clusterMixedData {MixAll} | R Documentation |
ClusterMixedDataModel
] classThis 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
].
clusterMixedData(data, models, nbCluster = 2, strategy = clusterStrategy(), criterion = "ICL", nbCore = 1)
data |
[ |
models |
a [ |
nbCluster |
[ |
strategy |
a [ |
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)