geepack (version 1.3-2)

genZcor: genZcor

Description

constructs the design matrix for the correlation structures: independence, echangeable, ar1 and unstructured The user will need this function only as a basis to construct a user defined correlation structure: use genZcor to get the design matrix Z for the unstructured correlation and define the specific correlation structure by linear combinations of the columns of Z.

Usage

genZcor(clusz, waves, corstrv)

Arguments

clusz

integer vector giving the number of observations in each cluster

waves

integer vector, obervations in the same cluster with values of wave i and j have the correlation \(latex\)

corstrv

correlation structures: 1=independence,2=exchangeable,3=ar1, 4=unstructured

Value

the design matrix for the correlation structure

See Also

fixed2Zcor

Examples

# NOT RUN {
#example to construct a Toeplitz correlation structure
#    sigma_ij=sigma_|i-j|

#data set with 5 clusters and maximally 4 observations (visits) per cluster
 gendat <- function() {
       id <- gl(5, 4, 20)
       visit <- rep(1:4, 5)
       y <- rnorm(id)
       dat <- data.frame(y, id, visit)[c(-2,-9),]
}

set.seed(88)
dat<-gendat()

#generating the design matrix for the unstructured correlation
zcor <- genZcor(clusz = table(dat$id), waves = dat$visit, corstrv=4)
# defining the Toeplitz structure 
zcor.toep<-matrix(NA, nrow(zcor),3)
zcor.toep[,1]<-apply(zcor[,c(1,4,6)],1,sum)
zcor.toep[,2]<-apply(zcor[,c(2,5)],1,sum)
zcor.toep[,3]<-zcor[,3]

zfit1 <- geese(y ~ 1,id = id, data = dat,
                   corstr = "userdefined", zcor = zcor.toep)


zfit2 <- geeglm(y ~ 1,id = id, data = dat,
                   corstr = "userdefined", zcor = zcor.toep)



# }