EMclust {mclust} | R Documentation

## BIC for Model-Based Clustering

### Description

BIC for EM initialized by hierarchical clustering for parameterized Gaussian mixture models.

### Usage

    EMclust(data, G, emModelNames, hcPairs, subset, eps, tol, itmax, equalPro,
            warnSingular, ...)

### 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 BIC is to be calculated. The default is `1:9`.
`emModelNames` | A vector of character strings indicating the models to be fitted in the EM phase of clustering. Possible models: "E" for spherical, equal variance (one-dimensional) "V" for spherical, variable variance (one-dimensional) "EII": spherical, equal volume "VII": spherical, unequal volume "EEI": diagonal, equal volume, equal shape "VEI": diagonal, varying volume, equal shape "EVI": diagonal, equal volume, varying shape "VVI": diagonal, varying volume, varying shape "EEE": ellipsoidal, equal volume, shape, and orientation "EEV": ellipsoidal, equal volume and equal shape "VEV": ellipsoidal, equal shape "VVV": ellipsoidal, varying volume, shape, and orientation The default is `.Mclust\$emModelNames`.
`hcPairs` | A matrix of merge pairs for hierarchical clustering such as produced by function `hc`. The default is to compute a hierarchical clustering tree by applying function `hc` with `modelName = .Mclust\$hcModelName[1]` to univariate data and `modelName = .Mclust\$hcModelName[2]` to multivariate data or a subset as indicated by the `subset` argument. The hierarchical clustering results are used as starting values for EM.
`subset` | A logical or numeric vector specifying the indices of a subset of the data to be used in the initial hierarchical clustering phase.
`eps` | A scalar tolerance for deciding when to terminate computations due to computational singularity in covariances. Smaller values of `eps` allow computations to proceed nearer to singularity. The default is `.Mclust\$eps`.
`tol` | A scalar tolerance for relative convergence of the loglikelihood. The default is `.Mclust\$tol`.
`itmax` | An integer limit on the number of EM iterations. The default is `.Mclust\$itmax`.
`equalPro` | Logical variable indicating whether or not the mixing proportions are equal in the model. The default is `.Mclust\$equalPro`.
`warnSingular` | A logical value indicating whether or not a warning should be issued whenever a singularity is encountered. The default is `warnSingular=FALSE`.
`...` | Provided to allow lists with elements other than the arguments can be passed in indirect or list calls with `do.call`.

### Value

Bayesian Information Criterion for the specified mixture models numbers of clusters. Auxiliary information returned as attributes.

### References

C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611:631. See http://www.stat.washington.edu/mclust.

C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.

### See Also

`summary.EMclust`, `EMclustN`, `hc`, `me`, `mclustOptions`

### Examples

    data(iris)
    irisMatrix <- as.matrix(iris[,1:4])

    irisBic <- EMclust(irisMatrix)
    irisBic
    plot(irisBic)

    irisBic <- EMclust(irisMatrix, subset = sample(1:nrow(irisMatrix), 100))
    irisBic
    plot(irisBic)

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