R/transFisher.R

In GGMridge: Gaussian Graphical Models Using Ridge Penalty Followed by Thresholding and Reestimation

#' Fisher's Z-Transformation
#'
#' Fisher's Z-transformation of (partial) correlation.
#' 
#' @param x A vector having entries between -1 and 1.
#'
#' @returns Fisher's Z-transformed values.
#' @author Min Jin Ha
#' 
#' @examples
#'  ###############################
#'  # Simulate data
#'  ###############################
#'  simulation <- simulateData(G = 100, etaA = 0.02, n = 50, r = 1)
#'  dat <- simulation$data[[1L]]
#'  stddat <- scale(x = dat, center = TRUE, scale = TRUE)
#'
#'  shrinkage.lambda <- lambda.TargetD(x = stddat)
#'
#'  ###############################
#'  # the ridge parameter
#'  ###############################
#'  ridge.lambda <- shrinkage.lambda / (1.0 - shrinkage.lambda)
#'
#'  ###############################
#'  # partial correlation matrix
#'  ###############################
#'  partial <- solve(cor(dat) + ridge.lambda * diag(ncol(dat)))
#'  partial <- -scaledMat(x = partial)
#'
#'  ###############################
#'  # Fisher's Z transformation of 
#'  # upper diagonal of the partial 
#'  # correlation matrix
#'  ###############################
#'  w.upper <- which(upper.tri(diag(nrow(dat))))
#'  psi <- transFisher(x = partial[w.upper])
#'  
#' @export
transFisher <-  function(x) {

  eps <- 1e-7

  x <- pmin(x,  1 - eps)

  x <- pmax(x, -1 + eps)

  0.5 * log( {1.0 + x} / {1.0 - x} )
}