R: Plotting method for MclustSSC semi-supervised classification

plot.MclustSSC {mclust} | R Documentation

## Plotting method for MclustSSC semi-supervised classification

### Description

Plots for semi-supervised classification based on Gaussian finite mixture models.

### Usage

    ## S3 method for class 'MclustSSC'
    plot(x, what = c("BIC", "classification", "uncertainty"), ...)

### Arguments

`x` | An object of class `'MclustSSC'` resulting from a call to `MclustSSC`.
`what` | A string specifying the type of graph requested. Available choices are: `"BIC"` = plot of BIC values used for model selection, i.e. for choosing the model class covariances. `"classification"` = a plot of data with points marked based on the known and the predicted classification. `"uncertainty"` = a plot of classification uncertainty. If not specified, in interactive sessions a menu of choices is proposed.
`...` | further arguments passed to or from other methods. See `plot.Mclust`.

### 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)
    summary(mod_SSC, parameters = TRUE)

    pred_SSC <- predict(mod_SSC)
    table(Predicted = pred_SSC$classification, Actual = class, useNA = "ifany")

    plot(mod_SSC, what = "BIC")
    plot(mod_SSC, what = "classification")
    plot(mod_SSC, what = "uncertainty")

[Package mclust version 5.4.9 Index]