covariance <- function(ds, FOM)
{
ret <- UtilPseudoValues(ds, FOM)
I <- dim(ret$jkFomValues)[1]
J <- dim(ret$jkFomValues)[2]
K <- dim(ret$jkFomValues)[3]
COV <- array(dim = c(I, I, J, J))
for (i in 1:I) {
for (ip in 1:I) {
for (j in 1:J) {
for (jp in 1:J) {
COV[i, ip, j, jp] <- cov(ret$jkFomValues[i, j, ], ret$jkFomValues[ip, jp, ])
}
}
}
}
COV <- COV * (K - 1)^2/K # see paper by Efron and Stein
# chk matrix is symmetric, no output if so
for (i in 1:I) {
for (ip in 1:I) {
for (j in 1:J) {
for (jp in 1:J) {
if((ip != i) && (jp != j)) {
if (COV[i, ip, j, jp] != COV[ip, i, jp, j]) cat("i, ip, j, jp = ", i, ip, j, jp, "\n")
}
}
}
}
}
return(COV)
}
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