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imputeResid <- function(rawResid, Delta, Gam, m, blkID)
## Imputes 'm' vectors of residuals for censored failure times and returns the
## resulting completed vector of residuals 'compResid' once standardized.
##
## 'rawResid': vector of raw residuals
## 'Delta': vector containing the censoring indicator
## 'Gam': estimated covariance matrix of the raw residuals
## 'blkID': vector with entries identifying correlated groups of observations
{
cn <- estimCondNorm(as.logical(Delta), rawResid, Gam)
# 'cn' is a list from which the mean and variance of the conditional
# normal distribution of the raw residuals of the censored failure times
# are retrieved.
censC <- Delta == 0
censTot <- sum(censC)
censInd <- 1:censTot
blkIDcens <- blkID[censC]
blkUniqCens <- unique(blkIDcens)
blkTot <- length(blkUniqCens)
impList <- apply(as.matrix(1:blkTot), MARGIN=1, FUN=imputeBlkResid,
blkUniqCens=blkUniqCens, blkIDcens=blkIDcens, cn=cn, m=m)
impVec <- unlist(impList)
impResid <- matrix(impVec, nrow=censTot, ncol=m, byrow=TRUE)
compResid <- numeric(length(rawResid))
compResid[cn$obsInd] <- cn$obsResid
compResid[cn$censInd] <- apply(impResid, 1, mean)
GamEig <- eigen(Gam)
GamEigVal <- GamEig$values
GamSqrt <- GamEig$vectors %*% diag(sqrt(GamEigVal)) %*%
solve(GamEig$vectors)
rho <- mean(compResid^2)
## Scale parameter which reflects the fact that we are using multiple
## imputed values rather than real observations.
return(as.vector(solve(GamSqrt) %*% compResid / sqrt(rho)))
}
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