Description Usage Arguments Value Author(s) Examples
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".
1 | clusterAlgo(algo = "EM", nbIteration = 200, epsilon = 1e-07)
|
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. |
a [ClusterAlgo
] object
Serge Iovleff
1 2 3 | clusterAlgo()
clusterAlgo(algo="SEM", nbIteration=50)
clusterAlgo(algo="CEM", epsilon = 1e-06)
|
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