R: Classification of multivariate observations by...

predict.MclustSSC {mclust} | R Documentation

## Classification of multivariate observations by semi-supervised Gaussian finite mixtures

### Description

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

### Usage

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

### Arguments

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

### 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

`MclustSSC`.

### Examples

    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(is.na(class), 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)

[Package mclust version 5.4.9 Index]