mclustDA {mclust} | R Documentation

## MclustDA discriminant analysis.

### Description

MclustDA training and testing.

### Usage

    mclustDA(trainingData, labels, testData, G=1:6, verbose = FALSE)

### Arguments

`trainingData` | A numeric vector, matrix, or data frame of training observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.
`labels` | A numeric or character vector assigning a class label to each training observation.
`testData` | A numeric vector, matrix, or data frame of training observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.
`G` | An integer vector specifying the numbers of mixture components (clusters) to be considered for each class. Default: `1:6`.
`verbose` | A logical variable telling whether or not to print an indication that the function is in the training phase, which may take some time to complete.

### Value

A list with the following components:

`testClassification` | `mclustDA` classification of the test data.
`trainingClassification` | `mclustDA` classification of the training data.
`VofIindex` | Meila's Variation of Information index, to compare classification of the training data to the known labels.
`summary` | Gives the best model and number of clusters for each training class.
`models` | The mixture models used to fit the known classes.
`postProb` | A matrix whose [i,k]th entry is the probability that observation i in the test data belongs to the kth class.

### Details

The following models are compared in `Mclust`:
"E" for spherical, equal variance (one-dimensional)
"V" for spherical, variable variance (one-dimensional)
"EII": spherical, equal volume
"VII": spherical, unequal volume
"EEI": diagonal, equal volume, equal shape
"VVI": diagonal, varying volume, varying shape
"EEE": ellipsoidal, equal volume, shape, and orientation
"VVV": ellipsoidal, varying volume, shape, and orientation

`mclustDA` is a simplified function combining `mclustDAtrain` and `mclustDAtest` and their summaries.

### References

C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust.

C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.

M. Meila (2002). Comparing clusterings. Technical Report 418, Department of Statistics, University of Washington. See http://www.stat.washington.edu/www/research/reports.

### See Also

`plot.mclustDA`, `mclustDAtrain`, `mclustDAtest`, `compareClass`, `classError`

### Examples

    n <- 250 ## create artificial data
    set.seed(0)
    x <- rbind(matrix(rnorm(n*2), n, 2) %*% diag(c(1,9)),
               matrix(rnorm(n*2), n, 2) %*% diag(c(1,9))[,2:1])
    xclass <- c(rep(1,n),rep(2,n))

    ## Not run:
    par(pty = "s")
    mclust2Dplot(x, classification = xclass, type="classification", ask=FALSE)
    ## End(Not run)

    odd <- seq(from = 1, to = 2*n, by = 2)
    even <- odd + 1
    testMclustDA <- mclustDA(trainingData = x[odd, ], labels = xclass[odd],
                             testData = x[even,])

    clEven <- testMclustDA$testClassification ## classify training set
    compareClass(clEven,xclass[even])
    ## Not run:
    plot(testMclustDA, trainingData = x[odd, ], labels = xclass[odd],
                  testData = x[even,])
    ## End(Not run)

[Package mclust version 2.1-10 Index]