MixAll (version 1.5.10)

IClusterModel: Interface base Class [IClusterModel] for Cluster models.

Description

This class encapsulate the common parameters of all the Cluster models.

Slots

nbSample

Integer with the number of samples of the model.

nbCluster

Integer with the number of cluster of the model.

pk

Vector of size K with the proportions of each mixture.

tik

Matrix of size \(n \times K\) with the posterior probability of the ith individual to belong to kth cluster.

lnFi

Vector of size n with the log-likelihood of the ith individuals.

zi

Vector of integer of size n with the attributed class label of the individuals.

ziFit

Vector of integer of size n with the fitted class label of the individuals (only used in supervised learning).

lnLikelihood

Real given the ln-liklihood of the Cluster model.

criterion

Real given the value of the AIC, BIC, ICL or ML criterion.

criterionName

string with the name of the criterion. Possible values are "BIC", "AIC", "ICL" or "ML". Default is "ICL".

nbFreeParameter

Integer given the number of free parameters of the model.

strategy

the instance of the [ClusterStrategy] used in the estimation process of the mixture. Default is clusterStrategy().

Author

Serge Iovleff

Details

A Cluster model is a model of the form $$ f({x}|\boldsymbol{\theta}) \sum_{k=1}^K p_k h({x};\boldsymbol{\lambda}_k,\boldsymbol{\alpha}) \quad {x} \in J. $$ where h can be either a pdf, a discrete probability, (homogeneous case) or a product of arbitrary pdf and discrete probabilities (mixed data case).

Examples

  getSlots("IClusterModel")