mclust (version 2.1-14)

mclustDA: 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))

par(pty = "s")
mclust2Dplot(x, classification = xclass, type="classification", ask=FALSE)

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])
plot(testMclustDA, trainingData = x[odd, ], labels = xclass[odd], 
              testData = x[even,])