Classify multivariate observations by Gaussian finite mixture modeling
Classify multivariate observations based on Gaussian finite mixture models estimated by MclustDA.
## S3 method for class 'MclustDA' predict(object, newdata, prop = object$prop, ...)
object |
an object of class |
newdata |
a data frame or matrix giving the data. If missing the train data obtained from the call to |
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. |
Returns a list of with the following components:
classification |
a factor of predicted class labels for |
z |
a matrix whose [i,k]th entry is the probability that
observation i in |
Luca Scrucca
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
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.