overfittingMFA_missing_values: Basic MCMC sampler for the case of missing data

In fabMix: Overfitting Bayesian Mixtures of Factor Analyzers with Parsimonious Covariance and Unknown Number of Components

Basic MCMC sampler for the case of missing data

Description

Gibbs sampling for fitting a mixture model of factor analyzers.

Usage

overfittingMFA_missing_values(missing_entries, x_data, originalX, outputDirectory, Kmax, 
	m, thinning, burn, g, h, alpha_prior, alpha_sigma, 
	beta_sigma, start_values, q, zStart, gibbs_z, lowerTriangular)

Arguments

missing_entries	list which contains the row number (1st entry) and column indexes (subsequent entries) for every row containing missing values.
x_data	normalized data
originalX	observed raw data (only for plotting purpose)
outputDirectory	Name of the output folder
Kmax	Number of mixture components
m	Number of iterations
thinning	Thinning of chain
burn	Burn-in period
g	Prior parameter g. Default value: g = 2.
h	Prior parameter h. Default value: h = 1.
alpha_prior	Parameters of the Dirichlet prior distribution of mixture weights.
alpha_sigma	Prior parameter \alpha. Default value: \alpha = 2.
beta_sigma	Prior parameter \beta. Default value: \beta = 1.
start_values	Optional (not used)
q	Number of factors.
zStart	Optional (not used)
gibbs_z	Optional
lowerTriangular	logical value indicating whether a lower triangular parameterization should be imposed on the matrix of factor loadings (if TRUE) or not. Default: TRUE.

Value

List of files

Author(s)

Panagiotis Papastamoulis

fabMix documentation built on May 29, 2024, 2:53 a.m.