# EM.Control-class: Class '"EM.Control"' In rebmix: Finite Mixture Modeling, Clustering & Classification

## Description

Object of class EM.Control.

## Objects from the Class

Objects can be created by calls of the form new("EM.Control", ...). Accessor methods for the slots are a.strategy(x = NULL), a.variant(x = NULL), a.acceleration(x = NULL), a.tolerance(x = NULL), a.acceleration.multiplier(x = NULL), a.maximum.iterations(x = NULL) and a.K(x = NULL) where x stands for an object of class EM.Control. Setter methods a.strategy(x = NULL), a.variant(x = NULL), a.acceleration(x = NULL), a.tolerance(x = NULL), a.acceleration.multiplier(x = NULL), a.maximum.iterations(x = NULL) and a.K(x = NULL) are provided to write to strategy, variant, acceleration, tolerance, acceleration.multiplier and maximum.iterations slot respectively.

## Slots

strategy:

a character containing the EM and REBMIX strategy. One of "none", "exhaustive", "best" and "single". The default value is "none".

variant:

a character containing the type of the EM algorithm to be used. One of "EM" of "ECM". The default value is "EM".

acceleration:

a character containing the type of acceleration of the EM iteration increment. One of "fixed", "line" or "golden". The default value is "fixed".

tolerance:

tolerance value for the EM convergence criteria. The default value is 1.0E-4.

acceleration.multiplier:

acceleration.multiplier a_{\mathrm{EM}}, 1.0 ≤q a_{\mathrm{EM}} ≤q 2.0. acceleration.multiplier for the EM step increment. The default value is 1.0.

maximum.iterations:

is positive integer containing the maximum allowed number of iterations of the EM algorithm. The default value is 1000.

K:

is integer containing the number of bins for the histogram based EM algorithm. This option can reduce computational time drastically if the datasets contain a large number of observations n and K is set to the value \ll n. The default value of 0 means that the EM algorithm runs over all n.

Branislav Panic

## References

B. Panic, J. Klemenc, M. Nagode. Improved initialization of the EM algorithm for mixture model parameter estimation. Mathematics, 8(3):373, 2020. doi: 10.3390/math8030373.

A. P. Dempster et al. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B, 39(1):1-38, 1977. https://www.jstor.org/stable/2984875.

G. Celeux and G. Govaert. A classification EM algorithm for clustering and two stochastic versions, Computational Statistics & Data Analysis, 14(3):315:332, 1992. doi: 10.1016/0167-9473(92)90042-E.

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 # Inline creation by function new call. EM <- new("EM.Control", strategy = "exhaustive", variant = "EM", acceleration = "fixed", tolerance = 1e-4, acceleration.multiplier = 1.0, maximum.iterations = 1000, K = 0) EM # Creation of EM object with setter functions. EM <- new("EM.Control") a.strategy(EM) <- "exhaustive" a.variant(EM) <- "EM" a.acceleration(EM) <- "fixed" a.tolerance(EM) <- 1e-4 a.acceleration.multiplier(EM) <- 1.0 a.maximum.iterations(EM) <- 1000 a.K(EM) <- 256 EM 

rebmix documentation built on July 28, 2021, 5:08 p.m.