Nothing
#----------------------------------------------------------------------#
# uFunc : Estimating equation #
#----------------------------------------------------------------------#
# #
# pars : Current parameter estimates #
# #
# data.y : Matrix of responses. It is assumed that the first column #
# contains integer patient IDs, the second column contains #
# the time of measurement, and the third column contains #
# the value of the measurement. #
# #
# data.x : Matrix of covariates. The columns contain only the values#
# of the covariates. #
# #
# kernel : a list, ith element containing a matrix of the yIs #
# distances #
# #
# xIs : list of length nrow(data.y), the elements of which list #
# the rows of data.x corresponding the patient in the ith #
# row of data.y #
# #
#----------------------------------------------------------------------#
# #
# Returns the value of the estimating equations. #
# #
#----------------------------------------------------------------------#
uFuncIden <- function(data.y,
data.x,
kernel,
xIs,
yIs,
nPatients) {
nCov <- ncol(data.x)
aMat <- matrix(data = 0.0, nrow = nCov, ncol = nCov)
bVec <- matrix(data = 0.0, nrow = nrow(data.x), ncol = nCov)
ones <- matrix(data = 1.0, nrow = 1L, ncol = nrow(data.x))
for( i in 1L:nPatients ) {
ly <- yIs[[i]]$n
if( ly < 0.5 ) next
for( j in 1L:ly ) {
k <- yIs[[i]]$v[j]
lx <- xIs[[ k ]]$n
if( lx < 0.5 ) next
xI <- xIs[[ k ]]$v
tx <- data.x[xI,,drop=FALSE] * kernel[[ k ]]
bVec[xI,] <- bVec[xI,] + tx * data.y[k,3L]
aMat <- aMat + t(tx) %*% data.x[xI,,drop=FALSE]
}
}
bVec <- as.vector(ones %*% bVec)
pars <- solve(aMat, bVec)
return(pars)
}
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