mclustDAtrain: MclustDA Training

Description

Training phase for MclustDA discriminant analysis.

Usage

mclustDAtrain(data, labels, G, emModelNames, eps, tol, itmax, 
              equalPro, warnSingular, verbose)

Arguments

data

A numeric vector, matrix, or data frame of 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 observation.

An integer vector specifying the numbers of Gaussian mixture components (clusters) for which the BIC is to be calculated (the same specification is used for all classes). Default: 1:9.

emModelNames

A vector of character strings indicating the models to be fitted in the EM phase of clustering. Possible models: "E" for spherical, equal variance (one-dimensional) "V" for spherical, variable variance (one-dimensional) "EII": spherical, equal volum

eps

A scalar tolerance for deciding when to terminate computations due to computational singularity in covariances. Smaller values of eps allow computations to proceed nearer to singularity. The default is .Mclust$eps.

tol

A scalar tolerance for relative convergence of the loglikelihood. The default is .Mclust$tol.

itmax

An integer limit on the number of EM iterations. The default is .Mclust$itmax.

equalPro

Logical variable indicating whether or not the mixing proportions are equal in the model. The default is .Mclust$equalPro.

warnSingular

A logical value indicating whether or not a warning should be issued whenever a singularity is encountered. The default is warnSingular=FALSE.

verbose

A logical value indicating whether or not to print the models and numbers of components for each class. Default:verbose=TRUE.

Value

A list in which each element gives the optimal parameters for the model best fitting each class according to BIC.

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)
par(pty = "s")
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(1, 2*n, 2)
train <- mclustDAtrain(x[odd, ], labels = xclass[odd]) ## training step
summary(train)

even <- odd + 1
test <- mclustDAtest(x[even, ], train) ## compute model densities
clEven <- summary(test)$class ## classify training set
compareClass(clEven,xclass[even])

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