clusterAlgo {MixAll}

Create an instance of the [ClusterAlgo] class

Description

There is three algorithms and two stopping rules possibles for an algorithm.

• Algorithms:

  • EM: The Expectation Maximisation algorithm

  • CEM: The Classification EM algorithm

  • SEM: The Stochastic EM algorithm

  • SemiSEM: The Semi-Stochastic EM algorithm

• Stopping rules:

  • nbIteration: Set the maximum number of iterations

  • epsilon: Set relative increase of the log-likelihood criterion

• Default values are 200 nbIteration of EM with an epsilon value of 1.e-8.

The epsilon value is not used when the algorithm is "SEM" or "SemiSEM".

Usage

clusterAlgo(algo = "EM", nbIteration = 200, epsilon = 1e-07)

Arguments

algo

character string with the estimation algorithm. Possible values are "EM", "SEM", "CEM", "SemiSEM". Default value is "EM".

nbIteration

Integer defining the maximal number of iterations. Default value is 200.

epsilon

Real defining the epsilon value for the algorithm. Not used by the "SEM" and "SemiSEM" algorithms. Default value is 1.e-7.

Value

a [ClusterAlgo] object

Author(s)

Serge Iovleff

Examples

clusterAlgo()
clusterAlgo(algo="SEM", nbIteration=50)
clusterAlgo(algo="CEM", epsilon = 1e-06)