clusterPoisson: Create an instance of the ['ClusterPoisson'] class

In MixAll: Clustering and Classification using Model-Based Mixture Models

Description

This 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].

Usage

clusterPoisson(data, nbCluster = 2, models = clusterPoissonNames(),
  strategy = clusterStrategy(), criterion = "ICL", nbCore = 1)

Arguments

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	[vector] listing the number of clusters to test.
models	[vector] of model names to run. By default all poisson models are estimated. All the model names are given by the method [clusterPoissonNames].
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 processor to use (default is 1, 0 for all).

Value

An instance of the [ClusterPoisson] class.

Author(s)

Serge Iovleff

Examples

## 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)

MixAll documentation built on Sept. 12, 2019, 5:05 p.m.