summary.mclustDAtrain {mclust} | R Documentation

## Models and classifications from mclustDAtrain

### Description

The models selected in `mclustDAtrain` and the corresponding classfications.

### Usage

    summary.mclustDAtrain(object, ...)

### Arguments

`object` | The output of `mclustDAtrain`.
`...` | Not used. For generic/method consistency.

### Value

A list identifying the model selected by `mclustDAtrain` for each class of training data and the corresponding classification.

### 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

`mclustDAtrain`

### Examples

    set.seed(0)
    n <- 100 ## create artificial data

    x <- rbind(matrix(rnorm(n*2), n, 2) %*% diag(c(1,9)),
               matrix(rnorm(n*2), n, 2) %*% diag(c(1,9))[,2:1])
    xclass <- c(rep(1,n),rep(2,n))
    ## Not run:
    par(pty = "s")
    mclust2Dplot(x, classification = xclass, type="classification", ask=FALSE)
    ## End(Not run)

    odd <- seq(1, 2*n, 2)
    train <- mclustDAtrain(x[odd, ], labels = xclass[odd]) ## training step
    summary(train)

[Package mclust version 2.1-8 Index]