R/plotTrees.R

In DCG: Data Cloud Geometry (DCG): Using Random Walks to Find Community Structure in Social Network Analysis

#' generate tree plots for each ensemble matrix
#' \code{plotCLUSTERS} plot all cluster trees
#'
#' @param EnsList a list in which elements are ensemble matrices.
#' @param mfrow A vector of the form \code{c(nr, nc)} passed to \code{\link{par}}.
#' @param mar plotting parameters with useful defaults (\code{\link{par}})
#' @param line plotting parameters with useful defaults (\code{\link{par}})
#' @param cex plotting parameters with useful defaults (\code{\link{par}})
#' @param ... further plotting parameters
#' @return a graph containing all tree plots with each tree plot corresponding to the community structure from each of the ensemble matrix.
#' @details
#' \code{plotCLUSTERS} plots all cluster trees with each tree corresponding to each ensemble matrix in the list of ens_list.
#' \code{EnsList} is the output from \code{getEnsList}.
#'
#' \code{mfrow} determines the arrangement of multiple plots. It takes the form of
#' \code{c(nr, nc)} with the first parameter being the number of rows and
#' the second parameter being the number of columns. When deciding parameters for mfrow,
#' one should take into considerations size of the plotting device and number of cluster plots.
#' For example, there are 20 cluster plots, mfrow can be set to \code{c(4, 5)} or \code{c(2, 10)}
#' depending on the size and shape of the plotting area.
#' @seealso \code{\link{getEnsList}}
#' @references
#' Fushing, H., & McAssey, M. P. (2010).
#' Time, temperature, and data cloud geometry.
#' Physical Review E, 82(6), 061110.
#'
#' Chen, C., & Fushing, H. (2012).
#' Multiscale community geometry in a network and its application.
#' Physical Review E, 86(4), 041120.
#'
#' Fushing, H., Wang, H., VanderWaal, K., McCowan, B., & Koehl, P. (2013).
#' Multi-scale clustering by building a robust and self correcting ultrametric topology on data points.
#' PloS one, 8(2), e56259.
#' @examples
#' symmetricMatrix <- as.symmetricAdjacencyMatrix(monkeyGrooming, weighted = TRUE, rule = "weak")
#' Sim <- as.SimilarityMatrix(symmetricMatrix)
#' temperatures <- temperatureSample(start = 0.01, end = 20, n = 20, method = 'random')
#' \dontrun{
#' # for illustration only. skip CRAN check because it ran forever.
#' Ens_list <- getEnsList(Sim, temperatures, MaxIt = 1000, m = 5)
#' }
#' \dontshow{
#' # for CRAN check only
#' Ens_list <- getEnsList(Sim, temperatures, MaxIt = 5, m = 5)
#' }
#' plotCLUSTERS(EnsList = Ens_list, mfrow = c(2, 10), mar = c(1, 1, 1, 1))
#' @export


plotCLUSTERS <- function(EnsList, mfrow,
                         mar = c(1, 1, 1, 1),
                         line = -1.5,
                         cex = 0.5, ...){
  # set arrangement
  op <- par(mfrow = mfrow,
            mar = mar)

  for (i in 1:length(EnsList)){
    plotTree(EnsList[[i]])
    mtext(paste0("Ens", i), 4)
  }

  par(op)

}



#' generate tree plots for selected ensemble matrix
#' \code{plotTrees} plot one cluster tree
#' @param EnsList a list in which elements are ensemble matrices.
#' @param index an integer. index of which ensemble matrix you want to plot.
#' @param ... plotting parameters passed to \code{\link{par}}.
#' @return a tree plot
#' @export

plotTheCluster <- function(EnsList, index, ...){
  plotTree(EnsList[[index]])
}




plotTree <- function(ens, ...){
  tree <- hclust(as.dist(1-ens))
  plot(tree)
}