learnMixedData: This function learn the optimal mixture model when the class...
In MixAll: Clustering and Classification using Model-Based Mixture Models

Description Usage Arguments Value Author(s) Examples

This function learn the optimal mixture model when the class labels are known according to the criterion among the list of model given in models.

1 2	learnMixedData(data, models, labels, prop = NULL, algo = "impute", nbIter = 100, epsilon = 1e-08, criterion = "ICL", nbCore = 1)

`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).

An instance of the [ClusterMixedDataModel] class.

Serge Iovleff

## A quantitative example with the heart disease data set
data(HeartDisease.cat)
data(HeartDisease.cont)
## with default values
ldata = list(HeartDisease.cat, HeartDisease.cont);
models = c("categorical_pk_pjk","gaussian_pk_sjk")
model <- clusterMixedData(ldata, models, nbCluster=2:5, strategy = clusterFastStrategy())

## get summary
summary(model)

## get estimated missing values
missingValues(model)

## Not run: 
## print model
print(model)
## use graphics functions
plot(model)

## End(Not run)