mclustDAtrain {mclust} | R Documentation

## 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.
`G` | 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 volume "VII": spherical, unequal volume "EEI": diagonal, equal volume, equal shape "VEI": diagonal, varying volume, equal shape "EVI": diagonal, equal volume, varying shape "VVI": diagonal, varying volume, varying shape "EEE": ellipsoidal, equal volume, shape, and orientation "EEV": ellipsoidal, equal volume and equal shape "VEV": ellipsoidal, equal shape "VVV": ellipsoidal, varying volume, shape, and orientation The default is `.Mclust\$emModelNames`.
`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.

### See Also

`summary.mclustDAtrain`, `mclustDAtest`, `EMclust`, `hc`, `mclustOptions`

### Examples

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

    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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