Last chance! 50% off unlimited learning
Sale ends in
Classify multivariate observations based on Gaussian finite mixture models estimated by MclustDA.
# S3 method for MclustDA
predict(object, newdata, prior, …)an object of class 'MclustDA' resulting from a call to MclustDA.
a data frame or matrix giving the data. If missing the train data obtained from the call to MclustDA are classified.
the prior probabilities of the classes; by default, this is set at the proportions in the training data.
further arguments passed to or from other methods.
Returns a list of with the following components:
a factor of predicted class labels for newdata.
a matrix whose [i,k]th entry is the probability that
observation i in newdata belongs to the kth class.
# NOT RUN {
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
# }
Run the code above in your browser using DataLab