Defaults.Mclust: 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.

Arguments

Value

A list with the following components:
epsA 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.
tolA 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).
itmaxA 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.
equalProLogical variable indicating whether or not the mixing proportions are equal in the model. Default: equalPro = FALSE.
warnSingularA logical value indicating whether or not a warning should be issued whenever a singularity is encountered. Default: warnSingular = TRUE.
emModelNamesA 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
hcModelNameA 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.
symbolsA 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.

Details

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

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))
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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