Supplies a list of values including tolerances for singularity and convergence assessment, for use functions involving EM within MCLUST.

emControl(eps, tol, itmax, equalPro)

## Arguments

eps

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. tol 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", "VEE", "EVE", "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.

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", "EVE", "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.

equalPro

Logical variable indicating whether or not the mixing proportions are equal in the model. Default: equalPro = FALSE.

## Value

A named list in which the names are the names of the arguments and the values are the values supplied to the arguments.

## Details

emControl is provided for assigning values and defaults for EM within MCLUST.

em, estep, me, mstep, mclustBIC

## Examples

irisBIC <- mclustBIC(iris[,-5], control = emControl(tol = 1.e-6))
summary(irisBIC, iris[,-5])
#> Best BIC values:
#>              VEV,2        VEV,3      VVV,2
#> BIC      -561.7285 -562.5508708 -574.01783
#> BIC diff    0.0000   -0.8224087  -12.28937
#>
#> Classification table for model (VEV,2):
#>
#>   1   2
#>  50 100