R/permutation.scale.R

In DIFM: Dynamic ICAR Spatiotemporal Factor Models

#' @title  Permute the dataset by the largest absolute value in each eigenvector, and scale
#' @description It finds the vector of permutation to permute data by its largest absolute value in each eigenvector. It sets the order by specified number of factors, and the rest is ordered as they were. The data is permuted, and if needed, scaled.
#'
#' @param data  The dataset
#' @param n.factors  Number of factors
#' @param return.scale  Scale data after permutation
#'
#' @return  The permuted and standardized dataset, either in matrix or array.
#' @export

permutation.scale <- function(data, n.factors, return.scale = FALSE){

  r <- dim(data)[2]
  eigvec <- eigen(cor(data))$vectors
  rownames(eigvec) <- 1:r
  colnames(eigvec) <- 1:r
  colseq <- seq(1, r)
  win.factors <- numeric(n.factors)
  win.factors[1] <- colseq[rank(abs(eigvec[,1])) == r]
  for(i in 2:n.factors){
    choose_again <- TRUE
    j <- 0
    while(choose_again){
      win.factors[i] <- colseq[rank(abs(eigvec[,i])) == (r-j)]
      choose_again <- any(win.factors[i] == win.factors[1:(i-1)])
      j <- j + 1
    }
  }
  rest.vars <- colseq[-win.factors]
  permutation <- c(win.factors, rest.vars)
  data.return <- data[,permutation]
  
  if(return.scale){
    data.return <- apply(data[,permutation], 2, scale)
  }

  return(data.return)
}