EM.Control-class | R Documentation |
"EM.Control"
Object of class EM.Control
.
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)
, a.K(x = NULL)
and a. eliminate.zero.components (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)
,
a.K(x = NULL)
and eliminate.zero.components
are provided to write to strategy
, variant
, acceleration
, tolerance
,
acceleration.multiplier
, maximum.iterations
and eliminate.zero.components
slot respectively.
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
:a positive integer containing the maximum allowed number of iterations of the EM algorithm. The default value is 1000.
K
:an 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.
eliminate.zero.components
:a logical indicating if the componenets with w_{l} = 0 should be eliminated from output. Only used with EMMIX-methods
.
Branislav Panic
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.
# 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
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