R: Information Criteria for Model-Based Clustering

Information Criteria {EMCluster} | R Documentation

## Information Criteria for Model-Based Clustering

### Description

These functions are tools for compute information criteria for the fitted models.

### Usage

    em.ic(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL,
          llhdval = NULL)
    em.aic(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL)
    em.bic(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL)
    em.clc(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL)
    em.icl(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL)
    em.icl.bic(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL)

### Arguments

`x` | the data matrix, dimension n * p.
`emobj` | the desired model which is a list mainly contains `pi`, `Mu`, and `LTSigma`, usually a returned object from `init.EM`.
`pi` | the mixing proportion, length K.
`Mu` | the centers of clusters, dimension K * p.
`LTSigma` | the lower triangular matrices of dispersion, K * p(p+1)/2.
`llhdval` | the total log likelihood value of `x` given `emobj`.

### Details

The `em.ic` calls all other functions to compute AIC (`em.aic`), BIC (`em.bic`), CLC (`em.clc`), ICL (`em.icl`), and ICL.BIC (`em.icl.bic`). All are useful information criteria for model selections, mainly choosing number of cluster.

### Value

`em.ic` returns a list containing all other information criteria for given the data `x` and the desired model `emobj`.

### Author(s)

Wei-Chen Chen wccsnow@gmail.com and Ranjan Maitra

### References

https://www.stat.iastate.edu/people/ranjan-maitra

### See Also

`init.EM`.

### Examples

    library(EMCluster, quietly = TRUE)
    x2 <- da2$da

    emobj <- list(pi = da2$pi, Mu = da2$Mu, LTSigma = da2$LTSigma)
    em.ic(x2, emobj = emobj)

[Package EMCluster version 0.2-13 Index]