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

Description Usage Arguments Value Author(s) Examples

This function computes the optimal Categorical mixture model according to the criterion among the list of model given in models and the number of clusters given in nbCluster, using the strategy specified in strategy.

1
2
3

clusterCategorical(data, nbCluster = 2,
  models = clusterCategoricalNames(probabilities = "free"),
  strategy = clusterStrategy(), criterion = "ICL", nbCore = 1)

`data`	a data.frame or a matrix containing the data. Rows correspond to observations and columns correspond to variables. data will be coerced as an integer matrix. If data set contains NA values, they will be estimated during the estimation process.
`nbCluster`	[`vector`] listing the number of clusters to test.
`models`	[`vector`] of model names to run. By default the categorical models "categorical_pk_pjk" and "categorical_p_pjk" are estimated.
`strategy`	a [`ClusterStrategy`] object containing the strategy to run. [`clusterStrategy`]() method by default.
`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 [ClusterCategorical] class.

Serge Iovleff

## A quantitative example with the birds data set
data(birds)
## add 10 missing values
x = as.matrix(birds); n <- nrow(x); p <- ncol(x)
indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2)
x[indexes] <- NA
## estimate model (using fast strategy, results may be misleading)
model <- clusterCategorical( data=x, nbCluster=2:3
                           , models=c( "categorical_pk_pjk", "categorical_p_pjk")
                           , strategy = clusterFastStrategy()
                           )

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

## End(Not run)

## get summary
summary(model)
## print model
## Not run: 
print(model)

## End(Not run)
## get estimated missing values
missingValues(model)