R/manova_functions.R

In Superpower: Simulation-Based Power Analysis for Factorial Designs

#functions for mlm table

Roy <- function(eig, q, df.res) {
  p <- length(eig)
  test <- max(eig)
  tmp1 <- max(p, q)
  tmp2 <- df.res - tmp1 + q
  c(test, (tmp2 * test)/tmp1, tmp1, tmp2)
}

Wilks <- function(eig, q, df.res)
{
  test <- prod(1/(1 + eig))
  p <- length(eig)
  tmp1 <- df.res - 0.5 * (p - q + 1)
  tmp2 <- (p * q - 2)/4
  tmp3 <- p^2 + q^2 - 5
  tmp3 <- if (tmp3 > 0)
    sqrt(((p * q)^2 - 4)/tmp3)
  else 1
  c(test, ((test^(-1/tmp3) - 1) * (tmp1 * tmp3 - 2 * tmp2))/p/q,
    p * q, tmp1 * tmp3 - 2 * tmp2)
}

HL <- function(eig, q, df.res)
{
  test <- sum(eig)
  p <- length(eig)
  m <- 0.5 * (abs(p - q) - 1)
  n <- 0.5 * (df.res - p - 1)
  s <- min(p, q)
  tmp1 <- 2 * m + s + 1
  tmp2 <- 2 * (s * n + 1)
  c(test, (tmp2 * test)/s/s/tmp1, s * tmp1, tmp2)
}

Pillai <- function(eig, q, df.res)
{
  test <- sum(eig/(1 + eig))
  p <- length(eig)
  s <- min(p, q)
  n <- 0.5 * (df.res - p - 1)
  m <- 0.5 * (abs(p - q) - 1)
  tmp1 <- 2 * m + s + 1
  tmp2 <- 2 * n + s + 1
  c(test, (tmp2/tmp1 * test)/(s - test), s * tmp1, s * tmp2)
}