MatrixMixt: Fitting for Matrix-Variate Mixture Models

In MatrixMixtures: Model-Based Clustering via Matrix-Variate Mixture Models

Description

Fits, by using expectation-maximization algorithms, mixtures of matrix-variate distributions (normal, t, contaminated normal) to the given data. Can be run in parallel. The Bayesian information criterion (BIC) is used to select the number of groups.

Usage

MatrixMixt(
  X,
  G = 1:3,
  mod,
  tol = 1e-05,
  maxiter = 10000,
  ncores = 1,
  verbose = TRUE
)

Arguments

X	A list of dimension N, where N is the sample size. Each element of the list corresponds to an observed p x r matrix.
G	A vector containing the numbers of groups to be tried.
mod	The matrix-variate distribution to be used for the mixture model. Possible values are: "MVN" for the normal distribution, "MVT" for the t distribution "MVCN" for the contaminated normal.
tol	Threshold for Aitken's acceleration procedure. Default value is 1.0e-05.
maxiter	Maximum number of iterations of the algorithms. Default value is 10000.
ncores	A positive integer indicating the number of cores used for running in parallel. Default value is 1.
verbose	Logical indicating whether the running output should be displayed.

Value

A list with the following elements:

flag	Convergence flag (TRUE - success, FALSE - failure).
pig	Vector of the estimated mixing proportions (length G).
nu	Vector of the estimated degree of freedoms (length G). Only for "MVT".
alpha	Vector of the estimated inliers proportions (length G). Only for "MVCN".
eta	Vector of the estimated inflation parameters (length G). Only for "MVCN".
M	Array of the mean matrices (p x r x G).
Sigma	Array of the estimated row covariance matrices (p x p x G).
Psi	Array of the estimated column covariance matrices (r x r x G).
class	Vector of estimated data classification.
z	Matrix of estimated posterior probabilities (N x G).
v	Matrix of estimated inlier probabilities (N x G). Only for "MVCN".
lik	Estimated log-likelihood.
BIC	Estimated BIC.

Examples

data(SimX)
res <- MatrixMixt(X = SimX, G = 2, mod = "MVCN")

MatrixMixtures documentation built on June 11, 2021, 9:06 a.m.