R/CV_partition.R

In vincentgarin/mppGxE: MPP GxE QTL analysis

################
# CV_partition #
################

#' Cross validation partition
#'
#' Partition the genotype indices into training and validation sets for
#' cross-validation (CV).
#'
#' The genotype indices are randomly assigned within cross to k subsets (folds).
#' Then each subset is used once as validation set, the remaining data go in the
#' training set.
#'
#' @param cross.ind \code{Character} vector with the same length as the number
#' of genotypes which specifies to which cross each genotype belongs.
#'
#' @param k \code{Numeric} value representing the number of subsets (fold) into
#' which data are spread within cross. Default = 5.
#'
#' @return Return:
#'
#' \item{fold}{\code{List} of k lists (one for each fold). Each fold list
#' contains two vectors with genotypes indices of the training (\code{$train.set}) and
#' the validation set (\code{$val.set}).}
#'
#'
#' @author Vincent Garin
#'
#' @seealso \code{\link{mppGE_CV}}, \code{\link{mppGE_oneS_CV}}
#'
#' @examples
#'
#' data(mppData_GE)
#'
#' part <- CV_partition(mppData_GE$cross.ind)
#'
#' @export
#'


CV_partition <- function(cross.ind, k =5) {

  # 1. define a general indices
  #############################


  gen.indices <- 1:length(cross.ind)


  # split the ith cross into k subsets
  ####################################

  cross.id <- unique(cross.ind)
  part.cross <- vector(mode = "list", length = length(cross.id))

  for(i in seq_along(cross.id)){

    cross.i <- gen.indices[cross.ind == cross.id[i]]

    int.div <- length(cross.i) %/% k
    rest <- length(cross.i) %% k

    within.part <- c(rep(int.div, (k-rest)), rep((int.div + 1), (rest)))

    part.cross[[i]] <- split(x = sample(cross.i),
                          f = as.factor(rep(letters[1:k], within.part)))

  }


  # rearrange the subsets into training and validation set
  ########################################################

  partition <- vector(mode = "list", length = k)

  for(i in 1:k){

    val.set <- c()

    for(j in seq_along(part.cross)){

     val.set <- c(val.set, part.cross[[j]][[i]])

    }

    val.set <- sort(val.set)

    train.set <- gen.indices[-val.set]


    partition[[i]] <- list(train.set = train.set, val.set = val.set)

  }

  return(partition)

}