calcul_e_single<-
function(dd5, pvalcutoff=0.95, tt1,
alpha = 1, S=colnames(tt1$Normal.mat)){
er <- tt1$Normal.mat[,
colnames(tt1$Normal.mat)%in%S]
er[er == 0] <- NA
w <- apply(er, 1, mean, na.rm = TRUE)
w2 <- apply(er, 1, sd, na.rm = TRUE)
w <- ifelse(is.na(w) == TRUE, 0, w)
w2 <- ifelse(is.na(w2) == TRUE, 0, w2)
m_f <- apply(tt1$flat.Nmat$mat[
rownames(tt1$Normal.mat),
colnames(tt1$flat.Nmat$mat)%in%S], 1, min)
m2_f <- apply(tt1$flat.Nmat$mat[
rownames(tt1$Normal.mat),
colnames(tt1$flat.Nmat$mat)%in%S], 1, max)
p2 <- lapply(seq_len(length(m_f)), function(i){
pnorm(m_f[i], w[i], w2[i], lower.tail = TRUE)
})
p2 <- unlist(p2)
p2p <- lapply(seq_len(length(m_f)), function(i){
pnorm(m_f[i] - alpha, w[i], w2[i], lower.tail = TRUE)})
p2p <- unlist(p2p)
p3 <- lapply(seq_len(length(m_f)), function(i){
pnorm(m2_f[i], w[i], w2[i], lower.tail = TRUE)})
p4 <- lapply(seq_len(length(m_f)), function(i){
pnorm(m_f[i], w[i], w2[i], lower.tail = TRUE)})
p3 <- unlist(p3)
p4 <- unlist(p4)
p3 <- p3 - p4
p1 <- lapply(seq_len(length(m_f)), function(i){
pnorm(m2_f[i], w[i], w2[i], lower.tail = FALSE)})
p1p <- lapply(seq_len(length(m_f)), function(i){
pnorm(m2_f[i] + alpha, w[i], w2[i], lower.tail = FALSE)})
p1 <- unlist(p1)
p1p <- unlist(p1p)
a3 <- 2 * ((p1 * p2p) + (p2 * p1p) - (p1p * p2p))
sl <- lapply(seq_len(length(as.vector(
dd5[upper.tri(dd5)]))), function(i){
ele <- as.vector(dd5[upper.tri(dd5)])[i]
#s <- pnorm(ele*length(m_f), mean = sum(a3,na.rm=TRUE),
# sd = sqrt(sum(a3*(1-a3),na.rm=TRUE)), lower.tail = TRUE)
s <- ppois(ele * length(m_f), sum(a3),
lower.tail = TRUE, log.p = FALSE)
ifelse(s < pvalcutoff, ele, 0)
})
s <- max(unlist(sl))
if(s <= 0){
sl <- lapply(seq_len(length(
as.vector(dd5[upper.tri(dd5)]))),function(i){
ele <- as.vector(dd5[upper.tri(dd5)])[i]
s <- ppois(ele * length(m_f), sum(a3),
lower.tail = TRUE, log.p = FALSE)
ifelse(s < 1-.Machine$double.neg.eps, ele, 0)
})
s <- max(unlist(sl))}
return(s)
}
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