Nothing
segregmix.init <- function (y, x, lambda = NULL, beta = NULL, s = NULL, k = 2, seg.Z,
psi, psi.locs = NULL)
{
n <- length(y)
p <- ncol(x)
psi.counts <- apply(psi>0,1,sum)
if(is.null(lambda)|is.null(beta)|is.null(s)|is.null(psi.locs)){
if (is.null(psi.locs)) {
psi.locs = vector("list",k)
psi.locs = lapply(1:k, function(i) if(psi.counts[i]>0) vector("list",psi.counts[i]) else NULL)
for(i in 1:k){
if(!is.null(psi.locs[[i]])){
temp.locs <- which(psi[i,]>0)
temp.labs=NULL
for(j in 1:length(temp.locs)){
psi.locs[[i]][[j]]=sort(runif(psi[i,temp.locs[j]],as.numeric(quantile(x[,temp.locs[j]],.05)),as.numeric(quantile(x[,temp.locs[j]],.95))))
temp.labs=c(temp.labs,colnames(x)[temp.locs[j]])
}
names(psi.locs[[i]])=temp.labs
}
}
} else k = length(psi.locs)
xnam <- colnames(x)
fmla <- as.formula(paste("y ~ ", paste(xnam, collapse= "+")))
TEMP.lm <- lm(fmla,data=x)
EM.out <- regmixEM(TEMP.lm$res,TEMP.lm$fit,k=k,epsilon=1e-2)
posts = apply(EM.out$post,1,which.max)
if (is.null(lambda)) {
lambda = EM.out$lambda
if(length(unique(posts))!=k) posts=rep(1:k,n)[1:n]
} else k = length(lambda)
A = round(lambda * n)
while (min(A) <= 4) {
lambda = runif(k)
lambda = lambda/sum(lambda)
A = round(lambda * n)
}
w = cbind(y, x)
w.bin = list()
for (j in 1:k) {
w.bin[[j]] <- w[posts==j, ]
}
all.w.bin=vector("list",gamma(k+1))
all.inds=perm(k,k)
all.X.aug=all.w.bin
all.lm.out=all.w.bin
avg.res=NULL
for(j in 1:length(all.w.bin)){
all.w.bin[[j]]=w.bin[all.inds[j,]]
X.aug <- lapply(1:k, function(i) cbind(1,aug.x(w.bin[[all.inds[j,i]]][,-1],unlist(psi.locs[[i]]),psi[i,],delta=NULL)))
sapply(X.aug,dim)
lm.out <- lapply(1:k, function(i) lm(w.bin[[all.inds[j,i]]][, 1] ~ X.aug[[i]][,-1]))
all.X.aug[[j]]=X.aug
all.lm.out[[j]]=lm.out
avg.res=c(avg.res,mean(as.vector(unlist(lapply(1:k,function(t) lm.out[[t]]$res)))^2))
}
IND=which.min(avg.res)
w.bin=all.w.bin[[IND]]
X.aug=all.X.aug[[IND]]
lm.out=all.lm.out[[IND]]
s.hyp = lapply(lm.out, anova)
s.hyp = as.vector(sqrt(sapply(1:k, function(i) tail(s.hyp[[i]]$Mean,1))))
s.hyp[(s.hyp <= 0) | (is.na(s.hyp) == 1)] = 1
if (is.null(s)) {
s = 1/rexp(k, rate = s.hyp)
} else k = length(s)
if (is.null(beta)) {
x.x <- lapply(1:k,function(i) try(solve(t(X.aug[[i]]) %*% X.aug[[i]]),silent=TRUE))
test <- sum(sapply(1:k, function(i) class(x.x[[i]])[1]=="try-error"))
if(test>0) stop("Lapack Routine Error")
beta.hyp = lapply(lm.out,coef) # matrix(sapply(lm.out, coef), ncol = k)
beta = vector("list",k)
for (j in 1:k) {
beta[[j]] = rnorm(length(beta.hyp[[j]]),mean=as.vector(beta.hyp[[j]]),
sd = (s.hyp[j] * sqrt(diag(x.x[[j]]))))
}
} else k = length(beta)
} else{
for(i in 1:k){
if(!is.null(psi.locs[[i]])){
temp.locs <- which(psi[i,]>0)
temp.labs=NULL
for(j in 1:length(temp.locs)){
temp.labs=c(temp.labs,colnames(x)[temp.locs[j]])
}
names(psi.locs[[i]])=temp.labs
}
}
}
list(lambda = lambda, beta = beta, s = s, k = k, psi.locs = psi.locs)
}
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