mlcc.reps.R

#' Multiple Latent Components Clustering - Subspace clustering assuming that the
#' number of clusters is known
#'
#' For a fixed number of cluster function returns the best partition and basis
#' for each subspace.
#'
#' In more detail, an algorithm \code{\link{mlcc.kmeans}} is run a
#' \emph{numb.runs} of times with random or custom initializations. The best
#' partition is selected according to the BIC.
#'
#'
#' @param X A data frame or a matrix with only continuous variables.
#' @param numb.clusters An integer, number of cluster.
#' @param numb.runs An integer, number of runs of \code{\link{mlcc.kmeans}
#'   algorithm} with random initialization.
#' @param stop.criterion An integer, if an iteration of
#'   \code{\link{mlcc.kmeans}} algorithm makes less changes in partitions than
#'   \code{stop.criterion}, \code{\link{mlcc.kmeans}} stops.
#' @param max.iter max.iter An integer, maximum number of iterations of the loop
#'   in \code{\link{mlcc.kmeans}} algorithm.
#' @param initial.segmentations A list of vectors, segmentations that user wants
#'   to be used as an initial segmentation in \code{\link{mlcc.kmeans}}
#'   algorithm.
#' @param max.dim An integer, maximal dimension of subspaces.
#' @param scale A boolean, if TRUE (value set by default) then variables in
#'   dataset are scaled to zero mean and unit variance.
#' @param numb.cores An integer, number of cores to be used, by default all
#'   cores are used.
#' @param estimate.dimensions A boolean, if TRUE (value set by default)
#'   subspaces dimensions are estimated.
#' @param flat.prior A boolean, if TRUE then, instead of a prior that takes into
#'   account number of models for a given number of clusters, flat prior is
#'   used.
#' @param show.warnings A boolean, if set to TRUE all warnings are displayed,
#'   default value is FALSE.
#' @export
#' @return A list consisting of \item{segmentation}{a vector containing the
#'   partition of the variables} \item{BIC}{a numeric, value of the mBIC}
#'   \item{basis}{a list of matrices, the factors for each of the subspaces}
#' @examples
#' \donttest{
#' sim.data <- data.simulation(n = 50, SNR = 1, K = 5, numb.vars = 50, max.dim = 3)
#' mlcc.res <- mlcc.reps(sim.data$X, numb.clusters = 5, numb.runs = 20, max.dim = 4, numb.cores = 1)
#' show.clusters(sim.data$X, mlcc.res$segmentation)
#' }
mlcc.reps <- function(X, numb.clusters = 2, numb.runs = 30, stop.criterion = 1, max.iter = 30,
                      initial.segmentations = NULL, max.dim = 4, scale = TRUE, numb.cores = NULL, estimate.dimensions = TRUE,
                      flat.prior = FALSE, show.warnings = FALSE) {
  if (is.data.frame(X)) {
    warning("X is not a matrix. Casting to matrix.")
    X <- as.matrix(X)
  }
  if (any(is.na(X))) {
    warning("Missing values are imputed by the mean of the variable")
    X[is.na(X)] <- matrix(apply(X, 2, mean, na.rm = TRUE),
      ncol = ncol(X), nrow = nrow(X),
      byrow = TRUE
    )[is.na(X)]
  }
  if (any(!sapply(X, is.numeric))) {
    auxi <- NULL
    for (j in 1:ncol(X)) {
      if (!is.numeric(X[, j])) {
        auxi <- c(auxi, j)
      }
    }
    stop(paste("\nThe following variables are not quantitative: ", auxi))
  }
  if (is.null(numb.cores)) {
    numb.cores <- max(1, detectCores() - 1)
  }
  cl <- makeCluster(numb.cores)
  registerDoParallel(cl)

  if (scale) {
    X <- scale(X)
  }

  i <- NULL
  BICs <- NULL
  segmentations <- NULL
  if (is.null(initial.segmentations)) {
    segmentations <- foreach(i = (1:numb.runs)) %dorng% {
      MLCC.res <- mlcc.kmeans(
        X = X, number.clusters = numb.clusters, max.subspace.dim = max.dim,
        max.iter = max.iter, estimate.dimensions = estimate.dimensions, show.warnings = show.warnings
      )
      current.segmentation <- MLCC.res$segmentation
      current.pcas <- MLCC.res$pcas
      list(
        current.segmentation, cluster.pca.BIC(X, current.segmentation, sapply(
          current.pcas,
          ncol
        ), numb.clusters, max.dim = max.dim, flat.prior = flat.prior),
        current.pcas
      )
    }
    # running user specified clusters
  } else {
    segmentations <- foreach(i = (1:length(initial.segmentations))) %dorng% {
      MLCC.res <- mlcc.kmeans(
        X = X, number.clusters = numb.clusters, max.subspace.dim = max.dim,
        max.iter = max.iter, initial.segmentation = initial.segmentations[[i]],
        estimate.dimensions = estimate.dimensions, show.warnings = show.warnings
      )
      current.segmentation <- MLCC.res$segmentation
      current.pcas <- MLCC.res$pcas
      list(
        current.segmentation, cluster.pca.BIC(X, current.segmentation, sapply(
          current.pcas,
          ncol
        ), numb.clusters, max.dim = max.dim, flat.prior = flat.prior),
        current.pcas
      )
    }
  }
  stopCluster(cl)
  BICs <- unlist(lapply(segmentations, function(x) x[2]))
  basis <- lapply(segmentations, function(x) x[3])
  segmentations <- lapply(segmentations, function(x) x[[1]])
  result <- list(
    segmentation = segmentations[[which.max(BICs)]], BIC = BICs[which.max(BICs)],
    basis = basis[[which.max(BICs)]][[1]]
  )
  class(result) <- "mlcc.reps.fit"
  return(result)
}