# Class `"EM.Control"` Object of class `EM.Control`. class ## 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)`, `a.K(x = NULL)`, `a.eliminate.zero.components(x = NULL)`, `a.likelihood.tolerance.check(x = NULL)` and `a.likelihood.estimation.rule(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)`, `a.eliminate.zero.components(x = NULL)`, `a.likelihood.tolerance.check{x = NULL}` and `a.likelihood.estimation.rule{x = NULL}` are provided to write to `strategy`, `variant`, `acceleration`, `tolerance`, `acceleration.multiplier`, `maximum.iterations`, `eliminate.zero.components`, `likelihood.tolerance.check` and `likelihood.estimation.rule` 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"`, `"ECM"`, `"SEM"`, `"SEM-EM"`, `"ECM-EM"`. The default value is `"EM"`. - **`acceleration`:**: a character containing the type of acceleration of the EM iteration increment. One of `"fixed"`, `"line"`, `"golden"`, `"stem1"`, `"stem2"`, `"stem3"`, `"square1"`, `"square2"` or `"square3"`. The default value is `"fixed"`. - **`tolerance`:**: tolerance value for the EM convergence criteria. The default value is 1.0E-4. - **`acceleration.multiplier`:**: acceleration.multiplier $a_{\mathrm{EM}}$, $1.0 \leq a_{\mathrm{EM}} \leq 2.0$. acceleration.multiplier for the EM step increment. The default value is 1.0. Only used when `acceleration == "fixed"`. - **`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`. - **`likelihood.tolerance.check`:**: a character containing the type of log likelihood convergence check. One of `"absolute"`, `"normalised"` and `"percentage"`. The default value is `"normalised"`. - **`likelihood.estimation.rule`:**: a character containing the type of log likelihood estimate. One of `"standard"`, `"aitken-lindsay"`, `"aitken-bohning"` and `"aitken-nicholas"`. The default value is `"standard"`. ## 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. tools:::Rd_expr_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. tools:::Rd_expr_doi("10.1016/0167-9473(92)90042-E") . R. Varadhan and C. Roland. Simple and Globally Convergent Methods for Accelerating the Convergence of Any EM Algorithm. Scandinavian Journal of Statistics, 35(2):335:353, 2008. tools:::Rd_expr_doi("https://doi.org/10.1111/j.1467-9469.2007.00585.x") . P. D. McNicholas et al. Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models. Computational Statistics & Data Analysis, 54(3):711:723. 2010. tools:::Rd_expr_doi("https://doi.org/10.1016/j.csda.2009.02.011") ## Examples ```r # Inline creation by new call. EM <- new("EM.Control", strategy = "exhaustive", variant = "EM", acceleration = "fixed", tolerance = 1e-4, acceleration.multiplier = 1.0, maximum.iterations = 1000, K = 0, likelihood.tolerance.check = "absolute", likelihood.estimation.rule = "standard") EM # Creation of EM object with setter method. 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 a.likelihood.tolerance.check(EM) <- "normalised" a.likelihood.estimation.rule(EM) <- "standard" EM ```

EM.Control-class function