clusterRandomMatrices: Estimate within cluster sum of squares

In jsemple19/EMclassifieR: Classify DSMF data using the Expectation Maximisation algorithm

Estimate within cluster sum of squares

Description

Get estimated total within cluster sum of squares by clustering random matrices

Usage

clusterRandomMatrices(
  dataMatrix,
  k_range = 2:8,
  maxB = 100,
  convergenceError = 1e-06,
  maxIterations = 100,
  nThreads = 1,
  setSeed = F,
  distMetric = list(name = "euclidean", rescale = F)
)

Arguments

dataMatrix	Matrix to be randomised and clustered
k_range	A vector indicating different numbers of classes to learn
maxB	The maximum number of randomisations to perform
convergenceError	An float indicating the convergence threshold for stopping iteration
maxIterations	An integer indicating the max number of iterations to perform even if the algorithm has not converged
nThreads	Number of threads to use for generating background distribution (default is 1)
setSeed	Logical value to determine if seed should be set for randomisation (default is FALSE)
distMetric	A list with the name of the distance metric and any parameters it might require

Value

A data frame with the average of the total within class sum of squares for multiple randomised matrices and different numbers of classes

jsemple19/EMclassifieR documentation built on Aug. 12, 2022, 2:57 p.m.