t2waypbg:

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

Usage

t2waypbg(J, K, x, alpha = 0.05, nboot = NA, grp = NA, est = onestep, ...)

Arguments

J
K
x
alpha
nboot
grp
est
...

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 (J, K, x, alpha = 0.05, nboot = NA, grp = NA, est = onestep, 
    ...) 
{
    if (is.data.frame(x)) 
        x = as.matrix(x)
    JK <- J * K
    if (is.matrix(x)) 
        x <- listm(x)
    if (!is.na(grp)) {
        yy <- x
        for (j in 1:length(grp)) x[[j]] <- yy[[grp[j]]]
    }
    if (!is.list(x)) 
        stop("Data must be stored in list mode or a matrix.")
    for (j in 1:JK) {
        xx <- x[[j]]
        x[[j]] <- xx[!is.na(xx)]
    }
    ij <- matrix(c(rep(1, J)), 1, J)
    ik <- matrix(c(rep(1, K)), 1, K)
    jm1 <- J - 1
    cj <- diag(1, jm1, J)
    for (i in 1:jm1) cj[i, i + 1] <- 0 - 1
    km1 <- K - 1
    ck <- diag(1, km1, K)
    for (i in 1:km1) ck[i, i + 1] <- 0 - 1
    conA <- t(kron(cj, ik))
    conB <- t(kron(ij, ck))
    conAB <- t(kron(cj, ck))
    ncon <- max(nrow(conA), nrow(conB), nrow(conAB))
    if (JK != length(x)) {
        print("Warning: The number of groups does not match")
        print("the number of contrast coefficients.")
    }
    if (is.na(nboot)) {
        nboot <- 5000
        if (ncon <= 4) 
            nboot <- 2000
    }
    m1 <- matrix(0, nrow = JK, ncol = nboot)
    set.seed(2)
    print("Taking bootstrap samples. Please wait.")
    for (j in 1:JK) {
        paste("Working on group ", j)
        data <- matrix(sample(x[[j]], size = length(x[[j]]) * 
            nboot, replace = TRUE), nrow = nboot)
        m1[j, ] <- apply(data, 1, est, ...)
    }
    bootA <- matrix(0, ncol(conA), nboot)
    bootB <- matrix(0, ncol(conB), nboot)
    bootAB <- matrix(0, ncol(conAB), nboot)
    testA <- NA
    testB <- NA
    testAB <- NA
    testvecA <- NA
    testvecB <- NA
    testvecAB <- NA
    for (d in 1:ncol(conA)) {
        bootA[d, ] <- apply(m1, 2, trimpartt, conA[, d])
        testA[d] <- sum((bootA[d, ] > 0))/nboot
        testA[d] <- min(testA[d], 1 - testA[d])
    }
    for (d in 1:ncol(conB)) {
        bootB[d, ] <- apply(m1, 2, trimpartt, conB[, d])
        testB[d] <- sum((bootB[d, ] > 0))/nboot
        testB[d] <- min(testB[d], 1 - testB[d])
    }
    for (d in 1:ncol(conAB)) {
        bootAB[d, ] <- apply(m1, 2, trimpartt, conAB[, d])
        testAB[d] <- sum((bootAB[d, ] > 0))/nboot
        testAB[d] <- min(testAB[d], 1 - testAB[d])
    }
    Jm <- J - 1
    Km <- K - 1
    JKm <- (J - 1) * (K - 1)
    dvecA <- alpha/c(1:Jm)
    dvecB <- alpha/c(1:Km)
    dvecAB <- alpha/c(1:JKm)
    testA <- (0 - 1) * sort(-2 * testA)
    testB <- (0 - 1) * sort(-2 * testB)
    testAB <- (0 - 1) * sort(-2 * testAB)
    sig <- sum((testA < dvecA[1:Jm]))
    if (sig > 0) 
        print("Significant result obtained for Factor A: Reject")
    if (sig == 0) 
        print("No significant result Factor A: Fail to reject")
    sig <- sum((testB < dvecB[1:Km]))
    if (sig > 0) 
        print("Significant result obtained for Factor B: Reject")
    if (sig == 0) 
        print("No significant result Factor B: Fail to reject")
    sig <- sum((testAB < dvecAB[1:JKm]))
    if (sig > 0) 
        print("Significant Interaction: Reject")
    if (sig == 0) 
        print("No significant Interaction: Fail to reject")
    list(testA = testA, crit.vecA = dvecA, testB = testB, crit.vecB = dvecB, 
        testAB = testAB, crit.vecAB = dvecAB)
  }

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