R: Classify multivariate observations by Gaussian finite mixture...

predict.MclustDA {mclust} | R Documentation

## 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 class '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(s)

Luca Scrucca

### See Also

`MclustDA`.

### Examples

    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

[Package mclust version 5.4.9 Index]