ELCIC (version 0.2.1)

QICc.gee: Joint selection procedure of marginal mean and correlation structures in longitudinal data based on QIC

Description

This function provides the Joint selection of marginal mean and correlation structures in longitudinal data based on QIC.

Usage

QICc.gee(x,y,id,dist,candidate.sets=NULL, name.var.sets=NULL,
   candidate.cor.sets=c("independence","exchangeable", "ar1"), joints=TRUE)

Value

A vector with each element containing QIC value for each candidate model. The row name of this vector is the selected correlation structure.

Arguments

x

A matrix containing covariates. The first column should be all ones corresponding to the intercept. Covariate matrix should be complete.

y

A vector containing outcomes.

id

A vector indicating subject id.

dist

A specified distribution. It can be "gaussian", "poisson",and "binomial".

candidate.sets

A list containing index corresponding to candidate covariates.

name.var.sets

A list containing names of candidate covariates. The names should be subset of column names of x matrix.

candidate.cor.sets

A vector containing candidate correlation structures. When joints=TRUE, it can be any subset of c("independence","exchangeable", "ar1"). The default is c("independence","exchangeable", "ar1"). When joints=FALSE, it should be either of "independence","exchangeable", "ar1". See more in details section.

joints

A logic value for joint selection of marginal mean and working correlation structure. The default is TRUE.

Details

Either arguments "index.var" or "name.var" is used to identify the candidate mean model. If both arguments are provided, only the argument "name.var" will be used.

When joints=TRUE, the argument "candidate.cor.sets" can contain multiple correlation structures; however, when joints=FALSE, it should contain either of "independence","exchangeable", "ar1". If multiple correlation structures are provided, only the first one will be used.

Examples

## tests
# load data
data(geesimdata)
x<-geesimdata$x
y<-geesimdata$y
id<-geesimdata$id
r<-rep(1,nrow(x))
time<-3
candidate.sets<-list(c(1,2),c(1,2,3))
candidate.cor.sets<-c("exchangeable")
dist="poisson"
criterion.qic<-QICc.gee(x=x,y=y,id=id,dist=dist,candidate.sets=candidate.sets,
                    name.var.sets=NULL,candidate.cor.sets=candidate.cor.sets)
criterion.qic