MixAll (version 1.2.0)

clusterMixedData: Create an instance of the [ClusterMixedData] class

Description

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

Usage

clusterMixedData(data, models, nbCluster = 2, strategy = clusterFastStrategy(), criterion = "ICL", nbCore = 1)

Arguments

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.

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". Default is "ICL".

nbCore

integer defining the number of processors to use (default is 1, 0 for all).

Value

An instance of the [ClusterMixedData] class.

Examples

## 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)