predict.MclustDA: Classify multivariate observations by Gaussian finite mixture modeling

Description

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

Usage

# S3 method for MclustDA
predict(object, newdata, prior, …)

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.

prior

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.

Value

Returns a list of with the following components:

classification

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.

References

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.

Examples

Run this code

# 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
# }