mixmodStrategy  R Documentation 
Strategy
] classThis class will contain all the parameters needed by the estimation algorithms.
mixmodStrategy(...)
... 
all arguments are transfered to the Strategy constructor. Valid arguments are:

There are different ways to initialize an algorithm :
Initialization from a random position is a standard way to
initialize an algorithm. This random initial position is obtained by
choosing at random centers in the data set. This simple strategy is
repeated 5
times (the user can choose the number of times) from
different random positions and the position that maximises the
likelihood is selected.
A maximum of 50
iterations of the EM algorithm according to the process : n_i
numbers of
iterations of EM are done (with random initialization) until the smallEM
stop criterion value has been
reached. This action is repeated until the sum of n_i
reaches 50
iterations (or if in one action 50
iterations are reached before the stop criterion value).\
It appears that repeating runs of EM is generally profitable since using a single run
of EM can often lead to suboptimal solutions.
10
repetitions of 50
iterations of the CEM algorithm are done.
One advantage of initializing an algorithm with CEM lies in the fact
that CEM converges generally in a small number of iterations. Thus,
without consuming a large amount of CPU times, several runs of CEM are
performed. Then EM is run with the best solution among the 10
repetitions.
A run of 500
iterations of SEM. The idea is that an SEM sequence is
expected to enter rapidly in the neighbourhood of the global maximum
of the likelihood function.
Defining the algorithms used in the strategy, the stopping rule and when to stop.
Algorithms :
Expectation Maximisation
Classification EM
Stochastic EM
Stopping rules for the algorithm :
Sets the maximum number of iterations
Sets relative increase of the loglikelihood criterion
Default values are 200
nbIterationInAlgo
of EM
with an epsilonInAlgo
value
of 103
.
a [Strategy
] object
Florent Langrognet and Remi Lebret and Christian Poli ans Serge Iovleff, with contributions from C. Biernacki and G. Celeux and G. Govaert contact@mixmod.org
Biernacki, C., Celeux, G., Govaert, G., 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate gaussian mixture models". Computational Statistics and Data Analysis 41, 561575.
mixmodStrategy()
mixmodStrategy(algo = "CEM", initMethod = "random", nbTry = 10, epsilonInInit = 0.00001)
mixmodStrategy(
algo = c("SEM", "EM"), nbIterationInAlgo = c(200, 100),
epsilonInAlgo = c(NA, 0.000001)
)
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