t2way:

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

Usage

t2way(J, K, x, tr = 0.2, grp = c(1:p), p = J * K, MAT = FALSE, lev.col = c(1:2), var.col = 3, pr = TRUE, IV1 = NULL, IV2 = NULL)

Arguments

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

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, tr = 0.2, grp = c(1:p), p = J * K, MAT = FALSE, 
    lev.col = c(1:2), var.col = 3, pr = TRUE, IV1 = NULL, IV2 = NULL) 
{
    if (is.data.frame(x)) 
        data = as.matrix(x)
    if (tr == 0.5) {
        print("For medians, use med2way if there are no ties")
        print("With ties, use linear contrasts in conjunction with medpb")
        stop("")
    }
    if (MAT) {
        if (!is.matrix(x)) 
            stop("With MAT=T, data must be a matrix")
        if (length(lev.col) != 2) 
            stop("Argument lev.col should have 3 values")
        temp = selby2(x, lev.col, var.col)
        lev1 = length(unique(temp$grpn[, 1]))
        lev2 = length(unique(temp$grpn[, 2]))
        gv = apply(temp$grpn, 2, rank)
        gvad = 10 * gv[, 1] + gv[, 2]
        grp = rank(gvad)
        if (pr) {
            print(paste("Factor 1 has", lev1, "levels"))
            print(paste("Factor 2 has", lev2, "levels"))
        }
        if (J != lev1) 
            warning("J is being reset to the number of levels found")
        if (K != lev2) 
            warning("K is being reset to the number of levels found")
        J = lev1
        K = lev2
        x = temp$x
    }
    if (!is.null(IV1[1])) {
        if (is.null(IV2[1])) 
            stop("IV2 is NULL")
        if (pr) 
            print("Assuming data is a vector containing all of the data; the dependent variable")
        xi = elimna(cbind(x, IV1, IV2))
        J = length(unique(xi[, 2]))
        K = length(unique(xi[, 3]))
        x = fac2list(xi[, 1], xi[, 2:3])
    }
    if (is.matrix(x)) 
        x = listm(x)
    if (!is.list(x)) 
        stop("Data are not stored in list mode")
    if (p != length(x)) {
        print("The total number of groups, based on the specified levels, is")
        print(p)
        print("The number of groups is")
        print(length(x))
        print("Warning: These two values are not equal")
    }
    tmeans <- 0
    h <- 0
    v <- 0
    for (i in 1:p) {
        x[[grp[i]]] = elimna(x[[grp[i]]])
        tmeans[i] <- mean(x[[grp[i]]], tr)
        h[i] <- length(x[[grp[i]]]) - 2 * floor(tr * length(x[[grp[i]]]))
        if (winvar(x[[grp[i]]], tr) == 0) 
            print(paste("The Winsorized variance is zero for group", 
                i))
        v[i] <- (length(x[[grp[i]]]) - 1) * winvar(x[[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)
    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
    cmat <- kron(cj, ik)
    alval <- c(1:999)/1000
    for (i in 1:999) {
        irem <- i
        Qa <- johan(cmat, tmeans, v, h, alval[i])
        if (i == 1) 
            dfA = Qa$df
        if (Qa$teststat > Qa$crit) 
            break
    }
    A.p.value = irem/1000
    cmat <- kron(ij, ck)
    for (i in 1:999) {
        irem <- i
        Qb <- johan(cmat, tmeans, v, h, alval[i])
        if (i == 1) 
            dfB = Qb$df
        if (Qb$teststat > Qb$crit) 
            break
    }
    B.p.value = irem/1000
    cmat <- kron(cj, ck)
    for (i in 1:999) {
        irem <- i
        Qab <- johan(cmat, tmeans, v, h, alval[i])
        if (i == 1) 
            dfAB = Qab$df
        if (Qab$teststat > Qab$crit) 
            break
    }
    AB.p.value = irem/1000
    tmeans = matrix(tmeans, J, K, byrow = T)
    list(Qa = Qa$teststat, A.p.value = A.p.value, df.A = dfA, 
        Qb = Qb$teststat, B.p.value = B.p.value, df.B = dfB, 
        Qab = Qab$teststat, AB.p.value = AB.p.value, df.AB = dfAB, 
        means = tmeans)
  }

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