clusterStrategy: A strategy is a multistage empirical process for finding a...
In MixAll: Clustering and Classification using Model-Based Mixture Models

Description Usage Arguments Details Value Author(s) Examples

A strategy is a way to find a good estimate of the parameters of a mixture model when using an EM algorithm or its variants. A “try” is composed of three stages

nbShortRun short iterations of the initialization step and of the EM, CEM, SEM or SemiSEM algorithm.
nbInit initializations using the [clusterInit] method.
A long run of the EM, CEM, SEM or SemiSEM algorithm.

For example if nbInit is 5 and nbShortRun is also 5, there will be 5 packets of 5 models initialized. In each packet, the best model will be ameliorated using a short run. Among the 5 models ameliorated one will be estimated until convergence using a long run. In total there were 25 initializations.

clusterSemiSEMStrategy() create an instance of [ClusterStrategy] for users with many missing values uning a semiSem algorithm.

clusterSEMStrategy() create an instance of [ClusterStrategy] for users with many missing values using a SEM algorithm.

clusterFastStrategy() create an instance of [ClusterStrategy] for impatient user.

clusterStrategy(nbTry = 1, nbInit = 5, initMethod = "class",
  initAlgo = "EM", nbInitIteration = 20, initEpsilon = 0.01,
  nbShortRun = 5, shortRunAlgo = "EM", nbShortIteration = 100,
  shortEpsilon = 1e-04, longRunAlgo = "EM", nbLongIteration = 1000,
  longEpsilon = 1e-07)

clusterSemiSEMStrategy()

clusterSEMStrategy()

clusterFastStrategy()

`nbTry`	number of estimation to attempt.
`nbInit`	Integer defining the number of initialization to try. Default value: 3.
`initMethod`	Character string with the initialization method, see [`clusterInit`]$ for possible values. Default is "class".
`initAlgo`	Character string with the algorithm to use in the initialization stage, [`clusterAlgo`] for possible values. Default value: "EM".
`nbInitIteration`	Integer defining the maximal number of iterations in initialization algorithm if `initAlgo` = "EM", "CEM" or "SemiSEM". This is the number of iterations if `initAlgo` = "SEM". Default value: 20.
`initEpsilon`	Real defining the epsilon value for the algorithm. `initEpsilon` is not used by the `SEM` algorithm. Default value: 0.01.
`nbShortRun`	Integer defining the number of short run to try (the strategy launch an initialization before each short run). Default value: 5.
`shortRunAlgo`	A character string with the algorithm to use in the short run stage Default value: "EM".
`nbShortIteration`	Integer defining the maximal number of iterations in the short runs if `shortRunAlgo` = "EM", "CEM" or "semiSEM", or the number of iterations if `shortRunAlgo` = "SEM". Default value: 100.
`shortEpsilon`	Real defining the epsilon value for the algorithm. epsilon is not used by the `SEM` algorithm. Default value: 1e-04.
`longRunAlgo`	A character string with the algorithm to use in the long run stage Default value: "EM".
`nbLongIteration`	Integer defining the maximal number of iterations in the short runs if `shortRunAlgo` = "EM", "CEM" or "SemiSEM", or the number of iterations if `shortRunAlgo` = "SEM". Default value: 1000.
`longEpsilon`	Real defining the epsilon value for the algorithm. epsilon is not used by the `SEM` algorithm. Default value: 1e-07.

The whole process can be repeated at least nbTry times. If a try success, the estimated model is returned, otherwise an empty model is returned.

a [ClusterStrategy] object

Serge Iovleff

   clusterStrategy()
   clusterStrategy(longRunAlgo= "CEM", nbLongIteration=100)
   clusterStrategy(nbTry = 1, nbInit= 1, shortRunAlgo= "SEM", nbShortIteration=100)

   clusterSemiSEMStrategy()

   clusterSEMStrategy()

   clusterFastStrategy()