R/preprocess_FreeSort.R

In ClustBlock: Clustering of Datasets

## =============================================================================


##' @title Preprocessing for Free Sorting Data
##'
##' @description
##' For Free Sorting Data, this preprocessing is needed.
##'
##' @usage
##' preprocess_FreeSort(Data, NameSub=NULL)
##'
##'
##' @param Data data frame or matrix. Corresponds to all variables that contain subjects results. Each column corresponds to a subject and gives the groups to which the products (rows) are assigned
##'
##' @param NameSub string vector. Name of each subject. Length must be equal to the number of clumn of the Data. If NULL, the names are S1,...Sm. Default: NULL
##'
##'
##' @return A list with:
##'         \itemize{
##'          \item new_Data: the Data transformed
##'          \item Blocks: the number of groups for each subject
##'          \item NameBlocks: the name of each subject
##'          }
##'
##'
##' @keywords FreeSorting
##'
##' @references
##' Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2018). Analysis and clustering of multiblock datasets by means of the STATIS and CLUSTATIS methods. Application to sensometrics. Food Quality and Preference, in Press.
##'
##'
##' @importFrom  FactoMineR tab.disjonctif
##'
##'
##' @examples
##' data(choc)
##' prepro=preprocess_FreeSort(choc)
##'
##' @seealso   \code{\link{clustatis}}, \code{\link{clustatis_FreeSort}}
##'
##' @export


## =============================================================================


preprocess_FreeSort <- function(Data, NameSub = NULL) {
  p <- ncol(Data)
  n <- nrow(Data)

  if (is.null(colnames(Data))) colnames(Data) <- paste0("S", 1:ncol(Data))
  if (is.null(NameSub)) NameSub <- paste("S", 1:p)

  # parapet for Namesub
  if (length(NameSub) != p) {
    stop("Namesub must have the length of ncol(Data)")
  }


  Yi <- list()
  for (i in 1:p)
  {
    Data[, i] <- factor(Data[, i])
    Yi[[i]] <- as.matrix(tab.disjonctif(Data[, i]))
    a <- apply(Yi[[i]], 2, mean)
    Yi[[i]] <- Yi[[i]] %*% diag(1 / sqrt(a)) # standardization
    colnames(Yi[[i]]) <- paste0(NameSub[i], "g", 1:ncol(Yi[[i]]))
  }

  dat <- NULL
  for (i in 1:p)
  {
    dat <- cbind(dat, Yi[[i]])
  }
  rownames(dat) <- rownames(Data)
  Blocks <- rep(0, p)
  for (i in 1:p)
  {
    Blocks[i] <- nlevels(as.factor(Data[, i]))
  }


  return(list(new_Data = dat, Blocks = Blocks, NameBlocks = NameSub))
}