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#' @title Consensus Cluster Plus without Plots
#'
#' @description
#' \code{consensusClusterNoPlots} is a wrapper function for
#' \code{\link[ConsensusClusterPlus]{ConsensusClusterPlus}}that suppresses
#' the creation of the plots that are created automatically.
#'
#' @param df A dataframe of network attributes containing only numeric values.
#' The columns of the dataframe should likely be normalized.
#' @param link_method The agglomeration method to be used for hierarchical
#' clustering. Defaults to the average linkage method. See other methods in
#' \code{\link[stats]{hclust}}.
#' @param dist_method The distance measure to be used between columns and
#' between rows of the dataframe. Distance is used as a measure of similarity.
#' Defaults to euclidean distance. See other options in
#' \code{\link[stats]{dist}}.
#' @param max_k The maximum number of clusters to consider in the consensus
#' clustering step. Consensus clustering will be performed for max_k-1
#' iterations, i.e. for 2, 3, ..., max_k clusters. Defaults to 10.
#' @param reps The number of subsamples taken at each iteration of the consensus
#' cluster algorithm. Defaults to 1000.
#' @param p_var The proportion of network variables to be subsampled during
#' consensus clustering. Defaults to 1.
#' @param p_net The proportion of networks to be subsampled during consensus
#' clustering. Defaults to 0.8.
#' @param cc_seed The seed used to ensure the reproducibility of the consensus
#' clustering. Defaults to 1.
#'
#' @author Philippe Boileau , \email{philippe_boileau@@berkeley.edu}
#'
#' @importFrom ConsensusClusterPlus ConsensusClusterPlus
#' @importFrom grDevices png dev.off
consensusClusterNoPlots <- function(df, link_method, dist_method,
max_k, reps, p_var, p_net, cc_seed){
# create the tempfile to save the plots in (this works for Unix and Windows)
ff <- tempfile()
grDevices::png(filename=ff)
# pass args to ConsensusClusterPlus
res <- suppressMessages(ConsensusClusterPlus(as.matrix(df),
maxK = max_k,
innerLinkage = link_method,
reps = reps,
pItem = p_var,
pFeature = p_net,
clusterAlg = "hc",
distance = dist_method,
seed = cc_seed,
plot = NULL))
# delete the tempfile containing the plots
grDevices::dev.off()
unlink(ff)
return(res)
}
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