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

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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

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

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