R: MclustDA cross-validation

cvMclustDA {mclust} | R Documentation

## MclustDA cross-validation

### Description

K-fold cross-validation for discriminant analysis based on Gaussian finite mixture modeling.

### Usage

    cvMclustDA(object, nfold = 10,
               metric = c("error", "brier"),
               prop = object$prop,
               verbose = interactive(), ...)

### Arguments

`object` | An object of class `'MclustDA'` resulting from a call to `MclustDA`.
`nfold` | An integer specifying the number of folds.
`metric` | A character string specifying the statistic to be used in the cross-validation resampling process. Possible values are `"error"` for the classification error, and `"brier"` for the Brier score.
`prop` | A vector of class prior probabilities, which if not provided default to the class proportions in the training data.
`verbose` | A logical controlling if a text progress bar is displayed during the cross-validation procedure. By default is `TRUE` if the session is interactive, and `FALSE` otherwise.
`...` | Further arguments passed to or from other methods.

### Value

The function returns a list with the following components:

`classification` | a factor of cross-validated class labels.
`z` | a matrix containing the cross-validated probabilites for class assignement.
`error` | the cross-validation classification error if `metric = "error"`, `NA` otherwise.
`brier` | the cross-validation Brier score if `metric = "brier"`, `NA` otherwise.
`se` | the standard error of the cross-validated statistic.

### Author(s)

Luca Scrucca

### See Also

`summary.MclustDA`, `plot.MclustDA`, `predict.MclustDA`, `classError`, `BrierScore`

### Examples

    ## Not run:
    X <- iris[,-5]
    Class <- iris[,5]

    # common EEE covariance structure (which is essentially equivalent to linear discriminant analysis)
    irisMclustDA <- MclustDA(X, Class, modelType = "EDDA", modelNames = "EEE")
    cv <- cvMclustDA(irisMclustDA)  # default 10-fold CV
    cv[c("error", "se")]

    cv <- cvMclustDA(irisMclustDA, nfold = length(Class))  # LOO-CV
    cv[c("error", "se")]

    cv <- cvMclustDA(irisMclustDA, metric = "brier")  # 10-fold CV with Brier score metric
    cv[c("brier", "se")]

    # general covariance structure selected by BIC
    irisMclustDA <- MclustDA(X, Class)
    cv <- cvMclustDA(irisMclustDA)  # default 10-fold CV
    cv[c("error", "se")]
    cv <- cvMclustDA(irisMclustDA, metric = "brier")  # 10-fold CV with Brier score metric
    cv[c("brier", "se")]

    ## End(Not run)

[Package mclust version 5.4.5 Index]