R: ICL Criterion for Model-Based Clustering

mclustICL {mclust} | R Documentation

## ICL Criterion for Model-Based Clustering

### Description

ICL (Integrated Complete-data Likelihood) for parameterized Gaussian mixture models fitted by EM algorithm initialized by model-based hierarchical clustering.

### Usage

    mclustICL(data, G = NULL, modelNames = NULL,
              initialization = list(hcPairs = NULL,
                                    subset = NULL,
                                    noise = NULL),
              x = NULL, ...)

    ## S3 method for class 'mclustICL'
    summary(object, G, modelNames, ...)

### Arguments

`data` | A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.
`G` | An integer vector specifying the numbers of mixture components (clusters) for which the criteria should be calculated. The default is `G = 1:9`.
`modelNames` | A vector of character strings indicating the models to be fitted in the EM phase of clustering. The help file for `mclustModelNames` describes the available models. The default is: `c("E", "V")` for univariate data `mclust.options("emModelNames")` for multivariate data (n > d) `c("EII", "VII", "EEI", "EVI", "VEI", "VVI")` the spherical and diagonal models for multivariate data (n <= d)
`initialization` | A list containing zero or more of the following components: `hcPairs` A matrix of merge pairs for hierarchical clustering such as produced by function `hc`. For multivariate data, the default is to compute a hierarchical clustering tree by applying function `hc` with `modelName = "VVV"` to the data or a subset as indicated by the `subset` argument. The hierarchical clustering results are to start EM. For univariate data, the default is to use quantiles to start EM. `subset` A logical or numeric vector specifying a subset of the data to be used in the initial hierarchical clustering phase.
`x` | An object of class `'mclustICL'`. If supplied, `mclustICL` will use the settings in `x` to produce another object of class `'mclustICL'`, but with `G` and `modelNames` as specified in the arguments. Models that have already been computed in `x` are not recomputed. All arguments to `mclustICL` except `data`, `G` and `modelName` are ignored and their values are set as specified in the attributes of `x`. Defaults for `G` and `modelNames` are taken from `x`.
`...` | Futher arguments used in the call to `Mclust`. See also `mclustBIC`.
`object` | An integer vector specifying the numbers of mixture components (clusters) for which the criteria should be calculated. The default is `G = 1:9`.

### Value

Returns an object of class `'mclustICL'` containing the the ICL criterion for the specified mixture models and numbers of clusters.

The corresponding `print` method shows the matrix of values and the top models according to the ICL criterion. The `summary` method shows only the top models.

### References

Biernacki, C., Celeux, G., Govaert, G. (2000). Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans. Pattern Analysis and Machine Intelligence, 22 (7), 719-725.

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

### See Also

`plot.mclustICL`, `Mclust`, `mclustBIC`, `mclustBootstrapLRT`, `bic`, `icl`

### Examples

    data(faithful)
    faithful.ICL <- mclustICL(faithful)
    faithful.ICL
    summary(faithful.ICL)
    plot(faithful.ICL)
    ## Not run:
    # compare with
    faithful.BIC = mclustBIC(faithful)
    faithful.BIC
    plot(faithful.BIC)

    ## End(Not run)

[Package mclust version 5.3 Index]