Rmixmod (version 2.1.1)

mixmodStrategy: Create an instance of ['>Strategy] class

Description

This class will contain all the parameters needed by the estimation algorithms.

Usage

# mixmodStrategy(algo = "EM", nbTry = 1,
#                        initMethod = "smallEM", nbTryInInit = 50,
#                        nbIterationInInit = 5, nbIterationInAlgo = 200,
#                        epsilonInInit = 0.001, epsilonInAlgo = 0.001,
#                        seed = NULL, parameter=NA, labels=NA)
                        
# NB: as an implementation detail, this function is variadic:
 mixmodStrategy(…)

Arguments

…

arguments passed to or from methods.

Value

a ['>Strategy] object

Details

There are different ways to initialize an algorithm :

random

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.

smallEM

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.

CEM

\(10\) 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.

SEMMax

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 :

  EM

  Expectation Maximisation

  CEM

  Classification EM

  SEM

  Stochastic EM

• Stopping rules for the algorithm :

  nbIterationInAlgo

  Sets the maximum number of iterations

  epsilonInAlgo

  Sets relative increase of the log-likelihood criterion

• Default values are \(200\) nbIterationInAlgo of EM with an epsilonInAlgo value of \(10-3\).

References

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.

Examples

# NOT RUN {
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))
# }