`cvMclustDA.Rd`

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

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

object | An object of class |
---|---|

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 |

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 |

... | Further arguments passed to or from other methods. |

The function returns a list with the following components:

a factor of cross-validated class labels.

a matrix containing the cross-validated probabilites for class assignement.

the cross-validation classification error if `metric = "error"`

, `NA`

otherwise.

the cross-validation Brier score if `metric = "brier"`

, `NA`

otherwise.

the standard error of the cross-validated statistic.

Luca Scrucca

if (FALSE) { 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")] }