plot.mclustDA: Plotting method for MclustDA discriminant analysis.

Description

Plots training and test data, known training data classification, mclustDA test data classification, and/or training errors.

Usage

plot.mclustDA(x, trainingData, labels, testData, dimens=c(1,2),
              scale = FALSE, identify=FALSE, ...)

Arguments

The object produced by applying mclustDA with trainingData and classification labels to testData.

trainingData

The numeric vector, matrix, or data frame of training observations used to obtain x.

labels

The 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.

dimens

An integer vector of length two specifying the dimensions for coordinate projections if the data is more than two-dimensional. The default is c(1,2) (the first two dimesions).

scale

A logical variable indicating whether or not the two chosen dimensions should be plotted on the same scale, and thus preserve the shape of the distribution. Default: scale=FALSE

identify

A logical variable indicating whether or not to print a title identifying the plot. Default: identify=FALSE

...

Further arguments to the lower level plotting functions.

Value

Plots selected via a menu including the following options: training and test data, known training data classification, mclustDA test data classification, training errors.

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.

Examples

Run this code

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))
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,])