Defaults.Mclust {mclust} | R Documentation

## List of values controlling defaults for some MCLUST functions.

### Description

A named list of values including tolerances for singularity and convergence assessment, and an enumeration of models used as defaults in MCLUST functions.

### Details

A function `mclustOptions` is supplied for assigning values to the `.Mclust` list.

### Value

A list with the following components:

`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 the relative machine precision `.Machine\$double.eps`, which is approximately $2e-16$ on IEEE-compliant machines.
`tol` | A vector of length two giving relative convergence tolerances for the loglikelihood and for parameter convergence in the inner loop for models with iterative M-step ("VEI", "VEE", "VVE", "VEV"), respectively. The default is `c(1.e-5,1.e-5)`.
`itmax` | A vector of length two giving integer limits on the number of EM iterations and on the number of iterations in the inner loop for models with iterative M-step ("VEI", "VEE", "VVE", "VEV"), respectively. The default is `c(Inf,Inf)` allowing termination to be completely governed by `tol`.
`equalPro` | Logical variable indicating whether or not the mixing proportions are equal in the model. Default: `equalPro = FALSE`.
`warnSingular` | A logical value indicating whether or not a warning should be issued whenever a singularity is encountered. Default: `warnSingular = TRUE`.
`emModelNames` | A vector of character strings indicating the models to be used for multivariate data in the functions such as `EMclust` and `mclustDAtrain` that involve multiple models. The default is all of the multivariate models available in MCLUST: "EII": spherical, equal volume "VII": spherical, unequal volume "EEI": diagonal, equal volume and shape "VEI": diagonal, varying volume, equal shape "EVI": diagonal, equal volume, varying shape "VVI": diagonal, varying volume and 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
`hcModelName` | A vector of two character strings giving the name of the model to be used in the hierarchical clustering phase for univariate and multivariate data, respectively, in `EMclust` and `EMclustN`. The default is `c("V","VVV")`, giving the unconstrained model in each case.
`symbols` | A vector whose entries are either integers corresponding to graphics symbols or single characters for plotting for classifications. Classes are assigned symbols in the given order.

### References

C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association. See http://www.stat.washington.edu/tech.reports (No. 380, 2000).

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/tech.reports.

### See Also

`mclustOptions`, `EMclust`, `mclustDAtrain`, `em`, `me`, `estep`, `mstep`

### 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))
    odd <- seq(1, 2*n, 2)
    train <- mclustDAtrain(x[odd, ], labels = xclass[odd]) ## training step
    even <- odd + 1
    test <- mclustDAtest(x[even, ], train) ## compute model densities

    data(iris)
    irisMatrix <- iris[,1:4]
    irisClass <- iris[,5]

    .Mclust
    .Mclust <- mclustOptions(tol = 1.e-6, emModelNames = c("VII", "VVI", "VVV"))
    .Mclust
    irisBic <- EMclust(irisMatrix)
    summary(irisBic, irisMatrix)
    .Mclust <- mclustOptions() # restore defaults
    .Mclust

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