R/non_linear_correl.R

In elpidiofilho/easyFit: Routines for fit classification and regression models in easy way

# Non Linear Correlation
#'
#' @title non_linear_correl
#' @description This function calculates non linear correlation between variables using rdc algorithm
#' @param df  dataframe with numerical data
#' @keywords RDC non linera correlation
#' @details details
#' @author Elpidio Filho, \email{elpidio@ufv.br}
#' @importFrom stats rnorm cancor
#' @examples
#' \dontrun{
#' non_linear_correl(df)
#' }
#' @export



non_linear_correl <- function(df) {
  stopifnot(is.data.frame(df))
  c <- c("peraser")
  mcnl <- matrix(nrow = ncol(df), ncol = ncol(df))
  for (i in seq_len(ncol(df))) {
    for (j in seq_len(ncol(df))) {
      mcnl[i, j] <- rdc(df[, i], df[, j])
    }
  }
  mcnl <- as.data.frame(mcnl)
  colnames(mcnl) <- colnames(df)
  rownames(mcnl) <- colnames(df)
  return(mcnl)
}



rdc <- function(x, y, k = 20, s = 1 / 6, f = sin) {
  x <- cbind(apply(as.matrix(x), 2, function(u) rank(u) / length(u)), 1)
  y <- cbind(apply(as.matrix(y), 2, function(u) rank(u) / length(u)), 1)
  set.seed(313)
  x <- s / ncol(x) * x %*% matrix(rnorm(ncol(x) * k), ncol(x))
  set.seed(313)
  y <- s / ncol(y) * y %*% matrix(rnorm(ncol(y) * k), ncol(y))
  cancor(cbind(f(x), 1), cbind(f(y), 1))$cor[1]
}