summary.EMclustN {mclust} | R Documentation

## summary function for EMclustN

### Description

Optimal model characteristics and classification for `EMclustN` results.

### Usage

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

### Arguments

`object` | An `"EMclustN"` object, whch is the result of a pplying `EMclustN` to `data` with an initail noise estimate.
`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

`EMclustN`

### Examples

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

    b <- apply( irisMatrix, 2, range)
    n <- 450
    set.seed(0)
    poissonNoise <- apply(b, 2, function(x, n=n)
                          runif(n, min = x[1]-0.1, max = x[2]+.1), n = n)
    set.seed(0)
    noiseInit <- sample(c(TRUE,FALSE),size=150+450,replace=TRUE,prob=c(3,1))
    irisNoise <- rbind(irisMatrix, poissonNoise)

    Bic <- EMclustN(data=irisNoise, noise = noiseInit)
    summary(Bic, irisNoise)
    summary(Bic, irisNoise, G = 0:6, modelName = c("VII", "VVI", "VVV"))

[Package mclust version 2.1-8 Index]