Supplies a list of values including tolerances for singularity and convergence assessment, for use functions involving EM within MCLUST.
emControl(eps, tol, itmax, equalPro)A scalar tolerance associated with 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.
A vector of length two giving relative convergence tolerances for the
log-likelihood and for parameter convergence in the inner loop for models
with iterative M-step ("VEI", "EVE", "VEE", "VVE", "VEV"), respectively.
The default is c(1.e-5,sqrt(.Machine$double.eps)).
If only one number is supplied, it is used as the tolerance
for the outer iterations and the tolerance for the inner
iterations is as in the default.
A vector of length two giving integer limits on the number of EM
iterations and on the number of iterations in the inner loop for
models with iterative M-step ("VEI", "EVE", "VEE", "VVE", "VEV"),
respectively. The default is
c(.Machine$integer.max, .Machine$integer.max)
allowing termination to be completely governed by tol.
If only one number is supplied, it is used as the iteration
limit for the outer iteration only.
Logical variable indicating whether or not the mixing proportions are
equal in the model. Default: equalPro = FALSE.
A named list in which the names are the names of the arguments and the values are the values supplied to the arguments.
emControl is provided for assigning values and defaults
for EM within MCLUST.
C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.
C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.
# NOT RUN {
irisBIC <- mclustBIC(iris[,-5], control = emControl(tol = 1.e-6))
summary(irisBIC, iris[,-5])
# }
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