overfittingMFA_Sj_missing_values: Basic MCMC sampler for the case of missing data and different...

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 and different error variance

Description

Gibbs sampling for fitting a mixture model of factor analyzers.

Usage

overfittingMFA_Sj_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.