View source: R/ClusterPoisson.R
clusterPoisson | 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 |
a data.frame or 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 |
[ |
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
plot(model)
## get summary
summary(model)
## print model (a very detailed output)
print(model)
## get estimated missing values
missingValues(model)
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