mixmodStrategy {Rmixmod} | 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 log-likelihood criterion
Default values are 200 nbIterationInAlgo
of EM
with an epsilonInAlgo
value of 10-3.
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, 561-575.
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))