mbc: Fit a mixture of multivariate Gaussians

In markajoc/MBCbigP: Model-Based Clustering for High-Dimensional Data

Description

Fit a mixture of multivariate Gaussians

Usage

mbc(x, groups = 2, maxiter = 500, likelihood = TRUE, verbose = FALSE,
  plot = FALSE, z = NULL)

mbc_cond(x_A, x_B, mean_A, sigma_AA, z, pro, groups, maxiter = 500,
  likelihood = TRUE, verbose = FALSE, plot = FALSE, abstol = 0.001,
  method_sigma_AB = c("analytic", "numeric"), updateA = FALSE)

Arguments

x, x_A, x_B	Data frame or a matrix
groups	The number of groups/mixture components to fit.
maxiter	The maximum number of iterations for the E-M algorithm.
likelihood	Logical indicating whether the log-likelihood should be calculated at each step and returned (defaults to TRUE).
verbose	Print verbose output if TRUE.
plot	Visualise the mixture model as it progresses.
z	A matrix of cluster probabilities.
mean_A	Mean vectors for previous batch.
sigma_AA	Covariance matrices for previous batch.
pro	Mixing proportions.
abstol	Stopping tolerance for likelihood.
method_sigma_AB	One of "analytic" (default) or "numeric", to choose the method of estimating the between-batch covariance for a cluster.
updateA	Logical, if TRUE, updates the parameters for the previous batch after estimating the paramters for the current batch.

Value

A list containing the estimated parameters for the mixture distribution.

Examples

library(mclust)
data(banknote)
mbc(x = banknote[, -1], groups = 2)

markajoc/MBCbigP documentation built on May 30, 2019, 8:39 a.m.