kmmStrategy | R Documentation |
ClusterStrategy
] classA strategy is a multistage empirical process for finding a good estimate in the clustering estimation process.
kmmStrategy(
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
)
nbTry |
Integer defining the 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 [ |
initAlgo |
Character string with the algorithm to use in the initialization stage,
[ |
nbInitIteration |
Integer defining the maximal number of iterations in
initialization algorithm if |
initEpsilon |
Real defining the epsilon value for the initialization algorithm.
Not used if |
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 during
sa hort run if |
shortEpsilon |
Real defining the epsilon value for the algorithm. Not used
if |
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 during
a long run algorithm if |
longEpsilon |
Real defining the epsilon value for the algorithm.
Nor used if |
A strategy is a way to find a good estimate of the parameters of a kernel mixture model when using an EM algorithm or its variants. A “try” of kmmStrategy is composed of three stages
nbShortRun
short iterations of the initialization step and
of the EM
, CEM
or SEM
algorithm.
nbInit
initializations using the [clusterInit
]
method.
A long run of the EM
, CEM
or SEM
algorithm.
For example if nbInit
is 5 and nbShortRun
is also 5, there will
be 5 times 5 models initialized. Five time, the best model (in the likelihood sense)
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 is 25 initializations.
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
kmmStrategy()
kmmStrategy(longRunAlgo= "CEM", nbLongIteration=100)
kmmStrategy(nbTry = 1, nbInit= 1, shortRunAlgo= "EM", nbShortIteration=100)
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