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repnormmix.init = function (x, lambda = NULL, mu = NULL, s = NULL, k = 2, arbmean = TRUE, arbvar = TRUE)
{
n <- ncol(x)
m <- nrow(x)
y <- apply(x, 2, mean)
x <- x[, order(y)]
x.bin = list()
for (j in 1:k) {
x.bin[[j]] <- x[, max(1, floor((j - 1) * n/k)):ceiling(j *
n/k)]
}
if (is.null(s)) {
s.hyp = sapply(lapply(x.bin, as.vector), sd)
if (arbvar) {
s = 1/rexp(k, rate = s.hyp)
}
else {
s.hyp = mean(s.hyp)
s = 1/rexp(1, rate = s.hyp)
}
}
if (is.null(s) == FALSE && arbvar == TRUE) {
k = length(s)
}
if (is.null(mu)) {
mu.hyp <- sapply(lapply(x.bin, as.vector), mean)
mu = rnorm(k, mean = mu.hyp, sd = s)
if(arbmean==FALSE){
mu = mean(mu)
}
}
if (is.null(mu)==FALSE && arbmean==TRUE){
k = length(mu)
}
if (is.null(lambda)) {
lambda = runif(k)
lambda = lambda/sum(lambda)
}
else k = length(lambda)
list(lambda = lambda, mu = mu, s = s, k = k)
}
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