GaussianMixtureMEM: Modal EM algorithm for Gaussian Mixtures

In mclustAddons: Addons for the 'mclust' Package

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:

• n The number of input data points.

• d The number of variables/features.

• parameters The Gaussian mixture parameters.

• iter The number of iterations of MEM algorithm.

• nmodes The number of modes estimated by the MEM algorithm.

• modes The coordinates of modes estimated by MEM algorithm.

• path If requested, the coordinates of full paths to modes for each data point.

• logdens The log-density at the estimated modes.

• logvol The log-volume used for denoising (if requested).

• classification The modal clustering classification of input data points.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi: 10.1002/sam.11527")}

See Also

MclustMEM().

mclustAddons documentation built on April 3, 2025, 11:19 p.m.