plot.mclustDA {mclust} | R Documentation |
Plots training and test data, known training data classification, mclustDA test data classification, and/or training errors.
plot.mclustDA(x, trainingData, labels, testData, dimens=c(1,2), scale = FALSE, identify=FALSE, ...)
x |
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. |
Plots selected via a menu including the following options: training and test data, known training data classification, mclustDA test data classification, training errors.
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.
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: 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)