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

Description Usage Arguments Value Note Author(s) Examples

This function computes the optimal kernel mixture model (KMM) according to the [criterion] among the number of clusters given in [nbCluster], using the strategy specified in [strategy].

kmm(data, nbCluster = 2, dim = 10, models = "kmm_pk_s",
  kernelName = "Gaussian", kernelParameters = c(1),
  kernelComputation = TRUE, strategy = kmmStrategy(),
  criterion = "ICL", nbCore = 1)

`data`	frame or matrix containing the data. Rows correspond to observations and columns correspond to variables.
`nbCluster`	[`vector`] listing the number of clusters to test.
`dim`	integer giving the dimension of the Gaussian density. Default is 10.
`models`	[`vector`] of model names to run. By default only "kmm_pk_s" is estimated. All the model names are given by the method [`kmmNames`].
`kernelName`	string with a kernel name. Possible values: "Gaussian", "polynomial", "Laplace", "linear", "rationalQuadratic_", "Hamming". Default is "Gaussian".
`kernelParameters`	[`vector`] with the parameters of the chosen kernel. Default is c(1).
`kernelComputation`	[`logical`] parameter. Should be `TRUE` if the Gram matrix is to be computed (faster but can be memory consuming), `FALSE` otherwise (times consuming). Default is `TRUE`. Recall that Gram matrix is a square matrix of size nbSample.
`strategy`	a [`ClusterStrategy`] object containing the strategy to run. [`kmmStrategy`]() 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).

An instance of the [KmmModel] class.

in KmmModel instance returned, the gram matrix is computed if and only if kernelComputation is TRUE.

Serge Iovleff

## A quantitative example with the famous bulls eye model
data(bullsEye)
## estimate model
model <- kmm( data=bullsEye, nbCluster=2:3, models= "kmm_pk_s")


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

## End(Not run)