R/acca.R

In meantrix/corrP: corrp (Compute correlations type analysis in parallel)

#' @useDynLib corrp, .registration=TRUE
#' @exportPattern "^[[:alpha:]]+"
#' @importFrom Rcpp evalCpp
#' @importFrom RcppArmadillo armadillo_version

#' @title Average correlation clustering algorithm
#'
#' @description A C++ implementation of the ACCA method
#' that works directly with the correlation matrix derived from the \code{\link{corr_matrix}} function.
#' In this sense, this implementation differs from the original,
#' it works with mixed data and several correlation methods.
#'
#' @param m  \[\code{matrix(1)}]\cr correlation matrix from \code{\link{corr_matrix}} or a distance matrix.
#' @param k \[\code{integer(1)}]\cr number of clusters considered.
#' @param maxrep \[\code{integer(1)}]\cr maximum number of interactions without change in the clusters.
#' @param maxiter \[\code{integer(1)}]\cr maximum number of interactions.
#' @param ... Additional arguments (TODO).
#'
#' @return \[\code{acca_list(k)}]\cr A list with the final result of the clustering method.
#'  That is, the name of the variables belonging to each cluster k.
#'
#' @author Igor D.S. Siciliani
#'
#' @keywords correlation , acca
#'
#' @references
#' Bhattacharya, Anindya, and Rajat K. De.
#' "Average correlation clustering algorithm (ACCA) for grouping of co-regulated
#' genes with similar pattern of variation in their expression values."
#' Journal of Biomedical Informatics 43.4 (2010): 560-568.
#'
#'
#' @examples
#' \dontrun{
#'
#' x = corrp::corrp(iris)
#' m = corrp::corr_matrix(x)
#' corrp::acca(m,2)
#'
#'}
#'
#' @export
#'
acca = function(m,...) {
UseMethod('acca',m)
}


#' @export
#' @rdname acca
acca.cmatrix <- function(m, k, maxrep = 2L, maxiter = 100L,...) {

  k = as.integer(k)
  maxrep = as.integer(maxrep)
  maxiter = as.integer(maxiter)
  checkmate::assert_int(k,lower = 2)
  checkmate::assert_int(maxrep,lower = 2,upper = maxiter)
  checkmate::assert_int(maxiter,lower = maxrep)

  allint = .Call(`_corrp_acca_main`, m, k, maxrep, maxiter)
  res = allint[length(allint)][[1]]
  names(res) =  paste0("cluster",1:k)
  return(structure(res ,class = c('acca_list','list') ) )
}

#' @export
#' @rdname acca
acca.matrix <- function(m, k, maxrep = 2L, maxiter = 100L,...) {

  warning("m is not an object of the 'cmatrix' class some results may go wrong.")


  k = as.integer(k)
  maxrep = as.integer(maxrep)
  maxiter = as.integer(maxiter)
  checkmate::assert_int(k,lower = 2)
  checkmate::assert_int(maxrep,lower = 2,upper = maxiter)
  checkmate::assert_int(maxiter,lower = maxrep)

  allint = .Call(`_corrp_acca_main`, m, k, maxrep, maxiter)
  res = allint[length(allint)][[1]]
  names(res) =  paste0("cluster",1:k)
  return(structure(res ,class = c('acca_list','list') ) )
}