Defaults.Mclust {mclust} | R Documentation |
A named list of values including tolerances for singularity and convergence assessment, and an enumeration of models used as defaults in MCLUST functions.
A function mclustOptions
is supplied for assigning values to
the .Mclust
list.
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. |
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.
mclustOptions
,
EMclust
,
mclustDAtrain
,
em
,
me
,
estep
,
mstep
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