Nothing
p.int2 <- function (object, delta = 50, N = -1, av = "median", p.adjust.method = "none")
{
if (!(class(object)=="marrayRaw") & !(class(object)=="marrayNorm")){
stop("Object should be of class marrayRaw or marrayNorm")
}
PpL <- list(NULL)
PnL <- list(NULL)
AL <- maA(object)
ML <- maM(object)
index <- c(1:dim(object)[[2]])
for (ii in index){
A <- AL[,ii]
M <- ML[,ii]
if (N < 0) {
N <- 100 * length(A)
}
MavP <- double(N)
#### GERNERATING EMPIRICAL DISTRIBUTION
if (av == "mean") {
for (i in 1:N) {
MavP[i] <- mean(sample(M, 2 * delta + 1))
}
}
if (av == "median") {
for (i in 1:N) {
MavP[i] <- median(sample(M, 2 * delta + 1))
}
}
#### STATISTIC FOR ORIGINAL DATA
Mav <- ma.vector(A, M, delta = delta, av = av)
Mav.l <- length(Mav)
pP <- double(length = length(A)) + NA
MavP.l <- length(MavP)
#### DETERMINING P-VALUES
for (i in 1:Mav.l) {
pP[i] <- sum(MavP >= Mav[i], na.rm = TRUE)/MavP.l
}
nP <- double(length = length(M)) + NA
for (i in 1:Mav.l) {
nP[i] <- sum(MavP <= Mav[i], na.rm = TRUE)/MavP.l
}
### ADJUSTMENT
pP[pP == 0] <- 1/N
nP[nP == 0] <- 1/N
pP.adjust <- p.adjust(pP, method = p.adjust.method)
nP.adjust <- p.adjust(nP, method = p.adjust.method)
pP.adjust[is.na(Mav)] <- NA
nP.adjust[is.na(Mav)] <- NA
PpL[[ii]] <- pP.adjust
PnL[[ii]] <- nP.adjust
}
list(Pp=PpL, Pn=PnL)
}
#######################################################################
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