R/cstats.table.R

In gabrielburcea/cvindia: Analysis of symptoms

#' cstats.table
#'
#' @param dist - compute the distance between the variables for clustering
#' @param tree - what type of tree to pass (in hierarchical clustering  I have different algoritms - e.g. pass divisive cluster / agglomerative one)
#' @param k - number of clusters 
#'
#' @return
#' @export
#'
#' @examples
cstats.table <- function(dist, tree, k) {
  clust.assess <-
    c(
      "cluster.number",
      "n",
      "within.cluster.ss",
      "average.within",
      "average.between",
      "wb.ratio",
      "dunn2",
      "avg.silwidth"
    )
  
  clust.size <- c("cluster.size")
  
  stats.names <- c()
  
  row.clust <- c()
  
  output.stats <- matrix(ncol = k, nrow = length(clust.assess))
  
  cluster.sizes <- matrix(ncol = k, nrow = k)
  for (i in c(1:k)) {
    row.clust[i] <- paste("Cluster-", i, " size")
  }
  for (i in c(2:k)) {
    stats.names[i] <- paste("Test", i - 1)
    
    for (j in seq_along(clust.assess)) {
      output.stats[j, i] <-
        unlist(cluster.stats(d = dist, clustering = cutree(tree, k = i))[clust.assess])[j]
      
    }
    
    for (d in 1:k) {
      cluster.sizes[d, i] <-
        unlist(cluster.stats(d = dist, clustering = cutree(tree, k = i))[clust.size])[d]
      dim(cluster.sizes[d, i]) <- c(length(cluster.sizes[i]), 1)
      cluster.sizes[d, i]
      
    }
  }
  
  output.stats.df <- data.frame(output.stats)
  cluster.sizes <- data.frame(cluster.sizes)
  cluster.sizes[is.na(cluster.sizes)] <- 0
  rows.all <- c(clust.assess, row.clust)
  # rownames(output.stats.df) <- clust.assess
  output <- rbind(output.stats.df, cluster.sizes)[, -1]
  colnames(output) <- stats.names[2:k]
  rownames(output) <- rows.all
  is.num <- sapply(output, is.numeric)
  output[is.num] <- lapply(output[is.num], round, 2)
  output
}