbox documentation built on Oct. 10, 2023, 9:06 a.m.