# MclustDA cross-validation V-fold cross-validation for classification models based on Gaussian finite mixture modelling. ```r cvMclustDA(object, nfold = 10, prop = object$prop, verbose = interactive(), ...) ``` ## Arguments - `object`: An object of class `'MclustDA'` resulting from a call to `MclustDA`. - `nfold`: An integer specifying the number of folds (by defaul 10-fold CV is used). - `prop`: A vector of class prior probabilities, which if not provided default to the class proportions in the training data. - `verbose`: A logical controlling if a text progress bar is displayed during the cross-validation procedure. By default is `TRUE` if the session is interactive, and `FALSE` otherwise. - ``...``: Further arguments passed to or from other methods. ## Details The function implements V-fold cross-validation for classification models fitted by `MclustDA`. Classification error and Brier score are the metrics returned, but other metrics can be computed using the output returned by this function (see Examples section below). ## Returns The function returns a list with the following components: - **classification**: a factor of cross-validated class labels. - **z**: a matrix containing the cross-validated probabilites for class assignment. - **ce**: the cross-validation classification error. - **se.ce**: the standard error of the cross-validated classification error. - **brier**: the cross-validation Brier score. - **se.brier**: the standard error of the cross-validated Brier score. ## Author(s) Luca Scrucca ## See Also `MclustDA`, `predict.MclustDA`, `classError`, `BrierScore` ## Examples ```r # Iris data Class <- iris$Species X <- iris[,1:4] ## EDDA model with common covariance (essentially equivalent to linear discriminant analysis) irisEDDA <- MclustDA(X, Class, modelType = "EDDA", modelNames = "EEE") cv <- cvMclustDA(irisEDDA) # 10-fold CV (default) str(cv) cv <- cvMclustDA(irisEDDA, nfold = length(Class)) # LOO-CV str(cv) ## MclustDA model selected by BIC irisMclustDA <- MclustDA(X, Class) cv <- cvMclustDA(irisMclustDA) # 10-fold CV (default) str(cv) # Banknote data data("banknote") Class <- banknote$Status X <- banknote[,2:7] ## EDDA model selected by BIC banknoteEDDA <- MclustDA(X, Class, modelType = "EDDA") cv <- cvMclustDA(banknoteEDDA) # 10-fold CV (default) str(cv) (ConfusionMatrix <- table(Pred = cv$classification, Class)) TP <- ConfusionMatrix[1,1] FP <- ConfusionMatrix[1,2] FN <- ConfusionMatrix[2,1] TN <- ConfusionMatrix[2,2] (Sensitivity <- TP/(TP+FN)) (Specificity <- TN/(FP+TN)) ```

cvMclustDA function