t3way:

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

Usage

t3way(J, K, L, x, tr = 0.2, grp = c(1:p), alpha = 0.05, p = J * K * L, MAT = FALSE, lev.col = c(1:3), var.col = 4, pr = TRUE, IV1 = NULL, IV2 = NULL, IV3 = NULL)

Arguments

J
K
L
x
tr
grp
alpha
p
MAT
lev.col
var.col
pr
IV1
IV2
IV3

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, L, x, tr = 0.2, grp = c(1:p), alpha = 0.05, p = J * 
    K * L, MAT = FALSE, lev.col = c(1:3), var.col = 4, pr = TRUE, 
    IV1 = NULL, IV2 = NULL, IV3 = NULL) 
{
    if (is.data.frame(x)) 
        x = as.matrix(x)
    if (!is.null(IV1[1])) {
        if (is.null(IV2[1])) 
            stop("IV2 is NULL")
        if (is.null(IV3[1])) 
            stop("IV3 is NULL")
        if (pr) 
            print("Assuming x is a vector containing all of the data; the dependent variable")
        xi = elimna(cbind(x, IV1, IV2, IV3))
        x = fac2list(xi[, 1], xi[, 2:4])
        J = length(unique(IV1))
        K = length(unique(IV2))
        L = length(unique(IV3))
        p = J * K * L
    }
    data = x
    if (MAT) {
        if (!is.matrix(data)) 
            stop("With MAT=T, data must be a matrix")
        if (length(lev.col) != 3) 
            stop("Argument lev.col should have 3 values")
        temp = selby2(data, lev.col, var.col)
        lev1 = length(unique(temp$grpn[, 1]))
        lev2 = length(unique(temp$grpn[, 2]))
        lev3 = length(unique(temp$grpn[, 3]))
        gv = apply(temp$grpn, 2, rank)
        gvad = 100 * gv[, 1] + 10 * gv[, 2] + gv[, 3]
        grp = rank(gvad)
        if (pr) {
            print(paste("Factor 1 has", lev1, "levels"))
            print(paste("Factor 2 has", lev2, "levels"))
            print(paste("Factor 3 has", lev3, "levels"))
        }
        if (J != lev1) 
            warning("J is being reset to the number of levels found")
        if (K != lev2) 
            warning("K is being reset to the number of levels found")
        if (L != lev3) 
            warning("K is being reset to the number of levels found")
        J = lev1
        K = lev2
        L = lev3
        data = temp$x
    }
    if (is.matrix(data)) 
        data = listm(data)
    if (!is.list(data)) 
        stop("Data are not stored in list mode")
    if (p != length(data)) {
        print("The total number of groups, based on the specified levels, is")
        print(p)
        print("The number of groups in data is")
        print(length(data))
        print("Warning: These two values are not equal")
    }
    tmeans <- 0
    h <- 0
    v <- 0
    for (i in 1:p) {
        tmeans[i] <- mean(data[[grp[i]]], tr)
        h[i] <- length(data[[grp[i]]]) - 2 * floor(tr * length(data[[grp[i]]]))
        v[i] <- (length(data[[grp[i]]]) - 1) * winvar(data[[grp[i]]], 
            tr)/(h[i] * (h[i] - 1))
    }
    v <- diag(v, p, p)
    ij <- matrix(c(rep(1, J)), 1, J)
    ik <- matrix(c(rep(1, K)), 1, K)
    il <- matrix(c(rep(1, L)), 1, L)
    jm1 <- J - 1
    cj <- diag(1, jm1, J)
    for (i in 1:jm1) cj[i, i + 1] <- 0 - 1
    km1 <- K - 1
    ck <- diag(1, km1, K)
    for (i in 1:km1) ck[i, i + 1] <- 0 - 1
    lm1 <- L - 1
    cl <- diag(1, lm1, L)
    for (i in 1:lm1) cl[i, i + 1] <- 0 - 1
    alval <- c(1:999)/1000
    cmat <- kron(cj, kron(ik, il))
    Qa <- johan(cmat, tmeans, v, h, alpha)
    A.p.value = t3pval(cmat, tmeans, v, h)
    cmat <- kron(ij, kron(ck, il))
    Qb <- johan(cmat, tmeans, v, h, alpha)
    B.p.value = t3pval(cmat, tmeans, v, h)
    cmat <- kron(ij, kron(ik, cl))
    for (i in 1:999) {
        irem <- i
        Qc <- johan(cmat, tmeans, v, h, alval[i])
        if (Qc$teststat > Qc$crit) 
            break
    }
    C.p.value = irem/1000
    cmat <- kron(cj, kron(ck, il))
    for (i in 1:999) {
        irem <- i
        Qab <- johan(cmat, tmeans, v, h, alval[i])
        if (Qab$teststat > Qab$crit) 
            break
    }
    AB.p.value = irem/1000
    cmat <- kron(cj, kron(ik, cl))
    for (i in 1:999) {
        irem <- i
        Qac <- johan(cmat, tmeans, v, h, alval[i])
        if (Qac$teststat > Qac$crit) 
            break
    }
    AC.p.value = irem/1000
    cmat <- kron(ij, kron(ck, cl))
    for (i in 1:999) {
        irem <- i
        Qbc <- johan(cmat, tmeans, v, h, alval[i])
        if (Qbc$teststat > Qbc$crit) 
            break
    }
    BC.p.value = irem/1000
    cmat <- kron(cj, kron(ck, cl))
    for (i in 1:999) {
        irem <- i
        Qabc <- johan(cmat, tmeans, v, h, alval[i])
        if (Qabc$teststat > Qabc$crit) 
            break
    }
    ABC.p.value = irem/1000
    list(Qa = Qa$teststat, Qa.crit = Qa$crit, A.p.value = A.p.value, 
        Qb = Qb$teststat, Qb.crit = Qb$crit, B.p.value = B.p.value, 
        Qc = Qc$teststat, Qc.crit = Qc$crit, C.p.value = C.p.value, 
        Qab = Qab$teststat, Qab.crit = Qab$crit, AB.p.value = AB.p.value, 
        Qac = Qac$teststat, Qac.crit = Qac$crit, AC.p.value = AC.p.value, 
        Qbc = Qbc$teststat, Qbc.crit = Qbc$crit, BC.p.value = BC.p.value, 
        Qabc = Qabc$teststat, Qabc.crit = Qabc$crit, ABC.p.value = ABC.p.value)
  }

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