R/soc_psm.R

In kyoustat/T4cluster: Tools for Cluster Analysis

#' Compute Posterior Similarity Matrix
#' 
#' Let \emph{clustering} be a label from data of \eqn{N} observations and suppose 
#' we are given \eqn{M} such labels. Posterior similarity matrix, as its name suggests, 
#' computes posterior probability for a pair of observations to belong to the same cluster, i.e., 
#' \deqn{P_{ij} = P(\textrm{label}(X_i) = \textrm{label}(X_j))}
#' under the scenario where multiple clusterings are samples drawn from a posterior distribution within 
#' the Bayesian framework. However, it can also be used for non-Bayesian settings as 
#' \code{psm} is a measure of uncertainty embedded in any algorithms with non-deterministic components.
#' 
#' @param partitions partitions can be provided in either (1) an \eqn{(M\times N)} matrix 
#' where each row is a clustering for \eqn{N} objects, or (2) a length-\eqn{M} list of 
#' length-\eqn{N} clustering labels. 
#' 
#' @return an \eqn{(N\times N)} matrix, whose elements \eqn{(i,j)} are posterior probability 
#' for an observation \eqn{i} and \eqn{j} belong to the same cluster.
#' 
#' @examples
#' # -------------------------------------------------------------
#' #               PSM with 'iris' dataset + k-means++
#' # -------------------------------------------------------------
#' ## PREPARE WITH SUBSET OF DATA
#' data(iris)
#' X     = as.matrix(iris[,1:4])
#' lab   = as.integer(as.factor(iris[,5]))
#' 
#' ## EMBEDDING WITH PCA
#' X2d = Rdimtools::do.pca(X, ndim=2)$Y
#' 
#' ## RUN K-MEANS++ 100 TIMES
#' partitions = list()
#' for (i in 1:100){
#'   partitions[[i]] = kmeanspp(X)$cluster
#' }
#' 
#' ## COMPUTE PSM
#' iris.psm = psm(partitions)
#' 
#' ## VISUALIZATION
#' opar <- par(no.readonly=TRUE)
#' par(mfrow=c(1,2), pty="s")
#' plot(X2d, col=lab, pch=19, main="true label")
#' image(iris.psm[,150:1], axes=FALSE, main="PSM")
#' par(opar)
#' 
#' @seealso \code{\link{pcm}}
#' @concept soc
#' @export
psm <- function(partitions){
  clmat   = soc_preproc(partitions, "psm")
  return(src_psm(clmat))
}