kmmMixedData: Create an instance of the ['KmmMixedDataModel'] class
In MixAll: Clustering and Classification using Model-Based Mixture Models

Description Usage Arguments Details Value Author(s) Examples

This function computes the optimal mixture model for mixed data using kernel mixture models according to the criterion among the number of clusters given in nbCluster using the strategy specified in [strategy].

1 2	kmmMixedData(ldata, lmodels, nbCluster = 2, strategy = clusterStrategy(), criterion = "ICL", nbCore = 1)

`ldata`	[`list`] containing the data sets (matrices and/or data.frames).
`lmodels`	a [`list`] of same length than data. It contains the model names, kernel names and kernel parameter names to use in order to fit each data set.
`nbCluster`	[`vector`] with the number of clusters to test.
`strategy`	a [`ClusterStrategy`] object containing the strategy to run. Default is clusterStrategy().
`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).

For each data set in data, we need to specify a list of parameters

An instance of the [KmmMixedDataModel] class.

Serge Iovleff

## An example with the bullsEye data set
data(bullsEye)
data(bullsEye.cat)
## with default values
ldata     <- list(bullsEye, bullsEye.cat)
modelcont <- list(modelName="kmm_pk_s", dim = 10, kernelName="Gaussian")
modelcat  <- list(modelName="kmm_pk_s", dim = 20, kernelName="Hamming", kernelParameters = c(0.6))
lmodels   <- list( modelcont, modelcat)

model <- kmmMixedData(ldata, lmodels, nbCluster=2:5, strategy = clusterFastStrategy())

## get summary
summary(model)


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

## End(Not run)