Nothing
# Compute crude consistent estimate ,that's, equation (6) in the reference paper.
# the initial sample size m1 is adjusted from 40 to 60.
y_crude_estimator <- function(mk_correlated_standard_norm,
invcdfnames, paramslists) {
if (class(mk_correlated_standard_norm) != "matrix") {
stop("mk_correlated_standard_norm must be matrix !")
}
if (length(invcdfnames) != length(paramslists)) {
stop("inversecdfs should have the same length paramslists !")
}
ndim <- ncol(mk_correlated_standard_norm)
mk <- nrow(mk_correlated_standard_norm)
if (mk >= 60) {
stop("the number of observations must be less than 60!")
}
transform_mat <- NULL
for (i in 1:ndim) {
funcall <- as.call(c(as.name(invcdfnames[i]),
list(pnorm(mk_correlated_standard_norm)[ ,i]), paramslists[[i]]))
transform_mat <- cbind(transform_mat, eval(funcall))
}
res <- matrix(rep(0, ndim * ndim), nrow = ndim)
diag(res) <- 1
for (i in 1:(ndim-1))
for (j in (i+1):ndim)
if (length(which(!duplicated(transform_mat[ ,i])[-1]))==0 ||
length(which(!duplicated(transform_mat[ ,j])[-1]))==0)
res[j,i] <- res[i,j] <- cor(transform_mat[ ,i], transform_mat[ ,j])
else
res[j,i] <- res[i,j] <- 0
res
}
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