clusterPoisson {MixAll} | R Documentation |
ClusterPoisson
] classThis function computes the optimal poisson 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
].
clusterPoisson(data, nbCluster = 2, models = clusterPoissonNames(), 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 processor to use (default is 1, 0 for all). |
An instance of the [ClusterPoisson
] class.
Serge Iovleff
## A quantitative example with the DebTrivedi data set. data(DebTrivedi) dt <- DebTrivedi[1:500, c(1, 6,8, 15)] model <- clusterPoisson( data=dt, nbCluster=2 , models=clusterPoissonNames(prop = "equal") , 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)