R/plotMyMultipartiteMatrix.R

In GrossSBM/sbm: Stochastic Blockmodels

#' Plot the matrices corresponding to a Multipartite Network
#'
#' @param listSBM  : a list of objects representing the multipartite network (see)
#' @param memberships : a list of length equal to the number of Functional Groups providing the clusterings inside each group.
#' @param plotOptions : a list containing the options. See details.
#' @details plotOptions is a list containing the following items
#'  * "normalized": Boolean. TRUE if the various matrices are presented in the same scale (between O and 1). FALSE otherwise. Default value FALSE
#'  * "compact": Boolean. Default value is TRUE if you ask for the matrices to be transposed to have a more compact view
#'  * "legend":  Boolean. Set TRUE if you   want to see the legend. Default value is FALSE
#'  * "legend.title":  Boolean. Set TRUE if you want to print the title of the legend. Default value is FALSE
#'  * "legend.position":  Position of the legend. Possible values are 'bottom', 'top','left,'right'. Default value is 'bottom'
#'  * "nodeNames":  Set true if the node Names must be plotted. Default value is FALSE
#'  * "line.color": The color of the lines to separate groups. Default value is red
#'  * "line.width": Width  of the lines to separate groups. Default value is NULL, automatically chosen
#'  * "title":  Title of the plot. Default value is NULL
#'
#' @return a ggplot object corresponding to the plot
#' @export
#'
#' @examples
#' data("multipartiteEcologicalNetwork")
#' Net <- multipartiteEcologicalNetwork
#' type='bipartite'
#' model = 'bernoulli'
#' directed = FALSE
#' listNet <- list()
#' listNet[[1]] = defineSBM(Net$Inc_plant_ant,
#'                          model,type,directed,
#'                          dimLabels = c(row = "Plants", col = "Ants"))
#' listNet[[2]] = defineSBM(Net$Inc_plant_bird,model,type,directed,
#'                       dimLabels =c(row = "Plants",col = "Birds"))
#' plotMyMultipartiteMatrix(listNet,plotOptions=list(legend = TRUE,title='Ecology'))
#'
#' listNet <- list()
#' listNet[[1]] <- defineSBM(matrix(rbinom(1000,1,0.5),20,50),
#'                    model = 'bernoulli',
#'                    type  ='bipartite', directed = NA,
#'                    dimLabels = c(row="Questions",col="Students"))
#' listNet[[2]] <- defineSBM(matrix(rpois(20*30,8),30,20),
#'                    model = 'poisson',
#'                    type  ='bipartite',directed = NA,
#'                    dimLabels = c(row="Competences",col="Questions"))
#' plotMyMultipartiteMatrix(listNet,plotOptions=list(legend = TRUE,compact = FALSE))
#' plotMyMultipartiteMatrix(listNet,plotOptions=list(legend = TRUE,normalized =  TRUE))
#'
#'
#'

plotMyMultipartiteMatrix = function(listSBM, memberships = NULL, plotOptions = list()){


  #########################

  myMSBMObject <- MultipartiteSBM_fit$new(listSBM)


  E <- myMSBMObject$architecture

  if (!is.null(memberships)) myMSBMObject$probMemberships <- lapply(memberships, as_indicator)
  ordered <- ifelse(is.null(memberships), FALSE, TRUE)
  g <- myMSBMObject$plot(type='data', ordered = ordered, plotOptions)
  g
}