initialize_em_univariate: Return initial estimates to the EM algorithm for GMM...

In bastienchassagnol/RGMMBench: A benchmark on Gaussian mixtures in R

Return initial estimates to the EM algorithm for GMM estimation

Description

One of the main drawback of the EM algorithm is that it requires initial guess as starting point. And so, careful initialisation, depending on the properties of the mixture, is required:

• initialize_em_univariate returns the initial estimates in the univariate dimension.

• initialize_em_multivariatereturns the initial estimates in a multivariate context. It's worth noting that quantiles initialisation method is not available in the multivariate context, as no unique set of parametrisation could be returned.

Usage

initialize_em_univariate(
  x = NULL,
  k = 2,
  nstart = 10L,
  short_iter = 200,
  short_eps = 10^-2,
  prior_prob = 0.05,
  initialisation_algorithm = c("kmeans", "quantiles", "random", "hc", "small em",
    "rebmix"),
  ...
)

initialize_em_multivariate(
  x,
  k = 2,
  nstart = 10L,
  short_iter = 200,
  short_eps = 10^-2,
  prior_prob = 0.05,
  initialisation_algorithm = c("kmeans", "random", "hc", "small em", "rebmix"),
  ...
)

Arguments

x	the vector of the observations
k	the number of components
nstart	the number of random restarts with kmeans, random and small EM method
short_iter, short_eps	hyperparameters of the small EM method
prior_prob	minimal uncertainty added to the minor components of each observation assigned by hierarchical clustering
initialisation_algorithm	the choice of the initialisation method, between kmeans, quantiles, random, hc, small em and rebmix method
...	additional hyperparameters supplied with some of the initialisation methods

Value

a list of the estimated parameters, ordered by increasing mean for identifiability issues

Author(s)

Bastien CHASSAGNOL

bastienchassagnol/RGMMBench documentation built on Oct. 26, 2023, 5:58 p.m.