emControl: Set control values for use with the EM algorithm

In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

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$double.eps, which is approximately 2e-16 on IEEE-compliant machines.
tol	A vector of length two giving relative convergence tolerances for the log-likelihood and for parameter convergence in the inner loop for models with iterative M-step ("VEI", "VEE", "EVE", "VVE", "VEV"), respectively. The default is c(1.e-5, sqrt(.Machine$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.

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.

See Also

em, estep, me, mstep, mclustBIC

Examples

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

mclust documentation built on May 29, 2024, 8:06 a.m.