- data
[list
] containing the data sets (matrices and/or data.frames).
If data sets contain NA values, these missing values will be estimated during
the estimation process.
- models
either a [vector
] of character or a [list
] of
same length than data. If models
is a vector, it contains the model
names to use in order to fit each data set. If models
is a list, it
must be of the form
models = list( modelName, dim, kernelName, modelParameters)
Only modelName is required.
- labels
vector or factors giving the label class.
- prop
[vector
] with the proportions of each class.
If NULL the proportions will be estimated using the labels.
- algo
character defining the algo to used in order to learn the model.
Possible values: "simul" (default), "impute" (faster but can produce biased results).
- nbIter
integer giving the number of iterations to do.
algo is "impute" this is the maximal authorized number of iterations. Default is 100.
- epsilon
real giving the variation of the log-likelihood for stopping the
iterations. Not used if algo is "simul". Default value is 1e-08.
- criterion
character defining the criterion to select the best model.
The best model is the one with the lowest criterion value.
Possible values: "BIC", "AIC", "ICL", "ML". Default is "ICL".
- nbCore
integer defining the number of processors to use (default is 1, 0 for all).