| cvMclustDA {mclust} | R Documentation |
K-fold cross-validation for discriminant analysis based on Gaussian finite mixture modeling.
cvMclustDA(object, nfold = 10, verbose = interactive(), ...)
object |
An object of class |
nfold |
An integer specifying the number of folds. |
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:
classification |
a factor of cross-validated class labels. |
error |
the cross-validation error. |
se |
the standard error of cv error. |
C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.
C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.
Luca Scrucca
summary.MclustDA,
plot.MclustDA,
predict.MclustDA,
classError
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")]
# compare with
# cv1EMtrain(X, Class, "EEE")
# general covariance structure selected by BIC
irisMclustDA <- MclustDA(X, Class)
cv <- cvMclustDA(irisMclustDA) # default 10-fold CV
cv[c("error", "se")]