cvMclustDA: MclustDA cross-validation

In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

MclustDA cross-validation

Description

V-fold cross-validation for classification models based on Gaussian finite mixture modelling.

Usage

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).

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 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

# 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))

mclust documentation built on Nov. 16, 2023, 5:10 p.m.