EM.Control-class: Class '"EM.Control"'

Description Objects from the Class Slots Author(s) References Examples

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.

Author(s)

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

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# 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.