clusterCategorical {MixAll} | R Documentation |
ClusterCategorical
] classThis 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
.
clusterCategorical(data, nbCluster = 2, models = clusterCategoricalNames(probabilities = "free"), strategy = clusterStrategy(), criterion = "ICL", nbCore = 1)
data |
frame or matrix containing the data. Rows correspond to observations and columns correspond to variables. If the data set contains NA values, they will be estimated during the estimation process. |
nbCluster |
[ |
models |
[ |
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 [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)