pbanova:

In musto101/wilcox_R: What the package does (short line)

Usage

pbanova(x, tr = 0.2, alpha = 0.05, nboot = NA, grp = NA, WIN = FALSE, win = 0.1)

Arguments

x
tr
alpha
nboot
grp
WIN
win

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (x, tr = 0.2, alpha = 0.05, nboot = NA, grp = NA, WIN = FALSE, 
    win = 0.1) 
{
    if (is.matrix(x)) 
        x <- listm(x)
    if (!is.list(x)) 
        stop("Data must be stored in list mode or in matrix mode.")
    if (!is.na(sum(grp))) {
        xx <- list()
        for (i in 1:length(grp)) xx[[i]] <- x[[grp[1]]]
        x <- xx
    }
    J <- length(x)
    tempn <- 0
    for (j in 1:J) {
        temp <- x[[j]]
        temp <- temp[!is.na(temp)]
        tempn[j] <- length(temp)
        x[[j]] <- temp
    }
    Jm <- J - 1
    if (WIN) {
        if (tr < 0.2) {
            print("Warning: When Winsorizing,")
            print("the amount of trimming should be at least.2")
        }
        if (win > tr) 
            stop("Amount of Winsorizing must be <= amount of trimming")
        if (min(tempn) < 15) {
            print("Warning: Winsorizing with sample sizes less than 15")
            print("can result in poor control over the probability of a Type I error")
        }
        for (j in 1:J) {
            x[[j]] <- winval(x[[j]], win)
        }
    }
    con <- matrix(0, J, J - 1)
    for (j in 1:Jm) {
        jp <- j + 1
        con[j, j] <- 1
        con[jp, j] <- 0 - 1
    }
    if (is.na(nboot)) {
        nboot <- 5000
        if (J <= 8) 
            nboot <- 4000
        if (J <= 3) 
            nboot <- 2000
    }
    if (alpha == 0.05) {
        dvec <- c(0.05, 0.025, 0.0169, 0.0127, 0.0102, 0.00851, 
            0.0073, 0.00639, 0.00568, 0.00511)
        if (Jm > 10) {
            avec <- 0.05/c(11:Jm)
            dvec <- c(dvec, avec)
        }
    }
    if (alpha == 0.01) {
        dvec <- c(0.01, 0.005, 0.00334, 0.00251, 0.00201, 0.00167, 
            0.00143, 0.00126, 0.00112, 0.00101)
        if (Jm > 10) {
            avec <- 0.01/c(11:Jm)
            dvec <- c(dvec, avec)
        }
    }
    if (alpha != 0.05 && alpha != 0.01) 
        dvec <- alpha/c(1:Jm)
    bvec <- matrix(NA, nrow = J, ncol = nboot)
    set.seed(2)
    print("Taking bootstrap samples. Please wait.")
    for (j in 1:J) {
        paste("Working on group ", j)
        data <- matrix(sample(x[[j]], size = length(x[[j]]) * 
            nboot, replace = TRUE), nrow = nboot)
        bvec[j, ] <- apply(data, 1, mean, tr)
    }
    test <- NA
    for (d in 1:Jm) {
        dp <- d + 1
        test[d] <- sum(bvec[d, ] > bvec[dp, ])/nboot
        if (test[d] > 0.5) 
            test[d] <- 1 - test[d]
    }
    test <- (0 - 1) * sort(-2 * test)
    sig <- sum((test < dvec[1:Jm]))
    if (sig > 0) 
        print("Significant result obtained: Reject")
    if (sig == 0) 
        print("No significant result obtained: Fail to reject")
    list(test.vec = test, crit.vec = dvec[1:Jm])
  }

musto101/wilcox_R documentation built on May 23, 2019, 10:52 a.m.