R: Classify multivariate observations on a dimension reduced...

predict.MclustDR {mclust} | R Documentation

## Classify multivariate observations on a dimension reduced subspace by Gaussian finite mixture modeling

### Description

Classify multivariate observations on a dimension reduced subspace estimated from a Gaussian finite mixture model.

### Usage

      ## S3 method for class 'MclustDR'
    predict(object, dim = 1:object$numdir, newdata, eval.points, ...)

### Arguments

`object` | an object of class `'MclustDR'` resulting from a call to `MclustDR.`
`dim` | the dimensions of the reduced subspace used for prediction.
`newdata` | a data frame or matrix giving the data. If missing the data obtained from the call to `MclustDR` are used.
`eval.points` | a data frame or matrix giving the data projected on the reduced subspace. If provided `newdata` is not used.
`...` | further arguments passed to or from other methods.

### Value

Returns a list of with the following components:

`dir` | a matrix containing the data projected onto the `dim` dimensions of the reduced subspace.
`density` | densities from mixture model for each data point.
`z` | a matrix whose [i,k]th entry is the probability that observation i in `newdata` belongs to the kth class.
`uncertainty` | The uncertainty associated with the classification.
`classification` | A vector of values giving the MAP classification.

### Author(s)

Luca Scrucca

### References

Scrucca, L. (2010) Dimension reduction for model-based clustering. Statistics and Computing, 20(4), pp. 471-484.

### See Also

`MclustDR`.

### Examples

    mod = Mclust(iris[,1:4])
    dr = MclustDR(mod)
    pred = predict(dr)
    str(pred)

    data(banknote)
    mod = MclustDA(banknote[,2:7], banknote$Status)
    dr = MclustDR(mod)
    pred = predict(dr)
    str(pred)

[Package mclust version 5.4.8 Index]