MclustDA cross-validation
cvMclustDA.RdV-fold cross-validation for classification models based on Gaussian finite mixture modelling.
Usage
cvMclustDA(object, nfold = 10,
prop = object$prop,
verbose = interactive(),
...)Arguments
- object
An object of class
'MclustDA'resulting from a call toMclustDA.- nfold
An integer specifying the number of folds (by defaul 10-fold CV is used).
- prop
A vector of class prior probabilities, which if not provided default to the class proportions in the training data.
- verbose
A logical controlling if a text progress bar is displayed during the cross-validation procedure. By default is
TRUEif the session is interactive, andFALSEotherwise.- ...
Further arguments passed to or from other methods.
Details
The function implements V-fold cross-validation for classification
models fitted by MclustDA.
Classification error and Brier score are the metrics returned, but other
metrics can be computed using the output returned by this function
(see Examples section below).
Value
The function returns a list with the following components:
- classification
a factor of cross-validated class labels.
- z
a matrix containing the cross-validated probabilites for class assignment.
- ce
the cross-validation classification error.
- se.ce
the standard error of the cross-validated classification error.
- brier
the cross-validation Brier score.
- se.brier
the standard error of the cross-validated Brier score.
Examples
# \donttest{
# Iris data
Class <- iris$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)
#> List of 6
#> $ classification: Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ z : num [1:150, 1:3] 1 1 1 1 1 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:3] "setosa" "versicolor" "virginica"
#> $ ce : num 0.02
#> $ se.ce : num 0.0102
#> $ brier : num 0.0187
#> $ se.brier : num 0.00703
cv <- cvMclustDA(irisEDDA, nfold = length(Class)) # LOO-CV
str(cv)
#> List of 6
#> $ classification: Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ z : num [1:150, 1:3] 1 1 1 1 1 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:3] "setosa" "versicolor" "virginica"
#> $ ce : num 0.02
#> $ se.ce : num 0.0115
#> $ brier : num 0.017
#> $ se.brier : num 0.00824
## MclustDA model selected by BIC
irisMclustDA <- MclustDA(X, Class)
cv <- cvMclustDA(irisMclustDA) # 10-fold CV (default)
str(cv)
#> List of 6
#> $ classification: Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ z : num [1:150, 1:3] 1 1 1 1 1 1 1 1 1 1 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:3] "setosa" "versicolor" "virginica"
#> $ ce : num 0.02
#> $ se.ce : num 0.0102
#> $ brier : num 0.0174
#> $ se.brier : num 0.00712
# 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)
#> List of 6
#> $ classification: Factor w/ 2 levels "counterfeit",..: 2 2 2 2 2 2 2 2 2 2 ...
#> $ z : num [1:200, 1:2] 2.45e-06 5.47e-25 4.80e-23 1.20e-24 7.38e-21 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:2] "counterfeit" "genuine"
#> $ ce : num 0.005
#> $ se.ce : num 0.005
#> $ brier : num 0.00499
#> $ se.brier : num 0.00494
(ConfusionMatrix <- table(Pred = cv$classification, Class))
#> Class
#> Pred counterfeit genuine
#> counterfeit 100 1
#> genuine 0 99
TP <- ConfusionMatrix[1,1]
FP <- ConfusionMatrix[1,2]
FN <- ConfusionMatrix[2,1]
TN <- ConfusionMatrix[2,2]
(Sensitivity <- TP/(TP+FN))
#> [1] 1
(Specificity <- TN/(FP+TN))
#> [1] 0.99
# }