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

# S3 method for MclustSSC
predict(object, newdata, ...)



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


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


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.


Luca Scrucca

See also


if (FALSE) { X <- iris[,1:4] class <- iris$Species # randomly remove class labels set.seed(123) class[sample(1:length(class), size = 120)] <- NA table(class, useNA = "ifany") clPairs(X, ifelse(, 0, class), symbols = c(0, 16, 17, 18), colors = c("grey", 4, 2, 3), main = "Partially classified data") # Fit semi-supervised classification model mod_SSC <- MclustSSC(X, class) pred_SSC <- predict(mod_SSC) table(Predicted = pred_SSC$classification, Actual = class, useNA = "ifany") X_new = data.frame(Sepal.Length = c(5, 8), Sepal.Width = c(3.1, 4), Petal.Length = c(2, 5), Petal.Width = c(0.5, 2)) predict(mod_SSC, newdata = X_new) }