GaussianMixtureMEM {mclustAddons}R Documentation

Modal EM algorithm for Gaussian Mixtures

Description

A function implementing a fast and efficient Modal EM algorithm for Gaussian mixtures.

Usage

GaussianMixtureMEM(
  data,
  pro,
  mu,
  sigma,
  control = list(eps = 1e-05, maxiter = 1000, stepsize = function(t) 1 - exp(-0.1 * t),
    denoise = TRUE, alpha = 0.01, keep.path = FALSE),
  ...
)

Arguments

data

A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations (n) and columns correspond to variables (d).

pro

A (G \times 1) vector of mixing probabilities for a Gaussian mixture of G components.

mu

A (d \times G) matrix of component means for a d-variate Gaussian mixture of G components.

sigma

A (d \times d \times G) array of component covariance matrices for a d-variate Gaussian mixture of G components.

control

A list of control parameters:

  • ⁠eps, maxiter⁠ Numerical values setting the tolerance and the maximum number of iterations of the MEM algorithm;

  • stepsize A function controlling the step size of the MEM algorithm;

  • denoise A logical, if TRUE a denoising procedure is used when d > 1 to discard all modes whose density is negligible;

  • alpha A numerical value used when denoise = TRUE for computing the hypervolume of central (1-\alpha)100 region of a multivariate Gaussian;

  • keep.path A logical controlling whether or not the full paths to modes must be returned.

...

Further arguments passed to or from other methods.

Value

Returns a list containing the following elements:

Author(s)

Luca Scrucca

References

Scrucca L. (2021) A fast and efficient Modal EM algorithm for Gaussian mixtures. Statistical Analysis and Data Mining, 14:4, 305–314. doi: 10.1002/sam.11527

See Also

MclustMEM().


[Package mclustAddons version 0.9 Index]