Classify multivariate observations based on Gaussian finite mixture models estimated by MclustDA.

# S3 method for MclustDA
predict(object, newdata, prop = object$prop, ...)

Arguments

object

an object of class 'MclustDA' resulting from a call to MclustDA.

newdata

a data frame or matrix giving the data. If missing the train data obtained from the call to MclustDA are classified.

prop

the class proportions or prior class probabilities to belong to each class; by default, this is set at the class proportions in the training data.

...

further arguments passed to or from other methods.

Value

Returns a list of with the following components:

classification

a factor of predicted class labels for newdata.

z

a matrix whose [i,k]th entry is the probability that observation i in newdata belongs to the kth class.

Author

Luca Scrucca

See also

Examples

if (FALSE) { odd <- seq(from = 1, to = nrow(iris), by = 2) even <- odd + 1 X.train <- iris[odd,-5] Class.train <- iris[odd,5] X.test <- iris[even,-5] Class.test <- iris[even,5] irisMclustDA <- MclustDA(X.train, Class.train) predTrain <- predict(irisMclustDA) predTrain predTest <- predict(irisMclustDA, X.test) predTest }