R/quality_CH.R

In kyoustat/T4cluster: Tools for Cluster Analysis

#' (+) CH index
#' 
#' Calinski and Harabasz index. Higher score means a good clustering.
#' 
#' @param data an \eqn{(n\times p)} matrix of row-stacked observations.
#' @param cluster a length-\eqn{n} vector of class labels (from \eqn{1:k}).
#' 
#' @return an index value.
#' 
#' @examples 
#' \donttest{
#' # -------------------------------------------------------------
#' #        clustering validity check with 3 Gaussians
#' # -------------------------------------------------------------
#' ## PREPARE
#' X1 = matrix(rnorm(30*3, mean=-5), ncol=3)
#' X2 = matrix(rnorm(30*3), ncol=3)
#' X3 = matrix(rnorm(30*3, mean=5), ncol=3)
#' XX = rbind(X1, X2, X3)
#' 
#' ## CLUSTERING WITH DIFFERENT K VALUES & COMPUTE QUALITY INDICES
#' vec_k  = 2:10
#' vec_cl = rep(0, 9)
#' for (i in 1:length(vec_k)){
#'   cl_now    = T4cluster::kmeans(XX, k=vec_k[i])$cluster
#'   vec_cl[i] = quality.CH(XX, cl_now)
#' }
#' 
#' 
#' ## VISUALIZE
#' opar <- par(no.readonly=TRUE)
#' plot(vec_k, vec_cl, type="b", lty=2, xlab="number of clusteres",
#'      ylab="score", main="CH index")
#' abline(v=3, lwd=2, col="red")
#' par(opar)
#' }
#' 
#' @concept quality
#' @export
quality.CH <- function(data, cluster){
  ## PREPARE : EXPLICIT INPUTS
  mydata = prec_input_matrix(data)
  mylab  = as.numeric(as.factor(cluster))-1
  
  ## COMPUTE & RETURN
  output = as.double(index_CH(data, mylab))
  return(output)
}