R/IniTransform.R

In PERMANOVA: Multivariate Analysis of Variance Based on Distances and Permutations

IniTransform <- function (X, InitTransform = "None", transform = "Standardize columns")
{
  n = nrow(X)
  p = ncol(X)
  RowNames = rownames(X)
  ColNames = colnames(X)
  InitTransforms = c("None", "Log", "Logit")
  if (is.numeric(InitTransform))
    InitTransform = InitTransforms[InitTransform]
  ContinuousDataTransform = c("Raw Data", "Substract the global mean",
                              "Double centering", "Column centering", "Standardize columns",
                              "Row centering", "Standardize rows", "Divide by the column means and center",
                              "Normalized residuals from independence")
  if (is.numeric(transform))
    transform = ContinuousDataTransform[transform]
  switch(InitTransform, Log = {
    if (sum(which(X <= 0)) > 0) stop("Initial log transformation is not compatible with negative or zero values")
    X = log(X)
  }, Logit = {
    if (sum(which(X <= 0)) > 0) stop("Initial logit transformation is not compatible with negative values")
    X = X + 0.01 * (X == 0) - 0.01 * (X == 1)
    x = log(X/(1 - X))
  })
  switch(transform, `Substract the global mean` = {
    gmean = mean(X)
    X = X - gmean
  }, `Double centering` = {
    X = (diag(n) - matrix(1, n, n)/n) %*% X %*% (diag(p) -
                                                   matrix(1, p, p)/p)
  }, `Column centering` = {
    means = apply(X, 2, mean)
    X = X - matrix(1, n, 1) %*% matrix(means, 1, p)
  }, `Standardize columns` = {
    means = apply(X, 2, mean)
    stdDevs = apply(X, 2, sd)
    X = (X - matrix(1, n, 1) %*% matrix(means, 1, p))/(matrix(1,
                                                              n, 1) %*% matrix(stdDevs, 1, p))
  }, `Row centering` = {
    means = apply(X, 1, mean)
    X = X %*% (diag(p) - matrix(1, p, p)/p)
  }, `Standardize rows` = {
    means = apply(X, 1, mean)
    stdDevs = apply(X, 1, sd)
    X = solve(diag(stdDevs)) %*% X %*% (diag(p) - matrix(1,
                                                         p, p)/p)
  }, `Divide by the column means and center` = {
    means = apply(X, 2, mean)
    for (i in (1:p)) X[, i] = X[, i]/means[i]
    X = (diag(n) - matrix(1, n, n)/n) %*% X
  }, `Normalized residuals from independence` = {
    nt = sum(sum(X))
    dr = apply(X, 1, sum)
    dc = apply(X, 2, sum)
    esp = (t(t(dr)) %*% dc)/nt
    var = t(t(1 - dr/nt)) %*% (1 - dc/nt)
    xp = ((xp - esp)/sqrt(esp))/sqrt(var)
  }, `Divide by the range` = {
    Rangos = apply(X, 2, max) - apply(X, 2, min)
    X = X %*% diag(1/Rangos)
  })
  rownames(X) = RowNames
  colnames(X) = ColNames
  return(X)
}