summary.EMclust {mclust} | R Documentation

## Summary function for EMclust

### Description

Optimal model characteristics and classification for `EMclust` results.

### Usage

    summary.EMclust(object, data, G, modelNames, ...)

### Arguments

`object` | An `"EMclust"` object, which is the result of applying `EMclust` to `data`.
`data` | The matrix or vector of observations used to generate `object'.
`G` | A vector of integers giving the numbers of mixture components (clusters) over which the summary is to take place (`as.character(G)` must be a subset of the column names of `object`). The default is to summarize over all of the numbers of mixture components used in the original analysis.
`modelNames` | A vector of character strings denoting the models over which the summary is to take place (must be a subset of the row names of `object'). The default is to summarize over all models used in the original analysis.
`...` | Not used. For generic/method consistency.

### Value

A list giving the optimal (according to BIC) parameters, conditional probabilities `z`, and loglikelihood, together with the associated classification and its uncertainty.

### 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

`EMclust`

### Examples

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

    irisBic <- EMclust(irisMatrix)
    summary(irisBic, irisMatrix)
    summary(irisBic, irisMatrix, G = 1:6, modelName = c("VII", "VVI", "VVV"))

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