R: Set control values for use with the EM algorithm.

emControl {mclust} | R Documentation

## Set control values for use with the EM algorithm.

### Description

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

### Usage

    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$double.eps`, which is approximately 2e-16 on IEEE-compliant machines.
`tol` | A vector of length two giving relative convergence tolerances for the log-likelihood and for parameter convergence in the inner loop for models with iterative M-step ("VEI", "EVE", "VEE", "VVE", "VEV"), respectively. The default is `c(1.e-5,sqrt(.Machine$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.
`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", "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.
`equalPro` | Logical variable indicating whether or not the mixing proportions are equal in the model. Default: `equalPro = FALSE`.

### Details

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

### Value

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

### References

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.

### See Also

`em`, `estep`, `me`, `mstep`, `mclustBIC`

### Examples

    irisBIC <- mclustBIC(iris[,-5], control = emControl(tol = 1.e-6))
    summary(irisBIC, iris[,-5])

[Package mclust version 5.3 Index]